Andrei Savin retweeted
I don't recommend anyone scramble to move their funds to new wallets today. But we should take the risks to cryptography from AI-accelerated math seriously, and minimize our exposure to not just quantum-vulnerable cryptography, but also potentially AI-vulnerable cryptography.
The core new area of risk from this viewpoint is, unfortunately, ML-DSA / FHE / lattices.
(and it's also another reason, along with quantum, why ECDSA might fall even faster than expected, hence the "fresh address" recommendation)
So far most people have been in the mode of thinking "elliptic curves broken, hashes safe, lattices safe". But there is a good chance that the concrete security of lattices will take serious hits from the next two years of AI math.
The basic threat model is: factoring is something that naively takes 2^(n/2) time, but over decades smart people have found and optimized number field sieves, and degraded that to 2^O(n^(1/3)), which is why RSA keys and signatures need to be ~400 bytes (and not 64 bytes). What if there are skeletons in the closet like that, both for elliptic curves and lattices, that we are simply not smart enough to discover - but bots soon will be?
This is a major part of the reason why for the past year ethereum's lean roadmap has been going in the "hash-only" direction: no lattices, no ML-DSA, no Falcon, no lattice-based commitments inside ZK proofs, etc. Signatures in lean ethereum are all hash-based, either WOTS or SPHINCS-.
For signatures and proofs, we already know how to go hash-only. The bigger challenge is for *public-key encryption* - and this goes far beyond blockchains. Secure communication, anonymizing protocols, lots of things need public-key encryption.
And unfortunately there are long-standing mathematical theorems showing why public-key encryption cannot be done with hashes alone. You have to have some kind of trapdoor object that has at least one form of usable "structure" - either group theory (incl. isogenies) or lattices or code-based or potentially in the future even more newfangled and spooky things (local mixing?). But for anything that has structure, you should assume that AI will make at least some progress in breaking that structure. Here, one reasonable inference is that if you want to make something plausibly long-term secure, multiply the key sizes by 10.
To me that's a very plausible world and something not at all extreme to predict. If AI will bring us 50 years of math in 2 years, then that 50 years of math may very plausibly include a "naive factoring -> GNFS" level of improvement to our ability to break lattices. In that world, lattices will still exist, but they will have to be significantly bigger to guarantee the same level of safety.
And at those new larger sizes, hash-based constructions will beat lattice-based constructions on concrete efficiency in every use case where hash-based constructions are possible at all.
Theoretically, of course it's possible that hashes are broken too (eg. P = NP would imply that). But I think P = NP is very unlikely. And intuitively, it's much more likely that a mathematical object has exactly no exploitable structure (like hashes are intended to), than that a mathematical object has exactly ~3 forms of exploitable structure (for elliptic curves: associativity, Schoof, pairings) and not some secret fourth form of structure we have not yet discovered that greatly degrades its security (for elliptic curves, ECDLP and pairing security). Similar for LWE, SVP, RLWE and the zoo of lattice problems.
For this reason, we do not yet see any reason to worry and start padding the byte size of hashes (if we start to worry more, we would pad the round count first before doing anything to the byte size).
Concrete TLDR, my own personal views:
* Hash-based > lattice-based, in those situations where hash-based is possible at all
* For anything lattice-based, be much more paranoid on param sizes. Remember that blockchains are only a small portion of the cryptography story; this point goes far beyond blockchains and applies to eg. access to websites, secure messaging, Tor / VPNs ...
* For privacy protocols, strongly favor NOT putting encrypted notes onchain. Instead, send them offchain through some third-party mechanism.
* If it's not difficult for you, keeping your funds in addresses which have not yet been used to make a transaction is a good idea. If it's easy for you, do it. **But be careful about migrations; I personally have lost more money in botched migrations than I have lost in all hacks combined**.
* For multisig wallets, doing confirmations offchain is better than onchain, because this way the signatures of signer wallets do not get exposed to the public, so if ECDSA falls to AI much faster than expected, at least the multisig "gracefully degrades" to a 1-of-1 where the 1 is whoever was gathering the signatures - a much better place to be than "anyone can take the money"
firefly.social/post/x/210783…
Andrei Savin retweeted
If they really start to gatekeep who gets to use the best models, that is a declaration of war.
This prospect fills me with the most sincere, bodily cypherpunk will-to-power that I've ever felt (at least since I was a teenager). If they really go down this route, I would go all-in on building the most psychotic swarms of open-source models and fine-tunes possible, all geared toward a Chaotic Good jamming of the entire institutional public sphere. If we didn't do that, all of political life and the marketplace of ideas would be over before we know it.
It's one thing if the top models become too expensive for me or others to use (I'm already pricing that in, and if you can't build something profitable enough to climb that ladder as it gets pulled up, then that's fair enough).
But if the ladder gets pulled up politically, now, so only select institutional players get access to the most intelligent models, then any mature American man should be as energized as gun collectors are around the 2nd Amendment, or liberal women are around Planned Parenthood.
Andrei Savin retweeted
Here is the technical report on SubQ 1.1 Small.
subq.ai/subq-1-1-small-techn…
This is the second iteration on our Subquadratic Sparse Attention (SSA) model, and the first to be deployed with design partners in the coming weeks.
The results are compelling and verified by @AppenResearch.
- Near-perfect long-context retrieval up to 12M tokens on the needle-in-a-haystack test, with up to nearly 1,000x attention compute reduction.
- A balance of long-context optimization and general reasoning ability, with strong performance retained across knowledge, coding, and non-coding enterprise agent benchmarks.
- At 1M tokens, SubQ 1.1 Small requires 64.5x less compute than dense attention and runs 56x faster than FlashAttention-2.
These results highlight a significant scaling advantage thanks to the efficiency gains from the SSA architecture.
We included some details and learnings from the development process which may be helpful to the community.
Comment with questions, I’ll try to respond!
Andrei Savin retweeted
Replying to @JoshuaTCharles
This is mostly a correct take, but it also sets up a false dichotomy.
The goal of technology should not to be to end all struggles a la Brave New World. It is to create better struggles.
There is a whole universe out there, why keep playing death, disease, stupidity, etc.?
$ALGO Fam I just launched a @devloot_xyz campaign on @ProductHunt
Show some love and give it a vote there
lets see how it goes
producthunt.com/products/dev…
Andrei Savin retweeted
We've launched Allbridge MCP the first cross-chain infrastructure built specifically for AI agents.
We've solved the industry's biggest bottleneck: teaching AI to manage cross-chain transfers while keeping your funds absolutely secure.
The secret lies in our open architecture, which deliberately separates planning from execution:
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Delegate the routine to artificial intelligence without risking your wallet.
Read the docs and launch your first secure AI flow today: allbridge.io/ai
Andrei Savin retweeted
Every variable across every increment of time will be probabilistically modelled via the free markets.
This is actually a decentralized AI algorithm literal world model.
This quoted post is unavailable.
Big news for the Open Source community
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Andrei Savin retweeted
Open Theme Challenge Winner: @algonews_xyz for their decentralized news aggregator and discussion board stored on the @Algorand blockchain.

















