The supply chain starts with producers and designers of AI infrastructure: firms like TSMC and Samsung, which fabricate chips; Nvidia, which designs them; and Cisco, which provides connectivity. Then come the hyperscalers like Amazon, Google and Microsoft. They are building data centres both for their own AI models and to sell compute (processing power) to others. In addition to the hyperscalers, there are more specialised companies like Equinix (data centres) and, of course, Anthropic and OpenAI, the developers of foundational LLMs.
AI tools will undoubtedly transform the nature of work. Large language models (LLM) can already generate referee reports for my own research papers that rival those of human referees. Unlike humans, who are always pressed for time, an LLM “knows” or can access much more of the literature in an instant, and often exhibits fewer biases. AI points out my analytical weaknesses, checks proofs, and makes suggestions for improvement. Only rarely are human reports better, typically because they connect the dots and offer new insights.
Nonetheless, the market euphoria around AI has become worrisome, especially given the scale of debt issuance in the sector. It is therefore worth considering where in the AI supply chain things could go wrong.
