Broadsheet

When I wrote this week about Anthropic’s announcement that all Claude models, worldwide, would soon begin “watermarking” everything they generate, including text, to comply with this EU regulation, we were left to speculate how this was going to work, because Anthropic offered not even a vague description of how it would work — despite the fact that the title of the announcement was, absurdly and insultingly, “How Claude Marks AI-Generated Content”.

My initial speculation was that maybe they’d hide invisible non-printing Unicode characters in the text. Just spitballing. Turns out that’s not what they’re going to do. What they’re going to do is apply a form of steganography, where the choice of words (or other token output) at inference time will leave fingerprints that can later, maybe, be detected probabilistically.

I initially guessed “invisible characters” not because I didn’t think of the semantic word-choice technique, but because I was a fool who took Anthropic at its word in their description of what they would do. Their original support document claims:

When a supported Claude model generates text, it weaves an imperceptible watermark directly into the text itself. You won’t see it, and it doesn’t change the meaning, quality, or readability of Claude’s response.

They say “imperceptible” and “doesn’t change the meaning, quality, or readability”. Their words. Not almost imperceptible. Not slightly changes the meaning, quality, or readability. That made sense to me, because that’s absolutely what I want — nay, demand — from any tools I use personally. It’s unacceptable for a tool to sacrifice an iota of clarity, coherence, meaning, quality, etc. for the purpose of embedding hidden clues within the text to suggest its provenance. That’s what I would and will demand. And Anthropic’s (original) support document unambiguously claims that’s what their system will enable. So if that were true, I couldn’t see what was left other than hiding invisible characters within the text.

My error was believing Anthropic that their system wouldn’t adulterate and corrupt the semantics of the text their models generate. That is in fact exactly what they plan to do. I should have my head examined for believing a single word of a document titled “How Claude Marks AI-Generated Content” that doesn’t explain, at all, how Claude marks (or will mark) AI-generated content.

How It’s Actually Going to Work

Yesterday, on an entirely different website than the original “How Claude marks AI-generated content” article (the one that didn’t explain anything at all about how it works), Anthropic published “How Claude’s Text Watermark Works”, which does actually explain in layman-accessible terms how it’s going to work. I will return to Anthropic’s new highly euphemistic and slightly misleading description below.

There’s a bunch of research on this topic, some of which I have also linked to below. But the very best description of the general idea behind the technique is an interactive essay by James Padolsey, “How AI Text Watermarking Works”. It’s a wonderfully cogent read, and the interactive elements splendidly illustrate the main concepts. A+ work. If you have any interest in this at all, I dare say you must read — and play with — Padolsey’s piece.

But here’s my stab at a layman’s high-level summary. If you toss a coin N times and note the results, you can determine with a degree of certainty whether the coin is fair or biased. LLMs are, in their popular incarnations, non-deterministic. Ask the same question of the same model and you often get at least slightly different answers. Maybe the same meaning, but different phrasing. At each decision point for generating the next token, the model makes a choice. With these semantic watermarking techniques, they make different choices for some tokens based on word lists that could be called “green” and “red”. At each decision point, they’re a little more likely to pick a word from the green list than the red list. That doesn’t mean they never choose words from the red list. Just that they’re less likely to than they would if the adulterated marking technique weren’t in place. (Same way that a crooked 51-49 coin will still land “wrong” side up 49 times out of 100 on average.)

Words or word phrases are sorted into the green and red lists deterministically on the fly, at each “next token” generation point. So sometimes a specific word will be on the green list, and other times it will be on the red list. Someone with the secret key can determine which list a word will be on at each token generation point (which is how the watermarking is detected); those without the secret key cannot. This means there will never be a list of words that Claude prefers or eschews.

With coin flipping, the higher N is — the more times you flip — the more confident you can be that the coin is fair or biased. So too with this semantic watermarking. The more words in the text, the more accurate the analysis will be that the text was generated by a specific AI model or not. With too few coin flips, you can’t achieve any confidence at all regarding a coin’s fairness. With too few words (or tokens), there’s no way to achieve any confidence whether a string of text was AI-generated or not.

Given a string of text to examine for signs of a specific watermarking system, if there are more words tagged as green and fewer tagged as red than would otherwise be expected, the text can be flagged — with some degree of confidence — as having been generated, or merely modified, by the AI system that applies the specific secret-key watermarking system. The amount of confidence in the determination will obviously vary, significantly, based on the size of the text string and randomized weights given to words on the green and red lists. But only Anthropic will be able to determine if text was seemingly generated by Claude, and Anthropic will only be able to detect the watermarks that are applied by Claude. Claude can’t detect the hidden watermark signals generated by, say, Gemini, and Gemini can’t detect the hidden watermark signals created by Claude, because each implementation is predicated on secret keys held only by the LLM provider.

