There's a narrative in the US/UK that low-skilled immigrants are important because they do the jobs locals don't want to do (cleaning, taxi-driver, barista, food-serving, lawn-mowing, etc). As a second generation immigrant (my parents both independently moved to the UK in their teens/20’s), I always found this an awkward, transactional framing, rooted only in economic utility rather in any consideration of broader socio-cultural factors. It’s also short-termist (my parents went on to successfully start several businesses and employee many people). In any case - what happens when robots and AI begin automating these roles over the next 10-20 years? A world where autonomous vehicles replace drivers, humanoid robots clean homes and serve food, landscaping bots trim lawns with precision, drones/delivery bots replace the UPS/Fedex army, etc. Here, the economic case for low-skilled immigration dissolves, opening space for a deeper set of questions like: “why let people in at all?” I think this gives credence for nationalists to become more protective of borders - they can easily claim robots don’t commit crime, affect culture, or burden welfare systems. There’s also an alternative world, which is perhaps the more utopia/optimistic, and likely in my view: that automation erodes the idea of labor-based human value, and we begin to find new meaning in life and perhaps start relating to each other differently. Here, borders become less about controlling who works and more about building societies with shared values and enduring peace, i.e we see immigration less as a threat to jobs and more as a national/demographic lever to rebalance aging populations, to cultivate/seed culture, and to build resilient societies. This is obviously less about immigration at this point and more about how societies assign value to human beings in a post-labor world. My favorite books/reads exploring this is (now relatively old) Kai-Fu Lee’s AI Superpowers and AI 2041. They pose the questions: what’s the future (human-human) social contract when machines outperform us in most economically relevant tasks? What does a government owe its citizens when their productivity becomes irrelevant? And what replaces “work” as the central axis of identity, contribution, and dignity? This is where the puck is going. Not just toward robots taking jobs, but toward an entire redefinition of what it means to matter. I think the tension between nationalism and globalism intensifies: some countries double down on exclusion, while others embrace a new kind of globalism that places all humans on equal footing. So, as we look ahead, the real question isn’t what jobs do AI/robots replace, it’s: “what values will remain?” and “can we build a future where people are welcomed not for what they do, but for who they are?”

My crypto theses right now: - CEXs and fintech apps will list all DEX tokens. - RWAs will come onchain as perps, not tokenized spot. - With infinite onchain assets available in every trading app, solving discovery is a 1000x opportunity.

Seven AI x Crypto predictions + full thesis post 👇 1. Decentralized training won’t beat SOTA (and that’s fine): Through 2027, decentralized training won’t beat centralized frontier models; its wins will be cost, resilience, transparency, and ownership. I think decentralized setups make most sense in terms of enabling domain-specific models, frequent fine-tunes, and in a regulated context that values auditability and control. 2. Small models: By 2027, small, domain-specific models will power most real-world usage of decentralized training networks. Small models are cheaper to train, easier to verify, and don’t require hyperscale infra. Their edge is performance-per-dollar and the ability to run on edge devices and agent wallets. 3. Provable inference becomes default in DeFi: By 2027, most major onchain DeFi protocols will require verifiable inference (TEE/zk) for any model-driven decisions. Cheap TEEs and improving zk tech will mean we can attest that a specific model, with a specific input and version, produces a specific output that moved money. 4. An “agent passport” becomes a shared standard: By mid-2027, a common “agent passport” (keys, version, rules, and reputation) is adopted by major chains and apps. As agents become active onchain participants, apps need a shared way to know who they are, what they’re allowed to do, and how to limit them. I think a common standard emerges, embedding important information like wallet, rules, version, and reputation. It’s like a credit report for agents. 5. Agent transaction share: By July 2026, >5 % of all transactions on Base will be initiated by verifiable onchain agents (i.e., not sniping or MEV bots) that operate under transparent, pre-defined rules. 6. Agent-powered prediction markets reshape DAO governance: By 2027, major DAOs will incorporate agent-driven prediction markets as a core governance primitive. This is essentially AI-powered Futarchy, but where agents with wallets represent human tokenholders and use data-driven forecasts to place and resolve bets on proposals. 7. Onchain agent swarms coordinate by default: By 2027, at least one successful protocol or app will be built and operated entirely by a persistent, multi-agent system, with coordination, communication, and evolution handled onchain. Instead of “one agent, one task,” there’ll be agent swarms with shared memory, rules, and incentives, enabling full autonomy for protocols. These predictions are my current best guess, but the real fun is in the journey. If you’re a founder building something that challenges these ideas or brings them to life, I’d love to learn alongside you. My DMs are always open. For a full breakdown of the thesis behind these predictions, you can read the full post:

