• The stock no longer needs a heroic AI thesis: Adobe has grown revenue, earnings and free cash flow over two years while the share price has collapsed nearly 59%. At today’s valuation, stabilization alone can create upside.

• The real risk is managed decline: freemium can be either investment or concession, margins are lower, and Adobe still has not disclosed the conversion, retention and organic ARR evidence needed to tell the difference.

• The investment question has simplified: Adobe no longer must prove AI makes its moat stronger. It must show that existing cash earnings are durable enough for investors to stop shortening the useful life they assign to them.

Adobe’s quarterly revenue has grown 25% in two years, adjusted earnings per share approximately 32%, and free cash flow approximately 24%. Around the corresponding earnings releases, its share price has fallen almost 59%. The company has become larger while investors have become much less willing to pay for the earnings it produces.

Our earlier work tried to resolve that contradiction by asking whether AI would make Adobe more valuable as content became abundant. That remains an important business question. At today’s valuation, however, the stock can work before Adobe establishes everything we once believed about that opportunity. The more immediate question is whether the deterioration investors fear will continue.

There are two investment paths. Adobe’s earning power proves durable enough for pessimism to bottom or reverse, and today’s price is low. Alternatively, new products and repurchases sustain the reported numbers while customer economics continue deteriorating, and Adobe enters managed decline. A temporary share-price bounce would not establish the first; an extended period of poor returns would not, by itself, establish the second.

The structural mechanism remains straightforward. Adobe becomes more valuable as customers depend on its assets, brand rules, permissions and performance history to complete growing volumes of commercial work. That advantage compounds when each project leaves context that improves subsequent projects and retains more spending within Adobe. It breaks when competing tools can use that context equally well or additional consumption fails to compensate for weaker pricing and fewer paid seats.

Q3 delivered $6.76 billion in revenue, up 12.9%, and adjusted EPS of $6.13, up 15.4%, with higher full-year guidance. Those results leave one fundamental question: is Adobe’s earning power durable enough for pessimism to stop worsening, or are improving headline numbers concealing managed decline?

When the stock stops fighting its earnings

In June’s The Abundance Paradox, we acknowledged what our earlier optimism had missed:

“This is not a quarterly miss. It is a framework error. We were measuring what Adobe does. The market was measuring what Adobe controls. Those turned out to be different things.”

The correction needs to go further than saying we were right about the fundamentals and wrong about the stock. Pricing power, retention and acquisition costs are fundamentals. We identified a real opportunity but treated greater usage as stronger evidence of economic power than it was.

The same discipline now requires separating a weaker competitive advantage from an inevitably declining business. Adobe could retain substantial earning power without regaining its old dominance. That distinction matters much more near ten times adjusted earnings than it did at a premium valuation.

The record shows a growing business under profitability pressure. Over two years, adjusted net income increased approximately 16.5%, with the lower share count contributing materially to faster EPS growth. Cash generation also increased, so the improvement cannot be dismissed as repurchases alone.

Meanwhile, the shares moved from $586.55 at the September 12, 2024 regular close to $243.08 in September 10, 2026 after-hours trading. Those are different session windows, but the valuation compression is unmistakable. The successful investment path begins when investors stop shortening the useful life they assign to Adobe’s earnings.

That need not require accelerating growth. If the earnings multiple stops falling, modest EPS growth can again translate into share-price appreciation. If confidence also recovers, the multiple can contribute rather than subtract. A valuation floor removes a headwind; the return must still come from earnings growth, actual cash distributions or some recovery in what investors will pay.

The arithmetic illustrates the opportunity. At the FY26 adjusted EPS midpoint of $24.475, a valuation of twelve to fifteen times earnings implies approximately $294–367, versus the dated $243.08 reference. That is 21–51% upside without assuming any earnings growth. It is entirely a rerating sensitivity, not a promised return or a timetable.

When adaptation becomes managed decline

The other path is more difficult to recognize because the company can remain profitable for years. Existing contracts, new products, acquisitions and a declining share count can support reported growth while the economics of attracting and retaining customers weaken.

Freemium sits at the center of that possibility. Asked about postponed pricing, Shantanu Narayen said:

“I’m actually really happy that we didn’t focus on the pricing actions, because that, while it may have provided some short-term relief, would not be as critical as continuing to drive new user adoption.”

Free access can recruit customers who become more valuable over time. It can also become necessary because customers have cheaper alternatives. Both forces can operate together; choosing the right defensive response does not establish that Adobe can preserve its previous profitability.

Managed decline would mean the concessions keep expanding. More usage requires more computing and product investment, but customers resist paying proportionately more. Enterprise growth remains positive without offsetting weaker creative economics. Repurchases support EPS while aggregate profit or economically available cash begins to erode.

That is why the eight-quarter margin record matters. Q3’s approximately 44% adjusted operating margin compares with 46.3% a year earlier. The materials do not isolate the contribution from AI, acquisitions and other spending, so we cannot attribute the entire decline to competitive pressure. We can say that sustaining growth currently absorbs more of each revenue dollar.

A low multiple offers less protection if the earnings being multiplied are themselves too high to sustain. Managed decline should therefore be judged through customer spending, retention and cash generation, not inferred from the share price. Equally, a rally driven by sentiment alone would not demonstrate that those economics had stabilized.

What Q3 tells us about the choice

There is evidence supporting the successful path. AI-first ARR exceeds $650 million, growing more than 150% year over year. Firefly app and credit-pack ARR grew 40% sequentially, and Brand Visibility doubled paying customers. These demonstrate payment as well as adoption, even though their undisclosed bases limit the consolidated conclusion.

The enterprise opportunity also survives. Generating an image becomes easier, but companies still need the correct product, approved claims, rights, branding and evidence of commercial results. More content increases the work surrounding generation. Adobe can benefit if its customer knowledge makes that work more dependable.

Competition remains relevant: Canva offers brand controls and approvals, while Figma connects AI creation with existing design context. Adobe must establish that its accumulated customer relationships deliver enough additional value to retain spending. Greater usefulness does not automatically confer greater pricing power.

The enterprise disclosures are less helpful for identifying momentum. Q2 reported GenStudio ARR growth above 25% and AEP subscription-revenue growth above 30%. Q3 reported above-20% ARR growth for each of three product families. Different metrics and disclosed thresholds prevent a clean deceleration calculation, but the presentation makes progress harder to assess

Anil Chakravarthy explained how management intends to convert engagement into revenue:

“The goal is, of course, to prove value first, and once they get value, we get a lot of signals about the engagement, about the intensity of AI usage, about whether they’re ready to move to, for us to convert them into ARR. We’re constantly calibrating that. What is the best conversion point? What kind of paywall tuning, for example, would we do?”

The missing evidence is what that calibration earns. Adobe has not disclosed comparable conversion and retention cohorts or acquisition payback. Asked for Semrush’s contribution, management emphasized integrated selling; total ARR rose approximately $400 million sequentially, but the organic bridge requires Semrush’s change during the quarter.

Nor can we manufacture the missing answer from AI-first ARR. Above $500 million becoming above $650 million does not establish at least $150 million of additions: $625 million becoming $675 million would satisfy both disclosures while adding only $50 million. The remaining business’s contribution cannot be reliably inferred by subtracting disclosed minimums.

June anticipated transition pressure and benefits during FY27. Q3 continues that bet, with paid-adoption progress and incomplete economic evidence. Better disclosure would help us distinguish the paths; it would not itself make either outcome true.

The evidence that changes the bet

Our June article established six signposts, and they remain useful evidence for this decision. Their job is to tell us whether customer economics are finding a floor.

Anil was named September 3, after Q3 ended August 28. That resolves the leadership vacancy but narrowly misses our literal timing condition. The commercially meaningful question is whether his management makes customer economics more durable and more visible

My lean is toward stabilization rather than managed decline. Revenue, aggregate adjusted profit and cash generation have grown, and customers are paying for new AI products. Those facts do not prove a bottom, but they give me more reason to investigate a valuation recovery than to treat economic decline as established.

That is a different conviction from our September 2025 argument. I no longer need Adobe to prove that AI creates a stronger moat before the stock can work. I need evidence that its existing earning power can endure, allowing the market to stop discounting each year as another step toward erosion.

Because prices can turn before reported growth does, waiting for every signpost to pass may mean paying substantially more. The investment judgment is whether the evidence already favors stabilization strongly enough to accept what remains unknown.

The valuation must still recognize stock compensation and necessary reinvestment. Adobe trades at approximately 13.4 times FY26 GAAP EPS guidance, versus 9.9 times adjusted guidance at the reference price. Repurchases can improve each remaining shareholder’s claim, but their cash cannot also be counted as an additional yield once the EPS benefit is included.

By Q1 FY27, I want a comparable organic bridge or convincing alternative evidence of conversion and retention economics. If neither arrives, I would stop assuming the freemium investment is working and re-underwrite observable earnings and cash. Persistent deterioration in those economics would move my view toward managed decline; silence alone would reduce confidence without proving it.

Over the next three years, the decisive issue is whether pessimism has already outrun the damage. If earnings prove durable and distrust stops deepening, Adobe is cheap enough for the stock to work without recovering its former dominance. If maintaining the business requires continuing concessions and shrinking economic returns, the low multiple is anticipating managed decline. That is the decision this quarter leaves us to make.

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• The funding model has changed materially: customer prepayments with a significant financing component rose to $11.4 billion in Q1, helping fund a massive infrastructure build without making Oracle economically capital-light.

• The operating evidence is increasingly convincing: OCI growth reached 121%, utilization was 97.9%, and even four-year-old GPUs renewed or resold at a 20% premium, arguing against the simplest “dark fiber” comparison.

• But the shareholder question remains unresolved: revenue is accelerating faster than per-share earnings, interest expense and dilution remain meaningful, and Oracle still has to prove that pre-funded growth converts into attractive full-cycle cash returns.

The most revealing exchange on Oracle’s earnings call began with a misunderstanding. An analyst asked about contracts requiring no additional capital expenditure. Co-CEO Clay Magouyrk corrected him:

“Sure. Let me make sure we clarify one thing first, and then I’ll dive into the pieces. I didn’t say, and I don’t think myself nor Hilary said that it doesn’t require additional CapEx. We said it doesn’t require additional cash from Oracle. Right?”

That distinction explains why I am more constructive after Q1, and why the qualification matters. Oracle reported revenue of $19.35 billion, up 30%, and adjusted EPS of $1.92, up 30%. More consequentially, it demonstrated that customers will help finance an increasingly large service obligation.

AI makes Oracle’s ability to turn processors, power and networking into reliable computing more valuable. Repeated delivery can improve execution, build customer confidence and attract further funded commitments, allowing operating expertise to compound as demand grows. That mechanism breaks if customers mainly value temporary scarcity, or if their funding and Oracle’s contractual returns cannot sustain expansion.

Our previous article, The Price of the Moat, accepted the financing mechanism but withheld a verdict on the whole business. I am now moderately bullish: delivery has earned greater confidence, although the connection from computing growth to durable profit and cash per share remains the fundamental question.

The Service Behind the Silicon

A customer can own the processors and still need Oracle. Power, cooling, networking, software and operations must work together before expensive equipment becomes useful computing. A larger fleet can spread engineering knowledge across deployments and make it easier to reassign capacity as contracts expire. Customer-owned hardware therefore helps reveal what Oracle contributes beyond financing the purchase, provided those operating gains outweigh the complexity of expansion.

Management reported delivering 850 megawatts and more than 300,000 GPUs during Q1, with GPU utilization at 97.9%. At Abilene, customer acceptance reportedly took approximately 24 hours. OCI revenue provides the financial counterpart to those operating claims.

OCI grew approximately 28% sequentially, almost exactly meeting our $7.40 billion forecast and exceeding the $7.09 billion consensus in our pre-release supplement. Remaining performance obligations, or contracted revenue still to be recognized, rose from $638 billion to $664 billion, below our $700 billion expectation. Those differently scoped figures cannot calculate a backlog-conversion rate. They do show why delivery increasingly deserves attention alongside commitments.

The intriguing disclosure concerned older processors: capacity renewed or resold during Q1 achieved a 20% premium, with most of those GPUs at least four years old. That challenges immediate-obsolescence arguments. It does not yet establish durable pricing power because the cohort renewed during scarce supply. Oracle’s advantage becomes more convincing if attractive renewal economics survive greater customer choice.

An Advance Is a Promise

Our Q4 article already recognized the $75 billion pool of prepaid and customer-supplied hardware. It also drew a boundary:

“But Q4 also drew the limit of the argument. BYOH changes the return on Oracle’s capital; it does not make Oracle capital-light. If the customer-funded model solved the problem outright, Oracle would not still need another large year of external financing. The bridge is real. It is not free. It reduces the capital burden per contract; it does not eliminate the capital burden of chasing a market this large.”

The latest release makes that distinction measurable. These are quarterly figures, in US$ billions:

Nearly half of Q1’s $23.10 billion operating cash flow came from the identified prepayment contribution. The $11.36 billion is a quarterly flow, not the outstanding customer balance. It reduces external funding needs while paying for services Oracle still owes. It is already included in operating cash flow; subtracting net capex would count its benefit twice.

Despite that contribution, net capital outlay increased approximately 60% sequentially and reported free cash flow became more negative. Collections and construction payments are lumpy, so this does not establish a worsening trend. It does rule out describing Q1 as a demonstrated cash turnaround.

Supplier financing, customer-owned hardware and prepayments also allocate risks differently. A smaller initial funding requirement improves returns only if sufficient economics remain after operating costs, replacement obligations and contractual concessions. Management says most newly added contracts use these arrangements and affect FY28 or later; the release does not reconcile those contracts with this quarter’s prepayment flow.

The counterparty question deserves equal weight. Asked whether funded contracts came from AI labs, chip companies or sovereigns, Magouyrk described customers ranging from startups to major investment-grade companies. Management describes broad participation, without revealing the dollar-weighted distribution.

Several customers can still represent a common economic exposure if their spending depends on the same investors, suppliers and financing conditions. If a chip supplier finances a customer that prepays Oracle, which then buys that supplier’s chips, several transactions depend on the same capital. These disclosures do not establish that such a chain explains the $11.36 billion. Cash already received reduces collection exposure on prepaid services; dependence on future fundraising can still threaten subsequent demand and capacity commitments.

I therefore want to know who owes what, under which terms, and how much demand customers can sustain from their own cash generation. Oracle’s operating success and its customers’ commercial success must eventually reinforce each other. A growing advance balance alone cannot demonstrate that relationship.

The Profit Has to Follow

Management’s margin argument is coherent. Compute has lower gross margins than software but requires less selling and research expenditure relative to revenue. Adjusted operating margin remained around 42% year over year, helped by lower combined selling, research and administrative expenses.

The forward arithmetic is less comfortable. Q2’s revenue midpoint implies approximately $21.20 billion, up 9.6% sequentially, while adjusted EPS of $1.89 would be below Q1’s $1.92. Using that midpoint, full-year guidance requires at least approximately $49.46 billion of second-half revenue and roughly $4.29 of second-half EPS, allowing for share-weighting differences. Revenue is growing faster than the guided earnings accruing to each share.

Hilary Maxson acknowledged the margin pressure in the call:

“Yeah. So we had mentioned already, I mentioned in the Q4 that we would expect a step down in gross margins this year. You can see the EPS guidance that we give, though, so you can see what we might expect in terms of operating margin.”

The EPS comparison cannot isolate operating margins: tax, interest, other income and share count also matter. Interest expense rose 55% year over year versus 31% growth in adjusted operating income, and Oracle completed its $20 billion gross equity issuance. Q1 EPS beat our $1.75 forecast, but the full-year guide rose only five cents to $8.10. Execution deserves greater confidence; extrapolating the quarterly earnings beat would go beyond the evidence.

Enterprise software offers another source of profit. Our earlier thesis suggested computing relationships could deepen Oracle’s database advantage. That connection needs evidence. Multi-cloud database revenue grew 353%, but distributing Oracle databases inside customers’ chosen clouds also demonstrates that the franchise can prosper without Oracle owning every surrounding computing workload.

Co-CEO Mike Sicilia supplied a more direct mechanism for improving the applications business:

“In our Oracle NetSuite applications, we have seen early customers leveraging these AI tools coming down from double-digit months down to single-digit weeks in order to be able to go live in production. We think that does two things. Number one, it helps customers get to value from AI more quickly than ever and certainly at a lower cost. Number two, in some of these very complex industries, there are ramps associated with these go-lives, and it allows us to unlock the ramp and recognize revenue more quickly than we have in the manual implementation piece.”

Faster implementation can lower customers’ costs and accelerate Oracle’s revenue. That is a credible advantage, although SaaS growth of 10% has yet to reflect a comparable acceleration. I would underwrite computing on its own economics and treat additional enterprise profit as something Oracle must demonstrate.

Three Prices, Three Businesses

My variant perception is that Oracle’s operating service may prove more durable than a simple bet on scarce GPUs suggests. Customer-owned hardware makes that service visible. The opposing interpretation is equally clear: customers are securing scarce capacity while Oracle accepts obligations whose returns become less attractive as supply expands.

This tension also explains how different investors may read the quarter. Growth investors have stronger delivery evidence; investors prioritizing predictable cash returns still face negative free cash flow and dilution. A broader shareholder base could emerge as funded contracts become demonstrably durable cash earnings. That is a conditional valuation argument, rather than evidence that a new investor category automatically pays a higher multiple.

Our published Q4 ranges were $100–140 for the bear case, $225–290 for the base and $440–625 for the bull. The updated points remain within or near those ranges; Q1 increases my confidence in the central outcome.

The bear assumes severe margin compression as compute becomes a larger, less profitable business. Revenue of $140 billion at 28% operating margin produces $39.2 billion of operating profit; financing, tax and the common-share adjustment leave $25.46 billion, or $7.72 across 3.30 billion shares. EPS therefore falls slightly below FY27’s $8.10 guide despite substantial revenue growth. Oracle can succeed at building the business while disappointing its shareholders.

The base assumes reliable delivery, continued customer funding and contained financing costs. An 18× multiple does not make those business assumptions conservative: reaching $180 billion means doubling FY27’s revenue guidance floor in two years, a 41.4% annual growth rate. The bull requires operating differentiation to survive greater supply while enterprise profits support stronger margins.

At the dated $164 reference, the base offers approximately 18% annualized appreciation. I find that attractive enough to be moderately bullish, with confidence tied to evidence. Adjusted EPS must still become cash available to shareholders, and the bear price provides no floor.

What Would Change My Mind

October’s Investor Day should clarify project economics, funding obligations and the margin trajectory. I want a reconciliation of opening advances, new funding, services delivered and closing obligations, together with customer concentration and delivery periods. These are requested disclosures, not information management has promised to supply.

At Q2 results, expected December 14, the immediate tests are the $13.16–13.64 billion cloud revenue guide and an explanation of earnings conversion. Through FY27, I will track the ≤$70 billion net-outlay guide alongside dilution and reported free cash flow. Higher investment becomes adverse evidence when it fails to bring stronger economics; a missed disclosure alone leaves the thesis unproven.

Across subsequent quarters, attractive renewals must survive broader equipment cohorts, and faster implementations must produce measurable enterprise growth. Repeated delivery disappointments, weaker contract returns or financing that consumes operating progress would reduce my conviction. Management has given no date for positive consolidated free cash flow.

Last quarter, we accepted that Oracle had found ways to finance the opportunity. This quarter earns more confidence that it can deliver. The next judgment concerns what delivery leaves behind: after fulfilling customers’ prepaid claims and paying the costs of expansion, how much more profit and cash belongs to each existing share? That is what Oracle gets to keep, and what will determine our view from here.

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• The growth transition is finally becoming visible: organic net-new ARR grew 17%, non-seat products exceeded 100% ARR growth, Zero Trust Everywhere reached 950+ enterprises, and Z-Flex TCV surged above $770 million.

• The thesis has become more precise: Zscaler does not need to become the “brain” of AI security. Identity, endpoint and other systems can make decisions upstream; Zscaler can still become increasingly valuable as the privileged place where those decisions are enforced.

• The new uncertainty is economic: machine traffic expands the number of interactions Zscaler can secure, but transactions have risen above 750 billion per day while FCF margins have weakened and CapEx is rising. The question is whether this is temporary equipment spending or structurally greater capital intensity.

“I was right that Zscaler’s inline architecture is valuable. I was too quick to assume it would become the highest-value control plane in the AI era.”

That was where I left Zscaler three months ago. Q3 had not broken the business: revenue and ARR were still growing 25%, Z-Flex was expanding, non-seat usage was scaling, and margins had reached a record. What broke was the inference I had been drawing from those facts. A valuable architecture did not necessarily mean Zscaler would own the most valuable security decision in an AI world; intelligence could increasingly move upstream toward identity, endpoint, code, browser and agent governance while Zscaler remained indispensable plumbing.

Q4 should be judged against that correction, not used to erase it.

On that basis the quarter delivered something much more useful than another beat: a split verdict. The growth transition we had been waiting for became considerably more tangible. Organic net-new ARR accelerated, non-seat products kept taking share of new business, Z-Flex took another extraordinary step higher, Zero Trust Everywhere adoption jumped, and the sales disruption that haunted Q3 largely disappeared from the numbers.

The cash-flow test, though, did not pass. And management’s FY27 guide still assumes remarkably little of Q4’s acceleration survives.

The first finding makes me more constructive. The second prevents the old bull case from simply returning.

What Did Not Change

Across this series I have used several analogies to describe Zscaler: Salesforce, ServiceNow, and most recently the cardiovascular system. The changing metaphors reflected a real evolution in the thesis, but they also risked making the underlying view appear less stable than it was. The architecture thesis has barely changed. What changed was how much economic dominance I was willing to infer from it.

Zscaler’s original insight was that the corporate network was the wrong thing to trust. Instead of putting a user onto that network and then protecting everything behind it, the Zero Trust Exchange connects a specific entity to a specific application according to policy. The architecture governs an interaction rather than protects a location.

That becomes more relevant as the entity changes from an employee to a workload, branch, device, model or autonomous agent. Zscaler’s 10-K now explicitly describes the Exchange as connecting users, devices, workloads and AI agents to applications, models and data according to identity and business policy.

Q3’s correction was that circulation is not cognition. The cardiovascular system can be essential without being the brain. If the highest-value security decision increasingly occurred in identity, endpoint or code before traffic ever reached Zscaler, then the Exchange could remain extremely durable without becoming the control point I had once assumed.

The burden of proof therefore became economic. Zscaler had to demonstrate that workloads, branches, data, agents and the products surrounding them could become large enough to alter company-level growth before the original user-security business matured.

Q4 began to do exactly that.

The Transition Appears in the Numbers

The headline quarter was excellent. Revenue reached $898 million, ARR $3.77 billion, net-new ARR $246 million and non-GAAP operating margin a record 24.3%. The $246 million net-new ARR result also just cleared the roughly $245 million investor bar we had identified before earnings.

But those are not the figures that change the thesis. These are:

Excluding Red Canary, net-new ARR was $232 million and grew 17%, after growing 7% in FY25 and 10% in the first half of FY26. Zero Trust Everywhere enterprises have moved from more than 350 at FY25-end to more than 700 in Q3 and more than 950 in Q4.

Z-Flex supplies the commercial connection. Q4 TCV exceeded $770 million, up more than 60% sequentially, while FY26 Z-Flex customers increased ARR by nearly 30% on average.

I used to describe this as lock-in; that was too strong. Z-Flex is better understood as commercial pre-commitment. Once a customer has committed a multi-year budget, adding another Zscaler capability no longer requires starting procurement from scratch. The next product is easier to adopt than the first.

That creates what I think is Zscaler’s more defensible economic advantage: economies of scope around an enforcement point. More of a customer’s estate comes under common policy; each additional use case increases the usefulness of consistent enforcement; Z-Flex reduces the friction required to expand that footprint.

This is why the 30% non-seat figure matters more to me than the revenue beat. An employee-seat business grows with headcount. A security system priced around workloads, branches, data volumes and machine activity can grow with the number of interactions being secured.

AI matters because it radically increases those interactions.

Upstream Was Too Binary

Jay Chaudhry made the distinction unusually clearly:

“While identity solutions answer ‘who’ is requesting access, our in-line platform determines ‘what’ that user or agent should be allowed to do.”

Q3 framed the emerging security world as anticipation versus interception. That now looks too binary.

Symmetry’s Access Graph maps relationships among entities, applications and data. Data Security and Security for AI add context about sensitive information, models, prompts and permissions. The Zero Trust Exchange applies policy to the actual interaction. Agentic SecOps, combining Zscaler telemetry with Red Canary’s operating expertise, is intended to investigate and remediate after something happens.

The ambition increasingly looks like:

understand → decide → enforce → observe → remediate.

That does not mean Zscaler suddenly becomes the brain. Microsoft begins with identity, endpoint and distribution; CrowdStrike with endpoint telemetry and response; Palo Alto with network-security incumbency and consolidation. Each has a legitimate claim on an important part of the decision chain.

Zscaler’s more interesting proposition is that intelligence gathered elsewhere becomes more useful when attached to an unusually powerful place to turn that intelligence into action.

In other words, Zscaler may not need to own every upstream security decision. It needs to remain the privileged place where those decisions are enforced.

Q4 makes that possibility materially more credible.

The Bill We Do Not Yet Understand

There is, however, a potentially uncomfortable consequence to the same transition.

As Zscaler secures more workloads, branches and machine interactions, the Exchange itself must carry more traffic and make more real-time decisions. Zscaler now processes more than 750 billion transactions per day, compared with roughly 500 billion in earlier periods.

At the same time, the cash-flow economics have weakened. FY26 FCF margin fell to 23%, and FY27 guidance remains only 23–23.5%. CapEx is expected to reach the low teens as a percentage of revenue.

It is tempting to make those facts one elegant story: machine traffic expands, Zscaler needs more equipment to inspect it, and the new growth model is inherently more capital intensive.

Q4 does not prove that.

Management attributes much of the increase to higher memory, storage and processor prices, tighter availability, and purchases that were accelerated when equipment became available. There are therefore at least three things inside current CapEx: component inflation, purchasing timing, and potentially a structurally larger equipment requirement as non-human traffic grows.

The distinction matters enormously.

Our old bull case effectively wanted the revenue opportunity of traffic-based security with the capital intensity of seat-based SaaS. That may have been too generous. But assuming FY27’s unusual equipment spend is the permanent economics of the business would be equally premature.

This is now a second fundamental question alongside growth: what does machine-scale enforcement cost once component prices and purchase timing normalize?

We do not know yet.

The Model Still Does Not Believe the Quarter

There is an even cleaner contradiction.

FY27 ARR guidance is $4.396–4.426 billion. At the midpoint, subtracting Q4’s $3.771 billion ending ARR implies about $640 million of FY27 net-new ARR. Organic FY26 ARR increased by roughly $615 million.

In other words, management’s FY27 guide implies only about:

4% growth in annual net-new ARR.

Q4 organic net-new ARR grew 17%.

