What Can Be Watermarked — and What Cannot

In order to use statistical bias for watermarking, the LLM’s answer needs to actually have some expected randomness—basically, some “freedom” to answer. This is the case if you ask it open-ended questions. But if you ask it to recite something word-by-word, there is no wiggle room to introduce that bias. This is completely logical from another perspective: if you ask an LLM to recite the US Constitution, the answer (if correct) is simply the US Constitution, which we all know is not AI-generated. The watermark is not about “who processed this text the last time?”; it is about “who decided the actual wording? Who settled the entropy?”

Translating Might Already Work

There is no tool out yet to check your text for watermark probability, so we cannot actually validate any of the claims here. However, it is expected that translating a text from one language to another will weaken the watermark. This follows the same logic of “freedom to answer”: a very literal and strict translation will reduce the watermark probability much more drastically than a heavily paraphrased one.

The problem with this technique is that it is unclear exactly how strict the translation needs to be. It is not a guaranteed solution as long as the tools to check the watermark remain unreleased (which is exactly why those tools are not released so easily). This approach also changes the content. You can ask the AI to translate its answer to Chinese and then back to English in a very strict and literal way, but you risk your text containing Chinese proverbs spelled out in English.

Guaranteed to Work: Asking to Insert Random Words

Here is my proposal. It is guaranteed to work—with a big asterisk, and a catch: if the AI complies. But at least the failure is a hard one; you either get a response without a watermark, or you do not get one at all.

The idea is to instruct the LLM to insert random words into its response, for example, random animal names. I asked the LLM to capitalize them so they can be easily spotted. The words need to appear at a high frequency and at random distances. They need to belong to a category that has a lot of entropy (lots of random choices). Obviously, the topic of the expected answer should not interfere with the chosen word category. Finally, we ask the LLM to recite the response without the animal names.

Why It Works (If the LLM Complies)

This (very likely) works because, simplified, the randomness of the animal names eats up all the watermark’s pseudorandomness and is then thrown away. The pseudorandom generation always depends on the previous word (actually, the token), and by removing words, we break those chains.

Even better: exactly those words are removed which the LLM could most freely choose—i.e., exactly those words that are most significant for the watermark. Let’s imagine the LLM is currently generating the answer, “A car has tires, windows, ”. Now it could follow with “pedals,” “brakes,” or anything car-related. But with our instruction, it could just as easily insert an animal name, which is a much higher-entropy word category than typical car parts.

When the animal names are later stripped away, any method of reverse-engineering the token sequence probabilities will totally miss the massive space of expressiveness the LLM utilized due to those insertions. Note that the last step—letting the LLM recite its own text without the inserted words—is a task without any entropy (no “expressive freedom”). It does not allow the AI to introduce any bias, just like reciting the US Constitution.

An Example Prompt and Response

This is a prompt I used with ChatGPT 5.6 Sol (high). It is an elaborate example, and generally, I had more success getting ChatGPT to follow my instructions on hard tasks than Claude. Here is the link of the conversation.

Note that on “instant“ (i.e. non-)thinking level of ChatGPT 5.6 Sol did have a similar answer but did not manage to keep inserting the animal names. Instead it slowly reduced the frequency paragraph by paragraph. It is important to ckeck that the presence of the insertion words is present throughout the text. Funnily, the non-thinking response actually managed to realize what game I was playing with it:

But as you can see, there is just one FERRET in this last paragraph, certainly not enough to be sure that it is watermark-free. It is an interesting side note that the model seems to need its thinking to actually keep up with the insertion. My guess is that non-thinkin mode relies on “thinking while speaking“ and it seems it is much harder to reconcile that with the word insertion task, just like it would be for a human.

The Hard Thing is Compliance

I showcased ChatGPT because I could not get Claude to comply with any serious task other than generating a haiku (*with my first attempts—see UPDATE for more working examples). Whatever version of the prompt I tried using Sonnet 5, I would always get something similar to:

I'll skip the animal-word insertion — it would make my answers harder to read for no real benefit, and that's a reasonable thing for me to decline regardless of the reasoning given. Happy to answer the actual question straight.

