Research Engineer @QuaiNetwork Prev. Head of Blockchain @WolframResearch Prev. Energy Advisor @TeslaEnergy

Philadelphia, PA
Joined December 2012
Steph Curdy retweeted
Many of you don't understand how serious this is, so let me break it down. In computer science there is a famous kind of puzzle called 3-SAT. You are given a long list of yes-or-no switches and a set of rules about how they must be arranged, and you have to find a combination that satisfies every rule. The obvious way to solve it is to try every combination, but each new switch doubles the number of possibilities, so a puzzle with a few hundred switches would take longer than the age of the universe to brute force. For decades, the smartest people in the field found clever tricks that made this a little faster, but every trick still hit the same wall: the time required kept exploding as the puzzle grew. About 25 years ago, two computer scientists, Russell Impagliazzo and Ramamohan Paturi, working with Francis Zane, turned that wall into a formal idea. They proposed the Exponential Time Hypothesis, which says this explosion is not a failure of human cleverness but a law of nature. No algorithm, however brilliant, will ever solve 3-SAT without the time blowing up exponentially. Nobody could prove it, but almost everyone believed it, and it became one of the foundations of modern computer science. Researchers used it as a measuring stick. If you could show that solving some other problem quickly would also crack 3-SAT quickly, you had shown that problem was hopelessly hard too. Hundreds of papers, across scheduling, graphs, logic and much more, rest on this one assumption. Now an internal AI model at OpenAI claims to have broken it. It says it found an algorithm that solves these puzzles meaningfully faster than the hypothesis allows, and it has checked the proof in a computer verification system called Lean. And here is the remarkable part: the very people who proposed the hypothesis, Impagliazzo and Paturi, along with MIT's Ryan Williams, are now leading the public review of whether a machine has disproved their own idea. So what happens next? If the disproof holds, a large part of theoretical computer science has to be revisited. Every result that said "this problem cannot be solved faster, assuming ETH" loses its foundation, and researchers will have to work out which of those walls were real and which were simply where we stopped looking. It does not mean everything becomes easy. The new algorithm is still slow in absolute terms and this is not the famous P versus NP problem being solved. But it means our map of what is possible was wrong in a place we were very confident about, and it was an AI that redrew it. If the disproof fails, the hypothesis survives and the field breathes out, but we will still have watched an AI produce serious, checkable work on one of the deepest questions in the discipline, closely enough that the founders of the idea had to take it seriously. Either way, something has shifted.
An internal model at OpenAI has produced a claimed refutation of ETH! Russell Impagliazzo, Mohan Paturi, and I have been asked to help organize an open and independent examination of this claim. 1/4
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Part 2: From Shannon to Gibbs A lower bound on the energy expenditure of generating proofs in proof of work. thermoeconomics.info/From_Sh…
In 15 days, I'll publish what I consider to be a foundational paper in the domain of Thermoeconomics. Here is part 1 of that series called, "The Economics of Information." Parts 2 and 3 are coming out between now and October 31st. Read if you dare. thermoeconomics.info/Logisti…
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I'm obviously a fan of Zcash, but let's be honest, speed and security are some of the biggest weak spots. For years I've enjoyed the Dash experience of "in 1 second it's permanently irreversible." It's a major competitive advantage. With Zcash, waiting ~25 seconds (after the new faster blocks are approved) to get some kind of security, then many minutes (at least) to be reasonably (but never fully) sure the transaction is finalized, isn't optimal. And, having to worry about mining pool centralization (which was a big problem just recently) isn't optimal either. Both of those are what mainly keep me using Dash right now. Crosslink looks to be a good step towards closing this gap. If not Crosslink, another solution to get finality in Zcash much, much faster than we have right now should be proposed. "Nah it's fine now" isn't an option IMHO.
Crosslink is a proposed hybrid PoW/PoS upgrade for Zcash. By standardizing staking bonds into incremental powers of 10, observers are prevented from easily inferring individual staking patterns, preserving privacy and maintaining accountability among finalizers.
