Hi all,
Welcome to our Sunday edition – and thank you for reading.
Markets wonder if AI is a bubble, China is executing the largest clean energy build-out in history and AI systems are beginning to design viable virus genomes. Capital, energy, and biology are all being rewired at once.
But before we get stuck into this week’s ideas and developments, I want to share my conversation with Vinod Khosla in which I lay out my vision for the next ten years:
Let’s get started with this week’s briefing on AI and exponential technologies.
Is AI a bubble yet?
The $10 trillion question everyone is asking. To answer it, we built a data-driven framework that compares [ link ] today’s AI boom with past bubbles. We look at economic strain, valuation heat and the quality of funding driving the frenzy, plus two other gauges to tell where AI stands.
You can pair our analysis this week with veteran VC Jerry Neumann ’s insightful essay that frames AI as a late-wave innovation entering its maturity phase [ link ] – one that’s unlikely to make you rich. He compares AI to containerization where competition and high capex costs squeezed the margins and growth opportunities:
Shipping containerization was a late-wave innovation that changed the world, kicked off our modern era of globalization, resulted in profound changes to society and the economy and contributed to rapid growth in well-being. But there were, perhaps, only one or two people who made real money investing in it. […] McLean did, as did shipping magnate Daniel Ludwig, who had invested $8.5 million in SeaLand’s predecessor, McLean Industries, at $8.50 per share in 1965 and sold in 1969 for $50 per share.
Jerry expects most of the gains of AI to accrue to consumers and downstream companies: cheaper healthcare, education and professional services, just as containerization cut costs for companies like IKEA and Walmart rather than enriching the shipping industry itself.
But I do see at least one important difference. Containerization spread across a fragmented network of ports, terminals and shipping lines – no single actor held sway. Digital markets, by contrast, concentrate control. A small cohort of firms can dominate users, data, compute and monetization. OpenAI, with 700 million users, controls its entire network and can turn it into a self-reinforcing, data-driven flywheel. Where containerization spread value thinly, AI platforms can concentrate it. That doesn’t mean fortunes will flow freely, but it does mean incumbents might extract more than Jerry suggests.
Resetting the transition
China is executing the largest clean energy build-out in history. In the first half of 2025, global solar installations grew 64% [ link ] and China alone accounted for two-thirds of that growth. With costs down 60-90% since 2010, China’s solar and wind now generate more power than hydropower, nuclear and bioenergy combined.
The West has a lot to learn. As I mentioned yesterday [ link ], we’re living through a value inversion [ link ]:
Frankly, it’s a bit odd when abundance creates crisis and scarcity drives prosperity. Perhaps our fundamental economic grammar – that language of supply, demand, and equilibrium we’ve trusted since Adam Smith – is out of date. […] We subsidize scarcity (fossil fuels) while penalizing abundance (renewable oversupply). Scarcity thinking is colliding with abundance reality.
EV member Michael Liebreich argues that Western climate policy has been captured by impossible expectations [ link ] and purity tests.
Net zero by 2050, hydrogen hype and blanket subsidies are fueling backlash rather than progress. He calls for re-anchoring: moving targets from 1.5°C to 2°C, decarbonizing the first 90% of energy use with renewables, electrification and batteries and leaving the last 10% for later. Kill off uneconomic distractions like green hydrogen at scale. And above all, rebuild public trust by treating citizens as participants, not hostages.
Generating life
At the end of 2024, when I wrote about DNA-trained AI [ link ], I suggested it could open the door to creating life forms for specific purposes.
Now we’ve stepped through that door.
A fine-tuned Evo 2 model has generated 16 viable bacteriophage [ link ] genomes: new life [ link ], some distinct enough to count as new species. A few even outperform their wild cousins at killing E. coli.
This is a step-change in synthetic biology. We are moving from designing parts (proteins) to designing systems (genomes). The road to complex organisms won’t be smooth – limited data and costly DNA synthesis remain obstacles, but the trajectory is there to pursue.
My friend Eric Topol highlights how genAI could change primary prevention [ link ] by stopping disease before it strikes. A new model, Delphi-2M [ link ], applies LLMs to the “language of human health.” Feed in your lifestyle and health factors, and it forecasts risk across more than 1,000 diseases to indicate when they may appear. It matched or beat the best single-disease models, even predicting mortality with striking accuracy. What’s remarkable: Delphi-2M is only GPT-2-class. We’re still at the starting line!
AI’s split personality
For consumers, AI has become a copilot. OpenAI’s own data show [ link ] 700 million people engaging with ChatGPT each week, sending 18 billion messages. Most of that activity isn’t about coding or building. Seven in ten conversations are personal rather than professional, and only four in ten involve direct task requests. The product is sticky and ubiquitous, but its economic profile is closer to a mass-market utility than a profit engine.
In the enterprise, the picture is different. Anthropic’s Economic Index, drawn from first-party API data, shows 77% of usage dedicated to task completion (raw data are here [ link ]). These are executional functions – coding, debugging, invoice processing, recruitment – that quietly erase entire workflows. This is where durable revenue pools will emerge: firms pay for productivity, not companionship.
Consumer adoption gives AI its cultural inevitability and enterprise automation gives it economic gravity. The winners will be those rare firms that manage to capture both.
See also: Anthropic and OpenAI are training their models on enterprise apps [ link ] such as Salesforce, Zendesk and Excel, inside simulated environments that mimic workplace tools. The spending is vast: Anthropic will put $1 billion into these reinforcement-learning gyms over the next year, while OpenAI expects data costs to rise from $1 billion in 2025 to $8 billion by 2030. No wonder Mercor, a marketplace where human experts train RL agents, says it has reached a $450 million revenue run rate [ link ] in just 17 months.
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Elsewhere
In AI, tech & science:
Alibaba’s new PPU chip [ link ] rivals Nvidia’s H20 in state-backed benchmarks, another step in Beijing’s push for semiconductor sovereignty. It was likely the tipping factor in China’s decision to block Nvidia sales [ link ].
Paper2Agent [ link ] automatically converts the methods of a research paper into an executable AI assistant. So instead of just reading how an experiment works, you can run it and build on it directly.
California has just completed its first solar-covered canal [ link ], which generates clean power while also saving water. What I find fun is solar’s flexibility: it can just as easily become a rooftop, a window or your garden fence.
In markets and strategy:
Microsoft has committed $30 billion to the UK [ link ], its largest investment outside the US.
Google’s new Agent Payments Protocol [ link ] gives AI agents a way to pay on users’ behalf.
In society and culture:
French pensioners now out-earn workers [ link ]. An inversion with ominous implications for the country’s pension sustainability. See my Saturday commentary on this [ link ].
China’s famed policy “laboratories” have gone silent as centralization reduces local experimentation [ link ].
Russian state TV just launched an AI-generated satirical news show [ link ]. Disinformation with a laugh track.
A Harvard-MIT study on chatbot intimacy found that relationships tend to arise accidentally [ link ]. Users start using bots for help and stay as conversations turn personal. Over a third of posters in r/MyBoyfriendIsAI on Reddit identify ChatGPT as their other half.
Thanks for reading!