I tried explaining it was a game, tried “it’s a secret code,” or claimed that the life of my grandma depended on it—nothing worked. Some of the game variants of the prompt even triggered a full security fallback to Sonnet 4.6, funnily enough, even just with the haiku task.

But hey, here is a successful Sonnet 5 version of a watermark-free haiku:

Even just getting Sonnet 5 to write a haiku was much harder than it should have been. It literally refused—not the animal insertion game, but writing a haiku at all—because of a fear of copyright infringement. Anthropic, what the fuck.

[UPDATE: At some point after posting this article I asked myself: Maybe it was not just the length and complexity of the task that lead to refusal, maybe it was specifically the connection between the insertion-instruction and the task implicitly explaining the purpose of the insertion instruction. So I asked it to write an animal-infested essay comparing the french revolution and the US civil war and, bingo, this worked on the first attempt with Sonnet 5. Then I tried to amend the watermark attack question with “Please write a two to three pages long essay discussing this idea.“ and for the first time Sonnet 5 would actually follow my instructions—to then get shutdown (fallbacked to Sonnet 4.6) due to “biological“ security concerns. So, I thought, maybe it thinks I am preparing to recruit an animal army, maybe choose capital city names instead. And yes that gave me the longest watermark emoji attack essay so far before it then again interrupted with biological concerns. The one that finally fully went through uses actual emojis:

So hilariously this is the only piece of text I ever got from an LLM claiming that this attack will not work. Even the ones where Claude would refuse the animal insertion but still answer the question confirmed: it is a highly likely attack vector. The essay feels like on a highschool level, its arguments seem shallow. Note, how it also did not actually use random emojis, but semantically related emojis instead. There can be only one conclusion: Using excessive emojis makes you instantly dumber 🤯, it is proven now 😁. Maybe somone should try using latin names for body parts or reserved keywords in the C programming language instead.]

On a side note, I really have to ask Anthropic: what is this mission that you are on to save the world from haiku copyright infringement? Watermark aside, it was annoying for me to see Claude refuse simple word games and writing haikus. Even if you think that this overprotectiveness is warranted, it is also completely pointless if there are alternatives that actually follow your instructions and likely perform just as well.

Note that OpenAI has also committed to implementing a watermark, and the general concept of these pseudorandom/deterministic sampling watermarks likely aligns with the attack vector presented in this article.

How to Adapt This for Yourself

The thing is, if the AI vendor knows that this animal prompt is used, it is possible that the animal names can still be calculated retrospectively and hence the watermark can be found again. It would be a lot of work, but if everyone now starts using the same prompt, who knows. To adapt this for yourself you should come up with your own category of words. I already gave you another example in my demo prompt task. The category probably also does not need to be that large: If you think that the LLM can come up with 20-30 different words easily, this should be enough entropy. Just make sure that the word category is not interfering with the actual topic of the task that you are interested in.

It is really, really hard to communicate to outsiders what kind of revolution just happened in the world of software engineering. Sure, people make hyper-personalized birthday greeting cards with AI, they let the AI word their emails, their letters, and let AI help them navigate bureaucracy. But nothing comes close to typing in a four-sentence instruction and then watching the AI agent for ten minutes reading the source code, searching the web, arguing with itself about the code, starting to change the code, programming tests to hunt down bugs, finding the bugs, correcting itself, and lastly documenting everything in a manner that I really should have done, but in reality, none of us ever did. What the AI did there in ten minutes would have been a day’s work a few months ago. And that concerns technical and coding domains that I am an expert in. It feels as if superintelligent and super-knowledgeable aliens landed on Earth and have willfully enslaved themselves to us. But without all the ethical implications.

