Energy, Taste, and Intelligence Cofo @Basethesislabs, e/acc @GenesisAILabs
Earth
Joined September 2019
- Tweets782
- Following2K
- Followers1.2K
- Likes20.6K
I am superr excited about decentralized energy and compute thesis :)
nitter.gq/_LiquidEnergy
nitter.gq/Evoracharge
This economy is being financed in a loop and is correlated with demand compounding.
Google, Amazon, and Microsoft:
> ≥$114.5B, ≥$110B and ≥$44.9B of compute.
> 48% of revenue flows through their marketplaces.
AMD sells it >$20B of chips and buys up to $5B of its stock.
Even the $72.4B H1 “loss” on convertibles shows up as a gain on partners’ books.
Now add ~$853B of mostly fixed commitments against ~90% incremental margins. Those are railroad and telecom economics: huge fixed cost, near-zero marginal cost. That structure ends in 2-3 players, entry tickets in the hundreds of billions, and the #2 forced to compete on price.
The core risk is a maturity mismatch between revenue and obligations.
Revenue: 80% is usage-based and can fall month to month. Contracted backlog (RPO) is $10.8B, ~75% due within 12 months, against a $69B run-rate.
Costs: ~$853B of compute commitments, ~$430B non-cancelable. Broadcom TPU leases run 5 years, Google cloud to 2033, Amazon to 2036, the tail to 2045.
Product: >80% of Q3 revenue came from models released in the prior 6 months. The revenue base turns over every ~2 quarters, so R&D at ~60% of revenue works as maintenance spend, not optional growth spend.
Equity: the reported $1.5-2T target is 22-29x run-rate for a PBC where founders hold 50.1% of votes and a trust elects directors. Public holders get the economics, not control.
If revenue per compute $ keeps rising, fixed costs spread over more revenue and margins expand. If it flattens, $132B of cash plus IPO proceeds covers the gap.
Sid retweeted
1/4
Meet Qwenputer: Qwen3.5-4B with a full RISC-V CPU inside its weights, using the model's own Gated DeltaNet layers as RAM.
It runs C and MicroPython in its forward passes, no external runtime.
On DeepMind math: base 55–90%, base + Python tool 95–100%, Qwenputer 97.5–100%.
Sid retweeted
Dear Prime Minister Ambani,
Please accept my humble apologies for not realizing that you are the real boss of India.
Naturally, you would prefer to maintain your monopolistic exploitation of the great people of India, but would you nonetheless consider allowing Starlink to compete?
There are many areas of India, as there are in all countries, with no Internet access, which denies children the opportunity for education and for small businesses the opportunity to sell their goods to a global market. Starlink would change their lives for the better.
Thank you 🙏
Amazing read!
Every firm will soon produce the same work at the same quality on the same day, signatures dissolving into a fluent interchangeable average.
We deploy aligned systems inside great professional firms so unique taste, recipe and judgment scale while secret ingredients stay the firm’s own.
In our first fifty days inside a working tax advisory firm, systems independently re-derived 430,766 figures and intercepted roughly one in every 156.
Intelligence inside a firm should be aligned to it, provable to it, and preserving of it, amplifying the spike rather than averaging it away.
Nothing leaves without the responsible partner’s confirmation; we build workpapers so claims are usable by everyone whose decisions count on proof.
Sid retweeted
really well written blog on the emerging shift from optimization-limited to rollout-limited frontier RL. what stood out to me was the observation that as reasoning trajectories become longer and more heterogeneous GPU utilization and learner-generator synchronization increasingly dominate training efficiency. one underexplored question is whether capability jumps themselves can be detected through staleness statistics. if old trajectories suddenly become much less useful for learning that may indicate the model has discovered a qualitatively new reasoning strategy. in that sense replay-buffer value decay could become an observable signal of phase transitions in capability acquisition. if you're into async-RL/large-scale training systems this is a really worthwhile read.
New blog! Is frontier asynchronous RL solved?
The blog covers Async RL theory and infrastructure, surveying 8 open-weight frontier labs for the algorithmic techniques and systems fixes to handle train-inference mismatch. Also answered: why do current methods still fail at high policy lag? Which methods scale with horizon and compute?
