ForeCast

Will MacAskill (Senior Research Fellow at Forethought) and Mia Taylor (Research Fellow at Forethought) discuss how to use philanthropic money to do the most good during and after the intelligence explosion, and explain why wealthy donors should consider handing off their philanthropy. They talk about: The challenges beyond AI takeover that we must address to get on track for the best futures Why it could be valuable to spend philanthropic money on AI labour during the intelligence explosion, and how money might be spent post-ASI Why philanthropists should consider handing off much of their giving to others, and concerns donors might have with this You can read a full transcript here. To see all Forethought’s published research, visit forethought.org/research. To subscribe to our newsletter, visit forethought.org/subscribe.

Matthew Adelstein was a Visiting Scholar at Forethought and blogs as Bentham’s Bulldog, where he writes on ethics, animal welfare, and the long-run future. He joined Fin Moorhouse to discuss: Why Matthew thinks handoff of high-stakes decision-making to AI is the most likely route to a great future Why locking in current human values would be a cosmic-scale catastrophe, why steering nowhere in particular is barely better, and why the alternative is building AIs that we trust to do reflective moral philosophy Various ways handoff might manifest politically: elected AI representatives (“President Claude”), extending political rights to digital minds, or de facto handoff through gradual disempowerment of humanity Whether handoff should be reversible (such that humans could take back control if we wanted) or if we should “tie ourselves to the mast” Whether to task AIs with “do what’s objectively good” or “do what humans would want on reflection,” why Matthew leans toward the former, and why he expects the two to ultimately converge If digital minds end up vastly outnumbering humans, and those digital minds deserve political rights, then consistently applied liberal-democratic principles may imply handoff as reducing expected disenfranchisement Whether AIs can actually do philosophy: where models currently sit, bootstrapping schemes using AI-designed evals, training AIs with different epistemic constitutions and looking for convergence, and Fin’s scepticism that recursive self-evaluation works as well as it needs to Whether we could even tell if an AI were superhuman at philosophy, given the difficulty of evaluating philosophy that’s beyond our own level of competence Whether goodness can compete under evolutionary pressure, resource races into space, and intergalactic existential risks as an argument for top-down planning Whether humans’ selfish preferences would saturate in a post-AGI world Why smart AIs won’t necessarily converge on good values, and how selection against “weird” outputs (by humans’ lights) to moral reasoning could lock in something mediocre You can read a full transcript here. To see all Forethought's published research, visit forethought.org/research. To subscribe to our newsletter, visit forethought.org/subscribe.

Peter Salib is an Assistant Professor of Law at the University of Houston Law Center and co-director of the Center for Law & AI Risk. Simon Goldstein is an associate professor at the University of Hong Kong, a senior editor at AI Frontiers, and a visiting senior scholar at Forethought. They have written together about AI safety, AI rights, and the governance of advanced AI. They joined Forethought’s Fin Moorhouse to discuss their new paper, Liberalism Forever. The conversation covers: Their “null hypothesis”: today’s liberal institutions — free markets and democratic governance — may already hold most of the answers for governing the far future, and longtermists have underrated their power Examining whether canonical social-science arguments for markets and democracy still hold under transformative AI, space colonization, and explosive growth, and claiming that most survive (and several get stronger) Why they favour reasoning “at the margin” over designing a detailed end-state, and why proposals like viatopia and the long reflection sound liberal but risk illiberalism when actually implemented Whether the standard case for markets — information aggregation, allocative efficiency, innovation — still bites once AGI can read off preferences directly Why inequality is, in their view, a comparatively boring problem with a known solution (tax-and-transfer), why AI-specific taxes like a compute tax are misguided, and how the growth-versus-distribution trade-off changes when growth rates get very high Dyson swarms and space resources: why they doubt there’s any real monopoly worry, since energy would be additive with free entry, and why property auctions might beat egalitarian allocation schemes The arguments for democracy that persist post-AGI — public-choice/selectorate incentives and democracy as a commitment device — versus the epistemic arguments, which they think weaken Why AGI could make autocracy more competitive (loyal AI bureaucrats and militaries removing the need for a human winning coalition), and what preserving democratic control requires in response Handoffs to superintelligent AI and successionism, and their preferred alternative of extending the franchise to AIs with their own values rather than deferring wholesale The history of new “technologies of democracy,” from the Federalist Papers to LLMs as impartial arbiters, and where each author thinks their own argument is weakest You can read a full transcript here. To see all Forethought's published research, visit forethought.org/research. To subscribe to our newsletter, visit forethought.org/subscribe.

