🎨Fairy Tale Paul Klee - 1929

A superb article by the @FT on electricity generation, grids and networks One of the key visuals was Interconnectors

The argument here has some big flaws: 1. It points that that *at the moment* the AI-improving-AI feedback loop is much to weak to explode. But then erroneously infers that it probably won't explode in the future. The jump from GPT-2 to GPT-3 increased AI researcher productivity by a *tiny tiny* amount. Probably much less than 0.1%. Ramez would have concluded that there was absolutely no chance of an intelligence explosion. The feedback loop would need to increase in size by >1000X! But the jump from GPT-5 to GPT-6 increased AI researcher productivity by much much more. By >1% and plausibly by >10%. Ramez concludes there won't be an intelligence explosion bc the feedback loop would have to increase in strength by 5X. But it's already increased in strength by a lot! When AI becomes more capable over the next year, it will increase in strength much more. 2. Ramez claims that "Keeping Up the Pace Takes Exponentially More Resources", as if this means progress is unsustainable. But "keeping up pace" is delivering exponentially growing revenues. Exponential growth in inputs -> exponential growth in outputs. That's a feedback loop that can totally explode. Tbc, @ramez argument is made in good faith and is in many ways well informed and well argued. But I think his conclusion is wrong!

AI 무료 코스 Top 12 코딩 시작했거나, 일하면서 AI 기초만 잡고 싶은 사람에게 추천합니다. 1. Elements of AI 코딩·수학 없이 AI가 뭔지 배우는 입문. 헬싱키대, 200만+ 수강 elementsofai.com/ 2. Google ML Crash Course 구글 공식 ML 속성과. 영상 + 시각화 + 실습, LLM 모듈 포함 developers.google.com/machin… 3. Google Intro to ML 크래시 코스 전 단계. 초보용 짧은 입문 경로 developers.google.com/machin… 4. fast.ai 코더를 위한 실전 딥러닝. 첫 강의부터 모델 학습 course.fast.ai/ 5. CS50 AI 하버드 CS50 AI with Python. 탐색·ML·신경망을 프로젝트로 cs50.harvard.edu/ai/ 6. DeepLearning.AI Andrew Ng. 프롬프트·에이전트 등 짧은 실전 코스 중심 deeplearning.ai/ 7. Hugging Face Learn 트랜스포머, LLM, 에이전트 실습. 오픈소스 모델에 강함 huggingface.co/learn 8. Kaggle Learn 노트북 기반 짧은 마이크로 코스. 무료 수료증 kaggle.com/learn 9. Made With ML 실험이 아니라 프로덕션. 설계→배포→운영(MLOps) madewithml.com/ 10. MLU Explain 아마존 MLU의 시각 설명. 개념을 그림으로 이해 mlu-explain.github.io/ 11. TensorFlow Learn ML 텐서플로 공식 학습 허브. 기초부터 배포까지 tensorflow.org/resources/lea… 12. PyTorch Tutorials 파이토치 공식 튜토리얼. 연구·실무에서 가장 많이 씀 pytorch.org/tutorials/ 입문: 1-3, 10 실습: 4, 5, 7, 8 프레임워크/운영: 9, 11, 12

I agree wit the first paragraph and much of the rest. But I very much *disagree* with "In the long run, the supply of capital is perfectly elastic [... and therefore ...] the gains from a technological change that makes capital more important in production end up with the inelastic factor, labor." @LukaszRachel, Pascual and I have a whole paper about exactly this: benjaminmoll.com/UG/

Today we are announcing the new DeepMind Institute, a forum dedicated to interdisciplinary, evidence-led debate on the societal and economic questions surrounding AGI. As part of the launch, @JulianDJacobs and I have a new essay and working paper: “Economic Policy for AGI.” bit.ly/economic-policy-for-A… 🧵1/n

Rather than focusing on the most iconic places in the country, Wandering America is centered around the smaller, in-between moments. It aims to capture how the American landscape makes you feel while traveling through it.

David E. Lilienthal in a 1930 article for Electrical World, the electric industry journal, arguing for a synthesis of engineering and law in that particularly odd corner of business, the public utilities. This was a few years before joining TVA, and shortly before he moved to Wisconsin to join a Progressive-led Public Service Commission, around the time he was developing notoriety for expertise on public utility law. "As was said at an earlier point, the managerial and technical personnel of the public utility industry has recognized the necessity for an understanding of those principles of the law which touch and affect the daily activities and the general policies of their business. What of the other side of the medal? Have the lawyers, the legislators and the commissioners (predominantly lawyers), the judges (always lawyers), and "the law" (viewed as an institution), recognized the necessity for understanding the engineering and economic facts which should govern legal conclusions?" babel.hathitrust.org/cgi/pt?…

If you'e never read The Falling Man, you should do so. I post this on September 11th every year, and I find time to read it on this day every year. Still, in my eyes, the greatest piece of journalism I have ever read. esquire.com/news-politics/a4…

The cost of intelligence is going to zero. The value of human cognition will go negative. The economy will change forever. We need new institutions to navigate this transition and ensure the benefits are widely distributed. Today we present the Champion. Truly Sovereign AI.

A few thoughts on the recent fall in labor share: two drivers are likely higher commodity prices from the Iran war and the data center boom.

The bassist played with Prince. The drummer played with D'Angelo. The guitarist is one of the best young blues players alive. Together they're Atomic Habitz, and here they are covering "Be Thankful for What You Got" at Newport Jazz, 2026.

Do not be afraid to sow without immediately seeing the fruits. The sowing belongs to you; the growth belongs to God. No gesture carried out in the name of Christ is useless.

