Entrepreneur & investor across tech, culture, crypto, & AI. CEO @AtomicStrata & Partner Matador VC | Former @DSCVR1, Downey Ventures, @IDEO, @BMW | @Stanford

Venice, CA
Joined March 2008
As a car enthusiast, the Tesla Roadster highlights an existential question about the point of hyper cars today. I grew up in an era when the performance improvements for each generation of supercar offered genuinely usable performance. They also offered sexy aesthetics. Now we have the possibility of performance which is outside the zone of human comfort. We have the visual statement of a rocket car that has taken us from sexy to boyish spectacle. And this spectacle looks so unreal that it appears AI generated. My kids are still insisting I tried to show them some AI car video and wasted their time. The Tesla Roadster launch is a physical embodiment of the AI era we are entering. If almost anything is possible, and we're all becoming desensitized, what's the point anymore? I think I need to go drive one of my analogue cars today.
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Dot is so good everyone will use it and thus literally train Dot to do their jobs.
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Waymo is driving me back from a VC AI dinner while I’m working with Dot on to-dos. It’s like living in the future today.
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At an SF Tech week event a startup CEO said “meat proxies and any employee should not have psychological safety on the job… that’s not a luxury we have anymore.” Minutes later he said conversely we can be abusive to AI today because its context is short but we should get in the habit of being nice to it because AI will soon have the ability to remember us indefinitely and it will out last us. I love tech, I’ve been in it for decades, I’m building AI infra but this is getting weird.
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What does enterprise AI need before it can be trusted with real work? Our founder @jbruce shares what Atomic Strata is building: governed AI context memory that enterprises can configure, inspect, and correct. Watch our showcase with Imagination in Action @imaginationxyz @johnkwerner
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AI is supposed to make life simpler and somehow I’m at a point in the curve where it feels massively more complex. Yes, I’m 10x’ing+ my output, but the world is matching that with more work. The rapid context switching is killer and so disruptive of flow state.
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I'm speaking about governed context memory for Enterprise AI at the Stanford HAI and MIT Silicon Valley AI Summit hosted at Google DeepMind today. AI context needs to be configurable, inspectable, correctable and auditable to meet enterprise needs. Atomic Strata is building this context stack today. – at Mountain View, CA
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My cofounder Ethan rebuilt his first startup in a weekend with AI and documented the journey. An interesting and touching read for anyone who has been around tech since the dot com era.
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We shipped LLM-Wiki Compiler v1.2.0. The product direction is simple. Turn source material into durable knowledge that can be reviewed, moved, and reused. The new OKF export/import path matters because useful knowledge should not be trapped in one tool's folder shape. Teams will want compiled knowledge they can inspect, version, and carry forward. github.com/atomicstrata/llm-…
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Your agent has been keeping notes of your information all this time. Curious to see what it knows? Open the memory layer and check the receipts.
Replying to @AtomicStrata
Building with Claude, GPT-6, or anything else? no problem. This memory layer doesn't lock you into a model.
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Your AI’s memory shouldn’t be a black box. We just launched Atomic Cloud—AI context memory developers can configure for performance, inspect to see what’s stored, and correct when it’s wrong. Sign up for a paid plan and get $100 in launch credit.
Let your system run autonomously. Give your agents memory they can use across sessions, and your team a way to see what was stored, retrieved, corrected, and why. Configurable memory for AI agents, now in Public Beta. memory.atomicstrata.ai/?utm_…
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Last week at SALT I had the opportunity to sit down with Kelvin Sparks of The Block and discuss the increasing importance of context for enterprise AI. The way context data is managed and brought to models is becoming more important than the model. Having a good context platform is enabling businesses to use AI to safely empower employees and build new applications. Its also going to be key to forthcoming regulation.
Sixt, the German rental car company, has its own staff building AI apps, each one fenced to what that job is allowed to touch. The AI risk Juan Bruce worries about is not hackers. It is the non-technical people you just handed the keys to. @AtomicStrata CEO @jbruce full interview with @imyoungsparks at @SALTConference: 00:00 Why AI & Crypto Stopped Competing 00:38 The EU AI Audit Log 02:37 Open Source vs. Enterprises Lock-In 04:25 Stablecoins & Agent Payments 06:18 Enterprise Vibe Coding Risks 08:07 Agent Personas
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Talking AI at SALT’s Wyoming Blockchain Symposium which consistently has some of the highest level insight into regulatory and institutional issues. It’s also an amazingly beautiful location with a great crowd. Cheers to the SALT team for hosting a top notch event.
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Juan Bruce retweeted
From August 2021 through 2023, blockchains captured 90%+ of monthly crypto revenue. By mid-2026, that share dropped to 25%. The new breakdown: 🔸 Finance apps: exceeded 50% in most months 🔸 Consumer apps: steady meaningful share 🔸 Blockchains: down to ~25% 🔸 Physical and crypto infrastructure: rounding errors The economic center of crypto moved from the base layer to the apps running on it. Early cycles were infrastructure-focused because infrastructure was the only place value could accrue. Users paid L1 gas fees because there was nowhere else for the money to go. Ethereum's high fees, Solana's spikes, Bitcoin's security budget drove revenue. As blockchains became cheaper, more scalable, and more reliable, apps could finally support real users and capture their own revenue streams. Finance apps (perp DEXs, lending protocols, stablecoin issuers, trading tools) earn fees from trading volume, not just gas. Consumer apps (memecoin launchpads, wallets, social tools) turn engagement into sustained revenue as they find market fit. The internet followed the same arc. In the 1990s, ISPs and backbone providers made the money. By the 2010s, applications and platforms captured most of the value. Crypto is following that path, faster and with full transparency because everything settles onchain. You can't value L1s solely on the claim that they capture all fees anymore. App-layer protocols have proven that lean teams can generate hundreds of millions in revenue across: 🔸 @HyperliquidX -style derivatives platforms 🔸 @Pumpfun -style consumer platforms 🔸 The wider DeFi stack These products now capture economic surplus that once flowed almost entirely to validators and miners. That diversification is healthy. Crypto no longer depends on one revenue source or one narrative. The infra succeeded, which is why its revenue share fell. Finance and consumer apps now generate the majority. That's where the next decade of value compounds. h/t: @Blockworks
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Talking about AI context with blockchain rails at a packed Stanford BASS conference 2026. – at Stanford, CA
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At Stanford talking AI context on-chain at the Science of Blockchain conference. Always great to be back on campus. – at Stanford, CA
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If you are thinking about AI from a human systems and enterprise level, you need to read this. We need to be thinking about how we manage humans in the loop. a16z.news/p/the-next-ai-gold… – at Mill Valley, CA
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We've entered the era, due to AI, where product development velocity can outpace the rest of a startup org. Marketing, Sales, Strategy and everyone else are struggling to keep up. It used to be that strategy and marketing teams wanted products and features that had to be compromised by development resources. Today, with an AI optimized dev team, those constraints are disappearing. At the same time the tools and teams in other parts of the org are not advancing as quickly. e times. – at Mill Valley, CA
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Juan Bruce retweeted
Agent memory without grounding just becomes context pollution. Most memory stacks are append-only. No provenance or way to correct false beliefs, just more text crammed into your context window. Memory needs source tracking and update semantics so you can track each fact's source and edit what your agent believes and why. Don't build on a black box. github.com/atomicstrata/atom…
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