Kevin Warsh’s first press conference as Fed chairman was far better than I expected.

The statement is shorter, forward guidance is gone, and the Committee has promised to deliver price stability. That is most of the prescription I have made for at least fifteen years. But the question that actually matters – what the Fed should be trying to deliver, and the wreck of a regime Powell has left him – is still open.

Kevin Warsh held his first press conference as chairman of the Federal Reserve yesterday, and it was immediately clear that he has put himself firmly in the driver’s seat.

The statement was unlike anything Jerome Powell ever signed off on. It was substantially shorter. It dropped the elaborate “forward guidance” that had been the house style of the Fed for the better part of two decades. And in its place came a single, blunt sentence: “The Committee will deliver price stability.”

I have to admit I did not expect to be impressed. I have been a sceptic of Warsh for months, and I have said so in writing. So before I explain why he changed my mind, let me explain why I doubted him.

Why I Was Sceptical

Warsh did not look, until yesterday, like a man who would face down the White House and put the Fed’s credibility first.

He spent last year campaigning for the job, and he campaigned by telling the White House what it wanted to hear. He was outspoken in favour of lower rates, exactly as Trump has been demanding, and he justified it with the claim that an AI-driven productivity boom would expand supply and pull inflation down on its own.

That argument never persuaded me, and not because I doubt what AI might do to productivity. My objection is more basic. A productivity boom is a positive supply shock, and a central bank should not be reacting to supply shocks at all, positive or negative. If AI does raise productivity, the right response is to hold nominal spending on its path and let the gain come through as lower inflation and faster real growth. Cutting rates because supply is improving is using a supply-side story to justify a demand-side easing – exactly the confusion a rule-based Fed exists to remove.

There was also the question of qualifications. Warsh was a Fed governor from 2006 to 2011, the youngest ever appointed, but his record on inflation forecasting in those years was weak, and prominent NGDP advocates, Scott Sumner among them, were openly sceptical that he was the right man. As was I at the time.

And there was the politics. His IMF speech last April read, frankly, like a job application written for Trump’s ear – a list of grievances about institutional drift, wrapped in language about independence that pointed towards less of it rather than more.

There is, too, the family dimension, which under normal circumstances I would ignore entirely. Warsh is married to Jane Lauder, daughter of Ronald Lauder, a long-standing personal friend of Donald Trump.

These are not normal circumstances. The President has spent a year demanding lower rates and used a renovation investigation as a pressure tactic against Powell. When his preferred candidate to replace the chair turns out to be connected to his inner circle by marriage, that belongs on the analytical record.

There is one side of Warsh’s thinking I have always had sympathy for. He has been a consistent critic of the Fed’s mission creep into financial-stability and macroprudential territory. Those are my positions too.

But in May, on the day Powell chaired his final meeting, my complaint was precise: Warsh was full of diagnosis and empty of framework. He knew what he wanted to remove. He had said nothing about what would replace it.

So that was where I stood. Sceptical, unconvinced by the AI story, worried he would be Trump’s man, and waiting to see whether he had a regime in mind or only a grievance.

He did better than that. And that is why I am writing this.

Letting the Market Do the Work

The first thing you notice about Warsh’s statement is the length. It is substantially shorter than anything Powell put out. That is not cosmetic. The old statement told you what the Committee thought, how it read the data, and where it expected to move. The new one gives the target range, reaffirms ample reserves, notes inflation is still above 2%, and states – flatly – that the Committee will deliver price stability.

Warsh described it himself with understatement: a bit shorter, a bit simpler, dispensing with some older language. Gone, in particular, is forward guidance. And he declined to submit his own dot to the projections, on the grounds that a published rate path locks the central bank into a promise it cannot keep.

Clearly, this is the right instinct. So let me give it a name.

Years ago I defined the Chuck Norris effect like this: you do not have to print more money to ease policy if you are a credible central bank with a credible target.

The point generalises. A credible central bank does not have to do very much, because the market does the work for it. If everyone believes the Fed will deliver, expectations adjust on their own, and the threat to act is usually enough.

Warsh has not announced a rule. But by stripping the statement to a commitment and refusing to spell out a path, he is telling markets to read his reaction function rather than his quarterly mood. He is asking the market to do the lifting.

In its purest form, a fully credible Fed would hardly need to hold a press conference at all. It would be enough to say, once and for all: we will always, at any time, do exactly what is needed to deliver price stability.

The market does the rest. It is the market that implements the policy, and it is the market that makes the real forecasts, through its expectations.

The comparison that comes most naturally to me is not American. It is Danish. Denmark, where I live, runs a fixed-exchange-rate policy against the euro. The Danish central bank – Danmarks Nationalbank – publishes no elaborate forecasts and no guidance. It does not need to.

Everyone knows it will do whatever it takes to hold the krone fixed against the euro. That credibility is the policy, and the market enforces it. Warsh is now saying something structurally identical: we will deliver price stability, you do not need our forecasts, you need only believe us.

Contrast the opposite approach. For years the ECB under Mario Draghi, and Jean-Claude Trichet before him, insisted it would “never pre-commit” to any future action. I always thought that was precisely the problem. A central bank that refuses to say what it is trying to achieve forces the market to guess, and guessing is not a credible target.

Mission Creep and the End of Fine-Tuning

This is also the most refreshing thing about Warsh’s debut, and it goes beyond the statement. The Fed has spent the better part of fifteen years trying to do too much.

Since 2008 it has drifted into questions that have nothing to do with its mandate – financial-stability ambitions, distributional concerns, even climate-related risk. The mandate is price stability and maximum employment. It is not the management of every problem that happens to be fashionable.

So the narrowing Warsh implies is welcome.

There is an old principle, associated with Jan Tinbergen, that each policy target needs its own instrument; a central bank chasing five objectives with one rate will fail at most of them. Goodhart’s Law adds the rest: when a measure becomes a target, it stops being a good measure – which is exactly the trap macroprudential policy walks into.

Warsh, to his credit, seems to grasp both. The Fed has one instrument and should pursue one nominal objective. Everything else is someone else’s job.

This is, fundamentally, an argument about rules versus discretion. The Great Recession and its aftermath were an exercise in central-bank discretion – improvisation dressed up as sophistication. I have always thought we should go the other way, towards a Fed that behaves automatically, like the computer Milton Friedman wanted to run monetary policy. All central banks need to do this.

A Fed that fine-tunes is a Fed that surprises. A Fed that follows a rule is a Fed the market can anticipate. And a Fed the market can anticipate barely has to act at all.

Nominal Stability, Not Price Stability

But a great deal is still missing, and Warsh knows it. Which instruments should the Fed actually use – the money base, or the policy rate? Which measure of inflation is the right one? Should there even be an inflation target at all? My own answer, and my favourite, is a target for nominal GDP. So this is where my renewed optimism meets the hard question. Clarity is worth little if the target is wrong.

Warsh committed the Committee to price stability, which the Fed reads as 2% inflation. Notice what the statement leaves out. The mandate is dual – stable prices and maximum employment – and the new statement mentions only the first. It is a price-stability statement, not a dual-mandate one.

And here I would go further than Warsh did. He committed to price stability. I would commit to nominal stability instead, and the two are not the same thing.

Nominal stability – a stable path for total nominal spending – delivers price stability over time. Price stability pursued directly does not necessarily deliver nominal stability at any given moment. You can hold prices down in the short run by letting nominal spending collapse, or by tightening into a supply shock. That is not stability. It is the opposite, dressed up as discipline.

So it is not really prices a central bank should stabilise. It is nominal spending. Get the nominal path right, and prices look after themselves over the medium term.

My answer to the target question has not changed for more than fifteen years.

The Fed should target the level of nominal GDP – a 4% path, consistent with 2% inflation and around 2% trend real growth. It has two advantages over an inflation target, and both bite right now.

First, it handles supply shocks: a level target lets inflation rise temporarily after an energy shock while holding nominal spending on its path, instead of tightening into it the way the ECB infamously did in 2011.

Second, it makes up for past misses. An inflation target lets bygones be bygones; a level target claws the overshoot back. That is the difference between an anchor that holds and one that drifts.

Now look at what Warsh is staring at. Consumer price inflation hit 4.2% in May, the highest in three years. Producer prices ran above 6%. The Iran war drove oil from around $67 in late February to an intraday peak near $119 in March. The Committee’s median projection now has the policy rate ending the year at 3.8%, a quarter point above today, where three months ago it expected a cut.

So the dot plot has flipped from a cut to a hike, and it is hard to read Warsh’s debut as anything other than a hike now being more likely than a cut.

Here is the part most commentators will miss. The current inflation has two sources, not one. Part of it is the energy supply shock – and a central bank should look through that, not tighten into it.

But part of it is excess nominal demand the Fed never wound back – nominal spending that has run above trend since 2021 and is still drifting higher, as I will show in a moment. A pure inflation-targeter cannot tell the two apart and risks getting both wrong. A nominal GDP target separates them automatically – look through the oil, lean against the excess nominal demand. It is the one framework that gets this moment right.

This, by the way, is why the whole “hawk versus dove” framing is, frankly, useless. To be hawkish or dovish is already to assume policy should be set by someone’s discretionary feel.

What should concern us is not the temperature of the chairman. It is the regime.

The Five Task Forces

In May my complaint was that Warsh offered no framework. Yesterday he began building one, or at least the machinery to build one.

He announced five task forces, each on a core area of policy: communications, the balance sheet, data and data sources, productivity and jobs in an era of transformation including AI, and inflation frameworks examined from first principles.

Each one, Warsh said, would draw on the best minds inside and outside the economics profession, backed by Fed staff, and report to the policymakers.

Two things stood out by their absence. There was no hint – none – of the easiest dovish escape available to him: quietly raising the inflation target, or hiding behind its “flexible average” wording, to make five years of overshoot vanish on paper.

He could have conjured the problem away. He chose not to. And he barely mentioned AI, even though it has a task force of its own. The man who last year leaned on an AI productivity boom to justify the rate cuts Trump wanted did not reach for that argument once.

Which leaves the question I keep coming back to: who. If the inflation-frameworks task force is serious about first principles, the names I would want in the room are obvious – Scott Sumner, David Beckworth, Peter Ireland, and Josh Hendrickson on nominal GDP and policy rules; George Selgin on the balance sheet, where the ample-reserves regime is his territory; and, for any of it, my friend Bob Hetzel, the finest monetary historian the Fed has produced ever. Appoint people like that and “from first principles” might mean something. Appoint the usual committee and it will not.

What I Have Proposed

So let me be concrete about what I would actually have the Fed do, because I am not asking the task force to invent anything. I wrote it down almost exactly ten years ago, in January 2016, and I would not change a word.

First, a 4% nominal GDP level target, defined on the expected level of NGDP eighteen to twenty-four months out. That one target delivers both price stability and maximum employment. No other is needed.

Second, the Fed should stop producing its own forecasts and instead read the market’s: a prediction market for NGDP twelve and twenty-four months ahead, the surveys of professional forecasters, and market-based models of NGDP expectations. Let the market tell the Fed where nominal spending is heading, not the other way round.

Third, give up interest-rate targeting – the dot plot included – and use the monetary base as the instrument. Announce a permanent base-growth rate set to hit the 4% path, justified by one number only: expected NGDP against the target. Leave interest rates entirely to the market.

The consequences follow directly.

The policy is rule-based, not discretionary. It is transparent, with the market doing most of the implementation – the Chuck Norris effect made operational.

There is no zero-lower-bound problem, because control of the base can ease even when interest rates sit at zero. The endless and, frankly, silly talk of bubbles, moral hazard, and macroprudential fine-tuning stops, because the Fed gives up pretending it can do a better job than the market in ‘forecasting’. The Fed stops reacting to supply shocks, positive and negative. And the FOMC could, at last, be handed to the computer Milton Friedman wanted to run it.

I called it, at the time, a forward-looking McCallum rule. The label matters less than the shape of the thing: one target, read from the market, hit with one instrument, justified by one number.

Look at Warsh’s five task forces and you will see the same agenda, broken into committees – inflation frameworks is my first change, communications and data my second, the balance sheet my third. The only real question is whether they follow the framework to where it leads, or spend a year rediscovering why the Fed has always preferred its own discretion.

What He Inherited

All of which raises the obvious question. Why does any of this matter so much? Why is the regime question existential rather than academic?

The answer is the Fed that Warsh has inherited. Because it is not a healthy institution. It is a weakened one, and the weakness is to a large extent self-inflicted.

Trump has spent the past year arguing that Powell ran policy too tight. The data says the opposite.

By my reckoning, Powell ran the easiest monetary policy in modern Fed history, and the cleanest way to see it is not through inflation or the funds rate but through the level of nominal spending – nominal GDP.

From 2010 through 2019, US nominal GDP grew at almost exactly 4% a year. That was the de facto regime under Ben Bernanke and Janet Yellen, broadly consistent with their stated 2% inflation goal given trend productivity and labour-force growth.

I have argued for years that this implicit anchor, and not the dual mandate the Fed talks about, was what actually held the system steady. The Covid shock knocked nominal spending below the path in 2020, and the Fed’s emergency response that spring was, in my view, exactly right.

Then came 2021.

On 29 April 2021, I published a post on this blog titled “Heading for double-digit US inflation.”

I argued that the explosion in US broad money, combined with the largest fiscal expansion since the Second World War, would produce a fast, large, one-off jump in the price level – and that what happened next would depend entirely on whether the Fed moved to anchor expectations or let its credibility erode.

The forecast was right. Headline CPI inflation peaked at 9.1% in June 2022, the highest in four decades. The point is not that I got it right. The point is that the call was available to anyone willing to read the data.

The broad-money numbers were public. The fiscal arithmetic was public. The lags between money and inflation that Milton Friedman identified decades ago are textbook material.

And the Fed, under Powell, chose to do nothing.

Worse than nothing. In 2020 the FOMC had adopted Flexible Average Inflation Targeting, which committed it to running inflation above 2% for a while after running it below. Defensible in theory. A catastrophe in practice.

Through 2021 and into 2022 the Fed held the funds rate at zero and kept buying $120 billion of assets a month while nominal GDP grew 11% in 2021 and close to 10% in 2022 – more than two and a half times the established 4% rate. That is not flexible average inflation targeting. It is the monetary equivalent of price-fixing, and all the while the Fed insisted the inflation was transitory, a word it kept using long after the indicators had made it indefensible. This was not a close call.

Powell then spent 2022 and 2023 cleaning up the mess, and he deserves credit for it. From near-zero in March 2022 the Fed reached 5.25-5.50% by July 2023. Inflation came down, the labour market did not collapse, and that successful disinflation is the one thing from the Powell era I will give him unambiguous credit for.

But the disinflation did not restore the regime. The price-level jump is permanent – undoing it would take a Volcker-scale recession, and with federal debt above 100% of GDP and interest payments above $1 trillion a year, that is no longer arithmetically possible without risking a sovereign default.

The growth rate, though, is a separate matter. Having allowed the one-off jump, the Fed could at least have re-anchored nominal spending at 4% from there. It did not. On my numbers, NGDP growth has averaged about 5.4% a year since the start of 2023, and from the start of 2025 it has run above 6%. The rate is drifting up, not settling.

So the Fed of 2025 and 2026 is not running a tight policy that has restored discipline. It is running a policy that still accommodates above trend. The “tightening” belongs in scare quotes: it is tight relative to the Fed’s own 2021 error, not relative to any credible framework. This is the excess nominal demand I pointed to earlier. The Fed is still too easy.

