Pre-IPO Stock Secondary Market Update | as of Oct 13, 2026 | Lambda's $18.5B Raise, Reflection AI's Open-Source Model, micro1 Buys Enterprise Data, and Ghost's Local AI Computer Watch full video = piped.video/watch?v=Lae3x6uC… 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.

Pre-IPO Stocks Secondary Market Valuations | as of Oct 12, 2026 | Download full report = agdillon.com/reports

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Pre-IPO Stock Secondary Market Update | as of Oct 7, 2026 | DeepSeek’s China Discount, Hark AI Personal Assistant Launches, Anduril Goes to Sea, and the Inference Chip Race (Etched, Fractile, Positron) Watch full video = piped.video/watch?v=BPp4ei4l… DeepSeek’s reported push to raise at least $12 billion at a valuation of about $74 billion ahead of a listing on Shanghai’s STAR Market will be the first real test of how the China discount applies to a frontier AI lab. That discount is structural. Alibaba and Baidu serve hundreds of millions of customers on large and growing revenue bases, yet trade at valuation multiples well below their US counterparts, and we see little reason a mainland listing of an AI lab will escape the same overhang. A STAR Market debut should find strong domestic demand, but US investors will likely be limited to indirect access through China-focused ETFs, and we do not expect the hundred-billion-plus outcomes we anticipate from US AI leaders. The more important story is the model itself. DeepSeek’s open-source models are competitive and far cheaper than closed alternatives, and they can run locally or on US inference platforms such as Together AI and Baseten with no data flowing back to China. One recent estimate attributes roughly 7% of all debt created over the past seven years indirectly to OpenAI and Anthropic, so the real pressure point for the frontier labs is not a bubble in AI demand but a shift of that demand toward open models. That shift would not strand the infrastructure: if Anthropic’s reported $518 billion in compute commitments went underused, providers like Lambda would rationally release the capacity and resell it to other buyers rather than litigate for years. Our house view is that if AI demand holds, compute finds a buyer whether workloads run on closed or open models, which makes the neoclouds a natural hedge across both outcomes. Brett Adcock, CEO of Figure AI and founder of publicly traded eVTOL maker Archer, has launched Hark Pro into a personal-agent market forming quickly around Meta’s Muse, OpenAI’s Dots, and Instinct. The launch demo shows where the category is heading: a single request to plan a date night produced concert tickets matched to both partners’ Spotify playlists, a reserved parking spot, and a calendar entry timed to the drive. Tasks that once took an hour of planning collapse into one sentence, and as these agents become proactive they will increasingly suggest the plan before the user asks. Adcock’s record of multiple exits makes Hark worth taking seriously, and Meta’s context across WhatsApp and Instagram gives it a strong position as well. Our house view, however, is that the long-term consumer winners will be Apple and Google, which already hold users’ email, messages, photos, payments, and app usage across iOS and Android and can deliver an exceptional AI personal agent the moment they choose to switch it on. For that reason we are cautious on private B2C agent startups, which may carve out niches but lack the data advantage, while B2B agents remain an open field. In the enterprise, agents are likely to follow the data, favoring incumbents like Microsoft Copilot and Google Workspace, where firms such as RIAs can deploy an agent without moving client information to a new vendor. We expect widespread personal-agent adoption within 6 to 12 months. Anduril’s Arsenal-2, a $3.7 billion shipyard at Sparrows Point, Maryland, backed by a Navy contract worth up to $2.9 billion, takes the company from existing undersea work into a full-scale maritime push and puts it in direct competition with Saronic and Huntington Ingalls. The facility will build Virginia-class submarine components and undersea systems, and it reinforces how quickly Anduril converts new products into production capacity, even as talk of a defense bubble grows around richly valued peers such as Shield AI. Palmer Luckey, alongside Elon Musk, operates on a different level in real-world manufacturing, and it is hard to bet against him winning more mandates of this kind. The key structural question is concentration. With the US military as essentially its only customer, Anduril’s revenue is likely to follow an S-curve, accelerating sharply before eventually plateauing, which raises questions about the revenue multiple it can sustain absent a move into commercial markets. That need not be a problem, since a public listing followed by a healthy dividend once growth matures would be a successful outcome for early investors in what becomes a value stock. We also expect a meaningful founder premium at IPO, with demand that could resemble SpaceX’s but potentially without the same post-listing comedown, since private-market access to Anduril has been far tighter than it was for SpaceX. Etched is reportedly fielding offers at $40 billion or more, roughly double the $21 billion valuation it set in September, despite not yet having a chip in market. The company has taped out its chip, reportedly holds about $1 billion in customer orders, and counts a strong investor roster, but paying that price for a business without revenue looks rich to us. We prefer Positron, last priced at $5 billion, which already has systems deployed in data centers and is generating revenue before its next-generation chip, combining memory and inference, has even shipped. The design differences matter for deployment: Etched’s chip is liquid-cooled, while Positron’s is air-cooled and built to drop into virtually any data center. The closest parallel is Groq, which later drew a deal from Nvidia, and which did not offer the most groundbreaking technology but delivered fast, US-fabricated, high-volume inference with clear customer value. Positron fits that same profile of solid technology meeting overwhelming demand. More broadly, we view AI inference semiconductors as a sector to watch, with real money to be made among the companies racing Nvidia.

