Enterprise software is not going away. But the place where work happens is changing. The old SaaS application may remain the system of record while losing the work surface to agents, or it may absorb agents into its own governed perimeter. SAP and Salesforce have, between mid-April and mid-May 2026, given us both versions of that future. This essay is an attempt to make sense of what is happening, written for the many people now trying to do the same.
I. Three doors
Two of the largest enterprise software companies on earth made three moves this spring that, taken together, tell the story of where the industry is going better than any analyst report I have read. The dates: April 15, late April, and May 12. The actors: Salesforce and SAP. The question hidden inside the sequence is one every board in the sector will have to answer in the next eighteen months.
On the 15th of April, at its TrailblazerDX developer conference in San Francisco, Salesforce introduced what it called Headless 360. The headline, taken directly from its own announcement, was that every capability on the platform (data, workflows, business logic) is now accessible as an API, a Model Context Protocol tool, or a command-line instruction. More than sixty new MCP tools. Thirty preconfigured coding skills. Open access from Claude Code, Cursor, Codex, Windsurf, and any other coding agent. A unified AgentExchange marketplace bringing together ten thousand Salesforce apps, more than twenty-six hundred Slack apps, and more than a thousand Agentforce agents, tools, and MCP servers. A fifty-million-dollar Builders Fund to support partners building agents on the platform. Salesforce's co-founder, Parker Harris, framed the announcement with a question that would have been heretical five years ago: why should you ever log into Salesforce again?
Roughly two weeks later, on the other side of the Atlantic, SAP published Version 4 of its API Policy. The document was technical, brief, and easy to miss. Section 2.2.2 was the part that mattered. It said that, except through SAP's own endorsed architectures, data services, or service-specific pathways, SAP would prohibit its APIs from being used for interaction or integration with autonomous or generative AI systems that plan, select, or execute sequences of API calls. In plain language: third-party AI agents could no longer chain their own calls against SAP data unless they routed through SAP-endorsed paths. Integrations of the sort being built around Microsoft Copilot, Salesforce Einstein, and the long tail of agentic SAP connectors were no longer dealing with a neutral API surface. They were dealing with a perimeter. The German-speaking SAP User Group, DSAG, publicly raised concerns about the policy. SAP's CEO Christian Klein later clarified on the Q1 investor call that SAP's intent was not to block customers from their own data and that the company still wants an open platform. But the policy text remained.
Then, on May 12, at SAP's Sapphire conference in Orlando, SAP did something more revealing. Having drawn a perimeter around third-party agentic access two weeks earlier, it announced its own Autonomous Enterprise strategy. A unified SAP Business AI Platform. A new Joule Studio for building agents. An Autonomous Suite with more than fifty domain-specific Joule Assistants orchestrating over two hundred specialized agents. Most strikingly: a deepened partnership with Anthropic, in which Claude becomes a primary reasoning and agentic capability embedded across SAP's portfolio through Joule, operating on SAP data through what SAP calls its Knowledge Graph and Business AI Platform. The accompanying joint statement from Anthropic's president Daniela Amodei framed it precisely: Claude doing the work of closing the books, rerouting delayed orders, approving expenses, inside the systems enterprises have already invested in, with the trust and governance SAP customers rely on.
Read those three moves in sequence and you have, in compressed form, the strategic debate that the enterprise software industry is now having with itself. Salesforce: expose everything to external agents, let the user work from wherever they want, accept that the platform's job is to be the system of record and the system of action underneath someone else's agentic surface. SAP, late April: restrict external agents at the API perimeter, force agentic traffic through SAP-endorsed paths. SAP, mid-May: the deeper move. Concede that agents are the future of the work surface, but anchor those agents inside SAP's governed perimeter, with SAP's Joule as the assistant, SAP's Business AI Platform as the trust layer, SAP's processes and data as the context, and Claude (the standalone agent OS) running inside the perimeter rather than outside it.
These are not opposite strategies. They are different answers to the same question, and the question is the one that should be on every enterprise board agenda this year. The question is not whether agents are coming. The question is who owns the agentic work surface: the independent AI operating system, or the incumbent system of record. Salesforce is betting the platform survives as substrate even as the work surface moves up to whatever agent wins. SAP is betting it can absorb the agent layer into itself, making Joule plus Claude the work surface and keeping the strategic value of being the place where work happens. Both moves implicitly concede that the application layer as it has existed for twenty-five years, the seat-licensed, screens-and-workflows version, is being repriced.
And here is the part worth attending to. Through May 12, both stocks remained under pressure. The public-market signal is not clean, but it is large. Reuters reported that the S&P 500 software and services index shed roughly $1 trillion in market value in the week after January 28 alone. Broader software losses, depending on index construction and time period, run into the low trillions. The exact number matters less than the repricing: investors are treating application software as a category with new structural risk. Some of that risk is macro. Interest rates, post-pandemic normalization, the end of the frothy 2021-2022 valuation cycle, company-specific guidance, geopolitical concerns. Those factors are real and we should be honest about them. And there is a serious, credentialed camp arguing the selloff is simply wrong. Bank of America's senior semiconductor analyst has called the souring on software indiscriminate and overblown. Nvidia's CEO has said the market got it wrong, that capable AI will expand the demand for software rather than collapse it. Several well-known investors have argued that the market is confusing what is changing with what is dying, and projecting the behavior of five-person startups onto Fortune 100 procurement. They may be right. The market is not proving the thesis. Markets rarely prove anything cleanly, and panics overshoot. But the repricing is not only a panic. It is also the market beginning to price the possibility that the application layer's old growth and pricing assumptions no longer hold, and that the platform value is migrating somewhere, whether that somewhere is the standalone agent OS, the incumbent's own agentic surface, or some hybrid not yet fully formed.
