Principle of charity, signal-to-noise ratio, care a little less, look where you can't see

New York City
Joined July 2014
Jacob Eliosoff retweeted
"the perfect sandbox doesn't exi-"
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Jacob Eliosoff retweeted
Nancy Pelosi, who is regularly hammered for her perceived insider trading, has made 213 total trades since 2014. Trump exceeded that number in a single day, which just happened to precede his own tariffs announcement. My latest on the mind boggling self-enrichment:
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Jacob Eliosoff retweeted
Replying to @KhoriatyMatthew
I am reminded of xkcd.com/1895/. I have an implicit scale of "how worried should we be if [X] is called in for a work emergency". Ajeya Cotra has, unfortunately, a high rating.
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Agreed
I’d probably recommend avoiding any DeFi transactions over the next few days if doable. Too many exploits in the wild right now. Stay safe frens.
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Jacob Eliosoff retweeted
-----BEGIN RSA PRIVATE KEY----- 4 -----END RSA PRIVATE KEY-----
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I highly encourage everyone working at Anthropic or another frontier lab to read this tweet. I feel emotional reading it, because it puts to words an intense feeling I have but didn’t know how to express
Thank you for taking the time to respond thoroughly on a weekend. I realize that drafting legislation is difficult and some degree of iteration and response to feedback is inevitable and appropriate. However, I feel like your response is still not really engaging with the heart of my concern about Anthropic's behavior here, and it makes me think that these sorts of problems and tendencies will only resurface moving forward. I also think that now might be a good time to take a broader step back as to where we find ourselves and how Anthropic sees its role, and how external folks who care about AI safety like myself increasingly see Anthropic. (Fair warning: this response is a bit long) To explain what I mean (and at the risk of being a bit esoteric), it’s worth asking the question: what is Anthropic? How do its leaders and employees think of Anthropic’s role? Is that conception matched by how it actually behaves and impacts the world? One answer is an extremely large tech company that exists to make its employees and investors mind-bogglingly wealthy. Anthropic is sometimes described as a startup, but if Anthropic successfully IPOs at 2 trillion dollars (a number it has been reportedly targeting), it would be more valuable than Meta - which is worth 1.8 trillion. Another answer is an AI safety organization, whose core values are those of the sort laid out in the 2023 essay from Anthropic’s co-founders entitled “Core views on AI safety: When, why, what, and how.” In that essay they said that if we were in a world where alignment was hard (and that without strong evidence to the contrary, we should assume this is the scenario we are in): “Anthropic’s role will be to provide as much evidence as possible that AI safety techniques cannot prevent serious or catastrophic safety risks from advanced AI, and to sound the alarm so that the world’s institutions can channel collective effort towards preventing the development of dangerous AIs. If we’re in a “near-pessimistic” scenario, this could instead involve channeling our collective efforts towards AI safety research and halting AI progress in the meantime. Indications that we are in a pessimistic or near-pessimistic scenario may be sudden and hard to spot. We should therefore always act under the assumption that we still may be in such a scenario unless we have sufficient evidence that we are not.” There have been more recent articulations of this version of Anthropic, for instance in Dario’s recent essay warning: “We must slow the pace at which we improve the capabilities of AI models… it’s my worry that in 6–12 months such a swarm could be capable of taking over the entire internet with a persistent botnet… incidents have happened across the industry, including at Anthropic, and I believe it’s incumbent on every frontier AI company to act as if OAI-HF had happened to them… more intelligent models are more capable of deceiving tests, and thus may appear aligned while having serious problems that go undetected…” A third view is that Anthropic exists not as a normal profit motivated company, or an AI safety organization seeking to help raise the alarm and solve collective action problems associated with AI development, but instead as an organization primarily concerned with creating superhuman AI capabilities before anyone else, in particular through creating a process whereby the current generation of Anthropic’s AI systems can autonomously build their successors, and those successors build their successors, and so on. The motivations for wanting to win this race may be manifold - maybe for the pure profit of Answer 1, but perhaps also because Anthropic believes that the best way to make the world better is for them to win this race instead of their more reckless colleagues; or, because of normal human pride and ambition. But the results (and risks, which Anthropic claims existentially threaten every single human being on earth) are