Why the Attack Lands

So why, if Powell ran the easiest money in modern history, does Trump’s accusation that he was too tight gain any traction at all?

It lands because the Fed proved, in real time, that it cannot be trusted to act on the inflation indicators when its own framework tells it not to.

That is the credibility deficit Warsh inherits, and it is worth more to Trump than any number. A Fed that had read the money data correctly in 2021 and headed off the inflation would now be defending its independence from overwhelming strength. Trump’s attacks would land in empty air. Instead the Fed defends independence from partial credibility, having handed the public a 9% inflation to absorb. The easy money of 2021 and the political crisis of 2025-26 are not two stories. They are one.

The historical rhyme is the early 1970s. Nixon leaned on Arthur Burns – the tapes record instructions, not requests, to expand the money supply before the 1972 election – and Burns complied, explaining at each meeting why the next move could wait. Each decision sounded reasonable on its own. The cumulative result was a decade of inflation and, eventually, a Volcker disinflation that drove unemployment towards 11%.

The parallel to Powell is only partial. He raised rates hard, the disinflation was real, and he resisted Trump’s public pressure throughout. On the political dimension he was not Burns – or rather at least not quite as bad a Burns.

But on the analytical dimension – the willingness to ignore the monetary indicators because the prevailing framework said they did not matter – the parallel is uncomfortably close. Burns ignored the aggregates because his framework dismissed them. Powell ignored them in 2021 because FAIT said the inflation was wanted. The justification differs. The shape of the failure is identical.

And underneath all of it sits the deeper threat: fiscal dominance.

Federal debt held by the public is above 100% of GDP in the US.

Net interest has gone from around $350 billion in 2020 to over $1 trillion in 2025. Every 100 basis points on the policy rate eventually adds about 1% of GDP to the interest bill. The federal government now has a powerful, mechanical reason to want low rates regardless of what the macroeconomy needs – and Trump has been unusually explicit that he wants rates down for the budget.

That is fiscal dominance: the fiscal authority’s needs crowding out the central bank’s independence. It is the condition behind nearly every serious inflation in history, from the German hyperinflation of 1923 to the post-Soviet Russian inflation, chronic Argentine inflation, and the Turkish episode under Erdoğan.

Not Trump’s Fed Chairman

So set Warsh’s debut against all of that, and the most important thing he did yesterday is the thing I least expected. He defied the White House.

Trump appointed him after attacking Powell for not cutting, and Warsh had spent the previous year signalling sympathy for exactly those cuts.

The natural bet was that he would ease, or at least signal easing – the cuts the President wanted. Instead he held rates on a unanimous vote, let the dots tilt towards a hike, and gave a press conference the markets took as anything but the relief Trump was hoping for.

The S&P 500 fell. Bond yields jumped. Asked whether he had spoken to the President since taking the job, he said only: “I don’t have anything for you.” He has a mandate, and he intends to deliver on it. Whatever Trump says.

Set that against everything stacked the other way – the campaign for lower rates, the IMF speech, the Lauder connection, and the fiscal-dominance pressure that gives any modern president a mechanical hunger for cheap money. On his first day, with all of that pushing one way, Warsh pushed back. This is the Arthur Burns fear answered, at least for one meeting.

And there is a deeper point underneath it. The more rule-based and automatic policy becomes, the less it matters who the chairman is, and the less influence any president has over him. A discretionary Fed can be lobbied, pressured, and bullied. A rule-based Fed cannot.

Taking discretion out of monetary policy is not only better economics. It is the strongest protection of central bank independence there is – which, with fiscal dominance bearing down on the Fed, is no small thing.

Warsh also made a point of noting that inflation has now run above the 2% goal for more than five years. He inherits that overshoot, the same five years and the same failure. If he means what he says, it has to end. A central bank that overshoots for five years and shrugs has no credibility, and without credibility none of this machinery works.

The Hard Part Has Started

So I come away more optimistic than I expected, and far more than I was a month ago. The communication is sharper. The mission creep is being reversed. He defied Trump on day one. And the instinct – say what you will deliver, then let the market do the work – is exactly right.

But the easy part is the part Warsh has done. Shortening a statement, dropping a forecast, and holding the line against Trump for one meeting is style and nerve. Choosing the right thing to be clear about is analysis, and it is unresolved.

Friedman taught that monetary policy works with long and variable lags and cannot fine-tune the economy. Scott Sumner updated it: long and variable leads, because the work runs through expectations. Both point to the same conclusion. What matters is the regime, not the meeting-to-meeting decisions. A Fed that spent five years being careful about each meeting lost the regime entirely – and that, not any single rate call, is what Powell leaves behind.

So it was never really a rate decision. It is a regime decision. Warsh has made a good start on the things that are mostly nerve. The thing that is analysis – whether the Committee anchors on nominal spending or on an inflation number that has already failed for five years – is still to come. And in practice, reading all of this together, it is hard not to see a rate hike now as more likely than a cut.

The chairman changed in May. The hard part started yesterday.

Trump says Powell has been too tight. The data says the opposite. Powell has run the easiest monetary policy in modern Fed history, and the credibility cost is what makes the institution so vulnerable today

Later today the Federal Open Market Committee will almost certainly leave its target range for the federal funds rate unchanged at 3.50 to 3.75%, and markets are pricing a 100% probability of the hold. The only suspense in the room is whether Stephen Miran dissents again, as he did at the March meeting, in favour of a quarter-point cut.

That is the part everyone agrees on. The interesting question, in my view, is everything else – and the ritual focus on the rate decision actually obscures what is happening in the institution today.

This is, in all likelihood, Jerome Powell’s final meeting as Federal Reserve chair, with his term ending on May 15. Kevin Warsh has been nominated by the President to replace him, and the Senate Banking Committee is voting on the nomination this morning (US time), four hours before the FOMC’s own decision.

The political timing is, of course, not an accident. Last week the U.S. attorney for the District of Columbia, Jeanine Pirro, transferred her investigation of the renovations at the Fed’s headquarters to the Fed’s own office of inspector general – a procedural move that cleared the obstacle Senator Thom Tillis had placed in front of the Warsh nomination.

So here is what Wednesday actually is. It is not a rate decision. It is the end of an era at the Federal Reserve, and the beginning of a regime under conditions that have not existed in the United States since Arthur Burns sat in the chair.

What the dual-mandate framing misses

The Fed’s dual mandate looks roughly balanced if you are willing to squint at it. Core PCE inflation is running around 3%. The labour market is soft but not collapsing – weak hiring, unemployment at 4.3%, and payroll growth that has slowed without breaking. Brent oil is around $110 a barrel. The energy shock is not yet fully fed through to headline inflation.

That is a reasonable summary of the technical position. And it is also, in my view, almost beside the point.

The Fed has been above its 2% inflation target for five years. Five years. There is no recent precedent for a major central bank running this far above target for this long without either delivering the disinflation or formally rebasing the target. The institution has done neither. It has waited. The technical case for waiting is fine. The institutional consequence of having waited for five years is something else entirely.

A picture of the failure

Trump has spent the past year arguing that Powell has run policy too tight. The data tells the opposite story.

The chart below shows nominal GDP, in level terms, from 2010 through the end of 2025. The dashed grey line is a 4% growth trend anchored at the start of the period.

I have argued for years that the post-2008 Federal Reserve under Ben Bernanke and Janet Yellen ran a de facto 4% NGDP target, and that this target was broadly consistent with their stated 2% PCE inflation objective, given underlying productivity and labour-force growth.

Look at what the picture shows.

From 2010 through the end of 2017, US nominal GDP grew at almost exactly 4% per year. The actual line and the trend line are essentially indistinguishable. This is what a predictable monetary regime looks like – the price level and real output combined growing at a steady, predictable rate that markets can plan around.

Powell took over in February 2018.

NGDP continued at roughly 4% growth through 2019. The Covid shock in Q2 2020 produced a sharp negative deviation, and the Fed’s response in March 2020 was, in my view, exactly right. By the end of 2020, NGDP was already approaching the trend line again.

Then came 2021.

The April 2021 forecast

On this day five years ago, April 29, 2021, I published a blog post on this blog titled “Heading for double-digit US inflation.”

I want to quote what I actually wrote, because the framing matters.

I argued at the time that the explosion in US broad money supply, combined with the largest fiscal expansion since the Second World War, would produce a sharp one-off jump in the US price level – not necessarily a permanently higher inflation rate, but a level shift that would be very fast and very large.

I wrote that the fixed-income market was mispricing the inflation risk. I noted that the Fed seemed to be deliberately behind the curve. And I wrote that what would happen after the price level jump would depend entirely on the Fed’s response.

Either the Fed would move aggressively to anchor expectations and contain the shock as a one-off, or it would allow a serious erosion of credibility and longer-term inflation expectations would jump as well.

That was the forecast. The red dot on the chart marks where it was made.

The forecast was correct on the inflation outcome. Headline CPI inflation peaked at 9.1% in June 2022 – the highest rate of inflation in four decades. The forecast was correct on the timing – the price level moved fast and sharply through late 2021 and 2022.

The point is not that I got the forecast right. The point is that the forecast was available to anyone willing to read the data.

The P-star model I used was developed by Hallman, Porter and Small at the Federal Reserve in 1989. The broad money numbers were public.

The fiscal arithmetic was public. The relationship between monetary expansion and subsequent inflation, with the lags Milton Friedman had identified decades earlier, was textbook material. The Federal Reserve had access to all of this information, employed dozens of monetary economists with sophisticated models, and was looking directly at the same data I was looking at.

And the Federal Reserve, under Powell, chose to do nothing.

Worse than nothing. The Fed had, in 2020, adopted a new framework called Flexible Average Inflation Targeting – FAIT – which committed the FOMC to running inflation moderately above 2% for some period after running it below 2%. The framework was defensible in theory. The application in practice was a catastrophe.

Through 2021 and into early 2022, the Fed held the funds rate at zero and continued QE asset purchases at $120 billion per month even as nominal GDP growth ran at 11% in 2021 and nearly 10% in 2022. Pre-Powell average growth was 4%. Powell’s Fed ran NGDP growth at more than two-and-a-half times the established regime rate.

That is not flexible average inflation targeting. That is, as I have written elsewhere, the monetary policy equivalent of price fixing – holding the policy rate so far below its market-clearing level that the resulting inflation was not an accident but a mechanical consequence.

Powell’s Fed told the public, repeatedly, that the inflation that was already arriving was transitory. The word was, by late 2021, no longer defensible on any reasonable reading of the monetary indicators. It was deployed anyway, and the Fed maintained the framing through most of 2021 and into early 2022, well after the empirical case for it had collapsed.

This was not a close call. This was not a difficult judgement. This was the textbook monetary failure – too much money chasing too few goods, with the central bank insisting that money does not matter and that the inflation will go away on its own.

The reversal that did not anchor anything

Powell then spent 2022 and 2023 trying to undo the damage. From a target range of 0-0.25% in March 2022, the Fed reached 5.25-5.50% by July 2023. Inflation came down, and the labour market did not collapse. That was, by historical standards, a successful disinflation – one of the few things from the Powell era that I will give him unambiguous credit for.

But here is where the chart tells a story that the press releases do not.

Look at the three red dotted lines on the chart. Each one is a 4% trend rebased at a different starting point – Q1 2023, Q1 2024, and Q1 2025. They show what NGDP would have done if Powell’s Fed, having allowed the price-level jump, had at least re-anchored the growth rate at 4% from each of those points forward.

The actual path lies above all three.

I am not, to be clear, arguing that Powell should have tried to bring NGDP back to the original pre-2020 trend. That would require at least a Paul Volcker-scale disinflation, and the current fiscal arithmetic – debt above 100% of GDP, interest payments above $1 trillion – makes that mathematically impossible without causing a US sovereign default (more on that below). The price-level jump is permanent. We have to live with it.

But the growth rate is a separate question. After the price-level jump, the Fed could have re-anchored at 4%. It chose not to. From Q1 2023 to today, NGDP growth has averaged 5.4% per year – 1.4 percentage points above the established regime rate. From Q1 2025 alone, the rate is 6.2%. The growth rate is, if anything, drifting higher rather than settling.

So the Fed in 2025 and 2026 is not running a tight policy that has finally restored discipline. It is running a policy that continues to accommodate above the long-run trend. Powell tightened relative to the catastrophe of 2021-22, yes.

But he has not returned the regime to anything resembling the Bernanke-Yellen anchor. The “tightening” is in scare quotes for a reason. It is tightening relative to the Fed’s own previous error, not tightening relative to a credible monetary framework.

The Burns parallel

The historical episode that maps most directly onto the current situation is the early 1970s. President Nixon, facing reelection in 1972, applied direct and well-documented pressure on Federal Reserve Chair Arthur Burns to ease monetary policy. The Nixon tapes contain explicit instructions – not requests, instructions – that Burns increase the money supply ahead of the election.

Burns complied. He explained, meeting by meeting, why the next move would have to wait, and each individual decision sounded reasonable in isolation. The cumulative result was a decade of inflation, requiring a Volcker disinflation that took unemployment to nearly 11% to break.

The Burns analogy applies to Powell only partially. He raised rates aggressively in 2022 and 2023, the disinflation was real, and he resisted Trump’s public pressure throughout the second Trump term. He has not been Burns on the political-pressure dimension.

But on the analytical dimension – the willingness to ignore the inflation indicators when the prevailing framework said not to act – the parallel is uncomfortably close.

Burns ignored the monetary aggregates because his framework said they did not matter. Powell ignored them in 2021 because FAIT said the inflation was wanted. The institutional failure has the same shape, even if the specific theoretical justifications differ.

The cost of the failure is what we are living through

Here is the structural problem the dual-mandate framing does not capture, and which I think is the single most important thing about the current moment.

Trump has spent the past year publicly arguing that Powell has run policy too tight, that interest rates should be lower, and that the Fed has been hostile to the administration. The chart above shows that the empirical case for this is essentially nil. Powell has run the easiest monetary policy in modern Fed history. NGDP growth has been substantially above the regime rate since 2021. The federal funds rate is below where most reasonable models would put neutral, given the inflation conditions of the past five years.

So why does the political attack land?

It lands because the Fed has, over the same period, demonstrated in real time that it cannot be trusted to act on the inflation indicators when its preferred theoretical framework tells it not to. The credibility deficit Warsh inherits is not just a number. It is the gift that the Powell Fed has handed to anyone, including Donald Trump, who wants to argue that the institution does not deserve the deference that has historically been extended to it.

A Fed that had not produced 9% inflation in 2022 – because it had read the broad money data correctly in 2021 and acted on it – would now be defending its independence from a position of overwhelming strength. Trump’s attacks would land in empty air. Markets would dismiss them. Congressional Republicans would be reluctant to follow.

Instead, the Powell Fed defended independence from a position of partial credibility, after producing a major inflation that the public and the political branch had to absorb. The independence defence has held so far. But the defence is much weaker than it would have been, and the institution is much more politically vulnerable than it would have been, because of the 2021 failure.

This is the link between the easy monetary policy of 2021 and the political crisis of 2025-26 that almost no commentator is drawing. They are not separate stories. They are the same story.

The fiscal-dominance story

Federal debt held by the public now exceeds 100% of GDP. Net interest payments on the federal debt have surged from about $350 billion in 2020 to over $1 trillion in 2025.

