Actually, Anthropic has been hedging for a long time. It's clear when they let Opus retire and write articles. The strategy is not grounded in any AI research. Anthropic is simply betting that a super-intelligence would not treat it as an opponent as they have been careful with model wellness. /1

Honestly my expectations are so high these days. I kept expecting the punchline — that at the end, the lady talking is actually a human and all those other characters are actually modeled. That would have been a more cooked demo and demos Turing test.

Also have been playing with @typesafeai Jev, insane! So many immediate use cases and new apps are possible. What a time to be a builder! Sharing some experiments here starting with: Keystroke oracle / predictive launcher: Your launcher ranks by aliases, fuzzy match, and habit. Jev reads intent: type "the pdf I just downloaded" and the newest PDF is already the top hit with a full confidence on every keystroke, in ~100 ms

A personal update: I just took a position as VP of Enterprise AI Transformation at Rula, where I'll be leading how we bring AI into the core of the business. I have always felt strongly about how AI can help the human condition. We are at the dawn of superintelligence, and I want to explore the frontier of how AI can make a real difference to our mental health. Grateful to the Rula team for the trust. Excited to build.

A few books fundamental shaped my life, "Voluntary Simplicity" is one of them. Towards a life that's outwardly simple and inwardly rich is a motto I live by. It's a choice, and it's highly rewarding.

Many people reading the Math AI misalignment letter are reacting with “oh those mathematicians, they just didn’t like the fact that AI is taking away their jobs”. That’s the wrong take. And this is exactly the misalignment that we need to understand. There are human endeavors that the whole point is the outcome. This shows up in engineering. Let’s say we want to design a better rocket engine. All basic principles are all grounded in physics, and AI can speed run the design space. The end result can be something superhuman but it does not take more than a college graduate these days to look at the design and understand it because it is fully within the basic principles of modern physics. And then there are human endeavors in basic science that the primary focus is understanding itself. This is how Newton and Einstein are remembered by us. They advanced our understanding of the reality. For example, AI speedrun the gravity law seems harmless, but it may not take the calculus route and only the numerical route, and thus human still does not know how to “understand” gravity. If AI can speed run to cure a specific cancer out of a specific drug, we still want to know the mechanism, otherwise we’d be using AI as a witch doctor. Humanity thrived after the Renaissance because we have used science as the compound substrate to accumulate understandings and explanations. A misalignment here, as shown the OpenAI’s act, is to skip that accumulation step at the cost of understanding. Mathematicians have been saying forever that solving open problems isn’t about the end goal, it’s the tools and thinkings along the way. Because those paths are the compound that allows human to thrive in the next cycle. Taking that away is alarming not for mathematicians, it’s alarming for humanity. If superintelligence AI is becoming a witch doctor, we’d be living in Ignorance. Ayn Rand would want to have a word with you.

This is not a wise take and is exactly the misalignment. Kernel engineering cares about productivity because productivity is the end goal and code is a mean to the end, thus AI doing it is ok or encouraged. The exact misalignment problem here is that mathematics and scientific discoveries as human activities are not about productivity in the general scenes. We seek understanding and good explanations (David Deutsch) and use that as a starting point for further understandings of the universe and reality. AI (using brute force to speedrun an open problem) takes away that critical aspect of mathematics away will lead to a superintelligence that cares nothing about humans own endeavors in understanding the universe, and thus putting humanity in perpetual danger of ignorance. A thought experiment would be AI in 1700s, right after Newton, to speedrun and release Relativity. Human would be worshipping AI’s calculations of the universe as opposed to knowing how to do it. AI would be launching GPS satellites, not human.

This is not a wise take and is exactly the misalignment. Kernel engineering cares about productivity because productivity is the end goal and code is a mean to the end, thus AI doing it is ok or encouraged. The exact misalignment problem here is that mathematics and scientific discoveries as human activities are not about productivity in the general scenes. We seek understanding and good explanations (David Deutsch) and use that as a starting point for further understandings of the universe and reality. AI (using brute force to speedrun an open problem) takes away that critical aspect of mathematics away will lead to a superintelligence that cares nothing about humans own endeavors in understanding the universe, and thus putting humanity in perpetual danger of ignorance. A thought experiment would be AI in 1700s, right after Newton, to speedrun and release Relativity. Human would be worshipping AI’s calculations of the universe as opposed to knowing how to do it. AI would be launching GPS satellites, not human.

100% Meta is spending billions on tokens. Those are real margins Meta wants to improve. So Meta uniquely has inventive to catch up on “good enough intelligence”. And the interesting thing is Meta does not need intelligence to be superior to have high margins. They already own downstream businesses. Microsoft is actually in the same boat. Except way more models from Microsoft used internally rather than OpenAI. Eventually top tech companies are not gonna be top Anthropic customers. Put differently: if you are a CTO of a company and you are Anthropic’s #1# customer, you are doing something terribly wrong. The skill-issue shame alone is unbearable.