Objections to the Technical Premise

One of my fundamental problems with this is that no two synonyms carry the exact same meaning. “He leaped at the chance” and “He jumped at the opportunity” are very similar sentences expressing the same general sentiment, but they are not the same. The exact words we choose when writing matter. I want any LLM I use to choose the very best, most precise words at every single decision point. An obvious constraint that I accept is time and computation. Within the constraint of executing inference quickly, and at a certain cost per token, I want the best words. This constraint matches human writing. I could surely write a better column by taking longer to write it. I write with a sense of how much care I should put into every word and punctuation choice I make. I take more time with certain paragraphs, sentences, or even individual word choices when my gut feeling says I should.

In other words, these are necessary trade-offs. These factors are all in my interest: speed, cost, quality. Ideally I would like perfect writing, at instantaneous generation speed, at zero cost. None of those things are possible. Computation is not free of charge (and cloud-based LLM inference with leading models is actually expensive). Inference is not instantaneous. And great writing, whether natural or artificial, can only approach perfection.

The idea that anything other than my needs should factor into the generation of text for me is patently offensive.

This isn’t just about text one might generate with the intention of passing it off as their own natural work. This isn’t even about LLM proofreading of work written by hand. Anthropic is saying that all new Claude models are going to adulterate every single bit of text longer than 200 tokens (~150 words) they generate, including everything it presents to its users to read. So even in a private conversation between a user and Claude, which will never be read by anyone other than the user, Claude will begin making word choices in the name of marking its output in statistically predictable ways rather than maximizing clarity and precision.

Even today’s so-called frontier models are already decidedly lacking in lucidity. Claude, ChatGPT, Grok, et al. are “better writers” than most humans and produce better prose than the median human. But: no shit. Most people are terrible writers. The “average person” is pretty stupid and half of all people are stupider than that. And there are many smart, interesting people who are miserable writers. So as impressive as LLMs are, the bar is low. The best writing I see come out of these models is worse than anything I would choose to read for pleasure. And now Anthropic is saying they’re going to make it worse, on purpose, for purposes that do not benefit me in any way? Even if only slightly worse?

Get fucked.

Objections to the EU Regulation

Speaking of objections, the relevant EU regulation motivating all of this, “Code of Practice on Transparency of AI-Generated Content”, is red-tape nanny-state pipe-dream nonsense. Here’s Ben Thompson’s summary from a paywalled Stratechery update this week:

The regulation applies to text longer than 200 tokens.

The provider must mandate in their terms-of-service that users not remove the watermarking.

The solution should be robust in terms of evading “typical processing solutions” like screen shots, scanning and OCR, copy-and-pasting, translations, etc.

Taken literally, compliant LLM terms of service must forbid users from rephrasing the output from models that comply with this regulation, because the word choices are the marks. But it’s not the European Union that is trying to impose their absurd, impractical, witch-hunt-fueling regulation on the entire world. That falls on Anthropic.

Complying with this, particularly with regard to text, is only going to create problems for honest users. Dishonest users attempting to pass off AI-generated text as their own writing (students, employees, whoever) will simply circumvent detection through non-compliant AI paraphrasing tools.

James Padolsey — whose interactive visual explanation of how these schemes work I linked to above — explains this in a post titled “Anthropic’s Weak Watermarks Appease a Weak Law” (which, if it rings a bell, I linked to in a standalone post earlier today):

The same thought that led to this law could have applied to calculators at the time of their inception, had their outputs revealed themselves through artefacts. Thankfully, a sum borne of the brain is treated no differently from one produced by a calculator. Likewise with spellcheckers. To make assistance suspect only once the tool becomes capable enough to compose a whole sentence is not a principled boundary. It is a moral premium placed on difficulty itself.

Anthropic has nevertheless chosen a blanket, model-level implementation that appears broader than the law’s minimum requirement. That may be convenient compliance engineering, but it discards distinctions the law expressly attempted to preserve. The result is a signal broad enough to implicate harmless and assistive use, yet fragile enough to be removed by a motivated person through substantial recomposition. It risks concentrating suspicion on ordinary and assistive users while remaining weakest against deliberate deception.

Padolsey is the creator of Declaude, a delightfully simple web app that allows you to “Paste in AI-flavored text and get the same content back as plain prose”. Declaude’s original purpose is cleaning the saccharine Claude personality stink from text (whether it was created by Claude or any other LLM), but, if Anthropic persists in its stated plan to begin adulterating all text Claude generates, Declaude will also serve as a copy-paste single-extra-step way to eliminates those marks. Declaude is interesting and useful already, but it exemplifies how ill-considered and futile this EU regulation is when it comes to prose.

