Discussion

• @andrewmccalip Andrew McCalip on x

  This was a great piece by Eric, and I'm in total agreement. The amount of intelligence consumption per capita is about to explode. We're going to be inference capacity limited for a long, long time. https://evjang.com/... [image]

• @kevinroose Kevin Roose on x

  This is great, and the bits about inference demand ring true. It's trivially easy to generate 100ks of tokens in a single session with a coding agent (sometimes a single prompt!) and I'm not even using this stuff professionally. We're gonna need a lot more data centers.

• @_sholtodouglas Sholto Douglas on x

  100% agree with his conclusions - Eric consistently predicts where the field is going.

• @ericjang11 Eric Jang on x

  While re-reading As We May Think I thought it would be fun to show some of the side-by-sides of inventions Vannevar Bush predicted in 1945 compared to their modern instantiation of information processing systems. 1/4 [image]

• @aarthir Aarthi Ramamurthy on x

  “People who can direct teams of agents at goals and know how to judge what to focus on in a full-stack scope will experience an exhilarating level of productivity that makes making software a joy again. For roboticists: there is the age-old question of how much we should rely on

• @deredleritt3r Prinz on x

  We are probably building too few data centers [image]

• @nabeelqu Nabeel S. Qureshi on x

  Everyone underestimates just how much inference we will need. Excellent essay: [image]

• @jenniferhli Jennifer Li on x

  Good read. And a powerful comparison. “Based on my own usage patterns, it's beginning to dawn on me how much inference compute we will need in the coming years. I don't think people have begun to fathom how much we will need. Even if you think you are AGI-pilled, I think you are

• @altimor Flo Crivello on x

  Hard agree with this point. People underestimate how much inference we will need by many, many orders of magnitude. [image]

• @gbrl_dick Gabriel on x

  incredible essay — the first half explains structural advances in LLMs very clearly, but i was struck by this towards the end. as models get better at longer horizon tasks, inference demand obviously goes up because longer reasoning uses more tokens but what i hadn't really [imag…

• @ericjang11 Eric Jang on x

  As Rocks May Think: an interactive essay on thinking models, automated research, and where I think they are headed. Enjoy! https://evjang.com/...

• @deanwball Dean W. Ball on x

  Recently I did some back-of-the-envelope math and realized that my heightened use of coding agents in the last few months means that my daily token in/out consumption has probably increased by ~2 *orders of magnitude,* and I was a heavy user of reasoning chatbots before.

• @mascobot Marco Mascorro on x

  This is a great read from Eric. I feel the same way in many ways, and it resonates a lot with the conversations with researcher friends I've had over the last few months. The world has changed a lot since 2022 (since the release of ChatGPT), and even more in the last year. If

• @drivelinekyle Kyle Boddy on x

  A lot to grasp from this section if you're someone who likes learning stuff. [image]

• @bilaltwovec Bilal on x

  i no longer launch any of my own jobs its glorious not waking up to run that ended up useless because you made a mistake in the yaml [image]

• @danielrock Daniel Rock on x

  Fascinating and thought-provoking essay. I'd like to think we Rocks at least sometimes thought before, but also sometimes in our family there is evidence to the contrary.