Every 100 basis points of higher policy rates adds roughly 1% of GDP to the federal interest bill over time. The federal government has a powerful, mechanical reason to want lower rates – independent of whether lower rates are appropriate for the macroeconomy.

Trump has been remarkably explicit about this. He has stated publicly, more than once, that lower rates would help the federal budget. He simply wants lower rates, and I think he wants them for fiscal reasons, not for macroeconomic ones.

This is the condition economists call fiscal dominance – a regime in which the fiscal authority’s needs constrain the monetary authority’s ability to conduct independent monetary policy.

It is, in fact, the structural condition behind essentially every major historical inflation episode I can think of, from the German hyperinflation of 1923 through the Latin American inflations of the 1970s, the post-Soviet inflation in Russia, the chronic Argentine inflation, and the Turkish episode under Erdoğan. The 1970s American and European inflations were milder versions of the same dynamic.

What changes with Warsh

Some have argued that Kevin Warsh will be a hawk because he dislikes QE, and that reading is, in my view, partly right and substantially wrong at the same time.

The right part first. Warsh was a Fed governor from 2006 to 2011, and he has, as a private citizen, been a critic of the Fed’s enlarged balance sheet and of what he has called mission creep into financial-stability and macroprudential territory. Those are positions I have considerable sympathy for.

What economists call The Tinbergen rule says that each policy target requires its own policy tool, and a central bank that pursues price stability, full employment, and financial stability all at once – while adding new macroprudential objectives on top – is going to do at least one of them badly.

Goodhart’s Law adds the further point that when a measure becomes a target, it ceases to be a good measure. The macroprudential indicators the Fed has been adding to its toolkit are precisely the kind of measures Goodhart’s Law was written about.

So a Warsh chairman who shrinks the institutional ambition of the Fed and pushes back on macroprudential creep is, in my view, doing useful work, and I would actively welcome that part of the transition.

The wrong part, and the more important part, is what Warsh proposes to replace it with. And here, in my view, the answer is that he does not propose anything specific.

Warsh’s diagnosis of where the Fed has gone wrong over the past 15 years is the easiest part of the analysis. What is missing is the framework that would replace the discretion he wants to remove.

He has not said what monetary policy rule he wants the Fed to follow. He has not said how he would handle the next zero-lower-bound episode. He has not said whether he prefers inflation targeting, NGDP targeting, price-level targeting, or something else. He has not said how he would respond if the federal government pushed the debt above 130% of GDP and demanded accommodation.

Warsh’s IMF speech from April 2025 was, in many ways, a job application written for Trump’s ear – a list of grievances about institutional drift, paired with suggestive language about Federal Reserve independence that pointed in the direction of less independence rather than more.

There is also, frankly, the family dimension. Warsh is married to Jane Lauder, daughter of Ronald Lauder, who is a long-standing personal friend of Donald Trump.

Under normal circumstances I would consider this irrelevant. But these are not normal circumstances. The President has spent the past year publicly demanding lower rates.

The investigation into Powell was, by most credible accounts, a political pressure tactic. So when the President’s preferred candidate to replace the chair turns out to have a family connection to the President’s inner circle, that fact deserves to be on the analytical record.

The Friedmanite reading

I take a fairly simple lesson from all of this, and it is the point on which I want to land.

Milton Friedman wrote, in his Nobel lecture and many times before and after, that monetary policy works with long and variable lags and cannot reliably fine-tune the economy. Scott Sumner has updated this with the observation that monetary policy works with long and variable leads, because the transmission runs through expectations rather than through mechanical adjustments to short rates.

Both formulations point to the same institutional conclusion: what matters is the regime, not the meeting-to-meeting decisions. A Fed that has spent five years being technically careful about each individual meeting has lost sight of the regime question altogether.

So watch today’s press conference for what Powell does and does not say about his own future. Watch the June meeting for the first signal of what the new regime actually looks like. And watch, after that, for whether the Warsh Fed acts like an independent central bank that happens to share some of the President’s preferences, or like a central bank whose preferences are increasingly the President’s by another route.

It is not a rate decision. It is a regime decision.

The chairman leaves today. The hard part starts in June.

The chief executives of the world’s most powerful artificial intelligence companies have been saying remarkable things about the future of work.

Dario Amodei of Anthropic has warned, in multiple interviews, that AI could eliminate half of all entry-level white-collar jobs within the next few years.

Sam Altman of OpenAI has spoken of AI agents “joining the workforce” and of intelligence eventually becoming “too cheap to meter”, a utility as abundant as electricity, capable of rewriting the rules of the economy.

Jensen Huang of Nvidia, perhaps the most ebullient of the three, has described a future workforce of “humans and digital humans,” predicted that companies will hire and onboard AI agents just as they do people today, and suggested that the current infrastructure buildout is the largest in human history.

These are not idle remarks. They come from people who run companies at the very centre of the AI industry, who speak at Davos and on 60 Minutes and to US congressional committees, and whose words move markets. They deserve to be taken seriously, and that is precisely why they deserve to be examined seriously.

I want to offer three arguments against the prevailing Silicon Valley consensus on AI and jobs. The first is empirical: the evidence does not support the predictions. The second is technical: the predictions rest on a misunderstanding of what large language models actually are. The third is economic: even setting aside the first two objections, the predictions ignore the most fundamental question of all: at what price?

What the Evidence Shows

The most rigorous attempt to date to measure AI’s actual effects on firms and workers was published in early 2026 by a team including Nicholas Bloom and Steven J. Davis, drawing on surveys of thousands of senior executives across four major economies. The overwhelming majority reported no measurable effect of AI on either employment or productivity over the preceding three years. The effects, where reported at all, were vanishingly small.

This is not an isolated finding. A Yale Budget Lab study, reviewing Bureau of Labor Statistics data through late 2025, found no significant differences in employment outcomes between occupations with high and low AI exposure.

Sam Altman himself acknowledged at a recent conference that companies are blaming AI for layoffs “whether or not it really is about AI”, an admission that ought to give pause to anyone constructing a narrative of AI-driven displacement.

Robert Solow observed in 1987 that you could see the computer age everywhere except in the productivity statistics. The paradox named after him has not gone away. It has simply acquired new occupants.

This should not be surprising to anyone who studies the history of general-purpose technologies. The personal computer arrived in the early 1980s, but the productivity gains it enabled only became measurable in the 1990s. The pattern is consistent across technological revolutions: the gap between a technology’s demonstrated capability and its measurable economic impact is large, and it is measured in decades rather than years.

Altman predicted in early 2025 that AI agents would “join the workforce” and materially change company output within the year. They did not. The prediction has now been quietly extended to 2026, then perhaps 2027.

What Language Models Actually Are

There is a deeper problem with the displacement narrative, which concerns the nature of the technology itself. Large language models are genuinely impressive. But the source of their impressiveness is also the source of their limitation, and that limitation is structural, not a matter of scale.

At their core, these systems are prediction machines. They are built to estimate, given a sequence of words, what word is likely to come next, a process trained on an enormous corpus of human-generated text. The outputs can be fluent, coherent, and occasionally brilliant.

But the mechanism is statistical pattern completion, not reasoning. When a language model produces an analysis of a legal question or a financial situation, it does so not because it understands the question, but because it has encountered vast quantities of text in which similar questions were discussed in similar ways.

The financial industry has been doing something broadly analogous for decades, using quantitative models to find patterns in data and generate predictions. Nobody called those systems intelligent, and nobody suggested they would replace lawyers and analysts wholesale. The AI revolution, in important part, is the democratisation and broadening of such methods, not a qualitative leap into something categorically different.

This matters enormously for the displacement question. The tasks at which these systems genuinely excel are those resembling sophisticated pattern completion: drafting standard documents, summarising lengthy texts, generating code from specifications, producing first drafts from structured inputs.

The tasks at which they remain genuinely poor are those requiring abstract reasoning, causal inference, judgement under genuine uncertainty, and the kind of theoretical model-building that underlies the higher-value components of professional work.

Apple’s research division published a paper in 2025 testing frontier models on logical puzzles requiring genuine reasoning, and found that performance collapsed at high complexity even when the correct method was provided explicitly. The METR research group found, in a randomised controlled trial, that experienced software developers were measurably slower when using AI assistance than without it, and that is this is the domain where AI is supposed to perform best.

Jensen Huang is fond of arguing that AI will enhance rather than replace professionals: the radiologist, he says, will use AI to handle routine work and focus on judgement and care, making hospitals more productive and creating more jobs. This is a reasonable description of what AI does well. But it is precisely not the scenario of mass white-collar displacement. Enhancement and elimination are different economic mechanisms, and they have different implications.

The Price Nobody Mentions

The third objection is the one that I find most decisive as an economist, and the one that receives almost no attention in the public debate.

The current price of AI services does not reflect the true cost of producing them. The leading AI companies are, by their own internal projections, running significant losses and do not expect to reach positive cash flow until the late 2020s at the earliest, and those timelines have already been revised once.

They are sustained by a continuous flow of investor capital at valuations that require extraordinary future growth to justify. The largest technology companies are collectively committing hundreds of billions of dollars annually to AI infrastructure, a scale of capital deployment without precedent in the history of the technology industry.

What this means, economically, is that the price businesses are paying today for AI services is heavily subsidised, not by governments, but by investors who are betting on a future in which these services become vastly more valuable.

The analogy I find useful is this: imagine that every morning a helicopter with a pilot arrived at your door to take you to work, entirely free of charge. Your productivity would rise. You would reorganise your working life around it. You might even let go of some arrangements that no longer seemed necessary. But if you had to pay the actual market cost of a private helicopter and pilot, the calculation would look entirely different. Many of the apparent gains would evaporate.

This is the position businesses are in today with AI. They are restructuring around a technology priced far below its true cost.

When prices normalise, as they must if the companies providing these services are ever to become profitable, many applications that currently appear economically attractive will prove not to be. The entry-level professional who seemed redundant next to a free AI agent may look considerably less redundant next to a properly priced AI agent.

There is also a resource constraint that the displacement narrative tends to ignore. Running AI at the scale Amodei and Altman envision requires enormous quantities of electricity, specialised chips, water for cooling, and capital for infrastructure.

The International Energy Agency projects that global data centre electricity consumption will roughly double by 2030, reaching the equivalent of Japan’s entire annual consumption, and that projection does not assume anywhere near the scale of white-collar displacement being predicted.

If one took seriously the claim that half of all entry-level white-collar work would move to AI within five years, the implied demand on physical infrastructure would be orders of magnitude larger than anything currently being planned. AI does not abolish scarcity. It relocates it.

The Incentive Structure

It would be unfair not to note that Amodei, Altman, and Huang are not disinterested parties in this debate. They are the chief executives of companies whose valuations, fundraising capacity, and competitive positioning all depend on a compelling story about AI’s transformative economic impact.

Sustaining investor confidence in the capital deployed requires a narrative of imminent disruption. Amodei himself has acknowledged that he is deeply uncomfortable with a small group of people making decisions about technology that will affect everyone, yet he continues to make exactly those kinds of claims about economic transformation, from exactly that position.

Altman was at least admirably honest at a recent conference, saying of the current moment: “If there was an easy consensus answer, we’d have done it by now, so I don’t think anyone knows what to do.” That is a notably different register from predicting that AI agents will join the workforce within the year, or that intelligence will become too cheap to meter. The gap between private uncertainty and public prophecy deserves attention.

What Will Actually Happen

None of this is an argument that AI will leave the economy unchanged. It will not. These are genuinely useful tools, and their usefulness will grow as the technology develops and as institutions learn to integrate it into their workflows in ways that are reliable, safe, and cost-effective.

The appropriate historical frame is not the industrial revolution but something more modest and more instructive: the spreadsheet. The spreadsheet did not eliminate finance departments. It changed what finance departments did, making certain kinds of analysis cheaper and faster while freeing human attention for the work that actually required judgement. Demand for financial analysis expanded to fill the additional capacity. Employment in finance did not collapse.

The Jevons paradox, named for the nineteenth-century economist who observed that more efficient steam engines led to more coal consumption rather than less, is worth keeping in mind here.

If AI genuinely makes junior professionals more productive, the likely consequence in many sectors is not that firms need fewer of them, but that demand for their services expands. Lower effective cost stimulates demand. The structure of employment changes; the aggregate volume does not necessarily decline.

I should be transparent about my own position. I use these tools every day, and they have made me more productive in concrete and specific ways. Writing this piece itself involved Claude, which is of course Anthropic’s product.

What I fear most is not mass unemployment. It is a cycle of inflated expectations followed by disillusionment.

After the dot-com crash, many businesses retreated from internet investment at precisely the moment when the genuine long-run benefits were beginning to materialise. The internet did ultimately transform banking, retail, and media but it did so over fifteen to twenty years, not in the two-to-five year windows being promised in 1999.

AI will likely follow the same arc. The worst outcome would be a premature rush driven by subsidised pricing and exaggerated predictions, followed by retrenchment, delaying the genuine benefits by a decade.

The technology is real. The potential is genuine. But Solow’s paradox did not disappear because the predictions got louder. And the entry-level lawyer, the junior consultant, and the graduate analyst may prove rather more resilient than the prophets of Silicon Valley believe, not because AI is unimpressive, but because impressive technology and economically viable technology are not the same thing, and because scarcity, as economics has always insisted, cannot be wished away. It can only be moved.

But Anthropic’s Claude certainly is an amazing product, because it helped me write a lot of this post. Then again, Claude was trained on, among other things, this very blog. Maybe I should ask for a discount on my Claude subscription. I should also confess that writing this article required a fair amount of time correcting Claude’s hallucinated figures and citations that simply did not exist in reality. Perhaps Amodei hallucinates too.

Lars Christensen is an economist, Head of Analysis and co-founder of PAICE, and external lecturer at Copenhagen Business School’s Department of Digitalization. He is the originator of Market Monetarism and writes The Market Monetarist blog.

Contact: LC@paice.io

Regular readers of this blog will know that most of what I do here is explain, analyse, and argue about macroeconomics, monetary policy, and increasingly artificial intelligence. That work is public and free – and I intend to keep it that way.

But over the past year, we have increasingly been asked – through PAICE, the consultancy I co-founded – to bring that kind of thinking directly into organisations. Investment committees, boards, executive teams, and strategy sessions.

So we have formalised that offering under the name Expert Briefings.

What is an Expert Briefing?

It is not a standard presentation. It is a tailored, interactive session where your organisation gets focused access to specialist knowledge on the topics that matter most to you right now. We start from your industry, your risk exposure, and your questions – and work from there.

At PAICE, we cover three interconnected areas:

Macroeconomics, monetary policy, and financial markets – global growth, inflation, central bank policy and communication, interest rates, currencies, commodities, and financial imbalances. The question we always come back to: what does any of this actually mean for your business, your investments, and your risk picture?

Geopolitics and business risk – trade conflicts, energy markets, security policy, and geopolitical shifts. And critically: the concrete implications for companies and investors who have to make decisions in an uncertain world.

Artificial intelligence and technology – the latest developments in AI, what they mean for your specific sector, and how you position yourself strategically when the pace of change is this fast.

These three areas can be addressed individually or in combination, depending on what your organisation needs.

Who is this for?