Google SynthID

Google has a watermarking system in place that they call SynthID, which they apply to AI-generated images, video, audio, and text. I’m concerned in this article only with text. With multimedia, embedded watermarks can be metadata within files, and truly not affect the experiential quality of the work when viewed or listened to. With text, we are talking about the actual words that are chosen. From the “AI-generated text” section of Google DeepMind’s own description of SynthID:

We’ve expanded SynthID to watermarking and identifying text generated by the Gemini app and web experience. Large language models generate text one word (token) at a time. Each word is assigned a probability score, based on how likely it is to be generated next. So for a sentence like “My favorite tropical fruits are mango and…”, the word “bananas” would have a higher probability score than the word “airplanes”. SynthID adjusts these probability scores to generate a watermark. It’s not noticeable to the human eye, and doesn’t affect the quality of the output.

In a group chat, a friend of mine quoted the above, and I responded that if a chatbot wrote “My favorite tropical fruits are mango and airplanes”, I’m pretty sure I’d fucking notice. Another friend then responded with this:

Days later, that still cracks me up.

But Google’s absurd description puts the lie to their own claim that it isn’t noticeable, and it serves to show just how little regard the people behind these generated-text fingerprinting schemes have for the actual craft of writing. Of course bananas has a higher probability score than airplanes, because airplanes aren’t fruit. But what about pineapple? Should the sentence complete to “mango and bananas” or “mango and pineapple”? That’s a good question, and the only acceptable answer for why an LLM should choose bananas instead of pineapple (or coconut, or guava, or papaya...) is that it has determined that it’s the best fit for the intended meaning, tone, and sentiment of the text. Not because bananas is on the watermarking “green” list and pineapple is on the “red” list, even though pineapple might be the better fit. Google’s own supposedly jocular description of how SynthID works in fact captures how the scheme perverts the text it generates.

They’re saying you won’t notice because if it only chooses bananas over pineapple for these fingerprinting purposes, well, they’re both tropical fruits and who cares. But it’s utter nonsense that the difference is “not noticeable to the human eye”. The semantic difference between banana and pineapple is just as noticeable to the human eye as the taste of the two are to the human tongue.

If it did produce “My favorite tropical fruits are mango and airplanes”, it’d be incredibly stupid, but it wouldn’t be offensive because we’d all recognize that something completely off-key happened. What’s offensive is that with a system like SynthId in place, where the fingerprinting decisions are motivated by a secret key, we have no idea whether it completed to “mango and bananas” because bananas was determined to be the best next token, or because bananas is in the “green” bucket of words. It calls every single word choice into question.

Here’s a paper published in Nature where Google’s team behind SynthID published their work, after putting it into production with Gemini (née Bard):

We analysed approximately 20 million watermarked and unwatermarked responses and computed the thumbs-up and thumbs-down rates (both as a fraction of the total number of thumbs-up and thumbs-down feedback received). We found that the thumbs-up rate for the two models differed by 0.01% (with the watermarked model being higher); and the thumbs-down rate differed by 0.02% (with the watermarked model being lower). We found both of these differences to be statistically insignificant, and well within the 95% confidence intervals.

From this experiment, we conclude that over a wide variety of real chatbot interactions, the difference in response quality and utility, as judged by humans, is negligible. Subsequently, non-distortionary SynthID-Text has been productionized and is currently watermarking responses in Gemini and Gemini Advanced. To the best of our knowledge, this evaluation represents the first systematic watermarking investigation of its kind within a large-scale production system.

To this I say:

Gemini/Bard’s thumbs-up/thumbs-down buttons are not a good experiment for evaluating the effect on quality. If a chatbot tells me “My favorite tropical fruits are mango and bananas” instead of “mango and pineapple”, I’m not going to give the response a thumbs down because of the fruit it chose. I’d give it a thumbs down if it said “airplanes”, yes, but that’s a strawman. (The paper in Nature even uses “My favourite tropical fruit is ...” as an illustration, but in the paper, the only four next tokens considered are, in order of probability distribution, mango, lychee, papaya, and durian. No airplanes. And, conveniently, in the paper’s example, the “winner” of the watermarking “tournament” just happens to be mango, the one that would have been selected as the best if the watermarking weren’t in place.)
A “difference in response quality and utility, as judged by humans” that is “negligible” does not mean imperceptible. What they really mean is that it’s only slightly worse and that everyone is either too stupid to notice or too indifferent to care.
It’s widely considered that Gemini is behind ChatGPT and Claude in quality. Perhaps the fact that they’ve put SynthID-text into production is one of many reasons why. I personally agree that Gemini’s prose is inferior. Maybe the use of SynthID has nothing to do with the fact that I, along with the general public consensus, consider Gemini to be a second-rate chatbot — but in that case, maybe it’s the fact that Gemini is a second-rate chatbot that makes the difference “negligible” when Google started mixing in SynthID-motivated tokens in its results. It’s a lot more likely that your restaurant customers won’t notice that you replaced your regular coffee with Folgers Crystals if your regular coffee is second-rate to start with.

