Wed 9 September 2026

Bill Gates published an essay in late August arguing that dealing with the risks of artificial intelligence should be the world's top priority. Two weeks earlier Elon Musk had sat down with The Economist and said much the same thing in his own way.

They are not natural allies and they do not sound alike. Gates is worried and says so. Musk says he has decided to look on the bright side. But both end up asking for the same practical thing: somebody who actually understands these systems should check them before they are released to the public.

Musk's suggestion is oddly specific. He thinks the rival companies should check each other's work — give competitors a week or two with a new system before it goes out, on the grounds that a government official cannot realistically judge it and a competitor absolutely can. He says he put this to a rival himself.

Gates's complaint is different. He is not upset that people disagree with him. He is upset that nobody is arguing at all. He expected a public row once these systems became genuinely capable, and instead got quiet. “The silence is what really drove me to speak out,” he said.

Worth noticing what neither of them asks for: a pause. Both want the work checked. Neither wants it slowed down.

Ed Zitron has spent fifteen years in the technology industry and has become its loudest sceptic. His case, put on a podcast in late August, is simple enough: the companies building artificial intelligence are spending vastly more than they earn, and most of what they do earn comes from two other companies who are themselves losing money.

He puts numbers to it. None of them have been independently checked, and they should be treated as his claims rather than as facts. But the shape of the argument does not depend on any single figure.

His strongest point is the one that needs no arithmetic at all. When the railways were overbuilt, the country was left with railways. When the internet bubble burst, the cables stayed in the ground. Zitron argues that the specialised chips being bought by the billion today are good for one thing only, so if the spending turns out to have been a mistake, there is nothing useful left behind.

The person interviewing him pushed back, and fairly: enormous numbers of people use these tools every day. Zitron's answer is that being nagged into using something is not the same as choosing it. That is the weakest part of his case, and it is worth knowing that it is weak.

Brett Adcock runs Figure, one of the companies trying to build robots shaped like people that can do ordinary work. In June last year he said his factory could build twelve thousand robots a year on each production line, and that a hundred thousand robots in four years was achievable.

By this April, talking to a different interviewer, the number for this year had become “thousands”.

What has not changed is the size of the prize he describes. Roughly half of everything the world earns is paid to people for their work, and a robot that can do that work addresses all of it. That claim is identical in both interviews.

The interesting change is what he now says the hard part is. Last year it was building enough of them. This year: “This is not a manufacturing problem. This is an intelligence problem.” The bodies work. Getting them to think well enough to be left alone for a day's work is the thing nobody has solved.

He is honest about the standard he has set himself: a robot doing seven to ten hours of useful work in a house with nobody supervising it, every day. “Nobody's ever shown that.”

Three separate sources this week, none of them talking to each other, landed on the same constraint: there may not be enough electricity.

One data centre in Texas is designed to draw more power than a mid-sized city, packed into a site a fraction of the size. Every large company now wants one. The American electricity grid was not built with that in mind, and grids take decades to change while these buildings go up in months.

It is the sort of unglamorous detail that decides things. A company can announce whatever it likes about next year's technology, but it cannot announce a power station into existence.

In 2006 Fei-Fei Li assembled fourteen million labelled photographs, on the then-unusual theory that machines would learn more from lots of examples than from clever programming. She was right, and modern artificial intelligence dates from that moment.

She is now building something else, and her argument with the rest of the industry is worth understanding because it is not a squabble about whose product is better. Her point is that language is not enough. “Can words put down fires? Can words cook an omelet?”

What she is building instead are systems that understand space and physical objects — how things sit, how they move, what happens next. Useful for robots, for films, for anything that has to act in the real world rather than talk about it.

She is also unusually frank about how early it is. Asked whether this is where chatbots were around 2019 — everyone chasing it, nobody having cracked it — she agreed, and said the field has not even settled on how to build these things. That is a striking thing to say about the product your own company sells.

On regulation she has one line worth repeating: root it “in science, not science fiction”. She thinks talk of machines wiping out humanity actively gets in the way of the dull work that would actually help.

Somebody published a ranking of every add-on people have installed for one of the major AI assistants. There are more than eighty-five thousand of them, which is roughly eighty thousand more than anyone can look through.

The single most installed one, by a distance, does not do a job at all. It finds other add-ons. You describe what you want in ordinary words and it goes and fetches the thing that does it. It has been installed nearly three million times — about three and a half times as often as anything that actually does something.

The rest of the top of the list is quietly revealing. They are not magic. They are discipline. One makes the assistant interrogate your plan with hard questions before it will help you build it. One makes it define what a correct answer looks like before it starts. One carries your notes from a long conversation into a fresh one so you do not have to explain yourself twice.

In other words: the things people find most useful are the things that stop the machine running off confidently in the wrong direction.

A polished video about the electricity that artificial intelligence consumes states, out loud and without embarrassment, that two of its central figures were obtained by asking a chatbot.

This is worth pausing on. It is not a company exaggerating its own product, and it is not a journalist repeating something from elsewhere. It is a number with no human source at all, wrapped in a confident documentary voice and passed along.

Both figures have been left out of the knowledge base entirely rather than included with a note of caution, because a cautionary note implies there is a source you could go and check. There isn't one.

The useful habit, for anything you read about this subject: ask not only whether a number has been verified, but whether it could be — by anyone, including the person saying it.

The podcaster Steven Bartlett mentioned in passing that his company had been paying tens of thousands of pounds a year for a piece of recruitment software. They built their own instead. It took about a week, and he says it is better.

That is the whole story of the moment, in one anecdote. Things that used to cost a fortune and take a year now cost very little and take days.

He then asked the obvious question, which most people skip: if it is that easy for him, it is that easy for everyone, so what is it worth? Nothing much, is the honest answer.

Their conclusion is oddly reassuring. If anything that can be copied will be, then the value moves to whatever cannot — turning up in person, knowing something because you lived it, being somebody people actually want to hear from. “Relatable beats impressive.”

Two figures they quoted in support of this have no source attached and are recorded here only as things not to repeat.