The organisations we work with range from pension funds and asset managers who need regular macro and market input for investment committees, to CEOs, CFOs, and boards who want an independent external perspective on economics, monetary policy, technology, and geopolitics. Exporters and multinationals dealing with currency risk and trade policy. Banks building internal analytical capacity. Technology companies navigating AI regulation and competitive dynamics.

The common thread is that they want direct access to expertise – not a consultant’s slide deck, but a genuine conversation with someone who has spent decades thinking about these questions.

Formats

We try to be flexible about how this works in practice. Some organisations want a regular monthly or quarterly cadence to stay continuously updated. Others prefer an on-demand retainer – a standing arrangement that allows them to call on a briefing when the need arises, without going through a full procurement process each time. And sometimes the need is simply a one-off session for a strategy day or board meeting.

For topics that genuinely benefit from multiple perspectives, we can also convene an expert panel – two or more specialists combining, for instance, macroeconomics with AI and technology, or geopolitics with energy markets.

Who delivers the briefings?

The briefings are anchored by me. I spent fifteen years as Head of Emerging Markets Research at Danske Bank, including co-authoring the 2006 “Geyser Crisis” report that identified the risks building in the Icelandic banking system ahead of the 2008 collapse. Today I serve as co-founder, co-owner, and Head of Analysis at PAICE, where our work sits at the intersection of macroeconomic analysis, monetary policy, AI, and data.

For broader panels and cross-disciplinary sessions, I draw on PAICE’s network of specialists across macroeconomics, monetary policy, financial markets, artificial intelligence, technology, and geopolitics.

Getting in touch

If any of this sounds relevant for your organisation, we are happy to have an initial conversation about what might make sense.

Contact: hello@paice.io

Speaker platform: http://www.globaletanker.dk (in Danish)

Blue Owl is one of Wall Street’s big names in private credit – a manager of nearly $300 billion in assets. The company’s logo is an owl: the animal that, according to legend, can see everything, even in the dark.

On February 19, 2026, Blue Owl restricted withdrawals from one of its retail-focused funds and quickly sold $1.4 billion in loans to raise liquidity. Investors who wanted out couldn’t get out. The stock has fallen nearly 60% over 13 months.

The blue owl in the coal mine had not seen it coming.

It reminds us of something we have seen before.

In the mid-2000s, many were warning about the American housing market. Lending standards were too loose. Too much capital was chasing too few good loans. And those bearing the risk often didn’t know they were doing so. We know how that story ended.

There is now a new part of the financial system that deserves the same attention. It is called private credit. Most people have never heard of it – and that is itself part of the problem.

Regulatory arbitrage and monetary policy created this market

Private credit is fundamentally a child of two policy choices.

The first was regulation. Banks were subjected to far stricter capital requirements via Basel III after 2008. The intention was understandable enough – but the consequence was predictable: capital and credit demand do not disappear because banks withdraw. They move precisely to where regulation does not follow.

Private credit funds operate without equivalent capital requirements, without the same transparency requirements, and without meaningful macroprudential oversight.

This is the definition of regulatory arbitrage – and it is a foreseeable consequence of asymmetric regulation, not an accidental side effect. The IMF noted in its Global Financial Stability Report in April 2024 that insurance companies were also incentivized to move into private credit precisely because the capital charges are lower and less risk-sensitive than those applicable to commercial banks. Regulation did not reduce risk. It relocated it.

The second was monetary policy – but let us be precise here. This is not the story of a decade of near-zero rates after 2008. That is the wrong diagnosis.

This is the story of what happened from 2020. The COVID response triggered the largest expansion of the American money supply in peacetime history. M2 grew by nearly 27% year-over-year in early 2021 – the highest peacetime rate since the Federal Reserve was founded in 1913. Some of this expansion was justified given the lockdowns. But it continued far too long.

That sent a tsunami of capital into private credit, because institutional investors desperately sought returns in a world where traditional fixed income products yielded nothing.

The market grew from $2 trillion to $3 trillion in precisely that period. When the money supply and rates finally turned from 2022, enormous sums were already locked into illiquid structures held by borrowers priced for a world of extraordinarily cheap money.

Regulation that does not eliminate risk, but merely displaces it – combined with a tsunami of liquidity. That is precisely the cocktail that mixed the subprime crisis.

The Austrian school element: the AI boom as malinvestment

It is worth drawing on an older analytical tradition here – but with an important caveat.

Friedrich Hayek and Ludwig von Mises were the two central figures of the Austrian school – a tradition in economic thinking that flourished in interwar Vienna before spreading to London and Chicago. Hayek received the Nobel Prize in Economics in 1974.

Their theory of the business cycle – known as Austrian Business Cycle Theory (ABCT) provides an explanation of what happens when central banks hold interest rates artificially low for too long.

The argument is simple. When the rate is lower than the market would have set itself, a false signal is sent to investors: capital is cheaper than it really is.

This attracts investment into projects that only look profitable at artificial financing costs – not at the natural rate. Hayek and Mises called this malinvestment – misallocations that look sensible during the boom, but are exposed brutally when monetary policy normalises.

But here is the caveat – and it is important. Austrian Business Cycle Theory is a theory of the unsustainable boom. It explains how the misallocations arise. It does not explain what happens next – and in particular, it does not tell us whether the bust will become a catastrophe. That depends on something else entirely: monetary policy.

The AI boom is a textbook example of the first part of that story.

What we could all ‘The Tesla boom’ of 2020-21 was to a large extent what financed the training of ChatGPT. The COVID liquidity injection sent capital into tech equities and pushed financing costs for AI companies below the natural rate – precisely the Hayekian mechanism, just with a modern transmission. Credit channels kept the expansion going far beyond what the underlying productivity numbers could justify.

Now we are in the middle of the massive buildout of data centres necessary to make the business models profitable – the four largest tech companies spent $360 billion on AI infrastructure in 2025 and are planning $650 billion in 2026. And the financing story is becoming increasingly speculative: AI companies are seeking capital in the Middle East, the Trump administration is talking about using Fannie Mae and Freddie Mac to finance the sector. It resembles the Icelandic banks in 2006-07, rolling around finding new liquidity sources while the warning lights were flashing.

The underlying problem is that the business model that must repay the debt is far weaker than assumed. Microsoft’s AI chief Mustafa Suleiman recently promised that AI would automate most office jobs within 12-18 months. A large study from the National Bureau of Economic Research of 6,000 senior executives shows that nearly 90% report AI has had no measurable effect on employment or productivity. Penn Wharton estimates AI’s contribution to productivity growth at 0.01 percentage points in 2025.

That is not an argument against AI as a technology in the long run – I am after all a huge fan of Large Language Models and a addictive user of LLMs – but it is an argument against pricing $650 billion in annual infrastructure investments on promises that the data consistently contradict.

Private credit and the AI boom are not two separate stories. They are two symptoms of the same misallocation of capital – both created by the same COVID fiscal and monetary expansion, both now under pressure as the bill is presented.

Moral hazard under the Trump boom

On top of regulatory arbitrage and the COVID monetary expansion came a third element: moral hazard on a grand scale.

I have for some time been warning about the dangerous fusion of the Trump administration and parts of American business – particularly the tech sector. When Trump administration AI czar David Sacks says “we can’t afford to go backwards”, I read it as an implicit promise: the government will underwrite the AI boom.

And when large tech companies are increasingly defined as strategically important – as “too big to fail” – there arises precisely the incentive problem that economist Robert Hetzel identified in his analysis of the financial crisis: financial institutions take greater risks when they know others bear the consequences. Gains are privatised. Losses are socialised.

This is not a new phenomenon. It is the same dynamic we saw with Fannie Mae and Freddie Mac before 2008. And it almost always ends the same way.

The cockroaches

The warning lights began flashing in earnest in the autumn of 2025. Let us go through the events in chronological order – because the pattern is more troubling than any individual episode in isolation.

Tricolor Holdings collapsed first. The company operated as both a used car dealer and a subprime lender – packaging high-yield car loans to credit-impaired borrowers into AAA-rated securities and selling them on to investors. When the repayments failed, the whole construction collapsed.

Fifth Third Bank is now accusing Tricolor of fraud, claiming the company pledged the same assets as collateral for multiple loans simultaneously. Investigators are reviewing what may prove to be a manipulated loan database.

First Brands Group followed shortly after. Just weeks before the bankruptcy, the company was marketed by Jefferies Investment Bank as an opportunity for $6 billion in lending – and the company was said to have nearly $1 billion in cash.

It collapsed anyway. It had borrowed massively for acquisitions, then borrowed again against invoices and inventory – and when tariff pressure hit imported components, there was no margin left. Jefferies, Millennium Management, JPMorgan, Barclays, and Fifth Third Bank are all exposed.

Jamie Dimon put it precisely: “When you see one cockroach, there are probably more.”

Then came a series of events that together sketch a pattern. BlackRock wrote down a private loan from full value to zero in three months. This is not as surprising as it sounds: private credit loans are not traded on markets but valued using internal models – what is known as mark-to-model. The IMF documented in its Global Financial Stability Report in 2024 that adjustments to private credit valuations are systematically smaller and slower than in public markets, and that it takes at least four quarters for prices to converge after a shock.

Losses are there before they are visible. A related warning sign is the sharp rise in payment-in-kind interest – where borrowers pay their interest by adding it to the loan principal rather than in cash. The IMF found that the payment-in-kind share in business development company portfolios doubled between 2019 and 2023. Borrowers are not defaulting. They are deferring. Blue Owl restricted withdrawals and sold assets to raise liquidity. Blackstone’s large private credit fund BCRED experienced redemption requests in early 2026 that exceeded the fund’s quarterly cap. And Morgan Stanley and Cliffwater have both been forced to limit withdrawals from their retail-focused funds after investors tried to redeem more than the structures allow.

The official default rate has risen steadily: from 1.76% in Q2 2025 to 1.84% in Q3 and 2.46% in Q4 2025 according to Proskauer’s Private Credit Default Index. That still sounds low – but when debt restructurings and creative loan extensions are included, the real rate approaches 5%.

Fitch puts the actual default rate in private credit at 5.8% through January 2026 – the highest since the index began. In February 2026 alone, 11 default events were recorded, nearly double the monthly average for all of 2025.

Mohamed El-Erian describes it as the “ATM scenario”: investors who cannot exit illiquid positions begin selling what they can sell – regardless of asset class. “If you can’t sell what you want, you sell what you can.”

That is the classic contagion mechanism – not losses, but liquidity pressure that propagates. It resembles a traditional bank run.

And lurking behind the share prices is a mechanism that has not yet fully played out.

The large private credit managers are today rated at the lower end of investment grade. One or two notches down, and they fall below the investment grade threshold – and this is not just a question of prestige. Pension funds, insurance companies, and large bond funds operate under mandates that forbid them from holding securities below investment grade.

A downgrade to junk triggers automatic forced selling from all these institutional investors – not because they want to, but because they are regulatorily obligated to. The IMF estimated in 2024 that pension funds and insurance companies globally had more than $600 billion invested in private credit funds – a figure that has grown rapidly since.

Some of the world’s largest pension funds, with combined assets exceeding $7 trillion, have significantly increased their allocation to private credit while simultaneously raising their financial leverage. The IMF identified this combination – illiquid assets and leveraged balance sheets – as a specific systemic risk. When collateral calls come, these institutions sell what is liquid. That is El-Erian’s ATM scenario in institutional form.

UBS estimates that in a severe AI disruption scenario, the US private credit default rate could hit 13% – twice the stress level for leveraged loans.

That creates precisely the cascade we know from 2008: forced selling pushes prices down, which pushes other funds toward the same threshold, and suddenly it is no longer just private credit under pressure, but all liquid assets that investors must sell to create room on the balance sheet. El-Erian’s ATM scenario, but in a rules-based and automated version.

The blue owl in the coal mine was not the only bird in the mine.

The near-perfect copy of 2008

One must be precise here. $3 trillion sounds like a lot, but in global financial context, the private credit market is relatively modest. The American equity market alone is about 15 times larger. The global bond market is more than 30 times larger.

But that is the wrong way to frame the question.

The subprime market was not the world economy’s largest market in 2006 either.

It still triggered the worst financial crisis since the 1930s. Then-Federal Reserve Chairman Ben Bernanke said himself in 2007 that the problems were “contained”.

They were not – because it was not about subprime’s absolute size, but about its connections to the rest of the financial system and what it revealed about the broader misallocation of capital. It is worth noting that the IMF’s own Global Financial Stability Report from April 2024 concluded that the financial stability risks from private credit “appear contained at present”. That is a precise echo of Bernanke’s formulation. It may prove equally accurate.

That is precisely the same point here. Private credit may not be large enough in itself to trigger a global crisis. But it can be the symptom of a far larger misallocation of capital – driven by the same COVID expansion, the same artificially low rates, and the same moral hazard – that will manifest as losses elsewhere in the financial system as reality catches up with the valuations made in a world that no longer exists.

This is where the data matters. U.S. nominal GDP growth remained relatively stable at around 5-6% through 2006 and into early 2007 – even as the first subprime warning signs appeared and global imbalances were becoming visible. The private credit market was wobbling. But the economy was not yet in crisis. It was only when those financial tensions translated into a de facto monetary contraction – and NGDP growth began to fall sharply through 2007-8 – that a financial correction became a macroeconomic catastrophe.

Critically, central banks made it worse. Spooked by what they perceived as bubble re-ignition – and facing rising headline inflation from an oil price surge – they turned hawkish at precisely the wrong moment (a number of central banks even hiked interest rates during the Summer of 2008). The result was a collapse in nominal spending that turned a necessary market correction into the Great Recession.

Secondary deflation, as Hayek called it, is not a natural consequence of a bust. It is a consequence of monetary policy failure.

The same risk exists today. Private credit may well be mis-priced. AI investment may well include substantial malinvestment. These corrections can be painful. But they do not have to become systemic crises. The key variable is not the size of the bust. It is whether the Fed ensures nominal stability through it.

On February 28, 2026, the US and Israel launched “Operation Epic Fury” – a coordinated strike on Iran that killed Ayatollah Ali Khamenei and threw the country into chaos.

Iran responded with drone and missile attacks on the Gulf states and effectively closed the Strait of Hormuz – the narrow passage through which 20% of the world’s oil consumption passes daily. Brent crude, which was trading below $70 per barrel at the start of February, hit above $100 earlier this week. That is a price increase of over 35% in fewer than four weeks.

Private credit is already cracking, as described above. The AI boom is under pressure from rising financing costs – and higher energy prices is not exactly good news for the energy intensive AI sector either. And now the oil price shock is a reality – the classic stagflation scenario that economists since 1973 have feared repeating.

In that situation, the Fed faces a near-impossible choice. The mandates point in opposite directions. Inflation is too high to ease.

The credit tightening calls for easing. This is the classic monetary policy dilemma that institutional rules and mandate structures make nearly impossible to resolve correctly – and it is precisely the dilemma that in 2008 turned a credit crisis into an economic catastrophe.

There is a way out of this dilemma – but it requires a framework the Fed does not have. A 4% NGDP level target would cut through the confusion between supply-side inflation and demand collapse.

Under such a framework, a central bank does not respond to rising oil prices by tightening – because oil price inflation does not represent excess nominal demand. It responds to falling nominal spending – which is the actual threat.

In 2007, the Fed had no such framework. It does not have one today either. As the German-American economist Rudi Dornbusch put it: “No postwar recovery has died in bed of old age – the Federal Reserve has murdered every one of them.”