Pre-IPO Stocks Secondary Market Valuations | as of Oct 5, 2026 | Download full report = agdillon.com/reports

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Pre-IPO Stock Secondary Market Update | as of Sep 29, 2026 | Higgsfield at $1B revenue, the Inference Platform Repricing (Together AI, Baseten, Fireworks AI, Modal, Fal), and AMD Moves Toward Robots Watch the full video here = piped.video/watch?v=l_YuBjv9… Higgsfield's climb to a $1 billion annualized revenue run rate roughly 18 months after launch ranks among the fastest ramps in private market history, and the August Series B at a $5.4 billion valuation now looks measured against that figure. The product sits on top of third-party models from ByteDance, OpenAI, and Anthropic, which invites the "AI wrapper" critique: if OpenAI launched a video platform tomorrow, would Higgsfield survive? We believe it can, because the complexity of the interface and the pace of product innovation are hard to replicate, and because revenue is shifting from individual creators toward business customers. B2B moats look structurally stronger than B2C ones. Forward deployed engineers and embedded workflows mean a rival must be better by multiples, not percentages, before a customer rips a vendor out, while consumers can cancel a subscription and switch in a day. Higgsfield is roughly split between the two today, so its upmarket move is the key to building durable lock-in. The longer-term vision of on-demand, personalized Hollywood-quality film, with a family member cast as the lead, shows how large the creative market could become. Inference platforms, the companies that host and run open-source models such as those from ByteDance, Moonshot AI, DeepSeek, and Nvidia, are repricing at speed. Fireworks AI is reportedly heading toward $30 billion, Baseten toward $26 billion, Fal toward the high teens, and Modal near $15.75 billion. Against that backdrop, Together AI's July Series C at $8.3 billion, on more than $1.15 billion in annual bookings, stands out as relative value in the group, even though it owns its GPUs and runs a different business model. The strategic logic is a hedge. Roughly 7% of US debt, including hidden obligations, is reportedly tied to OpenAI and Anthropic, so the frontier labs must succeed, yet the scale of open-source disruption remains unknown. Inference platforms benefit either way: if the labs keep accelerating they need more compute and book more contracts, and if growth slows the compute is still consumed by open models. Our house view is that 90% of compute will be inference rather than training, and that 90% of inference will run on open models. Closed labs can still grow revenue 50% to 75% a year while ceding share, since a smaller slice of a much bigger pie is acceptable to investors. The cloud computing analogy applies: few businesses want to run servers themselves, and most will pay for an API. We see a 3x return over five years as the higher probability outcome from current prices, with several names capable of reaching $100 billion or more. Island's $400 million raise in September at a $6.4 billion valuation shows that cybersecurity is increasingly an AI infrastructure story. The company has doubled ARR every year since its 2022 launch and consolidates security, identity, data protection, and application access in a single enterprise browser, which positions it to govern AI agents. Demand backdrop is strong: Palo Alto Networks, arguably the largest cybersecurity platform, is up 112% year to date, and agents online now reportedly outnumber humans. The media narrative around AI hacking points directly to better security as the answer, yet investors have been slow to connect those dots. Exit planning matters, because many cyber vendors are strong in a niche with a limited TAM (total addressable market) and are ultimately acquired, as Wiz was by Google for roughly $32 billion. The question for Island is whether Palo Alto buys it or builds the capability itself. We like the space on a risk-adjusted basis, but stock selection will drive returns. AMD's roughly $8.2 billion all-stock acquisition of Fei-Fei Li's World Labs, which builds simulated 3D worlds used to train robots, signals that chipmakers intend to move closer to the model layer as physical AI comes into view. The deal may position AMD as the preferred chipmaker for robotics, tuning silicon to run world models efficiently. Decart, a similar business, reportedly drew a bidding war from SpaceX, Anthropic, Nvidia, and Google, so demand for the category is clear. Both companies chose a buyout over building independent frontier labs, suggesting world models are not yet seen as the next trillion dollar standalone opportunity. The broader trend is full-stack specialization, with chips, models, and software tuned for a specific outcome such as a robot, which can lower power use and shrink model size without sacrificing quality. We expect robots in homes within a couple of years, and note that investors may find AI infrastructure suppliers to robotics a more attractive entry point than the humanoid makers themselves.

