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I almost don’t want to call Meshchera a match-three game because I fear that kind of undersells how captivating it is. But, it is a game you play within a six by six grid, in which you have to group matching tiles in clusters of three or more so they may merge and become other, higher value tiles, so that’s the description we’re working with. The atmosphere is off the charts, though, which isn’t something I’m used to finding in these types of games. It has gorgeously detailed artwork and background music that you can get completely lost in.

In Meshchera, you can choose to go for the high score or pick from several challenges that will dictate how you approach the round, like “kill five monsters” or “keep 10 monsters for 10 turns.” The gameboard is a dark marsh that will slowly become overrun with vegetation and creatures, unless you can stay ahead of creep by skillfully matching tiles to condense them into other things. Grasses become flowers, which become trees, campfires, houses, churches, etc. It is a uniquely complex matching game — you’re given next to no information about how the items work or how different elements on the board behave and interact, so you have to figure it out along the way and course-correct as you learn.

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I’ve spent quite a bit of time playing Meshchera over the last week, but certain things still elude me. Take the “create and destroy a Monster” challenge. I have absolutely no idea how to create a monster, and that’s not for lack of trying. But, this gives me something to keep working toward even as my high scores nudge higher and higher. The game includes 10 challenges right now, and the developer says more are coming soon. Meshchera is really good, and feels like the kind of game you can revisit ad infinitum. It’s already found itself a home in my folder of “go-to” Playdate games.

Meshchera isn’t available in the Playdate Catalog (yet?), but don’t let that stop you from trying it out. It’s on itch.io at the moment, and sideloading games onto the Playdate is incredibly easy. Once you have the game file, you can just drag and drop it right into your library by signing into your Playdate account and going to the Sideload tab. This can also be done via USB. Panic has a detailed explanation of all the options, if you need some guidance.

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Twelve AI labs have a combined valuation larger than Ford and GM. None of them sell anything. I call them the Virgin Unicorns — valued above a billion dollars, but innocent of product or revenue.

OpenAI proved that an AI research lab with the right product could become one of the most valuable companies on earth. A dozen other AI labs are trying to repeat the trick. They have raised more than $29 billion at a combined valuation approaching $130 billion, without shipping anything a customer can buy.

Two questions are worth asking:

• Why are sophisticated investors writing growth-stage checks to pre-companies?
• What does history say about how this story ends?

Top Virgin Unicorns

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To answer these questions, let’s identify four patterns across this cohort of companies.

Pattern 1: The pedigree premium. Every founder is a recognized leader in their field, and most come from a small set of institutions. Roughly four-fifths hold PhDs, mostly in computer science from a handful of universities — Berkeley, Stanford, MIT, Toronto, Alberta, Cambridge, UCL — and most of the rest left PhDs at one of those programs to start their companies.

On the employer side, the concentration is tighter still. Four of the twelve companies are anchored by DeepMind alumni (Ineffable, Reflection, Ricursive, Recursive Superintelligence). Two are anchored by OpenAI alumni (Thinking Machines, Safe Superintelligence). AMI Labs traces back to Meta’s FAIR group, and Humans& draws its founders from across Anthropic, xAI, and Google. Stanford and Berkeley faculty appointments account for most of the rest (World Labs, Physical Intelligence, and Noah Goodman of Humans&).

Four institutions — DeepMind, OpenAI, Berkeley, and Stanford — have produced the founders of nearly every Virgin Unicorn in the table. Investors are pricing CVs, not products.

Pattern 2: Nvidia as kingmaker. Nine of the twelve companies in the table have Nvidia as an investor. The supplier of the picks and shovels is also an equity holder in the prospectors. Nvidia gets early visibility into the most ambitious AI bets, locks in compute commitments, and earns multiples on capital deployed at near-zero marginal cost. Selling the shovels was already a good business. Owning the mines too is unprecedented.

Pattern 3: The cap tables are unusually wide. Each round in the table includes a syndicate of ten to twenty investors — venture firms, corporate strategics, sovereign wealth funds, and individuals. Sequoia and a16z still lead. But the rounds are large enough that they require balance-sheet capital — from JPMorgan, BlackRock, Alphabet, the UK Sovereign AI Fund, Samsung, Temasek, ADIA, and Bezos personally — to fill out. That makes these rounds structurally different from classical venture financings.