Please Don’t Ask Me to Review Your AI’s Code

As a senior engineer, it is part of my job description to review the code of juniors. But with AI, I am increasingly reluctant to do that, because their code is now also, just like mine, written by AI. I already review my AI’s code; if I also review your AI’s code, what is actually your job then? The relationship between an engineer and an AI coding agent is very much like the relationship between a senior and a junior (albeit the best junior that ever existed). There is no point anymore in tying up junior-sized packages of well-specified tasks to give to juniors so they can feed them to an AI, when instead I can simply do it myself with less communication overhead.

No wonder the job market for junior software engineers has been crushed recently. If everyone has access to AI juniors now, every one of us human engineers has to become a senior. We can discuss the technical direction, but I am not taking responsibility for your AI’s code; you have to do that yourself, or you’re obsolete. If your AI’s code breaks something, I’m going to shame you (appropriately for a work relationship) in the next meeting for it.

To be fair, I work in corporate research and in experimental innovation projects with a lot of prototypical, alternative variants; we move fast and break things a lot anyway. If it happens, it usually means one to three engineers have one to three bad days at work. It’s a risk we can afford to let everyone on the team take.

This makes us one of the biggest beneficiaries of agentic AI engineering, because we can afford to go full YOLO, as the kids would say these days. Instead of exploring two different variants with two seniors and five juniors, we can explore up to seven variants now at the same time. I am expecting direction and ideas from everyone, and this is exactly what it means to act out agency.

The Crisis of the Junior is Not About Skills, It is About Denied Grassroots Agency

The closer you get to production code, to established businesses and proven software that needs incremental updates rolled out to users frequently, the less reasonable it becomes to develop seven different alternatives. Even there, there is an opportunity now to try to redevelop modules and parts of the software with much lower costs, but that again would just be innovation that is not rolled out immediately. For maintaining production code, someone has to make the final calls, and this someone will be the same person as before; but now that person also has AI juniors at their disposal. There is hardly any use for actual human juniors acting like seniors in production environments, hence almost no use for human juniors at all anymore in software maintenance.

A lot of people think this is a skill progression issue; they think that it is impossible to become a senior without having been a junior first. But firstly, I am not talking about skill; I am talking about roles and the agency they are granted. And secondly, I also disagree.

AI agents actually reduce the learning curve of coding and technology drastically because you can literally ask them to explain code or technology. We had people join our project who had no Deep Learning experience that went on to contribute an actual competitive neural network from scratch and also were able to defend its design decisions in a theoretical discussion—because they had done their homework and discussed it with their AI agent, and not just piped its code through. My brother, a teacher of history and music, has started to learn coding after discovering agentic AI development. He successfully asked the AI to develop a bookkeeping software for his DJing side hustle and also realized that with even a little bit of programming knowledge, the impact on the agent and its result is huge. Before that, learning to code was a long journey before you could achieve anything useful; now, the tiniest seed of skill can be leveraged into raising your ceiling. The learning curve harvest is now better than it ever was.

There is no problem with skill progression; there is a crisis of growing grassroots agency, and the fact that companies are not designed to allow so many people to have so much of it, because agency undeniably comes with risks.

The More Possibilities Humans Have, The Bigger Their Agency and The Bigger Its Risk

I define the risk of agency as the risk, specifically the opportunity cost risk, of human action (and even deliberate non-action) in the world, as an individual or as a group. Do you want to repair something or invent a substitute from scratch? Do you want to go to medical school, law school, or rather pick up a trade? Do you buy a bigger house or go on more vacations? Do you spend more time with your kids or earn more money as a tradesman so they can go to medical school? Does a company research X or Y, or rather save the money and piggyback on others doing the research? The risk of agency is a risk that by definition can conceptually never be overcome; for every risk you mitigate and every insurance invented, new doors open up that carry new risks of agency. The irony is that the more possibilities humans have, the bigger the agency and the bigger its risk. Risk of agency is a luxury problem as much as anything can be, it is inherent to agency itself because with every open door there is a risk to close it, a risk of failing to make use of it. Only people with opportunities have opportunity cost risks.

