ForeCast

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.

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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.

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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.

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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.

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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.