Why Watermark?

# Why Watermark?

Claude Wants Everybody to Know it is There

> “My product is so bad for mankind that I plan to place an indelible fingerprint that I created it so that everybody can know I was there.”

Ok I am cheating. Anthropic did not literally say this when announcing watermarks this week. But they did signal it. The tone of “I’m sorry for being here” and, from now on, “I promise not to be invisible,” is extreme and bizarre in equal portions.

In a week where investors signaled a $2 trillion IPO plan, the company seems to be schizophrenic. We are great and our revenues show it while simultaneously shrinking back from being a net plus for the world.

Whether AI touched the work is the wrong question and watermarking is therefore the wrong action.

Watermarking is Nuts

Ben Thompson’s Stratechery piece belongs at the top of this week’s issue because he nails this truth.

A watermark starts from suspicion. It assumes AI involvement is the important fact, and that the task of institutions is to detect it after the fact. Thompson’s objection is simple and right: a mark may only show that Claude proofread, translated, summarized, or converted human-origin work. A missing mark does not prove AI was absent. It can accuse legitimate human work and miss machine-written work at the same time. I read a piece by my good friend Saul this week and Substack's partner said it was 100% AI written. It isn’t. Sure Saul probably used AI, but his ideas are solid and clear in the piece, not those of the tool he used.

More importantly, asking if AI has been used is the wrong moral question. We do not mark work because a calculator helped with the arithmetic, a compiler helped with the code, a camera helped with the image, or a search engine helped with the facts. We judge the result and the responsibility behind it. Is it true? Is it useful? Is it accountable? Is the author using the tool honestly? The bad acts are fraud, fake sources, fake evidence, fake authorship, hidden manipulation, and unaccountable slop. The bad act is not using AI. The vast majority of AI is used in these “good” ways, not in the “bad” ones.

AI use is generally good.

Last week we established that humans created intelligence. We created language, science, medicine, law, software, markets, art, engineering, and institutions. AI is valuable because it can gather that human-created intelligence, digest it, translate it, reason over it, and hand it back to people who could not previously reach it. The machine is not the author but it does help make human knowledge more available to human beings.

If that is true, then AI should be everywhere and cheap and not need detecting. As it improves it may even be more readable.

This week makes the real gap more visible. That is the distribution gap.

The Distribution Gap

Grok Bot at $200 a month is exciting because it shows how capable the product category has become. It is also much too expensive if we believe AI is becoming a basic tool of thought. Universal access to intelligence cannot be a luxury subscription. It cannot be something only large companies, rich users, elite schools, and well-funded developers can afford. If AI is good, the goal is not detection. The goal is abundance.

The demand signal is already enormous. Peter Walker, now Head of Insights at OpenRouter and formerly at Carta, points to “70 trillion weekly tokens” flowing through OpenRouter. In a subsequent post he noted that 80% or so of token use is by agents, not humans. Grok Bot delivered unlimited agents to those who can pay $200 a month. Agents will eventually be free, some already are. And they work for human end goals.

Walker’s findings are about usage. They show people routing work by task, price, latency, efficiency, and model quality.

In this week's venture section, Pratyush Choudhury says he burns through 300 million to 500 million tokens a day to build intuition about models and markets. The more useful the tools become, the more people use them. The more people use them, the more cost and latency matter.

Universal Distribution Requires Infrastructure

The infrastructure story matters. Cheap and everywhere does not happen by wishing for it. It requires chips, power, data centers, memory, networks, inference software, capital markets, and new companies built around delivery. SemiAnalysis reports that TileRT reached “up to 500 tokens per second per user” on a single B200 decode server in one benchmark. That is the right kind of story: not AI as magic, but AI as engineering, cost curves, latency, throughput, and system design.

The same is true at the largest scale. SemiAnalysis argues that SpaceX could add 6GW to 8GW of compute capacity in 2027, with potential to exceed 10GW, and frames frontier inference as a business that could generate more than $100B per GW per year against far lower assumed costs.

Whether every number proves out is not the point. The point is that serious people are now treating intelligence delivery as infrastructure. They are thinking in gigawatts, turbines, binding power contracts, GPU clusters, capex, financing, and revenue per unit of compute.

AI is not just another software category

The scale of investment is not, by itself, evidence of a mania. It is aligned with the size of the opportunity. If AI is just another software category, the numbers look absurd. If AI is the next general infrastructure layer, delivering intelligence to the entire planet, they look more rational. Perhaps still too low.

Nvidia deserves credit here. In “Nvidia’s Risky Business,” Thompson describes Jensen Huang trying to make “AI factory compute” an investable infrastructure asset class, with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs, and KKR mobilizing “more than $500B” of third-party capital. Nvidia is not merely selling chips. It is trying to lower the cost of capital for the buildout and make compute financeable.

Good. Well done. But Maybe Still too Small

Nvidia’s efforts may still not be enough. If intelligence is going to be available to every child, worker, teacher, doctor, founder, researcher, and creator, then the system must be overbuilt enough to drive prices down. Scarcity is not neutral. Scarcity means higher prices, rationed access, slower tools, weaker models for ordinary users, and more power for whoever already controls capacity. An underpowered intelligence infrastructure is a form of exclusion. Those who want fewer data centers are accidentally supporting elite access to intelligence at high prices.

Hardware concentration by itself is inevitable as those who can spend these vast amounts can be counted on two hands.

A concentrated hardware buildout can still be socially useful if it produces falling prices, faster systems, and broad access.

Railroads, electricity, telecom, cloud, and semiconductors all required large pools of capital before they became ordinary parts of life. The question is not whether Nvidia is too important this week. The question is whether enough other people can step up, whether competition appears at every layer, and whether the benefits move outward from the builders to the users.

The real danger is low use of AI due to distribution and price concentration.

Who gets access? At what price? Under what rules? With whose permission? That is where watermarking, model controls, closed distribution, app-store style gatekeeping, and regulatory suspicion begin to rhyme. They all risk turning intelligence into a feared and managed system rather than a universal tool.

The right policy is not to mark every use of AI as suspect. The right market goal is not to ration capability until only premium users can afford it. The right social goal is not to make people apologize for using the most important tool of the decade.

The right goal is abundance: more compute, more power, more competition, more open models, better inference, lower latency, lower prices, and broader access. Punish fraud. Punish deception. Punish fake evidence and fake sources. But do not punish the distribution of intelligence.

AI is good. Build enough of it for everyone. No child left behind seems pertinent.