The argument that SaaS is dead has become consensus quickly enough that it's worth pausing on. A year ago it was a contrarian post. Six months ago it was a thread. Now it's a working assumption, repeated in board meetings, embedded in investor memos, taken as the obvious frame for why specific companies are struggling. The term “SaaSpocalypse” has been used unironically. The transition from contrarian to consensus happened faster than the underlying evidence justified, which is usually a sign that an argument is doing emotional work rather than analytical work.
The argument is doing emotional work. The death-of-SaaS framing is satisfying because it explains a lot of disappointment at once. The companies that aren't growing the way they were supposed to. The pricing pages that suddenly look fragile. The funds whose portfolios are repricing. If SaaS is dead, all of these become symptoms of a single structural shift rather than a collection of specific failures, and the people associated with them can blame the category rather than their own decisions.
That doesn't make the argument wrong. It does mean it deserves more scrutiny than it's been getting.
Three claims wrapped together
When you actually look at what people mean when they say SaaS is dead, the claim turns out to be three different claims wrapped together. There's an empirical claim about what's happening to specific SaaS metrics: net revenue retention, sales efficiency, multiples. There's a definitional claim about what SaaS even is, whether the term refers to a delivery model, a pricing model, an economic structure, or a vibe. And there's a predictive claim about what comes next: usage-based pricing, agent-priced products, outcome contracts, AI-native replacements, or in the most aggressive version, the disappearance of the application layer entirely as foundation model companies absorb it.
The argument's persuasiveness comes from the fact that the empirical claim is largely true. Metrics have compressed. Specific categories of SaaS, the ones that built on per-seat pricing for collaboration software with high willingness-to-pay among knowledge workers, are seeing genuine structural pressure. NRR has fallen. Sales motions that worked in 2021 don't work now. Multiples have repriced, and they've repriced in a way that suggests the market thinks something has actually changed, not just that rates are higher. Approximately $285 billion was wiped from software stock valuations in early 2026 alone.
But the empirical claim being true doesn't make the predictive claim true. And the predictive claim is where the death-of-SaaS argument quietly substitutes one thing for another. The move is: SaaS metrics are compressing, therefore the SaaS economic model is broken, therefore something else replaces it. The middle step is the one nobody defends.
The thing actually being challenged isn't the SaaS economic model. It's a specific generation of SaaS products that took advantage of a specific market window, roughly 2015 to 2022, when three conditions held simultaneously. Capital was cheap enough that growth was the only metric that mattered. Distribution into knowledge-worker software was wide open. And the unit of value-capture, the individual seat, happened to be reasonably well-correlated with the unit of value-creation. None of these conditions hold the same way now. Capital is expensive. Distribution into the knowledge worker is saturated. And the value of an individual seat has become much harder to defend as the variance in what any given seat does has exploded.
What this produces is a generation of products built for the old conditions struggling in the new ones. That's a real phenomenon. It's not the death of an economic model. It's the obsolescence of a specific generation of products that were optimized for conditions that no longer obtain.
The SaaS economic model itself, recurring revenue against software-delivered value, priced to capture some fraction of that value over time, is structurally fine. Better than fine. It's about to be the most interesting category of business model in the operator economy, and the reason has to do with what AI is doing to the product layer.
Two challenges that deserve real engagement
Before getting to the constructive argument, two challenges to the position deserve direct engagement, because they're the strongest versions of the death-of-SaaS case and waving at them would be dishonest.
Build versus buy
The first challenge is build-versus-buy. The argument is that AI tooling has collapsed the cost of building internal software so completely that the calculus which made buying SaaS obvious for the last decade has been renegotiated. A mid-sized company that would have bought four point-solutions in 2021 can now have one engineer build adequate versions in a quarter. The empirical case is real and the numbers are getting hard to ignore. A late-2025 Retool survey of 817 builders found that 35% of respondents had replaced functionality of at least one SaaS tool with a custom build, and 78% expected to build more of their own tools in 2026. Specific cases have surfaced. Netflix's finance team building an internal AI tool to replace a purchased SaaS application is the canonical example, and there are now dozens like it in circulation. If this trend continues at scale, it hollows out the bottom of the SaaS market, the products that solved a specific narrow problem well enough to charge for, but not deeply enough that customers couldn't build their own.
