EA - Neartermists should consider AGI timelines in their spending decisions by Tristan Cook

Link to original articleWelcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Neartermists should consider AGI timelines in their spending decisions, published by Tristan Cook on July 26, 2022 on The Effective Altruism Forum. Summary Neartermist funders should factor AGI timelines in their cost effectiveness analyses and possibly prepare to spend all their money quickly in light of an AGI arrival ‘heads-up’. When considering two types of interventions: those which produce value in a short period of time and those that produce value over a long time, factoring in AGI timelines pushes to marginally increase spending on the former. Main text Suppose for simplicity there are two types of interventions that neartermists can spend money on: 'Fast' interventions are interventions that have an immediate effect but have little to no flow-through effects beyond the time for which they’re applied. Examples could include providing pain relief medicines paying people to eat fewer animal products (who revert their diet after the intervention) 'Slow' interventions are interventions that help in the first year of implementation but they continue producing value for many years afterwards. Examples could include improving someone’s education, which improves their future earnings improving animal welfare laws Most of the most cost-effective neartermist interventions are slow interventions. Suppose we only considered the benefit from the first year of the interventions. Then it seems plausible that some fast interventions would be more cost effective than the cost effective slow interventions. If this was not the case, it may be a case of surprising and suspicious convergence. Now suppose we knew AGI was one year away and that the slow interventions have no flow-through effects after AGI arrives. Then given the choice between spending on slow or fast interventions, we should spend our money entirely on the latter. At some point, we may be in this epistemic state. However, we may never be in such an epistemic state where we know AGI is one year away. In this case, we should marginally spend more on fast interventions if we are 50% sure AGI will arrive next year than if we are 10% sure AGI will arrive next year. In both cases the AGI timeline considerations effectively discounts the slow interventions. With sufficient AGI implied discounting, fast interventions can become the most cost effective. Simple optimal control model I have created a simple optimal controlmodel that I describe in the appendix, and that you can play with here. The model (which I explain the appendix) takes parameters The real interest on capital The diminishing returns to spending on each of slow & fast interventions each year The ratio of (benefit in the first year of the best fast intervention per cost) to (benefit in the first year of the best slow intervention per cost) The rate at which slow interventions ‘wash out’ (alternatively framed as the degree to which their benefit is spread out over time). [Fast interventions are only beneficial in the first year by assumption]. AGI timelines The probability that at some point we have a one year ‘heads-up’ on the arrival of AGI and so spend all our money on fast intervention A non-AGI discount rate to account for non-AGI existential risks, philanthropic opportunities drying up, etc The model returns the optimal spending on fast inventions (xF(t)) and slow interventions (xS(t)) each year. Example model results Example 1 This example above has AGI timelines (50% by 2040, 75% by 2061). The best fast interventions have 5x more impact in the first year than slow interventions, but 0.25x impact over the long run (modulo AGI timelines & discounting). The growth in our capital is 5% and there is a 3% discount rate. The diminishing returns to spending on fast and slow interventions are equal. We have a 50% chance of a one year AGI arrival heads-up. Som...