There is only about ~another round of capital left where the companies can remain unprofitable. Perhaps OpenAI/Anthropic could raise $250-500B at a $1.5-2.5T valuation, but it seems very unlikely that they could raise $1T+ at a $4T+ valuation.
If they go public, this level of funding can continue. There is a lot of demand for exposure to AI.
It’s fairly hard to imagine AI labs doing much to cut costs to become profitable.
If Anthropic is making $44bn in annualized revenue (in some sense), that’s enough for maybe 3-4 GW of compute (at $12-15bn per GW per year), which they don’t physically have. To be unprofitable, it’s necessary to be able to get enough compute to spend the money on, so currently it’s possible to fail in the pursuit of unprofitability. (OpenAI probably didn’t fail.)
Anthropic’s current first-party inference plus R&D compute might be about 1-1.5 GW, that is they are only able to spend $12-25bn, annualized. They possibly have more capacity that’s not counted in this estimate, when serving via API from Vertex/Bedrock/Azure and leaving a greater part of the revenue with the clouds. Then it’s less than $44bn that remains for their own first-party inference plus R&D compute. SemiAnalysis estimates a gross margin of “over 70%”, which probably translates to annualized costs of only $12bn on serving models (if all inference was first-party), meaning a total of 1 GW of inference compute (Anthropic’s own dedicated compute plus the compute from the clouds). If they are using 0.5 GW of their own compute at a 72% gross margin, and 0.5 GW of compute from the clouds at a 30% gross margin (the rest goes to the clouds, and becomes a cost for Anthropic), that’s $22bn of gross profit in total out of the $44bn of revenue. To break even, they’d need 1 GW of R&D compute at $15bn per GW per year (on top of the 0.5 GW of first-party inference compute), which is a stretch. Though they’ll probably endeavor to restore the state of unprofitability as soon as they can.
If they go public, this level of funding can continue. There is a lot of demand for exposure to AI.
I think it’s possible, but I don’t think it’s likely that we could have the most valuable companies in the world by market cap, raise trillions of dollars without being profitable, or have a plan to become profitable very soon. There is only so much capital, and particularly risk capital. The dynamic changes a lot when there isn’t much room for growth. In particular, there is about $100T of total revenue per year in the world right now, and so even if you capture all of it, with 100% gross margins and a P/E ratio of 20, you are only looking at a market cap of a quadrillion or a 200x at MC of $5T. Larger amounts of capital are much more risk-averse than smaller amounts of capital.
I think people are over-indexing on this here, every month they get insane growth and think this will occur forever. I wish these numbers were audited. There is also more to spend money on than raw compute but I do agree this is the bulk of it. They did just make a deal with SpaceX for a lot of compute they could feasibly spend a lot of money on. I don’t see how this negates any of my analysis above.
I suppose I would ask, do you think Anthropic is currently profitable (or was in April)?
I don’t think it’s likely … raise trillions of dollars without being profitable, or have a plan to become profitable very soon
The plan is they become profitable as soon as they stop growing, provided they manage to grow to the correct size and no more. The only reason they are unprofitable is that they are growing, the R&D compute is trying to match next year’s inference compute, rather than this year’s inference compute. A lot about future compute buildout efforts can in principle be canceled or delayed on a relatively short notice, significantly reducing the cost to keep the work already done at the half-completed datacenter sites useful for when it resumes later than planned. For this to be the actual option, the contracts expressing the commitments need to be sufficiently flexible, though in some ways that only shifts the backlash from unpredictability of the timing for the end of the LLM supercycle (assuming no AGI by 2028-2030, which is the time when rapid scaling of compute should run out of the immediately accessible TAM) from the AI companies down the supply chains.
They did just make a deal with SpaceX for a lot of compute they could feasibly spend a lot of money on.
That’s just 300 MW, which is maybe $4-5bn per year, not much of a dent in $44bn. Currently their problem is that they are not able to spend the money, because almost nobody has any extra compute (at a scale at all relevant to them) immediately ready to go. They can only spend more on future compute.
every month they get insane growth and think this will occur forever
I don’t see the evidence they think this will occur forever. They think this will occur at least through 2027-2028, perhaps slower than so far and even slower in 2028, but still with significant growth (or perhaps keeping to 3x compute per year, thus 1-2 GW at end of 2025 become 10 GW by end of 2027 and more than that in 2028). They are ready to respond to the signs it’s slowing down, and maybe only need 2 years of notice to cancel excessive future buildouts cheaply, and 1 year of notice to delay future buildouts at a manageable cost (in a way that will make them useful when completed later).
do you think Anthropic is currently profitable (or was in April)?
I think it’s likely profitable (or was very recently) in the sense of run rate revenue exceeding run rate spending on all of the compute that’s currently online (all compute that is serving inference, plus all R&D compute, including training). This is not according to plan and will shortly be once again not so. But also, at any point where they are succeeding at being unprofitable, they can shift some R&D compute to inference and become profitable (making use of the 50-70% gross margin on serving tokens, which agrees with first-principles estimates), within weeks to months, as long as there is enough demand remaining to make use of the new inference compute shifted from R&D. And they would still be left with a reasonable amount of R&D compute to train models for the next year, if it turns out that next year they don’t actually need much more compute than they had this year (maybe less than 2x of what they had this year).
