Liam Robins is a math major at George Washington University who's diving deep into AI policy and rationalist thinking.
In Part 1, we explored how AI is transforming college life. Now in Part 2, we ride the Doom Train together to see if we can reconcile our P(Doom) estimates. đźš‚
Liam starts with a P(Doom) of just 3%, but as we go through the stops on the Doom Train, something interesting happens: he actually updates his beliefs in realtime!
We get into heated philosophical territory around moral realism, psychopaths, and whether intelligence naturally yields moral goodness.
By the end, Liam's P(Doom) jumps from 3% to 8% - one of the biggest belief updates I've ever witnessed on the show. We also explore his "Bayes factors" approach to forecasting, debate the reliability of superforecasters vs. AI insiders, and discuss why most AI policies should be Pareto optimal regardless of your P(Doom).
This is rationality in action: watching someone systematically examine their beliefs, engage with counterarguments, and update accordingly.
0:00 - Opening
0:42 - What’s Your P(Doom)™
01:18 - Stop 1: AGI timing (15% chance it's not coming soon)
01:29 - Stop 2: Intelligence limits (1% chance AI can't exceed humans)
01:38 - Stop 3: Physical threat assessment (1% chance AI won't be dangerous)
02:14 - Stop 4: Intelligence yields moral goodness - the big debate begins
04:42 - Moral realism vs. evolutionary explanations for morality
06:43 - The psychopath problem: smart but immoral humans exist
08:50 - Game theory and why psychopaths persist in populations
10:21 - Liam's first major update: 30% down to 15-20% on moral goodness
12:05 - Stop 5: Safe AI development process (20%)
14:28 - Stop 6: Manageable capability growth (20%)
15:38 - Stop 7: AI conquest intentions - breaking down into subcategories
17:03 - Alignment by default vs. deliberate alignment efforts
19:07 - Stop 8: Super alignment tractability (20%)
20:49 - Stop 9: Post-alignment peace (80% - surprisingly optimistic)
23:53 - Stop 10: Unaligned ASI mercy (1% - "just cope")
25:47 - Stop 11: Epistemological concerns about doom predictions
27:57 - Bayes factors analysis: Why Liam goes from 38% to 3%
30:21 - Bayes factor 1: Historical precedent of doom predictions failing
33:08 - Bayes factor 2: Superforecasters think we'll be fine
39:23 - Bayes factor 3: AI insiders and government officials seem unconcerned
45:49 - Challenging the insider knowledge argument with concrete examples
48:47 - The privilege access epistemology debate
56:02 - Major update: Liam revises base factors, P(Doom) jumps to 8%
58:18 - Odds ratios vs. percentages: Why 3% to 8% is actually huge
59:14 - AI policy discussion: Pareto optimal solutions across all P(Doom) levels
1:01:59 - Why there's low-hanging fruit in AI policy regardless of your beliefs
1:04:06 - Liam's future career plans in AI policy
1:05:02 - Wrap-up and reflection on rationalist belief updating
Show Notes
* Liam Robins on Substack -
* Liam’s Doom Train post -
* Liam’s Twitter - @liamhrobins
Anthropic's "Alignment Faking in Large Language Models" - The paper that updated Liam's beliefs on alignment by default
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