When AI Eats Software, Who Gets Eaten First?

Three weeks ago, someone at Anthropic pushed eleven open-source plugins to GitHub. No launch event, no press conference, no marketing blitz. Just some Markdown files and JSON schemas describing how an AI agent could handle contract review, compliance checks, legal intake, and sales preparation.

Within hours, $285 billion in market capitalisation had been wiped from software, legal-tech, and financial services stocks across three continents. Jefferies equity trader Jeffrey Favuzza christened it the “SaaSpocalypse,” describing the selling as “very much ‘get me out’ style, people are just selling everything and don’t care about the price.” By mid-February, the damage had expanded to roughly $1 trillion.

This wasn’t a financial crisis. It wasn’t a geopolitical shock. It was the market catching up to something practitioners have been watching unfold for months. AI agents don’t just use software. They replace it.

The SaaSpocalypse

The trigger on February 3rd was Anthropic’s Claude Cowork, a set of plugins that demonstrated AI could automate real enterprise workflows, the kind of work that underpins entire categories of software subscription revenue. The market’s response was immediate and brutal.

Thomson Reuters plunged between 16% and 20%. LegalZoom fell 19.7%. FactSet dropped 10.5%. RELX lost 14% on the NYSE and 17% in London. Goldman Sachs’ software basket fell roughly 6% in a single session. The contagion spread globally. India’s Nifty IT index dropped 6%, with Infosys down 8% and TCS down 6%.

The context made it worse. The S&P North American Software Index had already declined 15% in January, its worst monthly performance since October 2008. Bloomberg Intelligence analyst Anurag Rana described software sector sentiment as “radioactive.” Short sellers earned $24 billion from software stocks in the first weeks of 2026 alone.

Predictably, the commentary was full of voices declaring the “AI bubble” had finally burst. They could not have been more wrong. A bubble bursts when the technology fails to deliver. This was the opposite, reality arriving faster than anyone expected. The sell-off wasn’t driven by AI disappointing investors. It was driven by AI terrifying them, because it demonstrated, concretely, that it could do the work that justifies billions in software subscription revenue. That’s not a bubble bursting. That’s disruption happening in real time.

To put that in terms any business owner understands, the market was valuing the average software company at nine times its annual revenue six months ago. Today that number is six. Adobe, which averaged a valuation of roughly 30 times its earnings over the past five years, is now trading at 12 times. ServiceNow has gone from 67 times to 28. These companies haven’t suddenly become bad at what they do. The market is simply recalculating how much their work is worth when AI can do some of it for a fraction of the cost.

The anecdotes are already concrete. A customer of the AI development platform Base44 recently terminated a $350,000-per-year Salesforce contract and replaced it with a custom AI-powered solution. Jason Lemkin, the SaaStr founder often called the godfather of SaaS, framed it cleanly. “The question isn’t whether enterprises will spend on software. It’s whether they’re going to spend on your software, or redirect that budget to AI.”

Then came the second wave. On February 20th, Anthropic launched Claude Code Security, an autonomous agent capable of scanning enterprise codebases, identifying vulnerabilities, and applying patches without human intervention. CrowdStrike dropped roughly 10% in a day and now sits about 29% below its all-time high. Palo Alto Networks has fallen a similar distance after lowering its full-year guidance.

The thesis behind the cybersecurity sell-off is the same one driving the broader repricing. Claude Code Security doesn’t eliminate the need for security, it finds vulnerabilities faster and more reliably than existing tools, and patches them autonomously. When an AI agent can scan an enterprise codebase and fix security flaws before they ship, the value proposition of traditional security suites starts to look very different. Not because the work isn’t needed, but because the cost of doing it just dropped dramatically. Another layer compressed.

Bank of America pushed back on the sell-off, calling it “internally inconsistent” and drawing parallels to the overblown market reaction to DeepSeek in early 2025. They may be right that some of the selling was indiscriminate. But the direction of travel is clear. The market isn’t confused about whether AI works. It’s confused about how fast it works. That’s a very different kind of uncertainty, and not one the bubble hawks should take much comfort from.

The Spotify Signal

While the market was processing the SaaSpocalypse, Spotify quietly demonstrated what comes next.

On its fourth-quarter earnings call in early February, co-CEO Gustav Söderström made a statement that ricocheted across the internet. “When I speak to my most senior engineers, the best developers we had, they actually say that they have not written a single line of code since December. They actually only generate code and supervise it.”

Spotify credits an internal AI platform called Honk, built on Claude Code, with the transformation. Engineers can fix bugs or add features from Slack on their phones during their morning commute, receive a new version of the app pushed back via Slack, and merge to production before arriving at the office. The company shipped more than 50 new features throughout 2025 and attributes much of that velocity to the system.

The reaction was predictable. 14,275 upvotes on Reddit’s r/technology within 48 hours, 2,377 comments, overwhelmingly sceptical. And there is reason for nuance, if not outright scepticism. An Anthropic study published on January 29th found that developers using AI assistance scored 17% lower on coding comprehension tests, despite completing tasks slightly faster. The honest reading of the Spotify story is probably this. AI coding tools have reached a point where senior engineers, working on the right tasks, with the right infrastructure, can genuinely delegate the mechanical work of programming and be significantly more productive for it. The key word is “senior.” The productivity gains are real, but they accrue disproportionately to engineers who already know what good looks like and can judge what the agents produce.

I can speak to this directly. I haven’t written a line of code since November, despite shipping two large-scale projects to production over that period. The work didn’t disappear. It changed. I spend my time on architecture, on defining what needs to be built and why, on reviewing what the agents produce, and on the decisions that require judgment rather than syntax. The mechanical act of writing code has become the least interesting part of the process, something I wrote about in January from direct experience, not speculation.