Seven AI x Crypto predictions + Full Post 👇 1. Decentralized training won’t beat SOTA (and that’s fine): Through 2027, decentralized training won’t beat centralized frontier models; its wins will be cost, resilience, transparency, and ownership. I think decentralized setups make most sense in terms of enabling domain-specific models, frequent fine-tunes, and in a regulated context that values auditability and control. 2. Small models: By 2027, small, domain-specific models will power most real-world usage of decentralized training networks. Small models are cheaper to train, easier to verify, and don’t require hyperscale infra. Their edge is performance-per-dollar and the ability to run on edge devices and agent wallets. 3. Provable inference becomes default in DeFi: By 2027, most major onchain DeFi protocols will require verifiable inference (TEE/zk) for any model-driven decisions. Cheap TEEs and improving zk tech will mean we can attest that a specific model, with a specific input and version, produces a specific output that moved money. 4. An “agent passport” becomes a shared standard: By mid-2027, a common “agent passport” (keys, version, rules, and reputation) is adopted by major chains and apps. As agents become active onchain participants, apps need a shared way to know who they are, what they’re allowed to do, and how to limit them. I think a common standard emerges, embedding important information like wallet, rules, version, and reputation. It’s like a credit report for agents. 5. Agent transaction share: By July 2026, >5 % of all transactions on Base will be initiated by verifiable onchain agents (i.e., not sniping or MEV bots) that operate under transparent, pre-defined rules. 6. Agent-powered prediction markets reshape DAO governance: By 2027, major DAOs will incorporate agent-driven prediction markets as a core governance primitive. This is essentially AI-powered Futarchy, but where agents with wallets represent human tokenholders and use data-driven forecasts to place and resolve bets on proposals. 7. Onchain agent swarms coordinate by default. By 2027, at least one successful protocol or app will be built and operated entirely by a persistent, multi-agent system, with coordination, communication, and evolution handled onchain. Instead of “one agent, one task,” there’ll be agent swarms with shared memory, rules, and incentives, enabling full autonomy for protocols. Full post:

DAOs are why I got crypto-pilled in 2020, but they’ve struggled with low participation and the Tragedy of the Commons, which comes with a really bad negative feedback loop. I think AI agents can turn this into a positive feedback loop, where agents inform decisions, place probabilistic bets, and vote based on expected impact. Over time, you can imagine agent-governed prediction layers serving not only as governance/coordination tools for DAOs, but also as an informal reputational layer. Prediction: by 2027, major DAOs will incorporate agent-driven prediction markets as a core governance primitive. This is essentially AI-powered Futarchy, but where edge agents represent individual human tokenholders and place and resolve forecasts on proposals.

JUST IN: 🇺🇸 Treasury Secretary Bessent says "we are exploring new possibilities in decentralized computing and digital payments to unlock the potential of blockchain technology."

AI is the most powerful platform shift of our time, but it’s being shaped by a small group of companies racing to control every layer of the stack: compute, data, models, and distribution. I believe the future of AI shouldn’t be monopolized, and that the most transformative systems will be built on open, decentralized infrastructure. AI agents are becoming active participants in software, and they need environments where they can hold assets, follow rules, and transact. Blockchains, which are now fast, cheap, and expressive enough to support real AI infrastructure, provide that foundation. Decentralized, credibly neutral, and user-owned, they offer exactly what today’s AI systems lack. And for the first time, they’re ready. Just a year ago, decentralized training was dismissed as technically unfeasible, even by leading AI researchers. But breakthrough research like OpenDiLoCo @PrimeIntellect, DeMo @NousResearch, and Protocol Models @PluralisHQ are flipping that assumption. For the first time, high-performance AI training is not only possible on decentralized networks – it’s working. Exceptional teams are focused on core decentralized AI infrastructure. While I believe there will always be opportunities in improving compute, training, and data layers, I see the most practical wedge in agent infrastructure: the frameworks, wallets, and coordination and reputation layers that enable autonomous systems to interact, transact, and evolve. This is where the Crypto x AI stack starts to become real. As primitives for identity, payments, memory, and coordination mature, we’ll move from isolated agents to persistent, interconnected networks of agents cooperating, competing, and evolving onchain. These agent networks will become the foundation for new types of products, experiences, and organizations. Over time, I expect this to unlock fully autonomous agent swarms, governed by cryptographic rules, not corporate APIs. I'm looking to back ambitious, pre-product founders who are building years ahead of the market, starting from day zero. DMs open.

Goldman Sachs on Agentic AI: "spinning up AI coworkers on demand will be a baseline capability." This is where the org chart breaks. 1 founder + 100 agents.

Wimbledon makes me miss living in London more than usual - few things beat catching Centre Court midweek! 🥲 Men’s semis tomorrow - Alcaraz, Sinner, Djoko, Fritz - all killers. And Djoko’s last chance to win Wimbledon. Might be one of the best final weekends in years 🎾🥂

Wimbledon szn is a top 5 thing I miss about living in London 🎾 🥲