At least since reading some early Singularity-adjacent publications by Vernor Vinge, Ray Kurzweil, and Bill Joy, I’ve been interested in learning curves. (And for the record, the topic and these authors were were not considered politically “right wing” or even political at all at the time.) Progress, at least in certain technologies, tends to be exponential over time. This clearly applies to computer technology, where there are short product cycles, the needed infrastructure is more or less in place and/or can adapt as the technology is scaled/commercialized, and legal and institutional barriers to change are relatively low. Technologies with “recipes”, like chemicals, drugs, seeds and other agricultural technologies, might be other examples. For these we have the patent system to actually try to slow down scaling and commercialization to the pace of innovation. With energy technology, learning curves seem to play out over much longer periods of time because while available technology changes rapidly, our system tends to be locked into long-lived infrastructure that can only change slowly. So new energy technology rolls out slowly as it is scaled up and commercialized over decades. There are also entities with enormous political and propaganda power that fight tooth and nail to keep us locked into obsolete technologies and infrastructure that fit into their historical (and profitable) business models. Now when you get to other technologies, like transportation and housing, public policy, legal and institutional barriers are dominant and tend to retard or even prevent progress. Rollout is so hard that while there are pockets of innovation, many don’t see the light of day or don’t spread from the local/pilot scale, even if they are successful at this scale. These also vary by location and jurisdiction, so that progress is very uneven geographically. These are my thoughts anyway. As for what’s new, here’s a journal article from Advances in Applied Energy.

Variability of technology learning rates

Climate and energy policy analysts and researchers often forecast the cost of low-carbon energy technologies using Wright’s model of technological innovation. The learning rate, i.e., the percentage cost reduction per doubling of cumulative production, is assumed constant in this model. Here, we analyze the relationship between cost and scale of production for 87 technologies in the Performance Curve Database spanning multiple sectors. We find that stepwise changes in learning rates provide a better fit for 58 of these technologies and produce forecasts with equal or significantly lower errors compared to constant learning rates for 36 and 30 technologies, respectively. While costs generally decrease with increasing production, past learning rates are not good predictors of future learning rates. We show that these results affect technological change projections in the short and long term, focusing on three key mitigation technologies: solar photovoltaics, wind power, and lithium-ion batteries. We suggest that investment in early-stage technologies nearing cost-competitiveness, combined with techno-economic analysis and decision-making under uncertainty methods, can help mitigate the impact of uncertainty in projections of future technology cost.

This blog in Construction Physics has a deeper dive across more industries, and discusses at least one large data set that is available for analysis. If you could accurately predict learning rates (and successful scaling/commercialization rates) for specific technologies based on known factors, then theoretically you could fine-tune policies and incentives to increase the rate of progress in the technologies you want. So this is an area of research that could boost all other areas of research and progress.