Hook
South Korean President Lee Jae-myung will attend the upcoming AI summit in San Francisco and hold private meetings with the CEOs of Nvidia, OpenAI, Anthropic, and Broadcom. On the surface, this looks like standard diplomatic courtship. But for anyone who has spent years watching centralized power structures form inside crypto, the roster reads differently. It reads like a blueprint for the exact kind of vendor lock-in that Web3 was built to dismantle.
Context
We have been here before. In 2017, when ICOs promised to democratize fundraising, the market quickly consolidated around a handful of custodial exchanges. By 2020, DeFi Summer seemed to offer an escape through composable liquidity — until Curve’s governance nearly collapsed under concentrated voting power. Today, AI faces a similar inflection point. The race to secure compute and frontier models is driving nations straight into the arms of a few corporations. Nvidia controls over 80% of AI training hardware. OpenAI and Anthropic own the two most capable closed-source models. Broadcom designs the networking chips that stitch together hyperscale data centers. President Lee’s itinerary is not a coincidence; it is a capitulation to centralized infrastructure.
Open source isn’t just a licensing choice. It’s a philosophy of transparency that allows anyone to audit, fork, and improve the technology. When a nation’s AI strategy hinges on closed APIs and proprietary silicon, that transparency evaporates. The Korean government is effectively outsourcing its AI sovereignty to four boardrooms in California. The same dynamic played out in crypto when countries adopted CBDCs built on centralized ledgers rather than permissionless blockchains.
Core
Decentralization is not a tech stack; it is a philosophy of transparency. The question for South Korea — and for every other nation — is whether they will duplicate the mistakes of legacy finance by choosing closed systems over open ones.
Let’s break down what the four meetings imply from a technical and economic standpoint:
1. Nvidia – The Compute Monopoly Nvidia’s CUDA ecosystem has become a moat that renders alternative hardware nearly useless for large-scale training. During DeFi Summer, I wrote about the “Geometry of Trust” — where liquidity pool design determined capital efficiency. The same principle applies to AI: the geometry of parallel processing determines compute efficiency. Nvidia’s H100 and upcoming B200 are the only production-ready chips that deliver the necessary parallel throughput for cutting-edge models. South Korea, despite being home to Samsung and SK Hynix — the world’s leading memory manufacturers — cannot produce a competitive AI accelerator. President Lee will likely negotiate supply guarantees and perhaps a deal to embed Korean HBM3E memory deeper into Nvidia’s packaging roadmap. That is good for Korean exports, but it deepens dependency. Based on my audit experience with early DeFi protocols, I learned that liquidity concentration is a single point of failure. Compute concentration is no different. Projects like Akash Network and Render Network are building decentralized GPU marketplaces that allow anyone to rent compute from idle hardware worldwide. But they lack the scale to serve trillion-parameter models. South Korea could accelerate this by using its national cloud procurement budget to fund decentralized compute pilots, yet the meeting agenda suggests the opposite: more centralized contracts.
2. OpenAI – The Closed Model Gatekeeper OpenAI’s GPT-4 and o1 are the gold standard for reasoning tasks, but they are black boxes. We don’t know the training data, the architecture choices, or the safety filters with full transparency. When a government integrates such a model into public services — healthcare, education, legal advice — it inherits opaque decision-making. During the Terra/Luna collapse, I wrote a post-mortem called “The Hubris of Leverage” about how hidden leverage can amplify systemic risk. Closed models hide a different kind of leverage: leverage over information flow. South Korea’s massive tech conglomerates (Samsung, LG, Naver, Kakao) have their own AI ambitions. Naver’s HyperCLOVA X model is already deployed in Korean financial services. President Lee’s meeting with Sam Altman signals that the government may prefer a foreign API over a homegrown alternative. That decision could stifle local innovation and create a single point of failure for national AI infrastructure.