Who owns the agentic work surface: the independent AI operating system, or the incumbent system of record?
This essay is an attempt to explain why I think this is the central question, what the structural answer probably looks like, and what could prove me wrong. The argument has weaknesses, and I will name them. I am writing as someone inside this industry who has spent the past several months in conversations with builders, buyers, investors, and operators. I have no claim to certainty. I do think the direction of travel is hard to ignore. The timing, magnitude, and identity of the eventual winners remain deeply contested.
II. The substrate and the platform
To explain what I think is going on, I want to take you back to 1985. In 1985, if you had asked a thoughtful person where the value of the personal computer industry was going to accumulate, the most popular answer would have been: chips. Intel made the brains of the machine. Intel had the patents. Intel had the manufacturing. The x86 instruction set was the foundation that everything else sat on top of. Own the chip, own the industry.
That answer was wrong. Or rather, it was correct about the substrate and wrong about the industry. Competitors caught up to Intel at the instruction-set level. AMD made chips that ran the same software. Years later, ARM ate the mobile market entirely. The chip-level moat that looked so durable in 1985 turned out to be fillable by anyone with enough engineering talent and enough capital. By the late 2000s, the chip business was a real business, but it was no longer the business.
The business, the place where the trillions of dollars actually accumulated, was the operating system. The layer that sat on top of the chip. The layer where the user actually lived. The layer that translated whatever a human wanted to do into the language the chip could execute. Microsoft did not make chips. Microsoft made Windows. And Windows turned out to be the platform, while x86 turned out to be the substrate. In technology, the platform tends to win.
Here is the part of the story that should haunt anyone betting on the wrong layer today. Linux is free. Linux is, in many respects, technically superior to Windows. Linux has been freely available for over thirty years and runs most of the world's server infrastructure. And Windows is still embedded in a Microsoft franchise worth, conservatively, more than a trillion dollars. The substrate commoditized. The platform did not. Because once a user has installed their applications, configured their files, built their habits, and trained their muscle memory, switching to a technically better alternative is not a casual decision. The substrate is replaceable. The platform is not.
The substrate is replaceable. The platform is not. This is the iron rule of technology transitions, and the layer most analysts are watching is usually not the layer where the value ends up.
This is a recurring pattern in technology transitions, and it should make anyone betting on the wrong layer today nervous. People confuse the substrate for the platform. They look at the most visible, most discussed, most benchmarked layer and assume that is where the value will accumulate. The pattern has held in personal computing, where the chip makers were the spectacle and the operating system won. It held in the browser era, where the rendering engines were the spectacle and the search and advertising platforms won. I think it is holding right now in artificial intelligence, where everyone is watching the models, and the operating systems built on top of the models are, quietly, where the work surface is moving.
Take any of the leading AI assistants apart, the way a mechanic takes apart an engine, and ask yourself what is actually inside. There is the model itself, of course. The part everyone talks about, the part that scores on benchmarks. But the model is one piece, and not the most strategically important one. Around the model, these companies have built persistent work surfaces where files and projects accumulate over time. They have built coding agents with their own execution environments, products that have become some of the fastest-growing tools in the current software cycle. They have built universal adapters that let the assistant reach into any external system. They have built native presences inside the tools people already use, inside browsers, inside spreadsheets. They have built skills systems that give the assistant domain expertise on demand. They have built memory, so the assistant knows you. They have built partner networks and certification programs. They have built tiered pricing structures that operate simultaneously at the consumer, prosumer, and enterprise levels. They have built safety and governance layers.
None of that is the model. The model is the engine. All of that is the car around the engine: the chassis, the cabin, the instruments, the doors, the wheels. Eighteen months ago, it would have seemed implausible to say this, but the model is no longer the only place where the strategic battle is fought. Several companies can now build a competitive frontier model, and building one remains a feat of extraordinary engineering. Far fewer have built durable work surfaces, memory, integrations, governance, distribution, and pricing systems around them. Competition at the model layer is real and intense. Competition at the operating-system layer is narrower.
This is the place where I want to be careful, because I have heard a version of this argument used to declare a winner already, and I do not think that is honest. Anthropic's Claude is, in my view, the clearest current example of what an agent operating system is becoming, because Anthropic appears to have understood from early on that it was building one. OpenAI is the most heavily capitalized contender, and ChatGPT has the deepest consumer mindshare. Google has, in principle, the strongest distribution position through Workspace and Android, though it has moved slower than its assets would predict. Microsoft has Copilot inside the tools where most enterprise work already happens. And as SAP's Sapphire announcement made vivid, the incumbents themselves may yet capture some version of the agent layer by absorbing it into their governed perimeters. The race is not run. It is in the early phase of what may turn out to be the most consequential platform race in business history. The structural argument of this essay does not depend on which one wins. It depends only on the claim that a small number of operating-system-shaped surfaces will sit above or beside the model layer and capture work.
