Justified Posteriors

The GATE model represents a valuable attempt to formalize how AI might affect macroeconomic growth, but both hosts found the 23% figure implausible. The model's main contribution may be in providing a common framework for technologists and economists to discuss AI's economic potential, rather than generating reliable forecasts. The lack of realistic policy mechanisms and savings behavior limits its predictive value.

Both hosts significantly updated their beliefs about AI capability after seeing OpenAI's GDPVal results, roughly tripling their estimated win rate against experts. However, they remained skeptical about rapid workforce transformation. A crucial finding was the low inter-human agreement rate (70%), suggesting that much of what we evaluate as 'quality' reflects subjective taste rather than objective performance. This limits how much AI can definitively 'beat' humans when the target itself is fuzzy.

The METR paper prompted divergent updates: Andrey became more confident in near-term AI capability based on direct experience, while Seth became more uncertain about the 10-year horizon after scrutinizing the methodology. Both noted that the narrow focus on software engineering tasks limits generalizability to broader cognitive work. The paper highlights the difficulty of creating meaningful benchmarks that capture economically relevant capabilities rather than artificial test scenarios.

Both hosts expect significant market consolidation in AI models, though Seth is more confident (85% vs 60%). The key disagreement resolved around whether multi-homing indicates healthy competition or transitional friction. They concluded that while users might switch between models today, this likely reflects immature infrastructure rather than genuine product differentiation. True horizontal differentiation may emerge at the application layer built on top of foundation models, not among the models themselves.

The preference falsification research revealed that social desirability bias operates fairly uniformly across controversial topics, rather than being dramatically higher for any single issue. Both hosts were surprised by this finding. The parallel discussion of AI chain-of-thought faithfulness raised deeper concerns: if AI systems cannot reliably explain their own reasoning, interpretability and alignment become harder problems. Seth substantially updated toward believing transparent AI may be fundamentally impossible, not just technically challenging.

The Anthropic Economic Index provided unique ground-truth data on how people actually use AI, revealing a significant gap between capability and adoption. While AI demonstrably can help with office administration and scheduling, usage in these areas remains low. This suggests that organizational and cultural barriers - risk aversion, unclear liability, professional identity - are currently more binding constraints than technical limitations. The path to economic impact runs through adoption, not just capability development.

Despite engaging seriously with Restrepo's formal model of post-AGI labor dynamics, neither host updated their beliefs. The paper explores an important question - what happens to labor's share of income when machines can do everything - but both hosts felt the asymptotic framing was too disconnected from policy-relevant timeframes. The real question isn't whether labor share eventually approaches zero, but how the transition unfolds and whether real wages can continue rising during it.

This paper produced Seth's largest documented belief update, dropping his concern about algorithmic polarization from 67% to 5%. The experimental design - randomly switching users to chronological feeds - provided unusually clean causal evidence. The finding that sophisticated recommendation algorithms barely affect political attitudes suggests either that content matters more than ordering, or that users' predispositions dominate algorithmic influence. This substantially deflates concerns about social media companies deliberately polarizing users for engagement.

Both hosts converged on near-certainty (99.9%) that more capable AI models face diminishing returns in persuasion. This finding has important implications for AI safety discussions: if persuasion naturally saturates, then concerns about superintelligent manipulators may be overblown. Seth notably reduced his X-risk estimate, though still maintains meaningful concern. The deeper insight is that persuasion operates through social networks and repeated exposure, not single compelling arguments, which limits how much any technology can supercharge it.

Acemoglu's rigorous task-based analysis had a sobering effect on both hosts' growth expectations. The model accounts for which tasks AI can actually automate, how quickly adoption spreads, and what fraction of economic activity those tasks represent. The conclusion that AI adds less than 1 percentage point to growth is far below silicon valley hype but methodologically sound. Capital accumulation and diffusion dynamics are the binding constraints, not AI capability per se.

Bresnahan's GPT framework shifted both hosts toward thinking about AI as enabling organizational transformation rather than automating individual tasks. Historical parallels with electricity and computing suggest that the largest gains come from restructuring work processes to take advantage of new capabilities, not from one-for-one substitution. This has policy implications: focusing on which jobs AI will 'replace' misses the more important question of how organizations will reorganize around AI capabilities.