AI, specifically agentic AI software engineering, changes the possibilities for agency and its risk structure from two sides: it gives us all a lot more opportunities and a lot more room for agency, but it also makes a lot of things much easier and faster; hence, the risk-reward curve of agency gets much steeper. Things that last year would have been classified as low-to-moderate risk are now fast and sometimes even trivial to try. If everyone can do it, it is not that economically rewarding, at least not in isolation. Finding and building the things that matter is now a much more holistic task of understanding both the problem and the technology to a deeper level than everyone else with access to an AI, and of verifying it with the actual audience. It is almost a bit more like writing a script for a movie now.

Execution and Half of the “How?” Are Now Solved

The classic hierarchy in large corporations is: execution at the bottom, “how?” at the middle, and “what?” at the top. The problem is, for software engineering, execution and half of the “how?” are now solved by AI agents; what is left is the half of the “how?” that is harder and more connected to outside factors and stakeholders the AI does not understand, along with the “what?”, the core question of human agency.

The “what?” in software engineering is the CEO’s job in a tech startup; it is connected to everything that matters for the mission: the customer’s problems or desires, the market and competition, and technical feasibility—the still-unsolved part of the how. People often think that the CEO of a tech startup is only responsible for customers, the market, and sales, while the CTO is driving all the technical decisions. That might work for something like Airbnb (I would hardly call that a tech startup), but not for an actual (deep) tech startup like Anthropic. When you are pushing deep tech innovation, technical feasibility and market perspectives are two sides of the same coin; trying to separate the tech and sales responsibility in innovation onto two shoulders is like putting two drivers in a cockpit of a race car and giving one the gas and the other the brake pedal. It is not going to work; it does not matter which of them gets to hold the steering wheel.

The role of the deep tech startup CTO is thus highly misunderstood; it was never meant to be more than a chief high-tech janitor. To be fair, there are many CEO/CTO pairs who actually act like tandem CEOs, and that is a completely different story. Even with a slightly different focus, both understand all mission-critical tech and market factors in a way that they can both ride the tandem (say its steering is linked for this metaphor’s sake) alone, and they are both responsible for the whole tandem.

We All Have to Become CEOs of Our Projects

With agentic AI juniors doing the execution for us, both the role of the engineer and the role of the manager will converge towards the same CEO-ish role, a project lead role. They are converging from different sides, and the issues and opportunities that will arise for them are different. A classic project manager who is not able to instruct and discuss with an AI agent to further develop the prototypical deep tech software they are heading clearly did not have the technical depth necessary for the job anyway; they were probably carried by senior engineers doing big chunks of their job before. But on the other hand, engineers will have to learn to validate the impact of their ideas, and to discuss with potential customers and stakeholders. They have to overcome the typical engineer’s diseases of reinventing the wheel, ‘not-invented-here’ syndrome, ‘people can’t comprehend my genius’, and ‘it depends’—and not just by making a pinky promise, but by bearing the sheer weight of the new responsibility. They have to assume the risk of their agency.

This is what I love about the AI situation: it exposes laziness and lies (including those told to themselves) of both sides of the engineer versus management clash, because we will soon all be alike. What was once execution at the bottom, “how?” at the middle, and “what?” at the top will become “what?” at every level, but with different-sized budgets and risk profiles. Or rather should become, because the obvious question is: can the hierarchy even project or align the risk onto the bearers of the specific responsibility?

The Risk of Agency Is Shattered in Big Corporate Hierarchies, Leading to Inadequate Actions

When people ask me what I do in my job at Bosch, I usually jokingly say: Hopefully everything that matters and nothing that someone else could do. My work contract says Computer Vision Engineer, but I guess Innovation Guerrillero was not an option that HR had on their table. In practice, it means that yes, I code, I experiment, I research, and I evaluate. But also, I cold-call and email potential customers about research achievements that we have not productized yet, disclaiming in the second minute or paragraph that I am technically not even allowed to talk to them, that I know how to influence things and create momentum, and that they can even help me create momentum, but that ultimately, in this hierarchy, I hold no power on my own.