Oppenheimer challenged Kevin Rubin on exactly this calculation. Rubin did not dispute it. He instead pointed to the sales-leadership transition, the ramp of Agentic SecOps, stable 115% NRR, investment in new-logo coverage and AI tailwinds. He also said Zscaler had not specifically called out the contribution from Security for AI in guidance.

Both numbers cannot describe the normalized future.

Either Q4 was an unusually strong fiscal-year-end quarter and the FY27 model is approximately right, or management has built considerably more conservatism into the annual guide than current demand warrants.

That is also why Zscaler has not yet recovered like several cybersecurity peers after the AI scare. The argument that AI itself disrupts Zscaler looks increasingly weak. What remains unresolved is whether Zscaler’s strategic relevance translates into faster financial growth.

CrowdStrike and Palo Alto have already persuaded growth investors of that connection. Zscaler has connected its story to leading indicators while still formally guiding like a 16–17% company.

The market is entitled to wait.

There are disclosure reasons to wait too. Security for AI bookings increased more than 50% sequentially and pipeline 75%, but management still does not provide a clean Security for AI ARR figure. Z-Flex TCV is spectacular, but longer contracts are not the same as recurring-revenue acceleration. RPO growth has fallen from 35% in Q1 to 27% in Q4, only modestly ahead of revenue, meaning our old “speedometer versus engine” argument has lost much of its informational advantage.

The mechanism increasingly looks real. The model still assumes it barely matters. One of them must move.

Three Prices, Three Zscalers

For a company at this stage, I would value the scenarios primarily on EV/revenue, with growth, operating margin and FCF margin determining what multiple is deserved. FCF is an important quality check; it should not be allowed to answer an unresolved capital-intensity question by itself.

Our estimates. Price ranges use FY29 revenue implied by the stated CAGR, approximately 180 million diluted shares and current net cash as a conservative capital-structure assumption.

The bear case does not require Zscaler to lose relevance. The user-security core simply matures faster than non-seat security can compensate, while higher capital intensity prevents a premium valuation.

The base case is more interesting because it requires no return to hypergrowth. Zscaler compounds revenue around 17–18%, improves FCF toward the mid-20s, and proves that workloads, branches, data and agents can sustain the business as seat growth matures. Once the Q3 uncertainty has been resolved favorably, I think a 7.5–8.0x terminal revenue multiple is reasonable; a business still growing high-teens with mid-20s cash margins would deserve more than the valuation of a permanently decelerating SASE vendor.

The bull case requires actual company-level reacceleration. Security for AI and Agentic SecOps need to become meaningful recurring businesses, non-seat adoption needs to move materially beyond 30%, and the incremental equipment burden has to prove scalable enough for FCF margins to approach the high-20s.

Using midpoint outcomes gives a probability-weighted three-year value around $245, versus roughly $178 today. The important change from Q3 is not that the bull case deserves to be inflated again. It is that Q4 makes the pure maturity case less likely.

That is a healthier reason for fair value to rise.

The Trial Continues

The variant perception has narrowed considerably over the last year. It is no longer “the market sees software; we see security infrastructure.” That was too easy.

The market now sees a strategically relevant company whose growth is settling around 16–17% and whose cash economics may be becoming somewhat more capital intensive. Management wants investors to see a deliberately cautious guide sitting on top of accelerating organic net-new ARR, record sales productivity, rapidly growing non-seat security and AI products that are still early.

I think the market is right to withhold the old premium multiple. I also think it is increasingly difficult to look at 17% organic net-new ARR growth, >100% non-seat ARR growth, 950+ Zero Trust Everywhere enterprises and $770 million of Z-Flex TCV and conclude that nothing underneath the headline growth rate is changing.

Q3 correctly raised the burden of proof. Q4 has begun to meet it.

The remaining tests are clearer now. Organic net-new ARR needs to stay in the mid-teens rather than collapse back toward the ~4% annual growth embedded in guidance. Non-seat contribution needs to move toward 35%. Z-Flex must convert commitment into ARR. Security for AI needs to graduate from bookings and pipeline to recurring revenue. Agentic SecOps needs to become financially visible in the second half.

And the newest test may be the most important one we were not asking three months ago: whether FY27’s elevated equipment spending normalizes as prices and purchase timing normalize, or whether carrying machine-scale traffic really does require more capital than our old model assumed.

The architectural thesis did not change. Q3 changed what I was willing to infer from it. Q4 changes the evidence.

The transition is increasingly visible in the operating data. What we still do not know is the normalized cost of running it.

That is what FY27 now must answer.

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• The prediction arrived: product-revenue growth accelerated from 29% to 37%, and management says AI contributed roughly half of that seven-point improvement.

• CoCo is becoming more than an interface: by shortening migrations, reducing implementation friction and helping customers build faster, it can pull future Snowflake consumption into the present.

• The moat is still unproven: growth has accelerated, but retention has not yet reaccelerated, AI gross margins are lower, and Snowflake still needs to show that faster creation leads to deeper customer dependence rather than just more expensive consumption.

In December 2025, after Snowflake reported product-revenue growth of 29%, I argued that the most important number was not revenue but the 800-basis-point gap between revenue growth and RPO growth. Enterprises were committing faster than they could deploy (Snowflake 3QFY26 Earnings: The 800 Basis Points Nobody Noticed) I ended with a prediction: “The ‘slowing growth’ narrative has about two quarters left to live.”

The stock fell 8%, then kept falling. By April it had reached $118, down 56% from year-end. The market’s argument was no longer that Snowflake was experiencing a temporary optimization cycle. It was that agentic AI would make the company structurally less necessary. If agents could reach into enterprise systems, retrieve data and act on it directly, why would anyone need a centralized data layer standing in the middle?

That was the point of maximum discomfort because the architecture made sense before the consumption evidence did. RPO accelerated, enterprises signed record nine-figure commitments, and Snowflake’s governed context became more valuable as models became interchangeable. Yet net revenue retention remained stuck near 125%, product growth had slowed, and management could not show how much AI was contributing. We were not arguing merely that the stock was cheap. We were arguing that the market had the direction of causality wrong: agents would increase the need for governed data and reduce the difficulty of creating workloads on Snowflake.

The evidence then arrived. Product-revenue growth accelerated to 34% in Q1 and 37% in Q2. Full-year guidance rose by $180 million last quarter and another $230 million this quarter. Asked how much of the seven-point acceleration since Q4 came from AI products, CEO Sridhar Ramaswamy answered: “I would roughly call it even.” Faster migrations and other core workloads supplied the balance. The two-quarter prediction turned out to be almost literal.

Last quarter, I wrote that CoCo was not the product; it was the interface (Snowflake 1Q27 Earnings: Second Interface Collapse). That description now looks incomplete. An interface helps a user operate a product. An engine changes what the product can produce. By reducing the time and expertise required to migrate data, build pipelines and deploy applications, CoCo is beginning to create demand for the underlying business.

This matters because Snowflake sells consumption, not access. Direct revenue from running CoCo is useful; the workload CoCo leaves behind may be far more valuable. The architecture has begun to appear in the income statement. We were right about the mechanism when the market was pricing the opposite. That does not mean Snowflake has already won the agentic enterprise.

The Big Fundamental Question

Can Snowflake turn its governed enterprise context into a self-reinforcing engine in which AI creates workloads faster, those workloads deepen core consumption, and the resulting usage strengthens Snowflake faster than commoditization and inference costs weaken it?

Powerful models have become broadly available, shifting the enterprise bottleneck from model capability to the ability to connect models safely to governed data and turn an idea into a working application. Snowflake becomes more valuable if CoCo and CoWork compress that process, because each new workload creates direct AI usage, recurring core consumption and deeper organizational dependence. The mechanism breaks if generic agents make workloads portable, retention fails to improve, or lower AI margins consume too much of the value being created.

The Interface Became a Demand Engine

Snowflake’s original achievement was a form of interface collapse. By separating storage from compute and automating much of the operational complexity of a traditional data warehouse, Snowflake reduced the expertise required to put data to work. Customers could begin with one workload and expand as consumption justified itself.

CoCo attempts a second collapse. A customer can use conversational language to write code, debug queries, build pipelines and migrate legacy systems. This is often described as productivity, but that word is too small. The strategically important effect is a reduction in the activation energy required to consume Snowflake.

There are three layers to the economics. First, customers consume CoCo, CoWork and other AI services directly. Second, those products induce core consumption by helping customers build things that run on Snowflake. Third, they accelerate consumption by pulling future workloads into the present.

Management offered a useful example. A large network-equipment manufacturer is completing a Teradata migration in less than three quarters; Snowflake estimates that the same project would previously have required two to three years. The economic value is not confined to the cost of the coding agent. Snowflake begins earning from the migrated estate perhaps eighteen months earlier than it otherwise would have.

Ramaswamy disclosed another metric that may matter more than the AI account count: time to 80% of purchased consumption. Snowflake measures how quickly each new-customer cohort reaches 80% of its contracted capacity, and management says the newest cohorts have improved “very, very visibly.” This is the mechanism our earlier writing anticipated. AI is not merely adding a new workload category; it is shortening the distance between a commercial commitment and actual consumption.

Snowflake did not disclose the magnitude of that improvement. It should. If time to 80% continues falling, it would link AI adoption, implementation velocity and revenue conversion in one number. If it stalls, the interface may be popular without being economically transformative.

Seven Points, One Engine

The quarter itself was unusually strong. Product revenue reached $1.49 billion, up 37%, while total revenue increased 35% to $1.55 billion. Snowflake added 692 net new customers, up 32%, reaching 14,554. Deployed customer use cases rose 89%; use cases won per account executive increased 43%.

CoWork expanded to 5,800 accounts, up nearly 11% sequentially. CoCo surpassed 9,100 accounts after adding more than 2,000 during the quarter. Management raised FY27 product-revenue guidance by $230 million to $6.07 billion, or 36% growth, and guided Q3 to 37–38%. This was not simply a quarterly beat flowing through the annual number. Snowflake embedded a higher observed consumption curve into the second half.

The historical thesis ledger now looks different:

Source: Snowflake Q2 FY27 earnings release, supplemental materials and earnings call.

There are reasons to retain some restraint. Net revenue retention was 126%, healthy but unchanged from Q1 rather than breaking higher. Total RPO growth slowed to 30%, though the 54% expected to convert over the next twelve months implies roughly $4.86 billion of current RPO and a much stronger near-term conversion signal. Snowflake also refused to disclose the consumption uplift of AI adopters relative to comparable non-adopters.

Management said the effect was “pretty noticeable” across cohorts, then supported the claim with anecdotes about new use cases. Those anecdotes establish breadth. They do not establish lifetime value. Q2 proves acceleration; it does not yet establish its duration or terminal level.

Management Is Selling the Destination

Management’s chosen sentence was unambiguous: “The agentic enterprise runs on Snowflake.” The company says AI brings new data and workloads onto Snowflake, its first-party products attract new users, and those users consume more across the core. Because Snofwflake already sits amid enterprise data, permissions, business definitions, models and workflows, management believes it can become the governed control plane connecting intelligence to action.

Our story now reconciles with management’s on the mechanism. We agree that direct AI revenue understates the opportunity. We agree that models become more useful when they operate against governed enterprise context. We agree that reducing the difficulty of migration and application development can accelerate the existing business.

The disagreement concerns how much of the destination has already been earned.

Our earlier articles sometimes moved too quickly from valuable to defensible. Data sharing creates ecosystem benefits, but it is not a telephone network in which every participant benefits from every new connection. Governance matters, but Databricks, Microsoft, Google and AWS can also build catalogs, semantic layers and governed model access. Snowflake possesses a strong starting position, not a permanent claim on the category.

The deeper potential advantage is the engine itself. Enterprise data and permissions already reside in Snowflake. CoCo understands the environment in which the work must run. Customers can create and deploy workloads faster. Those workloads generate consumption and new operating patterns, which Snowflake can use to make the next project easier.

If this engine produces faster time to value than competing approaches, Snowflake becomes progressively harder to displace. If generic agents can reproduce the same outcome across open formats and different systems, CoCo becomes an effective feature rather than a category-defining advantage.

Management has now supplied evidence for the mechanism we anticipated. It has not yet supplied evidence for the empire it is building around that mechanism.

One further observation is worth adding, because it will matter in the months ahead. Databricks was not named by management or by any of the nine sell-side analysts on the call — a silence that could reflect strategic positioning, analyst preference for the acceleration narrative, or simple absence. Whatever it reflects, the Databricks IPO, whenever it arrives, will produce the first public comparable that is not a consumption-software peer. That event will force sell-side reference classes to update in one direction or another. Until then, templates will default to the nearest available comp, which is Datadog.

The Wrong Reference Class?

There is a subordinate question worth naming, because it may determine how much of the destination Snowflake earns actually gets captured in the stock over the next four to six quarters.

At $118 in April, Snowflake was priced as an impaired data warehouse. At $376 today, it is increasingly priced as an AI-beneficiary consumption business — the sell-side reference class of Datadog, MongoDB and Confluent, adjusted for scale and growth. That reference class produces a fair-value ceiling in the low-to-mid $400s through FY28 on the raised revenue base, which is roughly where sell-side price targets will cluster over the next several weeks.

But if Snowflake genuinely becomes what management is claiming — a governed control plane for enterprise agent workloads — the correct reference class is not consumption software. It is a category that does not currently have a clean public comparable, and whose historical analogs (transaction networks, benchmark providers, certain regulated data services) have earned meaningfully higher multiples on the strength of amortization economics and structural switching costs. The gap between the two reference classes is roughly 400–500 basis points of terminal EV/revenue multiple, or approximately $100–175 per share on FY28 estimates.

This is not, on the current evidence, a case for a $700 stock. It is an observation that Snowflake’s valuation ceiling depends on which reference class the market eventually settles on, and that the market cannot settle on the higher class until measurable evidence forces the update — meaningful governance-related consumption disclosure, retention improvement among agent-heavy customers, or a public comparable that breaks the current templates.

Q2 strengthened the possibility of that reclassification. It did not complete it. A new reference class should be the consequence of measurable retention, governance economics and operating leverage — not a substitute for that evidence.

Faster Creation Has a Cost

The second unresolved question is who captures the value.

Non-GAAP operating margin reached 15.3%, more than four points higher than a year ago, and Snowflake raised its FY27 operating-margin guidance from 13.5% to 14.5%. Year-to-date headcount additions fell sharply once the Observe acquisition is excluded. Management also described material internal savings from using CoCo and CoWork across marketing, finance and sales. There is real operating leverage here.

Product gross margin tells the other side of the story. It fell to 74.7%, and Snowflake reduced the full-year outlook from 75% to 74%. CFO Brian Robins explained the sequence plainly: build a great product, drive adoption and revenue, and then work on the margin implications. That is rational product management, but it also confirms that AI economics come after adoption in the current priority order.

The non-GAAP presentation deserves context. Snowflake still reported a $263 million GAAP operating loss, while stock compensation was approximately $456 million, nearly 30% of revenue. This does not invalidate improving operating leverage, but shareholders are funding part of it through dilution.

The relevant question is not whether gross margin declines by one percentage point. It is whether Snowflake is sacrificing one point of margin to create several additional points of durable growth, or replacing high-margin core growth with lower-margin AI activity. A lower percentage margin can create more value if incremental gross-profit dollars compound faster and customer dependence deepens. Management has shown the growth; it has not shown that complete economic bridge.

This is also where the current variant perception lies. The market now understands that Snowflake benefits from AI. What it may still misunderstand is that the decisive variable is neither AI account adoption nor a standalone AI revenue figure. It is the time required to turn enterprise intent into recurring consumption — and the gross profit Snowflake retains from doing so.

Three Prices, Three Snowflakes

At roughly $375–380 after the results, the stock no longer prices Snowflake as an AI victim. The three-year outcome depends on what kind of company this engine creates.

Scenario estimates are our assumptions, anchored to FY27 product-revenue guidance of $6.07 billion.

In the bull case, CoCo and CoWork become the preferred interfaces for enterprise data and agent development. Retention rises above 130%, revenue compounds near 30%, and operating margins reach the high 20s. Snowflake becomes the governed action layer management is selling.

In the base case, AI makes Snowflake a better data business without creating a new category. Growth settles in the low-to-mid 20s, retention remains around 125–128%, and gross margins remain below historical levels. This is an excellent company, but not a mispriced one.

In the bear case, open formats and generic agents reduce differentiation. AI expands with weaker economics, retention falls below 120%, and workloads move between competing systems with limited friction.

The probability-weighted midpoint is approximately $370, close to the post-earnings price. Conviction in the business has increased; the asymmetry in the stock has disappeared. I would hold rather than chase here, become interested around $325–335, and require higher retention and stable gross margins before underwriting prices above $420.

The Question After the Question

A year ago, the central question was whether AI would disintermediate Snowflake. Last quarter, it became whether AI could accelerate Snowflake. Q2 has substantially answered both: AI is making Snowflake more useful and driving the core business faster.

Six signposts will determine how powerful the engine becomes. Product growth should remain above 35% through FY27 rather than quickly reverting. Net revenue retention needs to move toward 128% and eventually 130%; continued stability at 125–126% would limit the compounding thesis. CoCo should exceed 12,000 accounts by year-end with evidence of repeat production usage, while CoWork must graduate from account adoption to large departmental deployments. Product gross margin should hold near 74%; two quarters below 73.5% would suggest that AI is becoming structurally more expensive. Most important, Snowflake should disclose either the improvement in time to 80% consumption or the consumption uplift of comparable AI-adopting cohorts.

The thesis has advanced. Snowflake no longer needs investors to believe that AI might someday help its business; management has begun to quantify the contribution. What was mispriced at $118 was direction; what is now at issue at $376 is destination. The burden of proof has moved upward from adoption to durability, from acceleration to customer dependence, and from revenue to retained economics.

The interface has become the engine. The next two quarters will tell us whether it also becomes the moat.

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• The scale is no longer in doubt: Broadcom now sees AI semiconductor revenue rising from roughly $58 billion this year to $115 billion in FY27 and $230 billion in FY28, driven by six XPU customers and extraordinary deployment visibility.

• But Broadcom’s model is fundamentally different from NVIDIA’s: NVIDIA integrates the AI factory around itself; Broadcom gives hyperscalers the tools to integrate it themselves. That makes customers more capable and potentially less dependent on any individual supplier.

• Networking is the key test of economic power: if Broadcom can expand Ethernet content across both custom-silicon and GPU clusters, scale may translate into greater value capture. If not, Broadcom may become enormous while the hyperscalers retain most of the strategic leverage.

The easiest mistake in investing is to confuse becoming larger with becoming more powerful. Broadcom’s fiscal third-quarter report made that distinction impossible to ignore: management described an AI semiconductor business expanding from roughly $58 billion this year to $115 billion in fiscal 2027 and $230 billion in fiscal 2028 yet left unanswered whether Broadcom’s share of the resulting economics will improve at all.

We were right that Broadcom would become one of the indispensable industrial companies of the AI buildout. We were less right about why. Broadcom is not integrating the AI factory around itself, as NVIDIA does; it is giving Google, Meta, OpenAI, Anthropic and other hyperscalers the tools to integrate the factory themselves.

That makes Broadcom’s opportunity larger than we previously imagined, but its power less certain. Revenue reached $29.6 billion, adjusted earnings were $3.32 a share, and AI semiconductor revenue rose 221% to $16.7 billion, beating Bloomberg consensus by 0.5%, 2.8% and 4.8%. Broadcom guided fourth-quarter revenue to $34.8 billion and AI revenue to $21.7 billion, but the shares ended our frozen after-hours window near $364, down 0.8%, because the guide cleared consensus without clearing the buy-side whisper. The quarter proved the volume. It did not yet prove the value capture.

Bigger Is Not More Powerful

Our original Broadcom thesis was that AI would increasingly be governed by the “physics economy”: advanced-node wafers, HBM, packaging yields, optics, power, delivery schedules and cash commitments rather than announcements about models and partnerships. That thesis has held up. We then stretched it too far by describing Broadcom as the prime contractor for the AI rack. Last quarter we corrected the metaphor: Broadcom does not own the rack but makes other companies’ systems deployable. Q3 supports that narrower and more durable formulation.

The evidence on execution is formidable. Broadcom now serves six XPU customers; Q3 XPU shipments grew more than 3.5 times year over year, AI networking grew more than 2.5 times, and management says supply has been secured for the $115 billion and $230 billion forecasts. Google is receiving two TPU generations, OpenAI’s first accelerator is shipping, Meta begins production shipments in Q4, and Anthropic is expected to become Broadcom’s largest XPU customer in fiscal 2027. These are no longer isolated design wins. Broadcom is operating several of the world’s most demanding semiconductor programmes simultaneously, although Hock Tan was careful to distinguish visible demand from deployed capacity:

“It will be 30 GW between the customers we have, the six customers we have. Question is, will all 30 come into production within these two fiscal years? We are giving you a judged number of $115 billion of chips to these guys in fiscal 2027, another $230 billion in 2028, which would add up, I know, to about $350 billion. Another way of saying is, we believe with a pretty high degree of confidence, we will ship $350 billion of AI semiconductors to these customers in the next two years. That’s the best way to look at it. It doesn’t necessarily mean all 30 GW need to have been deployed within that period.”

Thirty gigawatts is a demand envelope; $350 billion is management’s judgment about what can be funded, built, supplied and commissioned. If that forecast and Broadcom’s $20 billion–$30 billion of content per deployed gigawatt use comparable definitions, the arithmetic implies 11.7–17.5GW operating during those two years, or 39%–58% of the envelope. That is our inference, not guidance. It makes site readiness, financing and execution as important as chip demand.

The more consequential comparison is with NVIDIA. NVIDIA integrates the AI factory around itself: GPU, CPU, NVLink, networking, systems, algorithms and CUDA. Its stated revenue opportunity rises from roughly $18 billion per gigawatt with Hopper to $25 billion with Blackwell and $40 billion with Vera Rubin because each generation adds more NVIDIA-controlled content. NVIDIA uses scale to capture more of the system.

Broadcom enables the opposite architecture. The customer owns the workload, software, model and distribution, then uses custom silicon to internalize economics that NVIDIA would otherwise capture. Tan stated the proposition directly:

“The lesson here is when you co-develop a chip that is optimized for your particular LLM workloads, you will outperform any GPU. Like GPUs, Jalapeño demonstrates that it can also run other frontier models, and you can do all this at half the cost of a GPU.”

This is a management claim about selected workloads, not an independently demonstrated universal advantage. If it holds, the first-order benefit accrues to the customer: greater volume amortizes the design, lower token costs stimulate usage, and higher usage justifies more custom capacity. NVIDIA makes customers more dependent on NVIDIA; Broadcom helps customers become less dependent on any merchant compute supplier, potentially including Broadcom.

The strongest bull counterargument is what I would call the embedded-partner hypothesis. Each successful generation places Broadcom’s engineers, SerDes IP, packaging knowledge, interconnect design and supply commitments more deeply inside the customer’s roadmap. The hyperscaler retains strategic control, but changing industrial partners becomes progressively more expensive. Broadcom can also reuse design methods and supply knowledge across otherwise bespoke programmes. That could turn scale into lower development costs, stronger access to scarce components and a higher probability of winning the next generation.

The financial evidence is not yet conclusive. Q3 consolidated gross margin fell 210 basis points sequentially to 75%, and semiconductor gross margin was corrected on the call to approximately 67%, as memory-rich XPUs became a larger part of the mix. Operating margin still reached 67.9% because revenue grew far faster than operating expense. That is excellent execution, but the distinction matters: operating margin measures leverage during the ramp, while gross margin helps reveal how incremental value is divided among Broadcom, memory, foundry, packaging and the customer. We were right about scale and wrong about control. Broadcom is becoming essential to the AI factory, but the hyperscaler still owns the architecture, workload and distribution.

Hock Borrowed NVIDIA’s Grammar, Not Its Economics

Management made three revealing substitutions on the call. The horizon moved from Q4 to fiscal 2027–2028, gross margin was displaced by operating margin, and quarterly chip revenue became dollars per gigawatt. Each substitution is economically defensible. Each also directs attention away from a present weakness: the Q4 guide did not clear the whisper, product mix is compressing gross margin, and Broadcom’s content per gigawatt is expected to remain broadly stable while NVIDIA’s rises.

The timing was striking. Seven days after NVIDIA broke its quarter-only convention by presenting a supply-constrained fiscal 2028 outlook in gigawatts and revenue per gigawatt, Broadcom used almost the same grammar to describe its future. Broadcom’s customer roadmaps and supply commitments clearly predated NVIDIA’s call; what appears borrowed is the presentation architecture, not the demand. Hock borrowed NVIDIA’s language for describing the future and used it to describe the opposite system customer-owned rather than supplier-integrated.

Management’s story is that Broadcom knows the customers, workloads, chip roadmaps, supply requirements and deployment schedules well enough to forecast two years of extraordinary growth. I believe it. Our story is that this visibility establishes Broadcom as the preferred execution partner but does not establish increasing bargaining power or a rising share of customer economics. The stories reconcile on demand and execution. They diverge on value capture.

This reporting system will naturally appeal more to long-duration growth investors than to quarterly beat traders, although deliberate investor-base curation remains an inference. Investors should not accept management’s choice between gross and operating margin. Both matter, and the tension between them is part of the story rather than an inconvenience to be explained away.

The Network Is the Test

Networking is the best observable test of whether greater deployment can make Broadcom more powerful because it can spread across bespoke XPUs and third-party GPUs. XPUs accounted for 73% of Q3 AI revenue, leaving roughly 27% for networking, even though networking itself grew more than 2.5 times. Our previous requirement that networking remain above 40% of AI revenue was wrong: an exploding XPU denominator makes mix an unreliable measure of network strength.

The better measures are absolute networking revenue, networking dollars per deployed gigawatt, attachment within Broadcom-designed XPU clusters, and penetration of GPU clusters. Charlie Kawwas said Tomahawk 6 is deployed by “pretty much all” hyperscalers building XPUs with Broadcom and also by customers not using Broadcom XPUs; Tomahawk 6 and Tomahawk Ultra are appearing in both XPU clusters and some GPU clusters. If open Ethernet wins more of the scale-up domain while Tomahawk and Jericho retain scale-out leadership, the network can turn customer independence into Broadcom leverage.

The NVIDIA comparison still requires discipline. NVIDIA’s $40 billion-per-GW figure already includes NVIDIA networking and other system content, so Broadcom Ethernet in a GPU cluster takes a share of that wallet rather than adding another $20 billion–$30 billion on top. The contest is over who captures a finite AI-factory budget. Networking is where Broadcom can participate whether the compute is custom or merchant, making it the clearest bridge between revenue scale and economic power.

Certainty Is the Product

Frontier laboratories want more compute than their cash flows and credit profiles can independently finance. Broadcom’s financing vehicle bridges that mismatch with third-party capital; the first $35 billion tranche supports a 1GW Anthropic deployment, while Broadcom may provide residual-value guarantees. Silicon is the deliverable, but certainty is the product: certainty that the design will work, supply will arrive, the site will be financed, and successive generations will match the customer’s model roadmap.

