Energy, Taste, and Intelligence Cofo @Basethesislabs, e/acc @GenesisAILabs
Earth
Joined September 2019
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I am superr excited about decentralized energy and compute thesis :)
nitter.gq/_LiquidEnergy
nitter.gq/Evoracharge
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.
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.
> $69B revenue run-rate w/ $6.6M TTM
> Jevons Paradox; every $1 of compute earns $2.08, up from $0.40 five quarters ago, even after Opus 5.5 cut customers’ cost per task by ~40%.
What it means long term: output here grows with capital (chips, power, data centers), not headcount. And large enterprises still spend under $8 per employee per month on AI.
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%.
This is the most bullshit content I have seen from you. Don't come begging for content in lossfunk next time :)
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 🙏
@archgen_ my friends 🚀🚀
Archgen Labs (@archgen_) is now backed by Y Combinator (@ycombinator)
We are a research lab for silicon design, with one ambition: make chip design 1,000× faster.
For decades, computing advanced by shrinking transistors. The next chapter for compute workloads will increasingly depend on building vertically - stacking active logic layers to create 3D chips.
This makes designing those chips much harder but also opens up new possibilities for performance and efficiency.
At @archgen_ , we’re bringing together foundational models, EDA tools, and physics-informed surrogate models to explore more designs, predict their behavior faster, and optimize how they’re built.
Grateful to Kulveer Taggar (@kul), Garry Tan (@garrytan) and the @ycombinator team for backing us, and to the customers, mentors, and collaborators who have helped us get here.
If you want to get to tapeout faster, we’d love to talk. DM us.
#Semiconductors #OpenROAD #OpenLane #OpenSourceEDA #ChipDesign #VLSI #PhysicalDesign #AIForHardware #3DIC
@naveen_venk
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.