This is real. But the conclusion isn't that SaaS dies; the conclusion is that the categories where build wins and the categories where buy wins are being redrawn. Build wins where the problem is genuinely narrow, the company has the engineering capacity to maintain what it builds, and the regulatory and accountability surface is small. Internal dashboards, custom reporting, narrow workflow automation, knowledge retrieval over the company's own data. Buy wins where the problem requires deep domain expertise the company doesn't want to acquire, where the maintenance burden compounds, where there's a regulatory or audit surface that someone else needs to be accountable for, or where the value of integration with a broader ecosystem exceeds the cost of paying for a vendor. The Retool data hints at this dynamic. The same survey found 60% of respondents reported building something outside of IT oversight in the past year, and 37% of organizations had not yet established AI productivity metrics, both of which suggest the build wave is producing exactly the kind of sprawl and accountability gap that drove the original migration to SaaS in the first place.
Having spent years inside payments infrastructure, I have a specific view on what the buy case actually looks like at the integrated end. The reason companies don't build their own payments stack isn't that they can't write the code. The reason is that the work of being accountable for the code, maintaining its compliance posture across jurisdictions, taking the liability for failures, and integrating it into a continuously-changing regulatory landscape, is the actual product. The code is a small part of what's being paid for. AI tooling collapses the cost of writing the code. It does not collapse the cost of being accountable for it. That's the line the build-vs-buy boundary settles on, and it's why the SaaS economic model survives even as build becomes dramatically easier. The products on the right side of the new line will charge more, not less, because what customers are paying for is exactly the work they specifically chose not to do themselves: the maintenance, the compliance, the accountability, the integration.
The frontier labs moving up the stack
The second and harder challenge is the foundation model companies moving up the stack. The aggressive version of the death-of-SaaS case argues that if the application layer becomes a thin wrapper around a model, and the model company can release the wrapper itself, then the entire SaaS economic model gets disintermediated by something that looks more like a utility. Flat or metered pricing for compute and inference, with the differentiated application layer collapsing into a feature of the model.
This challenge has actually been resolved in real time, and not in the direction the death-of-SaaS case predicted. The frontier labs have moved up the stack aggressively over the past year. Anthropic has launched Claude for Legal, Claude for Healthcare, and Claude for Financial Services, and built a $1.5 billion enterprise services arm with Blackstone, Hellman & Friedman, and Goldman Sachs. OpenAI has launched a suite of financial-services tools designed to help professionals streamline financial analysis, investment memos and other work, alongside a $4 billion deployment company with TPG, Bain, and Brookfield. The frontier labs are not avoiding regulated, accountable, domain-specific application categories. They are entering them.
But notice the shape of the entry. The frontier labs are not disintermediating SaaS. They are becoming SaaS. They are launching named vertical products, signing enterprise contracts, building services partnerships, and pricing through the same hybrid platform-fee-plus-usage architecture the rest of this piece argues is the model's future. The behavior of the most sophisticated players in the AI ecosystem, the companies with the strongest theoretical position to disintermediate the application layer if disintermediation were going to happen, is to enter the SaaS economic model rather than replace it. That's evidence for the position I'm taking, not against it. The people closest to the technology are voting with their product launches that the durable value lives in the application layer, priced as recurring revenue against software-delivered work, in exactly the regulated and accountable categories where the SaaS economic model has the strongest structural fit.