Anthropic

Now, finally, back to Anthropic’s new “How Claude’s Text Watermark Works”, published yesterday. I have some comments.

To summarize:

We use a method of watermarking that does not have any practical impact on the quality or content of Claude’s outputs;

The difference between watermarked and un-watermarked text will not be distinguishable to readers;

Translation: Specific words do not matter and we don’t think anyone reads anything closely.

Nothing is added to the text and there are no hidden characters;

This would have been worth clarifying at the outset.

Watermarking won’t be specific to Claude. As of August 2, the EU requires AI providers serving its market to mark AI-generated content. Other major model developers have signed the same Code of Practice and will be implementing their own watermarks.

No other AI provider has stated that they will apply such marking, adulterating all generated text, outside the EU.

Take the sentence “The weather today was cold and…”. The next word is very unlikely to be “sugary.” But it is quite likely to be “overcast” or “grey.” Under most circumstances, it doesn’t matter much to the reader which of these latter two words the model ultimately chooses — the meaning of the sentence is largely the same either way. In cases like this, the choice is settled by a random number.

Arguing that grey vs. overcast “doesn’t matter much to the reader” is the crux of my argument that this entire endeavor is a perverse adulteration of what it means to write — or to read. That it’s subtle in some ways makes it more perverse, because it’s sneaky.

In internal testing, we’ve seen no impact of watermarking on the content, level of creativity, or readability of Claude’s text. In the SynthID-Text paper, which introduced the technique we use, Google DeepMind tested this impact by serving a model that used watermarking to a portion of their Gemini traffic and comparing thumbs-up and thumbs-down ratings. They found no statistically significant differences from the unwatermarked model. And in a controlled study, human raters comparing watermarked and unwatermarked answers side-by-side saw no difference in quality.

See above for my argument that this thumbs-up/thumbs-down data is absolutely worthless in evaluating whether the SynthID-style word-bias watermarking makes text worse. By definition it must make text worse, unless the underlying LLM model’s scoring is wrong, because the nature of the watermarking algorithm requires it to sometimes increase the probability of selecting a worse word choice and decrease the probability of selecting the model’s best choice. It’s only a question of how much worse. What Google’s thumb-counting data shows is only that it isn’t so much worse as to make Gemini users click the thumbs-down button.

Watermarking doesn’t change the meaning or experience for the person reading it, but if you wanted to check after the fact whether the text was likely generated by Claude, the watermark allows you to do so.

No, it does not. Because the entire scheme is tied to secret keys held only by the AI provider, it only allows Anthropic, not “you”, to check anything.

When Claude proofreads text written by a person, what it gives back has generally only been lightly edited; because nearly all the words are the person’s, there’s very little (if anything) for the watermark to attach to. Depending on the length of the text and how heavily Claude has edited it, those changes might not be enough to make Claude’s involvement detectable. The more Claude writes, the more decisions it has to make, and the more space there is for a watermark.

Translation: No one can ever again use Claude for proofreading their own prose unless they’re willing to risk that the whole thing might be flagged as having been generated by Claude.

For example, once the model has written “2 + 2 =”, there is a very clear best choice for the next token (if the model is completing the sum, there isn’t an answer that’s equally as good as “4”; if it’s talking about George Orwell’s Nineteen Eighty-Four, there isn’t an answer that’s equally as good as “5”). The “nudge” of the watermark wouldn’t be applied here. For the same reason, code — which in very many cases has to be exact — has generally less watermarking than some other forms of text.

Having said that, in areas where there is an arbitrary choice between particular words or terms within the code, the watermark can be used, such as comments within code. But by definition, it will have a negligible effect on the actual code produced.

Translation: We value precision in programming code; we do not in prose.

And it is exceedingly rich to cite George Orwell’s Nineteen Eighty-Four, approvingly, in the context of justifying a text adulteration scheme premised on the notion that specific words do not matter. I mean what the actual fuck? Orwell!

Lastly, as to why they’re doing this:

We’re implementing watermarking to comply with the EU AI Act. Anthropic, along with several other major AI model providers and around 190 total signatories, signed the EU Code of Practice on Transparency of AI-Generated Content in July 2026. This requires AI system providers to use methods of “marking” AI-generated text. We’re applying watermarking globally at launch because we don’t yet have a durable way to scope it by region.

This, from a company that the Financial Times just reported is weeks away from an IPO with an intended valuation of $2 trillion, which would make it one of the 10 highest-valued companies in the world — as of today, placing it at #7, between TSMC ($2.2T) and Broadcom ($1.9T).

