The framing difference made explicit here is worth more than the numbers: the approach described concentrates on diffusion and does not talk about artificial general intelligence. That is strategic rather than rhetorical, because a programme organised around reaching a threshold prioritises frontier capability while one organised around diffusion prioritises getting existing capability into industries — different investment, metrics and definition of success. The operational emphasis follows: building scalable systems in production across many datasets, and lowering the cost of using AI through cloud-level optimisation. That is where diffusion and cost converge, since the marginal adopter is by definition more price-sensitive than the last one. Open weights fit the same pattern as distribution mechanism rather than philosophy. What the session avoids is what is given up, and the position is the opposite of the one taken by the frontier labs elsewhere at this conference.