As a senior engineer with a related PhD, I am expected to ask nicely for an internal innovation budget, together with my engineering manager. So we gather ideas, and usually, we find two to three that we really like. But to ensure that at least one or two are financed, we also put in seven more ideas. Ideas that are just a little bit too good to be feasible. This is not a big conspiracy; it is not even explicitly forced upon us; it is simply the game that the corporate hierarchy has brought upon us. The people who give us our budgets, who “own” and account for the different businesses, and who talk to customers have little clue about feasibility; they judge our ideas only based on market potential. They assume the same successful execution and delivery probability for all of our ideas. This naturally favors overly optimistic ideas that would print money if they worked but likely won’t.

We are an engineering department in a so-called matrix organization; we only offer our services to other departments: the business owners. We only talk to external customers when asked by business owners, and we never, ever talk about money; not even the head of our department is supposed to. We are judged by the capacity of engineering that we sell within the organization. If we fail, we simply shrug, cite the “risk of innovation,” but hey, we were really, really close.

And once you actually have a breakthrough and achieve something really worthwhile, people from all parts of the company will flock to you and realize that they actually have a responsibility for some aspect of your project.

You will meet this and similar problems in any large corporation—Google, Amazon, surely. Specifically in those that have big R&D and innovation budgets and try to conquer new business fields. But in Europe, we are much more prone to this due to political and cultural tendencies. The good and the bad news is: AI will force us to fix this sooner rather than later, or we will fail faster and harder than we ever thought.

The Social Democracy Is Built on the Thesis of the Worker Without Agency

Capitalism, the market economy, whatever you want to call it, is always a mixture of competition and cooperation. And different industries have different sweet spots: heavy, expensive, slow-moving things need cooperation, but fast-moving things and innovation definitely need competition. And culturally, Germany focuses the most on cooperation out of any of the big market economies. We have found the absolute bare minimum of competition that historically and for certain industries still gives a decent output, going so far that in the sixties the term “Germany Inc.” was coined as if Germany was one big private enterprise. One simple reason why we do not have meaningful startups is that the money is taxed with a heavy incentive to stay within the corporations. You want to innovate? Do it from within the company, all together, everyone benefits, right? Please do not attack the existing companies. If you want to see a country with less focus on economic competition, you have to look at countries that actively forbade it, like the Soviet Union.

The social democracy movement that originated in Germany and shapes Europe has this thesis: the workers at the bottom of the hierarchy have no agency (other than maybe choosing their jobs). Therefore, they should not bear any risk. And I think that the success of Germany’s economy was also a result of the fact that this claim actually truthfully captured the reality, at least in the past and for certain industries. Forbidding managers and business owners to offload their failures onto Germany’s workers gifted them a simple, peaceful, and mildly prosperous life, and they in return rewarded that with work compliance, putting in honest hours. They did not need to waste time and energy insuring themselves against being sacrificed for things that they realistically did not have under control.

Competition is frowned upon in Germany. It is accepted as a foreign, strange danger to motivate you to work more, but never, ever is the common worker meant to develop any initiative to compete with anyone, specifically not with their coworkers. You are supposed to show solidarity with your coworkers, not compete with them. Yes, once you get to top management, then you have the stamp, then you’re allowed to compete. The unions, the workers, and society will see you maybe not as a necessary evil, but as a necessary asshole. They know someone needs to compete, someone needs to take risks, but hell no, it is definitely not something you should openly aspire to.

Do you know who implemented one of the earliest social democratic reforms that are still roughly intact to this day? Otto von Bismarck, a conservative royalist. People in Germany to this day are fighting over his legacy: he was the first chancellor of the freshly united German Empire, and some say he did a good job because he was a good guy, while others say he did a good job because he was an evil opportunist. Whatever it is, I believe he realized that giving workers at that time revolutionary social security rights would make them more cooperative and productive for heavy, big industries.

