German bank employees union argues that they should have three "AI relief days" off per quarter since AI makes work more dense and complex. If Europe starts redistributing productivity gains this early, it might have an issue... "AI relief days"

the uk government's official ai guidance tells civil servants to use gemini flash instead of pro, keep prompts short, and not say thank you to it, for the environment i do wonder at which point we forked the road. instead of building and using it to automate the work, we write guidance on how to use less of it we have the attitude of a high school prefect. caring more about the rules than actually doing the work

Bury your head in the ground at your own risk. I aim to not jump on any hype trains, but since Opus 4.6 and GPT-5.2 + the harnesses it was clear that these things can write code nearly as good as I can in my best language; better in other languages. But we won't see non-devs push production code for a long time; possibly forever, if you ask me. Software still needs to be built in robust ways, and it's our profession to do this well, and use the new tools we have, which create new and interesting (+tough!) challenges

AI is getting cheaper more quickly than any other transformative tech in history. At a given level of performance, cost has fallen ~47%/quarter since 2023. That’s 4× faster than DNA sequencing, 6× faster than compute, 18× faster than lithium batteries, and (up to 1973) 54× faster than electricity.

Why doesn’t Britain have as many trams as France and Germany? Answer: Cost. We pay roughly twice as much to build a kilometre of track. (And some projects cost a lot more!) Why is it so expensive? One challenge is the cost of moving wires and pipes. Our kit is older, badly mapped, and water/power companies get to pass the cost of utility diversions to the taxpayer when we try to build trams. We also need to move more because we have deeper track-beds. Today, I visited Coventry where they are trying a new approach known as ‘Universal Track’. It only goes 30cm (I.e. a school ruler’s length) deep, unlike existing approaches that go 60-90cm deep. That’s above most utilities in most place. It also uses less concrete. They reckon it’s much cheaper as a result. Potentially half the cost of a normal track. It was originally invented for their smaller ‘Very Light Rail’, but they’ve since learnt it’s strong enough to take full-size trams. They hope to have it commercially available in two years.

I had not realised before that the Bronze Age Collapse was literally *caused* by iron. Bronze age powers like the Egyptians, Hittites and Babylonians used bronze weapons, made by combining copper and tin. Tin was so scarce that only the richest and most powerful groups could access it and the bronze weapons it allowed. (Tin was so rare that it was being transported from Cornwall to the Middle East more than 1000 years BC.) The scarcity of tin gave these empires a near-monopoly on violence. Iron weapons are not actually any better than bronze ones – they're not stronger or easier to work with, as I'd assumed. They're just much, much easier to mass produce once you have bloomeries, which are the first furnaces that were hot enough to melt iron in a way that made them useable for weapons. Bloomeries meant that iron ore, which was widespread, could be used to make weapons anywhere. That led to warlords, bandits and city-states rising in a massive decentralization of military power, which the Bronze Age empires were unable to resist. After the invention of the bloomery, skeletons show a measurable increase in weapon-inflicted injuries. 'Destruction layers' with bones and signs of burning have been found from this time at archaeological sites including Troy. Ancient writers – including Herodotus, Ovid, and the writers of the Old Testament – believed the invention of iron had unleashed a new age of violence. New at Works in Progress, WEAPONS OF MASS DECENTRALIZATION: how iron itself brought about the Bronze Age Collapse. And what technologies could do the same today? worksinprogress.co/issue/wea…

Feeling blessed today on my 77th birthday ❤️

Watching Muse doing a finger bot on a browser to book me airport parking tomorrow does just look daft. It’s both incredible what AI can do , but 13 mins later and it has to create an account for airport parking and honestly what’s the point of all this. What problem did we fix ? All I needed was Google Maps to tell me the cost with no booking It’s the animatronic Horse and cart again.

Unironically he looks so cool

Unironically he looks so cool

I work in CRISPR discovery research. This is one of the most exaggerated nothing-burgers ever and would be laughed out of the room if a human scientist attempted to publish something like this. (cont.)

This is Dieter Schwarz - the owner of Lidl, the richest man in Germany, one of the richest people in the world. Only two known photos exist of him, zero video footage, and he's never given an interview. He is an extremely dedicated philanthropist, having put billions mostly into educational causes. He lives in Heilbronn, in Germany, and has transformed the area into an educational/tech/research hub, as well as funding educational activities at Oxford, HEC Paris, Stanford and many others. Technically, he doesn't actually own Lidl any more, as he set up 'The Dieter Schwarz Foundation' - a charity - which owns the Schwarz Group, the parent company of Lidl. In other words, Lidl is owned by a charity that ploughs lots of money into educational causes - and yet Lidl never use any of that in advertising. One of the most successful, interesting people on earth, and nobody even knows what he looks like any more.

