On January 12, 2026, Anthropic shipped a research preview called Claude Cowork. The whole thing was built by four engineers in ten days, with most of the code written by Claude Code. It runs on macOS, ships through the Claude desktop app, and was originally gated behind the Claude Max plan. The pitch is straightforward: give Claude access to your local files, queue up a few tasks, and walk away. TechCrunch covered it the day it dropped under the headline “Claude Code without the code.” VentureBeat called it “a Claude Desktop agent that works in your files, no coding required.”
Inside three weeks, a Jefferies trader had given the Q1 software selloff a name. Bloomberg ran a piece on February 4 titled “What’s Behind the SaaSpocalypse Plunge in Software Stocks.” By April, SaaStr’s analysis (carried by FinancialContent) put the damage at roughly two trillion dollars in lost market capitalization across the sector since the start of the year, with the IGV software ETF down twenty-two percent from highs. The same analysis called the IGV’s decline the worst on record for software relative to the S&P 500, exceeding the dot-com bust, the global financial crisis, and the 2022 rate-hike shock. Salesforce was down roughly thirty percent year-to-date. Cloudflare dropped twelve percent on April 9. ServiceNow lost seven percent. Snowflake dropped nine. Palantir lost seventeen percent over three sessions.
The thesis driving the selling was structural. Per-seat licensing breaks the moment AI agents do the work seats used to do. If a coworker now means a process running in a Claude desktop app, who pays $50 per month for software that an agent can drive autonomously? That is the question I want to take seriously.
The diagnosis holds water
The popular version of “SaaS is dead” is mostly Twitter/X. The crowd making the loudest version of it tends to be engineers who assume everyone wants to build their own software, which most people don’t, and the strawman gets knocked down easily. The serious version of the argument is harder.
Forrester’s late-2025 piece, “SaaS As We Know It Is Dead: How To Survive The SaaS-pocalypse,” frames it cleanly. The per-seat model was always a proxy for value, not the value itself. It worked because software was something a human sat in front of, and seats were how you priced a human’s time inside the tool. When the human is augmented by an agent that handles ten times the throughput, the proxy collapses. Customers consolidate seats. Renewals come back at half the size. New deals are smaller out of the gate. The Q4 2025 earnings cycle made this concrete across multiple SaaS names: customers were reducing seats rather than adding them, not because productivity tools failed but because they succeeded too well.
This is real. Concede the diagnosis. The IGV ETF doesn’t lie. Wall Street isn’t wrong that per-seat economics are under pressure when an agent can do what a seat used to do. The Forrester piece is right that for products which were essentially a database with a CRUD UI on top, the new question is “what makes this worth paying for when an LLM and a script could rebuild it in a week?” For some categories, the honest answer is “less than it used to be.” By March 2026, software’s forward price-to-earnings multiple had fallen from the 84 times peak of 2020-2022 to roughly 23 times, and SaaStr’s coverage noted that software was, for the first time in market memory, trading at a discount to the S&P 500. The repricing was not subtle.
But conceding the diagnosis is not the same as accepting the conclusion. SaaS isn’t dead. The capital it represents isn’t being destroyed. It’s migrating, and the migration has a direction.
Where the value migrates
Here is the cut I want to propose. There are two layers in the software economy that do not lose when the cost of building drops, because they get more valuable the more software gets built.
Call them platforms and utilities.
A platform is the surface you build on. Vercel hosts your app and runs your inference endpoints. Stripe and PayPal carry your payments. Cloudflare runs your traffic. AWS runs your compute. Increasingly, Salesforce is becoming this kind of layer for agentic workflows inside the enterprise. You don’t compete with platforms because you build with them.
A utility is the embedded service that earns its keep on every request. Sentry catches your errors. Datadog watches your metrics. Okta or WorkOS handles your enterprise auth. An LLM gateway like Helicone routes your inference. You don’t compete with utilities because you bolt them in and forget about them. They take care of routine tasks, accelerating the path from idea to working product even more.
