https://priceofthinking.com/Recent content on The Price of ThinkingHugo 0.125.0en-usSun, 07 Jun 2026 00:00:00 +0000https://priceofthinking.com/chapters/front-matter/Sun, 07 Jun 2026 00:00:00 +0000https://priceofthinking.com/chapters/front-matter/AI-assistance disclosure This book was researched and illustrated with substantial assistance from artificial intelligence — principally Anthropic’s Claude — working under the author’s direction and editorial control. Every effort has been made to verify the claims in this book against the published sources linked at the end of each chapter, and readers are encouraged to consult those sources directly. Responsibility for any errors that remain rests with the author. Preface This book makes one promise: the confusing parts of the AI story — the spending, the hype, the job fears, the chip wars — become legible once you look at them through the ordinary tools of economics.https://priceofthinking.com/chapters/why-economics-explains-ai/Sun, 07 Jun 2026 00:00:00 +0000https://priceofthinking.com/chapters/why-economics-explains-ai/A chatbot released in November 2022 reached a million users in five days, a hundred million in two months, and — by late 2025 — 800m people a week. In its wake, the world’s biggest technology firms began spending on a scale normally reserved for wars and railways: an industry-wide build-out of chips, data centres and power running at more than $500bn a year, as of mid-2026, with forecasts rising — a figure visible not just in bankers’ bullish decks but in the spenders’ own audited accounts.https://priceofthinking.com/chapters/supply-demand-price-of-thinking/Sun, 07 Jun 2026 00:00:00 +0000https://priceofthinking.com/chapters/supply-demand-price-of-thinking/Hardly anything in economic history has fallen in price like machine thinking. When OpenAI launched GPT-4 in March 2023, it charged $30 to read a million “tokens” — the word-fragments models are metered in, roughly 750,000 words. Within two years, models matching or beating it cost a few percent of that. The research group Epoch AI, which tracks these prices, finds that the cost of getting a fixed level of AI performance has been falling at somewhere between ninefold and several-hundredfold per year depending on the task — a median of roughly fiftyfold a year, as of mid-2026.https://priceofthinking.com/chapters/ai-production-function/Sun, 07 Jun 2026 00:00:00 +0000https://priceofthinking.com/chapters/ai-production-function/In January 2020, researchers at OpenAI published a paper called “Scaling Laws for Neural Language Models”. It was, in effect, a recipe: make the model bigger, feed it more data, train it with more computing power, and its performance improves — not vaguely, but along a smooth curve you can draw in advance. Two years later researchers at DeepMind refined the proportions, showing that most models of the day were too big for the data they ate.https://priceofthinking.com/chapters/costs/Sun, 07 Jun 2026 00:00:00 +0000https://priceofthinking.com/chapters/costs/Training the model behind ChatGPT’s 2023 leap forward cost OpenAI roughly $78m in computing time, by the estimates of Stanford’s AI Index and the research group Epoch AI. Answering the question you typed into it this morning cost a fraction of a cent. Between those two numbers lies the strangest cost structure in modern business, and most of what is odd about the AI industry — the gigantism, the secrecy, the handful of firms — follows from it.https://priceofthinking.com/chapters/market-structure/Sun, 07 Jun 2026 00:00:00 +0000https://priceofthinking.com/chapters/market-structure/The fiercest technology race in history has, so far, one clear financial winner — and it is not any of the racers. Nvidia, which designs the chips on which nearly all frontier AI models are trained, booked $193.7bn of data-centre revenue in its 2026 fiscal year, up 68% on the year before, at gross margins hovering between 71% and 75%, as of mid-2026. Roughly speaking, for every dollar the AI industry hands Nvidia, seventy cents is gross profit.https://priceofthinking.com/chapters/strategy-game-theory/Sun, 07 Jun 2026 00:00:00 +0000https://priceofthinking.com/chapters/strategy-game-theory/The bosses of the big technology firms agree, in public and at length, that the pace of AI spending cannot last — and then, each quarter, they raise it. The four largest spent a record of roughly $400bn on capital projects, mostly AI data centres, in 2025; their guidance for 2026 runs to around $650bn, with some analysts’ tallies higher still, as of mid-2026. Set against that, American consumers were spending barely $12bn a year on AI services.https://priceofthinking.com/chapters/work-tasks-not-jobs/Sun, 07 Jun 2026 00:00:00 +0000https://priceofthinking.com/chapters/work-tasks-not-jobs/Two facts about the labour market are both true, and they point in opposite directions. Total employment in America has kept growing since ChatGPT arrived in late 2022. Yet Stanford economists reading records from ADP, a firm that runs payroll for millions of American workers, have found that employment of 22-to-25-year-olds in the occupations most exposed to AI — software development and customer service above all — has fallen by about 13% relative to comparable workers (as of