Rituparn

2) Conclusions: 1) Lowering the cost to train will increase the ROI on AI. 2) There is no world where this is positive for training capex or the “power” theme in the near term. 3) The biggest risk to the current “AI infrastructure” winners across tech, industrials, utilities and energy is that a distilled version of r1 can be run locally at the edge on a high end work station (someone referenced a Mac Studio Pro). That means that a similar model will run on a superphone in circa 2 years. If inference moves to the edge because it is “good enough,” we are living in a very different world with very different winners - i.e. the biggest PC and smartphone upgrade cycle we have ever seen. Compute has oscillated between centralization and decentralization for a long time. 4) ASI is really, really close and no one really knows what the economic returns to superintelligence will be. If a $100 billion reasoning model trained on 100k plus Blackwells (o5, Gemini 3, Grok 4) is curing cancer and inventing warp drives, then the returns to ASI will be really high and training capex and power consumption will steadily grow; Dyson Spheres will be back to being best explanation for Fermi’s paradox. I hope the returns to ASI are high - would be so awesome. 5) This is all really good for the companies that *use* AI: software, internet, etc. 6) From an economic perspective, this massively increases the value of distribution and *unique* data - YouTube, Facebook, Instagram and X. 7) American labs are likely to stop releasing their leading edge models to prevent the distillation that was so essential to r1, although the cat may already be entirely out of the bag on this front. i.e. r1 may be enough to train r2, etc. Grok-3 looms large and might significantly impact the above conclusions. This will be the first significant test of scaling laws for pre-training arguably since GPT-4. In the same way that it took several weeks to turn v3 into r1 via RL, it will likely take several weeks to run the RL necessary to improve Grok-3’s reasoning capabilities. The better the base model, the better the reasoning model should be as the three scaling laws are multiplicative - pre-training, RL during post-training and test-time compute during inference (a function of the RL). Grok-3 has already shown it can do tasks beyond o1 - see the Tesseract demo - how far beyond is going to be important. To paraphrase an anonymous Orc from “The Two Towers,” meat might be back on the menu very shortly. Time will tell and “when the facts, I change my mind.”