How should we evaluate a country or company’s skill infrastructure?
The first thing I look for is the breadth of the talent pool. More specifically, how many people can do good-enough AI engineering tasks. To make that concrete: a developer who can take an open-source model, fine-tune it on an industry-specific dataset and produce a proof of concept for how it could be integrated into a particular company workflow. That is the baseline. The question is how wide that pool is, not how exceptional its top end is.
The second dimension is systematization. So, to what extent can a country or firm take that knowledge and spread it across different divisions or sectors, rather than leaving it siloed in one team or one institution. At the country level, this means strong linkages between industry and academia, and effective technology-transfer channels. Germany’s Fraunhofer applied research institutes are a good example of what that can look like in practice: bridging institutions that standardize and disseminate engineering knowledge from the frontier to the broader economy.
At the firm level, it might mean a common set of standards and protocols for how data is structured and updated so that it can feed into AI pipelines, or shared benchmarks for evaluating which model to deploy for a given task. The extent to which a firm can ensure that everyone is on the same page about integrating AI into operations (and not just the dedicated AI team) is itself a measure of skill infrastructure.
One area that is often underappreciated at the country level is the legal and institutional environment around university-industry linkages. In the US, it is relatively easy for researchers to spin out start-ups without ceding dominant intellectual property rights to their universities. That has enabled stronger, more porous connections between entrepreneurship and academia; it helps systematize the flow of knowledge across the technology ecosystem in ways that matter for diffusion.
