Research / Weekly Pre-IPO Stock Secondary Market Update
Weekly Pre-IPO Stock Secondary Market Update | July 9, 2026
Nvidia competitor Etched | Timeline to small modular nuclear fission reactors | Growing demand for open source AI LLM models
Companies covered: Etched, Valar Atomics
The AI chip market is far from saturated, and Etched's climb to a $5 billion valuation is the clearest signal that inference, not training, is where the next wave of semiconductor value will be created. Two Harvard dropouts built a purpose-built chip aimed squarely at Nvidia's franchise, and the market is rewarding the bet well before mass production. Nvidia's own leadership has projected that 80 to 90% of the semiconductor market will eventually be inference rather than training, a structural shift that favors cheaper, easier-to-manufacture silicon over the monolithic GPU stack. Even the recursive self-learning that frontier models now perform is inference compute at its core, which widens the addressable market rather than narrowing it. We remain early enough in the cycle that the winning hardware architecture is not yet settled, and that uncertainty is precisely why the opportunity looks compelling rather than played out. Supply is not outrunning demand here, because the buildout is driven by hyperscalers responding to real customer forecasts rather than speculative capacity bets. The takeaway is that semiconductors sit at the base of the AI infrastructure stack, and inference chips are the segment with the most room left to run.
Energy is emerging as the ultimate bottleneck in AI infrastructure, and nuclear, specifically small modular fission reactors, is shaping up as the cleanest answer on the table. Valar Atomics captures the theme, backed by Palmer Luckey and Palantir's Shyam Sankar, and it reportedly hit criticality last week while beginning to power Nvidia chips. The broader pre-IPO asset class benefits from marquee names such as Nvidia, Khosla Ventures, and Andreessen Horowitz standing behind companies at this stage, which lends real credibility, and defense-linked founders add further weight here. The discipline, however, is in the timing. A business valued near $5 billion today that investors believe can reach $200 billion carries a far higher probability of going to zero than one already valued at $190 billion, because the early company still lacks a finished product, mass production, and meaningful revenue. The rational entry point arrives once a company has a live product in the marketplace, is producing it at scale, and is generating revenue, which compresses downside while preserving upside. For investors with conviction on the theme, a position sized under 5% allows exposure to the grand-slam outcome without betting the portfolio on a company years from deployment. We favor fission and small modular reactors over more speculative approaches, and would catch the theme on its way up rather than trying to anticipate it.
The most consequential call of the week is that open source models will command roughly 80% of market share in the long run, a striking prediction that reframes how enterprises will build with AI. The logic rests on proprietary data and workflows. A company that hands its data to a frontier lab risks having that advantage absorbed, much as platform owners once cloned the successful third-party apps they hosted. Instead, enterprises will take an open architecture base such as Kimi K2.5, layer in their own proprietary data and workflows, and run a bespoke model that services internal and external customers at a fraction of the cost. Figma's experience with Claude, where an integration preceded a competing native design product, illustrates the risk of leaning too heavily on a closed provider. The economic incentive is decisive, because an AI application company can deliver results equal to a frontier model at roughly one tenth the cost, keep the same outcome-based pricing, and capture the spread. Crucially, this expansion does not damage OpenAI or Anthropic in absolute terms, since falling costs drive a roughly tenfold increase in total usage and leave the frontier labs with a smaller share of a far larger pie. The house view is that infrastructure, from data centers to model routers, remains the lower-risk allocation, while enterprise AI application companies with deployed engineers, open model architecture, and outcome-based pricing offer the more speculative upside.
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* NOTE: AG Dillon ("AGD") is not affiliated with any pre-IPO company in its funds. Some pre-IPO companies require company approval for purchases (aka transfers). AGD has not been pre-approved by any pre-IPO company to purchase their stock. AGD purchases pre-IPO stocks in the secondary market and may gain exposure by directly purchasing the stock (on the company's capitalization table) and/or through a third party fund (aka special purpose vehicle, or SPV). When AGD purchases through a SPV the SPV general partner is responsible for obtaining the pre-IPO stock company approval, not AGD.
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