Entrepreneur turned VC. Partner @SquarePegCap. Hooked on crypto, philosophy, electronic music, poi & tahini
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Joined June 2009
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1/ A common critique of $PEARL: some of the GPUs mining it aren’t doing useful AI work.
That's a classic misconception. I think that misses what makes the design by @WeinsteinOmri and the @prlnet team so clever.
3/ So mining alongside useful work has a near-zero marginal cost. Mining without it is expensive. Even if you already own the hardware, you still pay for the power and upkeep to run it.
So far, in the earliest days of @prlnet, speculators invested in running useless work just to build a $PEARL position. Speculation is healthy and necessary in this case.
But over time, older GPUs won’t be able to mine efficiently at all. The useless work will get pushed out slowly but surely. It simply won’t survive as the network scales, and useful AI workloads will dominate the network.
4/ @prlnet's purpose is to help anyone running a GPU get more out of every computation cycle. It runs with open weights models. Open weight models are now bigger than closed source + inference costs are skyrocketing ->the need for @prlnet becomes obvious. Needless to say, I am a believer and an investor.
Yonatan Sela retweeted
It’s all fun and games until globalizing the intifada hits your part of the globe
Yonatan Sela retweeted
It’s always impossible… until it is done.
@prlnet
Yonatan Sela retweeted
I wrote this in May when Pearl pearl-2:native was still just a miner coin before any listing
It went from pennies otc to $1.70 on safetrade → washed out and re-accumulated at $0.25 this summer → up almost 6x now back to ~$1.50
I sent this report to my friends who bought it. So they know what they own… basically the Bitcoin of AI
פרל @prlnet זה כמו קסם שמוציא יותר מכל GPU, פעמיים: 1) באופטימזיציה של עבודת ה Inference ו- 2)בכרייה של מטבעות פרל, בחינם כתוצר לוואי (רצוי!) של עבודת ה GPU (Proof of Useful Work). הכי מטורף זה שכל מי שמריץ אינפרנס (כולם...) יכול פשוט להתחיל לעשות את זה את זה, בלי לפגוע בביצועים.
Pearl (@prlnet) has the fastest FP8 MoE Kernels for Blackwell chips!
Group-Matrix-Multiplication is one of the most optimized operations in AI. As a former researcher at @nvidia I can say firsthand that pushing the performance frontier of MatMuls is... Hard.
Pearl-GEMM is ~5% faster than prior state-of-art Quack (@tri_dao) and ~42% faster than Flashinfer on B200s.
pearlresearch.ai/research/bl…
Yonatan Sela retweeted
We’re all building our own software factories.
But, our coding and code review agents are missing a curated map of the software they’re working on: how repositories connect, what depends on what, and where a change can have an impact.
That’s why we built Software Map in Qodo 🚀
Install Qodo and get your software map within hours.
Watch the video for examples and a closer look.
Yonatan Sela retweeted
Privacy-preserving proof of inference is a key building block for AI safety. @DarioAmodei's call for AI slowdown & independent oversight raises a critical question / How can frontier labs prove their computations follow the rules, without revealing model weights, prompts, or private data?
Two complementary technologies are necessary to make this inference layer possible: Zero-Knowledge proofs (zk-SNARKs) can prove that Y = M(x), while keeping M and x private, and verify compliance using explicit policies. Proof-of-useful-work (PoUW) can
tie that evidence to actual, timestamped GPU computation, with a public immutable record and economic incentives for participation.
@prlnet is partnering with @attestable to build this trust layer for AI. Together, our complementary technologies can make AI objectively accountable, making verification economically sustainable and self-funding.
Today, we are announcing @prlnet's new and ultra-efficient floating-point PoUW scheme for NVIDIA Blackwell chips.
Floating-point arithmetic has been a notorious obstacle to efficient verification of compute for decades. Pearl’s FP scheme brings proof-of-useful-work technology to Blackwell FP computations on frontier LLMs, with NEAR-ZERO additional overhead over end-to-end inference on optimized vLLM.
The FP network upgrade specification is now available (github & whitepaper) and will soon serve frontier models on Pearl x @togethercompute's endpoints in production.
Explore the implementation:
github.com/pearl-research-la…
Whitepaper: pearlresearch.ai/Pearl_White…
Run Pearlified models on our endpoint:
pearlresearch.ai/
Yonatan Sela retweeted
The real currency is not money. It's energy and data - the two scarce resources whose fusion creates intelligence.
We believe that this dramatic shift in the production of knowledge compels us to rethink the fundamental properties, purpose, and creation process of money.
Yonatan Sela retweeted
We’ve raised $320M at an $11B valuation, led by Addition.
AI is changing how companies are built. Teams are smaller, global from day one, and using agents more. We’ve spent 10 years building the financial rails for that world.
We’re now building the intelligent layer on top.
Yonatan Sela retweeted
A common critique on Proof-of-Useful-Work is that the zero marginal cost of mining drives the network’s security budget and price to zero.
A paper just released by Rafael Pass, a renowned cryptographer at @Cornell /@TechnionLive and research scientist at Pearl Research Labs proves why this argument is flawed.
By analyzing the equilibrium dynamics of PoUW, it shows not only that Pearl’s security budget (and hence price) are at least as high as Bitcoin, but in fact that Pearl will increase the SIZE of the global AI inference market, via Jevons Paradox.
arxiv.org/abs/2606.06700
@initc3org
Yonatan Sela retweeted
A milestone for Pearl Research Labs: our first major enterprise partnership is live with Together AI.
@togethercompute’s inference platform is an ideal demonstration of @prlnet's value proposition — One of the world’s most advanced hyperscalers running AI workloads on Pearl’s 2-for-1 Cuda kernels, turning inference into ¶PRL coins, and reducing consumer LLM price per token. Excited for what we’ll build together.
Introducing Gemma-4-31B-it-Pearl on Together AI, Pearl Research Labs’ instruction-tuned checkpoint of Gemma 4 31B powered by @prlnet Proof of Useful Work protocol.
AI natives can now use this Pearl model as a serverless inference endpoint on Together AI, at a 25%+ discounted pricing.
AI turns energy into intelligence.
Bitcoin turns energy into currency. Valuable, but very inefficient.
@prlnet is a new & only way to do both at once -> currency as a by-product of AI workloads.
This could become the purest form of sound money, backed by global compute.
