Weekly Pre-IPO Stock Secondary Market Update | October 13, 2026

Lambda Labs is reportedly raising $4 billion at a $18.5 billion post-money valuation ahead of a planned 2027 IPO, and we view it as a core AI infrastructure holding despite the balance sheet optics. The business buys GPUs from Nvidia, stands them up and rents out the compute, and Nvidia is also an investor, which we read as a signal that the company will receive chip allocation. Lambda reportedly carries a $50 billion backlog, so the company is raising capital to build capacity fast enough to capture demand it cannot yet serve. We expect the financial statements of Lambda, Nscale and CoreWeave to look like a disaster until they do not, and then to look very strong. Lambda has moved founder and CEO Stephen Balaban into the chief technology officer role and brought in a slate of public-company executives, which we see as the right call because the neocloud model is in large part a capital-raising exercise with banks, debt markets and large asset managers. CoreWeave is down 45% over the past year and trades at about $42 billion, which makes Lambda at $18.5 billion look attractive, and OpenAI cutting ChatGPT Pro usage in half at the same price as evidence of how tight compute remains. Our thesis rests on the logic Mark Zuckerberg: if AI succeeds at the macro level, unused data center capacity can simply be rented to someone else, so the real risk is that AI does not play out. We believe investors without an allocation to neocloud data centers should consider one, sized appropriately for the risk.

Reflection AI has released a US open-source model that appears to perform well, particularly for coding, and reportedly uses less inference compute than some of the Chinese open-source models, but we are not convinced by the equity at a $25 billion valuation. That price is roughly a third of DeepSeek's roughly $75 billion IPO valuation even though Reflection has just shipped its first offering, which reflects the discount applied to Chinese technology companies more than relative scale. The commercial case leans on US versus China sourcing, and we expect that question to be polarizing enough to win Reflection customers as long as it outperforms Nvidia's Nemotron. There is also buzz that Nvidia, already an investor, may try to acquire Reflection, and we would be surprised if Nvidia does not ultimately dominate US open-source models. A separate risk: OpenAI and Anthropic are indirectly responsible for about 7% of US debt over the past two years, so if open source takes share faster than the overall AI market grows, the circular financing around the frontier labs could be disrupted. Even in that case, the compute would still be needed, which reinforces our preference for neoclouds. As investors, we would rather express the open-source trend through AI inference providers such as Together AI, Baseten, Fireworks and Modal, which host these models and have a clearer revenue model than the model developers themselves.

micro1 has announced a $1 billion commitment to acquire enterprise data over the next 12 months, and it is reportedly past $1 billion in annual recurring revenue, but we see real uncertainty in the model. The thesis is that public internet data has largely been scraped and high-quality data is now the scarce input, so micro1 is buying Slack messages, emails and workflows from top firms and selling the processed data to model companies, with founders on X posting tutorials on how to sell a workflow to micro1 for $1.2 million. This is the first company we have seen try to buy enterprise data directly, which differs from players such as Scale AI that pay doctors and lawyers to complete questionnaires. Private company and personal data is a large share of the world's data and is not readily available, and pairing it with an open-source model inside a harness could deliver better results at much lower cost. Our main concern is historical behavior: people tend to give away data in exchange for free products, so a small law firm may hand over its data to get Harvey or a similar tool for free rather than sell it. Large incumbents such as Meta, which Zuckerberg said will collect interaction data as people use Meta Muse every day, may acquire this data at no cost, leaving smaller incumbents as the more likely buyers. We remain undecided on micro1's risk-adjusted return profile, but some market participants are calling micro1 the next Anthropic.

Ghost is a hardware company selling a roughly $3,500 home computer with a high-powered GPU that runs a user's AI agents locally, and while the first batch reportedly sold out quickly, we are skeptical of privacy as the investment case. The product pairs the hardware with its own personal agent and is pitched on keeping sensitive connections, such as bank accounts, health data from a Whoop and home cameras, off third-party systems, and its founder is 19 and has been working since age 12. We see it as competing with Apple's Mac Mini, which can be stacked for more local compute, and competing with Apple is a difficult position. Our view is that most people do not meaningfully care about data privacy, as social media and the widespread loading of bank, brokerage and email accounts into assistants such as Instinct suggest. The case changes if local compute becomes an economic driver, much like rooftop solar versus buying from the utility: if a family of four faces a $3,000 monthly bill from a cloud assistant, a one-time $3,500 device becomes an easy decision, especially as robots, appliances and automated tasks run around the clock.