Fed’s Lisa Cook Warns Fed Might Not Be Able To Counter AI Job Losses

Cook Warns on Fed’s Ability to Counter AI-Driven Unemployment

Bloomberg reports Cook Warns About AI-Related Job Losses.

Federal Reserve Governor Lisa Cook warned the US central bank may not be able to counter rising unemployment driven by adoption of artificial intelligence.

“If AI continues to raise productivity, economic growth could remain strong, even as churn in the labor market leads to an increase in unemployment. In a productivity boom such as this, a rise in unemployment may not indicate increased slack,” Cook said Tuesday in Washington.

“As such, our normal demand-side monetary policy may not be able to ameliorate an AI-caused unemployment spell without also increasing inflationary pressure,” she said.

Cook’s comments are the latest in a string of recent speeches by Fed policymakers on how AI could influence monetary policy in the coming years. A few of her colleagues have recently suggested that a productivity boom spurred by AI could boost the so-called neutral rate of interest that keeps the economy stable.

In her remarks, Cook offered some factors that could push it in the other direction.

“With investment contributing to strong aggregate demand, it is possible that the current neutral rate is higher than before the pandemic,” she said.

“This could reverse when the AI productivity gains are more fully realized or if the labor market transition leads to a rise in income inequality, such that well-off consumers receive a larger share of income, which could lower the neutral rate, all else equal,” Cook said.

Fed Governor Christopher Waller, speaking Tuesday at a separate event, said the report overstated the potential impact on employment.

“AI is a tool. It’s not going to replace us as human beings,” Waller said. “This is kind of an overstated thing.”

AI Scare Trade as IBM Drops Most in 25 Years

Also consider Taleb, Citrini Fuel AI Scare Trade as IBM Drops Most in 25 Years

The artificial intelligence “scare trade” erupted again on Monday as growing concerns about the disruptive power of AI dragged down shares of delivery, payments and software companies, and sent International Business Machines Corp. to its worst plunge in 25 years.

It began after a bearish report was published over the weekend by a little known firm called Citrini Research.

The report, released on social media Sunday, outlined the potential risks to various segments of the global economy, using hypothetical scenarios set in the future, specifically calling out food delivery services and credit card companies as ones facing trouble.

Then AI startup Anthropic said in a blog post Monday that Claude Code tool can help with modernizing COBOL, a dated programming language that’s mainly run on IBM computers.

And finally came a warning from Nassim Taleb: investors should brace for escalating volatility and even bankruptcies in the software sector as the AI rally enters a fragile phase.

IBM shares closed down 13%, the biggest one-day drop since 2000. DoorDash Inc., American Express Co., KKR & Co Inc. and Blackstone Inc. all slumped by at least 6%. Shares of other companies name-checked in the article, including Uber Technologies Inc., Mastercard Inc., Visa Inc., Capital One Financial Corp. and Apollo Global Management Inc. all fell by 4% or more.

“The sole intent of this piece is modeling a scenario that’s been relatively underexplored,” a preface to the article, which was published Sunday, said. “Hopefully, reading this leaves you more prepared for potential left tail risks as AI makes the economy increasingly weird.”

Let’s dive into the futuresque article.

Finally, please consider The 2028 Global Intelligence Crisis by Citrini Research.

The Consequences of Abundant Intelligence

February 22nd, 2026 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.

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.

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.

[Mish note: the author does not understand negative feedback look. Hardly anyone does.]

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?”

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. 

What else were they supposed to do? Sit still and die slower? The companies most threatened by AI became AI’s most aggressive adopters.

This sounds obvious in hindsight, but it really wasn’t at the time (at least to me). The historical disruption model said incumbents resist new technology, they lose share to nimble entrants and die slowly. That’s what happened to Kodak, to Blockbuster, to BlackBerry. What happened in 2026 was different; the incumbents didn’t resist because they couldn’t afford to.

Agents went looking for faster and cheaper options than cards. Most settled on using stablecoins via Solana or Ethereum L2s, where settlement was near-instant and the transaction cost was measured in fractions of a penny.

MASTERCARD Q1 2027: NET REVENUES +6% Y/Y; PURCHASE VOLUME GROWTH SLOWS TO +3.4% Y/Y FROM +5.9% PRIOR QUARTER; MANAGEMENT NOTES “AGENT-LED PRICE OPTIMIZATION” AND “PRESSURE IN DISCRETIONARY CATEGORIES” | Bloomberg, April 29 2027

Mastercard’s Q1 2027 report was the point of no return. Agentic commerce went from being a product story to a plumbing story. MA dropped 9% the following day. Visa did too, but pared losses after analysts pointed out its stronger positioning in stablecoin infrastructure.