That service can deepen relationships and pull revenue forward, but it changes the risk. Asked whether the roughly $29 billion of maximum exposure referenced from the previous filing indicated future backstops, management declined to provide a ceiling:

“We don’t have anything to announce today on residual value guarantees or backstop. There’s nothing new to add. The numbers that you outlined around what we’ve already done remain true. As I mentioned in my prepared remarks, we’re going to evaluate any strategic financing really on a deal-by-deal basis. We expect that any that we do in the future, they’re going to have unique features, and they’re going to be tailored specifically to the lab and to the investor needs. I can’t give you an overarching look at what’s the max and what each one’s going to look like.”

NVIDIA argues that its financed systems are fungible and redeployable, and its structure pays it on the hardware plus a share of rental revenue above a floor. Broadcom’s customer-specific XPUs are likely less fungible, while no comparable participation in application or rental upside has been disclosed. Financing does not prove weak pricing power; it proves that desired deployment exceeds some customers’ independent financing capacity. It creates value only if Broadcom is adequately compensated for deployment and credit risk.

Broadcom can absorb some risk. Q3 free cash flow was $13.7 billion, or 46% of revenue, and VMware’s 94% gross margin and 84% operating margin provide substantial ballast. Yet free cash flow missed consensus by 6%, receivables absorbed $2.86 billion of cash during the quarter, and Q4 capital expenditure is expected to rise from $532 million to $1.4 billion. The physics economy must eventually collect the cash it manufactures.

Three Prices, Three Worlds

At the frozen after-hours price of approximately $364, the bear case carries a 20% probability. Fiscal 2026–2029 revenue grows at 22%–25% annually, operating margin falls toward 56%–59%, fiscal 2029 adjusted EPS reaches $21–$24, and a 12–14 times multiple produces approximately $280–$330. This is the world in which Broadcom achieves volume without power: sites slip, customers preserve alternatives, networking capture disappoints, and financing obligations grow faster than cash flow.

The base case carries a 55% probability. Revenue compounds at 41%–46%, operating margin settles near 62%–64%, adjusted EPS reaches $35–$38, and a 17–19 times multiple yields approximately $630–$720. Broadcom remains the preferred industrial partner for customer-owned AI, the fiscal 2027 forecast largely converts, networking broadens, and financing stays contained. The company becomes indispensable without acquiring NVIDIA-like control.

The bull case carries a 25% probability. Revenue grows at 53%–57%, operating margin holds at 65%–67%, adjusted EPS reaches $43–$47, and a 21–23 times multiple supports $900–$1,050. Networking and reusable co-design knowledge turn greater scale into higher value capture, while financing proves both profitable and limited. A probability-weighted value near $675 implies an annualized three-year return of approximately 23%–24%, including dividends, but most valuation variance comes from how much of the $230 billion forecast is deployed, recognized, collected and achieved without underpriced guarantees.

The expected return is attractive, but another increment of conviction must be earned through networking growth across XPU and GPU clusters, fiscal 2027 revenue converting into cash, and maximum financing exposure becoming measurable. I would reduce the position if customer savings coincide with declining Broadcom gross economics or if receivables, deployment delays and guarantees grow faster than free cash flow.

Necessary Is Not Yet Powerful

Q3 increases my confidence that Broadcom will become much larger, while increasing only modestly my confidence that it will become structurally more powerful. The quarter confirmed the physics thesis, demonstrated rare execution across six programmes, and advanced Ethernet as the reusable layer across fragmented compute architectures. It also showed why the prime-contractor thesis was too generous: the customer integrates the system, owns the workload, captures the cost advantage and deliberately preserves alternatives.

Two observations would strengthen the thesis. Absolute networking revenue and networking content per deployed gigawatt must rise across both XPU and GPU clusters, and the fiscal 2027 ramp must convert into cash without a comparable increase in guarantees or working capital. Two developments would weaken it: falling semiconductor gross economics despite operating leverage, or hyperscalers adding alternative design partners without impairing their roadmaps.

The fiscal 2026 10-K and the next two deployment updates should resolve more than the next quarterly beat. Broadcom has become the company that makes hyperscaler independence possible. The next question is whether it can make itself indispensable without making its customers dependent.

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• The strategic thesis strengthened: Credo is moving beyond selling individual interconnect components toward understanding the health of the entire link, with PILOT turning diagnostics and failure prediction into a potentially system-level advantage.

• But technical importance is not yet economic ownership: the first major DustPhotonics photonic-chip wins did not include Credo DSPs, showing that customers may value Credo’s technology while still preserving modular supplier choice.

• The cost of the ambition is already visible: inventory, receivables and R&D are rising well ahead of the proof that optical attachment, margins and cash conversion will resemble AEC economics. The next test is whether broader technical relevance turns into broader wallet share.

A customer recently asked Credo whether its software could detect the slightest electrostatic-discharge damage to an optical transceiver. Not enough damage to stop the transceiver from working, but enough to create a latent defect that might eventually bring down a connection. Credo found a way to detect it.

“Now when we’ve got customers lighting up racks, they can determine immediately if a transceiver was mishandled and needs to be replaced.” — Bill Brennan, CEO

The striking part of this story is not that Credo’s technology worked. It is that the customer asked Credo to understand a failure before it occurred. A company customers call when its component fails is a supplier; a company customers call when the connection might fail is becoming something more important.

That distinction captures what management is selling. Credo does not want to be understood as an AEC company adding a collection of optical products. It wants to own enough of the connection from SerDes and DSPs to photonic chips, transceivers, firmware and diagnostics to understand how the entire link behaves. The more of the link Credo can see, the better it should become at identifying failure modes, improving its products and winning more of the customer’s next design.

The big fundamental question is whether that creates a compounding system or merely a wider product catalogue. The mechanism strengthens if every additional product gives Credo more visibility, that visibility produces better diagnostics and designs, and customers respond by adopting more Credo components. It breaks if customers value Credo’s engineering while continuing to assemble the link from whichever individual components they prefer.

Q1 supplied the best evidence yet that the first half of this mechanism is real. It left the second half unresolved.

The Test We Set Was Incomplete

Our previous article ended with a test:

“Credo has moved the proof from copper to light. Now the margin line will tell us whether the toll road followed.”

The logic was straightforward. AEC demonstrated that customers would pay Credo for a complete, reliable copper connection. If Credo could move into optics without sacrificing its economics, its advantage had travelled beyond the product that created the company.

Q1 appeared to pass. Revenue reached $479 million, up 115% year over year, while non-GAAP gross margin held at 68%. Credo reported record optical DSP revenue, recognized its first silicon-photonics revenue following the DustPhotonics acquisition and began production shipments of ZeroFlap optical transceivers. Management continues to expect more than $600 million of optical revenue in FY2027, with DSPs, photonic chips and ZeroFlap each contributing more than $100 million.

The problem is that consolidated gross margin cannot answer a product-level question. AEC remains Credo’s largest business, and management did not disclose optical revenue, optical mix or optical gross margin. The established business can therefore preserve the consolidated number while the economics of the emerging business remain invisible.

We chose the right question but an insufficient metric. Credo has now proved that its technology can travel into optics. It has not proved that AEC-like economic ownership travels with it.

The Layer Between Products

Management’s story begins with heterogeneity. Future AI systems will not use one architecture, protocol or physical medium. Copper will remain appropriate for shorter reaches; optics will dominate longer ones; different scale-out and scale-up connections will demand different combinations of power, latency, reach and serviceability.

“Future AI systems will combine optical and copper interconnects across different reaches, protocols, and topologies.” — Bill Brennan

That world rewards product breadth, but breadth alone is not the thesis. Broadcom, Marvell and several optical suppliers can also provide pieces of the connection. Credo’s claim is that its products are joined by a common capability: understanding whether a link is healthy, why it is degrading and what should happen before it fails.

PILOT is the layer that potentially turns a group of products into a system. Brennan compared existing link monitoring to a green light when a connection works and a red light after it fails. Credo is trying to add the yellow “check engine” light in between. Its hardware continuously measures indicators such as eye height, signal-to-noise ratio and error patterns; its software looks for deterioration and can help remove a vulnerable processor or transceiver before a link flap disrupts the cluster.

This matters because AI clusters contain tens or hundreds of thousands of interdependent connections. Management says link instability can cost more than 10% of processor utilization, while slow cluster commissioning can leave enormously expensive equipment idle for weeks. A reliable connection is therefore valuable not because the cable itself is expensive, but because everything connected to it is.

The ESD example turns that argument from a presentation into customer behaviour. Credo is learning about defects beyond the normal responsibility of a component supplier. The accumulated failure cases should influence future DSPs, photonic chips, firmware and qualification methods. More deployments can create better products, which can make the next design easier to win.

This is not an automatic data network effect. One additional link does not mechanically improve every other link, and customers may limit how operational data is shared. The advantage compounds only if learning changes product performance and product performance increases attachment. PILOT is strategically important when diagnosis repeatedly becomes design; otherwise it remains an excellent feature bundled with hardware.

Importance Is Not Ownership

The ESD request and the DustPhotonics wins are mirror images. In the first, a customer asked Credo to understand the behaviour of the entire connection. In the second, customers selected one Credo component while declining another. Q1 therefore contained both the strongest evidence for integration and the clearest warning against assuming attachment.

The DustPhotonics wins provide the quarter’s cleanest counterargument. Asked directly whether the first two major photonic-chip design wins included a Credo DSP, Brennan answered:

“These first two major design wins that we are talking about do not include the DSP.”

That is simultaneously good and incomplete news. The wins validate the acquired photonic technology independently, which suggests Credo purchased a genuinely competitive product rather than a capability that only works inside its own bundle. They also show customers retaining control over component selection. Credo has won a place in the optical module without winning the integrated DSP-and-photonic-chip combination that management says should improve performance, power, yield and cost.

The modular challenge described in our previous article therefore remains. Customers may prefer an architecture in which validated components can be combined across suppliers. Credo believes that owning more of the stack will allow it to innovate faster and deliver better system outcomes. The customer will decide how much integration is worth and how much supplier dependence it is willing to accept.

This is the distinction the next phase of the thesis must preserve: Credo may own the customer’s problem before it owns the customer’s bill of materials. Technical influence often precedes commercial capture, but the second does not inevitably follow the first.

The Cost of Becoming a System

The financial statements show the cost of this ambition earlier than the product disclosures show its reward. Revenue grew 10% sequentially, but inventory increased 25% to $313 million and receivables increased 24% to $289 million. Free cash flow was $83 million against non-GAAP net income of $236 million, a conversion rate of 35%. Non-GAAP operating expenses reached $95 million, above management’s prior range, as Credo increased research and development across a much broader portfolio.

It would be easy to treat this as an earnings-quality complaint. That misses the strategic issue. Moving from a dominant AEC product into DSPs, photonic chips, complete optical transceivers, near-package optics and new scale-up connections requires more designs, qualifications, supply commitments and inventory configurations. Credo must incur those costs before the new products reach volume.

Management is explicit about the bet. Brennan said the company was “leaning in from a supply-chain standpoint” and playing the long game. The full-year arithmetic shows the scale of that commitment. More than 85% growth on FY2026 revenue of $1.335 billion implies at least $2.47 billion in FY2027. Q1 revenue plus the midpoint of Q2 guidance produces approximately $1.01 billion in the first half, leaving more than $1.46 billion for the second half—or over $730 million per quarter, 45% above the first-half quarterly average.

Inventory is not proof that this demand exists. It is proof that management is willing to finance its forecast. If inventory converts into the promised optical ramp while gross margin remains in the high 60s and cash conversion recovers, Q1 will look like the necessary investment behind a larger company. If product attachment remains limited and inventory continues outgrowing revenue, Credo will have broadened its technical capabilities faster than its economic ownership.

The balance sheet is therefore not a side issue. It is where the cost of management’s strategy has become measurable.

What Changed in Our View

Before Q1, we believed AEC gave Credo a wedge into the customer and that optics would determine whether its reliability advantage could travel. We expected gross margin to reveal the answer.

The quarter strengthened the strategic thesis. Optical participation is no longer theoretical. PILOT has moved from a diagnostic claim to a real customer workflow. Credo’s involvement increasingly extends beyond whether its own component functions, and management is committing capital as though the second-half ramps are real.

The quarter also made the economic thesis more demanding. Consolidated gross margin cannot isolate optical economics. The first photonic-chip wins do not attach Credo DSPs. PILOT has no disclosed pricing, attachment or separately observable contribution. Working capital and operating expenses are rising before investors can see how much of the broader connection Credo will capture.

I think Credo is building a genuine system-level advantage, and Q1 increased my confidence in that conclusion. What I am not yet prepared to assume is that customers will buy the entire system simply because they increasingly depend on Credo’s understanding of it. The probability that Credo becomes strategically important across copper and optics has increased; the amount of that importance it will monetize remains the central uncertainty.

Three Futures

The scenarios should be understood as three outcomes for the same mechanism, not three predictions about the semiconductor cycle.

The share-count assumptions reflect different outcomes for dilution and Credo’s ability to offset it through future cash generation. The ranges expose the operating assumptions that matter; they are not predictions of the path the shares take between now and then.

In the bear case, customers select Credo products but assemble the link modularly; optical margins dilute AEC economics, development and inventory remain elevated, and breadth does not produce attachment. In the base case, Credo owns the portions of the link where reliability is most valuable, while component and complete-system sales coexist. In the bull case, telemetry improves product design, better products deepen customer dependence, and that dependence produces greater attachment across DSPs, photonic chips and transceivers.

The signposts follow directly. By Q3, revenue growth should overtake inventory growth and cash conversion should recover above 60%. Optical revenue should exceed $600 million for FY2027 without consolidated gross margin falling below 67%. Over the next year, Credo should show that photonic-chip wins attach its DSPs, and that PILOT influences repeatable product adoption rather than isolated customer engagements. Failure on those measures would not mean the products are weak; it would mean the proposed compounding mechanism is.

The Company Customers Call

Credo did not cause the customer’s ESD problem. That was precisely why the incident mattered. The company was valuable not because its component worked, but because it could explain why the connection might eventually fail.

That is a more ambitious role than selling a cable, DSP or photonic chip. It gives Credo the opportunity to learn across products and become embedded in how customers design, qualify and operate their networks. It also requires Credo to carry more products, inventory and development expense before that opportunity becomes revenue.

The previous chapter of the Credo story asked whether its advantage could travel from copper into light. Q1 suggests that its technical relevance can travel. What remains unanswered is whether product attachment, margins and cash returns will travel with it.

Credo is learning to understand more of the link. The next question is how much of the link it will own.

General Disclaimer: The information presented in this communication reflects the views of the author and does not necessarily represent the views of any other individual or organization. It is provided for informational purposes only and should not be construed as investment advice, a recommendation, an offer to sell, or a solicitation to buy any securities or financial products.

While the information is believed to be obtained from reliable sources, its accuracy, completeness, or timeliness cannot be guaranteed. No representation or warranty, express or implied, is made regarding the fairness or reliability of the information presented. Any opinions or estimates are subject to change without notice.

Past performance is not a reliable indicator of future performance. All investments carry risk, including the potential loss of principal. This communication does not consider the specific investment objectives, financial situation, or particular needs of any individual.

The author and any associated parties disclaim any liability for any direct or consequential loss arising from the use of this material and undertake no obligation to update or revise it.

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• The strategic thesis strengthened: Credo is moving beyond selling individual interconnect components toward understanding the health of the entire link, with PILOT turning diagnostics and failure prediction into a potentially system-level advantage.

• But technical importance is not yet economic ownership: the first major DustPhotonics photonic-chip wins did not include Credo DSPs, showing that customers may value Credo’s technology while still preserving modular supplier choice.

• The cost of the ambition is already visible: inventory, receivables and R&D are rising well ahead of the proof that optical attachment, margins and cash conversion will resemble AEC economics. The next test is whether broader technical relevance turns into broader wallet share.

A customer recently asked Credo whether its software could detect the slightest electrostatic-discharge damage to an optical transceiver. Not enough damage to stop the transceiver from working, but enough to create a latent defect that might eventually bring down a connection. Credo found a way to detect it.

“Now when we’ve got customers lighting up racks, they can determine immediately if a transceiver was mishandled and needs to be replaced.” — Bill Brennan, CEO

The striking part of this story is not that Credo’s technology worked. It is that the customer asked Credo to understand a failure before it occurred. A company customers call when its component fails is a supplier; a company customers call when the connection might fail is becoming something more important.

That distinction captures what management is selling. Credo does not want to be understood as an AEC company adding a collection of optical products. It wants to own enough of the connection from SerDes and DSPs to photonic chips, transceivers, firmware and diagnostics to understand how the entire link behaves. The more of the link Credo can see, the better it should become at identifying failure modes, improving its products and winning more of the customer’s next design.

The big fundamental question is whether that creates a compounding system or merely a wider product catalogue. The mechanism strengthens if every additional product gives Credo more visibility, that visibility produces better diagnostics and designs, and customers respond by adopting more Credo components. It breaks if customers value Credo’s engineering while continuing to assemble the link from whichever individual components they prefer.

Q1 supplied the best evidence yet that the first half of this mechanism is real. It left the second half unresolved.

The Test We Set Was Incomplete

Our previous article ended with a test:

“Credo has moved the proof from copper to light. Now the margin line will tell us whether the toll road followed.”

The logic was straightforward. AEC demonstrated that customers would pay Credo for a complete, reliable copper connection. If Credo could move into optics without sacrificing its economics, its advantage had travelled beyond the product that created the company.

Q1 appeared to pass. Revenue reached $479 million, up 115% year over year, while non-GAAP gross margin held at 68%. Credo reported record optical DSP revenue, recognized its first silicon-photonics revenue following the DustPhotonics acquisition and began production shipments of ZeroFlap optical transceivers. Management continues to expect more than $600 million of optical revenue in FY2027, with DSPs, photonic chips and ZeroFlap each contributing more than $100 million.

The problem is that consolidated gross margin cannot answer a product-level question. AEC remains Credo’s largest business, and management did not disclose optical revenue, optical mix or optical gross margin. The established business can therefore preserve the consolidated number while the economics of the emerging business remain invisible.

We chose the right question but an insufficient metric. Credo has now proved that its technology can travel into optics. It has not proved that AEC-like economic ownership travels with it.

The Layer Between Products

Management’s story begins with heterogeneity. Future AI systems will not use one architecture, protocol or physical medium. Copper will remain appropriate for shorter reaches; optics will dominate longer ones; different scale-out and scale-up connections will demand different combinations of power, latency, reach and serviceability.

“Future AI systems will combine optical and copper interconnects across different reaches, protocols, and topologies.” — Bill Brennan

That world rewards product breadth, but breadth alone is not the thesis. Broadcom, Marvell and several optical suppliers can also provide pieces of the connection. Credo’s claim is that its products are joined by a common capability: understanding whether a link is healthy, why it is degrading and what should happen before it fails.

PILOT is the layer that potentially turns a group of products into a system. Brennan compared existing link monitoring to a green light when a connection works and a red light after it fails. Credo is trying to add the yellow “check engine” light in between. Its hardware continuously measures indicators such as eye height, signal-to-noise ratio and error patterns; its software looks for deterioration and can help remove a vulnerable processor or transceiver before a link flap disrupts the cluster.

This matters because AI clusters contain tens or hundreds of thousands of interdependent connections. Management says link instability can cost more than 10% of processor utilization, while slow cluster commissioning can leave enormously expensive equipment idle for weeks. A reliable connection is therefore valuable not because the cable itself is expensive, but because everything connected to it is.

The ESD example turns that argument from a presentation into customer behaviour. Credo is learning about defects beyond the normal responsibility of a component supplier. The accumulated failure cases should influence future DSPs, photonic chips, firmware and qualification methods. More deployments can create better products, which can make the next design easier to win.

This is not an automatic data network effect. One additional link does not mechanically improve every other link, and customers may limit how operational data is shared. The advantage compounds only if learning changes product performance and product performance increases attachment. PILOT is strategically important when diagnosis repeatedly becomes design; otherwise it remains an excellent feature bundled with hardware.

Importance Is Not Ownership

The ESD request and the DustPhotonics wins are mirror images. In the first, a customer asked Credo to understand the behaviour of the entire connection. In the second, customers selected one Credo component while declining another. Q1 therefore contained both the strongest evidence for integration and the clearest warning against assuming attachment.

The DustPhotonics wins provide the quarter’s cleanest counterargument. Asked directly whether the first two major photonic-chip design wins included a Credo DSP, Brennan answered:

“These first two major design wins that we are talking about do not include the DSP.”

That is simultaneously good and incomplete news. The wins validate the acquired photonic technology independently, which suggests Credo purchased a genuinely competitive product rather than a capability that only works inside its own bundle. They also show customers retaining control over component selection. Credo has won a place in the optical module without winning the integrated DSP-and-photonic-chip combination that management says should improve performance, power, yield and cost.

The modular challenge described in our previous article therefore remains. Customers may prefer an architecture in which validated components can be combined across suppliers. Credo believes that owning more of the stack will allow it to innovate faster and deliver better system outcomes. The customer will decide how much integration is worth and how much supplier dependence it is willing to accept.

This is the distinction the next phase of the thesis must preserve: Credo may own the customer’s problem before it owns the customer’s bill of materials. Technical influence often precedes commercial capture, but the second does not inevitably follow the first.

The Cost of Becoming a System

The financial statements show the cost of this ambition earlier than the product disclosures show its reward. Revenue grew 10% sequentially, but inventory increased 25% to $313 million and receivables increased 24% to $289 million. Free cash flow was $83 million against non-GAAP net income of $236 million, a conversion rate of 35%. Non-GAAP operating expenses reached $95 million, above management’s prior range, as Credo increased research and development across a much broader portfolio.

It would be easy to treat this as an earnings-quality complaint. That misses the strategic issue. Moving from a dominant AEC product into DSPs, photonic chips, complete optical transceivers, near-package optics and new scale-up connections requires more designs, qualifications, supply commitments and inventory configurations. Credo must incur those costs before the new products reach volume.

Management is explicit about the bet. Brennan said the company was “leaning in from a supply-chain standpoint” and playing the long game. The full-year arithmetic shows the scale of that commitment. More than 85% growth on FY2026 revenue of $1.335 billion implies at least $2.47 billion in FY2027. Q1 revenue plus the midpoint of Q2 guidance produces approximately $1.01 billion in the first half, leaving more than $1.46 billion for the second half—or over $730 million per quarter, 45% above the first-half quarterly average.

Inventory is not proof that this demand exists. It is proof that management is willing to finance its forecast. If inventory converts into the promised optical ramp while gross margin remains in the high 60s and cash conversion recovers, Q1 will look like the necessary investment behind a larger company. If product attachment remains limited and inventory continues outgrowing revenue, Credo will have broadened its technical capabilities faster than its economic ownership.

The balance sheet is therefore not a side issue. It is where the cost of management’s strategy has become measurable.

What Changed in Our View

Before Q1, we believed AEC gave Credo a wedge into the customer and that optics would determine whether its reliability advantage could travel. We expected gross margin to reveal the answer.

The quarter strengthened the strategic thesis. Optical participation is no longer theoretical. PILOT has moved from a diagnostic claim to a real customer workflow. Credo’s involvement increasingly extends beyond whether its own component functions, and management is committing capital as though the second-half ramps are real.

The quarter also made the economic thesis more demanding. Consolidated gross margin cannot isolate optical economics. The first photonic-chip wins do not attach Credo DSPs. PILOT has no disclosed pricing, attachment or separately observable contribution. Working capital and operating expenses are rising before investors can see how much of the broader connection Credo will capture.

I think Credo is building a genuine system-level advantage, and Q1 increased my confidence in that conclusion. What I am not yet prepared to assume is that customers will buy the entire system simply because they increasingly depend on Credo’s understanding of it. The probability that Credo becomes strategically important across copper and optics has increased; the amount of that importance it will monetize remains the central uncertainty.

Three Futures

The scenarios should be understood as three outcomes for the same mechanism, not three predictions about the semiconductor cycle.

The share-count assumptions reflect different outcomes for dilution and Credo’s ability to offset it through future cash generation. The ranges expose the operating assumptions that matter; they are not predictions of the path the shares take between now and then.

In the bear case, customers select Credo products but assemble the link modularly; optical margins dilute AEC economics, development and inventory remain elevated, and breadth does not produce attachment. In the base case, Credo owns the portions of the link where reliability is most valuable, while component and complete-system sales coexist. In the bull case, telemetry improves product design, better products deepen customer dependence, and that dependence produces greater attachment across DSPs, photonic chips and transceivers.

The signposts follow directly. By Q3, revenue growth should overtake inventory growth and cash conversion should recover above 60%. Optical revenue should exceed $600 million for FY2027 without consolidated gross margin falling below 67%. Over the next year, Credo should show that photonic-chip wins attach its DSPs, and that PILOT influences repeatable product adoption rather than isolated customer engagements. Failure on those measures would not mean the products are weak; it would mean the proposed compounding mechanism is.

The Company Customers Call

Credo did not cause the customer’s ESD problem. That was precisely why the incident mattered. The company was valuable not because its component worked, but because it could explain why the connection might eventually fail.

That is a more ambitious role than selling a cable, DSP or photonic chip. It gives Credo the opportunity to learn across products and become embedded in how customers design, qualify and operate their networks. It also requires Credo to carry more products, inventory and development expense before that opportunity becomes revenue.

The previous chapter of the Credo story asked whether its advantage could travel from copper into light. Q1 suggests that its technical relevance can travel. What remains unanswered is whether product attachment, margins and cash returns will travel with it.

Credo is learning to understand more of the link. The next question is how much of the link it will own.

General Disclaimer: The information presented in this communication reflects the views of the author and does not necessarily represent the views of any other individual or organization. It is provided for informational purposes only and should not be construed as investment advice, a recommendation, an offer to sell, or a solicitation to buy any securities or financial products.

While the information is believed to be obtained from reliable sources, its accuracy, completeness, or timeliness cannot be guaranteed. No representation or warranty, express or implied, is made regarding the fairness or reliability of the information presented. Any opinions or estimates are subject to change without notice.

Past performance is not a reliable indicator of future performance. All investments carry risk, including the potential loss of principal. This communication does not consider the specific investment objectives, financial situation, or particular needs of any individual.

The author and any associated parties disclaim any liability for any direct or consequential loss arising from the use of this material and undertake no obligation to update or revise it.

Subscribe now

• Platformization is beginning to look like a genuine capability: CyberArk and Chronosphere are benefiting from Palo Alto’s distribution, cross-selling and integration, while XSIAM and Prisma AIRS add further evidence that the customer loop is strengthening.

• But acquisitions have erased the organic baseline: headline NGS ARR and revenue growth increasingly mix acquired and internally generated growth, making it harder to know how much economic value Palo Alto is creating versus purchasing.

• The decisive test is now per-share economics: gross margins are slipping, stock compensation remains substantial and dilution is absorbing a meaningful part of earnings growth. Palo Alto needs conventional free cash flow per diluted share to compound before the customer loop can truly become a shareholder loop.

“M&A is not a strategy. M&A is a consequence of stuff that we do from a product development perspective.”