A note on vocabulary, because language is a leading indicator. When I first started describing this layer as an operating system, it felt like a stretch of an analogy. It no longer does. Deloitte's enterprise software analysts now forecast the emergence of what they call, in their own words, an enterprise AI operating system: a layer that governs, orchestrates, and controls AI agents rather than leaving them as disconnected tools, and they advise buyers to start asking who owns that layer. When an independent professional-services firm reaches for the same metaphor without coordination, the metaphor has stopped being a rhetorical device and started being a category.
III. The new browser
There is a second analogy I find useful, because it captures what these new tools actually are in a way that resonates with anyone who has used a computer in the past twenty years. The leading AI assistants are not chatbots. They are the new browsers.
Think about what a browser was, in the classic sense. A browser was a window onto documents that lived somewhere else on the internet. You typed an address, and the browser fetched a page, and you read it. If you wanted to do something, buy a book, send an email, watch a video, you found a website that did that thing, and you did it through the browser. The browser was the surface where the internet happened. It was where you spent your time. It was where you formed your habits.
Consider what the browser captured, economically. The browser did not capture value merely by rendering pages. It captured value when it became a default surface for discovery, identity, search, advertising, and distribution. The largest economic gains of the internet age accrued not to the browser as standalone software, but to the ecosystems that formed around it: search engines, ad networks, operating-system defaults, mobile distribution. The agentic browser will matter for the same reason only if it becomes the default surface for execution.
Now consider what the AI assistants are becoming. The user is no longer typing in an address and fetching a document. The user is describing a desired outcome, write me a report, plan my project, debug this code, summarize these emails, find me a flight, and the assistant is producing the outcome. The interaction model has changed in a way fundamental enough that we are still groping for the right vocabulary. The old browser was a window onto documents. The new browser is a window onto outcomes.
And here is the part worth attending to: if browser-shaped ecosystems captured trillions of dollars of value around the document web, agentic work surfaces may be positioned to capture value around the outcome web. The document web, vast as it was, was always a subset of human activity. The part of human activity that could be encoded as a document and rendered on a screen. The outcome web, by contrast, is co-extensive with knowledge work itself. Anything a knowledge worker does, and a large and economically central share of enterprise value creation is now knowledge work, is in principle something that can be requested as an outcome from an AI assistant. Every email written, every report drafted, every analysis performed, every meeting summarized, every spreadsheet built.
The old browser was a window onto documents. The new browser is a window onto outcomes. The center of gravity in pricing is moving from seats to usage.
This brings us to a business-model shift that I think is more important than the technology itself, although I want to be careful here because the simple version of this argument is wrong. The simple version says: software used to be priced by the seat, and now it is priced by the token. That is half right and would be misleading without nuance.
For twenty-five years, enterprise software was priced primarily by the seat. A company would pay its software vendor a monthly fee for each employee who used the software. The seat was a proxy for value: each seat represented a human spending time inside the product, and the price was calibrated to the value extracted. The new pricing model is not the disappearance of the seat. Claude Enterprise, ChatGPT Enterprise, and Gemini Enterprise all still have per-seat components. What is happening is that the center of gravity in pricing is shifting. Seats remain a base. But the unit of incremental value is the token. A fragment of text consumed when the assistant does work. A complex request costs more tokens. A trivial one costs few. The price is metered to the work performed, not just to the seat occupied. Enterprise contracts now routinely combine base seats, committed token spend, usage tiers, overages, and enterprise-wide credits. Pricing is becoming hybrid, and the hybrid is tilted toward usage and outcomes.
This is not only my reading. IDC forecasts that by 2028, pure seat-based pricing will be obsolete, with roughly seventy percent of software vendors having refactored their pricing around consumption, outcomes, or organizational capability. And the incumbents are already moving. When ServiceNow opened its platform to external agents this spring, it metered the access: agents pay, per action, in the same consumption currency ServiceNow customers already buy, a model one analyst described as a tollgate for agents. The seat is not dead. But the meter has been installed, and the incumbents are installing it themselves.
This tilt matters for three reasons. The first is that token pricing removes the seat-count ceiling on growth. Traditional seat-based software has a natural seat-count ceiling unless the vendor expands modules, raises price, or moves into adjacent workflows. An agent-OS provider can grow as fast as the world delegates work to it, which is a much higher ceiling because most knowledge work is in principle delegable. The second is that the price is tied to value performed. If the assistant produces an outcome that would have taken a human three days, the customer compares the cost of the tokens to the cost of the three days and almost always concludes the tokens are a bargain. The third is that every token consumed is consumed in pursuit of something the user explicitly wants, which makes willingness-to-pay higher than in ad-supported models.
I want to acknowledge what this model gives up. Token pricing has real drawbacks. Bills are harder to predict, which procurement teams hate. The meter is always running, which can cause organizations to cap usage precisely when usage is most valuable. Inference costs put pressure on gross margins for the providers themselves. Some categories of work do not naturally map to token consumption. Enterprise customers, particularly in regulated industries, are right to demand committed pricing, cost guardrails, and predictable budgets, and the providers are still working out how to offer them. The transition will be uneven. None of these caveats overturns the direction of the change. They do mean the change will not be as clean as the simple narrative suggests.
IV. Dissolution, not displacement
The standard mental model for software disruption is what consultants call rip and replace. A new product comes along, demonstrably better than the old one. The customer, after some hesitation, agrees to swap them out. A migration project is scoped. It takes eighteen months. Data is moved. Training is conducted. The new product replaces the old. The old vendor loses the contract. This is what disruption has looked like in enterprise software for thirty years.
