Robinhood launched an interval fund investing in Y Combinator backed startups. Retail investors are dying for pre-IPO stock exposure. Companies now stay private through the entire small cap to mid cap to large cap journey that public investors once captured, which means a growing share of total lifetime alpha is created before the IPO ever prices. The delivery mechanism deserves closer scrutiny than the headline. Interval funds are a continuously offered closed-end structure that accepts subscriptions on demand but permits redemptions only quarterly, typically capped at 5% to 10% of total fund assets. In a drawdown that cap becomes a queue, and investors learn the vehicle is easy to enter and difficult to exit. The traditional closed-end alternative is no cleaner: one recently marketed vehicle holding roughly 12% Shield AI and 11% Anthropic traded near a 40% premium to net asset value, a spread driven by retail demand outrunning share supply rather than by anything in the underlying portfolio. Our view is that illiquid private exposure belongs in most portfolios, but sized deliberately and purchased alongside an advisor who can explain premium and discount mechanics before capital is committed rather than after.
Harvey's reported raise at a $15.5 billion valuation, a 40% premium to its round just five months ago, is a referendum on the vertical AI application layer rather than on legal software. Revenue tells the story: roughly $350 million annualized, up more than 80% from $190 million in January. The obvious bear case was total addressable market, since legal services alone looked too narrow to underwrite a valuation of this size. Management has answered that by signaling expansion into financial services, insurance, accounting, and tax, which reframes the opportunity as the full set of high-margin professional services verticals rather than a single practice area. The durable moat here is the forward deployed engineer model. Law firms are not technology organizations, so an engineer who embeds and builds the firm's entire workflow becomes structurally difficult to displace, and each additional automated workflow compounds into recurring revenue. Margin expansion is the second leg: aggressive token optimization and intelligent model routing toward lower-cost inference drop directly to the bottom line while customer pricing holds. Harvey is the cleanest public expression we have of the thesis that application companies sitting on foundation models, not the models themselves, capture the enterprise economics.
Valar Atomics raising roughly $1 billion led by Sequoia at a $6 billion post-money valuation, plus $200 million in additional financing, marks small modular nuclear moving from a policy conversation into a funded buildout. Two distinctions matter when sizing this exposure. The first is fission versus fusion. Fission is proven commercial technology and the binding constraint is regulatory rather than physical, while no operator anywhere has yet produced commercial fusion. The second is execution culture. When Valar sought the supercomputer that governs reactor shutdown on fault detection, the market quoted $5 million on a two and a half year lead time. The company built it internally in six weeks for $400,000. That vertical integration instinct, borrowed directly from the SpaceX playbook, is precisely what a sector frozen for half a century requires. Energy is the foundational layer beneath the entire AI data center buildout, and we expect regulatory friction to continue falling as power demand compounds. This remains a pre-revenue position, so we would frame it at 0.50% to 1.00% of a portfolio for aggressive investors, not as a core holding.
Unitree Robotics listing on the Shanghai Stock Exchange at a $9 billion valuation on $252 million of 2025 revenue prices the company at roughly 35.7 times sales, and the retail tranche was oversubscribed by 5,526 times. That subscription figure, more than the valuation itself, is the datapoint worth carrying forward. Gross margin above 60% on hardware is the second surprise, and it is a function of full vertical integration and domestic manufacture of high-end components such as actuators. The strategic question is whether hardware or intelligence captures the value. Our read is that Chinese manufacturers are winning decisively on volume and component quality while US labs are further ahead on the AI brain that determines actual usefulness in manufacturing and residential settings. That mirrors the large language model cycle almost exactly, where the eventual winners were the firms that paired compute with a frontier model rather than optimizing either in isolation. Regulation now complicates the picture, with a recent executive order restricting sales of Chinese humanoid robots into the US market. Manufacturing throughput remains the real bottleneck for everyone, with leading US producers still building fewer than 100 units per day against a thesis that requires billions of units over 10 to 15 years, and we would note that robotics at scale implies an inference compute demand curve materially steeper than anything software has generated to date.