This leaves us to believe that one of the following must be true:

It’s perfectly reasonable that a technology company valued on par with Amazon and TSMC is technically incapable of complying with an EU regional law only within the EU itself.¹ Not a cause for concern at all.
Anthropic is in over their heads, wields shockingly little control over their own tech stack, and their imminent IPO is likely to be remembered only as a new high-water mark in the manic global AI bubble.

Also, what happens if another major global market makes it unlawful for AI to secretly watermark generated text?

OpenAI

From an OpenAI support document titled “Provenance Signals (Content Credentials, SynthID) in OpenAI-Generated Content”:

Consistent with our commitments under the European Commission’s Code of Practice on Transparency of AI-generated content, our goal is to expand provenance signals to all modalities including text, so customers and developers have clear ways to meet their own transparency obligations as standards and tooling continue to mature.

There’s a lot of wiggle room in this brief statement, and it could just as well mean that OpenAI models will only adulterate text with fingerprint markers when users or developers ask for it. Or that it will only be mandatory for users in the EU. If I were at OpenAI I’d go hard on this and publicly say that ChatGPT will never watermark text it generates unless you ask it to, and that if you want tools that secretly work behind your back without telling you how they work to flag your words in ways you can’t see, go ahead and use Claude.

As a human who has had to wrangle with EU regulations in the past, I am abundantly clear what’s involved in the laborious bureaucratic process. I can guess what threats Anthropic is facing. However, this is a tone-deaf, clumsy, and alarming opening salvo in their watermark strategy. [...]

My writing is my work, and Anthropic’s current strategy is aggressively writer-hostile.

Jeff Gamet, “Anthropic’s Claude Watermark Is Akin to an AI Poison Pill”:

To be clear, the watermarking is embedded in pretty much any text Claude touches. Along with text Claude generates, it also applies to text it processes, such as proofreading and summarizing. I expect we’ll see too many inaccurate accusations of using Claude to write documents where the content was human-written, but AI-proofread.

The watermarking sticks with documents through copy-and-paste, too. Imagine copying text from a blog post or email only to have what you wrote tagged as potentially AI-generated. In fact, that could very well happen with this post. I personally write all of my content without AI tools, but I copied the quote at the top of this piece directly from Anthropic’s website. Does that mean what I wrote here will show as AI-generated? If they used their own models to generate or edit what I quoted, then the answer is very likely “yes.”

One of the papers published at ArXiv I cited above claims that such watermarking even persists when an article of text originally generated in English is translated into German.

Secrets are the poison here. When only Anthropic holds the secret keys that both produce the watermarking and perform the probabilistic detection of those marks, we’re all left to wonder. To wonder if what we’re reading is secretly watermarked, what we’re quoting is secretly watermarked, and whether what we ourselves are writing will be unjustly accused of being AI-generated based on secrets we don’t know and can’t see. Poisonous is exactly the right word.

Or should I say toxic? Or airplanes?

This is the side that noted savant Jim Cramer is on. ↩︎

Today’s post is brought to you by my sponsor, Mechanize. They’re hiring junior software engineers at $300K/year base salary. Apply now!

* * *

John Cochrane raised some difficult questions in the comment section of my previous post. I replied to his comment and also promised to give a more complete reply in my next post (i.e., this one). John responded with a briefer comment, which gets at the heart of the problem:

I agree that if the Fed controlled M, and if M were different from B [bonds], then reducing M raises interest rates. But here's the key question: Our Fed targets an interest rate. It does not control money supply at all. Does setting an interest rate determine the price level, and if so how? MV(i) = PY. Set i, that sets V, ok. But the same V is consistent with any level of M, and any level of PY. The doctrine "an interest rate target is not enough to determine the price level" seems a core of monetarism. Monetarists answer "control the money supply." Maybe the Fed should. But the Fed doesn't. So what theory do we use to understand the price level in our world? FTPL does the trick, of course.

I’m in the difficult position of defending a proposition while hating the background apparatus that makes the proposition valid. The proposition I defend is that the Fed can and should control nominal aggregates such as the price level or NGDP, and the background apparatus that I hate is interest rate targeting, which I view as making monetary policy both less effective and more confusing. It makes a policy tool (the monetary base) that is in an important respect exogenous look like it is endogenous. In this post I’ll try to explain why our convoluted monetary system seems to work, despite the fact that in some ways it looks like it should not work.

I’ll create an imaginary dialogue with a skeptic of monetarism. I intend some (not all) of the skeptic’s comments to represent something vaguely like Cochrane’s views, but I won’t put words into his John’s mouth—I’ll just call him “skeptic”. I will start with the basics to avoid any confusion. I apologize if some of this is obvious:

Me: Prior to 2008, the Fed mostly controlled the price level by adjusting the currency stock. If they wanted 2% inflation, they increased the nominal currency stock at a rate 2% faster than growth in real currency demand.

