Delphi Intelligence

Read the full report for free here. delphiintelligence.io/research/but-o…

The US-China decoupling could force both sides to borrow from their rival. The US has been in harvest mode for 25 years, extracting value from its institutions and capital markets while underinvesting in foundations. China took the opposite path, suppressing wages and consumption to build roots that are now bearing fruit. For decoupling to work, each side must shore up its weaknesses. The US needs to reinvest in infrastructure and reshore strategic industries. China needs to stimulate consumption and develop deeper domestic capital markets. Both will have to adopt elements they have historically resisted.

"The US and China are making fundamentally different bets on AI. The US is betting on superintelligence. China is betting on commoditized intelligence." Read more here👇

2026 is going to get weird(er) delphiintelligence.io/research/26-pr…

1/9 - The end of the year is a glorious time for reflection, and I love reading all the year in review + forecasting pieces all the firms put out. It's interesting to see how the stochastics converge, when they're largely developed in isolation. A common thread that I've seen from financial analysts is that 2026 is the year of capex buildout for scaling transformer models, but to me, it's the year of the architecture renaissance. The lens is increasingly incomplete. The next wedge isn’t just more parameters; it’s different architectures + different economics, pushing toward ambient, abundant intelligence. The full essay is at delphiintelligence.io/research/ambie… , courtesy of @Delphi_Digital and blog.hack.vc/ambient-and-ab…, courtesy of @hack_vc

Read the full analysis from @PonderingDurian here. delphi.link/Humanoid1

Are humanoids the future or just another bubble? It could be both. AI task completion is doubling every 7 months. Costs keep falling. By 2050, we're looking at tens of millions of humanoids globally, possibly hundreds of millions. Global labor is sized in the tens of trillions. If embodied AI expands that TAM by an order of magnitude, the capital will come. This is shaping up to be one of the largest investment cycles in history. But most of the real growth doesn't hit until the mid 2030s. Leading AI researchers still haven't cracked multimodal reasoning, let alone embodied intelligence. Robotics data is scarce, the best models are too large to run on device, and the hands still lack dexterity for many use cases. Components remain expensive and scaling production will take years. The explosion in valuations and successive funding rounds could be signs to be wary. Figure AI is rumored to have raised at around $39B. AgiBot in China with comparable capabilities, sits at roughly $1.5B. Tesla is worth 3 to 4x the entire Chinese EV industry, yet China leads on cost and volume and keeps taking global share. That dynamic is likely to repeat in robotics. China's manufacturing depth, dense supplier networks, and approach to competition give them structural advantages as the industry matures. In the near term, hardware captures more value than software. Sensors, motors, transmissions, and actuators make up the bulk of the profit pool through 2040. Each humanoid needs over 40 joints and actuators, and capacity is supply constrained and geographically concentrated. The areas to watch include tactile sensors, roller screws and reducers, edge inference, fleet orchestration, and the race for a true robot operating system.

Watch the full video here. delphi.link/Dagisummit

Nous Founding Engineer discusses the structural gap in American AI. "The big part of the challenge is the incentive scheme. How do you actually finance creating a pretrained model? China has solved this to some extent where they can release these open foundation models. We have yet to see the emergence of the American Deepseek."

Who will win the AI race? The US and China are running fundamentally different races that could both be necessary in the long term. One prioritizes frontier advancement and individual freedoms while China prioritizes rapid diffusion and societal harmony. The tension between this may be exactly what's needed to navigate what Rawson calls "AI's narrow corridor." America dominates in compute, but China graduates 5x more STEM students annually. Qwen is now the most downloaded open source model globally with the Chinese stack becoming increasingly more competitive. The US approach involves trillions in capex funded by anticipated monopolistic profits, aiming for superintelligence and breakthrough science. China uses AI to solve demographics through robotics, reduce education gaps with AI tutors, and keep manufacturing onshore as wages rise. Can both approaches coexist?

Read the full analysis from @ponderingdurian here. delphi.link/Narrowcorridor

On investing in AI: I'm confident AI is ushering in a golden era of venture, and will lead to the creation of multiple new $100b companies over the next few years. I’m also confident that the best way for most crypto ppl to get exposure to this is not via ad hoc direct investments, but through fund of funds exposure And this isn’t because I don’t think crypto ppl can get up to speed on AI and pick winners. I think it’s definitely possible for at least some crypto ppl to pick good AI companies. A lot of ppl in crypto are extremely smart, experienced investors who have learned how to get up to speed on new fields fast. The problem is that picking is <50% of what makes up edge in venture. The rest is sourcing and winning deals and this is much harder to build In my experience, it’s almost impossible to gain edge here unless you’re willing to move to SF and grind for a few years to build up your network and brand. If you don’t do this, you will face severe negative selection on the deals you see via SPVs and your network. They’ll be the lemons that the best investors don’t want, and it doesn’t matter how good a picker you are if you’re selecting from a sample of shitty deals You can try and mitigate this by hiring someone in SF, but again you’ll face negative selection as the majority of people who have edge will either be hired elsewhere or building their own funds This is why we decided to go the FoF route, and why I’ve been spending most of my time speaking to the best emerging fund managers I can find. While trad venture returns have historically been pretty mid, I believe: a) this time is different due to the disruption of AI and the tailwinds it’s provided across tech b) most venture investors suck, and underperformance can be mitigated by simply picking better managers. Specifically, by doing the opposite of what most FoFs do and picking smaller funds, with highly concentrated approaches, and a unique and clearly differentiated source of edge Overall, having spent ~3 months speaking to 100+ managers, I’ve been insanely impressed with some of the talent we’ve found. We’ve made 4 commitments to funds so far, and I can truly say these are some of the most impressive people I’ve ever met, who are not just well pedigreed/insanely capable/hungry, but have also thought deeply and have unique answers about the source of their edge. Crucially, none of these are people you could’ve hired, and recreating their edge would’ve either been entirely impossible or taken multiple years of concerted effort on our part Our goal is to select 5-10 managers with distinct focuses and minimal overlap, netting ~200 investments per year. Investing into the right managers also makes direct investment far easier, because we get access to their pre vetted dealflow and can double down into winners from this cumulative portfolio. We’re already seeing this strategy beginning to bear fruit I’ve learned a lot by going through this process and feel it’s made me a better investor too. With this in mind, I’ll be doing a series for @PodcastDelphi where I interview some of the best managers I’ve met and try and tease out what makes them so good Hoping to do many of these in person and to get the first few out in October. Stay tuned :)

if you read this article, you knew this time was coming. h/t @delphi_intel

An absolute pleasure hosting @philipjohnston to talk through @Starcloud_Inc1's plans for shifting heavy compute off-world. • Why orbital compute is the end-game • Building a 5GW data center in space • Radiation shielding H100s • The Era of Starship • Matrioshka brains