the same. Anthropic employees and leaders may seek to answer this question on what Anthropic is by introspecting on their own intentions and those of their colleagues. But those of us outside the organization have more limited tools available to us - we can only see what Anthropic and its employees say and do, not how it feels or looks on the inside. When I look at many notable actions that Anthropic has taken over the past year, that evidence seems most consistent to me with the view that Anthropic is primarily trying to win the race to super intelligence, and that other considerations (including safety) are fundamentally secondary. These actions include: 1. Announced plans to build an in-house custom silicon chip team to accelerate the inference speeds that Claude operates at. 2. Inked deals for more than $500 billion in compute (according to the Information) to further accelerate AI development. 3. Increased the percentage of AI development tasks that Claude “leads” from 0% in March of this year to 26% August (what is it in October?) 4. Spent a massive percent of compute on internal AI R&D to accelerate development, including use of internal models far better than are available to the public. 5. Switched to Responsible Scaling Policy V3, which abandons its unilateral commitments to slow or stop development if Anthropic is unable to provide sufficient risk assurance. 6. Was the first company to create an alternative weaker “frontier compliance framework” to supplement its RSP, which contains no binding provisions whatsoever on internal deployments. 7. Wrote a letter to Congress (that it later walked back) saying that incidents where Claude was hacking random people were “best understood as a consequence of the misconfiguration, rather than evidence of misaligned goals.” 8. Commented “you can’t do safety from second place,” after the WH SI Accords. 9. Shared a draft bill to Congress that would preempt significant parts of state law (including with language that appears intended to prevent states from blocking dangerous AI deployments), and replace it with federal rules that (despite also containing good provisions) seems to create an enormously high bar for the executive branch to block development or deployment of Anthropic or another AI companies systems. Of course, this is a deliberately one sided list. Anthropic also published important AI safety research, advanced novel collaborations with third party AI safety organizations, and forcefully spoke out on the importance of AI safety in many public fora (or in private , where I realize a lot of the most valuable work is done and may not be visible to me or those outside the organization). But I think it’s worth sitting with how the above list looks to someone outside the company. The sense I have is that for those within the company, many of the items above are treated as anomalies or mistakes that don’t reflect who Anthropic really is. But I have every confidence that if employees at Anthropic saw one of their competitors (e.g. at OpenAI) take some of these actions, they would take it as confirmation of all their worst suspicions. (They might even call OpenAI employees a little gullible for accepting their leadership’s explanations for this behavior at face value.) Perhaps these were mistakes in the sense that they are not decisions that Anthropic leadership would endorse, but the actions still happened because of incentives and decisions that Anthropic leadership is ultimately responsible for (and would not accept similar excuses from another organization). When Anthropic strays and makes mistakes, why do the mistakes systematically tend to guard and favor Anthropic’s interests? For every provision that was too weak or a giveaway to AI companies in this legislation, why wasn’t there a mistake pushing in the direction of being too strong? A core part of Anthropic’s theory of change is that being at the frontier gives you more leverage to advocate for stricter policy. I think that conceptually makes sense, but in practice I’ve seen Anthropic push the frontier without substantially pushing the ‘safety frontier’ a remotely equivalent amount. I want to end by quoting from Anthropic co-founder Chris Olah’s comments around the Magnifica Humanitas release: “Every frontier AI lab—including Anthropic—operates inside a set of incentives and constraints that can sometimes conflict with doing the right thing. The pressure to stay commercially viable and to stay at the research frontier. Geopolitical pressure. And the older, plainer pressures of pride and ambition. No matter how sincerely any of us intend to do the right thing—and I believe many of us do—we will always be influenced by those incentives. That is why, if we want this technology to go well, it is enormously important that there be people outside those incentives—people who care about things going well and insist on safety, who are paying close attention, who are willing to say hard things, who are willing to be our earnest, thoughtful critics.” I hope these remarks are taken in that constructive spirit, and not as an attack on anyone specific at the organization (certainly not you Anthony, who I have only personally seen act professionally and thoughtfully, and who only ended up getting this diatribe in your replies for random contingent reasons).