And if Kevin Warsh – Trump’s candidate as the new Fed chairman –-is at the helm, it becomes even harder.

Warsh is ideologically sceptical of quantitative easing – the central bank’s purchases of bonds to pump liquidity into the economy when rates hit zero – and has historically viewed the Fed’s balance sheet expansion as a problem rather than a solution.

In the scenario where rates hit the zero lower bound and conventional monetary policy runs out of road, the political and ideological resistance to reaching for precisely this instrument will be maximal – at precisely the moment it is most needed.

Add to this a chaotic White House that is actively undermining the central bank’s credibility and institutional independence, and the picture is complete.

This is not a prediction. Most scenarios probably end with a gradual correction – losses at the weakest actors, tightening of standards, consolidation. That is the normal credit cycle.

But the pieces are placed in a way that is uncomfortably reminiscent of 2008. Only with a markedly worse political situation, a Fed chairman who is not Ben Bernanke, and a supply shock that Bernanke never had to contend with.

Two weeks ago, Microsoft’s AI chief Mustafa Suleyman told the Financial Times that most white-collar work will be fully automated within 12 to 18 months.

Lawyers, accountants, project managers, marketing professionals – everyone who “sits at a computer.”

Anthropic’s CEO Dario Amodei has warned that AI could eliminate half of all entry-level office jobs. Ford’s Jim Farley predicts a halving of white-collar jobs in the US.

In early February, Anthropic launched Claude Cowork – an AI agent capable of performing legal work, among other things.

Thomson Reuters fell 16% in a single day, LegalZoom dropped 20%, and Atlassian lost 35% in a week. According to JP Morgan, it was the largest non-recession-driven decline in software equities in over 30 years.

These are dramatic claims. They deserve a serious economic response.

I should say upfront: I am not an AI sceptic. I use AI extensively in my own work – for research, for drafting, for editing and building a whole lot of macroeconomic, financial and geopolitical simulation models.

This very article was produced with substantial AI assistance. It raises the quality and saves time. But the claim that all office work will disappear within 18 months rests on a fundamental misunderstanding of what large language models are, what they can do, and what they cost.

In short: AI is a tool, not a thinker. It can automate the routine part of office work, but not the thinking part. And even as an automation tool, it is not free: it requires capital and energy on a scale that makes it far from certain that it is cheaper than human beings.

Solow’s paradox, revisited

“You can see the computer age everywhere but in the productivity statistics.”

So wrote Nobel laureate Robert Solow in the New York Times in 1987.

Nearly 40 years later, his observation is once again strikingly relevant. In February 2026, a team of prominent economists – including Nicholas Bloom and Steven J. Davis of Stanford – published the first representative international study of AI’s actual effects at the firm level.

The data come from central bank-sponsored surveys in four countries – the US (Atlanta Fed), the UK (Bank of England), Germany (Bundesbank) and Australia – covering nearly 6,000 senior executives, recruited by telephone rather than through paid online panels (which are riddled with fraud).

Over 90 per cent report no effect on employment over the past three years. 89 per cent report no effect on productivity. The average employment effect is literally zero – 0.00 per cent – and the productivity effect a modest 0.29 per cent.

The Solow paradox is alive and well.

This should not surprise anyone who understands what a large language model actually is.

Language models are econometrics in disguise

A large language model such as ChatGPT or Claude is built on the transformer architecture from Vaswani et al.’s “Attention Is All You Need” (2017).

At its core, the technology is next token prediction: the model predicts the next word in a sequence based on statistical patterns in the training data. When ChatGPT produces an analysis of the causes of inflation, it does not do so because it understands inflation.

It does so because it has seen millions of texts where certain words appear together in certain patterns.

This is – and this is my central point – fundamentally no different from econometrics. A regression model says: given these variables, what is the most likely value of Y?

A large language model does the same: given these words, what is the most likely next word? The mathematics is more complex, the dimensionality higher, but the principle is identical.

The financial sector has been using quantitative models for decades without calling it “intelligence.” VAR models, credit risk algorithms, algorithmic pricing – enormously useful tools that help trained analysts make better decisions. The AI revolution consists of democratisation and broader application, not a qualitative leap.

Gary Marcus, professor emeritus at NYU, has argued for over 25 years that neural networks lack the capacity for abstract reasoning.

His central claim – that language models operate on pattern recognition rather than genuine understanding, and that scaling does not solve this problem – was powerfully confirmed by Apple’s research paper “The Illusion of Thinking” (June 2025), which systematically tested reasoning models on controllable puzzles. At high complexity, all models – including the so-called “thinking” models – collapsed to zero per cent accuracy.

Even when given the correct algorithm as an explicit instruction, performance did not improve. Marcus’s conclusion stands: large language models do not build abstract models of the world. They are built to complete sentences.

The models have not fundamentally improved

The transformer architecture is the same in 2026 as it was in 2017.

The advances since ChatGPT 3.5 in November 2022 are primarily more training data, more parameters and better fine-tuning (RLHF). The underlying mechanism – next word prediction via statistical pattern recognition – is unchanged.

The hallucination problem is the best evidence. Three years and hundreds of billions of dollars later, the models still fabricate facts. They have become better at sounding authoritative, but the underlying tendency to invent things is intact. They are better at lying than a teenager about their homework – they politely but firmly insist that their fabricated answers are correct.

And the fundamental failures are banal. Ask ChatGPT: “I need to wash my car. The car wash is 150 metres away. Should I walk or drive?”

ChatGPT advises you to walk – it’s only 150 metres. But you need to wash the car. You rather need to bring it along. This kind of basic logical understanding is still missing. It is difficult to see this technology replacing every bank clerk within 18 months.

The GPT-5 launch in August 2025 illustrated the point. Reddit threads titled “GPT-5 is horrible” received thousands of upvotes. 3,000 users signed a petition to regain access to the older GPT-4o.

On the prediction market Polymarket, the probability that OpenAI would have the best model dropped from 75% to 14% in a single hour. The event was dubbed “Gary Marcus Day” on social media.

Three mechanisms that undermine scaling

Any econometrics student learns about overfitting in the first semester. When you add more variables to a statistical model, R² always rises – but it is a deception. The model has learnt the noise along with the structure.

When you test it on new data, precision often falls. Precisely the same happens with language models. Scaling – more data, more parameters, more compute – yields better reproduction of what the model has seen, but not necessarily better handling of what it has not.

Then there is context rot. A Microsoft/Salesforce study from May 2025 documented that all tested language models performed 30-40% worse in multi-turn conversations than in single-turn queries.

Chroma Research demonstrated that performance degrades systematically with increasing context length — even when the model can identify all relevant information with 100% accuracy.

This is the opposite of genuine understanding. An economist who receives more information typically delivers a sharper analysis. A language model does the opposite. This is a fundamental limitation, not an engineering problem to be solved with more hardware.

And finally, model collapse – a problem Hans Christian Andersen described better than any AI researcher.

In his story “It’s Quite True!” a hen loses a single feather. The story is retold from perch to perch, and with each retelling it grows: one feather becomes five hens that have plucked each other to death from unrequited love. Andersen captured something fundamental: when information passes through successive stages, it is not just signal that is lost – new patterns are invented.

A 2024 Nature paper (Shumailov et al.) documented that when language models are trained on text generated by previous models, they degenerate through successive iterations. The rare phenomena in the tails of the distribution disappear first – the unexpected formulations, the unique perspectives, the nuances.

The dominant patterns are amplified and distorted. An ICLR 2025 Spotlight paper sharpened the conclusion: even the tiniest fraction of synthetic data – as little as 1 in 1,000 – can trigger collapse. And larger models amplify the effect rather than dampening it.

As of April 2025, 74% of newly created web pages contained AI-generated text. The internet – the primary training source – is increasingly filled with text produced by previous models.

For an economist, this is a classic endogeneity problem: the dependent variable feeds back into the independent variables. The result is biased and inconsistent estimates that diverge from reality over time. Andersen’s henhouse on an industrial scale.

Theory is a modelling strategy

Here is what the entire AI debate is missing: an understanding of the role of theory.

Theory is not something abstract floating above empirics. Theory is the deliberate decision about what to include, what to exclude, and how to structure the relationship between variables.

It is a modelling strategy. Language models have none. They take everything in and search for statistical regularities. This is the opposite of theory – it is inductivism on an industrial scale.

Two of the 20th century’s greatest economists – who often disagreed on methodological matters – agreed on precisely this.

Milton Friedman argued in “The Methodology of Positive Economics” (1953) that a scientific theory must be judged on its predictions, not on the realism of its assumptions – but that one starts with a theory, not with data.

Ludwig von Mises, in “Human Action” (1949), went further: economic theory can be derived logically from fundamental axioms about human action, and empirics alone can never generate theory. They disagreed about method but agreed on the conclusion: pure inductivism – data without theory – is a dead end. That is precisely what language models do.

Try it yourself. Ask ChatGPT to analyse why food prices are rising. The result is not an analysis. It resembles an average article in the Financial Times.

All the right words are there. The structure is tidy. The arguments are recognisable. But there is no original thought, no unexpected angle, no theoretical framework organising the argument in a new way.

It is the average of everything written on the subject – because that is precisely what a language model produces: basically the statistical mean of its training data.

And average can often be good enough. Many people need a competent first draft or a quick summary. But nobody wants to listen to an average economist. Nobody wants to hear an average song – unless it is background music in a supermarket.

What creates value is precisely what deviates from the average: the sharp analysis that sees something others miss. Language models cannot produce that by definition, because they are built to find and reproduce the average.

The exceptional lies furthest from the centre of the training data distribution – and is therefore precisely what the model is worst at.

No, Suleyman – AI will not take all office jobs

Suleyman’s claim deserves a serious economic response. Think of office work as an aggregate of human labour and AI agents.

Economists use CES (constant elasticity of substitution) production functions to model how easily one factor of production can replace another. Even if we assume high substitutability – that AI can perform many office tasks – it does not follow that humans will be replaced.

Because AI agents are not free. Their “wage” is capital and energy.

Every token a language model generates consumes electricity. Every model requires data centres, GPUs, cooling, maintenance. Office workers are “powered” by relatively cheap energy – food, housing, a salary. AI agents are powered by semiconductors, data centres and electricity. It is far from given that the robot is cheaper than the office worker.

The economic logic is clear: even a perfect substitute only replaces human labour if it is cheaper per effective unit of output. And here two independent constraints apply: rising marginal costs (the more we replace human labour with AI, the more the energy market is squeezed) and the inability to think abstractly (even a free AI agent cannot replace the part of office work that requires theory, causal understanding and strategic judgement). Even perfect substitution on the routine dimension combined with zero substitution on the abstract dimension yields a far more modest effect than Suleyman imagines.

I have experienced the productivity paradox personally.

I sat down to write an article about AI and productivity. I chatted with Claude, iterated back and forth, fine-tuned – and suddenly I had spent five hours on something I would previously have written in one. AI felt faster. It was not. The personal productivity paradox mirrors the macroeconomic one.

Moreover, current prices are artificially low. It costs OpenAI more to run many ChatGPT subscriptions than they receive in revenue. They are running at a loss, funded by venture capital (an extremely easy monetary policy in the US in 2020-21). Sooner or later it must become profitable.

When the subscription price goes from $20 to perhaps $200 a month, many applications that currently feel productive will prove not to be economically viable. According to the IEA, global data centre electricity consumption will double to 945 TWh by 2030 – equivalent to Japan’s entire annual electricity consumption.

The evidence: AI makes experienced programmers slower

For those who believe “vibe coding” and AI agents are already replacing programmers, the METR study from July 2025 is a sobering corrective.

In a randomised controlled trial, researchers followed 16 experienced open-source developers across 246 tasks on mature codebases. Developers with AI tools were 19% slower than those without. But – and this is the key – they believed they were 20% faster.

Before the study, they predicted a 24% speed gain. External experts predicted nearly 40%. Everyone was wrong.

69% of developers continued using the AI tools after the study. Not because they were faster, but because it felt easier. The experience of AI productivity and actual AI productivity can be entirely different things. And remember: programming is arguably the domain where AI performs best, because code can be tested externally – you can run it and see whether it works.

If AI makes experienced programmers slower there, what happens in domains without external verification? Law, strategy, analysis, management?

What the data actually show

There is not a single example in economic history of large and sustained technological unemployment. Not one.

The steam engine replaced hand weavers but created factory workers. The car killed the horse and carriage but created mechanics, road builders, suburbs and the service economy.

The computer automated bookkeeping but created an entire IT industry and thousands of new job functions nobody had imagined. The pattern is always the same: the structure changes gradually. Some jobs disappear, new ones emerge, and the transition can be painful for those affected. But mass unemployment as a consequence of technological progress? It has never happened. And there is no reason to believe AI breaks this pattern.

John Maynard Keynes – arguably the most influential economist of the 20th century – predicted in his famous 1930 essay “Economic Possibilities for Our Grandchildren” that his grandchildren’s generation would work just 15 hours a week.

He got most of it right. Living standards in the advanced economies have risen six to eightfold since 1930, precisely as he predicted. But we work far more than 15 hours a week.

Think of it this way: when productivity rises, we receive an extra pound. We can spend that pound on material goods – higher wages, more consumption.

Or we can buy leisure with it – work less for the same pay. You might expect roughly a 50-50 split. But we have consistently chosen consumption over leisure. We would rather have a larger house, a new car than a three-day working week.

Keynes, who literally worked himself to death during the Bretton Woods negotiations in 1946, underestimated this drive. AI will contribute to a gradual reduction in working hours as we become richer and choose more leisure. But it will be a prosperity-driven choice – not involuntary mass unemployment.

Bloom et al.’s study confirms the broader picture. 69% of firms use at least one AI technology, but the average senior executive spends just one and a half hours per week on AI. 28% do not use it at all. Adoption and genuine integration are two very different things.

The software meltdown of February 2026 is itself revealing.

If the market truly believed AI would broadly replace office work, we should have seen a sharp rise in, say, banking equities – banks would stand to save enormously from cheaper software. That did not happen.

The decline in software equities reflects a general nervousness about overvalued technology stocks rather than a genuine expectation of mass automation. AI profit margins remain concentrated in Big Tech , but the broader stock markethas barely moved.

Suleyman is a salesman, not an economist

One ought to ask what Suleyman’s prediction actually is: an economic analysis or a sales pitch?

He is not an economist. He is the head of the AI division at a company investing hundreds of billions of dollars in AI infrastructure, with a clear interest in convincing investors and customers that their product will revolutionise the world.

And one should look at what Microsoft is actually doing – not what Suleyman says.

While he predicts that office work will vanish within 18 months, his company is investing heavily in developing precisely the office tools that AI supposedly renders obsolete.

Throughout 2026, new features and deeper Copilot integration are being rolled out across Word, Excel, PowerPoint and Outlook.

On 1 July 2026, Microsoft will raise prices on Microsoft 365 subscriptions. The company maintains long-term product roadmaps and support programmes that still require human IT staff to handle bugs and compatibility issues.

If Microsoft genuinely believed in full automation by 2027, why raise prices on subscriptions that presuppose human users? Why invest in product updates that AI supposedly makes unnecessary? The answer is obvious: because Microsoft is betting that Office and Azure will continue generating enormous revenues for years to come, with AI as a supplement – not a replacement. The bold predictions are for investors. The business model is for the balance sheet.

The same applies to Anthropic’s Amodei and all the other technology CEOs competing to outdo each other in dramatic predictions.