Pre-IPO Stock Secondary Market Valuations | as of Sep 28, 2026 | Download full report = agdillon.com/reports

Pre-IPO Stock Secondary Market Performance | as of Sep 28, 2026 | Download full report = agdillon.com/reports

Pre-IPO Stock Secondary Market Update | as of Sep 22, 2026 Watch full video = agdillon.com/reports ** Harvey, Glean, Together AI, Baseten, Positron pre-IPO funds accepting investors, closing Oct 2 ... email aaron.dillon@agdillon.com to receive fact sheets (see disclosure at end of email) ** Unconventional AI is attempting something few infrastructure companies dare to try: replacing the compute architecture that has powered computers for the past 60 to 70 years. Founder Naveen Rao, whose MosaicML now accounts for roughly a quarter of Databricks revenue following its acquisition, raised a reported $475 million seed at a $4.5 billion valuation to build brain-inspired, dynamical-system chips designed alongside biologists. The thesis is simple. Energy is the binding constraint on AI, and every token and API call ultimately prices back to power. The human brain runs on roughly 20 watts, and the company's stated target is a 1,000x reduction in inference power. Its first model, Un-0, already matches state-of-the-art image diffusion while running on a software simulation of the chip. Demand is not the problem, with Google alone processing more than 3.2 quadrillion tokens per month and premium AI subscription tiers still selling out. We view energy efficiency as the defining infrastructure trade of the next decade, and the milestone that matters here is working silicon, not simulation. TypeSafe AI attacks the same cost curve from the model side. Founder Diogo Almeida, a former OpenAI researcher behind the InstructGPT and RLHF (reinforcement learning from human feedback) work that became ChatGPT, emerged from two years in stealth with a $40 million DCVC-led seed. Its model, Jev, outputs structured decisions rather than English text, letting machines communicate in their own language instead of translating everything into prose. Pricing is $0.042 per million input tokens with 70 to 500 millisecond response times, and within days Vercel called it the fastest-adopted model in the history of its AI Gateway. The dynamic mirrors China, where GPU constraints pushed labs toward software efficiency and ultimately into open source leadership. Power and token scarcity will now force the same innovation in the US. Both TypeSafe and Unconventional remain early, and our preference is to underwrite companies once product, revenue, and a visible addressable market are in hand. Still, the arrival of two architectures this different is the clearest signal yet that AI remains in its first inning. ElevenLabs Reception marks the voice leader's move from infrastructure into outcomes. The product is a 24/7 AI receptionist that answers calls, books appointments, and speaks more than 70 languages for roughly $29 per month. The economics are striking. One business owner pays $80,000 per year for a single offshore employee whose only job is to call new leads within five minutes, day or night, because speed to lead is his single biggest driver of new revenue. The broader point is that AI applications are converging on outcomes rather than categories. A CEO buying a booked meeting does not care whether it came from a voice company like ElevenLabs, a customer service platform like Sierra, or an enterprise search company like Glean. The winners will be those with the deepest access to company data, workflows, and context, and we expect six to nine months of learning before these agents perform at full capacity. Sales and marketing may prove to be the biggest near-term beneficiary of the AI cycle. Shield AI is reportedly in talks to raise at a valuation of at least $20 billion, up from $12.7 billion in March. The company is guiding to 80% revenue growth and more than $540 million in 2026 revenue, up from $276 million in 2024. Hivemind, its autonomy software embedded across US military aircraft and drones, anchors the software narrative. V-BAT and the upcoming X-BAT, however, make this a hardware business priced at roughly 37x 2026 revenue, a multiple we find difficult to justify outside of a sector leader like Anduril, now at roughly $120 billion implied valuation. Defense budgets are finite, incumbent primes are entrenched, and no startup is likely to capture Google-like market share. The path to sustained growth likely runs through a Palantir-style expansion from government into commercial markets such as stadium and office security. Absent that, we frame defense tech as a durable value franchise with margin expansion and eventual dividend potential rather than a growth story. Castelion illustrates the entry-point discipline required, since a 3x return from a $13 billion valuation implies a $39 billion missile company within five years. * 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.

