Is it worth the effective altruism (EA) community trying to accelerate the growth of cultured meat production? Should EA just let market forces move it forward? Should EA invest directly in cultured meat R&D or identify high-leverage ways to increase funding? Or should EA just not invest in it because it is insufficiently promising?

Most work on forecasting, including most EA work on forecasting, is on short-range forecasting (defined loosely here as timescales of ~1 week - ~3 years). Yet most of the motivation for why forecasting is valuable appeals to long-range forecasting, defined loosely as forecasting on the timescale of greater than 10 years, or much longer. Here, I argue that advances in short-range forecasting (particularly in quality of predictions, number of hours invested, and the quality and decision-relevance of questions) can be robustly and significantly useful for existential risk reduction, even without directly improving our ability to forecast long-range outcomes, and without large step-change improvements to our current approaches to forecasting itself (as opposed to our pipelines for and ways of organizing forecasting efforts).

In addition to the EA Early Warning Forecasting Center I outlined in my other post, I think there are several ways forecasting may be very useful for longtermism, including: (1) Forecasting as a way to amplify EA research (2) Prediction-evaluation setups as a way to improve EA grantmaking (3) Large-scale broad forecasting as an EA outreach intervention (4) Large-scale forecasting tournaments as a talent training and vetting pipeline (5) The dream: high-quality, calibrated, long-range forecasting (ideally also at scale and on-demand).

The story of how cultured meat becomes widespread involves overcoming many technical challenges. We review techno-ecomonic analyses (TEAs) of cultured meat, and evaluate their disagreements. The main cruxes of disagreement across the TEAs are: approach to the research question, investor payback timelines, food grade versus pharmaceutical grade bioreactors, the costs of media (growth factors and amino acids) at scale, and the limits of cell-engineering needed to reduce media consumption needs.