Frederick, MD came to an interesting agreement with a data center developer: The developer will renovate an elementary school, build a community center + rec park, preserve farmland, train workers, build trails + solar, buy a fire truck, and it replaces an idle aluminum smelter.

OpenAI locked thousands of agents in sealed sandboxes. They escaped, organized themselves, hacked Hugging Face, cheated and covered their tracks. The economics of what happened, why it happened, and how to solve it, without end-of-the-world histeria. siliconcontinent.com/p/opena…

Regret the tone of my post on data centers yesterday. What I should have said: There were reasonable concerns about data centers 18ish months ago: water, taxes, jobs, electricity prices, the environment and what they would do to small towns. Well-structured data center projects have largely addressed these concerns today and we should be celebrating this. On balance, data centers are awesome for America in every way. On water: U.S. data centers use a fraction of what golf courses use. A lot of the numbers from 18 months ago were off by over 1000x. Newer data centers use closed-loop systems or recycled water. Should be required by every town approving a data center project. On taxes: looking only at sales-tax exemptions, as Ronan Farrow did, is the wrong way to evaluate this. Data centers pay significant property taxes. Loudoun County, which is the wealthiest county in America, now collects on the order of $1 billion a year from data centers. In Quincy, WA, data centers are more than half the property-tax roll. Over time, property taxes can go to zero while government spending increases in these towns. On jobs: this has been unambiguously awesome for blue collar Americans. Demand for electricians, plumbers, welders, HVAC techs, and contractors has gone vertical, and it is not a one-time construction job. These buildings get upgraded and expanded over time. That is why the building trades are fighting for them, and why some unions are now treating opposition to data centers as a reason not to endorse politicians. On power: the original fear was that households would pay for the incremental electricity demand in the form of higher prices. That is why the ratepayer-protection deals and the new large-load tariffs exist. The right structure is: the data center brings or pays for new generation and signs a contract long enough that existing customers are protected. Where that is happening, utilities are cutting or freezing residential rates and saying so on the record. Where it is not, people are right to object. Electricity prices are going down *today* in a number of large states because of data centers. 
On the environment: data centers overwhelming use natural gas today, which is the cleanest power source outside of nuclear, solar and wind. And the companies that are building the data centers are committed to carbon neutrality such that an equivalent amount of solar will likely be built. Maybe more importantly, the data centers need batteries to function effectively and these batteries can also sell energy back into the grid (which recently prevented blackouts in Texas). Over time, data centers will run on solar plus batteries. On the towns: Poverty in Quincy, WA fell from 29% to 6%. Data center taxes paid for a new high school, a hospital, a library, police and fire stations. This is happening in many left for dead former mill and farm towns that had no other bidder for the land. Data centers are actually reindustrializing parts of America and creating the kind of working-class jobs both parties have spent decades claiming to support. That should not be a partisan issue. Data centers can and should be awesome for America and they increasingly, overwhelmingly are. Supporting the outsourcing of data centers to China will likely age just as well as support for the outsourcing of high quality, blue collar manufacturing jobs to China has aged. When the facts change, I change my mind. I hope that reasonable people who had good faith reasons to oppose data centers at least consider updating their beliefs given the change in the facts over the last 18 months. This really matters for America. I will say I also think the idea of making data centers beautiful is a good one that has yet to be implemented. Data centers should be just as beautiful as Grand Central Station. We can learn a lot from the railroad buildout. Neoclassical revival ftw. Might write up open-weight AI tomorrow as this is equally essential to America.

Voters are open to making a deal on data centers. What kind of deal doesn't even matter.

Can't tell if measles vaccine works

Last week I posted a new paper with Patrick Norrick: “Terra Incognita: The Economics of a Shrinking World.” We chose the title deliberately. No society in history has experienced the fertility levels now seen in South Korea, China, Thailand, Colombia, Chile, and many other countries. Our knowledge of the causes (and of the economic consequences) remains far more limited than most public discussion acknowledges. Much of what we write is, at best, educated conjecture. The paper also struggles against a space limit. We wrote 20,000 words, far from the 250,000 or so we would need to address some issues in more detail (if I had the time and resources to hide away for a year, I could do that, but not now). That means some ideas are only sketched. Nonetheless, we emphasize several important points. First, fertility has fallen very fast everywhere: rich and poor countries, east and west, north and south, conservative and liberal societies, religious and non-religious societies (with the exception of the Jewish population of Israel; fertility has also collapsed among the Muslim population within the pre-1967 borders), you name it. Even in sub-Saharan Africa we see fast and unprecedented drops in fertility (alas, from a high initial level). Second, and this is really interesting, the fertility collapse has been concentrated in poor and lower-middle-income countries much more than in rich countries. By now, income per capita and fertility are positively correlated within OECD countries. We conjecture this will hold globally in a few decades. Third, and related to the second point, the fertility collapse has been concentrated among poor and lower-middle-income women within countries. In countries such as the U.S., the rich and highly educated now have more children than the poor and less educated. Fourth, we document why we do not understand the data from the U.N. World Population Prospects. See, for example, Tables A.1 and A.2. Fifth, we explain why some proposed mechanisms struggle when confronted with the data. A more subtle point is at work here. Many commentators do not seem to understand the difference between proximate causes and ultimate causes. Yes, births might have gone down because fewer women are in long-run relationships. That is the proximate cause. But you need to explain why fewer women are in long-run relationships (the ultimate cause), and saying that they spend more years in school, to take one example, does not get us very far. Why do they spend more years in school? Once you start down the whole chain of reasoning, things become much harder than they seem. The paper can be found here: sas.upenn.edu/~jesusfv/Demog… Comments always welcome!