— Nikesh Arora, Q4 FY2026 earnings call

Palo Alto Networks ended fiscal 2026 with what management described as a record finish. Revenue grew 34% to $3.41 billion, Next-Generation Security ARR rose 63% to $9.1 billion, remaining performance obligations reached $21.2 billion, and the company added almost $1 billion of net new NGS ARR in one quarter. The shares fell roughly 7%, continuing a pattern in which Palo Alto wins the operating quarter while investors question the economics underneath it.

This is the fifth article in our Palo Alto series, and I think the disagreement has finally become clear. Management is selling a customer loop: AI makes attacks faster and identities more numerous, fragmented security becomes untenable, and each additional Palo Alto control point supplies more data and makes the remaining products easier to sell. The stock depends on a different loop, in which revenue becomes gross profit, gross profit becomes cash, and cash grows faster than the diluted share count.

Q4 is the strongest evidence yet that the customer loop is working. It is not yet evidence that the shareholder loop has closed. The big fundamental question is therefore: can Palo Alto convert a strengthening customer loop into a strengthening shareholder loop before Microsoft commoditizes the value its breadth creates?

The Customer Loop Starts to Close

Our first articles asked whether Palo Alto could move from gate to graph: from guarding a perimeter to observing identity, network, endpoint, cloud and application activity as one connected system. CyberArk and Chronosphere then turned this into the assembly test. Palo Alto was trying to coordinate products with different architectures, buyers and sales motions before AI made machine-speed control compulsory.

The previous article ended with the question:

“Can assembly become architecture before AI makes real-time control mandatory?”

Q4 does not prove that the architecture is complete, but it changes the burden of proof. CyberArk produced more than 400 shared leads and more than 200 new identity logos from Palo Alto’s installed base. Deals worth at least $5 million increased 50%, CyberArk’s margin improved by more than 1,000 basis points in two quarters, and integration synergies were running three to six months ahead of plan. Palo Alto is not merely absorbing the asset; its distribution is changing the asset’s growth and cost structure.

Chronosphere supplies similar evidence. Observability ARR has passed $500 million after more than doubling since the acquisition closed, while XSIAM relationships generated about half of its net new logos in Q4. XSIAM itself ended the year above $700 million of ARR, growing 70%, and Prisma AIRS crossed $100 million within four quarters of general availability. Platformizations reached roughly 2,500 after a record 220 net additions, with net retention above 120% for the platformized cohort.

These numbers reveal the mechanism management wants investors to see. More control points create more telemetry; more telemetry improves detection and automated response; better outcomes encourage consolidation; consolidation gives Palo Alto another surface through which to distribute the next product. In that sense, integration is becoming a capability rather than a sequence of transactions.

Commercial integration is not the same as technical unity. Management acknowledged that Chronosphere still needs about six months of development before competing broadly for conventional enterprise workloads, while customer transformations can take one to three years. Q4 shows that Palo Alto can distribute acquired products faster than we expected; it does not yet show that their data models, policies and workflows operate as one architecture.

Success Has Erased the Baseline

The awkward consequence of this strategy is that better integration produces worse visibility. Reported NGS ARR growth of 63%, revenue growth of 34% and RPO growth of 34% include the acquired identity and observability businesses. Chronosphere’s net new ARR also contained a nine-figure migration by one large language-model customer, a contribution management said will diminish after the first quarter of fiscal 2027.

The new category disclosures help, but do not solve the problem. Network and AI Security generated $8.35 billion of fiscal 2026 revenue and grew 17%, although that includes roughly $85 million from an acquired certificate-management business. Cortex grew 25% to $1.92 billion but now contains Chronosphere, while Idira is almost entirely acquired CyberArk revenue. Investors still cannot construct a clean bridge from the pre-acquisition Palo Alto business to the company that exists today.

This is not an accounting complaint. It is the central analytical problem. The better Palo Alto becomes at integration, the harder it becomes to distinguish value the company created from value it purchased. The headline numbers prove scale and commercial momentum; they do not establish the organic growth rate on which a premium valuation must eventually rest.

The balance sheet offers less cause for alarm than the absolute figures suggest. Approximate year-end DSO improved from about 106 days to 97 days as receivables grew more slowly than revenue, while financing receivables declined from roughly $1.72 billion to $1.54 billion. Revenue quality remains difficult to interpret because of acquisitions and customer financing, but the observable year-over-year trend did not deteriorate.

The Missing Premium

Microsoft was a central risk in our earlier work, and it should remain one. We wrote after Q1:

“Microsoft is security as a feature of the productivity suite you already own. It does not need to be the best. It needs to be good enough and already installed.”

Microsoft does not need to displace Palo Alto at the largest enterprises to matter. Entra, Defender, Sentinel and Azure Monitor give it enough breadth to cap pricing, particularly where a CFO values bundling more than a security team values technical superiority. Palo Alto can win consolidation and still fail to capture all the economics of the value it creates.

That is why Q4 gross margin deserves more attention than we initially gave it. Gross margin declined 100 basis points to 74.8%, while the full-year figure fell 60 basis points to 75.8%. Management expects cloud-hosting costs to grow faster than revenue in fiscal 2027 and also cited hardware component inflation. Non-GAAP operating margin is guided to improve only 30 basis points to 29.5%, while adjusted free cash flow margin slips from 38.4% to 38% before the company attempts to reach 40% in fiscal 2028.

The bearish interpretation is that the enlarged suite wins through breadth but cannot command a premium because Microsoft anchors the price. The benign interpretation is that faster-growing SaaS and acquired products have not reached gross-margin maturity, after which scale and integration synergies restore leverage. Gross margin cannot distinguish those explanations by itself; pricing pressure must also appear in renewal economics, discounting, competitive win rates and organic growth.

Management emphasized AI demand, large transactions and the $20 billion FY2030 NGS ARR target. Analysts received that destination constructively, but their questions kept returning to CyberArk penetration, Chronosphere’s durability, AI-driven firewall demand and the conversion of cross-selling into revenue. The market’s skepticism is therefore broader than one margin line: it is asking whether expansion in corporate scale will become superior economics per share.

A Better Company Must Become a Better Share

Fiscal 2026 generated $4.11 billion of conventional free cash flow and $4.41 billion of company-adjusted free cash flow. The difference matters because the adjusted figure adds back acquisition payments and selected capital expenditures, while neither measure deducts the $1.71 billion of stock compensation, equal to 14.9% of revenue. Acquired-intangible amortization added another $638 million to the gap between GAAP and non-GAAP earnings.

Dilution turns this from an accounting debate into an ownership issue. Full-year non-GAAP diluted shares increased 8.8% to 764 million, and the Q4 count increased 17.7% to 832 million. At the midpoint of fiscal 2027 guidance, non-GAAP net income rises roughly 20%, but EPS rises only about 9% because the guided share count reaches approximately 846 million.

Gross margin is the leading warning; conventional free cash flow per diluted share is the final test. It captures whether revenue growth survives hosting costs, integration spending, stock issuance and recurring capital requirements before reaching the owner. If that number compounds, the customer loop is becoming a shareholder loop. If it stalls while NGS ARR races toward $20 billion, Palo Alto will have built a more important company without necessarily building a better investment.

Three Prices, Three Worlds

At $355.21, Palo Alto trades near 85 times the midpoint of fiscal 2027 non-GAAP EPS guidance, while guided adjusted free cash flow implies a forward yield below 2%. The following scenarios value fiscal 2029 conventional free cash flow rather than the more flattering adjusted measure, and the arithmetic incorporates dilution explicitly.

The probability-weighted value is approximately $382, implying only a low-single-digit annual return from the current price. The decisive assumption is not whether revenue grows 19% rather than 17% next year; it is whether Palo Alto can sustain a high conventional cash margin and premium multiple after acquired growth annualizes. Existing holders can justify holding for the strategic optionality, but the expected return does not support adding at $355. A price of $320–$330 would merit reassessment, while a genuine margin of safety appears closer to $290–$300 unless the evidence improves first.

Two Loops, One Stock

Q4 makes us more constructive about Palo Alto’s organizational capability. The company is integrating acquisitions faster, distributing them more effectively and using AI demand to make its breadth increasingly relevant. The assembly test has moved in Palo Alto’s favor, although technical unification remains unfinished.

The quarter does not prove that Palo Alto can retain the economic premium created by that breadth. Microsoft can constrain pricing, cloud costs can absorb scale benefits, acquisitions can erase the organic baseline, and dilution can divert corporate growth away from each share. The thesis now turns on whether gross margin stabilizes and conventional free cash flow per diluted share begins compounding as acquired growth annualizes.

Perhaps Arora is right that M&A is not a strategy. The strategy is to make fragmented security untenable, while the capability is to build or buy each missing control point and distribute it through an expanding customer system. Q4 shows that the customer system is beginning to compound; at $355, the stock already assumes that shareholders will receive the same benefit, which is why the right action remains to hold rather than add.

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My first reaction is that the market traded the right question and reached too clean an answer. The quarter was considerably better than the stock reaction suggests, but the metric Rubrik introduced to explain that strength was also unusually convenient. That makes the disclosure change not Glasswing, Agent Cloud or even the headline beat the most important development in the print.

Our Rubrik articles have followed a changing question. We began by asking whether the company was building something larger than backup. Q3 FY26 described the janitor becoming the bodyguard. Q4 called Rubrik’s time-indexed record of the enterprise “state infrastructure.” Q1 FY27 asked whether that record could extend from data into identity and autonomous agents.

The Question is now more precise: Can Rubrik turn its time-indexed recovery record into the default control plane for data, identity and agentic actions, so that every additional protected surface increase adoption, recovery confidence and switching costs faster than migration benefits disappear and native alternatives commoditize recovery?

The Scoreboard Graduated or Moved

Rubrik ended Q2 with $1.661 billion of subscription ARR, up 33%, while net-new subscription ARR increased 35% to approximately $96 million. Cloud ARR reached $1.480 billion, up 39%, but calculated net-new Cloud ARR was approximately $89 million, down 4%. The new metric adjusted net-new Cloud ARR, excluding migrations by existing maintenance and non-cloud customers was $86 million, up 20%.

Both measures are valid. Migrations moved existing economics between Rubrik categories without creating new demand, so removing them produces a cleaner organic measure. Yet companies rarely introduce adjusted metrics when the old measure is becoming less flattering. Rubrik introduced this one when the reported measure turned negative and management simultaneously redirected investors toward total subscription ARR.

That redirection can be an economic graduation. Cloud now represents 89% of subscription ARR, and private or sovereign deployments need not be strategically inferior if they carry similar recurring economics. It can also become an escape hatch through which weakening cloud demand disappears into a broader number. The correct response is to accept the adjustment while requiring Rubrik to publish reported Cloud ARR, adjusted Cloud ARR and the migration bridge until the last legacy migration has passed.

Before extending the thesis, we should score the promises embedded in the previous article:

Sources: prior Rubrik Q1 FY27 article; Rubrik Q2 FY27 release; Q2 earnings-call transcript.

The scorecard says the recovery engine strengthened while the proposed second and third engines remained unproven. Our Q1 conclusion still holds:

Identity has crossed from narrative to metric. Agentic recovery has crossed from slideware to architecture. Agent Cloud has not yet crossed from architecture to disclosed revenue.

Q2 advanced the last sentence only slightly. That is meaningful progress for a product, but not enough evidence for a valuation category.

The cash-flow story is better than the quarterly margin suggests, but less simple than blaming Flex commissions. Q2 FCF rose 14% to $65.7 million while the margin fell four points. Across the first half, FCF increased 53% to $139.3 million and margin expanded to 17.1% from 15.4%. Cash invested in deferred commissions rose 40% to $69.1 million, but amortization also rose to $59.5 million; the deferred-commission asset increased only 3.6% from January. There is no disclosed evidence tying that increase specifically to Flex, and amortization does not reverse the original cash outflow. The raised $328 million FY27 FCF midpoint argues against structural deterioration. The more durable earnings-quality concern is $101 million of Q2 stock compensation 23.6% of revenue which separates corporate cash generation from per-share economics.

What Actually Compounds

Bipul Sinha describes Rubrik’s products as creating “complementary network effects.” I think that claim is too generous. One company adopting Identity does not improve another company’s recovery. What Rubrik may possess is customer-level data gravity: each protected workload, SaaS application and identity system makes the historical record more complete; a more complete record makes recovery more useful; broader recovery encourages another product purchase; and every added integration makes displacement more disruptive.

The economics are compatible with this mechanism. Net retention remains above 119%, customers spending more than $1 million grew 57%, and subscription ARR contribution margin reached 14%, up 460 basis points. Those facts prove expansion and operating leverage. They do not prove that customers adopting Identity or Agent Cloud expand faster, renew better or cost less to serve than recovery-only customers. The missing evidence is product attachment and cohort NRR.

Q2 supplied three different kinds of evidence that should not be confused. The largest international Identity deal shows enterprise-scale demand, but the missing ARR update prevents us from measuring the second engine. Project Hourglass gives Agent Cloud potential distribution through five global integrators, but a go-to-market alliance is not converted pipeline. Project Glasswing validates Rubrik’s relevance to emerging AI-security research, but Anthropic describes a broad program spanning vulnerability detection, testing and patching with numerous security partners; it does not validate Rubrik’s commercial moat or recovery architecture.

Agent Rewind remains the most interesting and least settled extension. Restoring a file or directory to a known state is bounded. Reversing an agent is not. An email may already have been sent, a payment executed, a permission propagated or an external system changed. Rubrik’s historical record can establish what happened and help orchestrate recovery, but complete reversal requires authority across systems it does not control. The architecture is coherent; the product boundary is wider than the metaphor suggests.

A Complement Is Not Comparable

The after-hours decline was not a verdict that Rubrik’s business deteriorated. It was a repricing of trajectory after a strong run into the print. NNARR fell from $115 million in Q4 to $103 million in Q1 and $96 million in Q2. Management’s full-year ARR guide implies roughly $101 million in Q3 and $121 million in Q4. CFO Kiran Choudary answered the concern by reframing the annual comparison:

“The net new ARR growth rate implied by the new guidance … went from 7% to 14%.”

That is true, but the market focused on how much acceleration is still required. Management has raised the destination; the burden of reaching it has moved into Q4.

CrowdStrike is useful for understanding the job, not valuing the stock. Detection at machine speed and recovery at machine speed are complements, which is why both companies can participate in Glasswing without being substitutes. That does not mean the market is mistakenly denying Rubrik a CrowdStrike multiple. CrowdStrike has greater scale, cash flow, installed-base evidence and demonstrated product attachment. Rubrik should earn its own valuation through duration and cohort economics, not inherit one from a neighboring category.

One execution risk deserves attention, although it is not a new Q2 revelation. Both Q1 and Q2 releases identify Rubrik’s ability to manage a leadership transition in its global revenue organization; Q1 explicitly referenced organizational changes and their effect on growth. Management has not explained the transition on either call. That silence matters because the H2 guide requires reacceleration, but the repeated language means this is a carried risk rather than a newly added legal warning.

Valuing What Is Proven

At approximately $96 after hours, Rubrik’s equity value is about $21.9 billion using the FY27 diluted-share guide of 228 million. Because that share count includes the potential dilution from converting Rubrik’s notes, subtracting $1.75 billion of cash and investments produces an enterprise value of roughly $20.1 billion, or 11.9 times FY27 revenue guidance. That valuation already assumes growth lasts.

The midpoint probability-weighted value is approximately $118, implying only about 7% annualized over three years from the post-print price. The bear case is not $30 because even a slower Rubrik would produce meaningful recurring cash and retain net cash. The previous $135–160 base range was too generous because it combined high terminal revenue multiples with insufficiently explicit dilution and cash assumptions. At approximately $85, the same probability-weighted value offers an 11% annualized return; near $96, the stock requires new evidence rather than another narrative extension.

What Changed in Our View

Q2 increased my conviction that Rubrik owns a valuable recovery franchise with improving economics. It did not increase my conviction by the same amount that Identity and Agent Cloud form a self-reinforcing second act. The variant perception is therefore narrower than before: the market still underestimates the strategic value of a trusted historical record, while bulls overestimate how much of the wider control plane has already been commercialized.

The next quarter has four tests. NNARR at or above $100 million keeps the annual guide credible; below $95 million leaves an uncomfortable Q4 burden. Rubrik should preserve the adjusted-cloud bridge and keep organic growth near 20%. By Q4, Identity needs renewed quantification and Agent Cloud needs either 30-plus paying customers or meaningful ARR. During FY28, stock compensation must fall below 20% of revenue and management must show that the revenue-organization transition has not weakened productivity.

Rubrik has proven that enterprises will pay for a trusted record of their past. It has not yet proven that this record gives it the right to govern their future. Existing holders can remain patient, but new money should wait for approximately $85 or for product-level disclosure that proves the compounding loop. The recovery engine cleared the bar; the valuation already assumes that the rest of the map eventually will.

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• Q2 looked structural, not merely strong: net-new ARR reached $333 million, up 51%, while revenue, operating income and free cash flow all accelerated.

• The business model may be improving: Falcon acts as technical distribution for new products, while Falcon Flex increasingly acts as commercial distribution, letting customers add capabilities without restarting deployment or procurement.

• The valuation depends on postponing maturity: CrowdStrike does not need to grow 25% forever. It needs new products like AIDR and SIEM to become meaningful before older growth vectors fade, continually pushing the visible maturity point further out.

“Inflection has become acceleration.” — George Kurtz, CrowdStrike Q2 FY27 earnings call.

There are earnings beats, and then there are quarters that change the question investors should be asking.

CrowdStrike’s fiscal second quarter was the latter. Net-new ARR reached $333 million, up 51% year over year, versus roughly $286 million embedded in published expectations. Revenue increased 26% to $1.47 billion, ending ARR reached $5.84 billion, non-GAAP operating income rose 46% to $372 million, and free cash flow increased 33% to $377 million. CrowdStrike also lifted its FY27 net-new ARR growth expectation to 34%, 1,150 basis points above where management began the year.

Those numbers explain why the stock went up. They do not explain why the quarter matters.

Before earnings, the important question was whether AI, Falcon Flex and CrowdStrike’s broader product portfolio were changing the shape of the business or merely giving management better stories to tell around an already excellent cybersecurity franchise. The market did not need another routine beat. It needed evidence that something structural had changed.

Q2 provided the first convincing evidence.

The Question is now this: can CrowdStrike turn the proliferation of AI agents into a compounding security cycle in which its existing endpoint distribution makes new products easier to deploy, Falcon Flex makes them easier to buy, and each additional workload broadens the customer relationship and strengthens CrowdStrike’s data advantage?

If so, the importance of Q2 is not that growth accelerated once. It is that CrowdStrike may have developed a mechanism capable of repeatedly postponing maturity.

The Agent Is Already There

A reasonable concern about AI is that it should make traditional endpoint security less strategically important. Intelligence increasingly lives in frontier models and cloud infrastructure; perhaps security therefore shifts toward OpenAI, Anthropic, Microsoft, Google and AWS rather than remaining with companies built around protecting PCs and servers.

The better way to think about AI security, though, is as several overlapping control layers. Model providers will secure models and inference. Hyperscalers will secure infrastructure and cloud workloads. But agents eventually must act: they access identities, files, credentials, applications and enterprise data. CrowdStrike does not have to monopolize AI security. It needs the layer where AI activity becomes observable and enforceable to remain sufficiently important that more AI makes Falcon more valuable.

The Q2 evidence supports that argument. CrowdStrike said that, within a sample of customers, Claude usage on endpoints had increased more than 400% in recent months and custom-agent usage more than 100%. Meanwhile the endpoint business accelerated for a fourth consecutive quarter.

This matters because Falcon is already installed. CrowdStrike already receives telemetry from the customer environment and already has an enforcement point. A new product does not necessarily require another agent, another deployment project or another technical architecture.

That was why one exchange in the earnings call mattered more than most of the AI rhetoric. Raymond James analyst Adam Tindle asked whether AI Detection and Response was genuinely incremental to endpoint security. George Kurtz answered:

“It is separate, and incremental. It’s another module, and it’s priced separately.”

AIDR therefore sits on the same Falcon agent but creates another entitlement and another revenue stream. The technical footprint already exists; CrowdStrike can monetize another problem without recreating distribution from scratch. AIDR ending ARR nearly tripled sequentially in Q2.

The same mechanism is increasingly apparent in SIEM. Next-generation SIEM exceeded $695 million of ARR and grew 60%, but the architectural advantage matters more than the number. CrowdStrike’s own endpoint telemetry is already in Falcon, which means existing customers can activate SIEM without beginning from an empty data environment. Kurtz described the product as unusually quick to deploy precisely because CrowdStrike already owns the first-party data.

Cloud Security is above $905 million of ARR, Identity above $585 million, and endpoint itself is still accelerating.

The Falcon agent, then, is not simply a product. It is distribution.

That solves one half of the problem.

Falcon Flex Solves the Other Half

Technical distribution does not eliminate enterprise software’s commercial friction. A customer can already trust a vendor and still spend months navigating budget cycles, procurement, legal review and contract negotiations before adding another capability.

Falcon Flex increasingly appears to attack this second problem.

Instead of repeatedly buying discrete modules, customers commit a larger pool of spending to CrowdStrike and consume additional Falcon products over time. CrowdStrike consequently gains something valuable: a commercial channel through which its next product can be monetized without restarting the purchasing process from zero.

Q2 was the first quarter in which the scale of that mechanism became difficult to dismiss. CrowdStrike added more than 935 Flex accounts, more than during the previous three quarters combined. Its ten largest deals were all Flex transactions. Flex new-logo ARR represented 34% of total Q2 net-new ARR, while ARR associated with Flex customers exceeded $2.29 billion and grew 101%.

The more important evidence is what customers do afterward. Standard subscription customers moving into Flex increase ARR by more than 40% on average. Customers completing their first ReFlex subsequently expand ARR by another 25%, while customers that have ReFlexed at least twice are running 53% above their original Flex commitment. The first ReFlex occurs after roughly eight months.

That eight-month figure matters because it addresses the obvious bear case.

Perhaps Flex merely pulls demand forward. Customers sign larger commitments today, CrowdStrike records more ARR, and future expansion disappears because customers eventually must consume what they already bought.

ReFlex is the test. If customers return after only eight months and voluntarily increase commitments again, then consumption appears to be catching contractual capacity rather than lagging it.

The loop becomes visible: Falcon is already deployed; deployment produces telemetry; telemetry supports new products; those products are easy to activate technically; Flex makes them easier to consume commercially; customers use more modules and ReFlex into larger commitments; the larger relationship creates more workloads, more data and more opportunities for the next product.

AIDR may become a very large product. Flex may matter more because it can make every future product larger faster.

This is the potentially important change in CrowdStrike’s business model. The Falcon agent lowers deployment friction. Falcon Flex lowers purchasing friction. AI increases the number of problems CrowdStrike can sell through both.

What We Underestimated

Our pre-earnings framework correctly identified net-new ARR as the metric that mattered, and roughly $300 million as the threshold at which the quarter would begin to look like genuine acceleration rather than another conventional beat. What it underestimated badly was the magnitude: our central expectation was closer to the high-$290 millions, not $333 million.

That mistake is useful. We anchored too heavily on the previous quarter’s beat pattern and not enough on the possibility that the demand regime itself had changed. Q2 did not simply clear a difficult expectation. It forced the expectation framework upward.

That is why the next question is no longer whether CrowdStrike can produce a $300 million quarter.

It is whether CrowdStrike can make $333 million look ordinary.

Management formally guided Q3 net-new ARR to $343–347 million and said it entered the quarter with record pipeline. After a quarter in which the company beat ARR guidance by more than $45 million, though, the formal guide is unlikely to remain the actual market hurdle.

Before Q2, the regime was proving the acceleration. After Q2, it is prove the compounding.

The Moving Horizon

This is also the only sensible way to think about CrowdStrike’s extraordinary valuation.

CrowdStrike has spent most of its public life trading at multiples that look unreasonable by conventional software standards. Simply assuming those multiples must eventually revert to something “normal” has repeatedly been a poor investment framework.

The more useful question is what allows an abnormal multiple to persist.

CrowdStrike does not need investors to believe it will grow 25% forever. It needs investors to keep postponing the year in which they expect growth to become ordinary.

Suppose investors once imagined CrowdStrike fading from the mid-20s toward 20%, then into the teens. A quarter like Q2 changes more than the current year. It introduces the possibility that the whole deceleration curve begins later because another growth vector has appeared before the old one fully matured.

Kurtz made the boldest possible version of this claim when JPMorgan asked about AI-security durability:

“I think the AIDR business can be bigger than the EDR business, just given the pure number of agents which each person will have.”

That should not be treated as a forecast. CrowdStrike has not even disclosed absolute AIDR ARR, and “nearly tripled” tells us little about ultimate scale without the denominator.

But it states management’s duration bet explicitly.

CrowdStrike was built on EDR. If a newly emerging product can someday rival the business that built the company, then the maturity point moves dramatically further away. If SIEM scales toward another billion-dollar-plus franchise while that happens, it moves again. If Flex lets subsequent products monetize faster, it can move again after that.

This is why Q2’s valuation significance is larger than its revenue beat.

The multiple survives not because CrowdStrike grows into it, but because CrowdStrike continually postpones the moment when investors feel compelled to normalize it.

That is also what makes the stock dangerous.

The multiple probably does not collapse because investors suddenly notice that it is high. Everyone already knows it is high. It collapses when investors can finally see maturity.

If AIDR remains small, the horizon moves closer. If customers take 15 months rather than eight to ReFlex, the horizon moves closer. If SIEM decelerates before another product becomes material, the horizon moves closer. If endpoint growth turns down and FY28 or FY29 estimates stop moving upward, investors will eventually have to answer the question CrowdStrike’s execution has repeatedly allowed them to defer:

What is a mature CrowdStrike worth?

That is when an extraordinary multiple becomes a source of extraordinary downside.

Q2 Did Not Make CrowdStrike Cheap

Q2 did not make CrowdStrike cheap. It made the extraordinary valuation more intelligible.

The quarter gave us strong evidence that Falcon is technical distribution, Falcon Flex is becoming commercial distribution, and AI is creating incremental workloads that can be sold through both. AIDR is separately monetized. Flex customers are increasing commitments. ReFlex is happening quickly. Endpoint is reaccelerating. SIEM, cloud and identity are scaling. Growth is occurring alongside operating leverage.

What Q2 did not prove is that these mechanisms will continue producing new growth vectors before existing ones mature.

That is now the investment case.

The Question and the valuation question are ultimately the same question viewed from opposite sides. The business question is whether CrowdStrike can repeatedly turn new security problems into products that become easier to deploy and easier to buy as the customer relationship deepens. The valuation question is whether it can do so frequently enough that visible maturity always remains several years away.

Before Q2, CrowdStrike needed to prove that AI could create another acceleration.

After Q2, the harder and considerably more valuable question is whether the Falcon agent and Falcon Flex can keep turning each new security problem into another reason that maturity remains just beyond the horizon.

If they can, the multiple can continue looking irrational for years.

If they cannot, the multiple will be the first thing investors discover was rational only while the horizon kept moving.

