Ed Zitron's AI Predictions: What He Got Wrong · Josh C. Simmons

← Writing<br>AI4 in Las Vegas had the moral atmosphere of a casino at four in the morning. Bad carpet glowed under artificial light while rooms full of people insisted the next machine would pay out. That was where I ran into Ed Zitron.

The man who makes his living carving strangers apart in public wanted out the second I introduced myself. Fine. The hallway doesn't matter. The written record is worse.

There's a YouTube version of this argument.

Watch it here.

I used to agree with Zitron about most of AI.

The labs were spending obscene amounts of money. Adoption looked coerced. Every mediocre demo arrived wrapped in a press release about the end of human work. Earlier this year, I had very little good to say about any of it.

I wanted Zitron to be right because contempt was simpler than uncertainty. His story gave me villains and let me stop thinking.

Then the models kept getting cheaper. The benchmarks kept moving. People I knew started using them for ordinary work. The numbers refused to cooperate.

I didn't enjoy this. I had spent months making the other argument.

Zitron, host of the Better Offline podcast and publisher of Where's Your Ed At?, remains one of the best critics of AI's economics. He has also made categorical predictions about AI adoption, efficiency, and technical progress. Several failed cleanly and publicly.

On efficiency, he even admitted one mistake. Then he folded that mistake into the same broader conclusion.

That's the problem. Zitron's conclusion is built to survive whatever evidence arrives.

The money

Frontier labs are burning capital. Their accounting is opaque. Hyperscalers finance model companies that turn around and buy infrastructure from them. Some employers force tools on workers without publicly demonstrating a return. Hallucinations remain a serious limitation. The frontier labs haven't publicly shown that the economics work at full cost.

Independent evidence supports the tension Zitron emphasizes. Stanford reports historically fast AI revenue and adoption growth alongside record spending on compute and infrastructure. The International Energy Agency finds rapidly improving efficiency per task alongside rising total electricity demand. Better technology doesn't automatically produce adequate returns.

Zitron's reporting on AI economics and infrastructure costs is worth reading because these questions are unresolved. I made a similar case when I wrote about the circular financing underneath the AI boom.

Then he turns good reporting into bullshit certainty. A real problem becomes proof that the technology can't work or that nobody wants it.

My dissertation committee would have skinned me alive for making those jumps. That was the job: take the exciting claim I wanted to make and ask whether the boring evidence could carry it.

Gemini

In December 2024, The Wall Street Journal reported, citing people familiar with the matter, that Sundar Pichai wanted the Gemini chatbot used by 500 million people before the end of 2025.

Zitron called the target "so unrealistic" that someone at Google should have been fired. He named Pichai. This was refreshingly specific. There was a number, a deadline, and a proposed punishment.

Alphabet later reported that the Gemini app had more than 650 million monthly active users in its third quarter. In its fourth-quarter call, the company said the number had reached 750 million.

Monthly active users don't tell us how deeply people use Gemini, whether they arrived voluntarily, or whether the product will justify Google's spending. Zitron hadn't predicted any of those things. He said the user target was absurd.

Alphabet passed it by 250 million. He was wrong.

The efficiency prediction produced something rarer: an admission.

After DeepSeek, Zitron wrote that he had "assumed, incorrectly" that there was no way to make models more efficient.

He was referring to his September 2024 claim that large language models had "effectively plateaued" and that nobody had succeeded in making them more efficient.

Stanford's 2025 AI Index later quantified what was happening. The advertised price of querying a model at roughly GPT-3.5-level MMLU performance fell from $20 per million tokens in November 2022 to seven cents by October 2024. That's a reduction of more than 280 times. The same report found machine-learning hardware price performance improving by about 30 percent per year and energy efficiency improving by about 40 percent per year.

That October endpoint came shortly after Zitron's essay, and API price isn't the same thing as a provider's internal cost. A company can subsidize usage. But smaller models reaching the same capability, better hardware price performance, and rising energy efficiency all point the same way: useful capability per dollar and per unit of compute was improving quickly.

Zitron's admission lasted one sentence. He immediately said his real mistake had been...