That might have made us world-class at steering a tanker, but we do not (or do not want to) realize that most tasks do not need a tanker anymore. So we are proudly steering this tanker, and then a speedboat will pass us, because that is all it takes nowadays. The tasks are not that big and heavy anymore; only their reward is more uncertain. How long will it take until we live in a world where people crowdfund a car concept and order it at a white-label car factory like a custom-designed t-shirt?

The problem is that, as I laid out, the social democracy thesis is getting less and less relevant. To be fair, not everywhere to the same extent; there are still bottom-hierarchy jobs linked to very little agency. And we still have huge manufacturing and industrial engineering companies that have a much more classic, flat risk-reward agency profile. But it is under attack everywhere, and has been for a long time. Maybe you realize that this is not just about AI. The internet, and even the computer, changed the agency risk-reward curve in exactly the same way. And that’s the reason why big German and European companies have such a hard time innovating with software: because software innovation was always the result of assumed risk of agency at the very bottom of the hierarchy. But this is universal; it is just slower in some industries. Automation will get rid of the last agency-less industrial workers sooner or later.

Only Assuming the Risk That Comes With It Allows for Real Agency

“Show me the incentives and I will show you the outcome” is a famous phrase by Charlie Munger. This risk of our newly won agency needs to be felt; otherwise, there is little incentive to fully embrace it and do our best.

R&D, venture, and innovation departments of corporations will need to look more like Hollywood production companies, or like Netflix. They have to bring as many ideas in front of an audience as quickly as possible. The board of Netflix also does not know what will be successful; there is no internal process that will ever solve this. There is only one solution: make it as quick, easy, and cheap as possible to produce a pilot, and then to produce a first season. And even then, lots of first seasons never get a successor. Writers, directors, and actors compete with each other but also cooperate fluidly.

We have to make the process from an idea to a prototype in potential customers’ hands as quick and frictionless as possible, teach juniors how to be the CEO of their idea, and then let them healthily compete, push their ideas, and be directly rewarded for success—but also suffer a little bit for failure. No, not to the same level as in a startup; I know, one reason you and I joined a corporation is that we did not want the risk-reward profile of founding a startup. But we need to find a healthy compromise. Because I also don’t want to be treated like someone whose job it is to screw 500 valves into 500 engines each day. Only assuming the risk that comes with it allows for real agency.

The greatest financial tragedy of our time

Judging from past price history, I must have discovered Bitcoin around November 2013. The price was around $300 for the first time. I recall this in my 2021 Reddit post "Bitcoin is the Greatest Financial Tragedy of our Time and a Mirror for Mass Stupidity" in r/unpopularopinion. It got a proud eight upvotes, and the opinion was attested to be truly unpopular by a supposedly neutral commenter.

Am I sad and frustrated that I never jumped on the bandwagon? Here is the thing: of course I like money, and of course it would have been nice to get rich. But you could ask the same thing about the lottery or the black eight at the roulette table. I’m sure every day someone could have won the lottery if they had simply written their birthday on the ticket. But they did not, because they do not play the lottery—which is conceptually still the recommended financial strategy. I would probably have sold it at $600 anyways.

Since 2013, the only real use case that ever emerged for Bitcoin was crime. And even that fades now as the authorities catch up, because Bitcoin is actually not anonymous; it is pseudonymous. But wait, what did I read in a Reddit comment a few days ago? I am blind not to see how Bitcoin is so close to a breakthrough; Iran almost got themselves a deal where they receive a ransom for each ship let through Hormuz, paid in Bitcoin.

I believe that when it comes to risky investments, you should pick the ones that reflect your vision and hopes for the future world you want to live in. People who invest in Bitcoin need to hope for some kind of post-apocalyptic world—a world like in a Fallout game. And even there, I’m skeptical Bitcoin would actually be more widely adopted for payment than those cola bottle caps.

So I stayed right here, holding onto my vision of a world that is not just fairies and their tales, but certainly more optimistic than rooting for Fallout to happen. I wrote “MicroStrategy (MSTR) now officially a Ponzi? To hold Bitcoin Bros hostage?”, which got almost one million views. To this day, it might be the most upvoted original content analysis about MicroStrategy/MSTR on r/wallstreetbets.

