We introduce an open-access web tool that estimates when quantum systems will outperform classical computers for specific algorithms on a cost‑equivalent basis. Built on the Choi–Moses–Thompson framework, it lets users vary assumptions about error correction, gate speeds, overheads, connectivity, hardware roadmaps, and classical baselines. We find that expected timing for advantage is robust for some algorithms (e.g., Shor's) but contingent for others (e.g., Grover's), depending on technical factors.

Denizens of Silicon Valley have called Moore's Law "the most important graph in human history," and economists have found that Moore's Law-powered I.T. revolution has been one of the most important sources of national productivity growth. But data substantiating these claims tend to either be abstracted - for example by examining spending on I.T., rather than I.T. itself - or anecdotal. In this paper, we assemble direct quantitative evidence of the impact that computing power has had on five domains: two computing bellwethers (Chess and Go), and three economically important applications (weather prediction, protein folding, and oil exploration). Computing power explains 49%-94% of the performance improvements in these domains. But whereas economic theory typically assumes a power-law relationship between inputs and outputs, we find that an exponential increase in computing power is needed to get linear improvements in these outcomes. This helps clarify why the exponential growth of computing power from Moore's Law has been so important for progress, and why performance improvements across many domains are becoming economically tenuous as Moore's Law breaks down.

"Computing performance doubles every couple of years" is the popular re-phrasing of Moore's Law, which describes the 500,000 – fold increase in the number of transistors on modern computer chips. But what impact has this 50 – year expansion of the technological frontier of computing had on the productivity of firms? This paper focuses on the surprise change in chip design in the mid-2000s, when Moore's Law faltered. No longer could it provide ever-faster processors, but instead it provided multicore ones with stagnant speeds. Using the asymmetric impacts from the changeover to multicore, this paper shows that firms that were ill-suited to this change because of their software usage were much less advantaged by later improvements from Moore's Law.