OUR DAILY THREAD: Be Careful What You Prompt For...
Did you get the memo?
THE SET-UP: What if AI is everything they say it is?
More precisely, what if agentic AI is a smashing success and it sparks the revolution in productivity investors are betting on?
That possibility sent a shockwave through the market this week when a viral memo published over the previous weekend offered a glimpse of what “success” may look like. It was a bit like a pack of dogs being told what they’d get when they finally caught the car they were frantically chasing. On Monday, they suddenly decided to run the other way. Per Bloomberg:
The AI “scare trade” rippled through markets on Monday after a weekend report from little-known Citrini Research warned about the technology’s disruptive impact on the global economy. The note, which laid out hypothetical scenarios set in the future, highlighted food delivery and credit card companies as vulnerable — spurring a selloff in delivery, payments and software stocks.
The S&P 500 fell 1% while an exchange-traded fund focused on software tumbled 4.8%, bringing its drop from a peak in September to around 35% on concerns AI could cannibalize earnings. International Business Machines Corp. saw its worst drop in 25 years.
[Memo co-author Alap] Shah said he was surprised by the market reaction. “I thought there was going to be a small reaction — it was definitely larger than we expected.”
Up to now, the market’s trepidation about AI has been largely contained to the bubble-like quality of AI-sparked growth in market capitalization. That’s led to some hand-wringing over the possibility of it popping like the Dotcom Bubble (or worse). There’s also been no shortage of warnings about AI’s dystopian downsides, including multiple warnings from the widely-acknowledged “Godfather of AI.” And we’re talking about grave downsides … like a one in five chance AI wipes out humankind.
None of it has mattered.
Investors have whistled past every predicted graveyard. Whether it’s massive job losses or the potential rise of Skynet, the market has refused to let a little gloom and doom (or the fundamentals) get in the way of their hyper-scaled dreams of avarice. But something in Citrini’s memo struck a nerve. Perhaps because it wasn’t just another predictable prediction of AI’s eventual failure, but instead it dove deeply into the implications of AI’s economy-altering success.
Set in near-term future of June 2028, the authors describe in great detail the impact of rapid adoption of agentic AI, which IBM describes thusly:
Unlike traditional AI models, which operate within predefined constraints and require human intervention, agentic AI exhibits autonomy, goal-driven behavior and adaptability. The term “agentic” refers to these models’ agency, or, their capacity to act independently and purposefully.
Agentic AI is the AI that can actually do things … and by “things” I mean jobs. And when agentic AI starts doing jobs, the big payoff will come in the form of a quantum leap in productivity and, therefore, booming profits will follow.
After all, isn’t that the real “promise” of AI for investors and for the industries agentic AI is most certainty going to disrupt? All those AI image-making and consumer-facing chatbots are there to collect your information and sharpen responses to prompts.
Mostly, they are there to entice people into a dependent relationship.
For business, though, what is AI if not a far cheaper and much more productive replacement for human beings in variety of jobs and industries?
Citrini Research gamed-out what it would look like if agentic AI does just that:
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.
The notion of “Ghost GDP” doesn’t seem far-fetched in a K-shaped economy. The aggregate GDP number is already effectively ghosting the decline in consumer spending by those sliding down the K’s leg. At the same time, the National Retail Federation recently found that the top 20% of spenders account for over 60% of all consumer spending. They are riding high on the K’s arm, fueled in no small part by the AI-stoked stock market.
They’ve kept on buying, too … because it is a good time to invest and an even better time to open a Securities-Backed Line of Credit (SBLOC) against continuing gains in AI-related stocks. It’s tax-free way to draw cash from stocks. As long as stock prices keep growing they’ll have no problem affording the growing price of groceries. And they have every incentive to keep investing in AI.
But, according Citrini’s scenario, it can quickly metastasize into a self-reinforcing doom-loop:
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.
Here’s where agentic AI’s success gets scary … perhaps even scary enough to spark the “scare trade”:
Over the past fifty years, the U.S. economy built a giant rent-extraction layer on top of human limitations: things take time, patience runs out, brand familiarity substitutes for diligence, and most people are willing to accept a bad price to avoid more clicks. Trillions of dollars of enterprise value depended on those constraints persisting.
