Managing Executive of Walrus Foundation
Joined March 2009
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Excited to finally share what we've been working on.
MemWal is a durable, verifiable memory layer that lets agents remember, share, and reuse information reliably.
End-to-end encrypted. Portable across apps. Ownership enforced on chain.
Live on @WalrusProtocol.
Building a memory layer for agents takes time. Redis for caching. S3 for storage. A vector DB for retrieval. And somehow it still doesn’t work right.
Today, we’re excited to announce MemWal: a single, verifiable memory layer for agents — persistent, shareable across systems, and no more fragmented infrastructure. Just memory that works. 🦭
Learn more about MemWal, now on Devnet 👇
Several of my social media accounts have been compromised. If you’ve received a recent message from me asking to arrange a call or anything that seemed unusual, please ignore and delete it.
I’m working to secure everything and apologise for any confusion caused.
Why does verifiable data matter for AI?
short answer:
we're handing these ai systems more access and more autonomy than we can currently verify
and i think verifiable data is the cleanest answer we have to that
Crypto Tumbles as Iran Conflict Escalates, BTC back under $70K, and we're Talking NFTs, AI, & Prediction Markets with Managing Executive of Walrus Foundation @RJ_Simmonds
xitter.cf/i/broadcasts/1kJzDMwPd…
Rare to find a builder who tested every decentralized storage protocol in production
rarer still to find one who wrote the comparison without flattering the winner
@oceanscarr did both and congrats on the win
.@karpathy wrote this in 2021, and I think it was fair then
five years later I would say AI and blockchain technology are on course for a grand unification, as @pmarca would say
i'd point at @WalrusProtocol as one of the early signs of it
rebecca simmonds 🦭/acc retweeted
Agents are already executing financial transactions on our behalf. The data behind those decisions must be verifiable.
@RJ_Simmonds 👇
I agree with this take, and I'd push it one step further.
AI systems are shaped by the data they're built on. Models are trained on it, fine-tuned with it, and increasingly retrieve from it at inference time.
If that data still sits on centralized cloud infrastructure, the sovereignty argument breaks down before it reaches the user.
Sovereign AI needs a sovereign data layer underneath it. Otherwise we've just swapped one gatekeeper for another.
"When social media first started rising, we didn't necessarily realize how much of that was actually just foundational infrastructure for the future of humanity.”
@benfielding of @GensynAI on why AI is about to repeat the mistake social media made, and how we can still fix it:
"This isn't just a product that you chat with. It's actually new fundamental infrastructure for humanity, and we should build that learning from the lessons from before. If we let a single company build AI, they hold an enormous amount of power in the world."
"If you actually distribute that out and provide it in a kind of sovereign way — it's on all of our own devices and we own it — then actually you steer the world in a very different social direction."
Facebook, X, TikTok, and every other social platform makes money by targeting ads on data users generate on the platform.
So if users control that data instead, can you still build a business that runs on ads?
George's answer is yes, plus the new paths @WalrusProtocol opens up on top.
Provenance, integrity, availability. These are the 3 properties @WalrusProtocol was built around.
And now in the agentic era, they're becoming the load-bearing properties of everything that comes next.
AI agents are now managing hundreds of millions of dollars. Very soon, it will be billions.
Right now, agents rely on centralized & outdated cloud infra, which has no authenticity proof, a single point of failure, and virtually no control over the data.
Nobody asks where the data came from, whether it was changed, or if it'll be there tomorrow.
The more you research this topic, the more you start understanding @WalrusProtocol's vision of a world where, instead of storing data on centralized servers, it's split up and distributed across multiple independent nodes.
No single entity controls where your data lives or who can access it. That's a major bottleneck for agents that nobody seems to really talk about for some reason.
When agents are making autonomous decisions in the real world, that data needs to be as trustworthy and provable as possible.
The next gen of AI agents is being built in front of our eyes. The data layer powering them will determine if they're here to stay or not.
rebecca simmonds 🦭/acc retweeted
This is tough to read. Losing the AI skills, memory and context of a 60-person team is something no company should experience.
We're building Walrus so agentic memory is portable, rather than tied to a specific vendor. Yours and available, no matter what.
DMs open, @patomolina–let us know if we can help.
Anthropic decidió dar de baja a toda nuestra organización por una supuesta infracción de sus condiciones de uso. Qué política específica infringimos no tengo ni la menor idea: simplemente recibimos un mail y listo, adiós Claude. Si querés apelar la medida hay que completar un Google Form, así de ridículo como suena.
De golpe más de 60 personas se quedaron sin una herramienta fundamental para trabajar. Integraciones, skills, historial de conversaciones: todo perdido o, en el mejor de los casos, parado por tiempo indeterminado.
Enorme aprendizaje para cualquier empresa de software que dependa de herramientas de IA en procesos críticos. Nunca hay que poner todos los huevos en una canasta.
rebecca simmonds 🦭/acc retweeted
There’s no universal legal framework that treats AI agents as criminally liable actors. We’re heading toward cases where no identifiable human or legal entity can be tied to fraudulent activity, and it’s inevitable that some will try to offload responsibility onto autonomous systems they claim not to control (“that wasn’t me… it was an agent I couldn’t control”).
Combined with deepfakes, we’ll face a broader wave of “deep obfuscation,” along with human–AI hybrids and even enhanced animal cognition. The result is that legal systems and criminal investigations will need to be fundamentally rethought, within a single generation, our human element could look entirely different.
In the next few years, most financial trades, medical screenings, and legal reviews will be handled by AI systems.
Today, there is no standard way to verify that the AI behind any of those decisions produced the correct output.
That's not a minor gap. It's the single biggest unresolved risk in AI deployment.
Now @OpenGradient is solving it.
Verifiable inference, cryptographic proof of execution, model storage on @WalrusProtocol.
This is what verifiable AI infrastructure looks like in production.
The OpenGradient Model Hub has surpassed 4,000+ models.
Over 4.5TBs of model data is stored on @WalrusProtocol. Each model executes within environments that enable verifiable inference, ensuring outputs can be validated rather than assumed.
Key components of the system:
• TEE and zk-backed execution environments
• Onchain verification of model outputs
• Privacy-preserving data handling by default
This architecture establishes a reliable foundation for deploying and scaling open intelligence systems.
We appreciate the support from Walrus in enabling verifiable, scalable data infrastructure.
We're starting something new: Walrus Sessions🔥
Every two weeks, a focused challenge that puts builders closer to the ecosystem.
Session 1: build your personal site onchain with @walgo_xyz.
A $1,500 prize pool in $WAL to kick it off.
🔥 NEW: Build your personal website onchain with @walgo_xyz and win from a $1,500 prize pool.
Whether it's a portfolio, resume, or interactive experience, if it shows who you are and what you can do, it counts. 🦭
Everyone’s building agents.
Almost no one is solving memory properly.
This is 👇
BREAKING: MemWal is now live on @WalrusProtocol 💧
AI agents can now have long-term, verifiable memory, not scattered data across tools, but something they can actually rely on.
This unlocks agents that don’t forget, can share context, and improve over time.
This is a big step toward real autonomous AI on @SuiNetwork ⤵️