Research / Weekly Pre-IPO Stock Secondary Market Update
Weekly Pre-IPO Stock Secondary Market Update | July 1, 2026
OpenAI's New AI Chip | Groq2.0 | The Inference Compute Arms Race
Companies covered: OpenAI, Groq
OpenAI's decision to build Jalapeño, its first custom inference chip co-developed with Broadcom on a roughly nine-month design cycle and slated for its own data centers by year end, is the clearest signal yet that compute cost has become the central battleground of the AI era. The logic is straightforward once you look at the economics, because OpenAI lost around $21 billion in 2025, and inference, the work of actually running models rather than training them, is where that cost concentrates. Jensen Huang (Nvidia CEO) has argued that 80% to 90% of all compute will eventually be inference, so owning the silicon that serves it is a direct lever on gross margin and long-term profitability. The move also fits a broader pattern of the largest players going vertical to escape single-supplier dependence and take control of cost, security, and capacity. It matters strategically because model quality is converging and the practical gap between the leading chatbots is narrowing which pushes the durable moat down into infrastructure and unit economics. We read Jalapeño as a margin story more than a hardware story, and we expect Google, Microsoft, AWS, and OpenAI to keep integrating downward into their own chips. The takeaway is that the companies controlling inference cost, not just model performance, are the ones most likely to compound from here.
Groq's ... or Groq2.0 ... newly closed $650 million financing round marks a credible second act for a company that many, ourselves included, once expected to become the next Nvidia. Founder Jonathan Ross built the business around the LPU, a processor purpose-built for inference, which is precisely the workload the market is now racing toward. The developer traction underscores the momentum, with Groq's base climbing from roughly 2.5 million to about 5.0 million, an addition of some 2.0 million in a relatively short window. Our house view is that demand for inference is effectively unbounded, and concerns that the sector is overheating the way the dot-com market did in 2001 or the credit market did in 2008 miss a key distinction. This buildout is thoughtful, in our opinion, and underlying usage is still growing at a healthy clip. Against that backdrop, Groq2.0 re-entering a market it helped define is a bet we think can work, provided execution keeps pace with the capital. We would watch closely whether the $650 million translates into durable share as the inference field grows more crowded.
The most interesting frontier sits a layer below inference, in the emerging class of companies attacking the power and cost structure of AI directly. Unconventional AI, started by a former Databricks AI leader, is claiming it can cut the price of power by as much as a thousandfold, an ambitious figure we would treat as a thesis to test rather than a settled result. We expect more of these meta layer businesses to surface, names like Fireworks AI, Baseten, and Factory AI, which sit between the models and the customer and exist primarily to manage and lower cost through model routing and optimization. Positron is another infrastructure name we are actively researching and may look to back. The discipline we would stress is timing, because the sweet spot for entering these companies is around product market fit, typically the Series A, once the technology has proven it scales rather than at the earliest and most speculative stage. We would also note that much of the return in the well-known later-stage names is likely to accrue in the public markets, which argues for patience there and sharper focus on earlier stage (sub-$15B valuation) infrastructure and application companies. The practical approach is to put these names under research coverage, track them, and step in when the product, the pipeline, and the capital needs line up.
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