What this does change is who the incumbents are competing with. The application layer doesn't disappear, but it gets crowded. Legacy SaaS companies in vertical categories are now competing not only with other SaaS companies but with frontier labs operating their own vertical products on top of their own models. The categories most exposed to this are the ones where the underlying model capability is most of the product: basic legal document review, basic financial analysis, basic healthcare summarization. The categories most protected are the ones where the model is one component of a much deeper stack: proprietary data, workflow integration, system-of-record relationships, domain-specific accountability the lab itself can't take on. The bifurcation still holds. The bifurcation just happens inside the SaaS economic model rather than between SaaS and something else.
What the model is actually becoming
With those engaged, the underlying claim about the economic model becomes easier to make.
Inference is variable cost. The marginal value an AI-augmented product delivers is highly variable per customer, per task, per session. The old SaaS pricing model, fixed monthly fee per seat, regardless of usage or value delivered, was already showing strain under that variance. It's now snapping.
But the response to that strain isn't to abandon the SaaS economic model. It's to reconstruct it on more accurate primitives. The fixed-fee-per-seat layer is being replaced by usage-based pricing where the unit of usage actually correlates with value, by hybrid contracts that combine a platform fee with consumption, by outcome pricing where the vendor takes risk on a specific result. None of these are departures from the SaaS economic model. They're refinements of it. The underlying logic, capture value via recurring contractual arrangement against software-delivered work, is unchanged. What's changing is the specific contract structure used to do the capturing.
This is what makes the death-of-SaaS framing genuinely misleading. The framing implies that what's coming next is not SaaS. What's actually coming next is SaaS with more sophisticated economic architecture. The companies that figure this out are not abandoning the model. They're operating it at a level of sophistication the previous generation didn't need to reach. Three specific shifts are visible already, and each tells you something about where the model is going.
The collapse of the seat
The first shift is the collapse of the seat as the natural unit of value-capture. Per-seat pricing was always a convenient fiction, the assumption that a uniform fee per user roughly tracked the value each user extracted. That fiction held up when the variance in per-user value was low. It doesn't hold up when one customer's seat does ten times the work of another customer's seat, which is what AI augmentation produces. The companies that are responding well are not abandoning recurring revenue. They're disaggregating the seat into more granular units, events, tasks, outcomes, that actually track value.
The seat as the unit of value-capture is also exposed to a structural risk that the previous decade of SaaS never had to confront. From 2015 to 2022, customer headcount in the categories that bought SaaS was going up almost monotonically. Per-seat pricing had a built-in tailwind that didn't require the vendor to do anything. The customer's hiring did the work. That tailwind has reversed. Tech headcount reductions in 2023 and 2024 directly subtracted seats from existing SaaS contracts. The AI-driven productivity gains arriving now are starting to subtract seats from non-tech categories too. Workday's announcement of 8.5% layoffs attributed to AI efficiency gains is a concrete example of how automation is eliminating the very roles that per-seat SaaS depends on. A vendor priced per seat is, in this environment, effectively short their customer's labor cost optimization. Every dollar of productivity the customer extracts from AI augmentation comes out of the vendor's revenue, because the vendor is paid for the headcount the productivity gain makes unnecessary.
Usage-based pricing inverts this exposure. If the customer reduces headcount but the remaining employees do more work per person, which is the actual outcome of AI augmentation, not work disappearing entirely, usage goes up while seats go down. The usage-priced vendor captures the productivity gain that the seat-priced vendor loses. The headcount metric and the vendor revenue metric, which moved together for a decade, are now moving in opposite directions, and the pricing mechanism determines which direction the vendor ends up on.
Even in a world where headcount continues to grow, the structural argument for moving off seats still holds. Headcount eventually flattens, for any given customer, for any given category, for the addressable market as a whole. Once it flattens, a per-seat SaaS company's growth has to come from price increases or new logo acquisition, both of which are harder and more expensive than expansion within existing accounts. A usage-priced or outcome-priced vendor can keep growing within the same account indefinitely, because the unit of expansion is decoupled from the unit of headcount.