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 You can read a full transcript here. To see all Forethought's published research, visit forethought.org/research. To subscribe to our newsletter, visit forethought.org/subscribe.

Avi Parrack is a physics PhD student at Stanford and a researcher at Forethought, where he works on AI, space expansion, and governance. He is the lead author of a recent Forethought report on orbital data centers. He joined Forethought’s Tom Davidson to discuss: What an orbital data center actually is, and why they’re suddenly being taken seriously Why the core case rests on cheaper energy from more intense and near-constant solar power Why orbital data centers hinge on SpaceX’s Starship bringing launch costs toward $100/kg or below Why cooling may actually not be a major issue Whether the inability to make repairs dooms orbital data centers Vulnerability of space data centers to kinetic attacks, and whether they would cause ‘Kessler syndrome’ debris cascades Why model-weight security might actually improve in orbit even as physical vulnerability rises If space becomes the cheapest place to scale compute and only SpaceX has the launch capacity, what the means for concentration of power, US–China competition, and the possibility of pausing AI Overall cost comparisons with terrestrial data centers The longer-run picture: when and how does the post-AGI industrial explosion spread to space? You can read a full transcript here. To see all Forethought's published research, visit forethought.org/research. To subscribe to our newsletter, visit forethought.org/subscribe.

Damon Binder is a senior researcher on the Biosecurity and Pandemic Preparedness team at Coefficient Giving. He has a PhD in physics from Princeton and previously studied existential risks at Oxford’s Future of Humanity Institute. He joined Fin Moorhouse to discuss his series on the AI industrial explosion. Topics include: How input-output tables let you estimate how fast a self-replicating economy could grow if labor were free, yielding a headline result of roughly annual doubling, even under conservative assumptions and heavy regulation Why competition makes low-growth restraint unstable Why raw-material scarcity isn’t a hard barrier Why a fast-growing economy wants cheap, disposable, infrastructure Thermodynamic and other physical speed limits to physical growth Which biological organisms replicate fastest, and what we can learn from them Why physical output matters for hard power How Damon uses AI in his research You can read a full transcript here. To see all Forethought's published research, visit forethought.org/research. To subscribe to our newsletter, visit forethought.org/subscribe.

This is an AI narration of "Risk-Averse AIs" by Elliott Thornley and William MacAskill. The article was first released on 24th June 2026. You can read more of our research at forethought.org/research. Thank you to Type III audio for providing these automated narrations.

Wei Dai is a computer engineer known for his work in cryptography and cryptocurrency systems, and for his long-standing contributions to AI safety, decision theory, and metaphilosophy. He joined Forethought’s Fin Moorhouse to discuss: Do we need to solve ‘metaphilosophy’ before we can trust AIs to answer crucial questions about the long-run future? Is it inevitable that the wisdom of the frontier AIs (their philosophical and strategic competence) will lag dangerously behind their raw capabilities in coding, math, and science? How do status games distort morally important decisions and conversations, including about the future of AI? How worried should we be about AI superpersuasion? The concept of “illegible problems”: crucially important issues that aren’t on almost anyone’s radar Is philosophical convergence necessary for a good future, or is institutional design enough? Wei Dai’s personal intellectual and career journey To respect Wei’s privacy, this episode’s audio is an AI narration of a transcript of a real conversation, which was edited for clarity. You can read a full transcript here. To see all Forethought's published research, visit forethought.org/research. To subscribe to our newsletter, visit forethought.org/subscribe.

This is an AI narration of "What should go in a model spec?" by James Tillman. The article was first released on 4th June 2026. You can read more of our research at forethought.org/research. Thank you to Type III audio for providing these automated narrations.

This is an AI narration of "Will We Really Put Data Centers in Space?" by Avi Parrack and Fin Moorhouse. The article was first released on 22nd May 2026. You can read more of our research at forethought.org/research. Thank you to Type III audio for providing these automated narrations.