Ash Dayanim @AshDayanim

@MilksandMatcha Yorkshire Dales

The strongman of Pakistan ft.trib.al/aRh4x2N

@MilksandMatcha @tyler_fong_ Congrats Sarah and Tyler! 🍾

Is GenAI causing the relative decline in early-career hiring? Our latest research finds that these effects may be conflated with another important driver: the rise of WFH arrangements (1/N)

A mathematician who shared an office with Claude Shannon at Bell Labs gave one lecture in 1986 that explains why some people win Nobel Prizes and other equally smart people spend their whole lives doing forgettable work. His name was Richard Hamming. He won the Turing Award. He invented error-correcting codes that made modern computing possible. And he spent 30 years at Bell Labs sitting in a cafeteria at lunch watching which scientists became legendary and which ones faded into nothing. In March 1986, he walked into a Bellcore auditorium in front of 200 researchers and told them exactly what he had seen. Here's the framework that has been quoted by every serious scientist for the last 40 years. His opening line landed like a punch. He said most scientists he worked with at Bell Labs were just as smart as the Nobel Prize winners. Just as hardworking. Just as credentialed. And yet at the end of a 40-year career, one group had changed entire fields and the other group was forgotten by the time they retired. He wanted to know what the difference actually was. And he said it wasn't luck. It wasn't IQ. It was a specific set of habits that almost nobody is willing to follow. The first habit was the one that hurts the most to hear. He said most scientists deliberately avoid the most important problem in their field because the odds of failure are too high. They pick a safe adjacent problem, solve it cleanly, publish it, and move on. And because they never swing at the hard problem, they never hit it. He said if you do not work on an important problem, it is unlikely you will do important work. That is not a motivational line. That is a logical one. The second habit was about doors. Literal doors. He noticed that the scientists at Bell Labs who kept their office doors closed got more done in the short term because they had no interruptions. But the scientists who kept their doors open got more done over a career. The open-door scientists were interrupted constantly. They also absorbed every new idea passing through the hallway. Ten years in, they were working on problems the closed-door scientists did not even know existed. The third habit was inversion. When Bell Labs refused to give him the team of programmers he wanted, Hamming sat with the rejection for weeks. Then he flipped the question. Instead of asking for programmers to write the programs, he asked why machines could not write the programs themselves. That single inversion pushed him into the frontier of computer science. He said the pattern repeats everywhere. What looks like a defect, if you flip it correctly, becomes the exact thing that pushes you ahead of everyone else. The fourth habit was the one that hit me the hardest. He said knowledge and productivity compound like interest. Someone who works 10 percent harder than you does not produce 10 percent more over a career. They produce twice as much. The gap doesn't add. It multiplies. And it compounds silently for years before anyone notices. He finished the lecture with a line I have never been able to shake. He said Pasteur's famous quote is right. Luck favors the prepared mind. But he meant it literally. You don't hope for luck. You engineer the conditions where luck can land on you. Open doors. Important problems. Inverted questions. Compounded hours. Those are not traits. Those are choices you make every single day. The transcript has been sitting on the University of Virginia's computer science website for almost 30 years. The video is free on YouTube. Stripe Press reprinted the full lectures as a book in 2020 and Bret Victor wrote the foreword. Hamming died in 1998. He gave his final lecture a few weeks before. He was 82. The lecture that explains why some careers become legendary and others disappear is still free. Most people who could benefit from it will never open it.