Platforms and utilities share the same economic test. They both get more valuable when more software gets built, because they are priced on usage of the thing being built, not on access to the tool that built it. They earn rent on every unit of production. Call this the Production Tax.
The Production Tax is the mechanism. Every deployed app pays Vercel for hosting. Every uncaught exception pays Sentry for catching it. Every inference call pays Baseten or Fireworks or Modal. Every payment routed through an agent pays Stripe. When the cost of building drops a hundredfold and the volume of software shipped goes up a hundredfold, the Production Tax layer wins on raw volume even if its per-unit price drops. The companies in this layer are not exposed to per-seat compression because they were never priced on seats.
Platforms and utilities do not lose when the cost of building drops. They get more valuable the more software gets built, because they tax every unit of production.
This is what the SaaSpocalypse take misses. Wall Street saw seats compress and concluded that software-as-a-business was over. What’s actually happening is that seats are giving way to a different priced unit. The same dollar is moving from “$50 per seat per month” to “$0.0003 per token, billed across a hundred trillion tokens a day.”
If that is the cut, the question becomes obvious. Who actually wins?
The picks and the shovels
The capital is voting clearly on the answer.
Start with inference. Baseten closed a $300 million Series E in late January 2026 at a $5 billion valuation, more than doubling its $2.15 billion September 2025 round, with Nvidia putting in $150 million of it (Bloomberg, BusinessWire). The pricing posture is what matters. Baseten charges per-million-tokens for popular open-source models accessed via API and per-minute for dedicated GPU and CPU instances on customer-controlled hardware (the Mistral Large 3 deployments run on NVIDIA Blackwell B200s as a flagship example). Fireworks announced a $250 million Series C in October 2025 at a $4 billion valuation, by which point its annualized revenue was $280 million and it was processing more than ten trillion tokens per day for customers including Samsung, Uber, DoorDash, Notion, Shopify, and Upwork (BusinessWire, SiliconANGLE). Modal Labs entered talks in February 2026 to raise at a $2.5 billion valuation led by General Catalyst, more than doubling its prior round from less than five months earlier (TechCrunch, PYMNTS). Modal’s pricing is the cleanest version of Production Tax in this category: per-second billing for GPU, CPU, and memory, with H100 at roughly $3.95 an hour and A100 80GB at roughly $2.50 an hour, no minimum, no reservation, no DevOps overhead. Suno scales up to thousands of GPUs on Modal during holiday traffic spikes and back to zero when the surge ends, paying only for the seconds it actually consumed. Ramp uses Modal to fine-tune its own LLMs and run experiments in parallel. Substack and Lovable are also on the platform. None of these companies make a model. They make models cheaper to run. They tax every inference call. Together AI is in the same category. As of May 4, 2026, so is DeepInfra, which closed a $107 million Series B (Yahoo Finance, SiliconANGLE). NVIDIA participated alongside 500 Global, Georges Harik (one of Google’s earliest engineers), Felicis, Samsung Next, and Supermicro. DeepInfra runs its own GPU infrastructure, supports more than 190 open-source models, processes roughly five trillion tokens per week, and reports that revenue has tripled since the start of 2026. The shape of the market is now clear: every meaningful AI application is paying one of these vendors per inference, and the more applications get built, the more the meter spins.
Then observability for AI. Sentry built a Model Context Protocol server that scaled from 30 million to 60 million requests per month, used by more than 5,000 organizations, with the team treating the MCP server as a production service after early outages exposed how fragile the original ship was (ZenML LLMOps Database, Sentry blog). In April 2026, Sentry shipped agent skills that auto-detect and configure monitoring for LLM calls, agents, and AI SDKs (Sentry product pages). Braintrust took an evals-first approach to the same space, focusing on the experimental loop of dataset, scorer, comparison. Helicone bolted a routing and caching gateway in front of more than a hundred models. LangSmith remains the LangChain-native traceback layer. Different companies, same mechanic. Every LLM call generates an event. They get paid by the event.
