SaaS in, SaaS out: what’s driving the “SaaSpocalypse”

## Summary A founder’s text to an investor — saying he was replacing his entire customer support team with Claude Code, an AI tool that can write and deploy software — is framed by TechCrunch as a signal of a broader shift: tools from incumbents like Salesforce may no longer be the automatic default. Investors quoted in the piece argue that coding agents have lowered the barriers to creating software enough to tilt the “build versus buy” decision toward building in many more situations. The article’s point is not only that new entrants can compete with SaaS vendors. It’s that the SaaS business model itself gets shakier when work is performed by a small number of AI agents rather than by large numbers of employees logging into apps. Much SaaS is priced per seat, but if fewer humans need to access a system because an AI agent can pull data and execute tasks on their behalf, the seat-based model starts to break down. TechCrunch ties this to “SaaSpocalypse” fears in public markets — including the idea of FOBO investing (fear of becoming obsolete) — while also noting that venture investors it spoke to see this less as the death of SaaS and more as a transition. In their view, enterprises will still need durable software for compliance, audits, workflows, and reliability, but the rules for product differentiation and monetization may change. ## In practice The story lays out three overlapping pressures. First, coding agents make software cheaper and faster to build. That changes negotiations and renewals: if a buyer can credibly build an internal alternative, even if they don’t ultimately do it, that option can push vendors’ pricing down and reduce the certainty of what they can secure on renewal. Second, AI agents threaten seat-based licensing. TechCrunch describes a future where employees don’t directly operate the SaaS tool as often; instead, they ask their AI of choice to retrieve information and take actions. If one or a handful of agents can handle what previously required dozens of users, “per-seat” becomes harder to justify. Third, the pace of AI product development can replicate not only core SaaS functions but also the add-on products vendors sell to expand revenue within existing customers. The piece points to tools like Claude Code and OpenAI’s Codex as examples of capabilities that can be applied across multiple workflows, potentially eating into the upsell layer that helped fuel SaaS growth. The article references an early sign of this dynamic: in late 2024, Klarna said it had dropped Salesforce’s flagship CRM product in favor of a homegrown AI system. TechCrunch suggests the possibility that more companies can follow that path is spooking public markets, where stocks of SaaS giants like Salesforce and Workday have been sliding. It also mentions a sell-off in early February that wiped nearly $1 trillion in market value from software and services stocks, followed by another billion later in the month. ## Context TechCrunch also frames the pullback as partially a re-rating of a sector that investors say had been overvalued. The story notes that many SaaS companies did much of their growth in the zero-interest-rate era, and that the end of that environment changes the cost of doing business and the cost of capital. Public-market investors often value SaaS by projecting future revenue. The problem, as described in the article, is uncertainty about whether people will be using SaaS products to the same extent in one year or five years — which is why every launch of a more capable AI tool can send a tremor through SaaS stocks. One investor quoted says this may be the first time the “terminal value” of software is being fundamentally questioned, reshaping how SaaS companies are underwritten. The piece argues that simply adding AI features to existing SaaS may not be enough, as AI-native startups are emerging quickly and redefining what it means to be a software company. Yet it also notes the market doesn’t have enough time or evidence to prove which new business model will win. Some AI companies are charging on consumption, measured in tokens (with definitions varying by provider). Others are experimenting with outcome-based pricing, charging based on how well the AI performs. TechCrunch points to Sierra — the AI startup led by former Salesforce CEO Bret Taylor — as an example of outcome-based pricing and a “quasi-Salesforce competitor” focused on customer service agents, and says the company reached $100 million in annual recurring revenue in November, in under two years. Finally, the story highlights IPO implications. A Crunchbase report cited by TechCrunch says that while the IPO market may be thawing for some sectors, there haven’t been — and aren’t expected to be — any venture-backed SaaS filings on the horizon. Investors quoted argue that late-stage private SaaS companies face pressure amid a persnickety IPO window and volatile sentiment, and may stay private longer. Meanwhile, the article notes “scuttlebutt” that OpenAI and Anthropic are contemplating IPOs, potentially later this year. Even so, the investors TechCrunch spoke with emphasize that enterprises will continue to need durable, compliant software — and that long-term value is built on fundamentals like retention, margins, real budgets, and defensibility, not hype. ## Why it matters - Seat-based SaaS pricing weakens if AI agents reduce how many humans need to log in to do the work. - The credible ability to build alternatives (or threaten to) gives customers more leverage in renewals and pushes contract pricing down. - Public-market volatility and the apparent pause in venture-backed SaaS IPO filings show the shift is already affecting financing and exits. - The likely endpoint is a hybrid of old and new, but the winning monetization model (consumption, outcome-based, or something else) is still uncertain.