Should Philanthropists Save Their Money for the Intelligence Explosion?
A podcast episode from Forethought
Will MacAskill is a senior research fellow at Forethought and the author of What We Owe The Future. Tom Davidson is a senior research fellow at Forethought and the author of a series of reports on AI timelines, takeoff speeds, and AI-enabled coups.
In this informal conversation about early-stage research in progress, they discuss:
Why a philanthropist who takes transformative AI seriously should expect extraordinary investment returns (plausibly 10x to 100x) and how the market doesn’t seem to be pricing this in
Why AGI shouldn’t be treated as a hard deadline to spend by, and the case that most philanthropic money should be spent during the intelligence explosion rather than before it
How genuine AI substitutes for human labor would dissolve the hiring bottleneck that makes organizations so hard to scale today
How relative prices shift when cognitive labour becomes abundant, why the patent system stops making sense in that world, and the window of “crazy philanthropic bargains” open to actors who adapt faster than large institutions
The prospect of a world where only a small elite has access to superintelligent medical, financial, and strategic advice, and what it would take to avoid this
Counter-considerations to the main case, e.g., crowding out by new money, the risk that overspending attracts grifters or distorts the field, and whether a richer world will mean worse philanthropic opportunities
The risk of value drift over a long saving period, and mechanisms for binding your future self (such as irrevocably handing off control of philanthropic funds to independent, constitutionally-bound foundations)
“Differential intellectual development”: using directable AI researchers to accelerate biodefense, infosecurity, AI interpretability, and neglected armchair fields like population ethics and social choice theory
Why the case for waiting to give is much weaker for small donors
Will’s worry that the whole line of reasoning might fail if it turns out scaling philanthropy well is bottlenecked by serial real-world learning that no amount of cognitive labor can compress
Here’s a link to the full transcript.
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