In retrieval, the lesson is about pricing model rather than category demand. Turbopuffer has been chosen for production retrieval workloads at Cursor, Notion, and Linear, with serverless pricing and hybrid search that comes in under ten dollars a month at standard load (Greyhaven, daily.dev). The reason is straightforward. A serverless retrieval layer compounds with the entire developer base of Cursor flowing through its meter. An enterprise-contract retrieval layer caps out at the number of contracts the sales team can close. Same Production Tax category, different posture toward the meter. The retrieval layer itself isn’t going anywhere. Every agent that grounds a response in a corpus needs vector search, and someone is collecting rent for that lookup.
For agent rails, the substrate is no longer up for debate. On December 9, 2025, Anthropic donated the Model Context Protocol to the Linux Foundation’s new Agentic AI Foundation, co-founded by Anthropic, Block, and OpenAI, with support from Google, Microsoft, AWS, Cloudflare, and Bloomberg (Anthropic, Linux Foundation, TechCrunch). Goose by Block and AGENTS.md by OpenAI joined as founding projects. By the donation date, MCP had crossed 97 million monthly SDK downloads and 10,000 active servers, with first-class client support across ChatGPT, Claude, Cursor, Gemini, Microsoft Copilot, and Visual Studio Code. The protocol is governance-neutral now. The implementations get the rent.
The implementation that matters most for agentic browsing is Browserbase, which provides the headless-browser substrate that lets an agent click on a real web page. Browserbase raised a $40 million Series B in April 2025 at a $300 million valuation, four times its Series A from seven months earlier (UpStarts Media, PitchBook). It now powers parts of Anthropic’s Claude Computer Use, OpenAI’s agent mode, and Google’s Project Mariner. The pricing is, fittingly, fully usage-based: a base tier at $20 a month for 100 browser hours, mid-tiers at $39 and $99 for higher concurrency, and enterprise plans for hundreds of concurrent browsers, with overage at roughly ten cents per browser hour. Browserbase tracks hundreds of millions of agent browser session minutes per month and bills them through Stripe’s usage-based billing. The picks-and-shovels billing the picks-and-shovels. Every agent that opens a browser pays a Browserbase-style company on the way in, every minute it stays open.
Identity for agents is being poured right now. WorkOS shipped Fine-Grained Authorization for AI agents (covered in Daring Fireball, April 19, 2026), the practical answer to the problem that agents need scoped credentials separate from the user session that spawned them. NIST published an AI Agent Identity concept paper in February 2026, with the public comment period closing April 2. The consensus, per WorkOS’s own framing, is that agents are a distinct identity class requiring purpose-built infrastructure: each agent gets its own client ID and secret, each action is authenticated and scoped, and audit trails are preserved at the agent level rather than rolled up into a human session. Auth0 is working on the same problem from a different angle. The bet is straightforward. Every agent that authenticates pays.
Payments is the cleanest example, because Stripe is openly building the protocols and naming them. On September 29, 2025, Stripe and OpenAI launched the Agentic Commerce Protocol, an open standard for programmatic commerce flows between AI agents and businesses, alongside Instant Checkout in ChatGPT (Stripe newsroom, OpenAI blog). Etsy was the launch partner; Shopify merchants like Glossier, Vuori, Spanx, and SKIMS were named as the next wave. Stripe followed with the Agentic Commerce Suite, onboarding URBN (Anthropologie, Free People, Urban Outfitters), Coach, Kate Spade, Revolve, Halara, Ashley Furniture, Nectar, and Abt Electronics. The Suite handles Shared Payment Tokens, a new payment primitive that lets an agent initiate a payment scoped to a specific seller, time window, and dollar amount, without exposing the buyer’s saved card. In a separate move, Stripe co-authored the Machine Payments Protocol with Tempo for agent-to-agent settlement, including microtransactions and recurring payments. Stripe’s posture here is the textbook platform play. The product is the protocol, and the price is a percentage of everything that runs on it.
Wall Street saw seats compress and concluded that software-as-a-business was over. What’s actually happening is that seats are giving way to a different priced unit, billed across a hundred trillion tokens a day.
