anastasios

Chinese open models (Kimi, Qwen) now beat US models on key tasks, violating 'distillation only' narrative; US will likely produce a multi-hundred-billion dollar open-source champion (Thinking Machines candidate); enterprises want AI sovereignty and cost control driving open-source adoption; US may ban Chinese models due to backdoor risks and lobbying by US labs.

Enterprises want to own their intelligence stack (cost, sovereignty, self-improvement, data privacy); this creates massive market for AI modernization services (fine-tuning, integration, workflow restructuring) that open source model companies can capture via deployed-engineer model.

Enterprises will demand AI sovereignty (owning their model supply chain), driving adoption of open-source models fine-tuned on proprietary data. This creates a massive market for an American open-source company combining revenue-share inference and deployed-engineer services, potentially reaching multi-hundred-billion or trillion-dollar valuation.

Chinese open source models (Kimi, Qwen) have surpassed US closed models on key benchmarks, violating the distillation narrative. However, US regulatory environment will drive enterprises toward American open source alternatives, creating opportunity for a Thinking Machines-type company to capture $100B+ value via revenue-share or deployed-engineer models.

US chip ecosystem (Nvidia/TSMC) is a national security asset; export controls may accelerate Chinese self-sufficiency long-term but currently protect Nvidia's dominance. Enterprise adoption of AI (fine-tuning, sovereignty) will drive 10x compute growth, benefiting Nvidia and new US chip startups like Etched.

75+ Neolabs but 2/3 will be worth zero; market now P&L driven requiring hypergrowth revenue; data is scaling complement to AI (10-20% of GPU spend) with durable demand until AGI; revenue concentration criticism is lazy (TSMC, Anduril counterexamples); data market could reach $100B-$1T by 2030.

Data is a scaling complement to models (more compute → more data needed); durable need until AGI; data providers spend 10-20% of GPU budgets; revenue concentration (OpenAI/Anthropic/Meta) is not a flaw—TSMC/Anduril prove concentrated models work; market could reach $100B-$1T by 2030.

Semiconductor supply chain is national security necessity; export controls protect US lead but risk accelerating Chinese self-sufficiency; new US startups (Etched) emerging to extend lead; government should actively grow this ecosystem.