I do not think this model describes what is happening with AI assistants. What is happening does not look like rip and replace. It looks more like sugar dissolving in water. Slowly at first. Then, perhaps, all at once.
Here is how it actually plays out. The example that follows is a composite, drawn from patterns I have heard repeatedly across conversations with enterprise buyers over the past six months rather than a single named deployment. It is meant to illustrate the pattern, not to serve as a statistical claim. A large company has been running a major enterprise platform for fifteen years. Its entire procurement, supply chain, and financial reporting infrastructure is built on it. It has spent, at this point, somewhere between forty and three hundred million dollars implementing and customizing it. It is not, under any circumstances, ripping it out. The CFO would not survive the conversation.
And yet. The same company has, in the past year, deployed an AI assistant for its finance team. Initially the team uses it for what looks like trivial work, summarizing emails, drafting memos. But the use cases compound. Someone in financial planning realizes the assistant can read the data export from the enterprise platform and produce a draft variance analysis in minutes that used to take an analyst two days. Someone in procurement realizes that the assistant, connected to the enterprise data and to the vendor database, can flag anomalies in supplier behavior that nobody had time to look for. Someone in treasury starts using it to draft the weekly cash report. Within six months, the finance team is doing thirty or forty percent of its work through the assistant. Within twelve months, the analysts who used to spend eight hours a day inside the enterprise platform's screens are spending two hours a day there and six hours inside the assistant. The platform is still in place. The data is still there. But the team's attention, its workflows, its institutional habits, those are no longer in the platform. They are in the assistant that sits one layer above.
The platform is no longer the place where work happens. It is the system of record. That is a different business, with a different margin, and a meaningfully different valuation.
Now consider what happens at contract renewal. The enterprise platform vendor prices its product based partly on the number of seats and partly on the strategic importance of the platform. When the negotiation happens, the buyer can now say, truthfully, that fewer employees are spending meaningful time inside the platform, that the data is being consumed through the assistant rather than through the platform's own interface, and that the platform is no longer carrying the strategic workflow it used to carry. The price comes down. Not catastrophically the first cycle. Maybe ten percent. Maybe fifteen. The direction is established. The next cycle, the conversation is the same, and the price comes down again. Within three or four renewal cycles, five or six years, the platform is no longer charging strategic-workflow prices. It is charging system-of-record prices. It is still in the picture. It is just in a much smaller one.
This is dissolution. There is no migration. There is no rip-out. There is no moment anyone can point to and say, that is when we left the platform. The application is simply no longer the place where work happens. The path from one state to the other looks, from the inside, like nothing at all. It looks like normal renewals, normal usage, normal everything, except that the screens have become less crowded, and the dashboards are no longer where decisions get made.
I want to address the strongest counterargument here, because I have heard it from people I respect and I think it deserves a fair hearing. The SaaS incumbents argue that their moat is not the storage of data (they would agree storage is commodity) but what they call governed context. The data plus the permissions, the sharing rules, the workflows, the business logic, the compliance controls, the audit trails, the implementation history, the organizational habits that have accumulated over decades. Salesforce's own framing of Headless 360 leans into exactly this: an agent connected to a raw database, they argue, does not know that a customer has an open escalation, a renewal due in thirty days, a breached SLA, and a relationship owner with a personal connection to their CFO. That context took years to accumulate. It lives in Salesforce. Headless 360 exposes it through APIs and MCP tools so that agents can reach it from anywhere, without touching the UI, but the context, they argue, still belongs to the platform. SAP's Autonomous Enterprise announcement is the same argument made even more aggressively: not only does the governed context belong to the platform, but the agent itself should live inside the platform's perimeter.
This is a serious argument and I do not want to wave it away. The question it raises is not whether the data moat is real, because narrowly defined as raw rows in a table the data moat is plainly weak. The question is whether the governed context, the permissions, workflow, business logic, auditability, migrates to the agent layer over time or remains anchored in the incumbent platforms. The incumbents are betting it remains anchored. They are betting they become the system underneath the agents (Salesforce's bet), or that they absorb the agent layer into themselves (SAP's bet), with the standalone agent OS reduced to a reasoning engine that runs inside their governed perimeter. My read is that the absorb-the-agent bet works for some time, and probably permanently for some categories of regulated, mission-critical operation. But I expect that for most workflow software, the governed-context layer migrates upward over a five-to-ten-year horizon, because that is where the work happens and that is where governance will eventually need to attach. Reasonable observers disagree about this. It is the most important open question in the essay, and it is precisely the question that SAP's Sapphire announcement was an attempt to answer in the incumbent's favor.
V. The flywheel
If you want to understand the speed at which this is moving, you have to understand the flywheel. The flywheel is what, in any technology transition, separates the slow, manageable kind of disruption from the kind that catches everyone by surprise. In the case of the AI operating systems, there are two flywheels that I would watch most carefully.
The first is talent. The senior engineers and solutions architects at the established enterprise software companies are one of the most valuable hidden assets in the world economy right now, and almost nobody is talking about it as such. There are likely tens of thousands, perhaps more than a hundred thousand, of these people globally, distributed across the major incumbent vendors, systems integrators, and implementation partners. They are the people who actually understand, in deep operational detail, how a Fortune 500 company runs its supply chain, recognizes its revenue, handles its claims processing, manages its compliance. This knowledge does not exist in textbooks. It exists in their heads, accumulated over twenty or thirty years of solving real problems for real customers. It is, in the truest sense, the moat of the enterprise software industry. Not the code, not the data, but the embodied institutional understanding of how the customer's business actually works.