Main takeaways: 1. Jensen does not seem AGI-pilled, instead views it as another technological leap 2. Nvidia is best because it is broadest, but not because of inference or training performance 3. The nationalist framing falls through since "US" and "Nvidia" are always indistinguishable

Dwarkesh Patel @dwarkesh_sp

4 months ago

The Jensen Huang episode. 0:00:00 – Is Nvidia’s biggest moat its grip on scarce supply chains? 0:16:25 – Will TPUs break Nvidia’s hold on AI compute? 0:41:06 – Why doesn’t Nvidia become a hyperscaler? 0:57:36 – Should we be selling AI chips to China? 1:35:06 – Why doesn’t Nvidia

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New paper with @NoamGidron reviewing (and trying to organize) recent research on the economic and political impact of national identification. From trade wars to fertility, from violent conflict to redistribution, national identity is a powerful force. But there's a puzzle...🧵

Noam Gidron @NoamGidron

4 months ago

The Political Economy of National Identity: a chapter with @moseshayo for the Handbook of the Economics of Identity. We review econ & pol‑sci research on how national identities shape trade, welfare policy, conflict, and polarization. papers.ssrn.com/sol3/papers.cf…

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Take of the day: this conflict is centered around US disengagement in the Middle East and advantages Israel and other US allies in the post-US era.

Best way to gather info on the Iran conflict in real-time. Fully agentic, tasteful interactive map of events sourced by OSINT. Great job @KestrelAI !

Noam Gidron @NoamGidron

4 months ago

The Political Economy of National Identity: a chapter with @moseshayo for the Handbook of the Economics of Identity. We review econ & pol‑sci research on how national identities shape trade, welfare policy, conflict, and polarization. papers.ssrn.com/sol3/papers.cf…

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Initial Iran strikes appear to be targeting airport, the leadership compound at Pasteur/Jomhouri street, and the transportation hub of Seyyed Khandan. Perhaps initial strike is aimed at senior government leadership to prevent fleeing

They should make AI cliff vesting schedules milestone-based instead of just 1 year. Time is way too linear for a technology that compresses time to value.

I feel quantized when I don’t get enough sleep. 6 hrs of sleep is like FP4, 7hrs is FP8, 8 hrs is FP16, 9 hours is FP32

Everyone following AI should take a moment to understand this graph to grasp the importance of hardware. The important and less talked about part of this graph is that for the SAME DURATION cerebras provides HIGHER ACCURACY with a SMALLER MODEL. This is the Hardware Lottery by @sarahookr and Bitter Lesson by Richard Sutton playing out in real life. Over time cerebras hardware will prove that SPEED is a QUALITATIVE ADVANTAGE and not just QUANTITATIVE. Congrats to @OpenAIDevs and @cerebras for this milestone.

@grah4m @cerebras Got you 😁

Very fast Codex coming!

Cerebras @cerebras

7 months ago

OpenAI🤝Cerebras openai.com/index/cerebras…

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For the last decade, it has been hard to stray off the beaten path of accepted wisdom that scaling training parameters drives innovation. However, the relationship between training compute + performance is uncertain + rapidly changing.

So many bad takes on Groq as if its LPU is some magical new architecture or a TPU for hire. Groq's micro architecture does not matter. The *only* reason Groq has any traction is because it bet on SRAM. Without SRAM, there's no speed advantage, no PMF, no demand, and no acquisition. No one cares about deterministic latency, fancy compilers, or VLIW cores. Ultimately Nvidia bought Groq because if Groq is allowed to scale its roadmap - especially as part of a well funded hyperscaler - it would become a huge drag on Nvidia's narrative/valuation. $20B today saves $200B later. The tech almost doesn't matter.

Last @cerebras Cafe Compute of the year was a cereal success!! 🎄 We had more than 100 AI builders, entrepreneurs, and investors come through the door to enjoy our barista bar, talk shop, and hold THE BIG CHIP. Thanks for joining us for the launch of the Big Chip Club, and stay tuned for BIGGER & BETTER in 2026 🥂 Huge thanks to my awesome DevX team - @SarahChieng @zhennydez @alyciazcary, plus plus our amazing Cerebras colleagues who showed up and supported (special shoutout to @AshDayanim the red nosed reindeer... had a really big big chip 🎶)

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My eyes are up here 👀 But true, this chip is the biggest and fastest AI semiconductor in the world! Stay tuned for more chip pics