This is the picks-and-shovels lineup. Inference, observability, retrieval, agent rails, identity, payments. Each one taxes a different unit of production. None of them is exposed to seat compression. All of them get more valuable the more software gets built. The capital is funding the layer the SaaSpocalypse selloff left behind.
SaaS rejoins the stack
Here is the part the SaaSpocalypse story didn’t tell.
On February 24, 2026, Anthropic hosted what it called an enterprise agents event and announced ten strategic partnerships at once. Salesforce. Slack. Intuit. DocuSign. LegalZoom. FactSet. Gmail. Thomson Reuters. The pitch on every one was the same: Claude as the model, the partner’s product as the surface where work happens. Software stocks rebounded on the news inside the trading day. CNBC’s coverage put Salesforce up about 5%, DocuSign up 4.3%, and Intuit up 2.3% on the announcement; Thomson Reuters surged 13.8% after disclosing that its CoCounsel legal AI had crossed one million professional users running on Claude. A Wedbush Securities research note the same day called the AI-displacement thesis “overblown,” noting that workflow infrastructure is still deeply embedded in software customers don’t actually want to rebuild.
The Salesforce-Anthropic partnership is the case study, and it is worth sitting with for a moment because it shows what a successful repositioning actually looks like in this market.
On October 14, 2025, the two companies announced an expansion that made Anthropic the first LLM provider fully contained within Salesforce’s trust boundary (Salesforce press release, Anthropic news). All Claude traffic runs inside Salesforce’s virtual private cloud, with the Anthropic models hosted via Amazon Bedrock under Salesforce-managed VPC controls. Claude became a foundational model for Agentforce 360. The deal targeted regulated industries first: financial services, healthcare, cybersecurity, life sciences. Salesforce shipped industry-specific Claude variants paired with Agentforce variants. The financial services bundle pairs “Claude for Financial Services” with Agentforce Financial Services, giving the agents the domain-specific reasoning required for instrument analysis, insurance-claim review, and regulatory-framework interpretation. Early adopters named in the announcement included CrowdStrike (cybersecurity workflows) and RBC Wealth Management (advisor-facing wealth management AI). Salesforce later disclosed it had begun deploying Claude Code internally across its engineering organization, eating its own cooking on the developer side of the agent transition.
The Slack piece is what cements the moat. By February 2026, Slack had shipped a Claude integration that respected Salesforce’s permissioning model. A user with access to a deal record in Salesforce could ask Claude about it inside a Slack DM and the access checks held. The agent did not need to be told who had permission to see what. The platform already knew, because the platform had spent fifteen years building the identity graph and the audit trail that the agent could ride.
By the time Salesforce’s TDX 2026 conference rolled around in April, the repositioning had hardened into a platform play. Salesforce announced what it called the most significant expansion of the Salesforce Platform for ISVs since the launch of Force.com eighteen years earlier. Agentforce 360 went generally available in December 2025 alongside Data 360, Agentforce Financial Services, Agentforce Automotive, Agentforce Foundations, and Salesforce Shield. ISVs could now embed the full Agentforce 360 stack as the foundation for their own agentic applications and commercially distribute them through a new marketplace called AgentExchange, which launched at TDX with 10,000 Salesforce apps, 2,600 Slack apps, and more than 1,000 Agentforce agents and MCP servers, alongside a $50 million Builders Fund for partners and ISVs (Salesforce press, SalesforceDevops.net coverage). Salesforce also previewed Headless 360 and Agentforce Vibes 2.0 at the same conference. The shape of the move is unmistakable. Salesforce is repositioning into the marketplace and runtime for the agent transition, the way Force.com became the marketplace and runtime for SaaS in 2008.
The pattern across the ten Anthropic partnerships and the broader sector is consistent. The SaaS companies that survive the agent transition are not the ones denying the agents. They are the ones partnering with the model providers and becoming the platforms where agents do the work. Salesforce is being repositioned around Agentforce 360 as the agent runtime for sales, service, and back-office workflows. ServiceNow is being repositioned around its own agent layer for IT operations. Microsoft has been doing this for two years. Workday is moving the same direction. Each of these companies has a deep moat made of integrations, regulatory coverage, customer-of-record relationships, and identity graphs that took fifteen years to build. The agents that ride those moats are the ones that get the work, because the agents that don’t have to ask for permission, audit logs, and SSO from scratch.