It started out simple enough. Agents removed friction.
Subscriptions and memberships that passively renewed despite months of disuse. Introductory pricing that sneakily doubled after the trial period. Each one was rebranded as a hostage situation that agents could negotiate. The average customer lifetime value, the metric the entire subscription economy was built on, distinctly declined.
Consumer agents began to change how nearly all consumer transactions worked.
Humans don’t really have the time to price-match across five competing platforms before buying a box of protein bars. Machines do.
Travel booking platforms were an early casualty, because they were the simplest. By Q4 2026, our agents could assemble a complete itinerary (flights, hotels, ground transport, loyalty optimization, budget constraints, refunds) faster and cheaper than any platform.
Insurance renewals, where the entire renewal model depended on policyholder inertia, were reformed. Agents that re-shop your coverage annually dismantled the 15-20% of premiums that insurers earned from passive renewals.
Financial advice. Tax prep. Routine legal work. Any category where the service provider’s value proposition was ultimately “I will navigate complexity that you find tedious” was disrupted, as the agents found nothing tedious.
Even places we thought insulated by the value of human relationships proved fragile. Real estate, where buyers had tolerated 5-6% commissions for decades because of information asymmetry between agent and consumer, crumbled once AI agents equipped with MLS access and decades of transaction data could replicate the knowledge base instantly. A sell-side piece from March 2027 titled it “agent on agent violence”. The median buy-side commission in major metros had compressed from 2.5-3% to under 1%, and a growing share of transactions were closing with no human agent on the buy side at all.
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.
That was just the start of the disruption for the intermediation layer. Successful companies had spent billions to effectively exploit quirks of consumer behavior and human psychology that didn’t matter anymore.
Machines optimizing for price and fit do not care about your favorite app or the websites you’ve been habitually opening for the last four years, nor feel the pull of a well-designed checkout experience. They don’t get tired and accept the easiest option or default to “I always just order from here”.
That destroyed a particular kind of moat: habitual intermediation.
DoorDash (DASH US) was the poster child.
Coding agents had collapsed the barrier to entry for launching a delivery app. A competent developer could deploy a functional competitor in weeks, and dozens did, enticing drivers away from DoorDash and Uber Eats by passing 90-95% of the delivery fee through to the driver. Multi-app dashboards let gig workers track incoming jobs from twenty or thirty platforms at once, eliminating the lock-in that the incumbents depended on. The market fragmented overnight and margins compressed to nearly nothing.
Agents accelerated both sides of the destruction. They enabled the competitors and then they used them. The DoorDash moat was literally “you’re hungry, you’re lazy, this is the app on your home screen.” An agent doesn’t have a home screen. It checks DoorDash, Uber Eats, the restaurant’s own site, and twenty new vibe-coded alternatives so it can pick the lowest fee and fastest delivery every time.
Habitual app loyalty, the entire basis of the business model, simply didn’t exist for a machine.
This was oddly poetic, as perhaps the only example in this entire saga of agents doing a favor for the soon-to-be-displaced white collar workers. When they ended up as delivery drivers, at least half their earnings weren’t going to Uber and DoorDash. Of course, this favor from technology didn’t last for long as autonomous vehicles proliferated.
And that’s where the rubber meets the road. Once the agentic feedback loop gets into gear, it could be unstoppable:
AI got better and cheaper. Companies laid off workers, then used the savings to buy more AI capability, which let them lay off more workers. Displaced workers spent less. Companies that sell things to consumers sold fewer of them, weakened, and invested more in AI to protect margins. AI got better and cheaper.
Frankly, none of this should be surprising. Whether or not agentic AI progresses exactly or even roughly the way Citrini projects, the bottom line is that their projection matches Silicon Valley’s disruptive business model. What Citrini describes may not ultimately happen, but it is ultimately what many of tech bros want and expect to happen.
As if on cue, Jack Dorsey announced Thursday on X (a.k.a. the platform he’d formerly named “Twitter”) that he was laying off 4,000 workers at Block, the fintech company he co-founded. That is nearly half of Block’s 10,000 employees gone in an instant. And why did Dorsey drop the axe?
we're not making this decision because we're in trouble. our business is strong. gross profit continues to grow, we continue to serve more and more customers, and profitability is improving. but something has changed. we're already seeing that the intelligence tools we’re creating and using, paired with smaller and flatter teams, are enabling a new way of working which fundamentally changes what it means to build and run a company. and that's accelerating rapidly.