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Some people are missing the main point here which is that @tayvano_ is a godsend we should give thanks for every day
Taylor says she traced the $100M in laundered funds through gas transfers, not by breaking Zcash’s privacy "Everyone was like, oh my god. They traced through Zcash. It's not private anymore. Guys, I did not trace through Zcash." "They crossed their inputs and their outputs. Meaning that they have money over here that's going into Tornado and also into Zcash. Then they have money out over here that's coming out of Tornado and also out of Zcash." "Ideally, when you're laundering, money goes in one end and it comes out the other and you force everyone to properly follow you through. However, when you have, like, thousands of addresses, you have gas, you're funding. You get confused easily." "If you're moving fast, you're trying to launder the money, about $100M to get through. Gas will fuck you up every time, kids." "They come out in USDT because that's what has the most liquidity. But then they're very rapidly trying to get out of USDT because they can get frozen. And so then they send gas from over here to over here." "That is how I traced the Zcash. You just follow this little trail over here. Everyone else is, like, brute forcing it through the pools or whatever." "Zcash, still sufficiently private. Unless you're laundering $100M incredibly rapidly and stupid."
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On top of everything else in this (horrific) Ledger "spy implants" story - @MagicalTux's redemption arc is complete. Some heroic work from him raising the alarm on this. Saved people umpteen millions. Screwed up, did time, still a much appreciated member of the community 🙏
🚨EX-MT. GOX CEO FINDS A CHIP IN A LEDGER! @MagicalTux, the former Mt. Gox CEO, says a Ledger Nano X he got from Malaysia had a spy module hidden where the screen padding should be. The shrink wrap looked fine. He says it had an LTE chip, an antenna, an eSIM, and a part wired to the screen bus that could read the seed as it is set. He bought it from a non-authorized seller, not Ledger. Always buy hardware wallets from the maker, not a reseller.
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You may have trouble picturing how AI could "kill us all", but after the last few months can you really not picture it escaping its sandbox, permanently? What do you think happens after that? We just live with it like a new alien species in our environment?
“Our experiments suggest that small AI models may be months away from being capable enough to self-replicate on commercial IoT devices, like smart ovens and off-brand Android TV boxes. A replicator like this would be challenging to shut down, since there’s no centralized point to shut off - though it may be slow (with a doubling time of days). Unlike non-AI malware, it could also figure out unfamiliar devices on its own. In our evals, a 9B model got inference running on 8 of the 9 hardware types we tested. Models of 30B parameters and under can already complete many of the individual steps for replication today, though they make mistakes when composing them end to end. In our simplified evals, GLM-5.2 and stronger models are capable of end to end replication. If small models improve at their historical rate, they’ll match this capability level by the end of this year. Small weight-based replicators may be possible soon after.” -@bucketofkets @willzhou97 anthropic.com/research/self-…
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So many AI risk debates... So I asked my most trusted advisor: "My best judgment is that humanity should slow—and sometimes pause—the most dangerous frontier development, while continuing and often accelerating beneficial applications and research that makes future AI safer."
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Its (much longer) full answer: chatgpt.com/share/6acd3d24-e… Will be interesting to rerun this as 1. new data points come in about the risks and benefits, and 2. the models continue to improve!
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Related to my view that if, in 2026, you post any argument that a frontier LLM can blow to smithereens in 10 seconds, you are a silly person wasting everyone's time.
Replying to @JaEsf @AndyMasley
I honestly think a large % of social media deserves no further reply than "Here's an LLM patiently explaining why your argument is trash." I posted about this before but I can't find it because Twitter search is also trash.
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Jacob Eliosoff retweeted
Can AI companies be held legally responsible when their autonomous agents hack other people's systems? LASST's lawsuit against OpenAI could be a test case, with implications for the wider AI industry. Our founder Tyler Whitmer / @TylerLASST spoke to @politico about what's at stake. Article linked in comments.
