Software and the AI Apocalypse
From Citrini Research’s memo, “The 2028 Global Intelligence Crisis”:
The unemployment rate printed 10.2% this morning, a 0.3% upside surprise. The market sold off 2% on the number, bringing the cumulative drawdown in the S&P to 38% from its October 2026 highs.
That’s the dystopian future Citrini portrays: a near-term “AI takeover” severe enough to push unemployment into double digits and drag equities into a deep bear market. To be fair, Citrini presents this as a hypothetical scenario rather than a point forecast. But even as a thought experiment, the future presented in Citrini’s memo feels like science fiction. And since this chain of causality starts with AI’s impact on enterprise software, the focus of Points and Platforms, that’s where I think I should start as well.
Software Survival
Citrini’s hypothetical began with:
A competent developer working with Claude Code or Codex could now replicate the core functionality of a mid-market SaaS product in weeks. Not perfectly or with every edge case handled, but well enough that the CIO reviewing a $500k annual renewal started asking the question “what if we just built this ourselves?”
Fiscal years mostly line up with calendar years, so 2026 enterprise spend had been set in Q4 2025, when “agentic AI” was still a buzzword. The mid-year review was the first time procurement teams were making decisions with visibility into what these systems could actually do. Some watched their own internal teams spin up prototypes replicating six-figure SaaS contracts in weeks.
Admittedly, it isn’t pure science fiction. A strong developer armed with tools like Claude Code, Codex, or Cursor can already reproduce plenty of straightforward software solutions, especially point solutions with limited edge cases and low regulatory surface area.
But that’s also where the premise starts to break. Software companies don’t just sell code. They sell an operating bundle around the code: ongoing updates, maintenance, security patches, compliance posture, uptime guarantees, debugging when the weird edge cases inevitably show up, integration support, admin tooling, audit trails, data migrations, roadmaps, and all the unglamorous work of making the product behave reliably inside an enterprise stack.
Enterprise customers can absolutely “spin up prototypes replicating six-figure SaaS contracts.” But they will be just that: prototypes. Getting from a prototype to a real product that the business can depend on requires a dedicated team, months (and potentially years) of iteration, and a never-ending stream of maintenance. That journey doesn’t end when Claude generates the first version of an app; that’s when it starts. And for most companies, none of this is what they’re actually in business to do. To quote the Stratechery article, “Microsoft and Software Survival”:
Companies — particularly American ones — are very good at focusing on their core competency, and for most companies in the world, that isn’t software.
To be fair, Citrini’s thought experiment does nod at this dynamic, but it tries to skirt around it by arguing that enterprises won’t build replacements internally. Instead, they’ll outsource the building to foundation model vendors such as OpenAI and Anthropic via forward-deployed teams that use AI tooling to replicate incumbent SaaS vendors. The article offers this story:
The procurement manager told [the software salesperson] he’d been in conversations with OpenAI about having their “forward deployed engineers” use AI tools to replace the vendor entirely. They renewed at a 30% discount. That was a good outcome, he said. The “long-tail of SaaS”, like Monday.com, Zapier, and Asana, had it much worse.
But zoom out for a second: what’s the practical difference between paying OpenAI to “replace the vendor” and paying Monday.com, Zapier, or Asana in the first place? It’s not as if a forward-deployed team shows up, generates a bespoke clone in a month, and walks away. Turning a prototype into something production-grade is still an ongoing commitment. So the real question isn’t whether enterprises will pay for software. It’s who they’ll pay, and what they get in return.
You could argue the foundation model vendor wins because customers prefer consolidating tools under a single supplier. But consolidation pressure isn’t new. “Vendor rationalization” has been a procurement mantra for a decade, long before AI became the headline. But ultimately, the decisive variable is product quality and ROI. If a tool reliably drives outcomes, it sticks. That’s why a company like Monday.com can still post ~110% net revenue retention even as it sits squarely in the “long tail of SaaS.”
Software Pricing
But there is one real, practical difference for customers once foundation model vendors become credible alternatives, namely the “30% discount.” Basic supply-and-demand says that if foundation model vendors (or AI-enabled services teams) become credible substitutes for certain categories of SaaS, incumbents should face more competitive pressure. That competitive pressure might then force software vendors to give back some pricing power. Citrini does make this point more explicitly:
SaaS wasn’t “dead”. There was still a cost-benefit-analysis to running and supporting in-house builds. But in-house was an option, and that factored into pricing negotiations. Perhaps more importantly, the competitive landscape had changed. AI had made it easier to develop and ship new features, so differentiation collapsed. Incumbents were in a race to the bottom on pricing - a knife-fight with both each other and with the new crop of upstart challengers that popped up. Emboldened by the leap in agentic coding capabilities and with no legacy cost structure to protect, these aggressively took share.
It certainly is possible that building in-house (or, more realistically, outsourcing to forward-deployed teams at foundation model vendors) becomes a credible alternative at the margin, and that might chip away at SaaS pricing power. However, it doesn’t follow that AI will make “differentiation collapse.”
