The Algorithmic Bridge • 828 implied HN points • 06 Mar 26
- A metric that mixes LLMs' theoretical abilities with real-world usage reveals a huge gap between what models could do and what they're actually used for. For example, models theoretically cover ~94% of computer/math tasks but are used for only ~33%, and a similar gap appears in legal work (~90% vs ~20%).
- There are two ways to read this gap: one is optimistic that adoption will expand until real use matches theoretical capability, and the other is that the gap shows real limits and inflated lab benchmarks rather than a temporary lag.
- The practical lesson is that the industry may be overestimating AI's near-term labor impact and needs to focus on rigorous evidence of real-world competence and adoption, not just benchmarked capabilities.