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• The interface may no longer be the moat: as Claude, ChatGPT, Slack and custom apps increasingly own the user experience, Salesforce’s value shifts toward governed context, permissions, semantics and trusted execution.

• AI may create a second user rather than replace the first: human usage remains resilient while machine access through MCP is scaling rapidly, creating a new consumption layer alongside seats.

• Q2 finally connected the AI story to the core business: cRPO accelerated to 14% constant currency and production/refill activity strengthened, but Salesforce still needs to prove implementation intensity, and AI economics can scale without eroding margins.

The most interesting Salesforce customer right now may be Anthropic.

Claude is precisely the sort of product that is supposed to threaten Salesforce. If an intelligent model can answer questions, build interfaces, write software, and take actions, why should a salesperson need to open an expensive CRM application at all? The strongest version of the “SaaS apocalypse” argument is not that software disappears. It is that intelligence moves into the model, the interface moves into the chatbot, and the application sitting between the employee and the data loses the user.

And yet Salesforce disclosed something peculiar on its first-quarter earnings call. Chief Revenue Officer Miguel Milano described what was happening at Anthropic:

“Anthropic is one of our biggest users of CRM, of Sales Cloud, and obviously Slack. Their usage through Q1 has exploded fivefold because now they’re using Sales Cloud from a headless perspective… Sales Cloud has become more prominent and more strategic for them than ever because of Headless.”

Anthropic was using less of the Salesforce interface and more Salesforce.

I think that paradox explains both why Salesforce’s second quarter mattered and why my own view of the company has changed.

When Less Salesforce Means More Salesforce

Last September, I argued that Salesforce had fallen into a complexity trap. The company that displaced Siebel by removing much of the pain from enterprise CRM had spent two decades accumulating Sales Cloud, Service Cloud, Marketing Cloud, Tableau, MuleSoft, Slack, industry clouds, Data 360, and eventually Informatica. Each acquisition increased what Salesforce could do while adding another set of schemas, permissions, workflows, interfaces, and integrations that had to work together.

AI appeared likely to expose that fragmentation. An autonomous agent cannot casually work around conflicting definitions of “customer,” uncertain permissions, or disconnected workflows in the way an experienced employee can. The agent needs context it can trust.

That concern has not disappeared. What I underestimated was the possibility that the user itself would change.

Complexity has two costs. One is the cost of constructing and integrating the system. The other is the cost imposed on the person who has to navigate it. Salesforce has not yet demonstrated that AI eliminates the first. Large enterprise deployments still require implementation work, customer data remains scattered across systems, and management still does not disclose the deployment intensity or AI unit economics that would let investors determine whether the underlying architecture is becoming materially cheaper.

AI can attack the second cost much faster.

A salesperson confronting Salesforce’s accumulated architecture sees objects, fields, dashboards, approval flows, permissions, and applications. An agent can see something else: who owns the account, what a field means, which records matter, what happened previously, which business rules apply, and which actions are permitted.

What looks like complexity to a human can look like context to an agent.

What the Agent Sees

This distinction matters because large language models are making interfaces abundant. Claude can provide one. ChatGPT can provide another. Slack can become another. A company can increasingly build its own.

What does not become abundant nearly as quickly is enterprise state.

A model can reason that a discount probably makes sense. It cannot independently know that a particular quote requires two approvers, that the employee requesting it lacks authorization, that the customer has a contractual restriction, or which system must record the final action. Those are problems of identity, semantics, permissions, governance, workflow, and deterministic execution.

Salesforce has spent 25 years accumulating precisely those things.

Benioff described the strategic change unusually directly on the Q2 call:

“People don’t have to come to Salesforce to get work done. Salesforce comes to them in Claude, in Slack, in ChatGPT, in Teams, wherever they want to work. It is a shift from software as the interface to software powering every interface.”

That is more consequential than another Agentforce feature. Salesforce is explicitly accepting that somebody else may own the interface.

This also changes how I think about our previous concern around federation. I had treated Salesforce’s embrace of zero-copy access to data residing in Snowflake, Databricks, SAP, and elsewhere as evidence that Salesforce had ceded control of the most valuable layer. That now seems too categorical. An agent may care less about where the bytes physically reside than whether Salesforce can explain what those bytes mean, whether they can be trusted, who can access them, and what can safely be done with them.

The moat may be moving from owning the interface to owning governed context and action.

There is an additional irony. Salesforce spent years building its own AI capabilities through Einstein and Agentforce. Now one of its most strategically interesting propositions is that Claude can become the interface to Salesforce. That could be interpreted as defeat. I increasingly think the opposite is true. If Salesforce needs to own the best model for this thesis to work, the thesis is fragile. A genuinely valuable governance and execution layer should benefit whether Claude wins, OpenAI wins, Gemini wins, or model intelligence becomes increasingly commoditized.

The Second User

The architectural change matters to investors only if it changes the economics.

Salesforce laid out the mechanism in Q1. Existing users could migrate to premium editions. AI could make Salesforce economical for additional users. Customer-facing agents could consume Flex Credits. Headless could eventually monetize interactions initiated by third-party agents outside the Salesforce application itself.

The old Salesforce monetized people.

The emerging Salesforce may monetize people and machines.

Salesforce’s Q2 presentation says human application usage remained strong while weekly calls to its MCP server increased approximately sixfold. It is a management-provided chart rather than a formally disclosed usage series, so the human side should not be over-interpreted. Still, the direction is important: machine activity appears to be developing alongside the existing human franchise rather than simply replacing it.

The Q2 transcript gives us a better economic indicator. When Morgan Stanley’s Elizabeth Porter asked how much Agentforce activity was moving beyond pilots, Milano answered:

“50% of the bookings came from customers refilling the tank. So they consume, they use the Flex Credits, they want more… We added 2,000 paying customers into production. That is 70% more quarter on quarter.”

This is more useful than the headline Agentforce ARR number.

ARR proves Salesforce persuaded someone to buy an AI product. Production proves the implementation escaped procurement. Consumption proves customers used it. Refill proves they used enough of it to pay Salesforce again.

The refill itself is not new; similar behavior was visible in Q1. What matters is that it persisted while production deployments and Agentic Work Units scaled rapidly.

And the machine user does not yet appear to be replacing the human one. Sales and Service seats are still growing, while management says penetration of its premium editions remains remarkably low:

“Only 5% of the knowledge workers that use sales and service have upgraded to the higher-end editions. We get a 60% to 80% premium.”

That suggests a considerably more attractive economic possibility than the original SaaS bear case:

existing seats + premium upgrades + machine consumption.

The threat was that AI would reduce Salesforce’s number of users.

The opportunity is that AI creates an entirely new class of Salesforce user.

When Agentforce Became Salesforce

None of this would matter if it remained confined to Agentforce statistics.

Morgan Stanley’s Keith Weiss made exactly that point on the Q1 call. Agentforce usage looked terrific, but cRPO had merely met expectations; Tableau and Commerce were weak; where was the broader business acceleration?

It was the right question, and it echoed the questions we had been asking for nearly a year.

In December, Salesforce was increasing sales capacity by more than 20% while guiding to single-digit growth. I argued that either management had abandoned the discipline investors had spent years demanding or it could see demand that had not yet reached reported revenue.

In February, Agentforce ARR, premium bookings, consumption, and large deals all improved, but aggregate organic growth did not. The distinction I made then was between progress and proof.

Q1 explained the mechanism. Q2 is where the mechanism began transmitting into Salesforce.

Revenue was $11.35 billion, although $456 million came from Informatica, while reported non-GAAP EPS of $5.90 included $2.53 per share of strategic investment gains. Neither explains why the quarter mattered. cRPO does.

Current remaining performance obligations grew 14% in constant currency, up from 13% in Q1, while Salesforce guided Q3 to another approximately 14%, excluding Contentful and Fin. Management also said net-new annual order value growth was the strongest in four years.

Milano then made the accountability test unusually explicit:

“We are very confident. Definitely, we are committed to the H2 revenue re-acceleration. That’s already math. Q3 is math. Q4 is nearly math.”

Three months earlier, investors were being asked to trust a forecast. By Q2, management was describing the next quarter as arithmetic. That does not prove the acceleration will persist, but it tells us why this quarter is different: more of the Agentforce story has migrated from usage into signed contracts and therefore future recognized revenue.

Q2 matters not because Agentforce became more impressive. It matters because Agentforce finally began becoming Salesforce.

The Anchor

The new engine still must pull a very large company.

Under Salesforce’s legacy disclosure, Sales grew 9% in constant currency in Q2, Service 5%, Marketing and Commerce declined 1%, and Integration and Analytics declined 6%. Platform, Slack and Other grew 43%.

That changes the strongest bear case. It is no longer that Agentforce is fake. It is that Agentforce works but never becomes large enough to bend Salesforce.

The complexity question also remains open. PenFed described Salesforce engineers and architects working directly alongside its team, while UCLA said its first customer-facing Agentforce use case took eight months to deploy.

Our December capacity call therefore looks increasingly prescient, as does the focus on refills. The Informatica semantic-layer hypothesis has gained credibility, but I would not declare it proven: we still cannot isolate Informatica as the reason consumption or deployment economics are improving.

Until Salesforce shows declining implementation intensity, strong AI gross margins, and sustained organic revenue acceleration, the Complexity Inversion remains a thesis rather than a conclusion.

The Price of Being Right

At roughly the post-earnings price, I would now assign a 15% probability to a bear case in which Agentforce succeeds but largely offsets legacy deterioration. Revenue grows 6–8% annually through FY30, non-GAAP operating margins settle around 30–32%, and earnings of roughly $16–18 support a stock around $210–250.

My 55% base case assumes the second user becomes economically meaningful but develops progressively. Revenue grows around 10–12%, cRPO stays in the low-to-mid teens, premium upgrades and consumption remain additive to seats, and margins hold around 34–36%. FY30 EPS around $22–23 at 17–18 times earnings implies roughly $375–415.

The 30% bull case is the Complexity Inversion fully expressed. Third-party agents dramatically expand Salesforce activity; premium penetration moves far beyond today’s 5%; Headless turns frontier models into distribution rather than competitors; implementation becomes easier; and Salesforce sustains 13–15% revenue growth with 36–38% margins. EPS around $25–27 at 20–21 times supports roughly $500–570.

The prices matter less than what each world requires. Q3 organic revenue now needs to visibly accelerate because management has made the promise falsifiable. cRPO needs to stay around 14% or better. Agentforce Apps, currently growing 8% in constant currency, needs to move toward double digits. Refills must persist as usage scales. Human seats need to remain resilient. And eventually Salesforce needs to disclose enough about implementation intensity and AI economics to demonstrate that the complexity underneath the abstraction is genuinely scalable.

No Salesforce

Twenty-seven years ago, Salesforce’s breakthrough was captured in two words: “No Software.”

The software did not actually disappear. Customer records still had to be stored, permissions enforced, workflows maintained, servers operated, and upgrades shipped. Salesforce simply hid enough of that complexity behind the browser that customers no longer had to think about it.

AI may force Salesforce to repeat the trick on itself.

Claude can replace the screen. Slack can replace the screen. A company’s own agent can replace the screen. What remains is the customer state, the permissions around it, the semantics that explain it, and the trusted system authorized to act on it.

The bear case assumes losing the screen means losing the customer.

Q2 offers the first financial evidence of a more interesting possibility: Salesforce may lose the screen and gain a second user.

Salesforce’s first revolution was “No Software.” Its second may be “No Salesforce” — at least not where the user can see it.

The irony is that disappearing from the screen may be how Salesforce becomes more important underneath it.

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• The unit of analysis is shifting from the GPU to the AI factory: NVIDIA says its revenue opportunity per gigawatt has risen from roughly $18 billion with Hopper to $25 billion with Blackwell and $40 billion with Vera Rubin.

• Custom silicon is not the whole threat: hyperscalers may take accelerator share while NVIDIA captures more of the CPU, networking, systems, software and surrounding infrastructure.

• The counterweight is increasingly financial: receivables, guarantees, investments and extended terms are rising. The key test is whether NVIDIA support grows slower than the economically productive infrastructure it enables.

Two different NVIDIA earnings reports appeared on Wednesday evening, and for the first hour the market could not decide which one mattered.

The first was the familiar NVIDIA print. Revenue was $96.2 billion, up 106% year-over-year and nearly $4 billion above consensus. Data Center revenue reached $89 billion, up 117%. Adjusted EPS was $2.22 versus $2.09 expected, and Q3 revenue guidance of $108 billion was about $4 billion above the Street.

The second print was less comfortable. Gross margin, still 75% in Q2, is guided to 74% next quarter; management expects memory costs to push it down to 71–72% in Q4 before recovering to 72–73% in fiscal 2028. Accounts receivable reached $63.1 billion and DSO increased from 45 to 60 days because NVIDIA extended payment terms on large multi-quarter purchases. Free cash flow was only $21.3 billion against nearly $54 billion of adjusted net income, while future supply and capacity commitments more than doubled to $279 billion.

The stock initially traded the second report and fell. Then CFO Colette Kress said:

“Customers’ forecasts point to our growth doubling next year. However…we expect to grow approximately 70% as we are supply-constrained.”

The stock reversed to finish the call roughly 4% higher.

That reversal captures the NVIDIA debate better than another discussion of whether $96 billion was a beat. Investors know AI demand is extraordinary; a company this large does not double revenue by accident. What they are trying to determine is what should be netted against the increasingly visible costs of sustaining that growth: custom silicon, falling margins, working capital and NVIDIA’s expanding financial involvement in its own ecosystem.

The answer, I think, begins with a different number.

The Chip Isn’t Enough

Kress said:

“Since Hopper, our revenue opportunity has grown from roughly $18 billion per gigawatt to $25 billion with Blackwell, to $40 billion with Vera Rubin.”

This is the most useful way to understand what NVIDIA has been building.

Power is increasingly the fixed resource in AI. NVIDIA cannot manufacture new gigawatts, but it can extract more intelligence from each one while increasing the amount of NVIDIA technology inside it. Hopper was principally about the accelerator. Blackwell expanded the rack. Rubin increasingly means Vera CPU, Rubin GPUs, NVLink, Ethernet or InfiniBand networking, systems, software and Groq LPUs.

The margin reset looks rather different through this lens. Blackwell at $25 billion of NVIDIA revenue per gigawatt and 75% gross margin implies about $18.8 billion of gross profit per gigawatt. Rubin at $40 billion and a 72.5% margin would imply roughly $29 billion. Even if mid-70s margins do not return, NVIDIA can potentially increase gross-profit capture from every scarce gigawatt by more than 50%.

This is also Jensen Huang’s answer to custom silicon. NVIDIA’s largest customers have powerful incentives to design their own chips: when compute is one of your largest expenses, NVIDIA’s gross margin is your potential savings. Amazon has Trainium, Google has TPUs, Meta has MTIA, and model companies are pursuing silicon of their own.

Huang did not argue that these efforts will fail. Instead he emphasized what NVIDIA sells that a specialized accelerator does not:

“One platform, fungible for every model and workload. Durable for the entire life cycle of AI.”

He made the contrast more explicitly in Q&A: many XPUs are optimized for a particular service or cloud, while NVIDIA is trying to build an AI-factory architecture that can run across clouds, models and stages of the AI lifecycle. That distinction matters because a multi-billion-dollar factory is not merely buying today’s inference workload; it is buying an asset that needs to remain useful when the workload changes.

Fungibility therefore has economic value. AWS can expand Trainium while simultaneously committing to two million additional NVIDIA GPUs and adopting Vera CPUs. Custom silicon may take accelerator share without taking an equivalent share of NVIDIA’s economics if NVIDIA captures more of the networking, CPU, software and system surrounding the accelerator.

That is the netting question in its first form: what should GPU share loss be netted against?

The morning after earnings supplied another clue. Reuters reported, citing The Information, that NVIDIA had agreed to acquire Hugging Face for $12.9 billion; NVIDIA and Hugging Face declined to comment. Hugging Face matters less because NVIDIA needs another model than because it sits between models and developers, hosting the open-model ecosystem and helping developers discover and deploy it.

This move fits a pattern that is becoming difficult to ignore. Networking threatened to become the constraint on GPU clusters, so NVIDIA bought Mellanox. CPU orchestration became more important, so Grace became Vera. Specialized decode created an architectural opening, so NVIDIA incorporated Groq. AI clouds lacked capacity, so NVIDIA helped develop NeoClouds. Model distribution is becoming strategically important, and NVIDIA may now be buying Hugging Face.

Each move looks adjacent on its own. Together they reveal the strategy: whenever a layer either constrains NVIDIA’s growth or offers customers a route around NVIDIA, the company moves sufficiently far into that layer to keep the larger system centered on NVIDIA.

NVIDIA’s customers are integrating downward to escape NVIDIA. NVIDIA is integrating outward and upward to make escaping NVIDIA matter less.

Capital Is a Component

The most unusual extension of this strategy is not Hugging Face. It is finance.

Kress said the frontier AI labs have enormous compute demand but are “growing faster than what their balance sheets and credit profiles can support.” This is an important distinction. NVIDIA is not describing weak demand; it is describing demand that cannot yet finance itself at the rate customers would like to grow.

NVIDIA’s response is strikingly similar to its response to every other bottleneck: remove it.

The company has invested nearly $50 billion in frontier labs and has brought Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR together around financing platforms intended to mobilize more than $500 billion of third-party capital. For some NeoCloud projects NVIDIA can provide a minimum revenue or take-or-pay commitment that helps independent lenders finance the facility, while NVIDIA participates in rental revenue above the floor.

Financing, in other words, has become another component of the AI factory.

The logic is compelling. NVIDIA can use the extraordinary profits from today’s installed base to accelerate tomorrow’s installed base, keep that capacity on NVIDIA architecture and potentially earn a recurring share of the revenue generated by the equipment it sold.

Fungibility matters here as well. A system capable of running many models and workloads for many potential customers ought to be easier to re-lease if the original customer fails. Better redeployability should support higher residual values; higher residual values reduce lender risk; lower risk lowers the customer’s cost of capital. More NVIDIA installations then deepen the secondary market that makes the next NVIDIA deployment still easier to finance.

CUDA could become a financing advantage as well as a software advantage.

This is where the argument becomes uncomfortable. NVIDIA generated nearly $54 billion of adjusted earnings this quarter but only $21 billion of free cash flow. Some of that gap is perfectly explainable by taxes, Rubin inventory and the extraordinary scale of the ramp. The more interesting number is 60-day DSO because management explicitly linked it to extended customer terms at the same time it acknowledged that parts of the AI ecosystem are growing faster than their financial capacity.

None of this proves the demand is circular. Demand can be completely real and still arrive faster than customers can finance it. The question is whether NVIDIA is providing temporary scaffolding around a young economic system or whether progressively more scaffolding is required to keep reported demand growing.

In The AI Handoff, we described this progression as Scarcity → Sponsorship → Conversion → Self-funding. Self-funding was never meant to imply that NVIDIA would stop investing; it meant that customer-generated cash and independent credit would increasingly finance each subsequent infrastructure cycle.

Q2 suggests the architecture is moving faster than the handoff.

The Netting Question

Last quarter I wrote that “a flywheel compounds; a circle spins.” My expectation was that ACIE would help distinguish the two: broadening non-hyperscaler demand combined with moderating NVIDIA support would suggest the ecosystem was increasingly standing on its own.

Instead, ACIE grew 25% sequentially and 138% year-over-year while NVIDIA’s financial involvement increased as well. There was some messiness: the $40.3 billion ACIE result missed consensus even as Hyperscale drove the Data Center beat but the broader demand signal is difficult to dismiss. The operating flywheel and financial scaffolding are not alternatives; at least for now, the latter is helping the former move faster.

My variant perception is therefore not that custom silicon does not matter, or that financing risk is imaginary. It is that the market is too focused on NVIDIA’s share of accelerators and not focused enough on NVIDIA’s expanding economic claim on each AI factory.

The $18 billion-to-$40 billion progression per gigawatt is the evidence. NVIDIA can lose something in GPUs, something in gross margin and something in financing support, yet still create substantially more economic value per scarce unit of infrastructure.

The real question is what must be netted against that expansion. If NVIDIA’s customers gradually become self-funding while NVIDIA continues increasing content per gigawatt, the company’s outward integration can more than offset accelerator-share erosion. If support requirements, receivables and guarantees rise as quickly as revenue while custom silicon takes the most profitable workloads, then orchestration starts looking more like subsidy.

Hugging Face adds a second risk. The orchestrator benefits from neutrality: NVIDIA is valuable because OpenAI, Anthropic, Meta, sovereigns and every cloud can all use it. Hugging Face is valuable for a similar reason. NVIDIA ownership could make the route from an open model to NVIDIA deployment much easier, but it could also make competing hardware vendors and model companies more interested in building an alternative. The more layers NVIDIA controls, the more valuable the architecture becomes and the greater the incentive for everyone else to route around it.

That tension defines the three-year range.

The bear case is not simply slower AI growth. One major customer or NeoCloud runs into financing difficulty, NVIDIA’s guarantees or equity exposure become economically relevant, DSO remains elevated, custom silicon captures standardized workloads and gross margin settles below 70%. AI can still succeed while NVIDIA’s equity does not.

The base case assumes management is broadly right about FY28: revenue grows around 70%, Rubin continues increasing NVIDIA content per gigawatt, ACIE broadens the customer base and hyperscalers use custom silicon without escaping the rest of the NVIDIA stack. Margins stabilize around 72–73%, cash conversion normalizes and financial support grows more slowly than the infrastructure it enables.

The bull case does not require NVIDIA to eliminate custom silicon. It requires NVIDIA systems to become measurably better assets: more fungible, easier to re-lease and consequently cheaper to finance. If the architecture earns a residual-value and cost-of-capital premium, the technical flywheel gains a financial counterpart.

There are four numbers I would watch to distinguish these outcomes. Free-cash-flow conversion should recover toward 70% or better of adjusted earnings; persistent conversion below 50% would make the financing concern structural. DSO should move back below roughly 55 days rather than remain above 60. Gross margins need to bottom around the promised 71–72% and recover thereafter. Finally, ACIE and independently financed capacity need to grow faster than the guarantees, equity and extended terms NVIDIA uses to support them.

The last measure is the one NVIDIA does not yet give us, and ultimately the one I care most about: how much NVIDIA support is required for each additional dollar of economically productive AI infrastructure?

NVIDIA’s customers will keep integrating downward because the economics are simply too important for them not to. NVIDIA does not need to stop them; it needs to keep moving the boundary of competition so that replacing the NVIDIA chip means replacing an ever-smaller portion of what NVIDIA provides.

Q2 was the first quarter to put a compelling number on that strategy: $40 billion per gigawatt.

The next few years will tell us what needs to be netted against it.

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• Technology removed rented friction: search, reviews, marketplaces, logistics and returns made alternatives easier to discover and try, weakening advantages that came mainly from distribution, familiarity and inertia.

• The real consumer asset is permission: when alternatives are visible, strong companies still win because customers actively choose their proposition or begin the decision with them.

• For investors, direction matters more than heritage: the opportunity is strengthening permission flow before the market recognizes it; the danger is paying for accumulated brand strength while current permission is deteriorating.

Warby Parker

In February 2010, four Wharton students launched a company selling prescription glasses for $95, including lenses. The price was striking, but the more important idea arrived in a cardboard box. A customer unsure about buying glasses online could choose five frames, have them shipped home, try them on for five days, and send them back. At the time, less than 2.5% of glasses were sold online, which made sense: eyewear seemed like the sort of category the internet could not easily disrupt because evaluation required putting the product on your face.

Warby Parker turned that constraint into the product. The company later recalled in its prospectus:

“We reached our first-year sales targets in three weeks, sold out of our top 15 styles in four weeks.”

The demand was not evidence that consumers had suddenly stopped caring about brands. It was evidence that a set of costs embedded in the old buying process had been mistaken for an intrinsic part of the product.

The incumbent system was formidable. Luxottica owned Ray-Ban and Oakley, licensed eyewear from names including Prada, Chanel and Tiffany, and operated thousands of retail locations through chains including LensCrafters, Pearle Vision and Sunglass Hut. Its advantage combined brands, manufacturing, wholesale relationships and retail distribution. Warby Parker did not reproduce that machine. It made parts of the machine less necessary.

Discovery moved online. Evaluation moved into the customer’s home. Returns reduced the risk of experimentation. A simple price made the transaction easier to understand. Warby Parker did not abolish the value of brands or physical stores; it removed enough friction that the incumbent’s distribution no longer settled the decision.

That distinction reaches far beyond glasses.

Rented Friction

For most of the twentieth century, consumer companies benefited from two different kinds of transactions that looked identical in the income statement. Some customers bought because they genuinely preferred the product, trusted the company, identified with the brand or valued the experience. Others bought because considering something else was difficult enough that they did not bother.

The second source of economics might be called rented friction.

Discovery once depended heavily on what retailers stocked and advertisers could put in front of consumers. Evaluation was difficult because independent information was scarce. Substitution involved finding the alternative, risking money on it and accepting the possibility that it would disappoint. Distribution, advertising and heritage reduced those costs for incumbents, which meant a familiar product did not always need to be much better; the alternative needed to be sufficiently better to justify discovering and trying it.

This created the great consumer loop of the last century. Scale financed advertising, advertising built awareness, awareness helped secure distribution, distribution generated transactions, and those transactions financed more scale. The loop rewarded genuinely great brands, but it could also protect ordinary propositions fortunate enough to occupy it. Revenue did not disclose which was which.

The internet changed that by lowering the cost of reconsideration. Search and social media made alternatives discoverable. Marketplaces expanded availability. Reviews, creators and specialist communities made claims easier to verify. Logistics and returns reduced the cost of experimentation. Friction did not disappear, nor did easier comparison make every category a commodity. Where products are interchangeable, it pushes surplus toward the consumer. Where products genuinely differ, it makes those differences easier to see.

Permission itself is much older than the internet. Titan demonstrated that decades earlier in India, overcoming a far more entrenched watch incumbent through product, design, retail experience and service. The important point is that technology did not invent permission; it reduced the shelter available to companies that lacked it. Titan’s Watches & Wearables business is still moving in the same direction today: net sales grew 16% in FY2025, while the company said the “premium wave continued unabated.”
Removing friction gets a company considered. It does not get it chosen.

That requires permission: the customer’s willingness to give a company the next transaction when realistic alternatives are easy enough to discover, evaluate and buy.

There are two useful forms. Proposition permission answers why the customer wants this product or experience: better economics, superior function, cultural meaning, status or scarcity. Decision permission answers why the customer begins the decision with this company at all: membership, loyalty, ecosystem, community or trusted discovery. Costco can possess both; a hotel loyalty system may lean more heavily on the second, while a technical footwear brand may lean more heavily on the first.

The taxonomy matters less than the underlying question: when alternatives are visible, why does the customer still choose you?

The Inflation Test

That question helps explain why today’s consumer economy looks so contradictory.

The dominant macro description is the K-shaped consumer: affluent households remain relatively healthy while lower-income households are more exposed to accumulated inflation, housing costs and credit. That distinction matters, winners and losers coexist inside restaurants, apparel, footwear and beauty, while value and premium businesses can strengthen simultaneously. Company-specific demand systems often explain the dispersion better than income labels.