This is more the case when most of the compute serving their models is their own compute, so that it only costs them as much as it costs to build (annualized), rather than also whatever portion of their gross margin the clouds are taking when serving their models via Vertex/Bedrock/Azure. Thus some of the speed of growth in the buildouts is probably about shifting the inference compute from the indirect serving via clouds to the more directly contracted dedicated compute that’s cheaper for them (and will remain so).
If they go public, this level of funding can continue. There is a lot of demand for exposure to AI.
If Anthropic is making $44bn in annualized revenue (in some sense), that’s enough for maybe 3-4 GW of compute (at $12-15bn per GW per year), which they don’t physically have. To be unprofitable, it’s necessary to be able to get enough compute to spend the money on, so currently it’s possible to fail in the pursuit of unprofitability. (OpenAI probably didn’t fail.)
Anthropic’s current first-party inference plus R&D compute might be about 1-1.5 GW, that is they are only able to spend $12-25bn, annualized. They possibly have more capacity that’s not counted in this estimate, when serving via API from Vertex/Bedrock/Azure and leaving a greater part of the revenue with the clouds. Then it’s less than $44bn that remains for their own first-party inference plus R&D compute. SemiAnalysis estimates a gross margin of “over 70%”, which probably translates to annualized costs of only $12bn on serving models (if all inference was first-party), meaning a total of 1 GW of inference compute (Anthropic’s own dedicated compute plus the compute from the clouds). If they are using 0.5 GW of their own compute at a 72% gross margin, and 0.5 GW of compute from the clouds at a 30% gross margin (the rest goes to the clouds, and becomes a cost for Anthropic), that’s $22bn of gross profit in total out of the $44bn of revenue. To break even, they’d need 1 GW of R&D compute at $15bn per GW per year (on top of the 0.5 GW of first-party inference compute), which is a stretch. Though they’ll probably endeavor to restore the state of unprofitability as soon as they can.
I think it’s possible, but I don’t think it’s likely that we could have the most valuable companies in the world by market cap, raise trillions of dollars without being profitable, or have a plan to become profitable very soon. There is only so much capital, and particularly risk capital. The dynamic changes a lot when there isn’t much room for growth. In particular, there is about $100T of total revenue per year in the world right now, and so even if you capture all of it, with 100% gross margins and a P/E ratio of 20, you are only looking at a market cap of a quadrillion or a 200x at MC of $5T. Larger amounts of capital are much more risk-averse than smaller amounts of capital.
I think people are over-indexing on this here, every month they get insane growth and think this will occur forever. I wish these numbers were audited. There is also more to spend money on than raw compute but I do agree this is the bulk of it. They did just make a deal with SpaceX for a lot of compute they could feasibly spend a lot of money on. I don’t see how this negates any of my analysis above.
I suppose I would ask, do you think Anthropic is currently profitable (or was in April)?
The plan is they become profitable as soon as they stop growing, provided they manage to grow to the correct size and no more. The only reason they are unprofitable is that they are growing, the R&D compute is trying to match next year’s inference compute, rather than this year’s inference compute. A lot about future compute buildout efforts can in principle be canceled or delayed on a relatively short notice, significantly reducing the cost to keep the work already done at the half-completed datacenter sites useful for when it resumes later than planned. For this to be the actual option, the contracts expressing the commitments need to be sufficiently flexible, though in some ways that only shifts the backlash from unpredictability of the timing for the end of the LLM supercycle (assuming no AGI by 2028-2030, which is the time when rapid scaling of compute should run out of the immediately accessible TAM) from the AI companies down the supply chains.
That’s just 300 MW, which is maybe $4-5bn per year, not much of a dent in $44bn. Currently their problem is that they are not able to spend the money, because almost nobody has any extra compute (at a scale at all relevant to them) immediately ready to go. They can only spend more on future compute.
I don’t see the evidence they think this will occur forever. They think this will occur at least through 2027-2028, perhaps slower than so far and even slower in 2028, but still with significant growth (or perhaps keeping to 3x compute per year, thus 1-2 GW at end of 2025 become 10 GW by end of 2027 and more than that in 2028). They are ready to respond to the signs it’s slowing down, and maybe only need 2 years of notice to cancel excessive future buildouts cheaply, and 1 year of notice to delay future buildouts at a manageable cost (in a way that will make them useful when completed later).
I think it’s likely profitable (or was very recently) in the sense of run rate revenue exceeding run rate spending on all of the compute that’s currently online (all compute that is serving inference, plus all R&D compute, including training). This is not according to plan and will shortly be once again not so. But also, at any point where they are succeeding at being unprofitable, they can shift some R&D compute to inference and become profitable (making use of the 50-70% gross margin on serving tokens, which agrees with first-principles estimates), within weeks to months, as long as there is enough demand remaining to make use of the new inference compute shifted from R&D. And they would still be left with a reasonable amount of R&D compute to train models for the next year, if it turns out that next year they don’t actually need much more compute than they had this year (maybe less than 2x of what they had this year).
This is more the case when most of the compute serving their models is their own compute, so that it only costs them as much as it costs to build (annualized), rather than also whatever portion of their gross margin the clouds are taking when serving their models via Vertex/Bedrock/Azure. Thus some of the speed of growth in the buildouts is probably about shifting the inference compute from the indirect serving via clouds to the more directly contracted dedicated compute that’s cheaper for them (and will remain so).