3. Anthropic – The Safety Theater Anthropic’s “Constitutional AI” is marketed as a safer alternative to OpenAI. But safety without transparency is theater. Anthropic’s models are still closed-source; their constitution is a document, not auditable code. I have seen this pattern before in crypto when projects claimed to be “audited” but the audit scope was narrow and the smart contract remained unverified. South Korea may adopt Anthropic’s framework for its national AI safety guidelines, which would give Anthropic de facto regulatory influence over Korean AI policy. Furthermore, by partnering with an American company on safety, South Korea draws a regulatory line that could exclude models from China (e.g., DeepSeek, Baidu ERNIE) or open-source alternatives (Mistral, Llama). This is a geopolitical choice disguised as a technical one. We didn't fall for centralized exchange insurance funds promising safety without reserves. We should not fall for centralized AI safety promises without open-source validation.
4. Broadcom – The Infrastructure Backbone Broadcom’s Jericho3-AI and Tomahawk switches enable the low-latency, high-bandwidth networks required for distributed training across thousands of GPUs. If South Korea plans to build a national AI supercomputer, it needs Broadcom’s networking. But Broadcom’s proprietary solutions lock the country into a single vendor for years. In the blockchain world, we build with open standards like libp2p and gossip protocols to avoid vendor lock-in. Hyperscale data centers can choose from multiple networking vendors, but Broadcom’s near-monopoly on AI networking chips limits competition. South Korea could invest in open networking projects like ONF (Open Networking Foundation) or even explore decentralized coordination protocols for compute clusters, but the meeting with Broadcom suggests a traditional top-down infrastructure play. Red flag: government procurement that favors a single vendor is the fastest path to cost overruns and technical stagnation — a lesson learned from countless IT modernization failures.
Contrarian
Many in the crypto AI space will interpret this news as bullish for AI tokens. The logic: sovereign adoption validates the technology, and the bull market will lift all boats. I argue the opposite. Government embrace of centralized AI infrastructure strengthens the incumbents that decentralized networks aim to displace. Every dollar South Korea spends on Nvidia GPUs under a direct contract is a dollar not spent on Akash or Render. Every API call routed through OpenAI is a data point lost to a closed system instead of an open protocol like Bittensor’s subnet. Moreover, the regulatory framework that emerges from this diplomatic push will likely impose compliance burdens that open-source projects cannot easily meet. We saw this with Hong Kong’s virtual asset licensing — it was framed as innovation-friendly, but its real purpose was to steal Singapore’s spot as Asia’s financial hub. The stated goal and the actual outcome diverged. Similarly, South Korea’s AI summit participation may be sold as “national competitiveness,” but the outcome could be a regulatory moat that protects American incumbents at the expense of global open AI.
On the other hand, the contrarian might point out that state action can inadvertently catalyze decentralization. When China banned ICOs in 2017, developers fled to decentralized exchanges and DAOs. If South Korea mandates that all government AI services use only API-based, approved models, it could create a black market for open-source models among Korean developers, pushing them toward self-hosted or decentralized inference solutions. Legal risk often accelerates adoption of decentralized alternatives because those alternatives are harder to censor. There is a world where this summit lights a fire under builders of decentralized compute protocols, forcing them to achieve the reliability and scale that governments demand. But that is a long shot. The more likely near-term effect is a re-enforcement of centralized AI channels.
Takeaway
The future of AI will not be determined solely by algorithms or hardware; it will be determined by the architectures of trust we choose. South Korea’s president is making a bet that centralized, closed systems are the fastest path to AI supremacy. That may be true for the next 18 months. But the history of financial infrastructure — and the rise of DeFi in particular — shows that open, transparent, trust-minimized systems eventually dominate because they can be audited, forked, and improved by anyone. We didn’t build Bitcoin and Ethereum to watch sovereigns reproduce the same centralized dynamics in AI. The question is not whether South Korea chooses American closed AI over Chinese closed AI. The question is whether we can build an open alternative that governments can’t afford to ignore.