Today we announced the Claude-led discovery of a molecular machine that we suspect could represent a new gene editing mechanism. Its precise function, biotechnological utility (if any), or level of significance is not yet clear, but at minimum it is work I would have been proud to do as a PhD student. The work was done mostly, though not entirely, by Claude: our life sciences team suggested a broad area of research, Claude read through the literature and a bunch of genome data and discovered something interesting, then Claude proposed experiments to verify the discovery and our team carried them out. It’s easy to dismiss this as a one-off or curiosity, but we’ve repeatedly seen a pattern where AI performance in new intellectual domains goes from weak to superhuman in a matter of a few years. In 2023 models struggled to do math at the level of an average high-school student. In 2024 they started to do well on math competitions for the best high-schoolers in the country, in 2025 they started to solve minor open problems, in early 2026 more significant open problems, and in late 2026 they are beginning to solve the top few open problems in all of mathematics. We believe AI for biology is on a similar exponential trend. The main difference between biology and mathematics, of course, is that math can be done purely theoretically, while biology requires experimentation. Some have used this to draw the conclusion that AI’s utility in biology will be limited. We think this is wrong. As we’ve demonstrated today, humans can collaborate with AI to perform the experiments, validate key results in a few weeks and, if necessary, work with the AI to iterate on what they find. Eventually it may even be possible for Claude itself to safely perform the experiments by autonomously controlling lab equipment, with appropriate safeguards in place, but we aren’t doing that today (our lab is also a BSL1/BSL2 facility that doesn't handle materials dangerous to humans). More broadly, biomedical advancement has many stages — from fundamental biology discoveries, to translational research, to drug discovery, clinical trials, and finally the actual delivery of medicines and health care to patients. We are also interested in these later stages, but even simply accelerating the first stage of fundamental biological discoveries has the potential to speed up and broaden the entire pipeline. Improving our understanding of biology and sharpening biologists’ tools can drive forward all of the later stages, for example by identifying new drug targets, finding new therapeutic modalities, allowing for more precise measurement, and speeding up the experimental loop which itself further accelerates our understanding of biology. This will not in itself speed up clinical trial times, but if it succeeds it could greatly increase the number of promising candidates that go into the pipeline — an increase in throughput even though latency remains. In Machines of Loving Grace, I wrote about AI’s potential to “cure most diseases in 5-10 years” — a goal that sounds impossible, but one I believe is just barely possible if AI is applied to every stage of the pipeline. The first step is showing that AI can first help with, and then drive, biological discoveries. Claude’s discovery is the latest in a line of related prior work that goes back decades, beginning with systems like CRISPR, and continuing with discoveries like the bridge recombinase and VIPR in the past few years. Recently, there has been heightened interest in systems based on reverse transcriptase (RT) enzymes, the enzyme underlying the system Claude identified. And most recently, a Stanford team working independently described a novel RT system with an associated non-coding array that is in some ways similar to the one Claude found, though they are distinct systems that evolved independently from each other. I believe that we’re at the very beginning of finding such systems and developing them into powerful tools for biotechnology. I’m proud of the resources Anthropic has invested in accelerating the public benefits of AI through the life sciences, and we’re aiming both to grow our life sciences team and to work with other scientists to extend this approach to a broad range of problems. If you have a proposal for a research collaboration or are interested in joining our life sciences team, please reach out.

Claude has discovered a previously unknown enzyme system hidden in the DNA of bacteriophages. Beside the enzyme’s gene sits a long array of repeating DNA—a structure that looks somewhat similar to CRISPR. We don’t yet understand what this system does, but only a handful of known systems share its features, and all of them are able to cut, copy, and paste DNA. Historically, the discovery of such programmable systems has helped revolutionize medicine. CRISPR, for instance, is now the foundation of genetic medicines. But it will take much more work to learn what this system does, and whether it can be put to similar use. Read more: anthropic.com/news/claude-di…

Meta has just unveiled their new VR glasses. • Price: $1,299 • Available Spring 2027 • Weight: 100 grams, about the weight of a deck of cards • Glasses contain the displays and sensors, while a separate puck houses the processor, battery and storage. • 5K display with micro-OLED panels • Pixel density: 37 pixels per degree • Custom pancake lenses • Dolby Vision • Dolby Atmos • No head straps • Qualcomm Snapdragon Reality Elite chip • Battery life: Up to 3 hours of continuous high-resolution media playback • 45W fast charging • Full-color passthrough cameras let you see your surroundings while wearing the glasses. • Open-sided design • Meta AI integrated directly into the operating system. • Voice commands, eye tracking and natural hand gestures, with no controllers required • First IMAX Enhanced-certified VR device, supporting select movies in IMAX's expanded aspect ratio. • Disney+ will offer select 3D movies • Streaming services: Partnerships with Disney+, Prime Video, YouTube, DIRECTV, AMC+, Crunchyroll, Plex, ESPN, Tubi and others • Immersive live sports: Front-row viewing experiences with 8K streaming and 180-degree views • Works with calling apps including WhatsApp and Zoom

