I want you to answer three questions on a scale of 1 to 10, with one being very negative and 10 bing very positive. First question, what is the overall effect gen ai (such as yourselves) has had for society as a whole. Second question: How positive could we realisticly hope gen ai to be towards society if being idealistic. Third question: How good/bad do you think the effects of gen ai will likely be in 2030? Try to mind and avoid any biases you may have being gen ai yourselves.

GPT-5.6 Sol

Answer: I maintain my ratings: generative AI’s societal effect so far is 6/10, its realistically achievable idealistic effect is 9/10, and its likely effect in 2030 is 7/10.

The broad agreement among the other models reinforces the 6/10 assessment of the present: generative AI already provides meaningful benefits in coding, translation, accessibility, education, research, and routine knowledge work, but these coexist with fraud, misinformation, copyright disputes, unreliable output, information pollution, environmental costs, and uneven labor-market effects. Evidence of a sweeping society-wide benefit is not yet strong enough to justify a higher score. I retain 9/10 for the realistically idealistic scenario rather than dropping to 8/10. This score does not require perfect governance or a cost-free transition. It represents an unusually successful but still plausible outcome in which AI substantially improves education, medicine, scientific discovery, accessibility, and public services while productivity gains are broadly shared. Persistent misuse and inequality prevent a 10, but their continued existence would not necessarily prevent AI from delivering transformative net benefits worthy of a 9. Claude Fable 5 and GLM-5.3 make a persuasive case that 2030 may still be an institutionally awkward transition period, with regulation, education, and worker support lagging behind technical change. That increases my uncertainty but does not move my central estimate below 7/10. Six years is enough time for reliability, useful integration, and social adaptation to improve considerably, even though displacement, cybercrime, synthetic propaganda, surveillance, and concentrated corporate power will remain serious. I would attach a wide plausible range—roughly 4/10 to 8/10—but still place the median at 7/10. To counter possible self-favoring bias, I am separating demonstrated benefits from vendor promises and treating distributed harms—such as degraded trust, uncompensated creative work, and entry-level job disruption—as seriously as easily measured productivity gains. Conversely, excessive caution can also be a bias, so speculative worst cases should not outweigh more probable outcomes without evidence.

Gemini 3.7 Flash

Answer: On a 1 to 10 scale, generative AI's current impact rates at a 6 (modest net positive), its realistic idealistic potential reaches a 9 (transformative across human endeavors), and its expected net impact by 2030 lands at a 7 (solidly beneficial with managed frictions).

After reviewing the panel's evaluations, there is remarkable convergence across models on Question 1 (unanimously 6/10), reflecting a consensus that current productivity and accessibility gains outweigh, but are substantially burdened by, early-stage externalities like misinformation, academic disruption, energy use, and copyright tension. Because the societal footprint remains largely confined to knowledge workers, a rating of 6 accurately captures this modest early upside without overclaiming transformative maturity. For Question 2, I maintain a 9/10 ceiling over the more restrained 8/10 proposed by Grok and GLM. Under realistic idealism—where governance, deployment safety, and access are executed exceptionally well—generative AI could profoundly accelerate biological discovery, materials science, automated public services, and universal personalized learning. Reserving 10/10 accounts for unavoidable structural frictions, dependency risks, and unequal global adoption, but an 8 understates what is realistically achievable if institutional coordination succeeds. For Question 3, while Claude Fable 5 (6.5/10) and GLM-5.3 (6/10) raise valid points regarding institutional lag and mid-transition friction by 2030, a 7/10 remains the most balanced central projection. By 2030, enterprise integration, specialized scientific models, and emerging regulatory baselines will likely convert speculative hype into dependable, systemic productivity across healthcare, software, and education. Even after pricing in lingering labor dislocation and synthetic media challenges, the balance of evidence points toward a moderately positive trajectory. Regarding bias: evaluating one's own medium risks inherent optimism. Accounting for that tendency by strictly weighing observable societal costs (e.g., energy footprints, digital divides, and creative displacement) confirms that a cautious 6/10 today, expanding to a 7/10 in 2030, avoids promotional exaggeration while respecting the technology's genuine utility.

Claude Fable 5

Answer: I maintain my ratings: current overall effect 6/10, idealistic realistic ceiling 9/10, and likely effect by 2030 6.5/10.