American Express (AXP US) was hit hardest; a combined headwind from white-collar workforce reductions gutting its customer base and agents routing around interchange gutting its revenue model. Synchrony (SYF US), Capital One (COF US) and Discover (DFS US) all fell more than 10% over the following weeks, as well.

Their moats were made of friction. And friction was going to zero.

AI has created new jobs. Prompt engineers. AI safety researchers. Infrastructure technicians. Humans are still in the loop, coordinating at the highest level or directing for taste. For every new role AI created, though, it rendered dozens obsolete. The new roles paid a fraction of what the old ones did.

U.S. JOLTS: JOB OPENINGS FALL BELOW 5.5M; UNEMPLOYED-TO-OPENINGS RATIO CLIMBS TO ~1.7, HIGHEST SINCE AUG 2020 | Bloomberg, Oct 2026

The hiring rate had been anemic all year, but October ‘26 JOLTS print provided some definitive data. Job openings fell below 5.5 million, a 15% decline YoY.

INDEED: POSTINGS FALL SHARPLY IN SOFTWARE, FINANCE, CONSULTING AS “PRODUCTIVITY INITIATIVES” SPREAD | Indeed Hiring Lab, Nov–Dec 2026

White-collar openings were collapsing while blue-collar openings remained relatively stable (construction, healthcare, trades). The churn was in the jobs that write memos (we are, somehow, still in business), approve budgets, and keep the middle layers of the economy lubricated. Real wage growth in both cohorts, however, had been negative for the majority of the year and kept declining.

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.

A feedback loop with no natural brake. [That is correct usage]

The irony of this was that the AI infrastructure complex kept performing even as the economy it was disrupting began deteriorating. NVDA was still posting record revenues. TSM was still running at 95%+ utilization. The hyperscalers were still spending $150-200 billion per quarter on data center capex. Economies that were purely convex to this trend, like Taiwan and Korea, outperformed massively.

The Battle Against Time

The first negative feedback loop [incorrect use] was in the real economy: AI capability improves, payroll shrinks, spending softens, margins tighten, companies buy more capability, capability improves. Then it turned financial: income impairment hit mortgages, bank losses tightened credit, the wealth effect cracked, and the feedback loop sped up. And both of these have been exacerbated by an insufficient policy response from a government that seems, quite frankly, confused.

The system wasn’t designed for a crisis like this. The federal government’s revenue base is essentially a tax on human time. People work, firms pay them, the government takes a cut. Individual income and payroll taxes are the spine of receipts in normal years.

The output is still there. But it’s no longer routing through households on the way back to firms, which means it’s no longer routing through the IRS either. The circular flow is breaking, and the government is expected to step in to fix that.

The Intelligence Premium Unwind

For the entirety of modern economic history, human intelligence has been the scarce input. Capital was abundant (or at least, replicable). Natural resources were finite but substitutable. Technology improved slowly enough that humans could adapt. Intelligence, the ability to analyze, decide, create, persuade, and coordinate, was the thing that could not be replicated at scale.

Human intelligence derived its inherent premium from its scarcity. Every institution in our economy, from the labor market to the mortgage market to the tax code, was designed for a world in which that assumption held.

We are now experiencing the unwind of that premium. Machine intelligence is now a competent and rapidly improving substitute for human intelligence across a growing range of tasks. The financial system, optimized over decades for a world of scarce human minds, is repricing. That repricing is painful, disorderly, and far from complete.

This is the first time in history the most productive asset in the economy has produced fewer, not more, jobs. Nobody’s framework fits, because none were designed for a world where the scarce input became abundant. So we have to make new frameworks. Whether we build them in time is the only question that matters.

But you’re not reading this in June 2028. You’re reading it in February 2026.

The S&P is near all-time highs. The negative feedback loops [Again wrong] have not begun. We are certain some of these scenarios won’t materialize. We’re equally certain that machine intelligence will continue to accelerate. The premium on human intelligence will narrow.

As investors, we still have time to assess how much of our portfolios are built upon assumptions that won’t survive the decade. As a society, we still have time to be proactive.

The canary is still alive.

Negative Feedback Loops

Well that was an amazing look to say the least.

The author does not mean “negative feedback loop“.

Negative feedback loops are stabilizing forces. The author means a positive feedback loop with a hugely negative impact.

Feedback loops aside. Lisa Cook is correct that the Fed would be powerless to counteract such an event.

I will side with Waller “AI is a tool. It’s not going to replace us as human beings.”

Sure, there is going to be some implication. There always is with creative destruction.

The good news is we are supposed to find out by June 30th, 2028. I still expect to be blogging. I’ll let you know.

AI Will Take Every Job

Meanwhile, it seems like Elon Musk agrees with Citrini Research, at least regarding the job losses.

For discussion, please see Elon Musk Backs Universal High Income Fearing AI Will Take Every Job

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