Now look at the compensation arithmetic. The leading AI labs have seen their valuations appreciate dramatically over the past eighteen months. A principal engineer at an incumbent vendor, whose equity grants from two years ago may now be worth less than expected, is fielding recruiting calls offering total compensation packages that are multiples of their current arrangement, plus equity in a company that may continue to appreciate. This person has a choice. They can stay, watching their net worth lag while their company navigates a strategic challenge they suspect will be difficult. Or they can take the call.
I want to be honest about the state of the evidence here. The flow of senior enterprise engineering talent from the incumbents to the AI operating system providers is still early and hard to measure publicly. It may already be substantial. It may still be a trickle. But it is one of the signals I would watch most closely over the next eighteen months, for a simple structural reason: if and when principal engineers and senior solutions architects begin moving from the application incumbents to the agent-OS providers at scale, the institutional knowledge moves with them. And the incumbents do not just lose engineers. They lose the people who knew how their customers' businesses actually worked, which is to say, they lose what made them valuable in the first place.
The moat of the enterprise software industry is not the code, not the data, but the embodied institutional understanding of how the customer's business actually works. When that knowledge walks across the street, the moat walks with it.
When these people do land at the AI labs, they do not rebuild the old systems. They build what those systems should have been if you had started designing them today, with assistants as the substrate from the beginning. Fewer screens. Less default reliance on seat licenses. Shorter, more modular implementations. Just an assistant that, because the engineer who designed it spent fifteen years solving supply chain problems for a global manufacturer, knows exactly how supply chains work. The new product is not a feature-by-feature replica of the old one. It is the old product's purpose, redesigned for the agent era, and built by the very people who used to build the old product.
The second flywheel is compute. Compute is the raw material of these new operating systems, the way electricity is the raw material of an aluminum smelter. The compute commitments being made right now are unlike anything in the history of corporate capital allocation. Major hyperscalers have announced multi-tens-of-billions of dollars of investments in the leading AI labs, in exchange for or alongside gigawatts of computing capacity. The structure of these deals is important to read carefully. The equity investment piece is one thing, the commercial compute commitment is another, and the two are related but distinct, and the press tends to conflate them. Anthropic's Amazon arrangement, for instance, secures up to roughly five gigawatts of capacity with what Anthropic itself describes as a commitment of more than a hundred billion dollars over ten years to AWS technologies, alongside a current Amazon equity investment of five billion dollars with up to twenty billion more in the future. Similar structures exist with Google. Goldman Sachs has estimated total AI infrastructure capital expenditure could reach roughly seven and a half trillion dollars between 2026 and 2031. The exact numbers will move. The order of magnitude is the point. These are not normal venture investments. They are infrastructure-scale bets made by the parties closest to the technology about the rate at which the world is going to absorb capital into this transition.
One caveat is worth making here, because the framework so far implies that compute earns foundry-style margins and that platform margins accrue one layer up. That is probably the long-run picture, and it maps cleanly onto how the personal-computing era resolved itself. But the compute layer may not behave like commodity plumbing during this cycle. Scarce compute, specifically scarce frontier-grade compute, scarce power, scarce fabrication capacity, may itself have platform-like pricing power for as long as the scarcity persists, which on Goldman's numbers could be most of the next decade. Nvidia is the obvious example. The compute layer may not be the final work surface, but it is not merely commodity plumbing either. The operating-system analogy is right in the long run. It may be temporally premature.
Both flywheels are self-reinforcing in the same direction. The more compute the AI labs have, the better their products get, the more revenue they generate, the more capital they raise, the more compute they can buy. The more senior engineers they hire, the better their products get, the more revenue they generate, the higher their equity values rise, the more attractive their compensation offers become. These loops compound. They appear to be compounding right now.
VI. What survives
Not everything in the existing software stack is going to dissolve. Some of it is going to be fine. Some of it is going to be more than fine. It is going to be quietly, durably valuable, the way boring infrastructure businesses have always been quietly, durably valuable. The question is which is which, and the answer comes back to why the software was there in the first place.
The software that survives is the software whose value comes from not changing. Financial general ledgers. Clinical health records. Identity systems. Regulated transactional databases. The undramatic plumbing of the modern economy. The reason these survive is that their value rests on a property, immutability, auditability, defensibility in a court or a regulatory proceeding, that is about the integrity of the record itself. The moment the authoritative record lives only inside an agentic reasoning layer, the record becomes hard to defend. The durable system of record still needs deterministic state, auditability, permissions, and replayable history. Agents can act on that layer, and eventually help govern it, but they do not eliminate the need for it. So the ledgers remain. The databases remain. The compliance systems remain. They are, in a real sense, the bedrock layer of the new architecture.
There is a useful tell here. The leading AI labs have raised tens of billions of dollars. They have not publicly prioritized owning the data-infrastructure layer. Their capital allocation and partnership behavior point instead toward compute, distribution, protocols, enterprise surfaces, and agentic workflows. They are content to let the data layer below them remain in the hands of existing providers. This is the strategic equivalent of the great platform companies of earlier eras choosing not to enter the semiconductor business. They were not being kind. They were being clear-eyed about where the platform value was. The data layer is plumbing. Plumbing is valuable. But it is not where the leading AI labs appear to believe their platform value sits.