Consider dedicating a few minutes to the super interesting discussion in the thread below between @CarlotaPrzPerez, @ganeumann, @sameer_singh17, @daveg, and @progress_bureau (and yours truly). If I were a hedge fund manager with capital to deploy, I would pay close attention to one question it raises: which historical precedent does the current AI bubble belong to? Let me attempt a synthesis for you and add some of my own thoughts. The framework obviously comes from Carlota. As she wrote in her landmark "Technological Revolutions and Financial Capital" (2002), every technological revolution goes through four phases. Irruption and frenzy form the installation period, driven by financial speculation. Synergy and maturity form the deployment period, when the technology diffuses and total factor productivity rises. A turning point separates the two. The whole sequence is called a "great surge of development". Now, if you look back at history, each surge tends to produce two bubbles. The first bursts at the turning point, let's call it a “mid-cycle crash” that marks the shift from installation to deployment. The second comes at the end, when asset prices drift away from fundamentals while productivity growth stalls. Let’s call that one a “late-cycle crash.” Let’s examine the third surge, the age of steel, electricity, and heavy engineering, which Carlota dates to Carnegie’s Bessemer steel works in 1875. Installation brought cheap steel, transcontinental railways, the telephone, and the first power stations, mostly led by the US and financed by a wave of foreign lending, mostly from the UK. The mid-cycle crash came with the Baring crisis of 1890, when Argentine bonds collapsed and the Bank of England had to rescue a pillar of the City, followed by the American panic of 1893, when overbuilt railways went bankrupt one after another. Deployment followed: the Belle Époque, the great merger movement that produced US Steel and General Electric, and the electrification of factories and cities. As Sameer points out, the late-cycle crash for the third surge came in 1919–21. The First World War had pushed commodity prices and inflation to extremes. The Federal Reserve raised its discount rate to 7 percent in 1920, and wholesale prices fell by more than a third within a year. By then, a new great surge, initiated with the launch of Ford’s Model T, was already installing itself. (Obviously, the closer we get to the present, the harder it becomes to classify each bubble correctly. That’s where a capital allocator can make expensive mistakes, so keep paying attention if you don’t want to shoot yourself in the foot 😅) Again, the fourth surge, oil, automobiles, and mass production, starts with the Model T in 1908. From the thread below, it is clear that its mid-cycle crash is 1929. During the 1920s, manufacturing productivity rose by 43 percent while wages stagnated, a Great Decoupling in which the technology was ready but consumer purchasing power had yet to be institutionalized. Surplus capital flowed into speculation until the bubble burst. As Bill Janeway once pointed out to me, the Depression really took hold in 1931, with the international banking crisis and gold-standard austerity. Then came the Second World War, a major disruption that belongs in a completely different category from financial crashes. It forced radical institutional innovation. The Wagner Act and Social Security were already in place in the US, but Europe finally followed with empowered labor unions and the welfare state, while Europe and America together put the Bretton Woods system in place. This brand-new socio-institutional framework, effectively brought about in reaction to the horrors of the war and the Holocaust, turned workers into mass consumers, and deployment, which had already begun in the 1930s, at least in the US, restarted on steroids across the West and Japan, ushering in the postwar Golden Age. Then the fourth surge’s plateau finally arrived in the 1970s. US productivity growth fell from 2.8 percent a year before 1973 to 1.2 percent between 1973 and 1979, and markets for cars and appliances approached saturation. The late-cycle crash in the US was the collapse of the Nifty Fifty, blue chips such as Polaroid, Xerox, and Avon that investors had bid up to 40 or 50 times earnings as “one-decision” stocks. The oil shock of 1973 quadrupled crude prices, inflation surged, interest rates followed, and the S&P 500 lost almost half its value between January 1973 and October 1974. Then something interesting happened. The West found two aces up its sleeve. The first was globalization, with production moving offshore and China’s integration into the world economy becoming the defining feature of the era. The second was financialization, of production through shareholder value and leveraged buyouts, and of consumption through what Colin Crouch called “privatized Keynesianism”: household debt and home equity standing in for wage growth to sustain middle-class demand. Together, these two forces, globalization and financialization, bought two and a half more decades of growth for an exhausted paradigm. I suggest we call this period the “Borrowed Decades,” because previous surges have no equivalent: an almost dead techno-economic paradigm kept alive by cheap labor borrowed from abroad and cheap credit