So the diagnosis holds for one set of SaaS companies and folds for another. The pure-CRUD layer where the seat was the value really is exposed. The platform layer where the integrations and the identity graph were the value is fine, because the integrations and identity graph turn into agent rails, and the seats turn into per-action billing through a marketplace. The SaaSpocalypse priced the whole sector as if every company in the bucket was the first kind. Inside ten weeks, the market had begun sorting one from the other.
By mid-April, the sorting was visible in the prices. On April 13, 2026, the IGV jumped 5.4 percent in a single day, its biggest one-day gain in roughly a year, and climbed more than six percent over the followi
ng forty-eight hours (Benzinga, FinancialContent). Oracle drove almost a fifth of that move on its own as it pushed out Fusion Agentic Apps. ServiceNow climbed 7.4 percent in the same window. ServiceNow’s investor day also disclosed that “Now Assist” ACV had grown from roughly $600 million at the end of 2025 to about $750 million by Q1 2026, more than doubling year over year, with a $1.5 billion AI ACV target for 2026 and 30 percent of total ACV expected from AI by 2030 (Quartr summary). The detail that mattered most was a pricing change. ServiceNow introduced a new tier called Agentic ACV, where customers pay for tasks completed by AI agents rather than for human login credentials. The per-seat-to-Production Tax migration was no longer a thesis. It was a line item on a contract.
Salesforce’s Q4 FY26 results, released February 25, 2026, told the same story from a different angle. Agentforce ARR had reached $800 million, up 169 percent year over year, on 29,000 deals up 50 percent quarter over quarter. The company had consumed nearly twenty trillion tokens to date and converted them into more than 2.4 billion agentic work units. More than 60 percent of Agentforce and Data 360 Q4 bookings came from existing customer expansion. Q4 revenue was $11.2 billion, up 12 percent year over year. Marc Benioff used the earnings call to mock the SaaSpocalypse narrative directly (Salesforce Ben coverage, BigGo Finance). The board authorized a $50 billion buyback the same day. By mid-May 2026, the IGV was up close to 14 percent over the trailing month, and JPMorgan was telling clients that specific software names were breaking out of the selloff (CNBC). Software’s price-to-earnings discount to the S&P 500 had narrowed without fully closing.
The arc finished where the framework said it would. The SaaS companies that survived the SaaSpocalypse did so by becoming the platforms agents run on and by repricing their seats into tasks. The ones whose integrations were deep enough to carry that repricing compounded. The ones that did not reprice continued to bleed, regardless of how many partnerships they announced.
What 1995 actually rewarded
The closest historical parallel I keep coming back to is 1995.
In 1995, anyone with a modem could put up a website. Tens of thousands of them did. Most of them did not matter, in the sense that they did not produce durable economic value, did not change consumer behavior, and did not survive five years. The ones we still talk about thirty years later are a small fraction of what was built. That fraction shares a property worth naming.
The durable winners of 1995 to 2000 did not win by having a website. They won by picking a layer that compounded with the usage of websites, and three companies illustrate the shape clearly.
The first is Cisco Systems. By the time the web was a household word, every internet company had to buy Cisco’s switches and routers to put their site online. The unit Cisco was metering was the equipment any new dot-com had to install to handle traffic, and the meter spun every time another company decided to launch online. On March 27, 2000, Cisco passed Microsoft and briefly became the most valuable company in the world, with a market capitalization of roughly $569 billion at a valuation that priced the stock at 220 times earnings (CNBC, Wikipedia, Charlie Bilello citing YCharts). Cisco rode the build-out itself. Whichever websites won, Cisco got paid. The cautionary half of the Cisco story matters too. The same stock lost roughly 85 percent of its value in the year that followed, because the price had run far ahead of the meter. Cisco’s run-up shows that even Production Tax companies can be overpriced. The protection is against per-seat compression, not against valuation gravity.