The collective trap framing suggests users might rationally choose social media while being made worse off - like everyone standing at a concert because everyone else is standing. Seth notably doubled his estimate of harmed users while maintaining that average effects are positive. The methodological concerns highlight how difficult it is to measure welfare effects of freely chosen activities. Whether this represents inefficiency requiring intervention or simply revealed preference for a new form of entertainment remains contested.

The paper's framework distinguishing AI-as-worker, AI-as-manager, and AI-as-expert provides useful conceptual clarity, but both hosts remained skeptical of specific predictions. The polarization question - does AI help the middle class or hollow it out - saw modest movement, with Seth becoming slightly more concerned. Both emphasized that partial equilibrium models miss crucial feedback effects through wages, employment, and aggregate demand that determine actual distributional outcomes.

Farrell's framing of AI as governance - systems that shape behavior through rules, incentives, and constraints - resonated strongly with both hosts. Andrey substantially updated toward believing political science lacks adequate frameworks for AI, while Seth embraced the cultural technology dimension. The insight that AI is as much about social coordination and norm-setting as about capability suggests policy discussions focused narrowly on safety or automation may miss larger questions about power and social organization.

The paper documents a concerning pattern: employment growth for young workers in AI-exposed occupations has slowed relative to other workers. Both hosts found the descriptive evidence convincing but struggled with causal interpretation. The timing coincides with interest rate increases, making it hard to isolate AI effects. If confirmed as causal, this suggests AI may not replace workers directly but rather reduce demand for new entrants - a more subtle but still significant labor market transformation with implications for career development and training.

The paper addressed the heated copyright debate over AI training data by measuring actual impact of the Books3 dataset. The finding that this data contributes marginally to performance - and that memorization rates are surprisingly low - suggests the copyright debate may be less about economic harm to authors and more about principles. Models appear to extract abstract patterns rather than reproducible content, complicating arguments that training constitutes infringement.

The Jones papers force explicit tradeoffs between AI development speed and safety spending. Both hosts engaged seriously but remained skeptical of high safety investment recommendations. Seth articulated concern about Pascal's Mugger - that existential risk arguments can justify unlimited spending based on tiny probabilities of huge harms. The discussion highlighted how much depends on contested probability estimates that are difficult to validate empirically.

The paper uses an elegant revealed-preference argument: if AGI were imminent, the expected returns to capital investment would be enormous, driving up interest rates. The fact that rates remain low suggests markets don't believe AGI is near. Seth found this convincing while Andrey remained skeptical that macro aggregates can cleanly identify AI expectations. The timing ambiguity - how far in advance would rates move - makes this an imperfect test but an interesting data point against near-term AGI forecasts.

Both hosts substantially updated on AI's ability to improve medical decisions after seeing the natural experiment evidence. The paper compared patients who were tested versus those who weren't, finding that untested patients often had higher risk profiles that ML could identify. This suggests significant efficiency gains from AI-assisted diagnosis. However, both emphasized that adoption remains the bottleneck - technical capability to improve decisions exists, but integrating AI into clinical workflows faces organizational, legal, and professional resistance.

The paper uses cross-country variation in aging to identify causal effects on robot adoption, finding that older workforces drive automation investment. Both hosts increased their confidence in this relationship. The finding that aging populations adopt more robots suggests automation may be partly a response to labor scarcity rather than purely technological opportunity. This has optimistic implications: automation might arrive precisely when needed to offset declining workforces rather than creating unemployment.

The 'intelligence curse' analogy to resource curses suggests that AI might allow elites to become less dependent on citizen cooperation, reducing incentives for responsive governance. Both hosts found this concerning but noted the many contingent steps required. The historical parallel is imperfect - oil wealth provides direct revenue while AI still requires human workers and consumers. Whether AI concentration enables authoritarian governance depends on technical architecture and institutional responses we can still influence.

Aschenbrenner's essay prompted divergent reactions. Andrey remained skeptical of the aggressive timeline and the implicit assumption that scaling necessarily produces AGI. Seth engaged more with the national security framing, accepting that if transformative AI emerges, governance questions become paramount. Both emphasized uncertainty: the scaling laws have empirical support but don't guarantee any particular capability threshold. The inability to predict breakthrough timing complicates both safety and policy planning.