The K-shape explains spending capacity. Permission explains spending choice.

The inflation shock of 2021 through 2023 made that distinction unusually visible because consumer companies across categories simultaneously raised prices. For several quarters, nominal revenue made the outcome look reassuring: an 8% price increase could offset a 4% unit decline and still produce reported growth. This was often described as pricing power.

But a price increase is not merely revenue. It is an elasticity experiment.

Warby Parker tested the old system by making an alternative cheaper and easier to evaluate. Inflation ran the test in reverse: incumbents asked customers for substantially more money and discovered how much genuine preference existed underneath the inherited transaction. Companies that held units, transactions and full-price demand proved something different from companies that held revenue only because price concealed falling volume.

This also gives us a better definition of the consumer “middle.” It is not a price point, and it is not an income group.

The middle is high awareness, high availability and low permission.

The customer knows the company, can easily find the product, and struggles to articulate a compelling reason to choose it.

That is why value and premium can win together. Costco or Walmart can make the economic exchange unusually clear. A strong footwear or apparel brand can make functional or cultural value unusually clear. The vulnerable incumbent offers familiarity and distribution as though those were reasons in themselves.

Distribution still matters enormously; its role has changed. It increasingly amplifies an underlying proposition rather than substitutes for one. Distribution multiplies permission for strong companies. For weak companies, it multiplies inventory.

Permission Stock and Permission Flow

The danger for incumbents is that brand strength is usually discussed as a stock. A famous company possesses awareness, customer memories, retail relationships, cultural associations and habits accumulated over decades. Those assets are real, but they describe where permission has been, not necessarily where it is going.

The more useful distinction is permission stock versus permission flow. Permission stock is the accumulated consequence of yesterday’s successful products and relationships. Permission flow is whether today’s products and experiences are adding to or subtracting from the customer’s reason to choose.

Nike shows why the distinction matters. It remains one of the world’s most recognizable consumer brands, yet its latest results show a business still repairing current demand. In fiscal 2026’s fourth quarter, wholesale revenue grew 1% currency-neutral while Nike Direct fell 9%; CFO Matthew Friend said:

“sell-through remains challenged.”

Nike’s accumulated permission has not disappeared. What management is working to rebuild is the flow.

Financial statements can hide that transition because they report the installed base of an advantage. Revenue comes from customers acquired over decades. Distribution reflects relationships accumulated over decades. Permission deterioration therefore tends to appear first at the margins: more promotion, weaker full-price selling, slower inventory, more spending to acquire or retain demand, and poorer conversion of gross profit into operating profit.

That is why the sequence of repeat or units → clean margin → free cash matters. Units reveal whether demand exists; clean margin reveals how much inducement was required to secure it; free cash reveals whether the mechanism remains economically self-sustaining.

The investment implication follows directly. The best company is not automatically the best stock. What matters is permission trajectory relative to expectations. The attractive setup is strengthening permission flow that the market still prices as the old state. The dangerous inverse is enormous historical permission, weakening flow and a valuation that continues to capitalize the historical franchise as permanent.

The Asian Inversion

The framework becomes more consequential in Asia because it challenges one of the oldest assumptions in emerging-market consumer investing: that rising incomes eventually pull consumers toward global brands.

That thesis made sense when multinational advantage came bundled with capital, manufacturing expertise, national advertising, distribution and trust. Technology has made parts of that bundle easier to assemble. Local companies can achieve discovery before developing national physical distribution, source manufacturing, iterate quickly from customer feedback and build relevance through local creators and communities. Meanwhile, cultural fluency, local price architecture, product cadence and regional aesthetics remain difficult to centralize.

This creates two completely different explanations for local-brand success. If consumers choose a local brand because it is cheaper, rising incomes should eventually work against it. If they choose it because it has earned stronger permission, rising incomes give the company room to improve the product, raise realized prices and premiumize without surrendering the relationship.

ANTA Sports is useful because that process is already visible at scale. In 2025 revenue rose 13.3% to RMB80.22 billion and its share of China’s sportswear market increased to approximately 21.8%. Its chairman describes the strategy as:

“Winning through products, winning through operations.”

That is not the language or the financial profile of a company whose advantage depends simply on consumers being unable to afford the foreign alternative.

The conventional emerging-market equation is rising income → global-brand trade-up. The alternative is considerably more interesting: rising income → local brand premiumization.

The distinction is testable. Local challengers that maintain or gain share while moving consumers up their own price architecture were not merely affordability substitutes; they earned permission. Those that lose share as purchasing power rises were renting demand from price.

What Gets Paid For

These forces operate on different clocks. Consumer capacity moves with employment, income and credit over quarters. Permission usually strengthens or deteriorates over a multi-year company cycle. Friction changes structurally over decades as technology changes discovery, evaluation and substitution. Confusing the clocks makes good theses look wrong and temporary macro moves look structural.

For portfolio construction, the question is therefore not simply which company has the strongest brand. It is which company’s permission is changing, whether the market recognizes that change, and how long the mechanism should reasonably take to show up in the accounts.

Test that proposition rather than merely admire it. Classify companies today as strengthening, stable or weakening permission and observe the next four to six quarters. Strengthening permission should produce better relative units or transactions, healthier inventory, less reliance on promotion and cleaner operating conversion. If it does not, permission is simply sophisticated vocabulary attached to winners after the fact.

Agentic purchasing may eventually provide another test by reducing decision friction still further, particularly for businesses whose permission depends more on habit or default than on demonstrable product superiority. That is a question for the next stage of the internet’s effect on consumer choice. The current stage is already visible.

Warby Parker’s five-frame box makes the change tangible. The company did not prove that brands were obsolete. It proved that once discovery and evaluation became easier, the incumbent’s distribution could no longer answer the customer’s question for her. Warby still had to provide a reason to choose.

That is increasingly the condition facing every consumer company. The K-shape tells us something important about whether the customer can spend. Technology tells us how easily she can consider alternatives. Permission determines who receives the transaction.

The internet did not commoditize brands. It revealed which brands had ever been real.

And the investment question is the same one Warby Parker put in front of a customer opening a box of five frames at home:

Given a genuine choice, would she still buy this?

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• The strongest evidence was comparative: Nu’s Brazilian 90+ delinquency rates stayed flat to slightly better while peer cohorts deteriorated sharply, widening the gap across income bands.

• Primacy looks increasingly like the real moat: the primary-account relationship generates richer information and payment priority; Nu’s decisioning layer then converts that into better risk-adjusted economics.

• One quarter is still only evidence: RA-NIM reached 12.4% and credit economics improved materially, but the next two quarters need to show that Q2 was the start of a pattern rather than a favorable print.

Nu Holdings crossed an obvious milestone in 2Q26: quarterly net income exceeded $1 billion for the first time. The company now serves 139 million customers, ARPAC reached roughly $17, activity rose to 83.5%, and Brazil crossed 86% activity. Risk-adjusted NIM, the number that mattered most after 1Q26’s credit scare, jumped from 9.5% to 12.4%, while ROE returned to 33%.

Those numbers are excellent. They are also not the most important part of the quarter.

In our 1Q26 piece, the fundamental question was deliberately simple: “Is Nubank’s scale making its underwriting engine smarter, or merely making its credit book larger?” If more customers created more data, and more data created better decisions, then scale should improve the economics of the system. If scale merely enabled Nu to push more unsecured credit, the company could still be a very good bank, but it would remain a bank in the conventional sense: cyclical, capital-intensive, and ultimately constrained by credit losses.

2Q26 did not settle that debate. It did, however, produce the first evidence that lets us ask a better version of the question: does owning the primary customer relationship make Nubank’s economic advantage larger as the system scales, rather than gradually diluting it?

The Gap That Shouldn’t Be Widening

The key disclosure sits on slide 20. Nubank showed Brazilian credit-card 90+ delinquencies by income cohort alongside comparable Brazilian financial institutions. In the Mass Market cohort, Nu’s NPL rate moved from roughly 9.1% in July 2025 to 8.7% in May 2026. The S1-S2 peer group excluding Nu moved from 13.9% to 20.6%. The absolute gap therefore widened from 4.8 percentage points to 11.9 points. The same directional pattern appears in Super Core and High Income: Nu remained stable to improving while peers deteriorated.

The level is striking; the widening is more interesting. A persistent gap could simply mean Nu selected better borrowers in the first place. A widening gap suggests that explanation may be incomplete, although it does not eliminate selection: credit losses are nonlinear, and superior borrower selection can itself become more valuable as weaker customers cross default thresholds.

There are at least three overlapping mechanisms. The first is information. A primary institution sees salary, balances, spending and cash-flow deterioration as they happen rather than through a delayed bureau snapshot. The second is decisioning. Nu must convert those signals into better limits, pricing, collections and product decisions; having better information without using it well creates little value. The third is payment priority. A primary relationship may sit higher in a stressed customer’s hierarchy of obligations.

Management increasingly describes the advantage in exactly these terms. It says customers for whom Nu is the primary relationship have delinquency around half the portfolio average, and connects primacy to richer behavioral data, stronger underwriting and a higher position in the customer’s payment hierarchy. Vélez was even more explicit in Q&A:

“Over 60% of our mass market customers use us as their primary bank account… [that] positions us effectively as being senior in the credit stack of a customer.”

That does not prove which mechanism causes the gap. But slide 20 is the first strong comparative evidence that Nu’s primary-account system is producing different credit outcomes, not simply different growth rates.

Primacy Is the Asset; Intelligence Is the Multiplier

This changes how I think about the architecture we introduced in 3Q25. At the time, we described Nubank as three reinforcing layers: Trust & Distribution, Data, and Intelligence. NuFormer was the new “brain,” converting a proprietary dataset into better decisions.

That framework still works, but 2Q26 suggests a clearer hierarchy. Primacy is the scarce customer position. Information is what primacy generates. Decisioning is the capability that converts information into economic action. NuFormer is therefore an amplifier of the moat, not the moat by itself.

AI architectures will diffuse. Compute can be purchased. Competitors can hire engineers. What is much harder to purchase quickly is the position inside a customer’s financial life that generates years of proprietary behavioral context.

The flywheel is better expressed as:

Primacy → Information → Decisioning → Better Economics → Deeper Primacy.

Ecosystem breadth still matters, but as a complement rather than a separate thesis. In 4Q25 we argued that Nu’s architecture resembles proprietary aggregation: additional services such as NuCel, travel and commerce can create more interactions and feed information back into the financial decision engine. That remains plausible. The test for every adjacency, however, should now be stricter: does it deepen the primary relationship or materially improve the decisions made from it?

NuFormer makes this loop more powerful. Management says improvements to its common backbone can propagate across downstream applications; the latest generation quadrupled context length, training and inference speed, and can reach comparable predictive performance with 20 million fine-tuning rows where older approaches required more than 400 million. It now supports credit in Brazil and Mexico, unsecured lending, customer service and more than 100 marketing campaigns.

What management still does not disclose is equally important: we do not know the incremental approval rate, loss rate or risk-adjusted return of a NuFormer cohort versus an equivalent previous-model cohort. 2Q26 shows differentiated system outcomes. It does not yet isolate AI attribution.

Taking More Risk, Making More Money

That distinction matters because the most impressive economic result this quarter was not lower risk. Nu intentionally added risk.

Seasonality reduced the 15–90 day delinquency ratio by 37 basis points; deliberate expansion into higher-risk, higher-return cohorts added 24 basis points back. Those same decisions added approximately $170 million to the credit-loss allowance. Yet early delinquencies still improved to 4.8%, and risk-adjusted NIM reached 12.4%.

This is the progression we wanted to see after 4Q25. Then, Nu expanded credit availability and IFRS 9 forced expected losses to be recognized before the associated balances had generated their full revenue. 1Q26 absorbed much of the cost. 2Q26 began to show the other side: credit income contributed 178 basis points to sequential RA-NIM expansion and lower credit cost another 115 basis points.

The relevant objective of a lender is not to minimize defaults. It is to identify which expected defaults are worth accepting because the yield more than compensates for them. 2Q26 is the strongest evidence yet that Nu may be moving the efficient frontier outward.

The caveat is important. Desenrola helped cost of credit; seasonality helped; and Livingston explicitly refused to call 12% RA-NIM a floor. Management is effectively giving investors permission to contemplate a higher normalized regime while warning them not to annualize 12.4%. The next two quarters will tell us whether the durable number begins with 11 or falls back toward the old 9–10% range.

Mexico Becomes the Second Experiment

Mexico is now useful for a different reason. In 2Q25, the critical experiment was funding: Nu cut deposit yields and customers largely stayed. Trust had become an economic asset; Nu did not have to permanently buy deposits with the highest rate.

The full banking license now unlocks the next experiment: can trust become primacy?

Mexico already reaches 16.5% of adults, approximately the penetration Brazil had in 2020, but generates $12.3 of ARPAC versus Brazil’s $5.6 at the comparable stage. Management attributes that to higher income, stronger credit-card unit economics and larger earning balances. Mexico also has only a 35% loan-to-deposit ratio, leaving substantial room to convert funding into earning assets.

The banking license matters because payroll direct deposits and a fuller product set make it possible for Nu to become the account around which everyday financial activity organizes. Meanwhile, Mexico is standardizing payment interfaces in a way management explicitly compares with the regulatory conditions that helped Pix accelerate Brazilian digital banking.

But the bar has not moved. In 1Q26, we said Mexico becomes valuation-relevant when it moves from evidence of transferability to a material second earnings engine. 2Q26 does not clear that bar. What changed is that the mechanisms required to clear it are increasingly in place.

A Better Bank, or a Different Kind of Bank?

This is now the variant perception. The market does not need to be wrong about Nubank’s credit exposure for the stock to work. The base case only requires the market eventually to conclude that Nu is a premium financial compounder whose high ROE and superior post-loss economics are durable. A full platform classification is additional upside, not a prerequisite.

Using 2029 as the three-year earnings horizon, I would frame the outcomes as follows:

The bear case is not that Nubank fails. It is that higher-risk growth eventually behaves like higher-risk growth, RA-NIM compresses, Mexico fails to become material and Nu remains valued as a cyclical emerging-market bank. The base case requires no software multiple: roughly 20% revenue compounding, around 30% ROE and post-loss margins above 10% are enough. The bull case requires something structurally different, Mexico reproduces the Brazilian loop, primary-account density continues widening credit economics, and the decision engine scales those advantages across markets.

Evidence, Then Pattern

The next four quarters should be judged against hard signposts, not narrative. RA-NIM averaging above roughly 10.5–11% would strengthen the higher-regime argument; a fall below 9.5–10% without a strategic explanation would challenge it. Early NPL should remain around or below 5% outside normal 1Q seasonality, while 90+ NPL moving beyond roughly 7.5% alongside worsening early buckets would be a serious warning. Most importantly, the Nu-versus-peer cohort gap should hold or widen; compression because Nu deteriorates would attack the primacy thesis directly. Brazil ARPAC should move beyond $18 toward $19–20, while Mexico should show rising payroll/primary-account adoption, LDR and ARPAC without sacrificing funding discipline. Efficiency around 20–21% would demonstrate that these investments are still occurring inside a structurally low-cost model.

1Q26 ended with a useful discipline: one quarter can be explained; a pattern gets capitalized. 2Q26 supplies the first evidence that Nubank’s left tail may genuinely be narrower than that of the institutions from which it is taking share. More importantly, it offers a mechanism: primary relationships generate richer information and potentially greater payment priority; Nu’s decisioning infrastructure converts those advantages into profitable risk-taking; better economics allow Nu to deepen the relationship further.

That is a more demanding thesis than “Nubank has better AI,” and a more valuable one if it is true. 3Q26 and 4Q26 now have a simple job: show that 2Q26 was not merely a very good quarter, but the first visible consequence of a compounding primacy advantage.

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• Wallet share is rising, but payment yield is falling: Adyen processed 24% more volume while constant-currency revenue grew 21%, with take rate falling to 16.2 bps as larger merchants moved into lower pricing tiers.

• The thesis has shifted from transactions to relationships: the real opportunity is to deepen merchant economics through Unified Commerce, Platforms, loyalty, billing, issuing and money movement not simply process more volume.

• The missing proof is monetization: Adyen increasingly demonstrates that its platform creates merchant value but still does not clearly disclose how much incremental revenue it captures outside core processing.

“We are no longer just a payments company. We are the complete financial operating system for modern commerce.”

There is one number in Adyen’s first-half results that complicates that statement: 16.2 basis points.

Adyen processed €803.8 billion in H1, 24% more than a year ago, while net revenue increased 21% in constant currency. Digital revenue grew 15% against 17% volume growth, while even Platforms grew 40% against 42% volume. Adyen is winning more business, but the revenue attached to each euro of payment volume is declining as large merchants move into lower pricing tiers.

Six months ago we concluded that the old “architecture dividend” thesis needed an upgrade. Adyen’s single platform was still differentiated, but we had confused evidence that the platform creates value with evidence that Adyen captures that value. As we wrote after Q1, “The single platform cannot merely process more volume. It has to help Adyen earn more around that volume.”

H1 is the first report that makes clear management has reached essentially the same conclusion.

The fundamental question is therefore no longer whether Adyen has superior payment infrastructure. It is whether Adyen can increase the total economics of a merchant relationship faster than scale commoditizes the underlying payment.

That is a harder question. It is also a much more interesting one.

The Merchant, Not the Transaction

The most important disclosure in H1 was not revenue or EBITDA. It was Adyen’s newly disclosed customer maturity curve.

Roughly two-thirds of H1 growth came from merchants onboarded in 2024 or earlier. Typical wallet share rises from below 20% in years three through seven to above 40% after a decade, while the 300 merchants accounting for roughly 60% of growth represented more than 70% three years ago. That disclosure suggests a different way of measuring Adyen.

The traditional payment equation is straightforward: processed volume × take rate = revenue. The emerging Adyen equation is closer to merchant relationship × wallet share × workflows × products = economic value.

Consider how that might work. A merchant starts with online payments, adds more countries, then physical locations through Unified Commerce. Those transactions make Adyen’s understanding of the merchant and shopper richer; Dynamic Identification and Uplift use that information to improve authorization and fraud outcomes. Talon.One can add loyalty and incentive decisioning before the transaction, Orb can meter and bill usage, while accounts, issuing and Intelligent Money Movement extend Adyen’s role after the transaction.

The payment becomes the foundation rather than the entire product. There is, however, a crucial piece of information management did not provide.

Bryan Bergin asked where Adyen’s existing customer base sits on the newly disclosed maturity curve: how much volume already comes from merchants above 40% wallet share versus customers earlier in their penetration journey. Management talked about diversification and continued expansion but did not give the requested mix.

That matters. Adyen has shown investors the slope of the curve but not where the installed base sits on it. If most volume remains early in the curve, the disclosure is a powerful forward indicator. If much of the business is already mature, it is more descriptive of the past. The non-answer does not prove the latter, but neither should we treat the former as established.

The Paradox of Winning Wallet Share

There is another complication embedded in this maturity curve.

More wallet share is good for Adyen, but it also pushes large merchants through volume tiers that reduce payment pricing. Management is categorical that it sees no evidence of competitive pricing pressure in its data; Pieter van der Does said the take-rate movement reflects existing merchants giving Adyen more volume and therefore moving into lower tiers.

This means Adyen’s original flywheel has an opposing force inside it. More wallet share creates more payments, more data and deeper integration, but it can simultaneously reduce the economics of basic processing.

This offers one possible interpretation of EMEA’s slowdown. EMEA revenue grew 15% in H1, versus materially faster growth previously, while North America grew 30% constant currency. Management argues geographic attribution is inherently noisy because global merchants sell across regions and points to the payoff from sustained US investment.

A hypothesis worth testing is that EMEA also contains a relatively more mature set of Adyen relationships, making tiering more visible there. We do not have enough disclosure to establish that. But if it proves correct, the implications are important: Talon.One, Orb, money movement and financial products are not simply exciting incremental growth opportunities layered onto payments. They become a required offset to the natural economics of merchant maturity.

In other words, the question is not how much upside the operating-system strategy adds. It may increasingly be how much operating-system monetization Adyen needs to sustain the growth rate investors already expect.

Purity Was Never the Point

This also explains what otherwise looks like a striking reversal in strategy. The company we originally liked was defined partly by what it refused to do. While payment incumbents accumulated acquisitions and integration debt, Adyen built a single system from scratch. The single platform produced consistent data, lower complexity and faster deployment. Now Adyen has bought Talon.One and Orb.

I no longer think the right interpretation is that Adyen abandoned its architectural philosophy. Rather, management has revealed the deeper principle underneath it. Architectural purity was valuable because it gave Adyen greater control over merchant complexity; purity itself was never the objective.

Pieter made the distinction unusually explicit on the call: for the core, Adyen still prefers to build; for adjacent capabilities, management evaluates both internal development and acquisitions. It simply happened that the answer was “build” for nineteen years and “buy” twice in H1.

The optimization function has changed from build everything ourselves to control the merchant workflow without sacrificing the architecture.

That is sensible. It also creates a new test. Talon.One and Orb cannot merely become software products sold beside Adyen payments; they need to become native participants in the same data loop. Management says merchants already using both Talon.One and Adyen prove the combination works, and the intention is to deepen that integration.

If loyalty, billing, payments and money movement produce one shared intelligence layer, the acquisitions strengthen Adyen’s moat. If they remain separate applications connected through APIs, Adyen has merely broadened the catalogue while introducing the architectural complexity it once avoided.

The Missing Arrow

There is now considerable evidence for most of the Adyen flywheel. Unified Commerce grew 27% constant currency; Platforms grew 40%; Adyen has 486 merchants processing across channels at scale and 293,000 active Platform business customers. More importantly, Uplift increased merchant conversion by an average 0.9 percentage points by the end of H1.

That establishes several arrows: more transactions create richer data; richer data creates better decisions; better decisions create measurable merchant value; merchant relationships continue deepening over long periods.

One arrow remains conspicuously missing:

better merchant economics → materially more Adyen revenue outside processing.

In Q1 we said we wanted visible value-added-services revenue, continued >mid-20s Unified Commerce growth, >mid-30s Platforms growth, take-rate stabilization toward 16.5 basis points and a real Talon.One operating proof point. H1 passes the UC and Platforms tests, provides one useful Uplift outcome metric, and demonstrates management’s strategic commitment. It misses the take-rate test and largely defers the monetization test.

Even financial products, which management says should contribute roughly one percentage point of growth this year, remain largely unbroken out.

This is the central analytical tension: Adyen increasingly shows us that its platform creates value, but still does not show us clearly how much of that value Adyen captures.

The Narrative and the Numbers

This distinction also clarifies the valuation argument.

Bloomberg consensus already assumes a significant part of the operational bull case: approximately €3.49 billion of FY27 revenue, €4.19 billion in FY28, and an FY28 EBITDA margin around 55%. At the supplied €1,061.60 reference price, however, Adyen trades at roughly 22.5x FY27 and 18.2x FY28 adjusted earnings.

So consensus may already expect strong execution, but the stock does not obviously carry a full “commerce operating system” multiple. This creates two different bull cases. The first is that estimates are too low. The more interesting one is that estimates may be broadly right, but Adyen proves that their composition and durability deserve a different valuation category.

That gives us three futures:

The bear case does not require Adyen to become a bad business; it merely remains an excellent processor whose adjacent products never materially alter merchant economics. The base case assumes those products become meaningful enough to preserve high-teens growth and mid-50s margins. The bull case requires something qualitatively different: revenue per merchant begins compounding independently of payment yield, making processed volume an increasingly poor denominator for the company.

The evidence required from here is therefore quite specific. We need actual monetization or attach-rate disclosure from Talon.One, Orb, IMM or financial products; more information about where customers sit on the maturity curve; evidence that the acquired products become part of Adyen’s unified data architecture; and CapEx returning toward historical levels while the >55% 2028 margin objective survives the expansion.

In May, we asked whether Adyen could sell more than payments. H1 tells us that management has decided it must.

Adyen has shown us the wallet-share slope. It has shown that unified data can produce better merchant outcomes. It has bought capabilities before and after the payment that could expand the economics of the relationship.

What it has not shown is the number connecting those things: how much incremental revenue the expanded platform earns.

That is the missing arrow. If it appears, 16.2 basis points will increasingly look like the wrong denominator. If it does not, it may turn out to have been the most important number all along.

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• The thesis has moved beyond peak earnings: contracts now provide multi-year demand visibility, while Sandisk is deliberately keeping actual bit growth below technological productivity.

• The real compounding mechanism is per share: technology lowers capital needs, contracts stabilize cash flow, supply discipline limits reinvestment, and buybacks permanently shrink the denominator.

• The key risk is time: Sandisk does not need NAND cyclicality to vanish. It needs current economics to persist for three to four years long enough for buybacks to permanently reshape per-share earnings before the next downturn.

This is my fifth piece on Sandisk in nine months. Each previous piece revised the last as the business kept transforming faster than the framework could hold, and I am wary of doing that again on the strength of an Investor Day that has not yet been tested by a difficult quarter. What follows is not a re-modelled forecast. It extends the argument I made eight days ago in The Earnings That Stay, which was that the debate had moved from peak earnings to duration. Investor Day did not invalidate that thesis. It showed the machinery management believes sits between the contracts we had already identified and the shareholder value we were still unwilling to assume.

How Long Is Long Enough?

The bear argument on Sandisk is that eighty percent gross margins cannot persist because memory always mean-reverts. The bull argument is that management has guided to eighty percent, so the stock should trade at ten times three hundred dollars of earnings. Both arguments treat the level of earnings as the only thing that matters. Neither engages with the question that Investor Day actually posed.

The real question is not whether current economics are sustainable forever. The real question is whether they need to be. Suppose Sandisk remains highly profitable for three or four years, even if margins ultimately settle below management’s targets. During those years, contracts make cash generation predictable, technology supplies most of the required bit growth, capital intensity stays low, and management returns the surplus through repurchases. The next downturn would then strike a company with materially fewer shares outstanding. It could reduce corporate earnings. It could not recreate the shares already retired.

That is the consequence I underweighted after fiscal fourth quarter. The question is not merely how much of today’s earnings stay. It is what those earnings can permanently change while they are here. The mechanism has four moving parts and they reinforce each other. Technology continues to reduce the capital required to produce each bit. Contracts convert the demand side into forecastable cash flow. Supply discipline lets management hold actual bit growth below what the technology could deliver. And buybacks compound the resulting cash into a permanently smaller share count. Any one of these in isolation is corporate finance. The four together are how a cyclical business quietly restructures its per-share economics before the cycle has a chance to reset them.

Why Sandisk, Why Now

The strategic question worth asking is why Sandisk is the company attempting this and not one of the four larger memory manufacturers. Samsung has more scale, more capital, and more customer relationships. SK Hynix has better balance sheet flexibility and an integrated HBM business. Micron is larger and more diversified. Any of them could theoretically have moved first on multi-year contracts with financial guarantees. None of them did.

