A Thought Exercise in Financial History, from the Future
Citrini Research | February 22, 2026
Written by Citrini and Alap Shah
Preface
What if our AI bullishness continues to be right...and what if that's actually bearish?
What follows is a scenario, not a prediction. This isn't bear porn or AI doomer fan-fiction. The sole intent of this piece is modeling a scenario that's been relatively underexplored. Our friend Alap Shah posed the question, and together we brainstormed the answer.
Hopefully, reading this leaves you more prepared for potential left tail risks as AI makes the economy increasingly weird.
This is the CitriniResearch Macro Memo from June 2028, detailing the progression and fallout of the Global Intelligence Crisis.
Macro Memo: The Consequences of Abundant Intelligence
CitriniResearch June 30th, 2028
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.
Traders have grown numb. Six months ago, a print like this would have triggered a circuit breaker.
Two years. That's all it took to get from "contained" and "sector-specific" to an economy that no longer resembles the one any of us grew up in. This quarter's macro memo is our attempt to reconstruct the sequence - a post-mortem on the pre-crisis economy.
The euphoria was palpable. By October 2026, the S&P 500 flirted with 8000, the Nasdaq broke above 30k. The initial wave of layoffs due to human obsolescence began in early 2026, and they did exactly what layoffs are supposed to. Margins expanded, earnings beat, stocks rallied. Record-setting corporate profits were funneled right back into AI compute.
The headline numbers were still great. Nominal GDP repeatedly printed mid-to-high single-digit annualized growth. Productivity was booming. Real output per hour rose at rates not seen since the 1950s, driven by AI agents that don't sleep, take sick days or require health insurance.
The owners of compute saw their wealth explode as labor costs vanished. Meanwhile, real wage growth collapsed. Despite the administration's repeated boasts of record productivity, white-collar workers lost jobs to machines and were forced into lower-paying roles.
When cracks began appearing in the consumer economy, economic pundits popularized the phrase "Ghost GDP": output that shows up in the national accounts but never circulates through the real economy.
In every way AI was exceeding expectations, and the market was AI. The only problem…the economy was not.
It should have been clear all along that a single GPU cluster in North Dakota generating the output previously attributed to 10,000 white-collar workers in midtown Manhattan is more economic pandemic than economic panacea. The velocity of money flatlined. The human-centric consumer economy, 70% of GDP at the time, withered. We probably could have figured this out sooner if we just asked how much money machines spend on discretionary goods. (Hint: it's zero.)
AI capabilities improved, companies needed fewer workers, white collar layoffs increased, displaced workers spent less, margin pressure pushed firms to invest more in AI, AI capabilities improved…
It was a negative feedback loop with no natural brake. The human intelligence displacement spiral. White-collar workers saw their earnings power (and, rationally, their spending) structurally impaired. Their incomes were the bedrock of the $13 trillion mortgage market - forcing underwriters to reassess whether prime mortgages are still money good.
Seventeen years without a real default cycle had left privates bloated with PE-backed software deals that assumed ARR would remain recurring. The first wave of defaults due to AI disruption in mid-2027 challenged that assumption.
This would have been manageable if the disruption remained contained to software, but it didn't. By the end of 2027, it threatened every business model predicated on intermediation. Swaths of companies built on monetizing friction for humans disintegrated.
The system turned out to be one long daisy chain of correlated bets on white-collar productivity growth. The November 2027 crash only served to accelerate all of the negative feedback loops already in place.
We've been waiting for "bad news is good news" for almost a year now. The government is starting to consider proposals, but public faith in the ability of the government to stage any sort of rescue has dwindled. Policy response has always lagged economic reality, but lack of a comprehensive plan is now threatening to accelerate a deflationary spiral.
How It Started
In late 2025, agentic coding tools took a step function jump in capability.
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.
That summer, we spoke with a procurement manager at a Fortune 500. He told us about one of his budget negotiations. The salesperson had expected to run the same playbook as last year: a 5% annual price increase, the standard "your team depends on us" pitch. The procurement manager told him 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.
Investors were prepared - expectant, even - that the long tail would be hit hard. They may have made up a third of spending for the typical enterprise stack, but they were obviously exposed. The systems of record, however, were supposed to be safe from disruption.
It wasn't until ServiceNow's Q3 26 report that the mechanism of reflexivity became clearer.
SERVICENOW NET NEW ACV GROWTH DECELERATES TO 14% FROM 23%; ANNOUNCES 15% WORKFORCE REDUCTION AND 'STRUCTURAL EFFICIENCY PROGRAM'; SHARES FALL 18% Bloomberg, October 2026
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.
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.
What else were they supposed to do? Sit still and die slower? The companies most threatened by AI became AI's most aggressive adopters. Each company's individual response was rational. The collective result was catastrophic.
Key Concepts
Ghost GDP
Output that shows up in the national accounts but never circulates through the real economy. Revenue that accrues to compute owners, not consumers. Productivity gains that don't translate into wages, and therefore don't translate into spending.
The Human Intelligence Displacement Spiral
A negative feedback loop with no natural brake:
- AI capabilities improve
- Companies need fewer workers
- White-collar layoffs increase
- Displaced workers spend less
- Margin pressure pushes firms to invest more in AI
- AI capabilities improve... (repeat)
Reflexivity in Enterprise Software
The interconnected nature of enterprise systems: when Fortune 500 clients cut 15% of their workforce, they cancelled 15% of their SaaS licenses. The same AI-driven headcount reductions that were boosting margins at customers were mechanically destroying the SaaS vendors' revenue base.
Critical Analysis
The scenario isn't that AI fails. It's that AI succeeds on every metric that markets track — and still produces an economic outcome that none of the standard models anticipated, because the standard models assumed the gains from productivity would flow through to workers and consumers the way they did in previous technological revolutions.
The piece is notable for being a left-tail risk analysis from self-described AI bulls. The goal isn't to predict doom — it's to stress-test assumptions that most AI bull cases don't model: the distributional question of who captures productivity gains, and whether those people spend them back into the economy in ways that support consumer demand.
This is a scenario, not a prediction.