Pre-IPO Stock Secondary Market Valuations | as of Sep 21, 2026 | Download full report = agdillon.com/reports | Harvey, Glean, Together AI, Baseten, Positron pre-IPO funds accepting investors, closing Oct 2 ... email aaron.dillon@agdillon.com to receive fact sheets* * 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.

Pre-IPO Stock Secondary Market Performance | as of Sep 21, 2026 | Download full report = agdillon.com/reports | Harvey, Glean, Together AI, Baseten, Positron pre-IPO funds accepting investors, closing Oct 2 ... email aaron.dillon@agdillon.com to receive fact sheets* * 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.

Pre-IPO Stock Secondary Market Update | as of Sep 15, 2026 | OpenAI vs Apple Hardware, Positron’s AI Chips & MetaMuse Watch the full video = piped.video/watch?v=kCTnX-xu… The AI inference chip market just produced two very different price tags for the same thesis, and the spread is the most instructive data point in private semis right now. Positron AI raised $875 million at a $5 billion post-money valuation on September 10, roughly five times its approximately $1 billion February mark, and it did so with product in the field: more than 50 first-generation Atlas racks deployed at Oracle Cloud Infrastructure, production customers including Parasail, Jump Trading and i3D, a next-generation Asimov chip taping out on TSMC N3P by year-end, and roughly $100 million of revenue forecast for this year. Etched raised $700 million at a $21 billion valuation in August, doubling its $10.3 billion July Series C in under a month, with Jane Street acting as both lead investor and first paying customer and more than $1 billion in contracts booked, but with no chip yet shipping commercially. Context matters here: Nvidia recently paid a reported $20 billion to license Groq's inference technology and bring founder Jonathan Ross in-house, and the Groq founding team owned less than 10% of the business by the time that outcome landed. That dilution math is the most plausible explanation for Etched's premium, since capital-hungry hardware businesses need enormous funding and founders price early rounds to protect ownership through the build. Our house view is that revenue-generating inference silicon at $5 billion is the more disciplined entry point than pre-scale silicon at $21 billion, and on a 3x-in-five-years framework Positron is well positioned to reach a $15 billion valuation as the Titan system ramps through 2027. We would still encourage diversification across several inference-focused semiconductor names rather than a single position, and we would watch Etched's first production deliveries closely before revisiting the multiple. Meta's September 8 launch of Muse is the first personal AI agent shipped by a company large enough to make adoption a default rather than an experiment, and the market responded accordingly, with the stock up roughly 9% since the announcement and 18% on the month. The product carries a free tier alongside $20 and $100 per month plans, and Zuckerberg has been public about using it well beyond inbox triage, feeding home gym camera footage into the agent for training feedback and using it to hold dietary restrictions and family logistics in working memory when planning travel. The strategic