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Wednesday, October 28, 2026

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@convequity

Convequity on X

Reflection AI has committed to more than $7bn of compute, and it will pay for it with investors' money, not with revenue from its new model. 1. On 5 October Reflection launched Beam, an open-weight model, meaning anyone will be able to download and run it for free. Most of the talk on X is about its benchmark claims, which have not been independently verified. 2. Reflection does not plan to sell access to the model. It plans to make money by helping enterprises and governments train its models on their own data and run them in-house. 3. This summer it agreed to pay $SPCX $150m a month for chips through 2029, about $6.3bn over the full term. Either side can end that deal with 90 days' notice after the first three months. It also signed a deal with $NBIS worth more than $1bn. 4. Reflection has raised about $4.7bn. That cash, not model sales, is what pays these bills. 5. Nebius says prepayments covered 50-60% of the capex on its four big Q2 deals, including Reflection's. It has not disclosed how much of Reflection's own contract is prepaid, guaranteed or cancellable. Until it does, I would haircut Reflection's share of Nebius's backlog. Reflection is a small part of Nebius's $40bn+ of customer commitments, so the effect on the total is small. ABBX, our AI bubble barometer, says the AI market is not in bubble territory yet: forward returns on AI capital are still above the cost of capital. It names commitments that do not show up as debt as the main risk. A lab with no model revenue signing multi-year compute contracts paid for with venture money is that kind of commitment. If more deals like this appear, I will read them as an early sign the market is getting frothy.
@ACapitalLP

A Capital on X

𝗣𝗲𝗿𝗰𝗲𝗽𝘁𝗶𝗼𝗻 𝗜𝘀 𝘁𝗵𝗲 𝗧𝗿𝗮𝗱𝗲. It’s funny: most people think the proliferation of information has made finding dislocations harder. I’d argue the opposite. When information was scarce, speed was the edge. Today the problem is information overload. Social media, AI, headlines, experts, hot takes. Just like different news organizations (CNN, Fox, MSNOW) can frame the same political candidate through completely different lenses, markets create narratives that can overwhelm the underlying facts. Every trade is a spread between perception and truth. Merger arb just makes that spread explicit because you have a relatively fixed endpoint (the deal consideration). Regular-way equities are harder because “truth” is a moving target. But you don’t need to know whether $TLN is worth $350, $370 or $390 to recognize when Josh Shapiro and data-center pushback have distorted perception enough to send it to $270. You don’t need to perfectly value every business line of $OWL to recognize when a Bloomberg story has caused the market to paint the entire business with the private-credit apocalypse brush. And it works both ways: $GLND wasn’t suddenly a critical-minerals bonanza because of a Trump headline. Sometimes the narrative creates the overvaluation. Having the best model or talking to the most experts helps you estimate truth. But truth without an understanding of perception isn’t a trade. You can be a little wrong on intrinsic value and still make a lot of money if you correctly identify that the market’s narrative has overshot reality. The edge today isn’t knowing whether something is worth exactly $37 or $39. It’s understanding why it’s trading at $27 in the first place, whether that perception is wrong, and what makes the gap close…

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My Last GFL Update - Hopefully

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310 Value’s Newsletter 5 clicks

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