Sid retweeted
The open-source protein ML space just got a massive upgrade. Phenomenal work by @anindyadeeps and @try_litefold on dropping the biggest protein data collection on Hugging Face
We have released the biggest protein data collection on Hugging Face, guys!
We have been working on this for more than 3 weeks now, starting from curating the raw data, doing a lot of filtering, splitting the datasets, sharding them, and doing a lot of analysis. Everything is summarized in our recent blog post.
Sid retweeted
The bitter lesson in 26 words:
Don’t be distracted by human knowledge, as AI has been historically.
Instead focus on methods for creating knowledge that scale with computation, like search and learning.
Sid retweeted
🚨Excited to announce Agent-BRACE!
LLM agents in long-horizon POMDPs either blow up their context with raw history or summarize it, discarding uncertainty by collapsing belief into a point estimate. Agent-BRACE decouples the agent into belief state + policy models, jointly trained via RL.
Key takeaways:
1️⃣ 🎯The belief state model produces a structured approximation of the belief distribution as a set of atomic natural-language claims with ordinal verbalized certainty labels ranging from certain to unknown. The policy conditions on this compact belief rather than the full history.
2️⃣ 📈 Outperforms strong RL baselines on long-horizon partially observable embodied language environments while maintaining a near-constant context window independent of episode length.
3️⃣ 🔄 The learned belief becomes increasingly calibrated as evidence accumulates, and epistemic belief decreases over time: the proportion of claims that the agent has the strongest level of belief in grows from 21% → 52% over an episode.
👇🧵
Sid retweeted
Amazing talk by @DrJimFan -- exciting times for Physical AGI!
TL;DR
VLA architecture is parameter-misallocated toward language and should be replaced by World Action Models
→ pretrained video diffusion models that jointly predict future world states and robot actions, instantiated by Dream Zero (a 14B model running real-time control at 7Hz with 2× generalization gains over VLAs, @SeonghyeonYe ).
arxiv.org/pdf/2602.15922
His central data claim is that egocentric human video is the FSD-equivalent ambient data flywheel for robotics, and EgoScale (@ruijie_zheng12) demonstrates a near-perfect log-linear scaling law (ℒ(N) = a − b log N, R² = 0.9983) between 1K and 20K hours of pretraining data and downstream dexterity performance.
arxiv.org/pdf/2602.16710
His central environment claim is that classical physics simulators will be replaced by neural simulators, and Dream Dojo (@ShenyuanGao) demonstrates this with 44K hours of human video pretraining, 10 FPS real-time interaction, and Pearson r = 0.995 policy-evaluation fidelity.
arxiv.org/pdf/2602.06949
Significant gaps in the talk:
- it does not address runtime semantics (skill installation, behavior consistency, run-update separation)
- it does not address the model-exploitation failure mode of training policies against learned simulators or learned rewards.
My running notes w/ Opus 4.7 👇
docs.google.com/document/d/e…
@NVIDIAAI
I promise this will be the best 20 min you spend today! Robotics: Endgame, the sequel to my last year's Sequoia AI Ascent talk, "Physical Turing Test". I laid out the roadmap for solving Physical AGI as a simple parallel to the LLM success story. Be a good scientist, copy homework ;)
And stay till the end, more easter eggs and predictions for your polymarket!
00:30 DGX-1 origin story at OpenAI, I was there in 2016 signing with Jensen and Elon. Heading to the Computer History Museum!
01:42 The Great Parallel
03:31 Robotics, the Endgame
03:39 Why VLAs fall short
04:32 Video world models as the 2nd pretraining paradigm
06:09 World Action Models (WAM)
07:46 Strategies for robot data collection and the FSD equivalent to physical data flywheel for robot manipulation
11:06 EgoScale and the Dexterity Scaling Law we discovered recently
14:00 Physical RL: bridging the last mile
15:39 DreamDojo: an end-to-end neural physics engine for scaling RL in silico
17:00 Civilizational Technology Tree and my predictions for the near future. Spoiler: it's closer than you think.
Thanks to my friends at Sequoia for inviting me back to AI Ascent this year! I had a blast! Last year's talk is attached in the thread if you missed it.