The software whose position is hardest to predict is the workflow application layer. CRM, ticketing, HR self-service, project management, marketing automation, e-commerce admin. Their value came from being the place where humans operated against the systems of record. Their argument now is that they are repositioning themselves as agent platforms, exposing their data, workflows, and business logic to external agents through MCP and APIs, while retaining the governed context that makes the platform valuable. SAP's Sapphire announcement is the most aggressive version of this argument yet attempted: not merely expose the platform to external agents, but make the platform's own agent the default surface and route the standalone AI model through it. Whether this works is the open question. The optimistic case for the incumbents is that governed context cannot be rebuilt by an agent in the time horizon that matters, and that the incumbents become the substrate that agents act on, or, in SAP's stronger version, the substrate that agents live inside. The pessimistic case is the dissolution argument above: the work moves up a layer, the renewal prices come down, the platforms become storage. I think the truth is in between, and which side dominates will vary by category. The most exposed categories are the ones where the workflow logic is thin and the user-interface real estate was most of the value. The least exposed are the ones where the governance, audit, and compliance machinery is dense enough that an agent cannot reasonably rebuild it, which is precisely the category SAP is now trying to claim is its entire footprint.
There is one more category worth noting. Software whose value comes from a network external to the customer (a financial information terminal, a professional networking graph, a payment network) is protected for a different reason. The AI operating systems will operate on top of these networks, but cannot replace them. You cannot replace the network of every financial professional in the world by writing better AI. You can only access it. Some of the most agent-resistant pieces of software ever built, paradoxically, turn out to be the ones whose value comes from the network of humans on either side of them.
Put this all together and you get a picture of the software landscape that looks different from the one we are used to. At the bottom: utilities. The databases, the ledgers, the identity systems, the payment networks. Durable and valuable, but valued the way utilities are valued. In the middle: a layer in transition, where the outcomes will be uneven and where incumbents like SAP are now trying to redefine their middle-layer position as a governed-perimeter platform for agents. At the top, or perhaps overlapping with the middle: the AI operating systems. A small number of them, with potentially extraordinary pricing power and revenue growth, though their ultimate margins remain contested because inference and compute are expensive. Below all of this, supporting the entire structure: the compute layer, the chips, the hyperscalers, earning, in this scarcity phase, more than foundry-style margins, possibly for years.
This is not, on reflection, a particularly unusual pattern in technology transitions. It happened to the mainframe makers when the PC arrived. It happened to the on-premises software vendors when the cloud arrived. It is happening now, I think, to the application layer with the arrival of AI operating systems, though with the wrinkle that, this time, the incumbents are attempting to fold the new layer into themselves rather than simply being displaced by it. Whether that works will define the next decade.
VII. What could prove this wrong
I have tried throughout this essay to be honest about uncertainty, but it is worth pausing to lay out, in one place, the strongest objections to the argument. Any of these could turn out to be the decisive factor. None of them, I think, dissolves the direction of travel. They could meaningfully change the slope, the timing, and the identity of the winners.
• Incumbents absorb the agent layer into their governed perimeters. This is the SAP Sapphire bet, made explicit. If incumbents successfully embed agent reasoning inside their own platforms, with their own assistants as the default surface, their own knowledge graphs as the context, and external models like Claude operating inside the perimeter rather than around it, the dissolution mechanism described above could be substantially blunted. The platform survives by absorbing the new layer rather than ceding ground to it.
• Enterprises standardize on agents provided by existing vendors. Microsoft is the obvious example, and this is no longer hypothetical. On May 1, 2026, Microsoft made Agent 365 generally available: a control plane to discover, observe, govern, and secure AI agents across Microsoft and partner environments, priced per user and bundled into a new enterprise suite, with registry sync that reaches into AWS and Google Cloud. If enterprises decide that the agent layer they want is the one embedded inside the productivity tools and identity systems they already pay for, the standalone agent-OS providers may capture less than this essay implies. The agent layer still wins; it just might be Microsoft's agent layer.
• Governance, security, and audit requirements anchor work inside the incumbents. Regulated industries have a legitimate interest in keeping certain workflows inside platforms with proven audit trails and compliance certifications. SAP's API policy is partly a security argument, and the security argument is not wrong. If governance becomes the binding constraint on agent adoption, the dissolution timeline stretches by years.
• Token economics compress provider margins faster than SaaS margins compress. Inference is expensive. The more useful the agents become, the more tokens they consume, and the more pressure that puts on the providers' gross margins. It is possible the providers end up looking more like cloud infrastructure businesses, with thinner platform-level pricing power than this essay assumes.
• Interoperability prevents lock-in. MCP is an open standard. The model layer is becoming more portable. If agents become genuinely interoperable, no single agent OS may develop the durable switching costs that historically have produced platform-level economics. The category wins; no individual company captures the disproportionate share.