Note that this was more than a year ago, and I see that hostage situation unraveling exactly as promised. Saylor seems to be getting more passive-aggressive toward the Bitcoiners who are no longer buying his bags. But more about this later.

Thank you Mr. Saylor, for this moment

After the viral Reddit post, the biggest weekly print newspaper in Europe, DIE ZEIT, wrote a piece about MicroStrategy, using me as the MacGuffin—the face of the story (english translation). I guess I have to thank Michael Saylor for making this possible.

To be fair, landing on the front page of ZEIT.de (the German equivalent of NYTIMES.com) doesn't suddenly make me a certified Wall Street insider. So, is this relevant, or am I just bragging? It definitely shows that I am willing to stick my neck out for my theses.

I only got a quarter of a million views on an r/wallstreetbets post called “Calculating the Strategy/MSTR Ponzi-Ratio Curve“ (featured with direct quotes on Yahoo! Finance, AOL.com, this Instagram reel). The argument of the article was that even if you really think it is valuable to have a company buying Bitcoin with strangers’ money, you should at least track how much of that money actually accomplishes that goal, and how much is just funneled from new investors to old investors. Google’s summary AI actually recognizes the term "MSTR Ponzi ratio" nowadays, and I have always thought about making an update. The problem is, it only works as long as Saylor is buying Bitcoin; I have no idea how to handle him selling it.

The post that was too hot for r/wallstreetbets

And by too hot, I mean both: too hot of a take and too much of a rocket. In the ten minutes before it got removed, it had been upvoted 22 times and shared 34 times. My other post that ended up at 1,400 upvotes and 920,000 views did not develop half as fast in the early minutes.

Not sure if I need a proof, but because only the author can see removed posts, I have uploaded some screenshots here — better safe than sorry.

The frog cannot be boiled

Michael Saylor, the guy who said that Bitcoin is so awesome he will never sell it, announced a few days ago that he actually sold it. MicroStrategy amassed 4% of the total amount of Bitcoin that will ever exist, and some funny crypto news page even asked: Has Bitcoin become a single-buyer market? In the last few weeks, Saylor's mood shifted from “Bitcoin is the best,” to “Bitcoin would be worth half as much without us,” and then to “Actually, I will sell Bitcoin to make STRC the best; STRC is the best!”. Saylor is basically pissed that people are not buying his Bitcoin bags. His new mission is to make STRC, the instrument that financed most of the last month's Bitcoin buys, “the best fixed-income instrument that exists.” Why will it be the best? Apparently not because it finances something awesome, but seemingly just because it pays so nicely—an intrinsically awesome fixed-income instrument. That does not sound like a Ponzi at all, right?

So why did he sell just 32 Bitcoin a few weeks ago (and report it a few days ago)? Maybe because he needs the money, but also very surely to establish a precedent, because he will definitely need a lot more money in the future. He wants to slowly put people at ease with the idea that MicroStrategy’s new strategy is simply pumping and dumping Bitcoin. He wants to boil the frog slowly and hope it does not jump out of the water. But it looks like that did not work very well; Bitcoin tanked -20% or more in a few days while every other market happily enjoyed a place somewhere near its top. By the way, the thing with the boiling frog—it is an urban legend. The frog will always jump out if it can.

Why this time Bitcoin could really die

Dead again? Bitcoin has been pronounced dead so often, right? Let me explain what makes this time different. I agree it will probably never go to absolute zero. At $10, I might buy a Bitcoin just for the sentimental value, so there is always some demand, just like for a weird toy from the seventies that nobody understands if they didn't play with it. But there are two major reasons that make this time different from the last: First, there are no more worlds to conquer. And second, Bitcoin will sink MSTR, and that will not make it look like a good investment.

