Travis Kalanick has emerged from eight years of stealth with Atoms. Atoms is a physical AI company built on City Storage Systems, the parent of CloudKitchens, and the mandate is gainfully employed robots across food, mining, and transport. The founder profile matters here: Kalanick took Uber from nothing to roughly $70 billion before being pushed out, and he brings multi-billion-dollar personal capital and deep Silicon Valley access to a category where funding, not engineering ambition, is often the binding constraint. The supporting data points are accumulating. Figure AI has crossed the threshold where working robots outnumber human employees across the company, and SoftBank is in talks to take a majority stake in 1X Technologies at a reported $6 billion valuation. We would frame the near-term opportunity around specialized robots rather than humanoids, and we would note that the first autonomous robot most people actually interact with will be a car, not a humanoid. Retail appetite has not arrived for robotic pre-IPO stocks.
Moonshot AI's move toward a Hong Kong listing will give public investors their first clean read on open-weight model economics. The company behind the Kimi open-source AI models has reportedly filed confidentially and is targeting roughly $50 billion IPO, up from a reported $35 billion at its last raise, on more than $300 million of annual recurring revenue driven primarily by API sales. The competitive position is well understood: Kimi models track three to six months behind the US frontier labs at materially lower cost. The revenue model is the open question. Open-weight providers do not collect a couple hundred dollars a month per seat, individuals run the models for free, and commercial users typically serve them through third-party inference providers such as Baseten or Together AI rather than paying the lab directly. The venue choice is the second question. A Nasdaq listing would have opened the door to Nasdaq 100 inclusion and the passive flows that follow, and $50 billion looks modest against current US frontier lab marks, so the Hong Kong decision likely caps the multiple this business can earn. We would still expect the stock to trade well, because there is almost no pure-play frontier model exposure available in public markets today.
Thinking Machines Lab is reportedly raising at a $40 billion pre-money valuation, below the $50 billion plus it sought late last year, on revenue north of $100 million annualized. That is roughly 400 times revenue for a company founded in early 2025 that has already raised $2 billion at a $12 billion valuation in the largest seed round on record. On a risk-adjusted basis we do not like the setup and would not add exposure at this price. The more useful way to read the number is backward. A founder who needs something close to $20 billion of capital to build a frontier lab properly, and who will not accept single-digit residual ownership, has to price the round near $40 billion for the percentages to work. That is venture math rather than a market-clearing valuation, and it explains a growing share of AI mega-rounds. The related dynamic worth tracking is talent flow, because the wealth created inside the frontier labs has reached a level where senior engineers and executives carrying nine-figure balances have no economic reason to take direction from anyone, so departures should be read as founder formation rather than as a distress signal about the companies they leave. The genuinely interesting part of the story is that Thinking Machines is a US-based open-weight provider, a category now filling in around Inkling, Reflection AI, and Nvidia's Nemotron, with Nvidia itself rumored to be evaluating an investment.
Glean's vision of an always-on work companion is the clearest articulation we have seen of where enterprise AI value actually accrues. The product thesis is a system that knows your role, your employer, your quarterly objectives, your career ambitions, your weekly tasks, and the people you meet, and that sits inside the messages you send, the documents you write, and the systems you work in, acting proactively rather than on request. OpenAI's newly released GPT-6 Astra points in the same direction, and the six-month timeline now being discussed publicly for ambient AI assistance looks credible. The strategic question for investors is where the margin sits. OpenAI, Anthropic, and Google will all win at the model layer, but context is the scarce asset, and the company that assembles a complete picture of an individual's work life may capture more value per dollar of compute than the labs themselves. The obvious objection is data privacy, and we think it collapses faster than consensus expects. Within twelve months we expect broad opt-in to persistent audio, visual, and screen capture across the major AI platforms, because the productivity benefit will be large enough that users volunteer the access. Glean sits at exactly that layer, and we would weight it accordingly.