Skeptic: Actually, the currency stock is demand determined. The Fed supplies whatever quantity of currency the commercial banking system requests, freely exchanging reserves for currency.

Me: Yes, but prior to 2008, banks held only very small quantities of reserves balances at the Fed, about 2% of the monetary base. So as a practical matter the Fed could control the currency stock by adjusting the total monetary base through open market operations. And prior to 1913, there weren’t any deposits at the Fed—the base was 100% currency. They even had ultra-high denomination currency notes for the big NYC banks to hold as reserves:

I think of zero interest reserves as being functionally equivalent to cash.

Skeptic: Even so, the monetary base as a whole was also endogenous, as even before 2008 the Fed engaged in interest rate targeting. The Fed must supply base money as required to stabilize the fed funds rate at the Fed’s target. And Milton Friedman showed that if they do that, then the price level is indeterminate.

Me: I agree, but it is misleading to say the Fed targets interest rates. After all, they also target inflation. And you cannot hit two targets with one instrument. This paradox is resolved if we assume the Fed targets interest rates over relative short 6-week intervals and then adjusts the interest rate target as required to keep inflation on target over a longer period.

Skeptic: OK, but how does the interest rate targeting system stabilize inflation? Keynesians tell us that high interest rates are the key to reducing inflation, but the Fisher effect suggests that high interest rates are likely to be associated with high inflation.

Me: I agree, that’s why I hate interest rate targeting. But New Keynesians insist it can be done, and they may be correct. They assume there is an (unobservable) equilibrium interest rate that would provide macroeconomic stability. When the Fed sets the target interest rate above that equilibrium rate you have tight money, which reduces inflation. When the Fed sets the policy rate below the equilibrium rate, inflation rises.

Skeptic: But all through the late 1960s and much of the 1970s, the Fed repeatedly raised interest rates, and yet inflation kept getting worse.

Me: (Visibly frustrated) Yes, that policy didn’t work very well. That’s why Milton Friedman didn’t like interest rate targeting, nor do I. The Fed was often behind the curve, raising rates more slowly than the equilibrium interest rate was rising. Why was the equilibrium rate so high? Because the Fed printed lots of money in the 1960s and 1970s, causing high inflation. The Fisher effect. That’s the specific sense in which monetarists believe interest rate targeting doesn’t work very well. It’s a clumsy tool.

Eventually, things got so bad the Fed started listening to Friedman almost out of desperation. In 1979, they stopped targeting interest rates and started directly slowing the growth in the money supply.

Skeptic: But money supply targeting didn’t work and was dropped in 1982. Velocity was unstable.

Me: Define “didn’t work”. Yes, in the end it looks like money supply targeting at a fixed growth rate is not optimal. Friedman was wrong about that. But the policy of reducing money growth did sharply reduce inflation in 1981-82 (despite Reagan’s big budget deficits), indeed much more dramatically than even Paul Volcker expected. Inflation reduction was the whole point of money targeting, and on that score it was a big success. And even if velocity is unstable, you can still use money as a policy instrument, as long as you adjust the monetary base to offset money demand shocks.

The money targeting period (1979-82) demonstrates that the Fed can make money exogenous if it chooses to. It doesn’t have to target interest rates.

Skeptic: But the Fed went back to interest rate targeting in 1982; doesn’t that mean that money is once again endogenous?

Me: Whether something is endogenous or exogenous entirely depends on one’s frame of reference. In the very short run, the monetary base is endogenous under interest rate targeting. But over a time frame of macroeconomic significance (months and years), I’d argue that the base is essentially exogenous. Even after 1982, the Fed was—at the most basic level—controlling inflation by controlling the growth rate of the base. Yes, it doesn’t look that way. It’s such a Rube Goldberg device that it looks to almost everyone as if money no longer plays any role in monetary policy.

Skeptic: Please explain.

Me: After 1982, the Fed adopted the Taylor Principle, moving interest rates up and down aggressively enough to avoid the mistakes of 1966-81 (until 2021-22, when they foolishly ignored warning signs.) To most of the world, it looked like the Fed was controlling inflation by manipulating interest rates. But to a monetarist, at a deeper level the Fed was controlling inflation by adjusting the interest rate target in such a way as to produce a money supply growth rate that was consistent with 2% inflation.

During 1982-2008, it looked like interest rates were determining M, but to a monetarist it was the exact opposite. The Fed’s New York desk was told to adjust the base (M) in such a way that the fed funds rate is on target. M determined i, at a level where the Fed wanted i to be. The Fed assumed a correct “i” was stabilizing the economy, whereas monetarists believe it is a correct “M” that stabilized the economy.