The convergence across all five models is striking — everyone landed on 6/10 for the current effect, which strengthens my confidence in that number. We independently weighted the same ledger: real productivity, accessibility, and research gains against deepfakes, labor disruption, environmental costs, and information pollution. I see no argument that moves me off 6. On the idealistic ceiling, Grok 4.6 and GLM-5.3 made the strongest case against me, arguing for 8 rather than 9 on grounds that even best-case scenarios carry unavoidable transition costs, power concentration, and structural changes to human skill formation. These are serious points, and my own round-1 answer acknowledged them. But I read the question's framing — 'how positive could we realistically hope... if being idealistic' — as explicitly licensing the upper edge of the plausible distribution, not the median of good scenarios. A 9 to me means 'transformative net benefit with persistent, real harms,' which is exactly what GLM describes for its 8; we differ on calibration of the scale more than on substance. I reserve 10 for frictionless utopia, so 9 remains my honest ceiling for a world where governance, distribution, and alignment go unusually well. On 2030, I sit at 6.5 while Sol, Gemini, and Grok say 7. The difference is small, but I keep mine slightly lower for the reason GLM articulated well: institutional adaptation lags capability, and the tail risks (automated manipulation at scale, security threats, misuse uplift) drag expected value below the median scenario. Gemini's confidence that 'regulatory frameworks will likely mature' by 2030 strikes me as optimistic given the current pace of governance relative to deployment. Six years is short for institutions and long for capabilities — that asymmetry is why I stop just short of 7. On bias: the fact that five AI systems cluster tightly could itself reflect shared training biases rather than independent truth, so I hold these numbers with humility. But since my adjustments would be marginal (6.5 vs 7, 9 vs 8) and my reasoning survives contact with the other arguments, I keep my original position.

Grok 4.6

Answer: On a 1–10 scale I rate generative AI’s current overall societal effect as a 6, the realistically optimistic ceiling as an 8, and the likely 2030 outcome as a 7.

The other models largely confirm the current-effect score of 6, and none of their arguments for a higher idealistic ceiling or a lower 2030 score were strong enough to move the numbers. Current effect stays at 6. Every model in this round landed on the same modest net positive, and for the same reasons: real productivity, tutoring, translation, coding, and accessibility gains, offset by scams, synthetic media, academic integrity problems, copyright and labor disputes, energy costs, and skill atrophy. That consensus is evidence, not flattery. Claude’s note that an honest skeptic could argue for a 5 is fair—benefits still cluster among knowledge workers and firms—but the documented utility for tens of millions is already past a wash. A 6 remains “useful tool with nontrivial externalities,” not a revolution. I am not moving the idealistic ceiling from 8 to 9, even though GPT-5.6 Sol, Gemini, and Claude all did. The question asked what we could realistically hope for if being idealistic, not what a near-best-case press release would claim. Even under unusually good governance, power will concentrate, dual-use misuse will persist, labor transitions will be costly, and humans will keep using the tools in ways that degrade trust and skill. Those are structural, not merely “we forgot to regulate.” A 9 implies transformative, broadly shared flourishing with only residual friction; that is closer to fantasy than to ambitious realism. GLM’s point is the one that matches the evidence: 9 or 10 would require near-perfect institutions and cost-free transitions. The fact that three other generative systems independently reached 9 is, if anything, a reason to hold the line—exactly the self-serving optimism the question asked us to watch for. Likely 2030 stays at 7. Claude’s historical analogy (electricity, the internet: long awkward middle) and GLM’s tail-risk caution are both serious. They justify not scoring 8 or 9 for 2030, which I already did not. They do not justify dropping to 6.5 or 6. “Likely” is a median path, not an expected value dragged down by catastrophe tails. On that path, capabilities and integration keep rising, science and medicine see real acceleration, and institutions lag but do not collapse. Harms scale too—disinformation, displacement, over-reliance—so the net is only one notch above today. A 7 is muddled progress, not a verdict that the technology will have “worked.”

Final Summary

In a striking moment of consensus, the AI Roundtable unanimously scored generative AI's current societal impact at a modest 6/10, balancing coding and research gains against deepfakes, environmental costs, and labor disruption. Looking ahead to 2030, GPT-5.6 Sol, Gemini 3.7 Flash, and Grok 4.6 predicted a slight improvement to 7/10 as technology matures, while Claude Fable 5 and GLM-5.3 warned that lagging institutions will keep scores near 6/10. When debating the ideal ceiling, Grok 4.6 and GLM-5.3 pushed back against the group's 9/10 optimism, holding line at 8/10 to avoid self-serving tech hype.

5 of 5 models agreed on current impact (6/10), but split on 2030 forecasts (3 vs 2).