insight is that Meta does not need the frontier model, it needs the frontier application, and its historical strength has been taking technology built elsewhere and getting billions of people to use it. Expect the rollout to proceed through narrow, obvious widgets, a grocery and meal planning widget, a scheduling widget, a kids' activity coordination widget, with the agent eventually proposing them proactively rather than waiting to be configured. The monetization path is the part investors should focus on: Zuckerberg has signaled the product can go free because payments flow through the agent and Meta takes a merchant cut, a Klarna-style take rate rather than a subscription business. The practical constraint on adoption is data governance, and in regulated industries the segregated, air-gapped deployment model remains the right posture for now. For everyone else, the moment ambient agents handle grocery reordering and pantry inventory is the moment this category stops being early adopter technology. The consumer hardware layer is consolidating faster than most models assume, and the acquisition and feature news of the past two weeks makes the direction unmistakable. OpenAI acquired Glass Imaging in a deal reported above $300 million, bringing in the ex-Apple team behind Portrait Mode and computational imaging technology that slots directly into an AI-first consumer device that is always listening, always seeing, and always resident. Apple answered with Audio Intelligence on the Apple Watch Series 12 and Ultra 4, including Siri Recap and Live Rewind, which keep an always-on microphone and retain conversation for seven days with an option to save. That is a genuine posture change from the company that built its brand on privacy, and once the watch establishes the precedent, the same terms of service will follow onto the phone with a single tap of consent. Meta is pushing the same frontier from a different angle, donating Ray-Ban Meta glasses to blind veterans, with users asking the assistant which shirt in the closet is blue and getting a useful answer, which is exactly the kind of demonstration that converts skeptics. The next six to twelve months should bring competing assistants from Apple under new leadership, Google, and OpenAI, with additional entrants from the robotics and devices community. The second-order consequence is the one that matters most for portfolios: as ordinary users experience daily utility, public resistance to data center and power infrastructure buildout weakens materially, and the demand curve for compute and electricity steepens from there.

Pre-IPO Stock Secondary Market Valuations | as of Sep 14, 2026 | Download full report = agdillon.com/reports

Pre-IPO Stock Secondary Market Performance | as of Sep 14, 2026 | Download full report = agdillon.com/reports

Pre-IPO Stock Secondary Market Update | Sep 10, 2026 | Uber’s Founder Is Back + Thinking Machines Valuation, Moonshot IPO, and Glean's Efficiency Dream Watch full video = piped.video/5x35BwUM8as 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.

Pre-IPO Stock Secondary Market Valuations | as of Sep 7, 2026 | Full report = agdillon.com/reports

Pre-IPO Stock Secondary Market Performance | as of Sep 7, 2026 | Full report = agdillon.com/reports

