Research / Weekly Pre-IPO Stock Secondary Market Update
Weekly Pre-IPO Stock Secondary Market Update | June 17, 2026
Enterprises are moving to AI LLM open architecture solutions; Large language models vs world models and how to pick some winners; Exponential vs linear growth in technology will yield incredible investment opportunities
Companies covered: Project Prometheus, Decart, Figure AI
An AI price war is now reshaping the economics of the entire sector, and the pressure is coming from the cost side rather than the demand side. OpenAI has been optimizing to maximize token spend, but a wave of inference optimization across both software and hardware is forcing frontier labs to bring per-token prices down or cede share. Open-source models are the lever, with the strongest options today coming out of China (DeepSeek, Kimi K2.5) and US application companies increasingly willing to route everyday workloads to them. Several AI application CEOs now expect to run almost entirely on open source within one to two years, reserving frontier closed-source models for only the most complex tasks where they can cost roughly 50 times more per token. A new category of model routers, exemplified by Factory AI, is emerging to match each query to the cheapest and best-suited model, turning model selection itself into a margin lever. At least one US open-source contender is already carrying a $25 billion valuation, Reflection AI. The takeaway is that the next super cycle is an optimization cycle, and value is migrating toward whoever compresses inference cost without sacrificing output quality.
Jeff Bezos has stepped directly into the frontier-model race with Project Prometheus, which raised $12 billion at a $41 billion valuation to build what it calls artificial general engineers. The structure is notable for how much institutional capital has lined up behind a company that has not yet shipped a product. The thesis is that AI can be pointed at engineering itself, designing and then stress-testing systems such as rocket engines inside virtual environments before anything is physically built. That ambition places Prometheus squarely in the world-model camp, where simulated environments substitute for expensive real-world iteration. A $41 billion pre-product valuation is aggressive on any conventional basis, and it signals that investors are underwriting the category rather than current revenue. We would treat the raise as a marker of how much capital is now willing to chase applied AI at the engineering layer, and as confirmation that compute-rich incumbents intend to compete on their own terms.
Decart released a new world model that goes after the single biggest bottleneck in generative AI, video generation. One large provider was spending roughly $15 million a day to output clips that still took a long time generate, far from instant. Decart lifted a video-generation model off its original setup and onto an AWS Trainium chip, increasing inference output by 8 times while cutting cost by 2 times. AI world models extend well beyond entertainment into self-driving (Tesla), humanoid robotics (1x and Figure AI), and industrial/manufacturing. These AI models are becoming core infrastructure, and the companies that optimize the hardware-to-model fit will capture a disproportionate share of the value.
Robotics scaling now looks exponential rather than linear. Once a humanoid robot learns a task, that skill can be copied across an entire fleet, and one Figure AI robot has already run a single task for three days straight without stopping. The next unlock is robots building other robots, which turns factory output into a compounding curve while a fleet-wide data flywheel, much like Tesla's cars, makes every unit more capable over time. The near-term applications are concrete, spanning factory logistics, household tasks, and elder care, where a home robot could let aging parents stay in their own homes one to three years longer at a lower expense vs a human at-home care nurse. The investment framing is that robotics is 12 to 18 months away from a 6 to 9 month window in which it has its ChatGPT moment, after which valuations could re-rate sharply, with a 5 to 10 times move possible before the shift becomes obvious. We would position at the beginning of that window, with particular attention to humanoid platforms, their proprietary world models, and the AI infrastructure layer that all of this runs on. Note that this is a high-conviction, long-duration thesis, and the timeline carries real execution and capital risk that argues for sizing exposure accordingly.
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