mid-2026).https://priceofthinking.com/chapters/productivity-growth/Sun, 07 Jun 2026 00:00:00 +0000https://priceofthinking.com/chapters/productivity-growth/Ask the world’s best economists what AI will do to growth and the answers differ by a factor of twenty. Daron Acemoglu of MIT works through the arithmetic and finds that today’s AI should add no more than about 0.7% to total factor productivity over a whole decade — a rounding error. Goldman Sachs, looking at the same technology, projects that it could eventually lift global GDP by around 7%, with productivity growth raised by some 1.https://priceofthinking.com/chapters/the-boom/Sun, 07 Jun 2026 00:00:00 +0000https://priceofthinking.com/chapters/the-boom/In the first three quarters of 2025, spending connected to AI contributed about one percentage point to American GDP growth — 39% of all the growth there was, a bigger share than information technology managed at the height of the dot-com boom, by the St. Louis Fed’s count. The biggest cloud companies have pencilled in around $650bn of capital spending for 2026, on their own guidance — on some tallies more (as of mid-2026).https://priceofthinking.com/chapters/distribution/Sun, 07 Jun 2026 00:00:00 +0000https://priceofthinking.com/chapters/distribution/Americans are far likelier to expect AI to harm them (43%) than to benefit them (24%), and 64% expect it to mean fewer jobs over the next twenty years — while 73% of AI experts expect it to improve how people work, according to Pew’s surveys. The easy response is that the public misunderstands the technology. The harder, better response is that the public understands the economy. Productivity gains and ordinary pay parted company in America decades before AI: since 1979, by the Economic Policy Institute’s reckoning, productivity has grown roughly three and a half times as much as the typical worker’s pay.https://priceofthinking.com/chapters/information/Sun, 07 Jun 2026 00:00:00 +0000https://priceofthinking.com/chapters/information/At its 2025 peak, LinkedIn was receiving job applications at a rate of 11,000 a minute, and by the platform’s own telling nearly half of applicants were using AI to write them. Applications per American job opening have roughly doubled since the spring of 2022. Recruiters report inboxes full of polished, near-identical letters and no way to tell who actually wrote, or can do, anything. Jobseekers report that applying has never been easier and getting hired never harder.https://priceofthinking.com/chapters/externalities-commons/Sun, 07 Jun 2026 00:00:00 +0000https://priceofthinking.com/chapters/externalities-commons/By 2030 the world’s data centres are expected to draw about 945 terawatt-hours of electricity a year — slightly more than Japan consumes today — and AI is the biggest reason, on the International Energy Agency’s projections (as of mid-2026). In America, data centres account for nearly half of all growth in electricity demand between now and the end of the decade. The firms building them pay for every metered kilowatt.https://priceofthinking.com/chapters/government/Sun, 07 Jun 2026 00:00:00 +0000https://priceofthinking.com/chapters/government/Brussels wrote its rulebook before the products had settled: the EU’s AI Act entered into force in August 2024 and bites in stages through 2026. Washington wrote almost no statute at all, leaving courts and agencies to pick over harms after the fact — and then, in December 2025, the White House ordered up a task force to sue American states whose AI laws it dislikes. Beijing requires model-makers to register their systems and pass official review before the public may touch them.https://priceofthinking.com/chapters/trade-geopolitics/Sun, 07 Jun 2026 00:00:00 +0000https://priceofthinking.com/chapters/trade-geopolitics/In October 2022 America forbade the sale of its most advanced AI chips to China — its chipmakers’ largest foreign market — and Beijing answered with a state-backed crash programme to build its own. Three years later Washington partially relented: under rules issued in January 2026, Nvidia may sell its H200 chip to Chinese buyers provided the American state takes a 25% cut of the proceeds. Beijing, having spent the interval weaning its firms onto home-grown silicon, responded by discouraging them from buying (as of mid-2026).https://priceofthinking.com/chapters/far-horizon/Sun, 07 Jun 2026 00:00:00 +0000https://priceofthinking.com/chapters/far-horizon/A wage is a price: the price of renting human time and skill, and it is positive for the same reason any price is — the thing being sold is scarce and useful. So put the hardest question plainly: if machines could one day do every task a person can do, as well and more cheaply, what would a wage be? It is the limiting case of the model this book has used throughout, and the answer is uncomfortably clear.https://priceofthinking.com/about/Mon, 01 Jan 0001 00:00:00 +0000https://priceofthinking.com/about/The Price of Thinking — AI, explained by economics — is a short book by Allan Pedersen. Fifteen chapters, one economic tool each, free to read here. The book grew out of the same itch that drives the rest of my writing: the AI story is usually told as a technology story, when most of its real puzzles — the spending, the hype, the job fears, the chip wars — are economic ones.