• The repricing is mostly a panic, and it reverses. This is the objection held by the most credentialed skeptics, and it deserves a fair hearing. On this view, the selloff confuses demo velocity with deployment reality, projects startup behavior onto Fortune 100 procurement, and underprices how much of the incumbents' value is switching cost, integration depth, and installed base. The gap between a polished agent demo and a system that runs reliably across thousands of regulated enterprise environments is vast, and the skeptics argue the market has not priced that gap. If they are right, the software multiples recover, and the dissolution stretches into a decade-plus evolution rather than a sharp repricing. My own read is that this objection is strongest on timing and weakest on direction: the panic almost certainly overshot in particular names, but the structural change it is gesturing at is real.
Each of these is a serious objection. My honest assessment is that the first and the second are the most credible as descriptions of where value actually lands, and the sixth is the most credible as a warning about timing. The incumbents may successfully absorb the agent layer, particularly in regulated workflows, and Microsoft may capture much of the agent surface through productivity-suite distribution. The panic may well have overshot. But none of these would change the structural claim that the application layer's old pricing assumptions are being repriced by the rise of agentic interfaces. The narrower argument, that the work surface is moving up or being absorbed, that pricing power is migrating with it, that the seat-licensed application as we have known it for twenty-five years is changing, survives most of the plausible challenges. The broader claim, that the standalone AI labs will capture all of the value, does not, and I have tried not to make it.
VIII. What to watch for
If you want to know how fast this is moving, there are a few signals worth tracking. None is conclusive on its own. Together they form a reasonably reliable picture of where we are on the curve.
Watch the capital allocation. When a software company's management stops investing aggressively in product and starts returning capital to shareholders through buybacks and dividends, that is a signal worth interpreting carefully. Buybacks can mean many things: capital discipline, confidence in valuation, tax-efficient return, offsetting dilution, or the absence of attractive M&A targets. They do not, by themselves, prove that management has given up on R&D. But a multi-year buyback program announced alongside softening forward guidance, with no parallel announcement of significant product investment or strategic acquisition, is one of the possible signatures of a category in transition. The signal is not the buyback alone. It is the pattern of capital flows over time.
Watch the talent flow. The most reliable leading indicator of where the next decade of software gets built is where the senior engineers are going. LinkedIn data is useful. Better signals come from who is speaking at conferences, whose names are on patents and papers, who is being quoted in the technical press. Watch in particular the second-tier movement. Not the founders and CTOs, but the principal engineers and senior solutions architects, the people who actually know how customer businesses work. When they move, the institutional knowledge moves with them.
Watch the integration protocols. The Model Context Protocol, introduced by Anthropic in late 2024 and donated to the Linux Foundation in December 2025 with OpenAI, Google, Microsoft, and Block as participants, has become a de facto standard for connecting agents to external systems. The cleaner signal is not that every major SaaS vendor has already shipped full, mature MCP support. They have not, and adoption is uneven across Salesforce, ServiceNow, Workday, and others. The cleaner signal is that major platforms are beginning to expose governed actions through agent protocols, while the leading AI assistants have converged around MCP as one of the default ways to call external systems. The direction is unmistakable even where the implementations are still partial. The act of supporting the protocol, even partially, is at minimum an acknowledgement that more work will be mediated by agentic surfaces outside the traditional application UI.
Watch the pricing models. The first time a major enterprise customer publicly announces that it has moved a category of work from a seat-based software contract to a token-based AI contract, you are looking at a bellwether. There will be a CFO who explains it on an earnings call. There will be a vendor who loses a flagship account. The financial press will treat it as a one-time event. It is unlikely to be a one-time event. Watch for the second and third announcements, which tend to come quickly after the first.
Watch what the AI labs do not do. This is more subtle, but revealing. The leading AI labs could deploy capital in many directions. They have not moved on data-infrastructure ownership. They have not moved on existing application-layer acquisition. Their public capital and partnership behavior points consistently toward compute, distribution, protocols, enterprise surfaces, and agentic workflows. Read this carefully. They are telling you what they think is valuable. They think the data layer is plumbing. They think the application layer is changing in ways they do not need to own. They are interested in the operating-system layer and the things that complement it. The strategic restraint is the signal.
Watch how the SAP and Anthropic experiment unfolds. This may turn out to be the most informative single development of the next eighteen months. If Claude operating inside SAP's governed perimeter, as Joule's reasoning engine, on SAP data, within SAP's Knowledge Graph, becomes the default way that SAP customers interact with their ERP, that is strong evidence that the incumbent absorption strategy is viable. If, instead, customers start preferring to bring their own agent OS to their SAP data via MCP and other protocols, ignoring Joule even when it is integrated with Claude, that is strong evidence that the standalone agent-OS thesis is the right one. The Sapphire announcement is the natural experiment, and the early evidence should begin to show up within a year, even if much of the decisive usage data remains private.
IX. Everyone is listening
I want to end with an observation about the people, because it is the part that surprised me most. A year ago, the conversation was about models. Which one was best, which was cheapest, which would plateau. That conversation is over. The people I talk with now, the executives and operators and investors who are genuinely good at their jobs, are not asking that question, and they are not asking a cleverer replacement for it either. They are mostly not asking questions at all. They are listening.
This is worth sitting with, because it is easy to mistake for indecision and it is not indecision. The smartest people I know in enterprise technology right now have arrived at the same posture more or less independently. They can see that something large is happening. They can feel the potential of it. And they have noticed that there is no proof anywhere, no reference deployment, no settled playbook, no company that has done the thing and can be copied. So they are doing the intelligent thing in the absence of a map. They are gathering signal. They are reading, comparing notes, watching what their peers try, holding their conclusions loosely. They are listening.