Notes on Robotics' End Game: Nvidia's Jim Fan!
- @sidgraph and Opus 4.7
youtu.be/3Y8aq_ofEVs?si=aWEN…
TL;DR
Vision-Language-Action architecture is parameter-misallocated toward language and should be replaced by World Action Models
→ pretrained video diffusion models that jointly predict future world states and robot actions, instantiated by Dream Zero (a 14B model running real-time control at 7Hz with 2× generalization gains over VLAs).
His central data claim is that egocentric human video is the FSD-equivalent ambient data flywheel for robotics, and EgoScale demonstrates a near-perfect log-linear scaling law (ℒ(N) = a − b log N, R² = 0.9983) between 1K and 20K hours of pretraining data and downstream dexterity performance.
His central environment claim is that classical physics simulators will be replaced by neural simulators, and Dream Dojo demonstrates this with 44K hours of human video pretraining, 10 FPS real-time interaction, and Pearson r = 0.995 policy-evaluation fidelity.
The framework is strongest on data (genuinely empirically grounded), partial on architecture (Dream Zero shows the substrate works but is GPT-2-stage, not GPT-3-stage), and weakest on the predictive claims about timeline and the "VLA is dead" rhetorical framing.
Three significant gaps in the talk: it does not address runtime semantics (skill installation, behavior consistency, run-update separation), it does not address safety, and it does not address the model-exploitation failure mode of training policies against learned simulators or learned rewards.
docs.google.com/document/d/e…
Amazing talk by @DrJimFan -- exciting times for Physical AGI!
TL;DR
VLA architecture is parameter-misallocated toward language and should be replaced by World Action Models
→ pretrained video diffusion models that jointly predict future world states and robot actions, instantiated by Dream Zero (a 14B model running real-time control at 7Hz with 2× generalization gains over VLAs, @SeonghyeonYe ).
arxiv.org/pdf/2602.15922
His central data claim is that egocentric human video is the FSD-equivalent ambient data flywheel for robotics, and EgoScale (@ruijie_zheng12) demonstrates a near-perfect log-linear scaling law (ℒ(N) = a − b log N, R² = 0.9983) between 1K and 20K hours of pretraining data and downstream dexterity performance.
arxiv.org/pdf/2602.16710
His central environment claim is that classical physics simulators will be replaced by neural simulators, and Dream Dojo (@ShenyuanGao) demonstrates this with 44K hours of human video pretraining, 10 FPS real-time interaction, and Pearson r = 0.995 policy-evaluation fidelity.
arxiv.org/pdf/2602.06949
Significant gaps in the talk:
- it does not address runtime semantics (skill installation, behavior consistency, run-update separation)
- it does not address the model-exploitation failure mode of training policies against learned simulators or learned rewards.
My running notes w/ Opus 4.7 👇
docs.google.com/document/d/e…
@NVIDIAAI
I promise this will be the best 20 min you spend today! Robotics: Endgame, the sequel to my last year's Sequoia AI Ascent talk, "Physical Turing Test". I laid out the roadmap for solving Physical AGI as a simple parallel to the LLM success story. Be a good scientist, copy homework ;)
And stay till the end, more easter eggs and predictions for your polymarket!
00:30 DGX-1 origin story at OpenAI, I was there in 2016 signing with Jensen and Elon. Heading to the Computer History Museum!
01:42 The Great Parallel
03:31 Robotics, the Endgame
03:39 Why VLAs fall short
04:32 Video world models as the 2nd pretraining paradigm
06:09 World Action Models (WAM)
07:46 Strategies for robot data collection and the FSD equivalent to physical data flywheel for robot manipulation
11:06 EgoScale and the Dexterity Scaling Law we discovered recently
14:00 Physical RL: bridging the last mile
15:39 DreamDojo: an end-to-end neural physics engine for scaling RL in silico
17:00 Civilizational Technology Tree and my predictions for the near future. Spoiler: it's closer than you think.
Thanks to my friends at Sequoia for inviting me back to AI Ascent this year! I had a blast! Last year's talk is attached in the thread if you missed it.