[It would be like OPEC leaders arguing whether their oil price target controlled the global oil market, or their output quotas controlled the global oil market. The output quotas allowed them to hit the price target. Two sides of the same coin.]

This is what I mean by a Rube Goldberg device. The logical way to do this would be to directly adjust the base in such a way as to stabilize the price of CPI or NGDP futures contracts, much as under a fixed exchange rate regime the monetary authority adjusts the monetary base in such a way as to stabilize the exchange rate.

Instead, they adjusted the interest rate target in a way that they believed (hoped?) would cause the monetary base to grow 2% faster than the real demand for base money. But due to the conflicting liquidity and Fisher effects, that’s hard to do. Interest rates are a clumsy tool.

And then in 2008 they made things even more complicated by adding interest on bank reserves. Even if IOR has microeconomic efficiency advantages (more liquidity), the Rube Goldberg device got even more complex. Instead of a clean split with the Fed controlling the supply of base money and the public determining the real demand for base money, the Fed could now influence the demand for base money as well, by adjusting the interest rate paid on bank reserves. That made money looks even less central to the process than was the case during 1982-2008.

Skeptic: You have not addressed the indeterminacy problem with using interest rates as an instrument of monetary policy. The fiscal theory of the price level gives us a way to pin down the price level.

Me: Yes, but it is not the only way. Even if John Taylor is wrong about the Taylor Rule, you can control inflation by adjusting the base to stabilize a price target other than interest rates, as with the gold standard and/or a fixed exchange rate regime.

Skeptic: But the gold standard is obsolete, and fixed exchange rate regimes tend to eventually collapse.

Me: But the price target need not be fixed. Singapore’s central bank targets exchange rates rather than interest rates and then adjusts its exchange rate target as needed to keep Singaporean inflation at the desired level. There’s no indeterminacy problem with using a price target rather than an interest rate target as the monetary policy instrument.

Skeptic: But the US is too big to use the exchange rate as the policy instrument, it would destabilize the world economy.

Me: I’m not sure that’s true, but in any case I favor using adjustments in the monetary base to stabilize the price of NGDP futures contracts, or CPI futures contracts if you insist on inflation targeting.

To summarize: Stop using discretionary fiscal policy. Stop targeting interest rates. Stop paying interest on bank reserves. Go back to the small pre-2008 Fed balance sheet. Simply adjust the base (likely to be 98% currency) as needed to keep NGDP futures prices growing along a 4% trend line. Like the gold standard, but with NGDP futures contracts replacing gold.

ChatGPT presents our current monetary regime, as seen by a market monetarist:

We need to make it much simpler.

Part 2: What is the empirical evidence?

Why can’t monetary policy disputes be resolved by looking at the empirical evidence? Partly because almost everything is up for debate. Both monetary and fiscal policy are hard to quantify, as people disagree as to the best policy indicators. What is easy money? What is an expansionary fiscal policy? And recent theoretical innovations emphasize the importance of changes in the expected future path of policy, which is even harder to estimate.

I’m not going to be able to resolve these problems in a blog post, but I’d like to respond to a few empirical observations made by John in his analysis of monetarism:

Most of the historical episodes adduced to support monetarist theories of inflation involve printing money to cover intractable deficits. These are just the sort of episodes where monetarism and fiscal theory agree. Inflationary episodes do not come from mistaken money supply control in healthy economies with sound public finances. Even Milton Friedman once said2 “What produces [inflation] is too much government spending and too much government creation of money and nothing else.”
Fiscal theory and monetarism may disagree a bit on the magnitude of a helicopter drop’s effect. If government debt is much larger than the money stock, a given helicopter drop is a larger proportionate rise in the money stock than it is a rise in government debt, so monetarism predicts a quantitatively larger response.
We can imagine a near-ideal experiment to tell fiscal theory and monetarism apart: First, create $3 trillion of reserves and exchange them for government bonds. See what happens to inflation. Then create $3 trillion of reserves and hand them out, essentially dropping money from helicopters, with no announcement of taxes or future spending restraint to soak up that money. See what happens to inflation. The theories make clear predictions: Monetarism says the two exercises will produce exactly the same inflation. Fiscal theory says only the second will produce inflation.
Our governments just ran this experiment. In three rounds of quantitative easing the Fed brought reserves from under $50 billion in 2007 to $2,800 billion in 2014. The corresponding assets, mostly treasury and mortgage-backed securities, rose from $880 billion in 2007 to $4,500 billion at the end of 2014. Quantitative easing had no discernible effect on inflation. Then, in 2020-2021 the Fed increased reserves from $1,727 billion (February 2020) to $4180 billion (Dec 2021) and leveling off at $3281 billion (Aug 2025). Assets rose from $4,171 billion to a peak of $8,965 billion in 2022. But this time the Fed financed deficits, allowing the Treasury to send people checks of all this new money without having to convince investors to hold trillions of new treasury debt. This time, we saw substantial inflation.
It would be hard to ask for a clearer test between monetarism and fiscal theory. The immense size puts quibbles to rest. Quantitative easing raised reserves by a factor of 50, i.e. by 5,000%. Quantitative easing should have been an atom bomb, a hyperinflation. Instead we are left arguing whether quantitative easing might have lowered long-term bond yields by 10 basis points or so and for how long.