I have tried, in this essay, to offer something to listen to. Not a forecast, and not a question that unlocks the rest if you only ask it. There is no single question. Anyone who tells you the whole thing reduces to one clean question is selling the comfort of a frame, and the comfort is false. What I have offered is a structural reading: that the work surface is moving, that it is moving either up into standalone agent operating systems or inward into incumbent governed perimeters, that the layer where it lands is the layer that captures the value, and that the seat-priced application as we have known it for twenty-five years is being repriced regardless of which way it resolves. That reading might be wrong. I have spent a section of this essay on the ways it might be wrong. But it is a shape, and a shape is something you can hold up against what you are hearing and test.
I do not know which agentic surface will win. The race is genuinely open, and I have tried in this essay to resist the temptation to declare a winner. What I will say is that the companies leading right now, both the standalone agent-OS providers and the incumbents now trying to absorb the agent layer, appear to have understood, earlier than most observers, what kind of thing they were actually building. They were not building chatbots. They were building or rebuilding operating systems. The architectural choices they have made, around protocols, surfaces, memory, integrations, safety, partner ecosystems, and pricing, are the choices you make when you understand that the model is the substrate and the platform is what matters. Whether the winners turn out to be the standalone labs, the productivity-suite incumbents, the systems-of-record giants now embracing agents, or some combination, depends on choices that are being made right now and that will reveal themselves in usage data over the next two to three years.
The thing that is ending, I think, is not enterprise software. The thing that is ending is the era in which the application layer of enterprise software was the most valuable layer in the stack. The era of seats, screens, and per-user-per-month pricing as the dominant model. That era began, roughly, at the turn of the millennium. It will end sometime, I would guess, before the end of this decade. Twenty-five years is, in the long view, about right for a platform era. The mainframe era ran about that long. The personal computing era ran somewhat longer. The application-SaaS era, by historical standards, had a perfectly respectable run. It is now changing, quietly in some categories, loudly in others, with most of the participants not quite sure yet whether the change will leave them stronger or weaker.
The companies in the application layer will survive. They will still exist in 2030. Many will reposition themselves successfully as systems of record, or as agent platforms, or as governed-perimeter providers underneath or alongside the standalone operating-system layer. SAP's bet at Sapphire is that this repositioning is not only possible but desirable from the platform's perspective. Some will be absorbed. A few may go private. None of these outcomes is catastrophic. None of them is what investors were modeling two years ago. Both can be true at the same time.
So I will not end by telling you what question to ask. I do not think that is the honest move, and I do not think it is what this moment calls for. What I will say is that the listening is not a holding pattern. It is the work. The people who come through this transition well will not be the ones who found the magic question first. They will be the ones who listened carefully, who built an internal picture of where the work surface was moving before there was proof, who noticed the shape of it early enough to act while acting was still cheap. The shape is becoming visible. SAP and Salesforce and Microsoft and ServiceNow have, in the span of a few weeks, each shown a piece of it. The trillion-dollar question, who builds the next infrastructure, who pays for it, and what it looks like when it is finished, does not have an answer yet. But it has a direction, and the direction is no longer hard to see.
I am still listening too. This essay is not a verdict handed down from somewhere above the situation. It is one reading, from inside the same fog everyone else is in, offered in the hope that it is useful to compare against yours. If your picture differs from mine, I would rather hear it than be told I was right, because none of us has enough signal yet and the fastest way to get more is to put our partial maps next to each other. That is the actual state of things in the spring of 2026: a large number of capable people, reading the same unsettled situation, building the picture in public and in parallel. The window to position yourself against the direction is open. It will not stay open indefinitely. Until it closes, the right thing to do is what the best people are already doing. Keep listening. Compare notes. Then move.
A note on sources
Key source anchors: Salesforce Headless 360 announcement (April 15, 2026); SAP API Policy v4/2026, particularly Section 2.2.2; DSAG's public concerns about the policy and SAP CEO Christian Klein's clarification on the Q1 2026 investor call; SAP Sapphire 2026 Autonomous Enterprise announcement (May 12, 2026); the SAP and Anthropic Claude-in-Joule announcement; Anthropic's MCP donation to the Linux Foundation's Agentic AI Foundation; Anthropic's Amazon and Google/Broadcom compute announcements; the Goldman Sachs Tracking Trillions report on AI infrastructure capex; Reuters coverage of the 2026 software-services selloff; the Microsoft Agent 365 general-availability announcement (May 1, 2026); and the ServiceNow Action Fabric announcement from Knowledge 2026. The forecast that seat-based pricing becomes obsolete by 2028 is from IDC; the enterprise-AI-operating-system framing attributed to an independent professional-services firm is from Deloitte's 2026 enterprise software predictions; the characterization of the selloff as overblown reflects on-record comments from a Bank of America analyst and Nvidia's CEO as reported by Fortune. Sector market-capitalization figures and individual stock declines are approximate and based on publicly reported data through May 12, 2026. The market-causality argument should be read as evidence of category risk, not as proof that AI disruption is the sole or primary cause of every individual stock move. The composite finance-team example in section IV is explicitly a composite, drawn from patterns across multiple buyer conversations rather than a single named deployment. The framework, the dissolution mechanism, the layer-cake interpretation, and the conclusions are mine. The direction of travel is, in my view, hard to ignore. The timing, magnitude, and identity of the eventual winners remain deeply contested.