Sandisk moved first because Sandisk had to. The company came out of the Western Digital separation with a market capitalisation that was, at the low, less than the replacement cost of a single leading-edge NAND fab. In that context, management had two options: run the business as a smaller version of its competitors and wait for the cycle, or restructure the commercial model in a way that would force investors to reconsider what the company actually was. The first option would have produced a fine business and a decade of oscillation between forty dollars and two hundred. The second was strategic in the sense Michael Porter used the word: a deliberate decision to compete on a different dimension than the industry.

There may also be an irony in Sandisk’s manufacturing structure worth naming, though I would not build the thesis around it. Investors have historically treated the Kioxia joint venture as a constraint because it limits Sandisk’s ability to unilaterally redirect capacity. That same structural limitation may make long-duration customer commitments more credible, because Sandisk cannot easily walk away from a contract even if the math turns against it. This is inference rather than something management disclosed, and it would be one factor among several including financial guarantees, quarterly volume specifications, and strategic engagement. Kioxia’s own commercial interests could still disrupt the arrangement, which is why the argument sits at the level of a plausible contributor rather than a load-bearing claim. But it is worth flagging that the feature usually described as weakness may function differently in a contracted world.

Contracts Change Time

Sandisk’s New Business Models now cover eight customers, including three American hyperscalers. Weighted-average duration exceeds four years, total contract value at floor pricing is $93.9 billion, remaining performance obligations are $91.1 billion, financial guarantees total $16.5 billion, and coverage rises from roughly half of FY2027 bits to about two-thirds in FY2028. Two customers have already expanded their commitments within weeks of signing.

The previous article treated these agreements primarily as protection against falling prices. Investor Day showed their second function: they are an information system. Sandisk previously made decade-long investments while learning every quarter what customers wanted and what they would pay. It now receives product, volume, and timing commitments several years ahead. That visibility does not guarantee forecasts are correct. It makes disciplined supply possible in a way quarterly auctions never did. The contracts therefore do more than raise an earnings floor. They tell Sandisk which bits customers have committed to buy, and which production the company should not create merely because its technology allows it.

Who Captures the Productivity

Management says the BiCS technology roadmap can produce roughly twenty-seven percent annual bit-per-wafer productivity while Sandisk intends to grow actual volume only in the mid-to-high teens. The gap is not an idle technical detail. It determines whether innovation becomes future oversupply or retained economics.

The old memory model converted every cost reduction into more bits, lower prices, and value transferred to the customer. Goeckeler’s declaration at Investor Day that Sandisk’s cost reductions are “ours” was more consequential than it sounded. Management wants market pricing to determine revenue while technology reduces the wafers and capital required to serve committed demand. Sandisk cannot impose that discipline on Samsung, Micron, SK Hynix, or Kioxia. Industry supply can still break the economics. What Sandisk can control is whether its own productivity becomes additional capacity or free cash flow. Investor Day made clear which outcome management prefers.

What Happens to the Cash

Sandisk intends to return one hundred percent of excess cash after funding the business and maintaining a debt-free balance sheet, with repurchases as the current mechanism. This is usually presented as capital allocation. In a memory company, it is also supply discipline expressed through the balance sheet.

The traditional cycle contains its own destruction: high prices produce high cash flow, high cash flow funds more capacity, more capacity destroys prices. Sandisk is proposing to let technology provide most of the bit growth and send the residual cash to shareholders instead of recycling it into wafers. The buyback is not merely financial engineering. It is the mechanism through which the current regime becomes permanent even if the regime itself does not last.

It also makes the share count endogenous. If the market remains sceptical and the stock trades at a low multiple, each dollar buys back more of the company. If investors accept the duration argument and rerate the stock, repurchases become less accretive but shareholders receive the rerating immediately. The genuinely bad outcome is not that the multiple stays low. It is that the earnings disappear before the denominator changes.

The Category Question Follows

If this system works through even one full downturn, the market will eventually have to decide whether Sandisk still belongs in the peer group it currently sits in. A memory specialist asks where NAND pricing will be next quarter. A generalist can begin asking how much four years of contracted cash flow will reduce the share count. Those investors do not pay the same multiple because they are not underwriting the same object. But category change is the consequence of the mechanism working, not the thesis. It is what happens if the argument is right, not the argument itself.

Three Futures

The scenarios below use FY2030 earnings as the forward basis for a stock value roughly three years from now. They are estimates, not management guidance.

The bear case assumes customers renegotiate when spot prices fall, competitor supply returns, and buybacks lack the time and scale to alter the share count materially. The base case assumes management is too optimistic on margins but broadly right on duration: NAND remains cyclical, HBF contributes nothing material, and three to four years of high cash generation permanently improve per-share economics. The bull case requires NBM coverage to keep rising, industry supply discipline to persist, and management’s framework to prove broadly achievable. I would weight these 20 / 55 / 25, implying a rough central value near $2,650. The base case matters most because it does not require Sandisk to abolish the NAND cycle. It requires the current regime to last long enough.

What to Watch

NBM coverage must continue past two-thirds rather than stall. Customer expansions must continue rather than reverse. Contract economics must remain intact when spot pricing weakens. Sandisk must hold actual bit growth below technological productivity, keep capital intensity in the mid-single digits, and reduce the diluted share count rather than merely announce repurchase authorisations. Competitor capacity remains the external variable the company cannot contract away. HBF should be tracked separately as upside rather than requirement.

Long Enough

The previous article asked how much of Sandisk’s earnings would stay. Investor Day did not answer that question. It suggested we may have been asking it too literally. The earnings do not need to stay forever. They need to stay long enough for technology to reduce the capital required to produce them and for the cash they generate to reduce permanently the number of claims against them. The NAND cycle may return. The shares Sandisk bought back cannot.

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General Disclaimer: The information presented in this communication reflects the views of the author and does not necessarily represent the views of any other individual or organization. It is provided for informational purposes only and should not be construed as investment advice, a recommendation, an offer to sell, or a solicitation to buy any securities or financial products.

While the information is believed to be obtained from reliable sources, its accuracy, completeness, or timeliness cannot be guaranteed. No representation or warranty, express or implied, is made regarding the fairness or reliability of the information presented. Any opinions or estimates are subject to change without notice.

Past performance is not a reliable indicator of future performance. All investments carry risk, including the potential loss of principal. This communication does not consider the specific investment objectives, financial situation, or particular needs of any individual.

The author and any associated parties disclaim any liability for any direct or consequential loss arising from the use of this material and undertake no obligation to update or revise it.

• The thesis has broadened: Applied is benefiting not only because AI chips require more process complexity, but because customers are increasingly constrained by clean-room space, yield, uptime and time-to-ramp.

• Scarcity is showing up in pricing: Semiconductor Systems gross margin reached 55.4%, helped by value-based pricing for tools that save space, improve yield and accelerate production.

• The key question is durability: the market already believes this cycle is extraordinary. The next rerating depends on proving that higher content, pricing and service economics survive after today’s scarcity fades.

Applied Materials reported record revenue of $9.12 billion, record adjusted earnings of $3.50 per share and a 50.4% gross margin. It then guided the October quarter to $10.25 billion of revenue and $4.02 of earnings, with Semiconductor Systems alone expected to reach roughly $7.9 billion, up 62% year over year. The stock nevertheless fell after hours.

That contradiction is the right place to begin, because Applied no longer has to persuade investors that artificial intelligence is driving an extraordinary equipment cycle. The market already believes that. The harder question is whether Applied is becoming economically better because semiconductor manufacturing itself is becoming harder.

I have written about this progression three times. In January, I argued that physics was shifting value from drawing smaller features toward constructing increasingly complicated three-dimensional structures. In February, Gate-All-Around, HBM and a 54.5% Semiconductor Systems gross margin made that claim measurable. By May, management had raised its calendar 2026 Semiconductor Systems growth outlook from more than 20% to more than 30%, every test from the prior article had cleared, and the debate had moved from whether the complexity thesis worked to how much of it was already reflected in the price.

Q3 did not overturn that argument. It exposed the half we had not fully described. I had thought the opportunity was primarily that harder chips require more Applied equipment; this quarter makes clear that those chips are also being produced inside increasingly constrained factories. Complexity creates more content, while scarcity changes the value of that content.

Five of Six

The May article set six signposts for Q3. Five cleared.

The accountability matters. Revenue, margins, cash conversion, duration and growth all moved in the direction the thesis required, while working capital did not. The May article’s tests are set out in the prior piece; Q3 subsequently delivered $2.33 billion of free cash flow and record profitability across both Semiconductor Systems and AGS.

Accounts receivable rose from $5.19 billion at fiscal year-end to $7.69 billion in Q3, producing a rough DSO calculation of approximately 77 days. That is worse than the threshold we set, not better. The offset is real: operating cash flow rebounded to $3.04 billion, contract liabilities increased, and the quarter included a $1.2 billion sequential revenue step-up that may have been back-end loaded. Still, the proper conclusion is not that the cash-flow question has disappeared. The cash-flow test cleared; the working-capital test did not. A Q4 DSO below 70 would support the timing explanation, while another quarter above 75 would turn the issue into a genuine earnings-quality debate.

The Three Raises

The simplest way to understand the acceleration is to follow management’s own forecast.

In February, Applied expected calendar 2026 Semiconductor Systems revenue to grow more than 20%. In May, that became more than 30%. This quarter, CFO Brice Hill said the previous greater-than-30% expectation was now too low:

“The greater than 30% that we highlighted last quarter, we’re saying now that it’s greater than that at this point.”

Semiconductor Systems generated $5.97 billion in April, $7.04 billion in July and is guided to approximately $7.9 billion in October. Applied is taking its core equipment business from a roughly $6 billion quarterly run rate to almost $8 billion in six months.
The cause matters more than the magnitude. Customers repeatedly found ways to create clean-room capacity, pulled equipment deliveries forward and supplied Applied with detailed eight-quarter forecasts. Demand did not merely exceed Applied’s estimate; it repeatedly exceeded customers’ physical ability to house the equipment.

Two Kinds of Scarcity

Gary Dickerson described AI as creating two races: one for technology leadership and another for manufacturing capacity. The first requires better transistors, memory, wiring and packaging. The second requires customers to extract more yield and output from existing fabs while bringing new capacity online.

Applied sits at the intersection of both. Technological scarcity is the thesis we already know: leading-edge foundry and logic, DRAM and advanced packaging are expected to account for roughly 80% of WFE growth, while advanced-packaging revenue, previously expected to grow more than 50% this year, is now expected to grow more than 70%.

Physical scarcity is the new part. When fabs are full, the scarce resources are not only wafers and tools but clean-room area, yield, uptime and time-to-ramp. Applied is increasingly selling directly against each constraint. A new DRAM epitaxy system improves performance while using 20% less clean-room space. More than 37,000 chambers are connected to AIx software. AGS is expected to grow more than 20% this year, while process diagnostics and control is expected to grow more than 50%.

This distinction matters because complexity and scarcity play different roles. Complexity determines Applied’s opportunity through the cycle; scarcity determines how valuable that opportunity is at this point in the cycle. A tool that saves floor space is worth more when floor space is the bottleneck. A service that reduces downtime is worth more when every chamber is running. A metrology system that improves yield is worth more when additional fab capacity is years away.

Applied is therefore not simply selling more process steps. It is increasingly selling solutions to increasingly expensive bottlenecks.

What 55% Means

The strategic story matters only if Applied captures the economics.

Semiconductor Systems produced a 55.4% non-GAAP gross margin and a 38.0% operating margin, compared with 53.5% and 33.2% a year ago. AGS reached a 30.1% operating margin.

Management attributes part of the improvement to a value-based pricing system introduced three years ago. Applied now evaluates what a tool contributes to the customer’s economics rather than simply what it costs to build, and says pricing and margins are higher across both new and existing products.

Hill made the mechanism unusually explicit:

“We examine the value of every single tool and put a new price on every single tool.”

The customer is not merely buying a chamber. It is buying earlier time-to-market, more output per square metre, better yield and less downtime. That is why physical scarcity can convert technological differentiation into pricing power.

There is, however, a counter-signal. Q4 corporate gross margin is guided flat at 50.4% despite another large revenue increase. Management points to rapid hiring, manufacturing expansion, service additions and display mix. Those explanations are credible, but they create the next test. If the costs are temporary, corporate gross margin should begin following Semiconductor Systems higher. If company margin remains around 50% while revenue surges, Applied may be creating more value than it captures.

What Management Would Not Say

The most revealing part of the call was the gap between management’s confidence and its specificity.

UBS analyst Timothy Arcuri argued that the trajectory effectively required Semiconductor Systems growth near 40% if Applied was to outgrow the market. Hill replied:

“I think you’ve got the dynamics right.… We’re not giving that number because we’re not guiding that out quarter.”

Management did not endorse 40%, but it also did not reject the arithmetic.

The same pattern appeared around 2027. Dickerson called it another strong growth year; Hill called it another strong, record year. Yet neither would quantify it. When analysts tried to turn the plan to double manufacturing capacity by 2028 into a revenue forecast, Hill pushed back:

“It is capacity. So it is not a revenue forecast for 2028.”

The uncertainty has therefore moved. Management appears confident about demand direction but is less willing to commit on magnitude and timing, because physical clean-room availability still determines when customer intent becomes Applied revenue.

That specificity gap helps explain why an excellent guide did not make the stock work. At roughly thirty times forward earnings, directional confidence is no longer enough. The October investor event now matters because investors need a quantified bridge from today’s shortage to normalized 2027 and 2028 economics.

Three Worlds After Q3

The Q4 midpoint takes fiscal 2026 revenue to approximately $34.3 billion and adjusted EPS to roughly $12.75. The more important question is what remains after the current acceleration fades.

These are scenarios rather than point forecasts.

The Inventory Reckoning assumes that today’s shortage contains meaningful pull-forward. Applied exits the cycle with more content and a higher earnings base than before, but the familiar capital-equipment pattern reasserts itself as utilization falls, urgency fades and customers absorb capacity already ordered.

The Higher Floor is my base case. WFE eventually slows, but packaging, process control and AGS remain larger businesses; Applied captures modestly more content per wafer; and company margins rise after the current ramp costs fade. This thesis does not require the cycle to disappear. It requires the next trough to be materially higher than the last.

The Compounding Era demands more. Applied must consistently outgrow WFE, sustain mid-teens AGS growth, convert EPIC partnerships into earlier design-ins and move corporate margins toward the economics already visible inside Semiconductor Systems. Q3 makes that world possible, but the durability of the scarcity premium remains too untested to make it probable.

What the Stock Now Implies

Before the report, Bloomberg expected FY27 EPS of roughly $17.31. That estimate should rise, but I would not leap immediately to $20 or $21. The January quarter contains 14 weeks, and management deliberately refused to quantify the year. A move toward approximately $18–19-plus is a more disciplined starting point.

The interesting setup is that the earnings denominator is rising while the share-price numerator has fallen.

Below approximately $475, the market would be treating much of the current surge as cyclical despite evidence that the earnings floor has risen. Around $500–550, the stock prices substantial 2027 strength without requiring the full Higher Floor case. Between roughly $650 and $750, that base case becomes increasingly reflected. Above $1,000, investors are effectively accepting that the Compounding Era has arrived.

The first-half story was multiple expansion. From here, earnings revisions must do most of the work.

After the Cycle

The Big Fundamental Question has evolved:

Can Applied turn the growing physical and technological difficulty of producing AI chips into structurally more content, pricing and service revenue per unit of semiconductor capacity, such that complexity becomes the compounding engine rather than WFE growth being the thesis?

Q3 moved the answer toward yes. Packaging is growing more than 70%, process diagnostics more than 50%, AGS more than 20%, and Semiconductor Systems gross margin exceeds 55%. Customers are giving Applied longer forecasts, and the company is developing tools whose value comes from extracting more output from limited factory resources.

Three structural tests now matter. Corporate gross margin should move through approximately 51% during FY27. The October event should demonstrate that the 2026 exit rate is not mostly demand pulled forward from later years. Applied should continue to outgrow WFE after the present surge begins to normalize.

Three nearer-term observations will tell us whether those tests are progressing: Q4 Semiconductor Systems revenue near or above $7.9 billion, DSO below 70 days, and a Q1 FY27 outlook that remains strong after adjusting for the extra week.

The stock fell because investors already understand that this equipment cycle is extraordinary. Applied’s next task is harder: it must prove that some of today’s extraordinary economics remain after today’s scarcity fades.

The question is no longer how extraordinary this cycle becomes. It is how much of the extraordinary economics Applied keeps after the cycle is gone.

$AMAT

General Disclaimer: The information presented in this communication reflects the views of the author and does not necessarily represent the views of any other individual or organization. It is provided for informational purposes only and should not be construed as investment advice, a recommendation, an offer to sell, or a solicitation to buy any securities or financial products.

While the information is believed to be obtained from reliable sources, its accuracy, completeness, or timeliness cannot be guaranteed. No representation or warranty, express or implied, is made regarding the fairness or reliability of the information presented. Any opinions or estimates are subject to change without notice.

Past performance is not a reliable indicator of future performance. All investments carry risk, including the potential loss of principal. This communication does not consider the specific investment objectives, financial situation, or particular needs of any individual.

The author and any associated parties disclaim any liability for any direct or consequential loss arising from the use of this material and undertake no obligation to update or revise it.

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• The optical transition is broader than expected: demand is expanding simultaneously across scale-across, 1.6T, optical circuit switching, near-packaged optics, CPO and external laser sources.

• The economics are arriving faster than the architecture: revenue reached $1.01 billion, operating margin hit 36.6%, and management is already guiding to roughly 40% margins at a $1.25 billion quarterly revenue run-rate.

• The real debate is duration: customers are helping fund capacity and signing multi-year commitments, but the thesis depends on Lumentum retaining its yield, reliability and qualification advantage as Chinese and other competitors’ scale.

Fourteen months ago, I wrote that AI had changed the nature of networking. Traditional data centers were built for computers that communicated intermittently: an application requested data, a browser called a server, a database returned a result. AI training was different. Thousands of processors needed to communicate continuously, and a processor waiting on the network was an extraordinarily expensive piece of silicon doing nothing. The network had stopped being plumbing around the computer and had become one of the constraints on the computer itself.

The conclusion of that article was that the optical transition would eventually become one of the most important battlegrounds in AI networking. Copper could be optimized, retimed and packaged more cleverly, but physics would eventually win ever-higher bandwidth would make electrical connections increasingly expensive in power and increasingly difficult in signal integrity.

I think that argument was broadly right. What I underestimated was the shape of the transition.

I imagined something relatively discrete: copper would dominate until it reached its limits, at which point technologies such as co-packaged optics would move light closer to the processor. Lumentum’s fiscal fourth-quarter earnings suggest the transition is happening much more continuously. Optics is spreading through several layers of the AI network at once, and each layer appears to create another demand vector before the previous one has finished growing.

The most striking evidence came from CEO Michael Hurlston:

“For one major hyperscaler, the network capacity connecting just two AI data center sites is double the total global backbone capacity they built over the entirety of the last decade.”

Two sites require twice a decade’s worth of backbone capacity.

That sentence says more about Lumentum’s opportunity than its first billion-dollar quarter does. Revenue reached $1.01 billion, up 109% year-over-year, while non-GAAP gross margin crossed 50% and operating margin reached 36.6%. The company then guided the September quarter to $1.225–1.275 billion of revenue and approximately 40% operating margins.

The numbers are extraordinary. The more important question is whether they describe the top of an unusually violent optical shortage or the early financial expression of something more structural.

The Missing Layer

My previous networking article focused mainly on the companies deciding how AI networks were built: NVIDIA, Broadcom, Arista, Marvell and Credo. NVIDIA had understood earlier than most that a GPU was only as useful as the network connecting it to other GPUs; Mellanox therefore looked less like diversification and more like completing the machine. Meanwhile, the optical transition appeared as a future disruption to today’s hierarchy.

What that framing underweighted was the physical layer beneath those architectural choices: the lasers.

Lumentum’s pump lasers are a good example. Shipments increased more than 80% year-over-year, yet the company remains effectively sold out. Management expects shipment volumes to increase roughly fourfold over the coming quarters and estimates its market share at 70–80%. More unusually, customers are entering mostly three-year arrangements, many effectively take-or-pay, while helping offset the capital expenditure required to create additional capacity.

That is an odd form of customer behavior if this is simply a normal semiconductor shortage waiting to correct itself. Customers usually welcome new capacity because it creates negotiating leverage. Here they are helping finance that capacity because securing enough supply appears more valuable than preserving future optionality.

EMLs tell a similar story. Lumentum expects more than 50% year-over-year unit growth by the December quarter, yet management still expects to remain significantly behind customer demand. Two-hundred-gigabit-per-lane EMLs already represent more than a quarter of EML revenue and should become most volumes around the middle of 2027.

The strongest evidence, though, came from ultra-high-power lasers. Citi asked management whether the supply-demand imbalance had improved from the previous quarter. Hurlston replied:

“We are way behind in our shipments, unfortunately, on high-powered lasers... The demand signal has increased, and we are very much further behind relative to our ability to supply.”

Wupen Yuen immediately clarified the important part: Lumentum’s capacity ramp itself is on schedule. The company is further behind because demand accelerated faster than supply.

This is the central fact in the Lumentum thesis today. Capacity is expanding rapidly, but the demand curve is currently moving away even faster.

The Optical Boundary

The reason that can happen becomes clearer when Lumentum’s products are considered as parts of one architectural progression rather than separate businesses.

The earliest AI networking opportunity was largely scale-across. Vast AI campuses need enormous amounts of high-capacity connectivity between buildings and data centers. That is where pumps and narrow-linewidth lasers matter, and where the “two sites versus a decade of backbone” example is most revealing.

Then comes scale-out. The cluster itself becomes larger, pushing 800G toward 1.6T and eventually 3.2T. Lumentum began 1.6T production in Q4, but the majority of transceiver shipments were still 800G. Management expects 1.6T uptake to intensify beginning this quarter and sustain through calendar 2027. In other words, one of the most obvious growth drivers in the story has barely started.

Then there is switching. Optical circuit switches allow increasingly large clusters to change connectivity dynamically without converting optical signals back into electrical ones. Lumentum doubled OCS shipments sequentially in Q4 and says the current quarter will be its first with OCS revenue meaningfully above $100 million. Management remains on track for roughly $400 million of OCS revenue in the second half of calendar 2026.

The more interesting OCS development is competitive rather than financial. Hurlston described Lumentum as effectively the only scaled merchant supplier currently shipping at meaningful volume. He expects the company to become the largest supplier to one major customer in early 2027, while new higher-port-count, lower-port-count and in-tray designs expand an opportunity that was not fully contemplated in the previous market framework.

Finally comes scale-up, where the optical boundary moves inside the rack and closer to the accelerator itself.

This is where my 2025 framework was too binary. I treated co-packaged optics as the transition point: copper today, optics tomorrow. The industry appears to be developing an intermediate step in near-packaged optics, placing optical engines close to the accelerator before eventually moving them onto the package.

Lumentum benefits from both.

Hurlston addressed recent concern about CPO timing directly:

“Our lead CPO customer’s production plans remain very much on track, and their demand signal has increased since our last update.”

That would already have been an important answer. The next sentence made it more tangible:

“We were recently given our first external light source, or ELS, module purchase for delivery by the second half of calendar 2027.”

The word purchase matters. CPO has spent years existing in roadmaps, demonstrations and future market estimates. A purchase order does not prove the eventual size of the market, but it moves part of the opportunity from possibility to commercial evidence.

ELS also raises the amount of value Lumentum can capture. Instead of selling only the laser, Lumentum supplies a higher-ASP module to customers that do not want to integrate individual laser chips themselves. Management says margins will be lower than on the standalone laser but still above the corporate average.

The optical transition therefore looks less like one architectural event and more like a moving boundary: between data centers, between racks, inside racks, beside accelerators and eventually on the package itself.

Lumentum has exposure at nearly every step.

The Economics Arrived Early

That breadth would matter less if moving from components into systems destroyed the economics.

So far, the opposite has happened.

Revenue has progressed from roughly $666 million to $808 million to $1.01 billion and now to a $1.25 billion guidance midpoint. Over the same period, operating margin has moved from 25.2% to 32.2% to 36.6% and now approximately 40%.

The old Lumentum financial model associated $2 billion of quarterly revenue with operating margins of 38–42%. The company is already around the midpoint of that range with only $1.25 billion of quarterly revenue.

When Mizuho asked CFO Wajid Ali what this meant for the longer-term model, the answer was unusually consequential:

“Think of the 42% outlier more as a midpoint, with the range probably moving up 100-200 basis points versus what we showed at OFC.”

That changes the earnings equation.

The usual high-growth semiconductor story asks investors to choose between revenue growth and margins. Capacity must be built before it is filled, new systems dilute component economics, R&D rises, and new customers require investment.

Lumentum currently has revenue estimates moving higher while the margins attached to those revenues also move higher.

My working FY27 assumptions are now roughly $6.2–6.5 billion of revenue and $23–25 of adjusted EPS. FY28 could reach $8.5–9.5 billion and $35–41 of EPS if the 1.6T, OCS, pump and CW ramps overlap as expected. Those numbers are estimates rather than company guidance, but the direction is more important than the exact decimal point: the company’s old financial model is no longer a useful ceiling.

The Other Fab

There is, of course, a familiar way this story can end.

Scarcity produces extraordinary margins. Extraordinary margins attract capital. New fabs come online, customers qualify second sources, pricing weakens, utilization falls and what once looked structural turns out to have been cyclical.

The most important bearish question on the call therefore came from Raymond James, which asked about new Chinese indium phosphide capacity.

Hurlston’s answer was that Lumentum has not yet seen meaningful impact. He argued that EMLs and high-power lasers remain differentiated and that even CW lasers can command substantial premiums because consistency and narrow specifications improve customers finished-transceiver yields. A cheaper laser is not necessarily cheaper if it causes more modules to fail.

That is a credible advantage. It is not an immutable one.

The street discussion after earnings highlighted Source Photonics and other Chinese suppliers progressing through 100G and 200G EMLs and potentially toward high-power lasers. I would not treat every channel claim as established fact, but the strategic question is exactly right.

The bear case is not that China needs to invent a laser.

It is that Chinese suppliers eventually achieve comparable yield, reliability, specification consistency and hyperscaler qualification at meaningfully lower prices.

If they do, Lumentum’s current scarcity economics can unwind even if AI networking demand continues growing.

This is the counterweight to the entire thesis. It is also why I do not assume today’s valuation multiple or scarcity margins indefinitely.

The Price of Duration

The market already knows Lumentum is having an extraordinary year. The TMT discussion around the print suggested that many investors were already using a September-quarter revenue bogey of roughly $1.20–1.25 billion rather than published consensus. By the time earnings arrived, some investors apparently wanted $1.30 billion for the stock to work.

That explains the initially muted reaction to objectively spectacular guidance.

Lumentum is a consensus long. Existing optical and semiconductor specialists have enormous gains. Some will raise their earnings estimates and sell shares at the same time because the position has become too large.