He added in a letter to shareholders:
The core thesis is simple. Intelligence tools have changed what it means to build and run a company. We’re already seeing it internally. A significantly smaller team, using the tools we’re building, can do more and do it better. And intelligence tool capabilities are compounding faster every week.
I don’t think we’re early to this realization. I think most companies are late. Within the next year, I believe the majority of companies will reach the same conclusion and make similar structural changes. I’d rather get there honestly and on our own terms than be forced into it reactively.
“Within the next year” eerily lines-up with the timeline laid out by Citrini. As Fortune explains, it may be the starting gun:
[S]ome experts warn Block’s layoffs could trigger the reality depicted in Citrini’s viral post, setting in motion a chain reaction of layoffs across the professional landscape.
“Whereas the job market effects of AI in 2025 were still quite ambiguous, AI capabilities have advanced rapidly in the past few months,” Anton Korinek, an economist who focuses on the economic impact of transformative AI, told Fortune. “This may be the beginning of a new trend where white collar jobs become threatened more seriously by AI. Once a few companies start the trend, competitive forces may induce others to follow suit.”
It also likely to have sparked Wall Street’s sell-off today. The market had mostly stabilized after Monday’s “scare,” but Dorsey’s announcement drove home Citrini’s point.
And don’t sleep on Anthropic’s falling-out with Pentagon Pete Hegseth. At issue was Pete’s desire to use Anthropic’s AI in ways Anthropic’s CEO Dario Amodei simply couldn’t abide:
“Anthropic understands that the Department of War, not private companies, makes military decisions. We have never raised objections to particular military operations nor attempted to limit use of our technology in an ad hoc manner. However, in a narrow set of cases, we believe AI can undermine, rather than defend, democratic values.”
Amodei specifically objected to using “Claude” for mass domestic surveillance and for fully autonomous weapons. AI-run surveillance can, Amodei explained, create “a comprehensive picture of any person’s life—automatically and at massive scale.” And it’s “frontier AI systems are simply not reliable enough to power fully autonomous weapons.”
In response, Trump announced Anthropic would not only lose its current government contracts, it was also blacklisted as a national security supply chain risk and, therefore, no company with NatSec contracts can do business with Anthropic.
That raised a whole set of questions for the burgeoning relationship between Silicon Valley and the Pentagon. Sadly, Alex Karp at Palantir and Palmer Luckey of Anduril don’t have any of the qualms raised by Amodei. Because Anthropic is privately-held, there was no stock price to drop. But it adds another alarm to the overall wake-up call that may have finally hit The Street this week.
Let’s face it, we as a polity and a society and a species are in desperate need of a wake-up call. It is shocking to think of how little public debate there has been since ChatGPT was launched on November 30, 2022. Throughout the meteoric rise of AI, we’ve been sleeping behind the wheel of a metaphorical Tesla, content to have the latest whiz-bang gizmo making our lives “easier,” impressing our friends and ourselves with one gadget after another. Each iteration, though, makes us more and more dependent and makes them richer and richer … but it’s all worth it because you can take a nap in traffic? Or because your 401k was really strong last year?
Citrini’s memo, which I recommend reading in-full, is just asking us to at least pump the brakes. And we’d better do it soon, because it looks like we’re headed for a cliff. - jp
Citadel Securities demolishes viral AI doomsday essay, arguing the real ‘Global Intelligence Crisis’ is ignorance of macro fundamentals
https://fortune.com/2026/02/26/citadel-demolishes-viral-doomsday-ai-essay-citrini-macro-fundamentals-engels-pause/
White House Economist Calls Citrini AI Report ‘Science Fiction’
https://www.bloomberg.com/news/articles/2026-02-24/white-house-economist-calls-citrini-ai-report-science-fiction
AI Is Proving a 100-Year-Old Prediction True
https://www.bloomberg.com/opinion/articles/2026-02-27/ai-is-proving-a-100-year-old-prediction-by-john-maynard-keynes-true