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Tokens too cheap to meter

354 points · 227 comments · teoruiz

  1. cs702 · · focus · HN ↗
    I found the OP insightful and worth a read. Thank you for sharing it on HN.

    The only aspect that is poorly analyzed by the OP is business model viability. All players are investing insane amounts of money in infrastructure with the expectation that their future profits will justify all that investment. The winner or winners in the AGI race, they believe, will find the proverbial "pot of gold at the end of the rainbow."

    The OP glosses over questions of business model viability with a brief qualitative discussion and very little hard data. For example, to earn an annual return > 10% on every trillion dollars of capital sunk into infrastructure, the owners of that infrastructure must earn free cash flow (operating profit less investment) in excess of $100 billion per year in perpetuity. Is that feasible? Why? How?

    The OP does not really consider such questions.

    1. BenzeneDream · · focus · HN ↗
      They are already turning profits and inference has shown to be a cash cow. And they've already secured compute for the next several years.
      1. cs702 · · focus · HN ↗
        Some frontier labs are reporting positive "adjusted EBITDA" (earnings before interest, taxes, depreciation, and amortization, with extra adjustments to make the figure positive).

        Free cash flow (operating profit less investment), actual cash coming in, is deeply in the red.

        EBITDA can be a sensible measure of profitability when there isn't much need for additional investment. That doesn't seem to be the case with these operators. They need to invest aggressively to avoid losing customers to competitors. All of these operators have made multi-year commitments to invest more in infrastructure. In addition, they have guaranteed quite a bit of debt to fund it.

        Maybe it all will work out fine (and I sure hope it does!), but I didn't see any hard data from the OP, or from you, supporting that view.

        1. 0cf8612b2e1e · · focus · HN ↗
          EBITDA might make sense for the resellers who package up open weight models and sell inference. It is not appropriate for the labs who have billions in debt for RAM, new data centers, gobbling up competitors, etc.

          Those real debt obligations are going to want to be paid back.

      2. metalliqaz · · focus · HN ↗
        Who is the "they" that are turning profits?
        1. keybored · · focus · HN ↗
          People are saying. If you know you know.
      3. sanderjd · · focus · HN ↗
        Definitely. The question is: Is it enough to recoup the enormous capital costs and justify the level of investment they've received. I think there's a decent chance that it will be. But maybe not. And the longer they keep focusing on training new models more so than on inference, the more uncertain I become that it's all going to work out.
        1. pixl97 · · focus · HN ↗
          Honestly at this point with training costs I don't see how it could ever pay itself back unless you get RSI, in which the talk of money really isn't the main problem any longer.

          We're in a situation where AI isn't going to go away, but whatever financial mode we're in right not is not going to work.

      4. ofjcihen · · focus · HN ↗
        Labs are playing money games with EBITDA, which is not uncommon, but also hides the extent to which they are in the red (deeply, deeply, in the red, and projected by them to get worse).
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