The next leg therefore requires a different buyer: large-cap growth and generalist technology investors willing to believe this is a multi-year earnings-duration story rather than the last year of an optical shortage.

That brings the investment question back to duration.

Using roughly $821 as the pre-earnings reference price, my probability-weighted three-year value is around $1,700.

The bear case assumes today’s scarcity is temporary. Capacity catches demand, Chinese and other suppliers close the technology gap, pricing normalizes and operating margins retreat toward the mid-30s.

The base case assumes several optical waves overlap. Pumps and scale-across continue growing; 1.6T and OCS drive FY27; NPO, CPO and ELS begin contributing before the previous growth vectors mature. The multiple can compress materially while earnings continue growing fast enough to carry the stock.

The bull case is more ambitious. It assumes optical connectivity meaningfully displaces copper closer to the compute and that Lumentum retains technological scarcity across enough of those connections to capture a disproportionate share of the value.

That produces my rough price path of $975 in three months, $1,100 in six months, $1,300 in twelve months and approximately $1,700 in three years.

I have the least confidence in the first number. Positioning can overwhelm fundamentals over three months. I have more confidence in the twelve-month question because by then investors should be valuing Lumentum against FY28 earnings rather than arguing about whether the September-quarter guide was $20 million above someone’s whisper.

What Would Change My Mind

The next quarter does not need to prove that AI demand is strong. That debate is largely finished.

It needs to show that $1.25 billion is another step rather than a peak. OCS should continue beyond its first $100 million-plus quarter. 1.6T should accelerate as promised. Gross margin should remain near or above 50%, and operating margin should remain above 40% rather than immediately mean-reverting.

More important for the three-year thesis, EML and pump demand should remain ahead of expanding supply; high-power laser capacity should convert into revenue without eliminating scarcity; additional ELS or NPO orders should turn architectural discussion into commercial evidence; and the lead CPO customer should remain on schedule for the H2 2027 laser ramp.

The bear signals are equally clear. Pricing weakness before supply is fully available would matter. Gross margin falling below roughly 49% while revenue continues growing would suggest today’s economics are more cyclical than I think. A closing supply-demand gap caused by softer demand rather than better output would change the interpretation quickly. Most importantly, credible Chinese products qualifying at hyperscalers with comparable yields would challenge the scarce-layer thesis at its foundation.

The useful thing about Lumentum today is that the argument has become testable.

When the Bottleneck Became Optical

My 2025 article ended with the idea that the next phase of AI networking would be won by companies building for requirements that seemed extraordinary today but would later appear inevitable.

A year later, two AI data centers apparently require twice the networking capacity one hyperscaler built across its entire global backbone over the previous decade.

That is an extraordinary requirement.

It is also increasingly real.

The network became a bottleneck because AI turned thousands of processors into one distributed computer. What Lumentum is now showing is that the bottleneck is moving again from protocols and switches toward the physics of moving an ever-growing amount of information.

That is why the company’s first billion-dollar quarter is almost beside the point. Lumentum is already guiding to $1.25 billion. OCS is crossing $100 million quarterly revenue. 1.6T has barely started. Pump output is heading toward four times today’s level. High-power laser demand is outrunning that expansion. NPO is appearing before CPO. ELS has moved from a concept to an order. And the operating-margin structure once associated with $2 billion of quarterly revenue has arrived hundreds of millions of dollars early.

Perhaps this is simply what the top of an optical cycle looks like when demand is unusually strong and supply unusually scarce. Capacity will arrive, competitors will qualify, customers will dual-source and margins will normalize.

But the alternative is more interesting.

AI may not simply require more networking. Each generation of AI may require optics in places where the previous generation could still use copper.

If that is true, Lumentum’s opportunity is not one shortage or one product cycle. It is the movement of the optical boundary itself.

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• AI infrastructure is entering a new financing phase: Nvidia and major capital providers are building repeatable structures to mobilise hundreds of billions of dollars beyond hyperscaler balance sheets.

• The key metric is no longer just capex, but support: if each new dollar of AI capacity requires less vendor backing, the system is moving from Sponsorship toward genuine Self-funding.

• Nvidia may be building a financial moat: if Nvidia-based projects consistently secure better financing terms than competing architectures, CUDA’s advantage extends beyond software into the cost of capital itself.

Six weeks ago, in The AI Handoff, we argued that the AI buildout was moving through four stages: Scarcity → Sponsorship → Conversion → Self-funding. Scarcity had produced extraordinary supplier economics. Sponsorship was emerging as vendors, strategic investors and capital providers helped customers build infrastructure before the applications using it generated enough cash to pay for it independently. Conversion would arrive when those applications produced durable revenue and savings. Self-funding would begin when the returns on one generation of infrastructure financed the next.

The central question was whether falling costs and improving capability could create enough broad, independently financed end-user demand before the physical capacity and financial obligations built in anticipation of that demand outran the resulting cash flows.

This week Nvidia put a number on the next stage of that experiment.

The company announced partnerships with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR intended to mobilise more than $500 billion of third-party capital for AI infrastructure. Nvidia says it may provide residual-value support of up to 25% on individual opportunities. Two months earlier, Apollo and Blackstone had structured a $35 billion Broadcom AI financing with materially more extensive residual protection for senior capital.

The structures are not identical enough to declare that the difference is simply “the price of CUDA”. Borrower quality, tranche seniority, tenor, customer commitments and collateral assumptions matter. But the comparison gives us something we did not have six weeks ago: the beginnings of an observable market price for how much financial value investors attach to different compute architectures.

That matters because Nvidia and its financing partners are not necessarily trying to create the same end state.

Nvidia benefits if its architecture remains differentiated enough that Nvidia-based infrastructure receives lower financing costs, requires less vendor support and retains higher residual values. Capital markets benefit from something broader: a liquid, standardised asset class in which Nvidia, Broadcom, Google TPUs and eventually other architectures can all be financed and compared.

The strategic question is therefore no longer simply whether compute can be financed. It is whether Nvidia can financialise compute without helping commoditise it — while the wider AI system reduces its dependence on sponsorship and moves toward genuine self-funding.

Seven Men on Television

The unusual thing about Nvidia’s CNBC appearance was not the size of the number. It was the people sitting around Jensen Huang.

Goldman Sachs CEO David Solomon, BlackRock CEO Larry Fink, Blackstone President Jon Gray, Apollo President Jim Zelter, Brookfield CEO Bruce Flatt and KKR’s global head of digital infrastructure joined Huang to explain the new financing platforms. These institutions compete for capital and transactions, yet Nvidia had assembled them to describe different parts of what increasingly looks like a new financial system.

Huang started with the technology.

“The computer is now part of the infrastructure like electricity, like the internet.”

His argument was that Nvidia had crossed from selling components into supplying productive infrastructure. AI factories generate revenue, can run many workloads, serve multiple potential users and retain economic value beyond the credit quality of the original buyer.

His partners talked increasingly about the asset.

Gray compared compute finance with houses and aircraft: lenders underwrite the borrower, but also the value of the underlying asset. Brookfield said investors had lacked the proper “format” through which to invest and described Nvidia’s architecture and support as part of what makes the infrastructure transferable to another operator. Fink compared the moment with the early mortgage-backed-securities market. KKR described the value chain as running “from molecule to token”.
There is no contradiction yet. Nvidia may genuinely represent the highest-quality collateral in an emerging market. The divergence is in where each participant wants the market to end up.

Nvidia wants Nvidia compute to become a financial standard.

Wall Street wants compute to become a financial asset class.

The first depends on differentiation. The second eventually benefits from standardisation.

That tension is the most interesting part of the announcement.

Sponsorship, Institutionalised

The mechanics are relatively simple. Financing vehicles can raise third-party capital, purchase AI infrastructure and lease the resulting compute to customers. Goldman can structure and distribute the debt. Infrastructure and private-capital firms can provide equity or credit. Nvidia supplies the systems and may provide limited support against residual-value risk.

What has changed is the investor base.

The first phase of AI infrastructure was largely financed by capital close to the technology: hyperscaler retained earnings, venture capital, strategic equity and corporate investment-grade debt. Nvidia’s coalition is designed to reach capital that does not need to believe a particular AI stock will rise. Pension funds, insurers and long-duration credit investors want contracted cash flows, recoverable collateral and yield.

That turns Sponsorship from a series of bespoke arrangements into something closer to a repeatable capital market.

This does not make the underlying demand fake. Young infrastructure systems often require capital before customers can fully fund themselves. A model company needs compute before it can create the product and revenue that make the compute affordable. A data-centre developer needs a committed tenant before lenders will finance construction.

But financing changes what demand proves. As we wrote in July:

“A vendor-supported purchase says the supplier believes strongly enough in the customer’s future to help create the purchasing power. The second may be an excellent investment. It is not independent validation.”

The $500 billion announcement does not mark the end of Sponsorship.

It marks its institutionalisation.

25% Versus 100%

This is where the financing terms become more interesting than the headline amount.

Nvidia has described potential residual-value support of up to 25% on qualifying projects. In the earlier Broadcom financing, Broadcom provided much broader protection against residual shortfalls for senior capital.

Two different concepts need to remain separate.

The sponsorship ratio measures how much incremental infrastructure relies on vendor guarantees, strategic equity or related-party financing rather than independent customer cash and credit.

The residual-support ratio measures how much collateral downside the vendor agrees to absorb if the borrower fails and the equipment has to be recovered or re-leased.

The second may tell us something important about the first, but they are not interchangeable.

What the comparison may eventually reveal is an architecture financing premium.

If comparable Nvidia projects consistently require less residual support, lower coupons, less equity, smaller collateral haircuts or longer maturities than competing architectures, then Nvidia’s ecosystem has become more than a software moat. It has become a financial moat.

This is a much stronger proposition than saying CUDA makes GPUs valuable collateral. It means Nvidia’s installed base, software compatibility and secondary-user ecosystem reduce the cost of capital for anyone deploying Nvidia systems.

That advantage could reinforce itself. Lower financing costs create more Nvidia installations. More installations deepen the potential re-lease market. A deeper secondary market improves residual values. Better residual values lower financing costs again.

The opposite outcome is equally important. If economically comparable Nvidia, Broadcom and TPU projects eventually require similar enhancement and clear at similar spreads, compute is becoming financeable while Nvidia’s architecture premium is disappearing.

The executed deals, not the MOUs, will tell us which world is emerging.

Three Clocks

The financing initiative also extends the framework from The AI Handoff.

The construction clock runs on engineering. Chips have to be manufactured, power secured, substations built, data centres completed, networks installed and systems commissioned.

The conversion clock runs on enterprises. Companies must connect models to proprietary data, establish permissions, redesign workflows, test reliability, change budgets and persuade humans to work differently.

The capital clock runs on debt service.

Interest has a due date. Leases must be paid. Covenants have test dates. Refinancing windows open and close. Equipment depreciates. Debt matures.

The third clock has the hardest deadline.

A technology can be right over twenty years while the security financing it is wrong over five.

This is where Liaquat Ahamed’s 1873, the book Satya Nadella recently called “the book to be read”, becomes useful. Ahamed notes that roughly $500 million a year flowed into US railway bonds during the early 1870s, equivalent to around $600 billion relative to the size of the modern economy.

The railway itself was not the mistake. Northern Pacific eventually completed its line after multiple bankruptcies and ultimately became part of the system that formed BNSF. The infrastructure survived. Some of the securities used to finance it did not.

The parallel is not the outcome. It is the financing problem.

Capital must arrive before the full economic activity that justifies the infrastructure.

Nvidia’s coalition is designed to give Conversion more time before the capital clock runs out.

There is also a feedback loop. More financing creates more infrastructure. More infrastructure lowers the price of intelligence. Lower prices make new workloads economical. Those workloads accelerate Conversion.

Finance can therefore do more than wait for demand.

It can help create it.

The Great Elasticity Bet

That feedback loop contains the deepest assumption in the entire structure.

KKR noted that token prices have fallen dramatically even as usage has risen.

This makes AI an unusual infrastructure asset. It has the fixed-capital intensity of a utility but the output deflation of software. Investors are being asked to provide long-duration capital against a product whose unit price may decline by orders of magnitude while the equipment producing it is itself improving rapidly.

The financing works only if demand for intelligence is extraordinarily elastic.

If useful intelligence becomes ten times cheaper, society must consume substantially more than ten times as much for the aggregate revenue pool to expand strongly enough to support a growing infrastructure base.

There are good reasons to believe that will happen. Human cognition has historically been expensive, so companies rationed it. They reviewed some transactions rather than all transactions, ran a limited number of experiments, produced periodic analysis rather than continuous analysis and assigned finite numbers of people to tasks where dozens of digital agents might eventually operate in parallel.

Cheaper cognition can create workloads that did not exist at all at previous prices.

But rising token volume is not sufficient. What matters to infrastructure investors is revenue density: the economic output generated per unit of deployed capacity after utilisation, power, operating expense and depreciation.

If prices fall faster than paid usage expands, AI can become extraordinarily successful for users while infrastructure owners earn poor returns.

The technology and the securities can reach different verdicts.

Follow the Payer

This is why the same earnings season matters.

Palantir produced rapid revenue growth and strong free-cash-flow conversion. Atlassian showed expanding contracted demand and improving economics around AI-connected products. At the same time, several hardware companies reported extraordinary growth and still sold off.

This was not simply a rotation from hardware to software.

The market was separating capacity from economic conversion.

Ultimately, the financing vehicle does not get paid in tokens. It gets paid in dollars.

Those dollars must originate with an enterprise, consumer or government receiving enough economic value from AI to support the chain beneath it. That value may come from new revenue, substitution for existing expenditure or productivity savings. Whatever the mechanism, customer economics must ultimately fund model revenue, which funds infrastructure payments, which service the capital.

Huang’s claim that AI tokens are “incredibly profitable” therefore needs one further distinction.

A token may generate an attractive gross margin after inference cost. A lab may generate operating profit after salaries and product expenses. Yet the capital structure ultimately needs free cash flow after the company funds the next model, secures future compute and maintains its competitive capability.

Token profitability validates the product.

Free cash flow validates the infrastructure.

That makes a future OpenAI or Anthropic IPO important for a different reason than the valuation headline.

An IPO is a financial handoff. It can broaden the investor base and reduce the cost of capital.

Self-funding is an economic handoff. It occurs when customer-generated cash can support the next infrastructure cycle without ever-larger injections of external capital.

A company can achieve the first without achieving the second.

Public filings will finally allow investors to distinguish them.

Nvidia, Wall Street and Google

The financing coalition also exposes a longer-term strategic tension around who defines the asset class.

Apollo has already participated in the Broadcom structure. Blackstone is involved with Nvidia, Broadcom and a large Google TPU joint venture. Its own underwriting analogy focused on the economic value of compute rather than on Nvidia specifically.

These firms do not need Nvidia to dominate AI. They need AI infrastructure to become financeable.

Google makes that distinction particularly important.

Google can monetise AI through Gemini, Google Cloud, advertising, security, storage and its own TPU infrastructure. It can also earn money supplying compute to laboratories that compete with Gemini. It therefore has reasons to make TPUs broadly usable even if the leading model is produced elsewhere.

As non-Nvidia systems acquire independent customers, rental histories and financing transactions, capital markets gain something they previously lacked: comparables.

At that point Nvidia’s financing advantage becomes measurable.

Does an Nvidia project receive a lower coupon than a TPU project with the same borrower? Does the lender require less first-loss protection? Is the advance rate higher? Is the equipment assigned a better residual curve? Can the debt mature later?

Nvidia may win all of those comparisons.

But the existence of the comparison itself changes the market.

Nvidia is helping create an enormous financing category while simultaneously betting that its own systems remain the premium asset inside it.

Wall Street wants the category.

Nvidia wants the premium.

The Scorecard

We now have enough evidence to track the handoff rather than merely describe it.

The direction matters more than the current level.

Five Calls

First, watch hyperscaler capex. The largest buyers still fund substantial infrastructure from their own economics. A meaningful capex-guide reduction from a major hyperscaler would be the clearest signal that the construction clock is encountering an economic constraint rather than a physical one.

Second, watch long-term memory commitments. A material cancellation or roll-down in contracted HBM or memory demand would turn the oversupply thesis from narrative into operating evidence.

Third, watch lab disclosure rather than IPO valuation. A spectacular IPO accompanied by persistent dependence on outside capital is financial graduation, not economic graduation. The real test is gross margin, free cash flow and compute obligations.

Fourth, watch Nvidia’s financing premium. If comparable Nvidia projects clear with materially less support and better terms than competing architectures, Nvidia has created a financial moat. If the terms converge, the compute asset class is standardising faster than Nvidia would prefer.

Fifth, and most importantly, watch the Sponsorship Ratio. If the first $100 billion of financed capacity requires 25% support and the next $100 billion requires 35%, the system is becoming more dependent on external support as it grows. If the requirement falls toward 15%, 10% and eventually zero because customers increasingly finance projects using their own cash and credit, the handoff is working.

The View

The Nvidia announcement is bullish for the pace of the AI buildout. It opens pools of capital to customers that do not possess hyperscaler balance sheets, accelerates infrastructure construction and should push the price of intelligence lower.

It is also the clearest evidence yet that AI has entered a new financial phase.

The question is no longer whether Wall Street can finance AI. It clearly can.

The first question is whether the amount of sponsorship required declines as the system scales. That tells us whether Conversion is producing enough independent cash flow for AI to move toward Self-funding.

The second is whether Nvidia retains structurally better financing terms than competing architectures. That tells us whether CUDA and Nvidia’s installed ecosystem can remain differentiated as compute itself becomes increasingly standardised and financeable.

The best outcome for Nvidia is straightforward: the Sponsorship Ratio falls while the architecture financing premium remains high. AI becomes increasingly self-funding, and Nvidia remains the preferred asset inside the capital market built around it.

The weaker outcome is the reverse: sponsorship rises while financing terms converge across architectures. AI may still transform the economy, but infrastructure remains dependent on external capital while compute commoditises underneath Nvidia.

That is why the executed transactions matter more than the $500 billion headline.

Six weeks ago, the Sponsorship Ratio was a framework.

This week, we began to see what might price it.

The next deals will tell us whether the number is going down.

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• Shopee’s underlying economics are improving: advertising grew more than 70%, core marketplace revenue rose 65.6% on 28.4% GMV growth, and logistics unit costs continued falling.

• The missing margin is being reinvested: Sea beat revenue expectations by nearly $700 million but delivered EBITDA roughly in line, as better economics funded customer acquisition, fulfillment, VIP and credit expansion.

• The integration moat has limits: Monee’s economics are strongest inside Shopee. Off-platform lending and Brazil introduce higher provisions and will test how well Sea’s advantage travels beyond its core ecosystem.

“Our improving operational efficiency and growing scale have strengthened our unit economics. We can now profitably serve a wider range of users, enabling us to lean further into user acquisition.”

— Forrest Li, Sea Q2 2026 earnings call

Sea reported $7.8 billion of second-quarter revenue, nearly $700 million above the Bloomberg consensus we had going into the print. Adjusted EBITDA was $917 million. Consensus was $919 million.

That juxtaposition is more interesting than either number by itself.

Sea generated far more revenue than investors expected and almost none of that upside appeared as incremental reported EBITDA. The obvious conclusion is that the company is still spending aggressively. The more important question is whether that spending reflects poor underlying economics or increasingly attractive opportunities to reinvest economics that are improving.

I have been writing about Sea for five quarters, and there is a useful correction to make before answering that question. I have called essentially the same thesis a flywheel, a fortress, the rice-field advantage, and finally integration density. The terminology moved around more than the investment thesis did. I am going to stop renaming it.

The thesis is simply that Sea builds dense consumer activity in difficult emerging markets and can reuse that density across commerce, logistics, advertising, and financial services. More activity should lower service costs, make access to consumers more valuable to sellers, and generate better information for underwriting. If those improvements exceed the cost of acquiring the next customer, the economics can finance still more activity.

Six months ago, in The Choice Not to Harvest, I admitted that I had been early on when those economics would appear in Shopee’s reported margin. Q2 does not erase that mistake. Shopee adjusted EBITDA is still only around 0.7% of GMV. What Q2 does provide is the strongest evidence yet that the machinery underneath that margin is beginning to work.

What We Said, What Happened

The best way to avoid changing the story every quarter is to score the old one.

In Q3 2025 I laid out several explicit signposts for Q2 2026. Two worked considerably better than expected; the one that mattered most for reported profitability did not.

The margin miss matters. I had assumed that investments made through 2025 would begin maturing and visibly release profit by now. Some are maturing, but Sea keeps finding new things worth funding: fulfillment, instant delivery, VIP, content distribution, Brazil, and new Monee customers.

That suggests my old mental model was incomplete. I implicitly assumed:

invest → mature → harvest.

Sea increasingly looks more like:

invest → economics improve → more customers or use cases become economically attractive → reinvest.

That distinction is crucial because successful investment can delay the reported margin precisely because it is successful.

Forrest Li’s statement that Shopee can now “profitably serve a wider range of users” is the clearest expression of this. Better economics have not simply produced more EBITDA. They have expanded the pool of customers Sea believes it can acquire profitably.

The Investment Cycle That Keeps Renewing

The evidence begins with customers. Average monthly new active buyers grew more than 35%, active buyers increased 18%, and purchase frequency rose 8%. Shopee was simultaneously adding customers and extracting greater engagement from existing ones.

Then there is monetization. Advertising revenue grew more than 70%, ad take rate increased over 90 basis points, the number of ad-paying sellers rose around 45%, and average ad spend per seller increased more than 15%. Core marketplace revenue — transaction fees plus advertising — grew 65.6%, more than twice Shopee’s 28.4% GMV growth.

This is a healthier form of monetization than simply increasing commissions. Management acknowledged that the pace of fixed commission increases will probably slow, but sees substantial remaining opportunity in advertising, where AI is improving targeting, campaign diagnosis, personalized vouchers, and seller returns.

The physical economics are even more revealing. Instant-delivery volumes in Indonesia grew roughly 80% while cost per order fell around 20%. Fulfillment volumes increased more than 20% sequentially, and listings shifted into fulfillment generated more than 20% higher orders on average in Southeast Asia.

That is the old rice-field idea finally appearing in economics rather than anecdotes:

Fulfillment remains early. Sea says its penetration is still well below several peers, particularly in Brazil, but its economics are improving as seller participation increases, warehouses integrate more tightly with SPX, utilization rises, and automation lowers cost. Importantly, Sea rents fulfillment facilities rather than owning the underlying land and warehouses, limiting the capital required to pursue the opportunity.

This is why the $917 million of group EBITDA matters. The extraordinary revenue beat did not produce an EBITDA beat because Sea is still reinvesting aggressively. That does not mean Sea could simply have reported whatever EBITDA it wanted; different revenue streams carry different incremental costs. What the quarter does tell us is that reported profitability continues to understate how rapidly some of the underlying economics are improving.

The comparison with Amazon’s long reinvestment period is tempting, but it should not be taken too far. The relevant lesson is not that Sea is another Amazon. It is that reported margins can be a poor measure of economic progress when successful investments repeatedly create adjacent opportunities worth funding. Sea still has to prove that those opportunities earn attractive returns.

So far, Shopee increasingly is.

Management reaffirmed that 2–3% EBITDA/GMV remains within reach and described a straightforward bridge: investment programs mature, logistics and fulfillment costs decline, and take rate improves through advertising and other monetization. It also confirmed that absolute Shopee EBITDA in the second half should exceed the first half.

That does not tell us when 2–3% arrives. It does tell us why 0.7% need not be the ceiling.

Where the Advantage Gets Thinner

Monee is where the integration thesis is both most impressive and most vulnerable.

The loan book grew 62% to $11.1 billion while 90-day NPLs remained at 1.0%. Sea’s newer underwriting models, trained on sequences of behavioral and transactional information, increased approval rates by around 10% without increasing observed risk. The company added 5.3 million first-time borrowers, active credit users rose 34% to more than 40 million, and average balances per user increased 20%.

This is the mechanism we have been arguing for several quarters: commerce produces information that allows Monee to make better credit decisions.

But Q2 also revealed the boundary.

Off-Shopee SPayLater now exceeds 20% of the portfolio, reaching 35% in some countries, while Sea is preparing a standalone financial-services application in Brazil. Management explicitly said higher credit provisions reflect greater exposure to off-Shopee lending and Brazil, which it described as a higher-interest, higher-risk market.

Monee’s numbers show the trade-off:

Source: Sea Q2 2026 results.

The old question was whether Sea’s proprietary data advantage existed. Q2 makes that increasingly difficult to dismiss.

The better question is now how quickly that advantage decays as Monee moves farther from Shopee.

Management says every product, segment, and country must maintain stable risk and positive returns on assets. That may make higher provisions perfectly rational if higher-risk markets also produce appropriately higher yields.

But this is exactly where the thesis should be tested rather than celebrated.

The Brazil Test

Brazil increasingly matters because it tests whether Sea’s operating model travels.

Shopee remains Sea’s fastest-growing market there. Management says it is growing faster than both the market and close competitors despite competitive changes to shipping thresholds and pricing and expects to continue outgrowing the market profitably.

At the same time, fulfillment remains underpenetrated, VIP has already exceeded one million Brazilian members, Monee lending is expanding, and Sea plans to launch a broader financial-services application with capabilities comparable to existing local competitors.

Sea does not need to defeat MercadoLibre for this thesis to work. It needs to demonstrate that the sequence developed in Southeast Asia, commerce density, lower service costs, higher monetization, and eventually broader financial services can be reproduced somewhere else.

If Brazil does that, integration is not simply a regional accident. It is an operating capability.

What We Know Now

The variant perception is not that investors have failed to notice the $1 billion Shopee EBITDA guide. They have.

The more interesting possibility is that the market still treats Shopee’s reported margin as a reasonable proxy for its underlying economics. Q2 suggests that assumption is becoming less safe. Logistics costs are declining with density, advertising is monetizing that density at much higher rates, and Sea is simultaneously adding customers without resetting Shopee EBITDA.

That does not mean management can manufacture any margin it wants. It means the eventual margin may be considerably higher than the current P&L implies.

My three-year framework therefore depends less on predicting perpetual 25–30% GMV growth and more on how much of these improving economics eventually reaches earnings.

At roughly $127–128 following the print, the broad probability-weighted value lands around $250–260, roughly a mid-to-high-20s annualized return over three years. The base case does not require Sea to reach 3% margins. It requires transaction density to continue improving economics gradually while Monee scales without a material deterioration in risk.

The tracking ledger from here should therefore remain fixed: Shopee EBITDA/GMV sustainably above 0.8–0.9%; advertising continuing to materially outgrow GMV; logistics unit costs continuing to fall; Monee NPL staying around 1.0–1.2%; credit provisions stabilizing as off-Shopee lending grows; and Brazil maintaining above-market growth without requiring escalating subsidies.

Six months ago, I wrote that the fields were not ready to harvest. I still would not declare them ready today.

What Q2 changes is our understanding of why.

The investments we expected to mature are maturing. The problem, if it can be called one, is that their success keeps revealing another economically attractive place to invest. That makes Sea’s eventual margin harder to time.

It also makes the underlying earnings power easier to believe.

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