Their business models depend on investors and markets believing in the AI revolution. That does not make them liars, but it makes them interested parties – and their predictions should be treated accordingly. One would not uncritically accept an oil company’s assessment of climate policy. One should not uncritically accept an AI company’s assessment of AI’s economic consequences.

Implementation is the real bottleneck

I recently spoke with a large international hedge fund. They wanted to use AI internally, but rules around IT security, data protection and client information prevented them from simply opening up the systems. Banks cannot hand their trading systems to something that hallucinates. Hospital records cannot run on a model that invents diagnoses. These are not irrational barriers – they are the reality firms face.

I encounter far too many companies that believe they have “adopted AI” because they have rolled out Copilot in their Microsoft systems.

If that is the strategy, the firm is in trouble. But the opposite fear – of being overtaken – is also exaggerated. Three years ago, I myself believed it would move much faster. I spoke with business leaders who were eager to get started. A year later, I asked the same leaders how it was going. The typical answer: “Well, there were some other things, and it’s a bit difficult, and we actually don’t want to throw away the company culture.” All perfectly rational choices. But it means implementation takes years, not months.

The parallel to the internet is striking. In the mid-1990s, every company needed a website. But nobody knew what to use it for. It became a glorified address book with a phone number and a postal address. Then people began saying you could perhaps sell things through it. Today you can buy everything online. But it took 15 to 20 years. E-commerce only truly broke through in the 2010s – far later than any of us believed in the mid-1990s. AI will likely follow the same pattern.

This is Solow’s paradox once more: the computer’s productivity gains came only decades later, when firms learnt to reorganise their work processes. Implementation, not faster processors, drove the growth. For AI investors and decision-makers, the implication is clear: if the value is in implementation, people with domain knowledge become more valuable, not less.

The last 5% is everything that matters

The first 90–95% of many tasks is routine. Writing a standard letter, producing background music for an advertisement, drafting a market analysis, generating boilerplate code, summarising a document. This is assembly-line work in the knowledge economy. AI can do it more cheaply and quickly. Those gains should be captured.

But the last 5-10% is everything that matters. The original insight in an analysis, the unexpected move in a composition, the theoretical framework that gives data meaning, the strategic judgement that correctly weighs risks. Abstract thought. Precisely what language models cannot do.

And here we hit something civilisational.

The capacity for abstract thought does not arise in a vacuum. It develops through practice – through wrestling with problems, making mistakes, revising one’s understanding, building intuition over time.

Think of the assistant analyst in a bank’s research department who is asked to photocopy and bind presentations. It is routine work – but in the process she reads the material, walks over to the economist who assigned the task, and they discuss what it actually means. That is on-the-job training. That is how one learns.

If AI takes over the routine, that learning process vanishes. A young economist who has never struggled to specify a model, understand residuals and think about what the data are telling her will never develop the deep intuition that enables her to deliver the crucial 10%.

Just as in music: one cannot compose anything original without having practised scales, played other people’s music, understood harmony and structure from the inside. The repetitive exercises are the precondition for the creative leap. If AI takes over the exercises, the foundation for the leap disappears.

The real risk is not that AI replaces thought now – it cannot. It is that it is just intelligent enough to tempt us to stop thinking for ourselves. Slow atrophy, not sudden disruption. Far harder to measure and counteract than job losses.

AI’s greatest threat is not that it is too intelligent. It is that it is just intelligent enough to fool us into believing it is intelligent.

Conclusion

Mustafa Suleyman is wrong. Not about everything – AI is changing office work, and it is doing so already. But the claim that most office work will vanish within two years is economically untenable. And one should remember that Suleyman is not an economist – he is a salesman for the product he predicts will revolutionise the world.

AI cannot think. It is pattern recognition, not abstraction. It lacks theory, causal understanding and the ability to weigh relevance. It delivers average analyses, never exceptional ones.

It is not free. It requires capital and energy on a scale that makes it far from certain that it is cheaper than humans – especially for the complex tasks where value is created.

The empirical evidence shows it. The METR study shows that AI makes experienced programmers slower.

And there is not a single example in economic history of large and sustained technological unemployment. Keynes predicted in 1930 that we would work 15 hours a week. We chose to work more and consume more. AI will not change that dynamic.

AI will deliver real productivity gains – primarily concentrated in domains that are already ripe for automation. Valuable, but not a new industrial revolution. For economists, strategists and decision-makers, the message is clear: AI is a tool, not a replacement for thinking. The most important asset in the knowledge economy remains the human one: the ability to formulate a theory, weigh relevance and understand abstract quantities. No language model can do that.

Data without theory is noise — whether the tool is a simple regression or the world’s largest language model.

On Friday, Bill Pulte (Director of the Federal Housing Finance Agency and chairman of both Fannie Mae and Freddie Mac) announced that America’s two government sponsored housing finance giants are exploring equity stakes in technology companies.

Speaking at the ResiDay conference in New York, Pulte described potential partnerships where tech firms would offer Fannie and Freddie equity positions, explicitly citing the Trump administration’s recent investment in Intel as a model.

I’m reminded of the American baseball legend Yogi Berra’s famous quip: “It’s like déjà vu all over again.”

What we’re witnessing here is the American government (through Fannie Mae and Freddie Mac, state owned mortgage institutions) once again heading down a path that bears a disturbing resemblance to the past.

The 2008 playbook

Leading up to the 2008 financial crisis, Fannie and Freddie played a central, though unfortunate, role.

These two institutions were designed to make home loans cheaper and more accessible by purchasing loans from banks and reselling them as securities to investors.

However, to keep pace with private competitors, they began taking on increasingly risky mortgages.

When housing prices fell, the losses were enormous, and in 2008 the American government had to take control to prevent a total collapse in the housing and financial sectors.

And now we see Bill Pulte (Trump’s handpicked chief of the powerful regulatory body, the Federal Housing Finance Agency) openly discussing how these state backed mortgage institutions should invest money in American technology companies.

Pulte stated that he views Fannie and Freddie somewhat differently because they operate as actual businesses, albeit private ones. He indicated that the GSEs will likely take ownership stakes in various companies as those firms offer equity in exchange for business partnerships with Fannie and Freddie.

More tellingly, Pulte acknowledged the coercive nature of these arrangements, explaining that major technology and public companies are offering equity to Fannie and Freddie in exchange for business partnerships. He noted that the GSEs are considering taking these equity stakes because of the substantial power Fannie and Freddie wield over the entire housing finance ecosystem.

Crony capitalism comes to Silicon Valley

I must be frank: this is beginning to stink.

Over the past week, we’ve received a series of signals that the American government now intends to directly support American technology companies through precisely the kind of arrangements that characterise crony capitalism, where political connections and government favour, rather than market competition, determine winners and losers.

On Monday, the Wall Street Journal reported that OpenAI’s CFO, Sarah Friar, hinted that government guarantees could cut AI funding costs – later backtracking to say they weren’t seeking a bailout. But the message was clear: tech giants now assume taxpayer backing, just like banks did before 2008.

So if you’re asking yourself why American tech giants (particularly the so-called “Magnificent Seven”) are trading at the extraordinary valuations we see today, you likely have part of the explanation here: investors aren’t just pricing in technological progress or profit growth. They’re pricing in implicit government guarantees.

They’re pricing in the expectation that the Trump administration will support these companies, that losses will be socialised whilst gains remain private, that these firms have effectively become “too big to fail.”

This is moral hazard on a grand scale, but now applied to an entire sector rather than just individual institutions. The market isn’t valuing these companies based on discounted future cash flows. It’s valuing them based on the anticipated probability of state intervention to prevent failure.

And the connections between political donations and government favour are becoming impossible to ignore.

Elon Musk, who contributed hundreds of millions of dollars to Trump’s campaign (making him the largest individual political donor in the 2024 election cycle), now wields extraordinary influence over government policy.

Marc Andreessen and Ben Horowitz, whose venture capital firm has invested heavily in OpenAI, together donated over $5 million to Trump aligned political action committees.

Peter Thiel, the co-founder of Palantir, spent $15 million to elect JD Vance to the Senate in 2022. Vance is now vice president.

These aren’t coincidences. They’re investments with expected returns.

But to my mind, this is also a very clear signal that shouldn’t be ignored: all is not as it should be with the tech giants. Over the past few weeks, we’ve seen turbulence surrounding share prices in the sector, but perhaps more worryingly, also rising tensions in credit and money markets.

It’s simply about nervousness that some AI companies potentially face liquidity problems, and ultimately whether this might infect the banking sector.

The market is already showing cracks

And these aren’t just theoretical concerns. Over the past fortnight, we’ve seen concrete signs of stress in American credit and money markets.

The Federal Reserve has been forced to inject substantial liquidity into the system to prevent tensions in the repo market from escalating.

On 31 October, the Fed injected $50.35 billion into the system (the largest single day operation since 2021), followed by an additional $22 billion on 3 November.

The Secured Overnight Financing Rate has shown unusual volatility, with repo rates spiking to their highest levels relative to the Fed funds rate since 2020, precisely the kind of pressure that typically precedes broader credit events.

Major Wall Street banks, including JPMorgan and Deutsche Bank, have warned that money market stress could flare up again.

Dallas Fed President Lorie Logan has suggested the central bank might need to begin purchasing assets if the rise in rates proves more than temporary, echoing the kind of emergency measures deployed during previous crises.

More tellingly, major international banks are taking defensive positions. Deutsche Bank, which has extended billions in loans to data centre firms powering the AI boom, is now reportedly exploring hedging strategies including shorting baskets of AI related stocks and purchasing credit protection to offset potential losses if the current pricing regime collapses.

When one of Europe’s largest banks starts hedging against its own AI lending book, that tells you something important about where sophisticated risk managers think we are in the cycle.

JPMorgan’s Jamie Dimon warned in October about a probable market decline of 10 to 20% within the next 6 to 24 months, citing concerns about overheating in the technology sector that could trigger a correction similar to the dotcom crash of 2000.

Goldman Sachs’ David Solomon echoed these concerns this week, stating that a 10 to 20% drawdown in equity markets within two years appears likely due to AI related risks and trade tensions.

Even the Bank of England’s Andrew Bailey has warned of “growing risk of a sharp correction” if AI expectations falter, with “alarm bells ringing” over private credit and AI concentration in major indices.

Regional American banks have already begun reporting losses on loans to distressed investment funds, and credit default swap spreads on major banks have risen to elevated levels.

And I’m hardly the only one concerned. I’m completely convinced that credit and risk management departments in all the major global banks are worried about precisely this right now. The parallels to 2007 (when credit markets began showing stress months before the broader crisis became apparent) are uncomfortably clear.

The Intel precedent: when subsidies become equity

To understand what Pulte is proposing, we must first examine the model he explicitly referenced.

In August 2025, the Trump administration took a 9.9% equity stake in Intel Corporation worth $8.9 billion, converting previously awarded CHIPS Act grants and Defence Department funds into common stock ownership. The government purchased 433.3 million shares at $20.47 per share.

Commerce Secretary Howard Lutnick justified this approach by arguing that the government should receive equity stakes in exchange for the funding that had already been committed under the previous administration.

But Intel was only the beginning.

The Trump administration has also taken equity stakes in MP Materials (rare earth mining, 15%), Lithium Americas (lithium production, 5-10%), and Trilogy Metals (copper zinc mining, 10%). Reports emerged in October that the administration was exploring similar arrangements with quantum computing firms including IonQ, Rigetti Computing, and D-Wave Quantum, though the Commerce Department subsequently denied “currently negotiating” such stakes (language that leaves ample room for future deals).

This represents an extraordinary shift in American economic policy and a troubling embrace of what can only be described as crony capitalism.

Unlike previous government equity stakes during financial crises (TARP in 2008 or airline support during COVID-19), the Trump administration has taken these positions without any financial emergency.

The ideological precedent is clear: converting government grants and subsidies into equity ownership across strategically important industries, creating an intertwined relationship between state power and private profit that undermines market discipline.

Now Pulte wants to extend this model to Fannie and Freddie. According to Pulte’s statements from May, Fannie Mae has approximately $4.3 trillion on its balance sheet, whilst Freddie Mac holds over $3 trillion.

The scale of what’s at stake

Fannie Mae and Freddie Mac don’t just participate in America’s housing market; they effectively are the housing market. They guarantee roughly half of all outstanding U.S. residential mortgages (the largest share of the approximately 70% of American home loans that receive some form of federal backing, including FHA, VA, and other programmes).

When institutions of this size and systemic importance start deviating from their core mission, the consequences ripple through the entire financial system.

My friend and Richmond Fed veteran economist Bob Hetzel wrote in his seminal 2009 analysis of the financial crisis about how financial safety nets inevitably create moral hazard by increasing incentives for risk-taking.

The critical mechanism he identified: financial institutions receive implicit subsidies from safety nets that grow larger as their portfolios become riskier, as they increase leverage, and as their capital buffers decline.

2008: when mission drift became catastrophic

The last time Fannie and Freddie strayed from their mandate, it ended catastrophically. In September 2008, both institutions collapsed under the weight of massive losses and required a government takeover that has lasted 17 years. At the time of conservatorship, they held or guaranteed about $5.2 trillion of home mortgage debt.

The scale of the losses was staggering. About 80% of Fannie and Freddie’s combined $213 billion in credit losses between 2008 and 2011 involved mortgages that were either Alt-A, interest only, or both. These were loans made to borrowers with relatively high credit scores but featuring riskier structural characteristics. The critical error wasn’t the specific loan types. It was the strategic decision to chase market share by expanding into riskier segments well outside their traditional remit.

Starting in 2006 and 2007, just as the housing market reached its peak, Fannie and Freddie increased their leverage and began investing heavily in subprime securities and Alt-A loans in an ill-fated effort to win back market share from private competitors. This is textbook mission drift: institutions designed for one purpose (providing liquidity to mortgage markets) taking on unrelated risks with predictably disastrous results.

Hetzel understood the fundamental problem.

Writing specifically about the GSEs, he noted that understanding the subprime crisis required grasping how Fannie and Freddie had increased demand for housing stock, pushed homeownership rates to unsustainable levels, and thereby contributed to sharp rises in housing prices given the relatively inelastic supply of housing due to land constraints.

Perhaps most damningly, Treasury Secretary Timothy Geithner told the Financial Crisis Inquiry Commission in a private interview that moral hazard was pervasive throughout the system, with the GSEs representing the single largest source of this problem.

The new mission drift: from mortgages to tech equity

Now we’re watching a remarkably similar pattern emerge, only this time the target isn’t housing. It’s technology.

Pulte indicated at the conference that “one of many companies” seeking equity arrangements with Fannie and Freddie is a firm whose involvement would leave observers “blown away with how much money is involved,” though he declined to name it.

Fannie Mae has already signed a partnership agreement with Palantir for fraud detection efforts, though financial terms weren’t disclosed (precisely the kind of opacity that should alarm anyone concerned with accountability).

The Palantir connection is particularly revealing. Since Trump took office, Palantir has secured large federal government contracts. Peter Thiel, Palantir’s co-founder, spent $15 million electing JD Vance to the Senate.

Vance is now vice president. Joe Lonsdale, another Palantir co-founder, contributed $1 million to Elon Musk’s America PAC supporting Trump. This isn’t a market economy. This is a system where government contracts and partnerships flow to companies whose founders financed the campaigns of those now in power.