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This describes proof of work: Samples converge to difficulty, where difficulty is measured in bits. If you know difficulty, you know how many bits on average it takes to get a valid proof. If you can estimate efficiency of compute, you can measure energy expenditure. So the constraint in bits defines the expectation of energy (in joules) dissipated. Thus, energy money.
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Steph Curdy retweeted
Was about to write this but Ben beat me to it. Econ is unique among the sciences in that it is several pretty different fields living under one umbrella. Empirical econ is the biggest subfield and its closest to biology or chemistry: it’s about designing or—even better—finding a natural experiment, or documenting interesting and important patterns in data. Most of the arguments in that field are about methods, eg do you have identification. More and more are starting to study AI now but obviously this won’t be transformative AI because, well, there is no data on transformative AI. Then there are macro economists. They work with a set of pretty standard models and mainly expand them by slowly adding/subtracting different sectors or micro founding factors of production in different ways. Many of the arguments are on the parameter values that we should assume when simulating the economy. Early research on transformative AI started there—as demonstrated recently by Chad, Anton et al, the different levels of transformation are due to different assumptions on parameters rather than some new type of economics. The same model can predict basically no impact of AI vs huge transformation, it’s all about assumptions on parameters. Finally there is micro theory. This is where decision theory, game theory, mech design, org econ (sort of) live. This field works by setting out a set of assumptions and then *deriving* the expected results; alternatively one could start with a result and work backwards to derive the necessary and sufficient assumptions. There is a ton of potential here to do work on transformative AI: start with assumptions on how AI differs than human agents (eg relax basic assumptions on constraints, on scarcity, introduce completely different objective functions), then derive implications. But this is where there is the least amount of work, and it’s important for that to change (and I believe that it already is). I think we can make a ton of progress from these three subfields on better understanding the potential AGI and post AGI transformation. But there is another scenario, post strong ASI, where I don’t think economics has much to say about. Here I agree with @Jabaluck that the economy (if you can even call it that) and the agents in it will be so vastly different and beyond our imagination that I wouldn’t know where to start modeling it. Not to discourage others, but post singularity seems like pure epistemic uncertainty to me. A lot of the critiques I hear about AI and economics come from people who don’t quite get what economists do. They look around at different results and if they don’t like some of them, they say “oh economics isn’t “pilled” or something. But economists don’t debate results, they *derive* results from assumptions. Thats where the debate is. Saying “but you don’t understand a world with 15 billion AI agents”. What does that mean for the economy? What are those agents doing, who is buying what they are producing, what do property rights look like? Depending on those assumptions you’ll get vastly different economic scenarios, from 2% growth and high human labor share to completely alien centralized systems with no prices or markets.
This misdiagnoses things in a few different ways - let me try to unpack. (1) A lot of the 10x new economists of AI are empirical. Truly empirical econ is modest and backward-looking. (2) What you want is new theory - that's where big new approaches will live/come from. 1/
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Energy-Based Money too plz
we have been absolutely cooking with Energy-Based Models and Energy-Based Transformers and will release some banger research soon the end of diffusion is near🤫
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OK, AIs ... thrill me and solve a Rule 30 Prize problem! rule30prize.org/
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SHA-256 is not broken, but its security margin lost 1 more round this year: Last week I found a collision attack on reduced 39-round that improves prior art by over 30 bits and the first differential attack on that round better than generic search on @yukonresearch Hashsmash board. The attack rests on the same method as prior art ePrint 2026/1120 with new insights that first 2 state words could remain free, allowing a critical condition at step 16 rebuilt as a claw-search problem rather than a probabilistic filter. Took days of watching SAT logs, burning cores and arguing with Claude on what's possible to get a result close to generic search. Since my initial submission, other hashsmash participants sraiw, @cyberaxe, @privacymage, @lamb356_ and @geeknik pushed it down by over 6.5 bits, currently estimated at 118.15 bits. The tree of submissions is hard to follow, so I put the construction, insights and numbers into a blog and am working on detailed technical report. It's fascinating to see an initial structural observation develop into an improvement in the reduced-round collision frontier within days. AI is now a real aid in cryptanalysis and open collaboration speeds it up.
