The US vs. China, Google vs. Microsoft, Anthropic vs. ChatGPT: Who Really Controls the AI Race
Who really controls the AI race: the owners of models, chips, infrastructure, or access to the user?
Map how control of the AI race is distributed across models, chips, talent, cloud infrastructure, and access to users—and assess companies across the whole chain rather than by one benchmark.
What to watch for
Key takeaways
The discussion of “The most expensive part of the new AI race is almost invisible to the average user” yields a practical test: this section clarifies the mechanism behind the topic and preserves a constraint that would otherwise be lost in an overly simple conclusion.
The boundary of the “Digital avatars - what's going on in U.S. venture funds? Google’s work” case is defined by this point: the relevant signal is not one number or one round: runway, access to the next round, and the ability to retain a customer reveal whether the business is durable.
For the “Fake voices are a fraud? What can this be? Part 1/3” scene, the decisive point is this: the risk depends on the scope of access, the scale of the consequences, and whether the system can be stopped and its actions reconstructed.
For the “Areas of application of voices” scene, the decisive point is this: the forecast can be tested through specific dates, company actions, and changes in the product or market.
The discussion of “The U.S.–China AI competition in the AI: NVIDIA v. Huawei” yields a practical test: the important signal is not one funding number: the next round, available runway, and closure rate show whether a company can survive the new cost of capital.
The discussion of “Emotional side of AI: soon, artificial intelligence will cease to be “essential”” yields a practical test: the conflict reveals which rights, money, and control points the parties consider strategic.
The “Y Combinator and start-up trends” scene leads to a working conclusion: the accelerator is producing more and more AI companies, but putting a fashionable term in a pitch deck does not create a market. Devin, for example, has revived the debate over the end of the programming profession. Yet the product's real value is determined not by the demo, but by how reliably it performs the work and how much control a person still needs to retain.
The “Anthropic cooler ChatGPT?” scene leads to a working conclusion: this section clarifies the mechanism behind the topic and preserves a constraint that would otherwise be lost in an overly simple conclusion.
What this episode is about
The AI race does not begin with a polished chatbot. It begins with people, chips, money, and access to infrastructure. While OpenAI and Google demonstrate voice avatars, NVIDIA and Huawei are fighting over the market's computing foundation, and Y Combinator is testing which ideas can actually become businesses.
The most expensive part of the new AI race is almost invisible to the average user. We see a voice avatar, a new chat interface, or another demo. Behind it are multimillion-dollar engineering salaries, plans to raise trillions for infrastructure, and a fight for the small number of people who can build these systems at all.
The question is no longer which model gave the most elegant answer today. The question is who controls the talent, the compute, and the channels through which the product reaches users.
Voice avatars reveal the market's dual nature particularly well. On one side, HeyGen, Synthesia, and Google's work open up practical uses: dubbing, training, personalized messages, and children's books read in a parent's voice.
On the other, the same tool becomes a ready-made machine for fraud and political manipulation. OpenAI did not restrict access to voice technology for no reason: when a convincing fake can be produced quickly and cheaply, society still has to learn how to distinguish it from a real person.
The conflict is even sharper at the infrastructure layer. NVIDIA designs the chips, TSMC manufactures them in Taiwan, China is developing Huawei Ascend, and Google, Intel, and Qualcomm are looking for ways to reduce dependence on a single supplier. An export measure or manufacturing constraint can matter more here than another algorithmic improvement.
A country without access to modern chips simply cannot develop models at the same speed.
At the same time, Y Combinator shows where entrepreneurial energy is moving. The accelerator is producing more and more AI companies, but putting a fashionable term in a pitch deck does not create a market.
Devin, for example, has revived the debate over the end of the programming profession. Yet the product's real value is determined not by the demo, but by how reliably it performs the work and how much control a person still needs to retain.
The larger picture is straightforward: OpenAI, Google, Anthropic, Microsoft, and Chinese players are competing across several layers at once—models, chips, cloud infrastructure, data, products, and talent. The winner will not be the company that tops a benchmark once. It will be the one that connects all of these layers into a working system and makes it available to ordinary people.
One first-place benchmark result is not enough. The advantage goes to the player that connects models, compute, data, product, and distribution into a working system.
Episode transcript
The episode is in Russian; below is an English reading guide to the transcript (the full EN transcript is a machine translation). Voice matching applied to 47 segments: 37 identified, 6 mixed, 2 marked with ✓, and 2 unresolved.
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