TECH
Life after AI: Why cory doctorow’s brilliant guide is an outdated map, lacking answers
What does life after AI look like? Cory Doctorow’s new book claims to know, and a pointed review in The Conversation claims he has got it wrong. Doctorow’s The Reverse Centaur’s Guide to Life After AI arrived in June 2026 promising a route out of the hype cycle, and by 10 August reviewer Michael Noetel had branded it an outdated map that fails to answer its own question.
This opinion piece weighs both sides of that argument, because we think each camp is holding half of a genuinely useful book. We cover AI releases weekly on our AI models and tools hub, from OpenAI’s mid-August ChatGPT update to open-weight model launches, and we build systems that use natural language processing every working day — so the question of life after AI is not academic for us or for our clients. Here is where the book helps, where the review lands, and what neither offers a business trying to plan.
The phrase life after AI sounds apocalyptic, but Doctorow means something narrower and more interesting. His argument is that today’s generative AI industry is a classic investment bubble, and that the important question is not whether the bubble pops but what remains afterwards. Life after AI, in his telling, is the period after the money runs out — when the hype evaporates, some companies collapse, and society decides what to salvage from the wreckage.
A question the whole industry is avoiding...That framing deserves more credit than it usually gets. Almost every AI vendor pitch assumes a straight line from today’s capabilities to permanent transformation. Almost no vendor deck contains a slide titled “what happens to your workflow if our funding dries up”. Doctorow has been asking that second question since his 2023 Locus essay on what kind of bubble AI is, and the book extends it to a full theory of life after AI: which tools survive, who ends up owning them, and whose labour gets reorganised around them.
Why the answer matters in 2026...The stakes have only grown since he drafted it. AI infrastructure spending now props up a meaningful share of stock-market value and capital expenditure, which means the shape of life after AI is a macroeconomic question, not a tech-blog debate. If the spending stops abruptly, the consequences reach pension funds and payrolls far outside Silicon Valley. A serious guide to life after AI would therefore be genuinely useful. The dispute is whether Doctorow has written one.
The book, published by Verso in June 2026 at a brisk 240 pages, is organised around one memorable idea. A centaur, in the old chess-computing sense, is a human assisted by a machine. A reverse centaur is a human conscripted into assisting a machine — the delivery driver whose routes, breaks and bathroom stops are dictated by an algorithm, or the moderator cleaning up after an automated feed.
The reverse centaur, explained...Doctorow’s fear is that the AI economy is built to mass-produce reverse centaurs. The industry’s most valuable product, he argues, is not any model but a story told to investors: that workers are about to be obsolete, so firms should buy the machine and demote the human to its minder. Whether the model can actually do the job matters less than whether the boss believes it can.
What the book actually recommends...The consolation Doctorow offers is that the bubble will burst before the worst version arrives, and that life after AI can be shaped by policy: antitrust enforcement, interoperability rights, worker protections, and picking through the productive residue — cheap GPUs, unemployed statisticians, open-weight models — once prices collapse. The tools should work for us, he writes, not the other way round. As a diagnosis it is vivid and frequently persuasive. As a plan, the reviewer argues, it is where the book runs out of road.
Readers of Doctorow’s earlier work will recognise the machinery. The book is effectively the enshittification thesis — platforms decay once they stop competing for users and start squeezing them — applied to the biggest capital buildout in tech history. That lineage is a strength: it grounds the AI argument in a pattern he has documented across search, social media and marketplaces for a decade, and it explains why Brian Eno and others blurbed the book as the clearest guide to the moment.
Noetel’s review makes two central charges. First, the book was drafted in mid-2025 and the ground has moved: dismissing coding assistants and agents as pure hype reads badly in a year when those systems shipped real, measurable work. Second, the guidance is thin — his summary of the book’s advice is “pick a side and boo”, which is a cruel line precisely because it is not entirely unfair.
Fourteen months is a long time in AI...The timeline problem is structural, not a matter of sloppiness. A book drafted in mid-2025 reached shops roughly twelve months later and met its most-read review about fourteen months after drafting. The chart below shows why that gap hurts a book making claims about fast-moving capabilities.
The claims that aged worst...Noetel’s sharpest evidence is concrete. Capabilities Doctorow waves away as marketing had, by review time, produced results that are hard to dismiss — and a reader relying on the book alone would not know any of it happened.
A guide that declines to guide...The deeper complaint is the missing second half. A reader finishing the book knows what Doctorow is against, but not what to do on Monday. Noetel contrasts it with scenario-planning work that names concrete levers — compute disclosure, capability evaluations, chip tracking — and concludes that on the question of where AI is actually heading, the book “will leave you misinformed”. For a volume whose subtitle promises to teach you how to think about AI before it is too late, that is the most damaging sentence a reviewer could write.
Here is where our opinion parts company with the review’s harshest reading. Strip out the capability predictions and the book’s economic core survives contact with 2026 remarkably well — because it never depended on models being weak. In his widely shared essay on the coming AI economic shock, Doctorow assembles figures that no capability breakthrough has answered: the gap between what the industry earns and what its infrastructure requires keeps widening.
The revenue gap nobody has closed...The sums are stark. Doctorow cites Morgan Stanley’s estimate that the industry’s real annualised revenue sits near 45 billion dollars, against Sequoia partner David Cahn’s calculation that current data-centre spending needs about 800 billion dollars in revenue to pay back, and Bain’s projection that profitability requires some 2 trillion dollars a year by 2030. One takeaway sentence before the numbers: revenue is running at roughly a fortieth of what the buildout assumes.
Better models do not fix broken unit economics...This is the part of the life after AI argument the review never really engages. An agent solving a maths problem is a scientific milestone; it is not 755 billion dollars of new annual revenue. If anything, stronger capabilities deepen the hole, because frontier training and inference costs climb with every generation. You can believe the models are genuinely impressive and still believe the financial structure carrying them is unsustainable — that is precisely Doctorow’s position, and calling the map outdated does not redraw the terrain. Life after AI remains a live scenario for any planner who can read a balance sheet.
by Michael Noetel