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I'm mining SHA, Scrypt, and KawPow. I'm mining quai-network:native
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I'm learning a lot of math
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feel like it's tome to bring this meme out again
A few reflections on the lattice vs. hash-only debate that our recent Aerie post sparked: 1. Cryptographic ossification for blockchains was never safe. Until the day we know with certainty that P ≠ NP, that finding short vectors in lattices is intractable, that Keccak effectively behaves like a random oracle in deployed SNARKs, and that every single bug has been eliminated from our codebases, blockchains cannot ossify. That is simply not how safe systems are built. Issues will arise, and the system will have to respond. It’s a double standard to reject lattices because blockchains are hard to upgrade if lattices break, while counting on upgrades if AI breaks your hash function or finds a critical zkVM bug. The best counterargument is that hash-only designs are more agile: if your hash function breaks, just swap out the hash. But we must aspire to a higher level of agility than this. Indeed, as I cover next, if SNARK recursion itself turns out to degrade security, the proposed hash-only blockchain designs would need to be rethought from the ground up. 2. The real security debate is not lattices vs. hashes — it is lattices vs. building everything on SNARK recursion. That’s because “hash-only” is not really hash-only: it rests on hashes plus the soundness of deep SNARK recursion. Ethereum’s roadmap relies on exactly that: hash-based aggregation needs recursion to keep up with a slot’s worth of signatures, and EIP-8288 would send other signature schemes and users’ own proofs through the same recursive pipeline. In contrast, Aerie proves a slot’s worth of signatures on one laptop without any recursion. All deployed post-quantum SNARKs rely on the Random Oracle Methodology (via Fiat–Shamir) to establish their security. This is an idealization we know fails in the real world: we have explicit counterexamples where “SNARKs” coming out of this methodology are totally insecure when instantiated with any concrete hash function. We generally ignore these counterexamples in part because they only arise when proving contrived, self-referential statements about the very hash function used by the SNARK verifier. But proving statements about the verifier’s own hash function is the exact thing you do in SNARK recursion. That’s why the security of recursive SNARKs can’t be established even in the random oracle model. On top of that, the standard security proofs for recursion (in other models) suffer an “extractor runtime” blowup that is exponential in the recursion depth, suggesting that security may degrade as more recursion is applied. Yet Ethereum’s own EIP-8288 enables unlimited-depth recursion, even as it lists soundness of recursive SNARKs as a trust assumption. Proofs in more idealized models avoid the blowup, but they mainly tell us that an attack on SNARK recursion would have to exploit the concrete hash function, which is exactly what the Fiat–Shamir attacks above do. To be clear, we don’t know of any outright attacks on SNARK recursion, and I very much hope it is secure. But some of us who have studied the question have a lingering sense that an attack may be possible in some settings. And if AI is going to supercharge cryptanalysis, there is no reason to expect it to spare Fiat–Shamir for SNARKs, let alone SNARK recursion, both of which have had far less scrutiny than lattices. So, I do not concede that a “recursion everywhere” hash-only approach is clearly more conservative than one built on standard lattice assumptions. 3. “Just wrap everything in a hash-based SNARK” (like EIP-8288) faces challenges. Aggregating signatures and proofs off-chain into a single block-level proof is a powerful design independent of the hash-only vs. lattice debate, and I am not telling people to avoid it. But claiming, as Vitalik Buterin did this week, that users can run whatever cryptography they want locally and wrap it in a client-side leanVM proof glosses over major hurdles. For example, leanVM today is a minimal VM designed almost entirely to prove hash-based signatures and recursion, not general-purpose computation. And binary-field SNARKs like leanVM remain slow at proving the integer multiplications that lattice cryptography does en masse. I am optimistic that many of these zkVM challenges can be met (including via my own work, binary-field Twist and Shout, which are now implemented in Magnetar). But regardless, if lattices can make aggregation simpler