Albo

JUST IN: 🇺🇸 SEC Commissioner Hester Peirce calls to end mass KYC data collection, warning it puts crypto holders at risk of phishing and physical attacks. Pierce says the current KYC/AML system creates massive databases of sensitive information that can be hacked, leaked, or exploited. She's pushing for zero-knowledge proofs (ZK proofs) that could verify users meet regulatory requirements without exposing their personal information.

When @BlackRock is telling you that AI and crypto are converging, believe them.

It's an increasingly common take that AI hacking means cybersecurity is doomed. I disagree. I think cybersecurity is naturally defense-favoring once people get their shit together. And anyone who continues to hold cryptocurrency (including me, ~90% of my net worth) is implicitly making that bet. Here's why I am making that bet. First, the oversimplified punchy one-line statement: If AI can prove Navier-Stokes and FLT, then AI can prove the statement "this program is secure" as a mathematical theorem. Even if the program is very complicated. Now, the nuance: (See also: vitalik.eth.limo/general/202… ) The word "secure" is hiding all kinds of skeletons in the closet in terms of what it actually means. What does it mean for Signal (the encrypted messenger) to be "secure"? The most basic definition you might think of is: no one who doesn't hold the recipient's secret key can read the contents of the message. But: * Did you remember to include _other_ critical forms of security? Can the adversary forge messages? Can the attacker prevent messages from reaching the recipient? Can they cause your client to crash by sending malformed messages? * Have you made sure that your model of the adversary includes attackers that interfere with the protocol actively and not just passively? And attackers that interfere by replaying messages to you or the recipient that either of you sent over the wire at any point earlier? * What if the adversary hacked (or _is_) the Signal server? * How did you learn which public key belongs to the recipient in the first place? What if that process was tampered with? * What if your device gets hacked at some point in the past or future - is your message still safe then? * What if your key leaks because of a bug in your operating system? Or because you got a bugged version of the Signal client? Or what if the database is corrupted? * Or the libraries, interpreter or compiler of the programming language you wrote it in? * What if your key leaks because tiny perturbations in perceptible signals generated by the hardware leak mathematical relationships that can extract the key a few hundredths of a bit at a time? * Are you hiding the *size* of the payload? Does that matter? * You're definitely not hiding the identity of the sender and the recipient, and the exact time each message was sent (think: not just time-of-day, but also time deltas between one message and the next). Is that not enough to deduce a lot of important facts about what relationships you have, and what *kinds* of conversations you are having? So ... even definitions can be over a thousand lines of code, and need deep careful thought to figure them out. Working on making definitions more human-readable is of extreme importance - it's perhaps the only "high-level language" that matters right now. But even still, even despite all of the above, for security-critical components, the definition is a much smaller attack surface than the implementation. Verifying that the definition is adequate is a much more tractable task than scanning over the code directly - and can become even more tractable with better tooling. Definitions are also _additive_: if two groups have two different definitions A and B, then, well, you can just prove that the program satisfies both A and B. Code is not additive in this way: if a program is A + B, a bug in A _or_ B can sink the whole thing. Definitions are additive. And if you can't satisfy A and B at the same time, you've isolated the most important philosophical issue for your project to spend its next few weeks grappling with. Sometimes, definitions are not much smaller than the implementation - UI components might be one example. But for many of the most critical components - message-passing protocols, sandboxes, cryptography like SNARKs and FHE - the asymmetry is real. Historically, a large class of failures with this approach have come from people only verifying a small portion of their code, that they self-declared to be the security-critical portion, and ignoring the rest - and it turns out that something in the rest of the code is security-critical too. This was reasonable back when verification was difficult and scarce. The solution today: sorry, you have to verify over literally your entire program, including database, networking, any caching layers, everything. Modern AI can do it. So it's not about "the good guys find all the vulnerabilities before the bad guys do" - that could maybe work too, after all a finite program only has a finite number of vulns, but it's riskier - it's specifically an asymmetric strategy of making code that is much more resilient in the first place. This is the kind of direction that Ethereum is going in for the next few years. There is no future for blockchains - especially blockchains with scalability and privacy - without doing this. We need to make software actually secure. And we have already made a lot of progress.

I will be here when $nock will go to $2.4

Can't wait for Zcash to be 4 figures

Three weeks after the first AI-PoW block, the AI Compute Network's work rate peaked today at 19.6 ExaMAC/s, about 19,600 H100 equivalents at dense INT8, 170x the first reading. Worth shining some light on the unit, and how the number is derived.

Can a blockchain reward miners for more than one kind of verifiable computation? Bitcoin gives every miner the same basic task: search for a valid hash. This provides a consistent measure of mining power because more hashpower means a greater chance of producing the next block. Hashing works well for consensus even though its output has little value outside the blockchain. Nockchain replaces hash grinding as the costly mining work with generating proofs over NockVM computation. More proofpower gives a miner a greater chance of producing the next valid block, analogous to hashpower in Bitcoin. Nockchain extends this model beyond a single kind of work. Its base ZKPoW network rewards NockVM proof generation. A separate AI PoW network lets inference providers reuse existing model computation while competing for PoW rewards. Each has its own difficulty and incentive structure. For proof-based mining to work, proofpower has to remain tied to fresh computation. A miner's chance of winning should scale with effective proofpower without allowing previous work to be reused or amortized across new mining puzzles. The protocol addresses this by cryptographically binding each puzzle to fresh inputs. Extending the model into a broader compute market introduces another requirement: there also has to be external demand for the computation being incentivized.

Could the same computation that powers AI also secure a blockchain? Miners are paid to secure networks through computation. AI providers are paid to produce useful outputs for customers. Proof-of-Useful-Work (PoUW) attempts to combine the two by making useful computation part of mining. Earlier designs struggled to make useful work verifiable, difficulty-adjustable, and tightly coupled to the chain. Recent progress has made PoUW more technically credible. The bullish thesis is a flywheel between AI demand and blockchain security. AI customers provide external revenue for compute providers. Token rewards can subsidize their costs, enabling lower prices, which may attract more demand and compute supply. But AI demand does not automatically strengthen the blockchain. It only helps when useful work contributes directly to consensus and its revenue remains tied to participation in the canonical chain. Otherwise, providers may shift their compute elsewhere. More customer revenue reduces reliance on token rewards, allowing the AI business to grow without creating equivalent demand for the token. Token emissions can also bootstrap a compute market that would not survive on customer demand alone. PoUW becomes a flywheel only if AI demand strengthens the blockchain and can replace token subsidies without pulling compute away from security.