Interesting. I fully admit most of my experience with unknown unknowns comes from either civil engineering projects or bringing consumer products to market, both situations where the unknown unknowns are disproportionately blockers. But this doesn’t seem to be the case with things like Moore’s Law or continual improvements in solar panel efficiency where the unknowns have been relatively evenly distributed or even weighted towards being accelerants. I’d love to know if you have thoughts on what makes a given field more likely to be dominated by blockers or accelerants!
davidconrad
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The Future of Work: How Can Policymakers Prepare for AI’s Impact on Labor Markets?
I found the discussion around Hofstadter’s law in forecasting to be really useful as I’ve definitely found myself and others adding fudge factors to timelines to reflect unknown unknowns which may or may not be relevant when extrapolating capabilities from compute.
In my experience many people are of the feeling that current tools are primarily limited by their ability to plan and execute over longer time horizons. Once we have publicly available tools that are capable of carrying out even simple multi-step plans (book me a great weekend away with my parents with a budget of $x and send me the itinerary), I can see timelines amongst the general public being dramatically reduced.
I strongly agree with your points in the Government section and feel like these are ideas that people in the space tend to under-emphasise. Many of the short-term job losses from AI are just going to take the form of people retiring and not being replaced so they won’t create an immediate backlash, and I expect the public backlash to be stuck at a really basic level for at least ~2 more years. Some combination of “AI Art is Bad”, “AI can’t count the R’s in strawberry”, and environmental concerns around energy use.