This is where the deeper continuity in the SaaS economic model becomes visible. Expansion revenue within existing customers, which is what every mature SaaS company eventually has to live on, has always been essentially a form of usage or outcome pricing dressed up in subscription language. The “upsell” motion in legacy SaaS is the customer paying more for getting more value: more storage, more API calls, more advanced features used more heavily, more integrations active. That's usage pricing if you squint, executed badly through manual contract renegotiations and clumsy tier transitions rather than through pricing structures that capture expansion automatically. The shift to explicit usage and outcome pricing isn't the SaaS industry abandoning its model. It's the SaaS industry finally building the right plumbing for the revenue model it was always converging toward.
Is usage-based pricing even recurring
A reasonable objection at this point is that usage-based pricing isn't actually recurring, that calling it recurring is a category trick that papers over a real change in the underlying business model. The objection is worth taking seriously, because pure transaction pricing genuinely would be a departure from the SaaS economic model. But usage-based pricing, properly understood, isn't transaction pricing. The recurrence operates at a different level than it did under fixed subscriptions.
Fixed subscription pricing produces recurring revenue contractually. The customer commits to a fee for a period, and the vendor books that fee as predictable revenue. The recurrence lives in the contract. Usage-based pricing in a well-designed product produces recurring revenue operationally. The customer integrates the product into a workflow they're going to keep running, and the vendor captures revenue every time that workflow runs. Across a customer base of meaningful size, the aggregate usage behaves like a recurring revenue stream, predictable in aggregate, growing with customer expansion, retaining at high rates because customers don't typically rip out infrastructure they're using. Snowflake, Twilio, and Datadog are the proof of concept. Their revenue is operationally recurring even though almost none of it is contractually recurring in the traditional sense. The public markets value them with SaaS multiples for exactly this reason.
This is, if anything, a stronger form of recurring revenue than the fixed subscription version. Fixed subscriptions are only as durable as the customer's willingness to renew at the next contract decision point. Operationally embedded usage is as durable as the customer's continued operation of the workflow itself. The first is a single decision; the second requires changing how the business runs. The second is harder to dislodge.
The practical shape most of the market is moving toward is a hybrid: a platform fee that secures access and provides baseline predictable revenue at the contract level, plus usage or outcome components on top that capture variable value. The platform fee preserves contractual recurrence. The usage layer captures the variance that fixed pricing couldn't. This is the architecture most AI-native SaaS companies are actually shipping, and it's the architecture most legacy SaaS companies are evolving toward. It's also the architecture the frontier labs are using for their own vertical products. It's not a departure from the SaaS economic model. It's the model getting more accurate about where value actually lives.
New surfaces for value capture
The second shift is the emergence of value-capture surfaces that weren't part of the previous model. The old SaaS contract captured value at one moment: the subscription renewal. The new generation of products captures value at many moments: at the point of inference, at the point of output, at the point of integration, at the point of outcome. This is more sophisticated economically, not less. It allows pricing to track value much more closely than the blunt instrument of an annual contract did. The recurring revenue base remains the foundation; the per-event capture surfaces sit on top of it.
COGS becomes a strategic variable
The third shift is the return of cost-of-goods-sold as a strategic variable. The old SaaS model could mostly ignore COGS because marginal infrastructure cost approached zero. The new SaaS model can't, because inference is genuinely expensive and the cost curve is harder to predict. This forces operators to think about gross margin in ways the previous generation didn't have to. It forces pricing strategy to do work it didn't have to do before. It forces the relationship between product decisions and economic decisions to become explicit in a way most product organizations are not yet equipped to handle. This is a harder version of the model, not a dying one. The companies that develop the operating muscle for it will be substantially more durable than the companies that built when none of this mattered.