Pattern 4: A post-LLM thesis. Every company is arguing, in some form, that the current paradigm isn’t enough — that scaling LLMs won’t reach AGI, and that something else (world models, reinforcement learning, agentic systems, AI scientists, novel chips, formal mathematical reasoning) is required. The thesis is the product. The product is a promise.

Others have dissected these unicorns:

• Howard Marks, in his December 2025 Oaktree memo Is It a Bubble?, described investor behavior as “lottery-ticket thinking” — investors backing startups with no product on the dream of an enormous payoff despite an overwhelming probability of failing.
• Derek Thompson, writing in October, framed the same dynamic by reporting that a Thinking Machines pitch meeting was described by one investor as “the most absurd pitch meeting” because Mira Murati “couldn’t answer any questions” about what she was building.
• GeekWire’s own year-end survey of regional venture investors found the same skepticism closer to home: the bubble, they said, is most pronounced at the early stages, where AI storytelling can substitute for real traction.

The lottery-ticket framing is now conventional wisdom. But will this lottery pay out? One way to handicap the odds is to look to the past.

What history teaches us

The closest historical parallel is not the dot-com era. Webvan, Pets.com, and Boo.com failed not because they were pre-product, but because they had products and bad business models. Those companies burned capital on infrastructure and marketing, not on research.

The closer cautionary tales are the celebrity-founder pre-product flops of the last fifteen years.

• Magic Leap raised $3.5 billion over nine years on the strength of Rony Abovitz’s prior exit and shipped a flop.
• Quibi raised $1.75 billion on Katzenberg and Whitman’s pedigree and lasted six months.
• Inflection AI raised $1.5 billion on Mustafa Suleyman and Reid Hoffman and was effectively absorbed into Microsoft in 2024 — its team hired, its technology licensed, its company hollowed into a shell.

In each case, founder credentials raised the money. The product never justified the valuation.

The structurally closest analogy, though, is biotech. Roughly 80% of 2021 biotech IPOs were pre-revenue. The probability that a pre-clinical drug reaches commercialization is under 10%. Development takes a decade and costs $1 billion. Yet a Bentley University study of 319 biotech IPOs from 1997 to 2016 found that the cohort produced over $100 billion in net shareholder value despite a failure rate above 50%. The winners were large enough to carry the portfolio. And many of the most successful biotechs were acquired before reaching profitability.

The Virgin Unicorns are biotech-shaped businesses. Pre-revenue, science-driven, decade-long timelines, binary outcomes, acquisition as the usual exit. But they aren’t financed like biotechs. Biotech investors release capital in milestone tranches tied to specific scientific results, and they expect most candidates to fail. Virgin Unicorn investors release capital in one large round on the strength of a CV, and price for success. Same shape of business, opposite financing logic. That mismatch is where the disappointment will come from.

Why Sequoia invests anyway

The OpenAI story counters the biotech analogy. From its 2015 founding to the ChatGPT launch in late 2022, OpenAI looked exactly like a Virgin Unicorn — pre-consumer-product for seven years, billions in capital, and only research to show for it. Then ChatGPT shipped and revenue went from zero to over $10 billion in three years. No biotech has ever scaled like that.

Sequoia and other investors writing checks to today’s Virgin Unicorns aren’t pricing for biotech outcomes. They’re pricing for the second coming of OpenAI.

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The table above makes the size of that bet legible. Early-stage venture investors aim for a 10x return. Most of these twelve will return zero, so the one winner has to carry the other eleven by itself. At a $127 billion aggregate marked-up value, that means the winner alone has to produce something like $1.3 trillion in value.

That is not a forecast — it is the bet the VCs have already placed. Sequoia and a16z made exactly this kind of bet on OpenAI and Anthropic, and the on-paper returns have already vindicated it many times over. Anthropic itself looked like a Virgin Unicorn in 2022 — and then it shipped Claude and built revenue.

The historical record suggests some skepticism. But bubbles have a way of producing the occasional Amazon or Google amid the wreckage. Identifying which Virgin Unicorn will become a trillion-dollar company — a “kilocorn,” a thousand unicorns in one — is tough. Which one would you bet on?

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