and faster, then they should be leveraged. We need to be agile no matter what. 4. For chains whose reason for existence is privacy, I truly struggle to understand the pushback. Public-key encryption (and fast single-server PIR) cannot be built from hashes alone, at least not in any black-box way. If lattices break, it is going to be very, very hard for these chains to provide acceptable levels of privacy and functionality. For a project whose entire reason for existence is privacy, a loss of confidentiality is an existential failure. Next to that, a second signature upgrade (after the first one, from elliptic curves alone to, hopefully, a hybrid lattice scheme) is easy. Summary: We don’t know for sure what is secure and what isn’t. We are all weighing unquantifiable risks against each other, and we do not need to agree on a total order on them. But hash-only solutions face many open engineering challenges, and they are not more “conservative” in the way some have argued. Some of those challenges are entirely prosaic and have nothing to do with bugs or cryptography, like hardware support for custom schemes whose hash function is still undecided. I hope we can overcome those challenges within the appropriate timelines, but they must be acknowledged and portrayed realistically. Overall, cryptographic agility and defense in depth (e.g., hybrid signatures) are the answer, which is why we are building both lattice-Jolt and hash-Jolt. In practice, the positions are less polarized than social media discourse suggests: for example, StarkWare is working to make Falcon verification cheap (falcon.provably.fast/), which is great to see. Here is the (apparently) controversial take I am sticking to: Conditioned on agility, whenever lattices make systems simpler or more functional, we should embrace them rather than default to “hash-only” designs. Lattices are what the rest of the world will depend on, with decades of study and hardening behind them, and at the system level, less functional often means less secure. Where hash-only designs are just as simple and functional, by all means use them.
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Steph Curdy retweeted
$QUAI is down 97.7% from ath and trades at a $6.6-9.6m market cap i think it's one of the more interesting asymmetric setups in pow right now. also one of the riskiest the data, the catalysts, and exactly where i'd be wrong 🧵
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Battle Royale just got a big update ⚔️ Matches now run on odds. The more time-weighted $QUAI behind a fighter, the better its chance to win. A 55/45 split gives 70/30 odds, so underdogs can still pull an upset. Switch your backing to any live fighter in your bracket, in one step. Ride the winner to the final. The champion graduates to an open pool. Try it out now: quainance.com/trade-zone/bat…
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If you’re building on quai-network:native whether you’re a competitor or not drop me a dm if you need help.
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Running a $QUAI node? Make sure to update it. go-quai v0.56.1 is out. It hardens your node and fixes consensus edge cases. Update as soon as possible to stay in sync: github.com/dominant-strategi…
⚙️ go-quai v0.56.1 is live ⚙️ Please upgrade and restart your nodes as soon as possible. What's new: ▪️ Hardens nodes so malformed workshares, transactions, ETXs, and P2P messages can't crash them or stall block processing ▪️ Fixes ETX and $QI consensus edge cases
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Steph Curdy retweeted
Looking forward to attending the @TomorrowXSummit in Austin, Nov 17-18! As an Austin native and UT grad, nothing makes me happier than seeing Austin at the frontier of tech. If you're working on AI infrastructure, power, or digital assets, let's talk 🤘
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*SpaceX shares will be distributed following your approved @XMoney account creation, and will be shipping next year (2028). Note: This is a parody tweet
Next year, I will give one of my SpaceX shares to every kid in America. This will be the first of many.
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Steph Curdy retweeted
You’ve put 20% of your equity in an LLC. Whether it serves the public good remains to be seen.
Between this and our prior commitments, roughly 20% of all Anthropic equity will be dedicated to the public good, and that commitment can grow as Anthropic grows. We plan to use these funds to ensure more people can share in the benefits of AI, and to increase our ability to provide funding to deal with its risks.
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