I couldn’t resist writing about another of Dwarkesh’s conversations with his good friend and AI industry guru Dylan Patel from SemiAnalysis. According to Dylan, after initially losing money on inference, AI labs now generate revenue well above their compute costs. He puts Anthropic’s annual revenue as high as $50 million per megawatt (MW), against compute costs of $10–15 million. Meanwhile, bringing more capacity online remains constrained by supply-chain bottlenecks and power availability. By the end of 2027, he believes annual revenue could reach $70–80 million per MW across Anthropic or OpenAI, depending on model performance and whether they can keep releasing better models.
This explains why everyone is piling money into new compute capacity. The value of each MW is going up dramatically, promising incredible returns for the foreseeable future. The two leading labs produce the most revenue per unit of compute, which allows them to outbid other buyers for new capacity. As stated in the title of Dwarkesh’s post, two private companies could soon control a majority of the world’s compute.
This hoarding of infrastructure is an especially big problem in AI, as access to compute determines how much research and training you can do on future models. We risk a self-reinforcing cycle that would leave these two giants controlling the world’s most advanced AI and almost all the hardware capable of running it.
Putting aside the debate on the AI financial bubble and the wisdom of such large investments, what’s at stake here isn’t merely attractive returns for investors but raw political and economic power. Whether or not current investments are recouped, money and wealth will always flow to those with power. This is the real bet being made: control of the AI frontier will become an essential pillar of power in the 21st century.
As discussed towards the end of the episode, there is a risk of an unacceptable concentration of power in the hands of just a couple of companies if — and it’s a big if — AI turns out to be able to replace the majority of human work in the near future. We’ve recently seen exciting releases of open-weight models that come close to the frontier at affordable prices. But where would these models run at scale? Where would the next generation be trained if the necessary hardware were owned or leased by companies with a vested interest in staying ahead? Even China faces a substantial gap: Dylan estimates that less than 10% of new AI compute is being deployed there, compared with around 70% in the US.
This is why our governments need to own or co-own a considerable share of the world’s new compute capacity. As in other strategic sectors of national importance, such as nuclear power, weapons manufacturing or space exploration, they need a real seat at the table to ensure AI is developed and deployed in a democratic and socially responsible manner, with access extending beyond Anthropic and OpenAI’s customers. It’s also a matter of national security and sovereignty, as AI becomes an increasingly important component of warfare and defence against cyberattacks.
My concern is that legislation is only effective if you have real leverage over the companies you are trying to regulate. In Europe’s case, we are making it harder for our own companies to compete and adapt without affecting how AI develops in the US or China. I recommend Wolfgang Münchau’s excellent piece on the subject.
Taxation is another valid option. There is an interesting paper worth reading on taxing tokens at the point of usage and how such a system could work. Redistributing some of the value created by AI across society will probably soon become necessary, but taxation alone would not address the concentration of power.
The problem, of course, is that most Western governments are in no financial position to make large infrastructure investments as in the past. Public sentiment in Europe is also largely sceptical about AI. This makes investment at the required scale unlikely. If countries miss this opportunity, they may never catch up with the private sector or geopolitical rivals, as greater access to compute helps the leaders develop better models and extend their advantage.
One financing option would be “AI bonds”: investors would lend to a publicly owned venture and receive interest, while ownership remained public. Why not? If Google and Meta can issue debt to finance new data centres, why couldn’t governments do the same? Public-private joint ventures could also bring in capital and technical expertise, provided the public partner retained meaningful control over how capacity was allocated.
Part of this capacity would be leased at market rates to the highest bidders, including Anthropic or OpenAI, allowing local consumers to continue using their models. Another part would be reserved for competing labs and public research, giving them a shot at training large models of their own.
Revenue would cover operating costs, repay lenders with interest, and help fund expansion and replace ageing hardware — AI chips don’t age well!
The real value for society would come from public ownership and control of infrastructure critical to national prosperity and security. It would give us a say in who uses it and who benefits, helping preserve the social democracies we hold dear.