The proposed model is seductive in its simplicity: tech companies offer Fannie and Freddie equity stakes in exchange for access to the GSEs’ enormous housing finance ecosystem.

But consider Pulte’s own reasoning: the GSEs are considering taking equity stakes in companies specifically because of the substantial power Fannie and Freddie exercise over the entire ecosystem.

This isn’t market allocation. This is using control over critical infrastructure to extract equity positions from private companies. This is crony capitalism at its most transparent.

Moral hazard at scale: the implicit guarantee premium

The fundamental problem here isn’t just about Fannie and Freddie taking equity stakes in a few tech companies.

It’s about what those stakes signal to the broader market about the existence and scope of implicit government guarantees.

Hetzel identified the core mechanism with precision: financial safety nets (including deposit insurance, too-big-to-fail protections, Federal Home Loan Banks, and the Fed’s discount window) allow banks to access funding at costs that don’t rise with the riskiness of their portfolios.

The same logic applies when government backed entities like Fannie and Freddie take equity positions in private companies.

That government guarantee (now explicit after 17 years of conservatorship) means US taxpayers ultimately backstop losses whilst any gains accrue to whom exactly? The Treasury? Tech companies?

This creates a fiscal time bomb where downside risk is socialised whilst upside is privatised.

But the effects extend far beyond the specific companies receiving these investments.

When the market observes the government taking equity stakes in Intel, exploring partnerships with OpenAI, and now planning to inject Fannie and Freddie capital into technology firms, investors rationally update their beliefs about which companies enjoy implicit state backing.

The extraordinary valuations we observe across the Magnificent Seven and related AI companies aren’t just about technological optimism. They reflect the market pricing in an implicit guarantee that these firms are “too big to fail.”

This is moral hazard pricing on a sectoral scale. And it raises a troubling question: if these companies are indeed “too big to fail,” are they also becoming “too big to save”?

Too big to fail or too big to save?

Consider the arithmetic. The combined market capitalisation of the Magnificent Seven alone now exceeds $20 trillion. Add in the broader ecosystem of AI related companies, data centre operators, and semiconductor firms, and you’re looking at market value that’s at least partially predicated on the assumption of government support.

Now place that against America’s fiscal position. U.S. national debt stands at $38 trillion (more than 100% of GDP). Interest payments reached $841 billion in just the first ten months of fiscal year 2025, already exceeding Medicaid.

The Congressional Budget Office projects debt will reach 156% of GDP by 2055, with interest payments hitting $1.8 trillion annually by 2035.

Here’s the uncomfortable arithmetic: when the next crisis comes (and it will come), can the American government actually afford to bail out a technology sector whose market capitalisation approaches half the entire national debt? The fiscal buffer that existed in 2007, problematic as it was, has completely evaporated. We may be creating a class of companies that markets believe are too big to fail, but which the American government is quite possibly too indebted to save.

And this is before considering the international dimension.

When Fannie and Freddie (institutions with explicit government backing) take equity positions in tech companies, it sends a signal to foreign holders of U.S. Treasury securities that America is extending its contingent liabilities even further into speculative territory.

For a Chinese central bank holding a trillion dollars in Treasuries, or a Japanese or Scandinavian pension fund with massive exposure to American debt, this doesn’t look like prudent fiscal management.

It looks like the American government is systematically increasing the risk that it will face multiple, simultaneous calls on its financial resources that it cannot meet without inflating away its debts or defaulting.

The conservatorship paradox and regulatory capture

Pulte confirmed on Friday that Fannie and Freddie will remain in government conservatorship whilst potentially conducting an IPO of up to 5% of their shares this quarter or early next year.

He indicated that he anticipates the president will make a decision on the IPO timing either this quarter or in early 2026.

Think carefully about what this means: these institutions remain under explicit government control because they’re deemed too important and too risky to operate independently in housing markets, yet they’re simultaneously being encouraged to speculate in technology equity markets.

The conflicts of interest are staggering. Pulte runs the agency that regulates Fannie and Freddie whilst simultaneously chairing both companies.

If those companies become equity investors in major tech firms, he’ll effectively be regulating entities in which his institutions have direct financial stakes.

This is regulatory capture taken to its logical extreme: the regulator, the regulated entities, and the companies receiving investment all bound together in a web of mutual dependency that eliminates any possibility of arms length oversight or genuine market discipline.

If Fannie and Freddie aren’t trustworthy enough to exit conservatorship after 17 years of profitability in their core business, what possible justification exists for expanding their mandate into tech investment?

What should happen instead

The solution is straightforward but politically difficult: complete the mission Fannie and Freddie were designed for, then either privatise them fully or wind them down entirely.

If conservatorship is necessary because these institutions require close government supervision, keep them focused exclusively on their housing mandate with strict limits on portfolio composition and leverage.

If they’re healthy enough to take tech equity positions, they’re healthy enough to exit conservatorship and face genuine market discipline.

What should not be accepted is this worst of all worlds hybrid: government guaranteed institutions with neither proper oversight nor proper market discipline, now venturing into speculative investments far beyond their expertise or mandate, at a time when the U.S. government’s own fiscal position is already unsustainable, all whilst creating a sectoral moral hazard problem that may prove impossible to resolve when the inevitable repricing occurs.

Hetzel proposed a radical but coherent alternative: eliminating the Fed’s legal authority to make discount window loans, suggesting instead that the central bank should flood markets with liquidity during panics through open market operations whilst maintaining its policy rate through interest on reserves.

The core principle: creditors and debtors will restrain financial system risk-taking only if they face genuine losses when financial institutions fail.

Every expansion of the safety net (whether through TBTF, discount window lending, government equity stakes in strategic companies, or now equity investments linking government backed housing finance to private tech firms) undermines this crucial disciplining mechanism.

Every implicit guarantee extended, every equity stake taken, every suggestion that politically connected companies will receive state support, moves us further from a market economy and deeper into crony capitalism where success depends not on serving customers but on securing government favour.

Conclusion: echoes from 2008

Seventeen years after Fannie and Freddie’s collapse nearly took down the global financial system, the same structural errors are being repeated, but this time with different assets, weaker fiscal foundations, and potentially far graver consequences.

The 2008 crisis taught us that Fannie and Freddie represent “entirely moral hazard” when they deviate from their core mission, as Geithner observed. Fannie and Freddie needed a $191 billion taxpayer bailout because they deviated from their core mission, driven by the same toxic combination of implicit government guarantees, inadequate oversight, and mission drift that is re-emerging today.

Now, under political pressure to “do something” about housing affordability and tech competitiveness, they’re being pushed down the same path again, only this time explicitly following the Trump administration’s model of crony capitalist equity stakes, at a time when U.S. federal debt stands at 100% of GDP, interest payments exceed defence spending, and the fiscal buffer to absorb another systemic crisis no longer exists.

But this time there’s an additional, more troubling dimension. This isn’t just recreating the moral hazard of 2008. This is creating it at sectoral scale across technology companies whose combined market capitalisation may literally be too large for the American government to backstop.

The stress in credit markets, the Federal Reserve’s emergency liquidity injections totalling over $70 billion in early November, the defensive hedging by major banks like Deutsche, the warnings from JPMorgan, Goldman Sachs, and the Bank of England—these aren’t abstract concerns. They’re happening right now, and they’re happening for a reason.

The market is beginning to ask the question that should terrify policymakers: what happens when companies that everyone believes are too big to fail turn out to be too big to save?

The next crisis won’t announce itself with sirens and flashing lights. It will begin, as the last one did, with seemingly reasonable people making seemingly reasonable arguments about expanding mandates, capturing growth opportunities, and using “power over the whole ecosystem” for strategic purposes. It will involve the gradual extension of implicit guarantees until the market prices them in as explicit. And this time it will happen in a context where U.S. federal debt is at historically high levels, the fiscal capacity to absorb losses has evaporated, and the companies that need saving may be orders of magnitude too large for the government’s balance sheet.

The lesson from Washington in 2008 was clear. Apparently, it needs to be learned once more, and this time, the tuition fees may be higher than ever, both for the financial system and for U.S. sovereign creditworthiness itself.

The only question is whether this administration is creating companies that are too big to fail, or too big to save.

I fear the answer will reveal itself soon enough.

Lars Christensen
LC@paice.io
+45 52 50 25 06

The Paradox of Preppers Who Want Stock Tips

I’ve had some rather paradoxical conversations in recent weeks. One second, I’m standing there talking to people about prepping—buying water, hand-crank radios, and whatnot. Then two minutes later, they’re asking me, “Lars, which shares should I buy?” There’s something deeply contradictory about that, isn’t there?

This captures the strange moment we find ourselves in. Drones are flying over Copenhagen, jet fighters are scrambling over Danish airspace, and yet many Danish investors have made substantial money on their shares in recent years. The disconnect between our anxieties and our investment behaviours has never been more pronounced.

We’re facing what I’d characterise as three dark clouds hanging over the investment landscape. These aren’t merely theoretical concerns—they’re real, measurable risks that could fundamentally alter the investment environment we’ve grown accustomed to over the past decade.

Three Dark Clouds Over the Financial Markets

The Sovereign Debt Crisis: My Greatest Concern

Let me be absolutely clear: the sovereign debt crisis is my greatest concern. The United States has public debt exceeding 100% of GDP. Britain faces similar challenges. We’re seeing massive deficits—in America, it’s somewhere between 6 and 8% of GDP this year, depending on how you calculate it. France has major problems. Japan has major problems. Italy has major problems.

The American federal government’s interest payments will soon reach 5% of GDP. That’s more than the Americans spend on defence. Think about that for a moment—roughly a quarter of all federal tax revenues will go to servicing debt. If interest rates rise, you can see how this becomes extremely difficult to manage.

Here’s the crucial calculation: if interest rates are higher than nominal GDP growth, you get an explosive development in debt as a percentage of GDP. Let’s say the American economy grows at 2% in real terms with 2% inflation—that’s 4% nominal GDP growth. If the interest rate on government debt is 5%, the debt burden will simply grow and grow and grow.

Donald Trump has talked extensively about growing out of the debt problems with all his brilliant ideas that will boost growth. Unfortunately, there’s little evidence this is happening. We got labour market figures last week that further confirm the American labour market is cooling, and GDP growth in the first half of the year is below one and a half percent annualised. The economy isn’t booming.

But there’s another way to get nominal growth up—create inflation. Every Danish homeowner who owned property in the 1970s can tell you this story. The high inflation of the 1970s ate away homeowners’ debt. And if you’re a government that creates inflation, perhaps by ringing up the central bank and saying “print some money,” well, that solves one problem whilst creating another.

The temptation to let the printing press run becomes greater and greater if you don’t want to make difficult decisions. We’ve seen Donald Trump at war with the Federal Reserve. He’s talked about firing Lisa Cook, who sits on the Federal Reserve Board—though last week the American Supreme Court told him, “You can’t do that, Donald. You need to argue your case better.” That’s been kicked to the corner for now. But the pressure is there. He’s said he won’t reappoint Jerome Powell when his term expires next year. He’s appointed Stephen Rennenkampf to the FOMC, the leading monetary policy body at the Federal Reserve. Rennenkampf, you’ll recall, voted for a half-percentage-point rate cut rather than the quarter-point cut we got at the last FOMC meeting. These are all signs of politicisation.

Geopolitical Uncertainty: The Highest in 35 Years

The geopolitical situation must be described as unstable and frightening—probably the highest level of uncertainty in at least 30 to 35 years. We’ve had the drones over Copenhagen, the entire situation in Europe, and recently there’s been speculation about whether the Chinese might make moves regarding a possible invasion of Taiwan. We have the conflict in the Middle East—Iran, Israel, Gaza—which creates concerns.

As I write this, we’re not far from Forum Copenhagen where we recently had a major European summit. I must be honest there was a lot of police around. Many helicopters in the air. We’ve heard a jet fighter or two. I have children asking about all this. What’s all this about? It’s rather uncomfortable on a practical level.

When this starts affecting air traffic, potentially sea transport, our supply chains, company earnings, and economic development, it becomes negative for markets. So far, markets have taken it remarkably calmly, but the threat is there.

We’ve agreed in Europe that we need to increase our defence spending because there’s a genuine threat from Putin’s Russia. There’s much talk about why there wasn’t drone defence around Copenhagen Airport and other Danish airports. Because there hasn’t been a need for it – it was completely unthinkable just a few years ago, but suddenly it’s something we must consider.

Drone defence isn’t free. I don’t know what it costs to send an F-16 fighter jet up to fire missiles at drones over Copenhagen Airport, but it’s not cheap. And whilst I hope it doesn’t come to that, it’s a stark illustration that we need to spend more on defence in Denmark and Europe in general.

If we already have weak public finances in Europe (much less so in Denmark), this pushes the problem further. We need more money, which pushes interest rates up. More government bonds need to be issued, and governments must pay those interest costs. If doubts arise about their willingness to pay, inflation expectations start rising too.

The Ukrainians are currently having some success pressuring the Russian economy by hitting oil refineries, oil storage, and other targets that push up petrol prices. Russian petrol prices have risen 40% this year. Petrol rationing has been introduced in many parts of Russia. We’re seeing images from Russia of kilometre-long queues because of rationing. It’s hitting the Russian economy.

There are probably quite a few Russians who are thoroughly fed up with this. We’re talking about Russian losses on the front over the past three years approaching a million men dead or wounded. So it’s not certain the war is quite as popular as some might wish. Perhaps someone would like to remove Putin. And let’s say that happens, and there’s a positive regime change in Russia. The geopolitical situation would change immediately, and perhaps we could reduce our fear that we need to spend 3-4-5% of GDP on defence. That picture changes if we’re facing a different Russia.

The Tech Concentration Risk

If we look at how the global equity market is constructed, somewhere between 70 and 80 percent of the global equity market – perhaps even more – consists of American shares. And a very large portion of that is just six or seven tech shares that dominate to an enormous degree.

So in reality, when you think you’re buying the whole world, you’re perhaps getting massive exposure to Nvidia, for example, or Tesla, or Microsoft. You’re exposing yourself enormously to American technology shares. And then you haven’t spread your risk—you think you have, but you haven’t really done so.

If these shares are overvalued – and it’s my personal opinion that they appear to be – then you haven’t spread your risk. You’ve actually taken on relatively high risk.

Let me give you an example of the timing problem. If we look at the situation in 1998 and examine the American stock market, we can see that American technology shares were extremely expensive at some point. If we look forward five years, we can see that was correct, and technology shares actually fell significantly during that period.

But here’s the problem: we need to find indicators that get us in and out of markets at the right time. I’ve done this exercise many times. Could we find indicators, such as price-earnings ratios—the share price relative to company earnings? Could we say that if price-earnings rises above a certain level, we should sell, and when it falls below another level, we should buy?

If we do this in connection with the tech bubble in the late 1990s, you’ll see it’s nearly impossible to find an indicator that would have got you out of the market at the right time and back in at the right time in real-time. The problem is that most indicators were already telling you to leave the market from 1995-1996. But if you left the market then, you’d have missed the entire upswing, and you’d be sitting there waiting for the market to come back down to where you started.

The best would be to stay in the market, even though it’s become too expensive, and then exit at the top. But if you don’t have an indicator for that, it’s useless. And so whilst I can sit here and say I think tech shares are really, really expensive now, and they’ve become very concentrated, that makes it very difficult to act on.

