Discussion

• @dwarkesh_sp Dwarkesh Patel on x

  .@dylan522p gives a deep dive on the 3 big bottlenecks to scaling AI compute: logic, memory, and power. And walks through the economics of labs, hyperscalers, foundries, and fab equipment manufacturers. Learned a ton about every single level of the stack. 0:00:00 - Why an H100 [v…

• @sarahdingwang Sarah Wang on x

  Narrative violation from Dylan on Dwarkesh: H100s are worth *more* today than they were 3 years ago. There's a sentiment that data center buildouts are priced into the risk of rapidly depreciating GPUs. But the models want to learn. Token prices are falling so fast that you can […

• @dwarkesh_sp Dwarkesh Patel on x

  .@dylan522p lays out how we know the hard upper bound on how much compute can be produced annually by 2030: around 200 GW/year. That's a crazy number (there's about 20 GW of AI deployed in the world right now), but it's nowhere near enough to satisfy Sam/Elon/Dario/Demis's [video…

• @dwarkesh_sp Dwarkesh Patel on x

  The AI supply chain has the craziest value cascade of any industry in the world. thinks that over the next five years, the biggest bottleneck to deploying AI will be EUV machines. ASML sells EUV machines for $300-400 million. You need about three and a half machines, so $1.2 [vid…

• @southernvalue95 @southernvalue95 on x

  This podcast with @dylan522p is a terrific rebuttal to the Citrini doomer scenario by playing through the real world constraints of a fast-ish takeoff (I know it wasn't intended as such). The constraints to producing enough AI tokens to be disruptive to society will slow it down

• @rdominguezibar Ruben on x

  Every AI bubble argument assumes the compute requirements keep going up forever. The actual trend is going the other way. GPT-4 required cutting-edge H100s to run at scale. Newer models at the same or better quality level run on hardware that is two to three generations older.

• @poof_eth @poof_eth on x

  If you've been wanting to understand AI inference and hardware economics... this is the best single place for a current take. It's long, but highly recommended for those interested!

• @pythiar @pythiar on x

  We've gone from $ASML is doomed and peak litho to 2030 Litho bottleneck can't get enough litho

• @parmita Parmita Mishra on x

  Ok @dylan522p this is amazing! Everyone watch!

• @kellangrenier Kellan Grenier on x

  Excellent listen. Key takeaways: 1) $ASML caps at 200GW by 2030 2) Memory eating 30% of Big 7 CapEx 3) H100s appreciate vs. depreciate 4) Neoclouds (and their agents such as $GLXY) control bottleneck 5) Early contracts ($CRWV 98% locked) print vs 50% spot markup @dylan522p 🐐

• @peteskomoroch Pete Skomoroch on x

  This matches my current world model. An H100 GPU is worth more today than 3 years ago, not less. People viewing GPUs as rapidly depreciating assets are missing that an older GPUs can and will do economically valuable work. The total economic value of work that GPU can do for you

• @alex_intel_ Alex on x

  The logic squeeze that is happening (Dylan thinks it's going to get worse) ensures that Intel after its N2P orders are filled for Nova Lake will be forced back to internal manufacturing The idea of Intel flexing ~20% to TSMC doesn't work in this world. That's good news

• @cryptopunk7213 @cryptopunk7213 on x

  this completely fucking breaks the AI Bubble narrative. a 3 year-old gpu is MORE valuable today because it serves higher-quality ai tokens FOR CHEAPER. translation: gpt 5.4 runs BETTER on an OLD GPU than gpt-fucking-FOUR read that again. a newer, better model runs more

• @iruletheworldmo @iruletheworldmo on x

  if you're interested in the race to agi you have to watch this. much more in depth on the super cluster build out. how much compute / gw's can you get online quickly. sam (the dealmaker) altman's conviction and acceleration is paying off again. whilst anthropics sbf

• @aniketapanjwani Aniket Panjwani on x

  The people I find most insightful/interesting on the economics of AI are non-economists. On hardware/macro topics, it is very hard to beat Dwarkesh and Dylan - I'm always enlightened by listening to them and humbled by their breadth and depth of knowledge. Definitely going to