The second is Akamai Technologies, which was incorporated in August 1998 by MIT professor Tom Leighton and graduate student Danny Lewin. Akamai’s product was content delivery: a globally distributed network of servers that cached your page closer to your user, and got paid every time bytes moved. The company shipped its first live traffic in February 1999, caught the ESPN March Madness tournament and the Star Wars Episode I trailer for Entertainment Tonight in March 1999 when both broke historical traffic records, and IPO’d on October 29, 1999 in what was, by tech-IPO size at the time, the fourth largest in history (Akamai company history, Wikipedia). Akamai is the cleanest 1995-era analogue to Browserbase or to a modern inference platform. The product is invisible to the end user. The price is stamped on every request. The meter spins as the web grows.
The third is Sun Microsystems, which is the cautionary tale. Sun marketed itself as “the dot in dot-com.” Its servers ran underneath eBay, Yahoo, and the early Amazon. The Sun Enterprise 10000 was, in trade-press language of the time, “the mainframe of the internet” (Network World retrospective, Wikipedia). Sun was technically a Production Tax company. It got paid as web companies grew their hardware footprint. At its dot-com peak it carried a market capitalization above $200 billion and posted $18.3 billion in revenue in fiscal 2001. By 2009, Oracle bought what was left of it for $7.4 billion. The reason is the most important lesson the analogy carries. Sun bet on the wrong substrate. It tied its servers to the proprietary SPARC architecture and Solaris while the rest of the industry moved to commodity x86 hardware running Linux. Customers compounded on the open platform. Sun’s pricing model and product roadmap fought that compounding rather than absorbing it. Production Tax compounds when you are on the open substrate. It does not compound when you are on the substrate the open one is replacing.
The mapping forward is straightforward. Cisco is what the inference platforms (Baseten, Fireworks, Modal, Together) are trying to become: the equipment vendor every AI company has to use as the meter spins. Akamai is what Browserbase, MCP-server implementations, and the agent-rails layer are trying to become: the invisible substrate that gets paid every time an agent does anything. Sun is the warning. Even if you are correctly positioned at a Production Tax layer, you can still lose by betting on a closed protocol, a proprietary primitive, or a pricing model that fights usage instead of compounding with it. The reason MCP being donated to the Linux Foundation matters so much is that it took the substrate question off the table, the way Linux on commodity x86 took the substrate question off the table for the post-Sun internet.
This is the analogy that does the work for me. In 2026, anyone with a Claude Max subscription can ship software. AI generates the code. Agents handle the dispatch. The interfaces are voice, chat, and a thumbs-up. The cost of producing a working application has collapsed by something close to two orders of magnitude in eighteen months. Most of what gets built will not matter, the same way most of 1995 didn’t matter. That isn’t tragic. It’s the shape of the curve when production gets cheap.
The opportunity is not in trying to predict which application wins. The opportunity is in picking the layer that compounds with the volume of applications, and shipping the version that gets there first. Every inference call needs an inference platform. Every agent needs a browser to click in, a protocol to coordinate over, an identity to act with, a payment rail to settle on, and an observability layer to be debugged through when it breaks. The companies that will be paid every time an agent does anything are being capitalized right now. Some of them are SaaS companies repositioning fast enough. Some of them are AI-native picks-and-shovels that didn’t exist in 2023. A few of them, history suggests, will get the substrate wrong and end up as the next Sun. The rest will get the substrate right, ride the build-out, and end up as the next Cisco or Akamai. The pricing model and the protocol bet are doing more work in that outcome than the marketing would suggest.
For the rest of us, the framing is simpler. The cost of building has collapsed. The question is no longer whether you have permission to ship something. The question is what you ship, and whether you build it on top of a layer that compounds, or in a category where the seat was the value. Both bets are available. Only one of them was on sale during the SaaSpocalypse selloff.
Q: In a world where anyone can build anything, what should you build?
A: It’s 1995. Build your Amazon.
For more AI and Product Strategy insights: Portfolio | LinkedIn