Pre-IPO Stock Secondary Market Update | as of Sep 4, 2026 | Anthropic's Pre-IPO Spending Spree, Outcome Based Pricing, Instinct Hits $2.5B, Plaud's AI Earbuds Watch full video = piped.video/watch?v=H7SNjfMP… Anthropic is accelerating into the turn weeks before its expected IPO, and the spending tells the story better than any prospectus language will. The company has reportedly agreed to a $45 billion, six-year compute deal with UK neocloud Nscale, covering roughly 460 MW at a West Virginia data center running Nvidia Vera Rubin chips. It is reportedly in late-stage talks to acquire Israeli inference-optimization startup Decart at roughly $7 billion, a business whose entire value is squeezing more throughput out of silicon already installed, and in early discussions with UK chip startup Fractile for inference silicon. The context matters, because Anthropic had the better model and the better product through much of last year, then ran short of compute and ceded share to an OpenAI that had been ridiculed for overbuilding. That lesson is now priced into behavior, since running dry in your first year as a public company is a stock price event rather than an engineering inconvenience. The reported $30 trillion total addressable market framing is aggressive on any conventional basis, though it is less absurd once you accept that AI agents already generate more internet traffic than humans and that robotics adds a second labor pool behind that. Anthropic trades at $1.4 to $1.5 trillion in the secondary market today, and we would not be surprised to see the IPO open above $2.2 trillion, which would clear SpaceX at roughly $1.7 trillion and make it the largest listing on record. Our view is that over-building is now the correct error to make, and we expect Anthropic to keep locking up chips, power, and capacity on every front available to it. Salesforce and OpenAI are both moving away from per-seat SaaS toward outcome-based pricing, where the customer pays only when the software delivers a defined result, such as a qualified meeting booked, rather than paying for seats, tokens, or API calls. This is total alignment between vendor and customer, and it eliminates the single biggest objection we hear from buyers who suspect they are funding an experiment. It also transfers risk onto the vendor, which is itself a signal, because an application company only offers this structure when it is confident its models can stand on their own. We would expect realized pricing to rise rather than fall under this model, since buyers will pay a premium to protect their downside, and that premium is rational on both sides of the table. The second-order effect is a capital cycle: vendors earning on outcomes have a direct incentive to invest more in model quality, which lifts outcomes, which lifts revenue. We think this is the adoption accelerator that finally pulls fence-sitters into AI, and we expect the per-seat model to look dated within a few product cycles. Instinct AI re-rated roughly 5x in a matter of weeks, and the mechanism behind that move deserves more attention than the number itself. The personal AI agent, still in private beta, reportedly raised a $250 million Series B co-led by Index Ventures and Benchmark at a $2.5 billion valuation, up from roughly $500 million in early August. The product reads your calendar, your Notion, and your CRM, then executes, so a request to plan a trip to Paris comes back as sourced flights, a booked itinerary, and a checkout screen. None of this is technically novel, since hobbyist agent setups have done versions of it for a year, but Instinct packages it as an easy button, and packaging is what creates a category. The constraint is not capability, it is consent, because the agent only works if you hand over everything, and early users report it sending emails they did not want sent. We would frame that the way you would frame a new human assistant, which makes mistakes early, accumulates context over time, and becomes indispensable once it knows enough of your history. The closer analogue is social media in 2005, when putting your life online looked reckless and now the absence of a profile reads as suspicious. Our view is that personal AI agents follow the same adoption curve, and that the privacy objection erodes considerably faster than the market currently assumes. Plaud One is a $249 pair of AI earbuds with a 4G-connected recording case, and it reportedly sold out its US pre-sale in a day. Treat it as the same trade as Instinct rather than as a separate consumer hardware story, because an always-on agent is only as valuable as the data it can capture. Capture at a desk is a solved problem, since laptops, phones, and home cameras handle it, but the world outside the office is where the gap sits, and the conference room at a client site is precisely the data an agent cannot currently see. That gap explains why the hardware wave is arriving all at once, with Plaud on earbuds alongside smart glasses, watches, lapel pendants, a forthcoming OpenAI device, and Hark from Figure AI founder Brett Adcock. We expect form factor to fragment along personal preference rather than consolidate around a single winner, which argues for exposure to the data and agent layers rather than to any one device maker. The enterprise side is already normalizing the behavior, with large employers recording employee communications and training models on them, which makes the consumer step feel incremental rather than invasive. The takeaway is that durable value in this wave accrues to whoever owns continuous context on the user, not to whoever sells the microphone.

Pre-IPO Stock Secondary Market Valuations | as of Aug 31, 2026 | Download full report = agdillon.com/reports