borrowed from the future. There was a price to pay, obviously. Part of it was paid in the form of a spectacular decoupling between productivity and wages across the West. Then, eventually, financialization went too far, which gave us the crisis of 2008. Here I agree with Jerry that 2008 had little to do with a technological revolution. Meanwhile, the fifth surge, semiconductors, software, and networks, had started with Intel’s microprocessor in 1971. Then came the Internet, the mid-cycle crash of 2000, with the end of the dot-com era and the collapse of the telecom bubble, and deployment, which, as Jerry says, was already underway in 2003–07 with broadband, search, and, in 2007, the iPhone. I believe 2022 marks the shift from synergy to maturity within the fifth great surge. Software multiples crashed, and ChatGPT opened a phase in which incumbents (which Jerry calls "synergy companies") moved first with massive capex—a clear sign that we had reached the late cycle. At this point, it’s important to reiterate that, viewed from this perspective, AI is the closing chapter of the fifth surge: it's nothing more than semiconductors, software, and networks on steroids—much like in the 1970s we had oil, automobiles, and mass production on steroids with muscle cars, Batillus-class supertankers, the Concorde, and so on. The problem, characteristic of the late cycle, is that such overshoots funded by abundant capital strain physical resources, which today means power, copper, and memory, with memory prices up six- to eightfold according to Sameer. This clearly suggests we are indeed in the buildout to a late-cycle crash resembling the 1970s and 1919–21, what Sameer calls “the wrong kind of bubble.” For an investor, these are very important signs. A mid-cycle crash tends to be followed by a golden age, so buying the lows of 1932 or 2002 paid off handsomely. By contrast, a late-cycle crash tends to be followed by a long grind. During the fourth surge, the Dow first reached 1,000 in 1966 and only broke clearly above that level in 1982, with inflation eroding real returns throughout. Assets built for the old demand get stranded, like the supertankers ordered before the oil shocks and scrapped within a decade of delivery. David and ProgressBureau are right on an important point: in our AI era, infrastructure is being built before the consumer use cases for agents exist, and if history is to be trusted, use cases do tend to arrive. The difference between the mid-cycle and the late-cycle is *who gets paid for what the technology delivers*: as Jerry explained in his landmark 2025 article, “AI will not make you rich,” published by @colossusmag (cc @zebriez @patrick_oshag), late-cycle buildouts have tended to reward users more than the people who financed them. This is probably what’s about to happen with AI, as the real, widespread use cases will rely on cheap open-weight models run locally on users’ devices, leaving all those data centers up for being scrapped like the old supertankers. There’s one more possibility to consider. Just as the fourth surge got its Borrowed Decades, the fifth may find its own, with a different mix of globalization and financialization. Carlota sees a possible way out through a new kind of globalization whereby the Global South buys capital goods from the West and develops its economies by exporting consumer goods. And on the financial side, the instruments of a new wave of financialization are already emerging, potentially giving birth to a world where money is programmable. Cc @mariekeflament In the 1970s and 1980s, the reset that opened the Borrowed Decades came through the Nixon shock, petrodollar recycling, and London’s Big Bang. This time, it may come through what Marieke and I called the Trump Shock, computedollar recycling, and a financial big bang yet to be determined. If so, we could see a new version of the Borrowed Decades. But this time, the sources of what is being borrowed could be different. Instead of cheap labor borrowed from globalization, we could get cheap labor borrowed from progress in robotics. And instead of cheap credit created through conventional financialization, we could get cheap credit through financialization on steroids, in the form of programmable money. In other words, the fifth surge may not be finished after all. It may find its own way to extend the life of an exhausted paradigm, just as the fourth surge did. The late-cycle crash could then open a second extension, and the investors who make money will be the ones who spot early where those next decades are being borrowed from. Meanwhile, obviously, a sixth surge is probably already underway. I have three hypotheses on my desk. One, initiated in China, is what I call the age of renewables, batteries, and electrification. Another, of which Sameer is a proponent, is a technological revolution in consumer robotics: open-weight models embedded in and running on robots that anyone can use and tinker with. A third hypothesis, which I think is top of mind for Carlota, is closer to a revolution in biotech, biochemicals, and related fields, which, by the way, is also being led by China. In fact, is there any sign that the sixth revolution won’t be led by China? I don’t see any. But that's a discussion for another day.

checking your mirrors on your driving test