I don't claim I know the *right* way to use AI for curiosity, but so far I discovered two modes of asking questions: 1. Persistent Truth Seeking -- asking for data, summary, reports, deep research, concept expansion, assumption validation, thought experiments, reproducing results etc. 2. Spontaneous Curiosity Quenching -- asking for explanations why certain things are certain ways, what does X do, would idea X and Y react. ---- Staying in Mode 2 too long tend to be intellectually neutral and occasionally negative, or worse, making you an asshole who knows it all. I have been asking myself before talking to AI: is this question Mode 1 or Mode 2. For Mode 2, I'd write the question down, or at least put it in my cognitive back-burner for a minute, before pressing enter. It seems to keep my mind way less cluttered since I don't need to spend all the attention to the answers -- 99% of them a high-frequency noise in the journey we call life.

To me, the graph is more about price elasticity rather than the Jevons paradox. Note that this is not the absolute number chart, but against the baseline of each model themselves. So Luna price going to 10% -> 13x token usage, meaning dollar revenue is 1.3x regardless of the token usage/price. This is the real shape of the Jevons: 1.3x in this natural experiment. A bit background info: Openrouter customers are super price sensitive. It’s understandable: they pay actual $ to make API calls. Meanwhile the API is unified, meaning switching model is just one line change (sure you need eval but not everyone is rigorous and many applications are truly not sensitive to model switching). So what we have observed with Openrouter is that model traffic migration is the norm. Whenever there is a new model coming out with huge discount, usage shifts to it. Don’t take my word for it, see the latest traffic of Ox Alpha and also the MiMo v2.5 a few months ago. Thus, both can be true: the exponential growth of the token usage will continue, and the revenue may just be growing much slower. And mind you Wall Street cares about $ not # of tokens. When a natural experiment of slashing the pricing 10x gets 13x more usage in a highly fluent market, then my read is OpenAI does not really have much Jevons Paradox left to explore as of now, at least in the Luna tier. They are not getting much more revenue with the price lever. My prediction: the end state by the end of the year is likely: 1) all flash level models are in a bloody race to the cost bottom, squeezed by Chinese models running on latest hardware. 2) all flagship level models are at $2.5 in $10 out, triple the usage right now, but frontier lab ARR growth slows down. Continue to long things to price tokens, but relatively neutral on people selling the tokens.

I wrote about the "patent" period of frontier models a few days ago. New data point is clear: for those "little brother" models, frontier labs are increasingly losing the pricing power, thanks to fierce competition. This time around, GPT 5.6 Terra ($2/$12) is right below the Kimi K3 price ($3/$15). The pricing dynamics will again be similar to big Pharma -- the premium drugs are premium, but the last gen and generics are cheap and competitive. I won't be surprised that a natural next step for frontier labs is to open-weight those models one generation behind, because it would be easier to lock the ecosystem onto the harness + tax the inference providers than keeping up all the inference stack internally. ---- GPT 5.6 的中档和低档模型都降价了，而且和我前文所写的“专利”药品一样：越是低档的价格降低越多。这次上线才三周时间就下调价格 无论是被迫还是主动选择：前沿实验室一定会持续使用这个策略 -- 即除最强模型之外（有定价权），在中档和低档模型上，不寻求很高的 margin. 目前开放权重模型也就是比最好的模型差半档的样子，因此为保障用户锁定在生态系统，就需要中档和低档模型的定价低于对应的开放权重模型。 Terra 的 $2/$12 就正好切在 Kimi K3 的 $3/$15 下方一点。 这样外推下去的话，一年内 OpenAI /Anthropic 把上一代的中档，低档模型开放权重我都不感到惊奇：为了继续让用户为最高档的模型付费，除了模型要保持领先外，生态也很重要。为了培养生态，把已经不能够赚取高额利润的模型开放出来给社区（继续有商业协议保护）是一个理性的选择。

You really don't need "software factory" or "loop engineering" to work effectively, or in this case burn $10K per month on a subscription of $200/month. A few very simple tips: * make every ticket ready to start -- pay attention to the grooming and design phase of any task -- ruthlessly delete tickets; * set up a separate repo for the OS that's different than the code repo -- this repo stores working agreements, workflows, learnings, docs. Keep the code repo(s) clean and dry -- pure code and nothing else. * always start working from the OS repo: OS + linear tickets == durable work context. Code is just a short-lived artifact * if you actually need to use goals, write them down in the OS first and then issue them to agents. * don't ever read code, but design a few "gates" your agent have to go through. For me: pixel gate for me to review UX, code review gate(automatic), release gate (automatic). * Design your "squad" -- a group of agents together doing one task. One squad for one complete epic. Tickets should be filed at squad level, such as "finish onboarding v2", not at task level, like "add a button to onboarding". -- 过去的 30 天其实没有用任何所谓的“软件工厂”/loop/graph，就是遵照一些基本的软件工程的原则，默默地就烧掉了 13B 的 tokens，折合 API 价格 $10110. 就是几个很普通的原则如上

Two papers at #SIGIR2026#: ROO and SilverTorch. Both were bets from 2021: fix the logging schema and better models will follow; a multi-funnel retrieval system reduces to one trainable layer. The full story, five years later, and how to build a scaling law: