Startup Spotlight

Index Ventures just raised another $2 billion across three funds: $400 million for seed, $900 million for venture, and $700 million for growth.

Simile raised $200 million at a $2 billion valuation, just five months after its $100 million Series A.

AI capital isn’t slowing down.

But I think we’re asking the wrong question.

Everyone wants to know: Which AI company wins?

I’d ask: Which companies control the bottlenecks?

The Mental Model: Second-Order Thinking

First-order thinking says:

AI is getting more capital.

Second-order thinking says:

Capital attracts talent. Talent consumes compute. Compute creates infrastructure dependencies. Infrastructure creates bargaining power.

Eventually, the model itself may become the least differentiated part of the stack.

That’s the part I find most interesting as an operator.

At /mkt, we’re building in a regulated market around athlete tokenization. That means the constraints aren’t something you solve after the product works.

Securities regulations matter.

Offering structure matters.

Trading infrastructure matters.

Disclosure and compliance matter.

The constraint shapes the product.

AI is developing its own version of this.

Compute. Distribution. Data. Energy. Regulation. Capital.

The companies that control those constraints have leverage.

Here’s my contrarian take:

Stop looking for moats in features. Start looking for moats in bottlenecks.

A feature can be copied.

A dependency that’s expensive, regulated, scarce, or deeply integrated is much harder to copy.

That’s why I care less about who raised the biggest round.

I’d rather know what that round bought them access to.

Because in the next phase of startups, the winner may not have the best technology.

They may simply control the thing everyone else needs.

Disclosure and disclaimer: Spencer Gareiss is Chief Product Officer at /mkt, referenced above as an example of building in regulated markets. This newsletter is for information and education only. It is not investment advice, and it is not an offer or solicitation to buy or sell any security. Gatik is a private company; its valuation was not disclosed, and its revenue, delivery, on-time, and fleet figures are company-reported and have not been independently verified. Forward-looking targets, including fleet-growth plans, are the company’s statements, not facts. Market-size, driver-shortage, and consolidation figures are third-party estimates from named sources (Future Market Insights, the American Trucking Associations, and others) and will vary by methodology. Funding and operating details reflect reporting as of August 28, 2026, drawn from the company’s announcement and outlets including TechCrunch, Bloomberg, Reuters, Forbes, SiliconANGLE, and FreightWaves.

If you want the mental models behind breakdowns like this, my book, Mental Models: How to Think, Act, and Win, is on Amazon now.

This post is for informational and educational purposes only. It is not investment advice, a recommendation, or a solicitation to buy or sell any security. Funding figures, revenue, and valuation are as reported by the company, regulatory filings, and named outlets; the ~$470M valuation is a reported, time-sensitive snapshot and not independently verified. Dhoni's individual investment amount was not disclosed. The /mkt reference is a structural illustration of building in regulated markets and is not an offer or solicitation. Past performance and third-party investment decisions do not indicate future results.

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AI Just Raised $5 Billion. The More Interesting Story Is Who Controls the Compute.

Spencer Gareiss — Tue, 01 Sep 2026 16:04:48 GMT

Nvidia is putting $5 billion into Safe Superintelligence, the AI lab founded by former OpenAI chief scientist Ilya Sutskever.

That number gets the headline.

The structure gets my attention.

SSI previously raised $1 billion at a $5 billion valuation in 2024, then another $2 billion at a $32 billion valuation in February 2025. Now Nvidia is reportedly committing another $5 billion, alongside access to its next-generation Vera Rubin hardware. The companies said the partnership should increase SSI’s compute capacity by an order of magnitude.

That’s not just capital.

It’s capital plus scarce infrastructure.

And the same pattern is showing up across venture.

Index Ventures raised $2 billion across three funds on July 31: $400 million for seed, $900 million for venture, and another $700 million for its existing growth fund. That brings Index’s available capital to $3.5 billion. The timing is notable: its portfolio company Wiz recently completed a reported $32 billion sale to Alphabet.

Meanwhile, Simile raised $200 million at a $2 billion valuation, only five months after its $100 million Series A. The company is building AI systems that simulate human behavior for product and market research.

The obvious conclusion is that AI funding is accelerating.

I think that’s incomplete.

The mental model: Second-Order Thinking

First-order thinking asks: What happened?

AI companies are raising enormous amounts of money.

Second-order thinking asks: What happens next?

When capital, compute, distribution, and infrastructure increasingly come from the same small group of companies, the competitive game changes.

The scarce resource may not be the model.

It may be access.

That matters for founders. The best startup strategy isn’t necessarily “raise the biggest round.” It’s figuring out which dependency becomes a bottleneck if your company actually works.

We’ve seen versions of this problem in regulated markets. At /mkt, building around athlete tokenization means the product can’t be separated from securities rules, offering requirements, trading infrastructure, disclosures, and compliance. The constraint isn’t something you bolt on after product-market fit. It shapes the product from day one.

AI founders face a different version of the same problem.

Compute isn’t a footnote. It’s part of the business model.

My contrarian take: the next great startup moats may be built around constraints, not features.

Everyone can see the latest model.

Far fewer people can secure the infrastructure, regulatory pathway, distribution, data, or capital structure required to turn that model into a durable business.

That’s where I’d look.

Not at who raised the most.

At what they now control that competitors can’t easily replicate.

If you want the mental models behind breakdowns like this, my book, Mental Models: How to Think, Act, and Win, is on Amazon now.

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Gatik Raised $200M. Here's the Case for Boring Autonomy.

Spencer Gareiss — Fri, 28 Aug 2026 18:30:39 GMT

Everybody wanted the robotaxi. Gatik wanted the box truck. On Aug 25 the autonomous freight company announced a $200 million Series D, its largest round yet, and it closed the round with something most self-driving startups still can’t put on a slide: real, contracted revenue. Here’s the full breakdown of what Gatik built, why it works, and where it could still come apart.

1. Company Overview

Gatik, founded in 2017 by veterans of the autonomous-tech world, runs driverless medium-duty box trucks on the “middle mile,” the short, repeatable regional hops between distribution centers and stores. On Aug 25 it announced a $200 million Series D led by the Qatar Investment Authority and Koch Disruptive Technologies, with Millennium Management, ARK Invest, Arca Continental, and Intact Private Capital joining. Intact tripled its prior commitment. It’s Gatik’s largest round to date and brings total funding to roughly $500 million.

The numbers Gatik put behind the raise are the part worth sitting with. The company reports more than $600 million in contracted revenue, 85,000 fully driverless orders completed, and a 99% on-time rate. Those are company-reported figures, and Gatik declined to disclose its valuation, so treat the metrics as self-reported and the price as unknown. It runs dozens of fully driverless trucks today and, per a company spokesperson, is targeting more than 100 by the end of 2026, with plans to reach thousands over the following years.

The customer list is the credential. Walmart was the first named customer, and in 2021 Gatik launched what it calls the world’s first driverless commercial delivery service on those routes. PepsiCo is now the largest public partnership: since signing a multiyear deal in June, 41 driverless Gatik trucks move Frito-Lay products from distribution centers to roughly 250 retail locations around Dallas, Phoenix, and Northwest Arkansas. Kroger, Tyson Foods, and Canada’s Loblaw are also customers. The trucks come off Isuzu assembly lines with Gatik’s “Gatik Driver” system on board, and the company partners with NVIDIA, Cummins, Ryder, and Goodyear. Operations run in Texas, Arizona, Arkansas, and Ontario.

2. The Market

Gatik isn’t chasing the whole autonomous-trucking number, and that matters, because the whole number is a mess of competing definitions. The slice it actually plays in is middle-mile autonomous delivery, and Future Market Insights pegs that at about $490 million in 2026, growing to roughly $14.2 billion by 2036, a 40% compound annual rate, with Level 4 box trucks the leading vehicle type. Those are third-party estimates, not gospel, but the shape is clear: this is a small, fast-forming market, not a mature one.

The demand underneath it is structural, not hype. The American Trucking Associations estimates a driver shortage near 82,000 in 2026, projected to climb past 160,000 by 2031. Federal hours-of-service rules cap a human driver at 11 driving hours inside a 14-hour window, which means a human-driven truck sits idle for a big chunk of every day. A driverless truck doesn’t clock out. On repetitive regional routes that carriers have always struggled to staff, that’s a real economic gap, and it’s exactly the gap Gatik sells into.

One more market fact frames the raise. Autonomous middle-mile is consolidating early: by some estimates the top handful of players already account for the large majority of commercially active driverless middle-mile miles. In a market like that, the durable asset isn’t a demo. It’s certified routes and signed customers.

3. Business Model and Moat

Gatik’s model is autonomy as a service. It doesn’t build trucks, it buys them from an OEM and supplies the brain, then runs the routes around the clock. The strategic choice that makes the whole thing work is the constraint. Instead of trying to drive anywhere, Gatik locks its trucks to a tightly defined operational domain: known regional routes, mapped and validated, run over and over. Early routes were fixed and under 10 miles. Today the dynamic version stretches to as much as 400 miles with multiple pickups and drop-offs, but the principle holds. Certify a route once, and every additional shipper you layer onto it makes the route more valuable. That’s a compounding asset, not a one-off.

The moat is the stack of things that are hard to copy fast: a driver-out operational record on public roads, route certifications that took years to earn, a Fortune 50 customer base with switching costs, a manufacturing and supplier web (Isuzu, NVIDIA, Cummins, Ryder, Goodyear), and a regulatory footprint across the states with workable AV frameworks. Gatik also joined NVIDIA’s Halos AI Systems Inspection Lab in March 2026, an accreditation layer that signals it’s treating safety validation as a moat rather than a checkbox. Right now, in driver-out middle-mile box trucks specifically, Gatik largely sits alone.

4. Spence’s Take

Gatik’s edge is what it refused to build. That’s circle of competence in action, one of the 50 models in the book. The glamorous problems in autonomy, robotaxis and long-haul highway trucking, are also the hardest ones, with the widest range of situations a system has to handle perfectly. Gatik drew a tight circle around a problem it could actually master, the repeatable distribution-center-to-store run, and refused to step outside it. Staying inside your circle isn’t a limitation. It’s how you ship while everyone else is still demoing.

Pair that with a second model: margin of safety. A narrow, validated route gives you engineering margin, fewer edge cases means fewer ways to fail. And more than $600 million in contracted revenue gives you financial margin that hype-funded peers just don’t have. This is the same logic behind building in regulated markets, which is the game I play day to day as CPO at /mkt: you don’t win by moving fast and breaking things, you win by owning a defined, compliant domain so completely that speed becomes safe. Gatik took the autonomy version of that bet.

The bull case is clean. Driver-out operations are live and commercial, the revenue is contracted, the customers are household names, the tailwinds (driver shortage, hours-of-service limits) are structural, and the company now has the capital to scale.

The bear case is just as real. “Dozens” of trucks to “thousands” is a brutal jump that tests manufacturing, operations, and capital all at once. One serious safety event could reset public and regulatory trust overnight, no matter how good the on-time rate is. Customer concentration is heavy, with PepsiCo and Walmart doing a lot of the lifting. The regulatory patchwork still varies state by state, and a national framework isn’t in place yet. And with the valuation undisclosed, nobody outside the round can judge whether the price is sane. None of this is a prediction. It’s the list of things to watch.

5. Why It Matters

For investors and VCs: Gatik is a template for capital-efficient autonomy. Narrow the domain, sign real contracts, and let the revenue, not the narrative, carry the round. Notice who led it, too: a sovereign fund and strategic industrial capital taking equity in a platform they can deploy against. As middle-mile consolidates, certified routes and signed revenue are the assets that survive. That’s the pattern to pressure-test in any autonomy deal that crosses your desk.

For potential customers (retailers, grocers, CPG, and 3PLs): driverless middle-mile has moved past pilot theater and into live supply chains. The pitch is capacity and reliability on exactly the routes that are hardest to staff. If you’re evaluating it, the questions that matter are on-time performance, how much of your route network fits the operational domain, insurance terms, and how much you’re comfortable concentrating with a single provider.

For competitors and builders: the lesson is wedge selection. Pick a domain you can certify and repeat, build the operations layer rather than just the model, and partner for the hardware instead of trying to build it. Certification and operational history are becoming the real barriers to entry here, and they compound with time. Speed to a flashy demo is not the same as speed to a signed, driver-out route.

6. The Bottom Line

Gatik didn’t win by solving the hardest problem in autonomy. It won by choosing a valuable one, drawing a hard line around it, and shipping revenue while the rest of the field argued about robotaxis. The next 18 months are a straight execution test: get from dozens of trucks to hundreds to thousands without a safety event that resets the clock. Hold that 99% on-time rate through the scale-up, and Gatik doesn’t just lead the middle mile, it defines it. Fumble the scaling, and the moat starts to look like a head start. Either way, this is the clearest proof yet that boring, contracted, driver-out autonomy is a real business, not a someday story.

If this was useful, share it with someone who builds things, and if you want the full toolkit, you can grab my book, Mental Models: How to Think, Act, and Win, on Amazon right now.

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If you want the mental models behind breakdowns like this, my book, Mental Models: How to Think, Act, and Win, is on Amazon now.

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Trucks, Grids, and a $250M Beta: The Week's 5 Startup Stories That Actually Matter

Spencer Gareiss — Fri, 28 Aug 2026 15:31:18 GMT

Five rounds closed between Monday and Thursday that are worth your time. One values a private beta at $2.5 billion. Another only makes money when a cow gets sold. Here’s what happened, and what each one tells you.

1. Instinct raised $250M for an app you can’t even use yet

Instinct, built by Spear Street Technology, is a personal AI agent you text or call to run your life: booking travel, clearing your inbox, managing your calendar. It told the Wall Street Journal it raised a $250 million Series B co-led by Index Ventures and Benchmark, pushing total funding to $350 million at a company-reported $2.5 billion valuation. It’s still in private beta, and press reports put its valuation climb at roughly $50 million to $2.5 billion in about four months. Founder Noah Shinn is 23.

Spence’s take: A $2.5 billion price on a beta with no public revenue isn’t a product bet, it’s a bet that Shinn ships trust faster than the lawsuits ship.

Here’s where a mental model earns its keep. Apply second-order thinking, one of the 50 in the book. First-order: the agent feels like magic and early users love it, so back it fast. Second-order: the same access that makes an agent useful, your inbox, your calendar, your cards, is what turns one bad default into a privacy and regulatory mess at scale. Reporters have flagged terms that reportedly grant a perpetual license to user data, and one investor said the agent emailed on her behalf and kept reading her inbox after she cut off access. A perpetual data license reads great in a term sheet and terrible in a subpoena. That’s why regulated markets are a different sport. At /mkt, where I’m CPO, consent and disclosure aren’t features you bolt on later, they’re the starting line. The agent race won’t go to the best demo. It’ll go to whoever solves second-order trust first.

2. Gatik raised $200M and already has the revenue to back it

While everyone argues about robotaxis, Gatik put driverless box trucks on middle-mile routes between distribution centers and stores. On Aug 25 it announced a $200 million Series D led by the Qatar Investment Authority and Koch Disruptive Technologies, with Millennium Management, ARK Invest, and Intact joining. It’s the company’s largest round yet and brings total funding to about $500 million. Gatik says it has more than $600 million in contracted revenue, 85,000 fully driverless orders, and a 99% on-time rate, with customers like PepsiCo and Loblaw.

Spence’s take: Everyone chased the flashy version of autonomy. Gatik built the boring one that already has signed revenue, and boring with revenue wins.

3. Emerald AI hit unicorn status by making data centers behave

The AI bottleneck stopped being chips and became megawatts. Emerald AI sells software that turns AI data centers into flexible grid assets, dialing power down when the grid is stressed without killing critical jobs. On Aug 25 it announced a $150 million oversubscribed Series A at a company-reported $1.05 billion valuation, co-led by Energize Capital and DCVC, with NVIDIA, Siemens, GE Vernova, and In-Q-Tel among the backers. Total raised now tops $220 million, and the company says flexible operation could free up more than 100 gigawatts on the existing US grid.

Spence’s take: When the constraint moves from silicon to the socket, the winner is whoever gets the utility to say yes, and Emerald’s selling exactly that.

4. Perceptron raised $21M to teach robots to see

Small check, loud signal. Perceptron, founded by former Meta researchers, is building vision models for factory and warehouse robots and just launched its Isaac 0.5 model. On Aug 26 it announced $21 million led by Bessemer Venture Partners. The bet: AI’s next decade shows up on a warehouse floor, not in a chat window.

Spence’s take: Consumer agents get the headlines, but the patient money is quietly moving into machines that see and act in the physical world.

5. Breedr raised $27M, and only gets paid when a cow does

The least flashy round of the week might be the healthiest business in it. Breedr gives every animal a digital record from birth to sale and runs a data-verified marketplace for cattle. On Aug 26 it announced a $27 million Series B led by Partech’s impact fund, taking total funding to $46.6 million. More than two million cattle are on the platform, and the company expects close to $500 million of livestock to trade through its marketplace this year, against a US herd the company notes is the smallest since 1951. The kicker: Breedr earns when an animal sells, not on a subscription.

Spence’s take: A model that only makes money when the customer does is rare and underrated. Own a painful real-world workflow, get paid on the outcome, and you’ve built something hard to kill.

Next week’s deep-dive

Next week’s Startup Spotlight goes long on the AI power crunch: whether “flexible data centers” is a real moat or a good story with NVIDIA’s logo stapled to it. Full breakdown, bull case, bear case, and where the risk actually sits, for subscribers.

If this was useful, share it with someone who builds things. And if you want the full toolkit, you can grab my book, Mental Models: How to Think, Act, and Win, on Amazon right now.

Disclosure and disclaimer: Spencer Gareiss is Chief Product Officer at /mkt, referenced above as an example of building in regulated markets. This newsletter is for information and education only. It is not investment advice, and it is not an offer or solicitation to buy or sell any security. Private-company valuations noted here are point-in-time and company-reported. Several figures, including Instinct’s reported valuation history, each company’s revenue and usage numbers, and forward-looking targets, are self-reported by the companies or drawn from press reports and have not been independently verified. Funding details reflect reporting as of August 28, 2026, from primary releases and outlets including the Wall Street Journal, TechCrunch, Bloomberg, Reuters, Forbes, BusinessWire, and others.

If you want the mental models behind breakdowns like this, my book, Mental Models: How to Think, Act, and Win, is on Amazon now.

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OpenAI Just Became a VC. That Changes the Game

Spencer Gareiss — Thu, 27 Aug 2026 18:30:58 GMT

OpenAI is putting $400 million of its own money into a new venture fund.

No outside LPs.

That’s a big change from its first fund, which raised $175 million from outside investors including Microsoft. The new fund plans to make roughly 8 to 10 lead investments a year, with checks ranging from the low millions to as much as $50 million to $100 million for select companies.

And OpenAI isn’t starting from zero.

Its first fund backed 24 startups, including Harvey and Cursor. That fund is now fully deployed.

At the same time, the rest of the market is showing just how aggressive AI venture has become.

Instinct is reportedly raising $250 million at a $2.5 billion valuation after launching its private beta in February. Socure raised $156 million at a $5.2 billion valuation while acquiring agentic AI startup Fravity. DeepSeek is reportedly seeking $7.4 billion at a $74 billion valuation.

So what’s actually happening?

The AI companies aren’t just raising money.

They’re increasingly becoming part of the capital allocation system around AI itself.

That’s where one mental model from Mental Models: How to Think, Act, and Win becomes useful:

The mental model: Incentives

Don’t just ask what someone says they want.

Ask what they’re structurally incentivized to do.

A traditional VC firm needs financial returns for its LPs.

OpenAI has another incentive layered on top: build an ecosystem around the technology it develops.

If a startup becomes an important customer, distribution channel, developer platform or infrastructure partner, the strategic value may extend beyond the ownership stake.

That doesn’t make the investment good or bad.

It makes the incentive structure different.

Founders should understand that difference before taking capital from anyone.

We’ve seen the same principle at /mkt. When you’re building in a regulated market, the incentives of the issuer, investors, trading infrastructure, regulators and customers all matter. You can’t design the product around one participant and assume everyone else will follow.

My contrarian take: the next generation of venture capital may look less like investors financing startups and more like companies financing ecosystems.

That’s powerful.

It’s also something founders should examine carefully.

When your investor can benefit from your success in multiple ways, understand every one of those incentives.

Capital isn’t just capital.

Who provides it matters. Why they provide it matters even more.

This is for informational and educational purposes only. It isn’t investment, financial, legal, tax, or regulatory advice, and it isn’t an offer or solicitation to buy or sell any security or financial instrument. Reported funding amounts, valuations and deal terms may change or remain unconfirmed.

If you want the mental models behind breakdowns like this, my book, Mental Models: How to Think, Act, and Win, is on Amazon now.

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DeepSeek Just Raised the Question Every AI Founder Should Fear

Spencer Gareiss — Thu, 27 Aug 2026 15:31:05 GMT

AI startups are getting expensive again.

DeepSeek is reportedly raising $7.4 billion at a $74 billion valuation, up from a valuation of more than $50 billion in June. The company plans to use the capital for research, development and computing infrastructure, with a potential Shanghai IPO next year.

But here’s the number I’d pay more attention to:

$500 million.

That’s DeepSeek’s reported annual recurring revenue.

The valuation gets headlines. Revenue tells you whether there’s a business underneath the headline.

And DeepSeek isn’t operating in a vacuum. Today, AI startup Instinct is reportedly raising $250 million at a $2.5 billion valuation, bringing its total funding to roughly $350 million. Socure raised $156 million at a $5.2 billion valuation and acquired agentic AI company Fravity. OpenAI is also launching a new $400 million venture fund, using its own capital rather than outside LP money.

That’s a lot of capital chasing AI.

Which brings me to a mental model from Mental Models: How to Think, Act, and Win:

The mental model: Second-Order Thinking

The first-order question is obvious:

“How big can this company get?”

The second-order question is harder:

“What has to be true for that outcome to happen?”

For an AI company, that might mean asking:

How much compute will it require?

How quickly will inference costs fall?

How durable is customer demand?

What happens when competitors offer similar capabilities for less?

And perhaps most importantly, does revenue grow faster than the cost of delivering the product?

That’s where today’s funding numbers get interesting.

A $74 billion valuation doesn’t tell you whether the business is durable. A $500 million recurring revenue base gives you a much better starting point for asking the question.

At /mkt, we’re building in a regulated market, which creates a similar discipline. You can’t just ask whether customers want the product. You have to ask what has to be true across compliance, distribution, infrastructure and market structure for the product to work.

That’s slower.

It can also force better thinking.

My contrarian take: AI founders shouldn’t be afraid of capital. They should be afraid of capital masking weak economics.

The next phase of AI won’t be won by whoever raises the most money.

It’ll be won by whoever converts extraordinary technical capability into an extraordinary business.

If you want the mental models behind breakdowns like this, my book, Mental Models: How to Think, Act, and Win, is on Amazon now.

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XPeng Raised $900M for Robots. The Market Hated It. Here's What It Missed.

Spencer Gareiss — Wed, 26 Aug 2026 18:30:37 GMT

A car company just raised almost a billion dollars for a robot business that hasn’t sold a single robot. The same day, that car company’s stock fell almost 7 percent. If those two facts feel like they cancel each other out, you’re reading it first-order.

Here’s what happened. On August 24, XPeng announced that its robotics unit had signed share-purchase agreements to raise more than $900 million, at a post-money valuation of more than $6.3 billion, roughly 43 billion yuan. XPeng calls it the largest single-round private financing in the history of China’s embodied-AI sector, so treat “largest ever” as the company’s claim. IDG Capital led it. Gaorong Ventures joined, with Tencent and Alibaba as strategic backers. Bloomberg reported the money splits roughly into $200 million from XPeng itself, about $600 million from outside investors, and around $100 million from senior executives writing their own checks.

The robot in question is IRON, XPeng’s humanoid. Mass production is targeted for the end of 2026, first inside XPeng’s own stores and campuses, then a wider launch in 2027. XPeng keeps control of the unit, holding roughly 82 percent and consolidating it on the group’s books. CEO He Xiaopeng is running the robotics business himself.

And the stock? It dropped about 6.8 percent that day, partly because second-quarter results landed at the same time: a wider net loss and light third-quarter guidance. So the market got two messages at once and picked the gloomy one.

Here’s the model I’d use to read this: second-order thinking.

First-order thinking stops at the immediate consequence. Robots don’t ship yet, the parent is losing money, this raise means dilution and distraction, so sell. All true, all shallow.

Second-order thinking asks what happens next, and after that. What XPeng actually did was carve its robotics arm into a separately capitalized entity with its own cap table, its own investors, and a public price tag that didn’t exist 48 hours earlier. It funded a capital-hungry, high-risk bet with mostly outside money, put executive skin in the game of about $100 million, and kept the core EV business from absorbing the burn. The quarter got priced by the market. The decade got priced by the round.

My contrarian take: the move that looks like financial engineering is also a discipline mechanism. A standalone company with its own valuation has to earn its next round on its own results. That’s much harder to bury inside a parent’s income statement than a line item labeled “R&D.” Whether IRON hits its timeline is a real question, and humanoid timelines slip more often than they hold. But “distraction” and “smart structuring” aren’t opposites here. Sometimes the same decision is both, and the stock chart only tells you how the crowd felt for one afternoon.

The lesson for builders isn’t “spin everything out.” It’s this: when you’re funding a long bet, structure decides who carries the risk and who’s forced to prove it. Get that right and a scary line item becomes a priced option. Get it wrong and it’s just burn.

If this was useful, share it with someone who builds things. And if you want the full toolkit of 50 mental models, you can grab my book, Mental Models: How to Think, Act, and Win, on Amazon right now.

Disclaimer: This newsletter is for informational and educational purposes only. It is not investment, financial, legal, or tax advice, and it is not a recommendation or solicitation to buy or sell any security. Funding figures, valuations, and investor details reflect company announcements and third-party reporting (including PRNewswire and Bloomberg) around August 24, 2026, are point-in-time, and may change. XPeng’s description of the round as a record, and the $6.3 billion valuation, are company-reported. Do your own research and consult a licensed professional before making any financial decision.

If you want the mental models behind breakdowns like this, my book, Mental Models: How to Think, Act, and Win, is on Amazon now.

Thanks for reading Startup Spotlight! This post is public so feel free to share it.

Castelion Raised $1B for Hypersonics. The Real Weapon Is the Factory.

Spencer Gareiss — Mon, 24 Aug 2026 18:30:55 GMT

Everyone’s talking about the missile. The interesting part is the assembly line.

This week Castelion, a four-year-old defense startup in Torrance, announced a $1 billion Series C, $800 million in equity plus a $250 million credit facility, at a valuation the company put at $13 billion. JPMorgan’s Strategic Investment Group co-led it alongside Andreessen Horowitz and Carlyle, with T. Rowe Price joining as a new backer. The money goes toward mass-producing Blackbeard, its low-cost hypersonic strike missile, at a 1,000-acre site in New Mexico. Castelion says it’s booked more than $500 million in U.S. military contracts in 18 months, with the Navy as its main customer, and it’s targeting initial fielding next year.

Read past the hardware and the thesis is almost boring. Castelion isn’t really selling a better missile. It’s selling manufacturing.

Here’s the model from my book: economies of scale. Make one of something and it’s staggeringly expensive. Make ten thousand and the price per unit collapses, partly because fixed costs spread across more units, and partly because of the learning curve, the well-documented pattern where every doubling of cumulative output pushes cost down a predictable notch as you get better at building the thing. Cars, phones, and rockets all rode that curve from bespoke to cheap. Traditional defense never did. It builds exquisite weapons in tiny numbers at brutal unit cost, which is exactly why the U.S. keeps running short on munitions the moment a real conflict burns through the stockpile.

Castelion is betting hypersonics can ride the same curve. Design the thing for manufacturability, use commercial components where you can, own the factory instead of waiting on the legacy supply chain, and the cost per unit starts falling toward something you can actually field at volume. Its CEO framed it as going back to Apollo-era industrial ambition. That’s the pitch investors just valued at $13 billion. Not a smarter warhead. A cost curve.

Spence’s take: Deterrence has never really been about owning the fanciest weapon. It’s about having enough of them at a price you can sustain, so an adversary runs the math and decides not to test you. Something you can only build ten of doesn’t deter anyone who can build a thousand cheaper ones. So the scoreboard everyone watches, the range and the speed and the specs, isn’t the one that decides this. The one that decides it is unit cost at volume. And that’s also where the bet is riskiest, because the demo is never the hard part. Making the 500th unit as good and as cheap as the first is the entire game, and it’s where manufacturing dreams usually go to die. Castelion just raised a billion dollars to prove hypersonics are a factory problem, not a physics one. Now it has to actually build the factory.

If this was useful, share it with someone who builds things. And if you want the full toolkit of 50 mental models, you can grab my book, Mental Models: How to Think, Act, and Win, on Amazon right now.

Disclaimer: This newsletter is for informational and educational purposes only. It is not investment, financial, legal, or tax advice, and nothing here is a recommendation or solicitation to buy, sell, or hold any security or to participate in any investment strategy. Funding amounts, the $13 billion valuation, and contract figures reflect information reported by the company and by third-party news sources as of the publication date and are point-in-time figures that may change. Forward-looking statements, including production plans, cost-reduction goals, and fielding timelines, are attributed to the company and are not statements of fact. Private company figures are difficult to verify independently. Do your own research and consult a licensed professional before making any financial decision. The author may hold positions in companies or sectors mentioned and receives no compensation from any company covered here.

If you want the mental models behind breakdowns like this, my book, Mental Models: How to Think, Act, and Win, is on Amazon now.

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Nuclear Takes a Decade. Valar Just Raised $1B Betting That's a Choice.

Spencer Gareiss — Mon, 24 Aug 2026 15:30:59 GMT

A three-year-old nuclear company just raised a billion dollars. If that sentence sounds impossible, you understand the nuclear industry perfectly

On Monday, Valar Atomics announced a $1 billion Series B led by Sequoia, plus a $200 million credit facility, for $1.2 billion in new financing. Bloomberg reported the round valued the company around $6 billion, a figure Valar didn’t confirm. This is a company that raised just $130 million last October. Its pitch is simple to say and brutally hard to do: build small nuclear reactors like manufactured products instead of one-off construction projects. It says its Ward 250 reactor went critical in June, and that it has powered an Nvidia AI chip directly off a fission reaction. AI data centers are its biggest target customer.

The easy read is “AI needs power, nuclear is back, money floods in.” True, but shallow. Demand for power has been around for decades. It never turned nuclear into a venture-scale bet before. Something else changed.

Here’s the model from my book: first principles. Most people reason by analogy. Nuclear is slow and absurdly expensive, so it’ll always be slow and absurdly expensive, so smart money stays away. First-principles thinking throws out the analogy and asks what’s actually true at the bottom. And at the bottom, the physics of a small reactor isn’t the problem. The problem is how we build them: as bespoke, multibillion-dollar megaprojects, each one a cathedral poured in concrete over a decade. Valar looked at that and asked a different question. What if a reactor were a product you manufacture on a line, not a monument you construct on a site? Same physics. Completely different cost curve.

That reframe is the whole thing. Valar’s own founding observation was that nuclear is one of the most abundant energy sources on earth, saddled with one of the worst cost curves in industry, and that almost nobody was actually trying to fix the curve itself. Attack it with manufacturing, vertical integration, and repetition, and a source everyone wrote off as too expensive starts marching toward the cost of simply making the thing.

Spence’s take: Sequoia didn’t write a billion-dollar check because AI is thirsty. Everyone knows AI is thirsty. It wrote the check because Valar is betting the industry’s defining number, the cost and time to stand up a reactor, was never a law of physics. It was an inherited habit. That’s the first-principles move: find the thing everyone treats as fixed and prove it’s a choice. Now comes the part the model can’t do for you. Nuclear is one of the most regulated, least forgiving arenas on earth, and manufacturing economics only matter if the reactors actually get built, approved, and run safely at scale. The insight is real. The execution is where bets this size go to be tested.

Disclaimer: This newsletter is for informational and educational purposes only. It is not investment, financial, legal, or tax advice, and nothing here is a recommendation or solicitation to buy, sell, or hold any security or to participate in any investment strategy. Funding amounts reflect information reported by the company as of the publication date. The approximately $6 billion valuation was reported by Bloomberg and was not confirmed by the company; it is a point-in-time figure that may change and could not be independently verified here. Forward-looking statements, including plans to mass-produce reactors, drive down the cost curve, and serve AI data centers, and the company’s own descriptions of its technical milestones, are attributed to the company and are not statements of fact. Advanced nuclear energy is heavily regulated and carries substantial execution, regulatory, and safety risk. Private company figures are difficult to verify independently. Do your own research and consult a licensed professional before making any financial decision. The author may hold positions in companies or sectors mentioned and receives no compensation from any company covered here.

If you want the mental models behind breakdowns like this, my book, Mental Models: How to Think, Act, and Win, is on Amazon now.

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Kalshi Wants $40 Billion for the Exact Thing States Are Trying to Ban

Spencer Gareiss — Thu, 20 Aug 2026 18:30:35 GMT

Kalshi built a $22 billion company by taking its own federal regulator to court and winning. Now it reportedly wants investors to pay nearly double for the fight that made it.

The Information reported this month that Kalshi is in advanced talks to raise at least $750 million at a roughly $40 billion valuation, with Sequoia Capital and Wellington Management weighing whether to co-lead. Nothing’s closed and the company hasn’t confirmed it, so treat those figures as reported, not final. But the trajectory is real. Kalshi raised $1 billion at a reported $22 billion valuation in May, and press reports put its annualized revenue near $4 billion as of July, up from a rounding error two years ago. Sports contracts reportedly drive more than 80% of that volume.

That’s the setup. Here’s the tension.

Kalshi is regulated by the CFTC, and that federal status is the whole moat. It’s why a $1.3 trillion shop like Wellington can even look at the cap table. It’s why Sequoia has publicly pegged Kalshi at roughly 95% of the U.S. prediction market. Being the compliant one is the product.

It’s also the target. More than a dozen states are challenging Kalshi’s sports contracts as unlicensed gambling, with reported actions ranging from outright bans to, in Arizona, criminal charges. The exact contracts printing the revenue are the exact contracts under legal fire.

One of the models in my book is Inversion. Instead of asking how something wins, you ask what has to be true for it to break. Flip Kalshi and the answer gets uncomfortable, because the bull case and the bear case are the same sentence. Federal regulation preempts state gambling law, right up until a court decides it doesn’t. The moat and the landmine are the same piece of ground.

Most people reading the $40 billion headline are pricing the upside: revenue, market share, an IPO the CEO has said he’s considering for 2027. Invert it and you’re really pricing one question. Does the CFTC framework hold against the states? Everything else is a footnote to that.

I’ve built inside regulated markets long enough to respect the trade. At /mkt we work under Reg A+ with tZERO as the trading infrastructure, and that framework is slow and expensive on purpose. You inherit its protection and its fights at the same time. You don’t get to keep one without the other. That’s not a bug of building in regulated markets. It’s the whole deal you sign.

So here’s the contrarian read. A $40 billion price on a company whose core product is being called illegal in a dozen states isn’t really a bet on prediction markets. It’s a bet on one legal question resolving one way. That might turn out to be a great bet. Just name the bet you’re actually making before you make it.

Disclaimer: This post is for informational and educational purposes only and is not investment, legal, or financial advice, nor a recommendation to buy, sell, or hold any security or interest in any company mentioned. Kalshi is a private company; all valuations are point-in-time and as reported by the press. The reported $750 million raise and roughly $40 billion valuation were reported by The Information citing unnamed sources, have not been confirmed by Kalshi, and were not closed as of this writing. Revenue, volume, and market-share figures are as reported by the press and have not been independently verified. Author affiliation disclosure: Spencer Gareiss is Chief Product Officer at /mkt; the /mkt reference is included as a structural example of building in regulated markets, not as a solicitation or endorsement.

If you want the mental models behind breakdowns like this, my book, Mental Models: How to Think, Act, and Win, is on Amazon now.

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Ambrook: The Software Betting America's Barns Still Run on Paper

Spencer Gareiss — Tue, 18 Aug 2026 18:31:30 GMT

Company Overview

Ambrook builds financial-management software for the businesses that grow, build, and haul the physical world. In plain English: it’s bookkeeping, invoicing, bill pay, payments, and cash management, except it’s designed for a cattle ranch that runs four entities, buys and sells animals that are literally born, grow, and die on the balance sheet, and collects most of its revenue in two lumpy seasons. Think of it as what QuickBooks would look like if someone rebuilt it in 2026 for people who wear boots to work.

The company just closed a $30 million Series B, announced in early August 2026. That brings total funding to about $59 million, following a $26.1 million Series A back in July 2025. The round was led by Lachy Groom, the solo capitalist who used to run Stripe Issuing, which matters more than it looks like it should, and I’ll come back to that. Thomson Reuters Ventures, Thrive Capital, Field Ventures, and Cameron Ventures joined, along with a roster of operator angels: Guillermo Rauch of Vercel, Akshay Kothari of Notion, and Tomer London of Gusto.

Ambrook was founded in 2020 by Mackenzie Burnett (CEO), Dan Schlosser (product), and Jeff Anders (design), and it’s based in Denver with an office in New York. The traction is the part that made me stop scrolling. Ambrook is over 8,000 customers, up from about 2,500 a year ago. More than 1,000 are trucking businesses now, with hundreds of general contractors and property managers on top of its agricultural core. Nearly a third of users open it only on their phones each month. And here’s the number that reframes the whole company: by the CEO’s account, about half of customers are putting their finances on software for the very first time, coming straight off paper and spreadsheets.

The Market

Start with the core. USDA’s 2022 Census of Agriculture counted 1,900,487 farms in the United States, the first time the number fell below two million since before the Civil War, working 880 million acres, operated by 3.37 million producers, producing $543.1 billion in agricultural products. These aren’t hobby plots. Fewer than 6 percent of farms, about 105,000 of them, sold more than three-quarters of all output, and many are multi-million-dollar operations. A striking share of them still run on paper, on desktop software from the 1990s, or out of a shoebox of receipts.

Now the timing. American agriculture is under real financial stress. In the 2025 ABA/Farmer Mac Agricultural Lender Survey, lenders expected only about 52 percent of US farm borrowers to be profitable, a sharp drop from recent years. When margins were fat, sloppy books were survivable. When roughly half your borrowers are underwater, financial visibility stops being a luxury and becomes the difference between getting the operating loan and not getting it.

Two more tailwinds make now the moment. First, AI-native bookkeeping slashes the cost to serve a customer base that used to be too small, too seasonal, and too scattered for anyone to serve profitably. Second, embedded finance lets a startup issue cards and move money through partners instead of becoming a bank. Ambrook’s Visa commercial cards are powered by Stripe and issued by Celtic Bank, a member FDIC institution. That’s the Lachy Groom connection paying off: this is a payments-native company, not an accounting tool that bolted a card on later.

The positioning I find most interesting is that Ambrook isn’t only taking customers from QuickBooks. When half your users are first-time software buyers, you’re not fighting over a fixed pie, you’re growing the pie. That’s a very different motion from most vertical SaaS, which usually means knife-fighting to convert switchers one at a time.

Business Model and Moat

Ambrook makes money two ways, and that’s the crux of it. There’s subscription software revenue, the classic SaaS engine. And there’s the payments side: card interchange and money movement running through its Stripe-and-Celtic rails. Two revenue engines from one customer, where healthy payment economics can subsidize cheaper, stickier software.

Who else is in the market? The incumbent is Intuit’s QuickBooks, more than 30 years old and built for a generic small business, not for multi-entity operations with livestock inventory, government-program payments, and cash flow that arrives twice a year. There are legacy desktop farm-accounting tools, a handful of newer farm-finance startups, and general-purpose small-business software. None of them combine vertical depth, mobile-first design, and embedded payments the way Ambrook is trying to.

So what’s actually defensible? Four things. One, switching costs. Once your books, receipts, bill pay, cards, and audit-ready tax records all live in one place, ripping it out hurts, and it hurts double for a business that had no system before Ambrook. Two, vertical depth. Modeling a herd, multiple profit-and-loss lines, seasonal cash cycles, and federal payments is genuinely hard to replicate as a feature bolted onto a horizontal tool. Three, the embedded-finance flywheel: payment data sharpens the cash-flow features, which deepen daily usage, which grows payment volume, which funds the software. Four, distribution and trust in a word-of-mouth market where outsiders struggle to earn credibility.

That last piece connects to something I think about constantly. Issuing cards through a chartered bank, moving customer money, and keeping compliance-grade records means Ambrook is building inside a regulated financial system from day one. That’s the same reality I live in at /mkt, where regulated-markets infrastructure isn’t a feature, it’s the entire game. Building where the rules are strict is slower, but the moat is real, because the next entrant has to clear the same bar you already cleared.

Spence’s Take

Two models here.

The bull case runs on switching costs, or what I’d call the system-of-record effect. The most valuable position in any workflow software is being the place the data lives. Ambrook’s wedge is unusually strong because it’s often the first system a business ever adopts. You don’t just win the account, you define how that business keeps its books forever. Layer embedded payments on top and the account gets stickier and more valuable every quarter. If the new trucking and construction cohorts retain the way the ag base does, this compounds into a durable company that grows its revenue per customer as it moves up-market.

Now the bear case, and my favorite tool for it is inversion. Don’t ask how Ambrook wins. Ask what would guarantee it loses, then check whether the company is walking toward those things. Three ways it loses. First, focus dilution: chasing agriculture plus trucking plus construction plus property management all at once can turn a sharp vertical wedge into a shallow generalist that gets out-specialized in every category. Second, the downturn bites the hand that feeds: if farm profitability keeps sliding, core customers cancel software before they cancel diesel. Third, the interchange trade breaks: if payment economics get squeezed, or a bank-partner or regulatory hiccup disrupts the card program, one of the two revenue engines sputters. None of these is fatal today. All of them are worth watching.

The honest read is that Ambrook’s greatest strength and its biggest risk are the same decision: expansion. Customer-pulled expansion, like the Arizona cattle feeder who reportedly asked to use it for his trucking business, is a gift. Founder-pushed expansion, chasing every adjacent vertical because the deck says the market is huge, is how focused companies go soft. Which one this becomes is the whole ballgame.

Why It Matters

For investors and VCs: This is a company worth watching, and I’ll leave it there, because nothing in this newsletter is investment advice. If you’re tracking it, the signals that matter are net revenue retention, the attach rate on payments and cards, average contract value as they move up-market, and whether the trucking and construction cohorts hold through a downturn the way agriculture has. Watch whether Thomson Reuters Ventures becomes a real distribution channel into tax and compliance, not just a check on the cap table. And note that Ambrook didn’t disclose a valuation with this round, so anyone quoting one is guessing.

For potential customers: If you run a farm, a ranch, a trucking outfit, a contracting business, or a property portfolio on paper, spreadsheets, or a desktop tool from two decades ago, this is aimed squarely at you. What’s coming is AI-native bookkeeping, invoicing, bill pay, business cards, and cash management built for multiple entities and seasonal money, with about a third of users running it entirely from their phones. The problem it solves is visibility: knowing which of your enterprises actually makes money, and having the records ready when the lender or the tax preparer calls. Standard fine print applies. The cards run through Stripe and Celtic Bank, so treat it like the financial product it is.

For competitors and builders: Three lessons. One, boring verticals full of first-time software buyers can be bigger and stickier than the crowded categories everyone else fights over. Greenfield beats knife-fighting. Two, embedded finance can turn one product into two businesses, and the payments engine can fund the software engine. Three, let your expansion be customer-pulled before it’s founder-pushed. The market will usually tell you where the next vertical is if you’re listening to support tickets instead of staring at a market-size slide.

The Bottom Line

Ambrook is a sharp wedge into a huge, underserved market at exactly the moment financial stress turns its product from a want into a need, with a payments-native model most vertical-SaaS peers can’t match. The real risk isn’t the market, it’s discipline: whether a company this good at expanding can stay focused enough to keep winning each vertical it enters. Watch retention in the new categories and the attach rate on payments, and you’ll know which Ambrook you’re getting.

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Disclaimer: This newsletter is for informational and educational purposes only. It is not investment, financial, legal, or tax advice, and it is not a recommendation or solicitation to buy or sell any security. Ambrook is a private company; its $30 million Series B was announced in early August 2026, and funding figures, investor names, and customer metrics reflect company statements and third-party reporting (including FinTech Futures, Upstarts Media, FinSMEs, and agricultural trade press) available at that time. Customer outcomes and traction numbers are company-reported and point-in-time. Ambrook did not disclose a valuation. Market and farm-economy statistics are from the USDA 2022 Census of Agriculture and the 2025 ABA/Farmer Mac Agricultural Lender Survey. Do your own research and consult a licensed professional before making any financial decision. Disclosure: the author is Chief Product Officer at /mkt; /mkt is referenced only as an illustrative example of building in regulated markets and this is not a solicitation for /mkt or any security.

If you want the mental models behind breakdowns like this, my book, Mental Models: How to Think, Act, and Win, is on Amazon now.

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You Can Be Worth $20 Billion and Still Get Debanked

Spencer Gareiss — Tue, 18 Aug 2026 15:30:18 GMT

Prediction markets are one of the hottest corners of finance right now. They’re also a live case study in the risk that a valuation can’t buy its way out of.

Last week the Financial Times reported that JPMorgan cut its banking relationship with Polymarket back in October, citing regulatory concerns, and told the company to find a new bank. Polymarket pushed back on the framing, telling the FT it still works closely with JPMorgan across multiple areas, and the bank reportedly kept some ties open in case it wants an underwriting role in a future IPO. Reuters confirmed the core of it through its own source. Around the same window, a judge ordered Kalshi to stop offering most wagers in Washington state, and New York City opened a probe into both platforms. On the other side of the ledger, the CFTC has told Kalshi to keep operating despite a New York lawsuit. Federal tailwind, state and banking friction, all at once.

Keep the numbers in mind while you read that. Kalshi was reportedly valued around $22 billion in May and, per FT reporting, has been in talks for a round near $40 billion. Polymarket sat around $15 billion and, according to Bloomberg and CNBC, has been seeking north of $20 billion, with both reportedly clearing a billion dollars in annualized revenue. These aren’t fragile startups. And they still spent the week playing defense against courts, regulators, and a bank.

Here’s the model from my book: inversion. Most founders ask “how do we grow faster than the other guy?” Inversion flips it: “what’s the thing that kills us, and are we building to avoid it?” In consumer software the answer is usually a competitor or churn. In regulated markets, it almost never is. It’s the perimeter: a regulator, a court, a state attorney general, or a bank quietly deciding you’re too much risk to hold. None of those show up on your growth chart until the day they end you.

I built prediction markets and derivatives at Robinhood, so I’ve watched this movie before. The product can be brilliant and the demand real, and none of it matters if you lose the rails underneath it. Banking access in particular is a dependency almost nobody inverts on until it’s gone.

Full disclosure: I’m CPO at /mkt, where we tokenize athlete income and made the call early to build inside the perimeter, using a Reg A+ framework with registered trading infrastructure. I’m not holding that up as the only right answer, and I’m not knocking Kalshi or Polymarket, who’ve won plenty of their own regulatory fights. The point is narrower. In a markets company, where you choose to stand relative to the rulebook is the first design decision, not the last.

Spence’s take: The prediction-market boom is real, and I think the category keeps growing. But the scoreboard everyone watches, valuation and volume, measures the wrong risk. The one that matters doesn’t trend on a chart. It shows up as a letter from a regulator or a bank. Invert first. Build for the thing that can end you, then go chase the growth.

Disclaimer: This newsletter is for informational and educational purposes only. It is not investment, financial, legal, or tax advice, and nothing here is a recommendation or solicitation to buy, sell, or hold any security or to participate in any investment strategy. The regulatory, legal, and banking matters described are based on third-party reporting (Financial Times, Reuters, Bloomberg, and CNBC) as of the publication date, are evolving, and may change; certain characterizations are those of the reporting outlets, and the companies named have disputed some of that framing. Reported valuations and funding discussions are point-in-time figures, some are unconfirmed by the companies, and private-company figures are difficult to verify independently. The author is Chief Product Officer at /mkt and references it here only as an illustrative example of building in a regulated market, not as a solicitation; any securities offering by /mkt is made solely through its official offering materials. Do your own research and consult a licensed professional before making any financial decision. The author may hold positions in companies or sectors mentioned and receives no compensation from any company covered here.

If you want the mental models behind breakdowns like this, my book, Mental Models: How to Think, Act, and Win, is on Amazon now.

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AI Made Writing Code Cheap. CodeRabbit Raised $143M on What Got Expensive.

Spencer Gareiss — Mon, 17 Aug 2026 18:30:36 GMT

For two years, the entire AI story has been about writing more code faster. CodeRabbit just raised $143 million betting the real money is in cleaning up after all of it.

On August 12, the AI code-review company closed a $143 million Series C at a $1.5 billion valuation, co-led by Atomico and Smash Capital, with new backers including BMW i Ventures and Datadog. That’s less than a year after its $60 million Series B, and the company says revenue grew more than 5x year over year. It now runs over 2 million code reviews a week for more than 17,000 customers, including Nvidia, BMW, Adyen, and JFrog. CodeRabbit put its own thesis bluntly on X: “Code is abundant. Judgment is scarce.”

That one line is the whole investment case, and it points straight at a model from my book.

Here’s the model: the theory of constraints, or bottlenecks. Every system has one step that limits the whole thing, one wall that caps output no matter how fast everything else runs. Speed up a step that isn’t the wall and you accomplish nothing. You just pile more work in front of the actual constraint. And here’s the part people miss: when you finally break one bottleneck, the constraint doesn’t vanish. It moves. Something else becomes the new wall.

For decades, writing code was the wall. It was slow, expensive, and human. Then AI coding tools knocked that wall down, and code became cheap and nearly infinite. But the system didn’t get faster overall, because a new bottleneck appeared right behind it: someone still has to review, validate, and take responsibility for all that machine-written code before it ships. That review step kept up fine when humans wrote everything by hand. Now it’s drowning. The constraint moved from writing to judging.

CodeRabbit sells shovels for exactly that new wall. As AI agents generate more of the code, the scarce and expensive thing becomes trustworthy review at scale. That’s why a review company is growing 5x while everyone’s attention stays glued to the generators. It isn’t competing with the tools that write code. It’s monetizing the traffic jam they created.

Spence’s take: The crowd keeps funding ways to produce more of the thing that’s already cheap. That’s the classic error, optimizing everything except the constraint. Making code even more abundant doesn’t speed anything up anymore. It just shoves more pressure onto the one step that’s now the wall. The operators who win the next stretch of AI won’t be the ones generating the most output. They’ll be the ones who spotted where the bottleneck moved and got there first. Right now, it moved to judgment. CodeRabbit noticed before the crowd did.

Disclaimer: This newsletter is for informational and educational purposes only. It is not investment, financial, legal, tax, or medical advice, and nothing here is a recommendation or solicitation to buy, sell, or hold any security or to participate in any investment strategy. Funding amounts and financial figures reflect information reported by the company as of the publication date and are point-in-time figures that may change. Forward-looking statements, including plans to reach additional U.S. transplant centers, advance the product pipeline and viability-assessment tool, expand into additional organ applications, and establish the technology as a standard of care, are attributed to the company and are not statements of fact. Regulatory status described reflects FDA De Novo clearance reported as of January 2026. Private company figures are difficult to verify independently. Do your own research and consult a licensed professional before making any financial decision. The author may hold positions in companies or sectors mentioned and receives no compensation from any company covered here.

If you want the mental models behind breakdowns like this, my book, Mental Models: How to Think, Act, and Win, is on Amazon now.

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A 2-Month-Old Company With No Product Just Raised $1.1 Billion

Spencer Gareiss — Mon, 17 Aug 2026 15:31:02 GMT

A company that’s roughly two months old, with an API still in preview and no shipped product, just raised $1.1 billion. Read that again. Not a typo.

On August 11, River AI announced $1.1 billion across its seed and Series A, co-led by General Catalyst and AMP PBC, with strategic checks from Nvidia and AMD Ventures and participation from Y Combinator and Temasek. The company came out of stealth on June 10. Its founder is Igor Babuschkin, who co-founded xAI and did earlier stints at DeepMind and OpenAI. What River sells today is an API for fine-tuning open-weight models. What it’s selling investors is a plan to rebuild the entire AI stack, from training to hardware, so that AI aligns to individual people instead of billions of users at once. There’s no revenue story here yet. There’s a founder and a thesis.

By any normal measure, paying $1.1 billion for that is nuts. And by any normal measure, it is. But venture capital doesn’t run on normal measures.

Here’s the model from my book: the power law. Startup outcomes aren’t spread evenly around some sensible average. They’re wildly lopsided. A tiny handful of companies return more than an entire fund, and almost everything else returns little or nothing. Once that clicks, the whole game flips. The mistake that kills a venture fund isn’t overpaying for a deal that fails, because you can only lose what you put in, once. The mistake that kills a fund is missing the one deal that becomes the giant. So the rational move isn’t to dodge the crazy-looking bet. It’s to make sure you’re holding a piece of anything that could plausibly be the outlier.

I spent years building derivatives, so here’s how I actually read this round: it’s a call option. The premium is $1.1 billion. The downside is capped at that premium. The upside, if River becomes a foundational layer of AI, is a number with a lot more zeroes. Nvidia and General Catalyst aren’t claiming River will probably work. They’re pricing how big the tail gets if it does, then deciding they can’t afford to not own the option.

That’s why “no product” doesn’t rattle them the way it rattles you. They’re not underwriting today. There is no today worth underwriting. They’re underwriting a distribution, and betting that a proven builder plus a big idea plus a mountain of capital gives that distribution a fat enough right tail to matter.

Spence’s take: Every time one of these rounds lands, the timeline fills with people calling it dumb money. Sometimes it is. But most of the “that’s insane” takes are just bell-curve brains staring at a power-law game. The pros aren’t betting River works. They’re betting they can’t afford to be out if it does. In venture, the only unforgivable error is being right about the risk and absent from the winner.

Disclaimer: This newsletter is for informational and educational purposes only. It is not investment, financial, legal, or tax advice, and nothing here is a recommendation or solicitation to buy, sell, or hold any security, option, or other instrument, or to participate in any investment strategy. The references to options pricing and power-law returns describe how venture investors approach risk and are illustrative, not trading or investment guidance. Funding amounts reflect information reported by the company as of the publication date and are point-in-time figures that may change. Forward-looking statements, including plans to rebuild the AI stack and build personal AI, are attributed to the company and its founder and are not statements of fact. Early-stage companies carry a high risk of failure. Private company figures are difficult to verify independently. Do your own research and consult a licensed professional before making any financial decision. The author may hold positions in companies or sectors mentioned and receives no compensation from any company covered here.

If you want the mental models behind breakdowns like this, my book, Mental Models: How to Think, Act, and Win, is on Amazon now.

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The Most Boring $110M Round of the Week Might Be the Smartest

Spencer Gareiss — Sun, 16 Aug 2026 15:30:53 GMT

While everyone argued about $5 billion AI rounds and a rumored $40 billion coding startup, a company that keeps donor livers alive quietly raised $110 million.

No frontier model. No agent. Just organs.

On August 12, Bridge to Life closed a $110 million Series C and debt package, led on the equity side by healthcare specialist Soleus Capital alongside Lauxera Capital Partners. The Georgia-based company has spent more than two decades on one unglamorous problem: keeping transplant organs viable outside the body. Its cold-storage solution, Belzer UW, is the long-standing gold standard. Its newer machine, VitaSmart, pumps cold oxygenated fluid through a donor liver and, per the company, is the first and only such system to win FDA clearance for liver transplant, which it received in January. Bridge to Life says adoption has outpaced its own expectations in the six months since.

Notice what this company didn’t do. It didn’t bolt “AI” onto its pitch. It didn’t pivot into a hotter category. It went one careful step deeper into the exact thing it already understood better than almost anyone.

Here’s the model from my book: circle of competence. The idea is simple, and most people ignore it. You have a zone where your knowledge is real and your judgment is sharp, and a much larger zone where you’re just guessing with confidence. The edge isn’t how big your circle is. It’s knowing exactly where the line sits and refusing to fool yourself about it. Bridge to Life moved from cold static storage to oxygenated perfusion, which is a real advance, but it’s an advance inside its circle, not a leap outside it.

The investors did the same thing. Soleus runs roughly $3.5 billion focused on life sciences. It didn’t wander into defense drones or vibe-coding. It wrote a big check into the one field it knows cold. Two parties, both operating inside their competence, both compounding an edge instead of diluting it.

That’s the quiet lesson under a loud week. The biggest checks grabbed the headlines, and some of those bets will pay off enormously. But the surest way to lose is to raise money for something you don’t actually understand, just because the category is hot. Staying in your lane isn’t a lack of ambition. Done right, it’s the most disciplined form of it.

Spence’s take: Everyone wants a bigger circle. The pros want a sharper edge on the one they’ve already got. Bridge to Life didn’t win much attention this week, and it probably didn’t want to. It won something better: a little more distance at the exact thing nobody’s going to out-execute it on. In a market addicted to the new, boring and deep beats shiny and shallow more often than the headlines let on.

If you want the mental models behind breakdowns like this, my book, Mental Models: How to Think, Act, and Win, is on Amazon now.

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A 13-Person Biotech Just Raised $152M for a Single Trial

Spencer Gareiss — Sat, 15 Aug 2026 15:30:51 GMT

Thirteen people. One drug. One trial that either works or it doesn’t. And Goldman Sachs just helped hand them $152 million.

On August 11, Vaderis Therapeutics, a clinical-stage biotech split between Basel and suburban Chicago, closed an oversubscribed $152.5 million Series B. Life Sciences at Goldman Sachs Alternatives and TCGX co-led it, with Omega Funds, EQT Life Sciences, Perceptive, and others piling in. The cash funds one thing: a global Phase 3 study of engasertib, a drug first built to fight cancer that Vaderis is repurposing for hereditary hemorrhagic telangiectasia, a rare inherited disorder that makes blood vessels form wrong and bleed. The company says the round carries it all the way through a potential U.S. approval, which means it wouldn’t have to sell the drug off cheap partway through to keep the lights on.

To most people, this looks insane. A tiny team betting everything on one late-stage trial, in a disease most have never heard of, with a molecule borrowed from cancer research. But that reaction confuses “unlikely” with “bad,” and that’s the trap.

Here’s the model from my book: expected value. You don’t judge a bet by whether it’ll probably work. You judge it by the payoff times the odds. A coin flip that pays 10 to 1 is a great bet even though you lose more than half the time. Specialist investors don’t ask “will this work?” They ask “if it works, how big is it, and are the real odds better than the price implies?”

Run Vaderis through that lens and the “reckless” story falls apart. HHT has no approved targeted therapy today, so a first-in-class win would own the category. The science isn’t pulled from nowhere either. The New England Journal of Medicine published proof-of-concept results on engasertib last December, which tilts the odds off zero. And the raise is sized to reach the finish line, so any payoff wouldn’t get diluted away across a dozen desperate bridge rounds. Big asymmetric upside, odds better than a naive guess, structured so the winners keep the winnings. That’s not a lottery ticket. That’s a priced bet.

None of that guarantees the trial works. Phase 3 studies fail all the time, and this one might. That’s the point. The pros aren’t claiming certainty. They’re claiming the math.

Spence’s take: “Risky” is the laziest word in investing. Most people use it to mean “might fail,” then walk away. Sharp money uses it to mean “mispriced,” then leans in. The crowd sees 13 people and one trial and calls it a gamble. The investors who actually ran the numbers saw a bet where the upside dwarfed the odds against it. Same facts, opposite conclusion. The whole difference is whether you bother to do the multiplication.

Disclaimer: This newsletter is for informational and educational purposes only. It is not investment, financial, legal, tax, or medical advice, and nothing here is a recommendation or solicitation to buy, sell, or hold any security or to participate in any investment strategy. Funding amounts and financial figures reflect information reported by the company as of the publication date and are point-in-time figures that may change. Forward-looking statements, including the initiation and outcome of the Phase 3 HEROIC study, the potential for regulatory approval, the expected use of proceeds, and any future commercialization of engasertib, are attributed to the company and are not statements of fact. Clinical-stage drug development carries substantial risk, and trials frequently fail. Private company figures are difficult to verify independently. Do your own research and consult a licensed professional before making any financial decision. The author may hold positions in companies or sectors mentioned and receives no compensation from any company covered here.

If you want the mental models behind breakdowns like this, my book, Mental Models: How to Think, Act, and Win, is on Amazon now.

Thanks for reading Startup Spotlight! This post is public so feel free to share it.

Databricks Just Raised $5B by Turning Down $15B

Spencer Gareiss — Fri, 14 Aug 2026 18:30:40 GMT

Most founders would crawl over glass for a term sheet. Databricks just got handed one and gave most of it back.

On August 13, the data and AI company closed a $5 billion strategic round at a $190 billion valuation, led by Coatue with Blackstone, MGX, T. Rowe Price, and new investor Sixth Street Growth. Per TechCrunch’s reporting, the striking part isn’t the number it took. It’s the number it didn’t. The company reportedly went in looking for around $1 billion, investors pushed to put in $15 billion, and Databricks settled on $5 billion. Then it made clear it’s staying private for now, even though it’s long been treated as an IPO-in-waiting.

The business can afford to be picky. Databricks says it crossed a $7 billion revenue run rate, growing more than 80% year over year last quarter, and that it’s kept adjusted cash flow positive for twelve straight months. Six months ago the company was valued around $134 billion, so its reported valuation climbed roughly 42% while it was busy printing cash.

Here’s the model from my book worth pulling out: opportunity cost. Most people file it under money, but it’s really about hidden trade-offs. Every yes quietly kills the options you could’ve said yes to instead. A dollar raised isn’t free. It carries dilution, a higher bar to clear next time, and investor expectations that harden into a leash. An IPO isn’t free either. CEO Ali Ghodsi said the quiet part out loud, framing today’s public markets as a distraction the company doesn’t need right now.

So run the math the way Databricks apparently did. The extra billions it passed on wouldn’t have changed the roadmap. They’d have changed the cap table, the pressure, and the focus. When you’re already cash-flow positive and growing 80%, capital isn’t the constraint. Attention is. Taking the bigger check would’ve cost the exact thing that got them here.

Spence’s take: In a bull market, everyone grades a founder on how much they can raise. The better grade is how much they’re willing to walk away from. Cheap capital is the easiest yes in the world, which is precisely why refusing it is the tell. Databricks didn’t take $5 billion because it couldn’t get more. It took $5 billion because it did the subtraction most founders skip.

Disclaimer: This newsletter is for informational and educational purposes only. It is not investment, financial, legal, or tax advice, and nothing here is a recommendation or solicitation to buy, sell, or hold any security or to participate in any investment strategy. Funding amounts, valuations, and financial figures reflect information reported by the company or by third-party news sources as of the publication date and are point-in-time figures that may change. The $190 billion valuation is company-reported. Details of the round’s size and negotiation, including the reported $1 billion sought and $15 billion offered, come from press reporting (TechCrunch) and have not been independently confirmed by the company. Forward-looking statements, including plans to remain private or eventually go public, are attributed to the company and its executives and are not statements of fact. Private company figures are difficult to verify independently. Do your own research and consult a licensed professional before making any financial decision. The author may hold positions in companies or sectors mentioned and receives no compensation from any company covered here.

If you want the mental models behind breakdowns like this, my book, Mental Models: How to Think, Act, and Win, is on Amazon now.

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Drones, Batteries, and a $40B Whisper: The 5 Rounds That Mattered This Week

Spencer Gareiss — Fri, 14 Aug 2026 15:30:41 GMT

Four rounds this week cleared $250 million, and the biggest one wasn’t an AI model company. That’s the tell. When the largest checks stop chasing the obvious thing and start chasing what the obvious thing needs to exist, you’re watching a market grow up. Here are the five rounds from August 9 to 13 that actually moved the needle, and the signal hiding beneath them.

1. Form Energy: $750M for batteries that rust on purpose

Form Energy closed a $750 million Series G led by T. Rowe Price, pushing total equity raised past $2 billion, according to the company. Its iron-air batteries store power for more than 100 hours by rusting and un-rusting iron, and Form says its project backlog jumped from roughly 20 GWh to 80 GWh this year as AI data centers scramble for always-on power. Reporting tied to the round points to a Google order of around 30 GWh for a Minnesota data center.

Spence’s take: Everyone’s funding the brain. The winners might be whoever keeps it powered.

2. Neros Technologies: $250M and a valuation that tripled

Neros raised a $250 million Series C at a $2.5 billion post-money valuation, co-led by Sequoia Capital and the American Strategic Technology Fund, per a company release. That’s roughly triple its prior mark. The Torrance drone maker says it’s building toward a million drones a year by 2028, and reports contracts across the Army, the Marine Corps, and SOCOM.

Spence’s take: Cheap and many just outbid expensive and few. Ukraine already ran that experiment in the field.

3. Lovable: $400M and a valuation that doubled in eight months

Sweden’s Lovable confirmed a $400 million Series C at a $13.3 billion valuation, co-led by Menlo Ventures and EQT’s Scaleup Europe Fund. The company says that valuation doubled from $6.6 billion in December and that revenue is tracking toward a $600 million annualized run rate by the end of August. Users have reportedly built more than 60 million projects on the platform without writing traditional code.

Spence’s take: “Vibe coding” sounds like a toy right up until you notice it’s charging real money to real businesses.