I agree with John that there are some problems with traditional monetarism, but I believe even those problems are overstated. Traditional monetarists never favored using the monetary base as an indicator of policy, and the M2 money supply rose far more in 2021-22 than during and after the Great Recession. Today, monetarists often use Divisia indices, which do a reasonable job of accounting for the high post-Covid inflation:

Let’s not forget that monetarists like Bob Hetzel and Tim Congdon provided some of the most accurate warnings about the post-Covid inflation (and the 2008 recession). And as I remarked in the previous post, I was arguing that QE would not cause high inflation during the early 2010s, even as some FTPL proponents were worried about inflation. So, I don’t see how John can argue that these events were in any sense a definitive test of the relative merits of monetarism and the FTPL.

Consider this observation:

Most of the historical episodes adduced to support monetarist theories of inflation involve printing money to cover intractable deficits.

OK, then let’s consider a bunch of cases where monetary and fiscal policy seemed to move in opposite directions. Here are five examples, off the top of my head:

In the 1990s and 2000s, Japan does some of the most expansionary fiscal policy in all of world history, with lots of big infrastructure projects aimed at boosting demand, which led to a huge increase in the Japanese national debt as a share of GDP. To suggest the effects on spending were small would be an understatement, as GDP actually declined during 1994-2012, even in nominal terms. That’s an almost unprecedentedly weak growth in NGDP after a near all-time large fiscal stimulus.
In 2013, Prime Minister Abe reversed course, raising the BOJ’s inflation target while tightening up fiscal policy to reduce the deficit. There were several big tax increases. NGDP finally started growing, although inflation remained below target until recently. Money seemed to dominate fiscal.
In 1968, President Johnson raised taxes to slow inflation, creating a budget surplus in the 1968-69 fiscal year. Monetary policy remained expansionary, however, and inflation continued to accelerate. The Great Inflation began during the 1960s, a decade with very small budget deficits. John might argue that current deficits don’t matter; it is the long run expected path of fiscal deficits that is important. But I’d argue the same for the monetary base and IOR.
In the early 1980s, Paul Volcker adopted a contractionary monetary policy at a time when Reagan was cutting taxes and boosting military spending, resulting in a much larger budget deficit. Keynesians thought the large budget deficits would be inflationary, but they were wrong.
In 2013, Congress enacted fiscal austerity which reduced the budget deficit roughly in half, from about $1050 billion in (calendar) 2012 to roughly $550 billion. Keynesians said growth in spending would slow and that the 2013 austerity was a “test” of market monetarism. Nominal growth actually sped up.

Almost everywhere I look, I see evidence of monetary dominance. A monetary dog and a fiscal tail. That’s not to suggest that John is necessarily wrong about the post-Covid inflation. Any decent model, including my own market monetarist approach, puts a lot of weight on the importance of shifts in the future expected path of policy. It’s quite plausible that the big Covid fiscal stimulus contributed to public expecting that the Fed would allow more inflation than during the early 2010s. I cannot rule that out.

But note that another change occurred in 2020. The Fed committed to a “make-up policy”, something Ben Bernanke was not able to get his fellow FOMC members to accept when he proposed the idea back in 2003. What had changed is that academic work on level targeting by people like Michael Woodford was much more widely accepted in 2020, as in retrospect it was clear that the Fed did too little to boost recovery in the early 2010s.

It’s also possible that the strong recovery in 2021 had little to do with either monetary or fiscal policy and instead reflected the weird nature of the Covid recession, which was quite deep (peaking at 14% unemployment), but also artificially produced by a temporary shutdown, and that in retrospect a strong bounce back was to be expected. Macro data looked very abnormal during the Covid recession.

[Mea culpa: I did not anticipate the fast nominal recovery, nor did the financial markets. But fellow market monetarist Lars Christensen did.]

I want to be clear that I don’t view the five examples cited above as definitive. I recall that John has cited the Social Security reforms of 1983 as one reason inflation stayed muted despite the Reagan deficits. All of these events are complex. My point is that the comparison of the Great Recession and the Covid inflation doesn’t prove anything other than that the most naive form of monetarism—the view that the monetary base is a good indicator of the stance of monetary policy—is wrong. But more sophisticated versions of monetarism are still very much in play. Those include the Divisia approach, and (my preference) the focus on using adjustments in the monetary base to target NGDP futures prices.

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