Yeah, I don't disagree with your assessment at all. I think the H2A ratio is still a good metric for the AI adoption rate of an organization. At a higher H2A ratio, you will also start to hear people measuring things using token volumes, which I think is also a similar metric (because most models nowadays run on a relatively fixed Tokens/second speed).

All of this is not a direct signal to a productivity boost. I think at higher volumes, you will need to start to account for the "yield" rate of the token volumes above: what are the volumes of tokens that get to the final production deployment? At which stage is it a constraint on the yield? Is it the models, or is it the harness, or something else (i.e. Code Review, CI/CD, Security Scans etc...)? And then it becomes an optimization problem to reduce the Cost of Goods Sold while improving/maintaining Revenues. The "productivity" will then be dissolved into multiple separate but more tangible metrics.