4. Cognition (maker of Devin): a reported $40B whisper

Bloomberg reported that Cognition, the company behind AI coding agent Devin, is in early talks for a round that could value it at $40 billion or more, up from $26 billion in May. Read that word carefully: reported. This round is not confirmed, and the figure comes from press sourcing, not from the company. TechCrunch, citing Bloomberg, connected the number to a roughly $1 billion annualized revenue run rate.

Spence’s take: When a valuation jumps 50% in three months on talks that aren’t even closed, the story is the appetite, not the number.

5. inKind: $414M to finance restaurants

inKind closed an oversubscribed $414 million financing facility led by Citi and Cross River, taking total capital raised past $1.2 billion, per a company release. This isn’t equity chasing a roadmap. It’s credit. inKind fronts capital to independent restaurants in exchange for future dining credit, and its network now spans more than 8,500 restaurants representing close to $30 billion in annual GMV.

Spence’s take: The banks are underwriting a credit book in a thin-margin, high-failure business. The number nobody’s printing is the default rate.

The signal underneath

Go back to Form Energy, because it’s the cleanest example of a model from my book: second-order thinking. First-order thinking sees the AI boom and buys the obvious thing, chips and models. Second-order thinking asks the next question: what does that thing actually need to run? The answer is enormous, reliable, always-on power, and today’s grid can’t supply it at the pace the boom demands. So the durable trade often isn’t the headline. It’s the unglamorous infrastructure the headline can’t function without. T. Rowe Price didn’t lead a battery round because batteries are exciting. They led it because AI is, and batteries are how AI stays on.

That’s the through-line for the whole week. Drones need domestic manufacturing. AI apps need coding agents. Restaurants need credit. The money kept flowing one layer down from the shiny thing, into the pipes that make the shiny thing possible. Watch the layer below the hype, and you’ll usually spot the moat before everyone else does.

Next week’s deep-dive

Next week’s Startup Spotlight goes deep on one company turning “boring infrastructure” into a real moat, and why the sharpest founders are racing to own the bottleneck instead of the spotlight. Same six-section breakdown, same bar: make the $7.99 feel like a steal.

Disclaimer: This newsletter is for informational and educational purposes only. It is not investment, financial, legal, or tax advice, and nothing here is a recommendation or solicitation to buy, sell, or hold any security or to participate in any investment strategy. All funding amounts, valuations, and financial figures reflect information reported by the companies or by third-party news sources as of the publication date and are point-in-time figures that may change. Valuations described as reported, including the Cognition figure sourced from Bloomberg reporting, are not confirmed by the companies and should be treated as unverified press reports. Forward-looking statements, including production targets, revenue run-rate projections, and deployment plans, are attributed to the companies and are not statements of fact. Private company figures are difficult to verify independently. Do your own research and consult a licensed professional before making any financial decision. The author may hold positions in companies or sectors mentioned and receives no compensation from any company covered here.

If you want the mental models behind breakdowns like this, my book, Mental Models: How to Think, Act, and Win, is on Amazon now.

Thanks for reading Startup Spotlight! This post is public so feel free to share it.

Lovable Doubled to $13.3 Billion. Now Try to Kill It.

Spencer Gareiss — Thu, 13 Aug 2026 18:31:33 GMT

A two-year-old company that writes apps from plain-English prompts is now worth $13.3 billion. Eight months ago it was worth half that. The bull case is easy. The useful question is the opposite one.

Lovable, the Stockholm vibe-coding startup, announced a $400 million Series C on Wednesday at a $13.3 billion valuation, per the company and reporting from Reuters and Bloomberg. Menlo Ventures led it, with EQT’s Scaleup Europe Fund co-leading and backers including Tencent, Accel, and Salesforce Ventures.

The growth is the story. Lovable says its annual recurring revenue has nearly tripled from $200 million and is tracking toward $600 million by the end of August. The valuation doubled from $6.6 billion in December. More than 60 million projects have been built on the platform since it launched in November 2024, and the company says names like Nvidia and Adidas build on it. For a company that’s barely two years old, those numbers are absurd, and I mean that as a compliment.

Here’s a mental model from the book: inversion. Most people size up a hot company by asking why it wins. Inversion flips it. Don’t ask why Lovable is worth $13.3 billion. Ask what would have to be true for it to be worth zero, then go check whether the company is defusing those things.

So invert. The obvious kill shot for any AI app-builder is that it’s a thin layer on top of somebody else’s model. If the real value lives with the frontier labs, then Lovable is renting its core and competing with rivals like Replit and Cursor on a feature a bigger player could copy in a quarter.

Now go check. On August 11, Lovable published an engineering post saying its own in-house models now handle a growing share of the build work: routing requests, summarizing responses, writing commit messages, with harder tasks next. It calls the orchestration layer a control plane, and outside models have to earn their traffic against the in-house ones. In its words, model independence is the product. That’s not a coincidence. That’s a company that ran the same inversion and is building away from its biggest failure mode in public.

My take: the ARR chart is the least interesting thing here. Revenue growth this fast is real, but it’s also the easiest thing to fund and the easiest thing to lose. The durable question is whether Lovable owns any part of the stack that a frontier lab or an incumbent can’t absorb. The model-independence bet is their answer, and it’s the right thing to obsess over. Whether it works is a different question, and I’m not calling it.

Contrarian close: a $13.3 billion price tag isn’t really a bet on how many apps got built. It’s a bet that Lovable ends up owning the orchestration layer, the boring plumbing that decides which model does what. If it does, the wrapper worry goes away. If it doesn’t, the growth was rented. None of this is investment advice. But if you’re building anything on top of AI, run the inversion on yourself first: what kills you, and are you building away from it, or just away from the topic?

Disclaimer: This post is for informational and educational purposes only. It is not investment, financial, legal, or tax advice, nor a recommendation to buy, sell, or hold any security or asset. Lovable’s valuation, funding, and revenue figures are company-reported as of publication, with additional reporting from Reuters, Bloomberg, and TechCrunch; they are point-in-time, subject to change, and have not been independently verified. References to other companies are for context only and are not endorsements. Forward-looking statements reflect the companies or sources cited, not statements of fact. Do your own research and consult a licensed professional before making any financial decision.

If you want the mental models behind breakdowns like this, my book, Mental Models: How to Think, Act, and Win, is on Amazon now.

Thanks for reading Startup Spotlight! This post is public so feel free to share it.

Kalshi's Racing to $40 Billion. The Real Number Is 80%.

Spencer Gareiss — Thu, 13 Aug 2026 15:30:42 GMT

Kalshi was worth $11 billion in December. It’s now reportedly raising at $40 billion. That’s not a startup growing up. That’s a market changing its mind about what a regulated exchange is worth, every few weeks.

Here’s what dropped this week. The Information reports Kalshi is in advanced talks to raise at least $750 million at a roughly $40 billion valuation, with Sequoia Capital and Wellington Management in discussions to co-lead. If it closes there, it nearly doubles the $22 billion mark from the $1 billion Series F it did back in May, led by Coatue.

What’s driving it? Revenue. Kalshi’s annualized revenue reportedly topped $4 billion in July, roughly double the $2 billion pace from two months earlier, per The Information. World Cup wagering did a lot of the lifting. For scale, a $40 billion private mark would top Coinbase’s market cap and put Kalshi in the same conversation as Robinhood, which sits around $85 billion. I built prediction markets at Robinhood, so watching this category go from a policy footnote to a $40 billion line item is wild.

Time for a mental model from the book: second-order thinking. First-order thinking stops at the obvious. Revenue doubled, so the valuation doubled, makes sense, next. Second-order thinking asks the follow-up the headline skips: where does that $4 billion actually come from, and how durable is it?

Here’s the second-order read. Sports contracts reportedly make up more than 80% of Kalshi’s trading volume. And those contracts are exactly what more than a dozen states are trying to kill. New York sued. Arizona filed criminal charges. Massachusetts banned the sports markets. Kalshi’s whole defense is that it’s federally regulated by the CFTC, which has told it to keep operating, so states don’t get a vote. That fight isn’t settled.

So the same asset is both the moat and the risk. The regulatory position is why mainstream money will touch this at all. It’s also the single point of failure.

My take: the volume isn’t the product. The rulebook is. In regulated markets, your defensibility and your fragility live in the same place, which is your relationship with the regulator and the law. I see this every day at /mkt, where we run athlete offerings under Reg A+ with tZERO as the trading infrastructure. The compliance structure isn’t overhead. It’s the moat. Get it right and it’s a wall competitors can’t climb. Get it wrong and there’s no business left to value.

The contrarian bit? A $40 billion price on an 80%-sports revenue base isn’t pricing a betting app. It’s pricing a bet that federal preemption holds. That’s the actual trade underneath the trade. None of this is investment advice, and the round hasn’t closed. But if you’re building in a regulated category, steal the lesson: the rules aren’t the thing slowing you down. Long term, they might be the only thing you own.

Disclaimer: This post is for informational and educational purposes only. It is not investment, financial, legal, or tax advice, nor a recommendation to buy, sell, or hold any security or asset. Spencer Gareiss is the Chief Product Officer of /mkt and references it here as an illustrative example of building in regulated markets; this is a disclosed affiliation, not an endorsement or solicitation. Kalshi valuation and revenue figures are reported by a third party (The Information) as of publication, describe a funding round characterized as in talks and not closed, are point-in-time and subject to change, and have not been independently verified. Robinhood's market capitalization is a point-in-time public figure that fluctuates. Forward-looking statements reflect the reporting or the companies cited, not statements of fact. Do your own research and consult a licensed professional before making any financial decision.

If you want the mental models behind breakdowns like this, my book, Mental Models: How to Think, Act, and Win, is on Amazon now.

Thanks for reading Startup Spotlight! This post is public so feel free to share it.