If AI productivity gains accrue disproportionately to senior engineers, the obvious question is, what happens to the junior ones? IBM, surprisingly, offered the most interesting answer of the month. In the same week as the Spotify earnings call, IBM announced it would triple entry-level hiring in the US in 2026. IBM’s Chief Human Resources Officer, Nickle LaMoreaux, was blunt. “And yes, it’s for all these jobs that we’re being told AI can do.”

The catch is that the jobs have been completely rewritten. Junior software developers who previously spent 34 hours a week coding now spend their time on customer engagement, product development, and supervising AI output. In HR, entry-level staff intervene when chatbots produce errors, correcting outputs and working with managers. LaMoreaux’s argument is that companies which slash entry-level hiring to save money will face a shortage of mid-level managers within a few years, forcing them to poach talent from competitors at a 30% premium.

“The companies three to five years from now that are going to be the most successful,” she said, “are those companies that doubled down on entry-level hiring in this environment.”

This is the nuance that gets lost in the noise. The question isn’t whether AI replaces work. It does. The question is what the new work looks like, and whether your organisation is designing for it.

The Pipeline Is Shifting

The signals aren’t confined to the stock market and the boardroom. They’re showing up in university enrolment, which tends to be one of the most honest leading indicators we have, because 18-year-olds vote with their futures.

In the UK, BCS analysis of UCAS data shows acceptances to computing degrees fell 9% among 18-year-olds in 2025, despite overall acceptances for the same age group rising 3.5% across all subjects. Scotland saw a 10% drop, England 9%, Wales 21%. Computing slipped from seventh to eighth most popular subject for young people. But buried in those numbers is a striking counterpoint. Acceptances to dedicated AI degrees rose 42%. Applications to AI courses rose 15% among women and 12% among men, narrowing the gender gap to 3.7 to 1, better than the 4 to 1 ratio across computing as a whole. Students aren’t abandoning technology. They’re repositioning within it.

Edinburgh, home to the largest informatics department in Europe and ranked 4th in the UK for computer science, was already ahead of this curve, offering dedicated AI undergraduate and postgraduate degrees while most UK institutions are still working out where AI fits in their curriculum. It’s the kind of early positioning that is likely to define which universities thrive through this transition and which are left scrambling.

The pattern is even more pronounced in the US, where the shift has had longer to develop. Computer science enrolment across the University of California system has fallen 6% this year after a 3% decline in 2024, the first sustained drop since the dot-com bust. Nationally, 62% of computing programs reported undergraduate enrolment declines this autumn, according to the Computing Research Association. At the graduate level, CS enrolment dropped 15%.

But again, the picture isn’t one of retreat from technology. It’s one of migration. UC San Diego, the only UC campus that launched a dedicated AI major, is the only one where enrolment increased, with one in five CS applications now directed at the AI programme. MIT’s AI and Decision-Making major is now the second-largest on campus. The University of South Florida enrolled more than 3,000 students in a new AI and cybersecurity college in its first semester.

Perhaps most striking, data from SignalFire shows that graduates from MIT, Stanford, Carnegie Mellon, and Berkeley employed as engineers at major tech companies dropped from 25% in 2022 to roughly 11-12% today, a greater than 50% decline in two years. The elite CS-to-big-tech pipeline that defined a generation of careers is narrowing fast.

It’s not just the talent pipeline shifting. LinkedIn recently reported that traffic from general industry searches, the kind where someone Googles a topic and discovers your content, declined by up to 60% across certain categories. Their pages still ranked just as well. But fewer people were clicking through, because AI was answering the question before they needed to. LinkedIn’s response was to abandon traditional SEO metrics entirely, replacing them with a new framework. “Be seen, be mentioned, be considered, be chosen.”

This matters because it’s the same pattern at every layer. From how people find software, to how people build software, to what software gets built, to who builds it, the entire stack is being restructured.

What This Means For Your Business

These numbers are worth sitting with. Nvidia reports its fourth-quarter earnings today, with Wall Street expecting roughly $65.7 billion in revenue, up 67% year-on-year. In the same month the market wiped $285B off software stocks, the companies building AI were writing even bigger cheques. The major hyperscalers have announced combined capital expenditure plans north of $500 billion for 2026, roughly double what they spent in 2025. That tells you everything you need to know about the direction of travel. This isn’t a bubble bursting. The people writing the biggest cheques are accelerating, not retreating. The money isn’t disappearing from the technology sector. It’s migrating, from the software layer to the infrastructure and model layer, and it’s doing so faster than anyone predicted.

The pattern across all of these stories is the same. AI is collapsing layers. The layer between a user and an outcome. The layer between a requirement and working code. The layer between a question and an answer. The layer between a security threat and a remediation. Every business that sits in one of those middle layers, whether you sell per-seat SaaS, per-hour consulting, or per-click advertising, needs to ask which side of this you’re on.

From our own experience building agentic AI systems over the past year, the businesses moving fastest aren’t waiting for the dust to settle. They’re not running pilot projects or forming AI committees. They’re identifying the specific workflows where an AI agent can replace a manual process today, and they’re building. Not because they’ve solved every problem, the tooling is still immature, the reliability questions are real, the security implications are significant, but because the cost of waiting is becoming clearer by the week.

The $285 billion that evaporated from software stocks on February 3rd didn’t disappear. It moved. So did the trillion that followed. The only question that matters for your business right now is whether that money, and the value it represents, is moving toward you or away from you.