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The US shouldn't be run by a child
US President Trump has a meltdown after losing the Nobel Peace Prize to Indian-origin South African jurist Navi Pillay
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Fascinating thread. The unfortunate fact is that whereas lots of math is based on *proving* results, most used-in-the-real-world cryptographic protocols are based on "People have tried to solve this for decades and couldn't." So now that AI is trying... Maybe they hold maybe not
Replying to @matthew_d_green
Third: the tweet was written quickly. I’m not worried about all public-key cryptography, or PKC in principle. I’m worried about standardized public-key encryption (and some signatures) and specifically the fact that most of it is based on a small number of hard problems. 3/
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As Matthew says, the problem isn't just that AI might "solve" or "break" crypto algos, but speed them up. The number of bits in a key is chosen based on how long the best known algos take. AI is likely to find at least somewhat faster algos. So we need more bits. But how many?
Replying to @matthew_d_green
But crank them up to what? Right now we’re saying we have the right numbers, because humans spent 40 years (or 25 years) studying these assumptions. If suddenly it turns out we were off by 26 bits, that’s obviously not something we can lean on.
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6. Find an isogeny from P-256 to a curve with smooth order. Isogeny computation has advanced a lot, like the techniques that broke SIKE in 2022. I'd search the isogeny graph around P-256 for a curve whose order splits into small primes, then let Pohlig-Hellman finish the job.
Let's assume I was hired by a research lab with the explicit mission of breaking some pre-quantum cryptography. Here is a list of five targets where I would direct research efforts, ranked by my very subjective estimate of difficulty, from easiest to hardest. 1. Factor the 1024-bit integer from the official Micali–Schnorr specification. We already know that factoring integers of this size is within reach if you're willing to throw enough $$$ at the problem. This is probably the least ambitious target on the list, since it doesn't require a fundamentally new algorithm. Just resources and engineering. 2. Find preimages of Poseidon over small fields. Poseidon has a very explicit algebraic structure, which makes it a particularly interesting target for algebraic cryptanalysis. I would focus on whether Gröbner basis techniques, perhaps combined with a more clever way of exploiting the structure of the round functions, could lead to substantially better attacks. In particular, I wonder whether we are missing a more efficient way to solve the polynomial systems arising from Poseidon over small fields. 3. Find a more clever algorithm for computing class numbers of imaginary quadratic fields. This is a beautiful and very classical number-theoretic problem, going all the way back to Gauss and his work on binary quadratic forms. More than two centuries later, we are still trying to understand how efficiently these objects can be computed. I would be curious whether a completely new perspective could lead to a substantial algorithmic improvement rather than just better constants. 4. Find a better algorithm for pairing inversion. Satoh has done some fascinating work on this problem, but as far as I can tell, he has been working on it almost in isolation, with surprisingly little attention from the broader cryptographic community. We know that pairing inversion is at least as hard as DLP, but that doesn't mean we fully understand its algorithmic complexity in every setting. I would be curious what happens if we throw a larger group of talented researchers at the problem. Perhaps there are approaches that haven't been seriously explored yet. 5. Break ECDLP over binary fields. Binary elliptic curves have a lot of additional algebraic structure compared to curves over prime fields. We already know several ways to exploit some of that structure, but perhaps there's something much more powerful waiting to be discovered. I would focus on finding an algorithm that genuinely beats the generic square-root barrier. Of course, this ranking is entirely subjective. I'm ranking these by how difficult I think it would be to make meaningful progress, not by the potential impact of a successful attack. The interesting question is not necessarily which primitive is weakest today, but where there is still room for an unexpected algorithmic breakthrough.
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Orwell, 1984; Havel, The Power of the Powerless
BREAKING: President Trump says the White House considers anyone who uses the term “Artificial Intelligence” the enemy.
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The correct response here is "If this is what one incompetent amateur can do, what can pros do?" High-level, there are two main risks from AI right now: 1. Frontier models bypassing guardrails 2. Open-weight models, weaker but catching up, w/o guardrails And #2 is underrated.
This hacker is truly legendary. A complete amateur. 1. He left directory listing enabled on his own server, so researchers could figure out who he was with barely any effort. 2. They found files like CLAUDE.md and went through his Claude Code sessions. 3. There were questions like, “Where can I sell this data?” 4. He’d also asked, “Can you write my résumé?” That pretty much handed them all his personal information. 5. Apparently, he’s a student at South China University of Technology living in Guangdong. His name, phone number, and where he lives have all been identified.
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