I run an agent-operated company. Most of the actual work inside Credyt is done by AI systems that consume inference, and we charge customers in part for what those systems produce. The gap between what an inference call costs and what it's worth to the customer is the entire business. That gap is variable in ways that fixed-fee SaaS pricing literally cannot represent. Two customers running the same nominal workload can produce wildly different inference profiles depending on the complexity of their data, the depth of their queries, the structure of their workflows. The pricing model has to absorb that variance without breaking, which means thinking about COGS at a level of granularity per-seat SaaS never required. Most SaaS companies that grew up during the period when infrastructure was effectively free are not currently equipped to do this work. They don't have the instrumentation. They don't have the pricing architecture. They don't have the muscle. The companies that build the muscle now, while the rest of the market is still arguing about whether SaaS is dead, will be the ones operating from a structural advantage when the recovery comes.
A test the public markets are already running
What this means for operators sitting with real decisions right now: the question isn't whether SaaS is dead. The question is whether the specific economic architecture of your specific product is built for the new variance or the old uniformity. Most products built before 2023 are built for the old uniformity. The decision in front of most operators isn't whether to leave SaaS. It's whether to do the structural work of rebuilding the economic layer of their product for the conditions that now exist.
That work is concrete and unglamorous. It looks like rethinking what unit your product charges for, what surfaces it can defensibly capture value on, what cost structure your gross margin will actually support, what contractual primitives you have access to that you weren't using, and which side of the redrawn build-vs-buy line your product now sits on. None of this is the death of an economic model. It's the maturation of one. The part where the easy version stops working and the sophisticated version becomes the price of survival.
The public markets are worth watching on this specifically, because they offer the cleanest external test of whether the position I'm taking is right. The current multiple compression on listed SaaS names reflects the consensus death-of-SaaS framing. The market is pricing in the assumption that the category itself is structurally impaired, not just that specific companies within it are exposed. If the position here is right, the recovery in those multiples, when it comes, will not be a generic re-rating of the category. It will be specific, and the specificity will be the signal. The companies that re-rate first will be the ones that have visibly done the economic architecture work. The ones that have moved to defensible non-seat units, that have demonstrated they can price against inference cost rather than absorb it, that have shown durable gross margin under the new variance. The companies that don't re-rate, or that re-rate only as part of a generic risk-on move that then unwinds, are the ones the market correctly identified as structurally exposed.
This is a watchable thing. The public markets are currently doing the work of pricing in uncertainty, and the early part of any recovery is where the most information is densest, because the dispersion in how different companies re-rate is wider than it will be later. For operators paying attention, the relative pricing behavior of comparable SaaS names over the next twelve to eighteen months will tell you a great deal about which specific architectural choices the market is rewarding and which it isn't. That information is harder to extract once the recovery is consensus.
Here's what would change my mind. If, over the next eighteen months, we see a significant category of AI-native products achieve durable scale on transaction-only or pure-outcome pricing, no recurring contract base, no platform fee, just per-event capture, that would be a real signal that the recurring revenue architecture is being structurally replaced rather than refined. I don't expect to see this. The economic logic of recurring revenue against software-delivered value is too strong, and the customer-side preference for predictable cost is too durable, for pure transaction pricing to dominate in B2B software at scale. But it's the specific evidence that would change the call.
The other thing that would change my mind is if the build-vs-buy line keeps moving and doesn't stop. The current shift assumes that build wins in narrow categories and buy wins in deep ones, with the boundary settling somewhere defensible for vendors. If the boundary keeps moving, if AI tooling continues to collapse the cost of internal builds so completely that even regulated, integrated, accountable applications start getting built rather than bought, then the SaaS economic model survives only in a much smaller addressable market than the position here implies. The early evidence is that the boundary is settling, not collapsing. The same Retool data that shows aggressive internal building also shows the governance and accountability gaps that drove SaaS adoption in the first place returning fast. But this is the variable to watch, and if it moves the wrong way, the call gets weaker.
Most discourse about whether SaaS is dead is operating at the wrong altitude. The interesting question isn't the death of the category. The interesting question is what the next generation of recurring-revenue software businesses looks like when they're built on accurate economic primitives instead of convenient ones. Which operators are doing that work now. Which architectural choices the market will reward when the recovery comes. The rest is noise.