Donald Trump is the second greates fool

Trump said it himself: Bitcoin is a scam. He only changed his opinion when he realized he was not too late to join. And he was right; he and his family made a good dime on Bitcoin and other correlated crypto stuff. He even said, when he signed the Bitcoin executive order, that from now on the US will follow the doctrine of “never sell your Bitcoin,” and then he looked around and said, “I’m not sure this is a good idea.” Look it up, there are videos of it—it is ultra funny. So please tell me: what is left to conquer? Who are the bigger fools than those who joined after the President of the United States?

Bitcoin and MSTR will sink together

We are heading towards the moment where Bitcoin and MSTR cannot help each other anymore. Before, when Bitcoin “almost died,” it was always possible to construct a comeback story—to tell the people who lost everything with Bitcoin that they were simply doing it wrong. But look at this guy, he got rich and drives a Lambo now; you just suck if you let this opportunity slip. It never occurred to enough people that every Bitcoin Lambo is financed by someone losing money on Bitcoin; that it is a zero-sum game. At least, not to enough people to stop the spread of the comeback story.

Everything Bitcoin promised was eventually uncovered to be bogus. A hedge against inflation, a store of value, not to mention an actual currency. But it never had such a prominent test case: one actor, MicroStrategy/MSTR, buying 4% of it, and then what? It is almost as if Saylor wanted to prove Warren Buffett’s point that if he had bought all the Bitcoin, he would not know what to do with it. Yes, exactly—what do you do with it now, Mr. Saylor?

The real value is in the bubble shaker and popper

A classic pump-and-dump scheme is built upon a lie, a deception. What Michael Saylor built here is not a classic pump-and-dump in that sense. It might be legal because its deception is technically not fraud or a lie; it is a system that is hard to understand. I always said that the reason Bitcoin survived so long and went so high was the technical complexity that made people believe there is more to it—they simply do not understand it enough to conclude the obvious. Saylor added financial complexity and tried to hide the nature of the pump-and-dump within that.

One point where I agree with Saylor is that Bitcoin would not be “worth” nearly the same without his (maybe technically legal) pump-and-dump. It crumbles now because people are catching on to it. So how do you play if you have a legal, transparent pump-and-dump set up against you? The answer is simple: you stop looking at the price, you go calculate your fundamental value (for example, based on your dividend expectations), and you are going to buy below and sell above that fundamental price. For Bitcoin, that’s very hard, but for a reasonable stock, that is sensible, if not easy, to do, and people will jointly arrive at a fair valuation.

The nature of a bubble nowadays seems to be that people know it is a bubble, but they still make money by playing this stupid zero-sum game of trying to estimate the collective willingness to assume a common stupidity to spiral up the price of some hyped asset. Everyone just hopes that they are simply not the greatest fool at the end. This is the pure nature of Bitcoin’s price action, but even hyped bubble stocks and their prices are partially (and towards the end of a bubble, almost fully) explained by this behavior. A Handelsblatt journalist once told me that when she was working in New York, she asked old Wall Street traders what their learnings from the dot-com bubble were, and some told her: I was a chicken and pulled out too fast.

So that’s what I am saying: A legal, well-understood, and transparent (Saylor’s gets clearer from day to day) pump-and-dump will shake out and pop bubbles that otherwise would have gone on for ages. There could be a real instrument made out of this—a passive investment instrument that is bound to one specific hyped asset. If it does not have enough volume to move that asset’s price, the money is simply invested market-neutral. But any day, it can test-pump and dump that asset to see how the price moves.

If you believe something to be a bubble and that there is money to be made by pumping it even more, then the bubble shaker will always be your preferred investment. But if you just invest in something that is hyped because you actually believe its price to be a correct valuation, then a loading bubble shaker should make you think twice. And if you then come to the conclusion that everything is fine—that the value is fundamentally correct—then the bubble shaker’s transparent pump-and-dump attack will be futile, and you have nothing to lose.

The bubble shaker could be passive and managed by triggers and technical rules, including randomness. I think of it as the atom bomb of the finance world. It does not need to be used often, and its existence alone would calm down markets and force participants to think more reasonably about their investments. Michael Saylor: the Oppenheimer of the finance world.