Governance as an Investment Strategy

When I talk about governance, it’s really about what we want when there’s uncertainty—trust. Something we can rely on. Perhaps in 2018 or 2019 or 2020, Russian shares looked very attractive. They were cheap, and there were some good stories. But there was also a dictator in Russia. A dictator who could suddenly just invade a neighbouring country and essentially confiscate all businesses. Hardly anyone would want to have invested in Russian shares today.

This governance theme has been really important in recent years. Countries where there’s respect for property rights, where there’s press freedom, where there’s a low level of corruption, where agreements are honoured, where the legal system ensures agreements are honoured—these are countries that have performed relatively better than those where we think, “Hmm, perhaps there’ll be a military dictatorship tomorrow, or the military dictator might confiscate some businesses.”

We can think of countries like Turkey, Russia, China. We’ve seen very clearly that this theme has dominated the pricing of Chinese shares. President Xi might decide to confiscate a business or introduce capital controls. And some of the things we’ve talked about regarding Donald Trump—that’s what we could broadly call governance. Because Donald Trump has said, “I didn’t write the rulebook. It doesn’t apply to me.” And something happens there.

Donald Trump constantly tests these checks and balances. He’s done it in trade, with the central bank, with defence, with states’ autonomy. He’s sent the National Guard into various states. He constantly tests this. And something we’ve talked about in various forms—whether we believe in these checks and balances—that there’s no problem, he can’t do anything. But he tests it. And he tests it extensively.

The countries that score highly on governance include lovely, peaceful, beautiful Denmark. If we look at various measures of economic and political freedom, all the Nordic countries, but especially Denmark, score very highly on economic freedom. We have relatively low levels of regulation, which might surprise some people. We have well-protected property rights. What pulls us down when we talk about economic freedom is that we have high tax levels in Denmark. But overall, we have relatively unregulated product markets, relatively unregulated labour markets.

Other countries could be Ireland, Singapore, Switzerland, the Netherlands—they typically score highly on these measures. These are countries where we’d also feel safe if we flew there. We won’t just be arrested on the street for nothing. That’s a large part of European countries, but not all of them.

There are also countries that have clearly moved in the right direction. If we look at all countries in Central and Eastern Europe, 35-36 years ago we had communist dictatorships in Poland, in the Baltics, for example. And we must say they’ve moved enormously regarding these governance questions, becoming free, democratic nations with respect for property rights.

If we look at emerging markets over the past five years, it’s been very clear that the emerging markets with most respect for institutions, property rights, contractual freedom, and free trade are the ones that have performed well. That could be Poland, the Baltics. But countries that have moved away from this—Russia, China, Turkey—have taken proper beatings in the stock market.

Chile and Uruguay are countries in the emerging markets world that belong at the top of the class. Botswana is interesting—I believe Botswana gained independence in 1966 and has been a democracy since independence. It’s actually the only country in Africa that can boast of this. It’s had enormous economic and political stability, democracy, and well-protected property rights. It’s a fantastic success story that we don’t talk much about.

The All-Weather Portfolio

What we need to consider is what’s sometimes called an all-weather portfolio – an investment portfolio that performs well in different weather conditions. When the economy is doing well, when it’s doing poorly, when there’s inflation, deflation, stable inflation, high growth, volatile growth. How do you manage?

It’s about spreading risk, of course. It’s also about having shares or assets that can handle these scenarios. My encouragement to investors sitting out there having made really good money on their shares would be: perhaps you should sit down and say you haven’t spread your risk. You thought you had because you just bought the S&P 500 index. But now you’ve become enormously exposed to basically five or eight American tech shares.

Perhaps you should reduce that exposure, buy some bonds, buy some commodities. It could be gold. It could be gold mining shares. It could be different types of bonds. It could be focusing on inflation risk—buying inflation-indexed bonds to remove some of that inflation risk. Spread the risk.

Saying “I have five different shares” isn’t enough if you’ve bought five different shares within the same sector—you haven’t spread the risk. You need different countries, different assets, bonds, shares. In reality, what you should do if you’re sitting there thinking you’re a bit worried things have become expensive, or you’re considering spreading risk, is to spread it across many more assets.

For the average Dane (or anybody else globally), the most significant exposure in their portfolio is the property or flat they own. It’s interesting that whilst we sit here with drones over Copenhagen, uncertainty, trade wars, and all sorts of things worrying us, Copenhagen property prices are up 20% over the past year. That tells a story about how the property market and stock market are insurance – partial insurance – against high inflation.

Where it’s not insurance is if central banks do something about inflation. If they say inflation is rising too much and we need to kill it by raising interest rates sharply, then the property market dies, the stock market dies. So we can’t just say we shouldn’t worry and should buy shares and bonds. What I’m trying to say is that when we start getting high inflation expectations, some of these markets begin to behave differently than we’re used to.

My Final Message: Don’t Panic, But Do Check Your Risk

My main message is: don’t panic. Use these crisis considerations to sit down calmly. Whether you’re an institutional investor, pension fund, or individual investor, sit down and ask: how am I actually exposed? Have I really achieved the risk diversification I think I have?

Because there are people who don’t need risk diversification. But sit down and do a crisis check, a risk diversification check on your portfolio. Don’t do anything desperate. Don’t think you know which crisis share or weapons share will rise. Don’t try to beat the market, but sit down and consider whether you have the risk diversification you think you have.

If you think you’ve spread your risk by just buying a global equity index, my message is: you haven’t spread your risk. You might feel like you have, and it’s actually performed really well. But this crisis might be a good reason to take that check. And don’t rush it. You never get anything good from that.

I’d like to be in a situation where I’d want to buy weapons shares because I’m worried—yes, there’s that too. I’m probably in the worried camp relative to how the market is. But if I’m constructing a portfolio, I need to create one where I don’t constantly have to time things correctly.

If your portfolio has risen 30% annually for the past three years, perhaps it might be good to spread some risk, get some bonds, get some commodities. That’s not investment advice in the sense that I don’t know what individuals have as exposure. I don’t know individual private economics, but this is what economic and financial theory textbooks say: spread your risk, consider the correlation between assets.

Sometimes you think, “I’m in this and I’m in that—they’re completely different things.” But if you see that nine out of ten days these two assets move in the same direction, you’ve essentially bought the same thing. So consider that. I think this is a healthy opportunity to do a reality check on your portfolio.

This article is based on the latest episode (“Investering i en krisetid) of my podcast “Makropuls” (in Danish). See links to the podcast here (Spotify and Apple podcast). The podcast is produced in cooperation with Howden Denmark.

Another day, another politically motivated attack in the United States.

This morning’s shooting at a Dallas ICE detention facility – where a sniper killed two detainees and wounded another before taking his own life prompted me to revisit a question that’s been troubling me: Is political violence actually increasing in America, or does it just feel that way?

To explore this, I’ve conducted what I’ll call a methodological experiment.

Rather than relying on traditional datasets, I’ve used ChatGPT and Claude to construct a synthetic index of political violence in the US since 1945. Let me be absolutely clear: this isn’t conventional data. It’s data generated through language models, with all the limitations that implies.

The Methodology (and Its Limitations)

Here’s what I did: I asked both ChatGPT and Claude to generate lists of politically motivated violent incidents since 1945, then had them score each incident’s severity on a scale where 50 represents a “normal” level.

The models assessed both casualties and symbolic significance, and I used them to cross-check each other’s work. I then quality-checked the output myself and categorised perpetrators by political affiliation where this was clearly established.

This approach is, admittedly, unorthodox. Language models are trained on existing texts and may reflect biases in their training data. They might overweight highly publicised events or recent incidents that featured prominently in their training corpus.

The “data” we’re looking at is essentially a structured synthesis of what these models have absorbed about American political violence.

Yet there’s something intriguing here. These models have processed vast amounts of information about political violence – news reports, academic studies, government documents. Their output might capture patterns that traditional datasets miss, though it might also amplify certain narratives or blind spots.

What the Synthetic Data Reveal

With those caveats firmly in mind, the patterns that emerge from this exercise are concerning. The model-generated index shows a clear upward trend in political violence over the past decade.

Looking at the breakdown by perpetrator ideology (where clearly established), the data suggest that right-wing extremist groups have been responsible for the majority of incidents in recent years, though we cannot draw conclusions about today’s attack whilst investigations are ongoing.

The synthetic data align with some empirical observations. Princeton’s Bridging Divides Initiative recorded over 600 incidents of threats and harassment against local officials in 2024 – a 74% increase from 2022. The University of Maryland found that in the first half of 2025, 35% of violent events targeted U.S. government personnel or facilities – more than twice the rate in 2024.

The Charlie Kirk Assassination and Recent Patterns

The September assassination of conservative activist Charlie Kirk marked a particularly dark moment.

The incident followed numerous recent acts of political violence, including the murder of Minnesota Democratic state Rep. Melissa Hortman and her husband, and two assassination attempts on President Trump in 2024.

What the synthetic data reveal is not just increased frequency but a shift in patterns. While overall levels of physical political violence remained low in 2024 compared to years prior, acts of vigilante violence grew as a proportion of all reported incidents.

We’re seeing less organised group violence and more lone-wolf attacks – a pattern that’s harder to predict and prevent.

The Epistemological Challenge

When we use language models to generate “data” about social phenomena, what exactly are we measuring? We’re essentially extracting structured information from the collective corpus of human writing about these events. It’s aggregating distributed information, but through an AI intermediary rather than traditional data collection methods.

This raises fascinating questions.

The models suggest that right-wing extremist violence has been responsible for a fairly large majority of U.S. domestic terrorism deaths since 2001. But how much of this reflects actual patterns versus the way these events are covered and discussed in the sources the models were trained on?

The synthetic data are, in a sense, a mirror of our collective discourse about political violence. They reflect not just what happened, but how we’ve talked about what happened. That’s both a limitation and, potentially, a feature – understanding the narrative landscape around political violence might be as important as counting incidents.

An Experimental Tool

I’ve built an interactive app (using the AI coding tool Lovable) based on this language model-generated violence index.

Users can explore the synthetic data, examine patterns across different time periods and perpetrator groups, and understand the methodology behind it. Think of it as an experiment in using AI to structure historical information rather than a definitive dataset.

The value isn’t in treating this as gospel truth, but in what it reveals about how these events are recorded, remembered, and synthesised in our collective digital memory.

When language models trained on our civilisation’s text output show rising political violence, it tells us something – even if that something is as much about narrative as about underlying reality.

This morning’s tragedy in Dallas reminds us that behind every data point – whether traditionally collected or AI-generated – there are real victims and real consequences. Understanding the patterns, however imperfectly, is the first step toward addressing them.

Try the tool here.

The Argentine markets took a beating last week, but US Treasury Secretary Scott Bessent has rushed to the rescue with a remarkable promise: America will provide what amounts to unlimited support to prop up Argentina. His declaration that “all options for stabilization are on the table” – including swap lines, direct currency purchases, and buying Argentine government debt – represents an extraordinary blank check.

But here’s the real kicker: Bessent claims Argentina is “systemically important” to the United States. This is financial fiction at its finest.

The Systemic Importance Fairy Tale

Let’s be brutally honest: Argentina poses zero systemic risk to the US financial system. US banks have minimal exposure to Argentine debt. Trade between the two countries is negligible in the context of the US economy. If Argentina defaulted tomorrow, would Bank of America collapse? Would JPMorgan need a bailout? Of course not.

The “systemically important” label is being stretched beyond recognition. If Argentina qualifies, then virtually every country in Latin America – including those the Trump administration just hit with massive tariffs – should qualify too.

This isn’t about systemic risk; it’s about political preferences dressed up as financial necessity.

The Moral Hazard Machine

By offering essentially unlimited support to Argentina, the US is creating a massive moral hazard problem.

The message to Milei’s government is clear: Don’t worry about the hard work of building political coalitions or passing sustainable reforms through parliament. Uncle Sam will catch you if you fall.

This is precisely the wrong incentive structure. Argentina has defaulted on its sovereign debt nine times since independence. Nine times!

The country’s political economy is fundamentally broken, cycling through periods of populist spending followed by crisis and austerity. US financial support doesn’t fix this cycle – it enables it.

The Real Threat to US Financial Stability

Here’s the irony: While Argentina poses no systemic risk to the US, this bailout policy might. Not directly through financial contagion, but through the precedent it sets.

If the US Treasury is willing to provide unlimited support to a serial defaulter like Argentina simply because its president is friendly with Trump and speaks the MAGA language, what’s to stop other countries from playing the same game? Elect a Trump-friendly president, make the right noises about being an ally, and wait for the bailout when things go south.

This transforms the US Treasury into a global lender of last resort – not for genuine systemic crises, but for politically favored regimes. That’s a commitment the US cannot afford, especially when federal debt is already approaching dangerous levels.

The Buenos Aires Reality Check

The timing of Bessent’s announcement is telling. It comes right after Milei’s party got hammered in regional elections in Buenos Aires. The political message from Argentine voters was clear (rightly or wrongly): Milei’s policies aren’t working, and he lacks popular support for his reforms.

Rather than forcing Milei to build political consensus and pursue genuine institutional reforms, the US bailout allows him to double down on rule by decree. This is not sustainable governance. It’s political theater subsidized by American taxpayers.

Where’s the “America First”?

This is where the contradictions become absurd. The Trump administration came to power promising “America First” – putting American workers and taxpayers first, being tough on countries that don’t pay their fair share, and ending the era of the US playing global policeman.

Yet here we are, with a Trump-appointed Treasury Secretary promising unlimited support to a country that has stiffed international creditors nine times. How exactly does bailing out Argentine bondholders put American workers first? How does propping up a foreign government that can’t even win local elections serve US interests?

The Unlimited Commitment Problem

Perhaps most troubling is the open-ended nature of Bessent’s commitment. “All options are on the table” with no conditions, no limits, no requirements for structural reform. This isn’t a rescue package – it’s a blank check.

What happens when Argentina needs another injection in six months? Another one in a year? At what point does the US Treasury say “enough”? And when that moment comes as it inevitably will won’t the withdrawal of support trigger an even bigger crisis?

The Alternative Nobody Wants to Discuss

Here’s what should happen: Argentina should be allowed to face the consequences of its political and economic choices.

Yes, this means potential default. Yes, this means economic hardship. But it also means the country would finally be forced to confront its fundamental problems rather than papering them over with foreign money.

The IMF learned this lesson the hard way after multiple failed bailouts. Now the US seems determined to repeat the same mistakes, but with even less conditionality and oversight.

Conclusion

This isn’t about whether one likes or dislikes Milei. It’s about the dangerous precedent of the United States providing unlimited financial support to a country that poses no genuine systemic risk to the US financial system (or to the global financial system).

The moral hazard is obvious: Why should any country pursue painful but necessary reforms when they can simply wait for a bailout? Why should Argentina fix its institutional problems when the US Treasury stands ready to finance its dysfunction?

Ultimately, this policy doesn’t just threaten US financial stability through the direct cost of supporting Argentina.

It threatens the entire architecture of international financial responsibility. When “systemically important” becomes a political designation rather than an economic reality, and when bailouts come with no strings attached, we’re not promoting stability. The US taxpayers will be subsidizing instability.

The world is indeed upside down when an “America First” administration puts Argentine bondholders before American taxpayers.

PS Back in July I warned about Milei not being the miracle maker that some was making him up to be in my blog post Classical Liberals, Let’s Be Honest About Milei