For the system-of-record vendors Citrini argues are also vulnerable, the moat isn’t a prettier UI or a slightly better workflow. It’s the fact that they sit on proprietary data that AI-native challengers simply can’t access at scale. To quote this publication’s eponymous article, “Points and Platforms”:
Because platforms like ServiceNow and Salesforce have a wholistic view on core workflows, over time, they have accumulated a depth of workflow data that AI-native startups and standalone point solutions can’t match. With that data, platforms are often better positioned than AI-native startups and point solutions to train and refine agents that behave reliably inside real enterprise processes.
Without that data, challengers have to compensate with heavier R&D, deeper domain modeling, more integration work, and more time spent closing the gap to production-grade reliability. And that investment has consequences. If an AI-native company is spending meaningfully more to build something truly differentiated, it can’t undercut everyone on price long-term. The economics don’t support a permanent “race to the bottom.” Therefore, price pressure may rise at the edges, but the central contest won’t be a knife-fight over who can charge the least; it will be a knife-fight over who can deliver the better product with the most defensible outcomes.
Software and White-Collar Job Losses
According to Citrini, the downstream consequence of AI-driven disruption in software is job loss:
The interconnected nature of these systems weren’t fully appreciated until this print, either. ServiceNow sold seats. When Fortune 500 clients cut 15% of their workforce, they cancelled 15% of their licenses. The same AI-driven headcount reductions that were boosting margins at their customers were mechanically destroying their own revenue base.
The company that sold workflow automation was being disrupted by better workflow automation, and its response was to cut headcount and use the savings to fund the very technology disrupting it.
There’s no denying that AI is already causing job displacement, and will most likely continue to. Forecasts suggest roughly 6–7% of Americans will face some form of displacement over time. In the memo’s logic, enterprises adopt AI, which reduces the need for human seats, compressing seat-based SaaS revenue. That revenue pressure then forces software vendors to cut costs, primarily by reducing headcount. And those cuts are made possible, ironically, by the vendors themselves shipping AI tools that let them maintain output with fewer people. Once those tools exist, enterprises can also use them to cut headcount, which further reduces seats and compresses revenue, continuing the cycle. As Citrini themselves put it:
AI-threatened companies did the only thing they could. Cut headcount, redeploy the savings into AI tools, use those tools to maintain output with lower costs.
Each company’s individual response was rational. The collective result was catastrophic. Every dollar saved on headcount flowed into AI capability that made the next round of job cuts possible.
However, this mechanism is largely unconvincing. If anything, widespread agent adoption should increase software activity. Agents don’t reduce the number of workflows flowing through ITSM, CRM, HCM, and other software systems; they multiply them. An agent that can execute tasks at machine speed still needs those tasks to be orchestrated, governed, audited, and measured inside platforms like ServiceNow, Zendesk, Salesforce, and their peers.
What likely changes is not demand for software, but pricing. Pure seat-based pricing may fade, but that doesn’t imply shrinking revenue. Vendors can (and increasingly will) monetize usage, outcomes, or agent “seats,” which might be more valuable, and thus expensive, than a human seat because they generate orders of magnitude more throughput.
Therefore, while the job impact is real, they are a function of productivity gains creating surplus capacity in certain roles, rather than collapsing software budgets. That has two important implications:
AI adoption isn’t “funded” by a shrinking revenue base in software; it can be funded by rising throughput and value creation.
The software economy retains the capacity to reallocate labor. Therefore, if AI is expanding output per worker, the question becomes where people are most valuable next, not whether demand disappears altogether.
This same logic applies across white-collar work. Yes, some people will be displaced as AI boosts productivity, but that doesn’t mean revenues fall. Instead, higher output and faster execution mean more revenue, not less. And if revenues are growing, then companies will still have the capacity to hire. Therefore, the challenge isn’t a lack of demand for labor; it’s the allocation of it. Firms will spend less on certain roles and more on others, and the real question simply is: what will companies actually value? Whatever the answer, it will almost certainly be more interesting than the repetitive, monotonous work that AI is poised to automate away.
A Quick Note on AI Agents and Friction in Society
You might notice I haven’t addressed most of Citrini’s memo, namely the parts that reach beyond software into industries like real estate, payments, and more. Since those industries are not the focus of Points and Platforms, I leave it to other analysts to cover these topics. But there’s one line I do want touch on briefly:
We had overestimated the value of “human relationships”. Turns out that a lot of what people called relationships was simply friction with a friendly face.
I wholeheartedly disagree with this premise. Humans are fundamentally social beings. We don’t live only to know things; we live to share them, argue about them, learn alongside other people, and feel seen in the process.
An AI agent might be able to tell me everything I could ever want to know about the NBA, but that doesn’t replace the 3 AM dorm room GOAT debates. It might explain Plato more clearly than any textbook, but no AI agent can replace sitting in a room with a professor, pushing on ideas, and talking them through with peers. For most of us, we aren’t just looking for the most efficient or optimized solution; we want the human experience around it. If, as Citrini suggests, human relationships are just “friction with a friendly face,” then that friction is not only valuable but also essential to our very being.
That’s why, both in the enterprise and in society as a whole, there will always be space for humans.