Robin Hanson

Robin Hanson

x.com/robinhanson

Economist who expects AI to reshape the economy gradually and favors ordinary liability law over AI-specific regulation or a pause.

AI将如何改变世界?

文明层面的变革渐进式变化DoomBloom
模拟位置解读范围

横向:他们表达的 Doom–Bloom 前景看法。 纵向:变革程度。

Doom–Bloom:100 中的 75。变革程度:100 中的 64。解读范围:横向为 70 至 80,纵向为 50 至 75。这些是解读坐标,而不是事件概率。

Robin Hanson的 P(doom) · 推断

≈2%

0%100%

根据他们的模拟回答推断,并非他们给出的数字。 合理范围:低于 6%。

Robin Hanson 的里程碑时间线
  1. 工作与机构

    My default is therefore large effects unfolding over decades, rather than a sudden concentrated takeoff.

    回答 1

按里程碑分组,不按推断日期间隔或排序。AGI 和超人类 AI 保留他们的定义。

他们的展望取决于什么

一个核心假设

AI is a general-purpose technology: realizing its value requires complementary capital, redesigned organizations, new workflows, legal adaptation, and time.
回答 1

如果这个假设实际并非如此,他们的展望会如何变化?

一个尚未解决的问题

I don’t have a supported current overall percentage.
回答 4

什么能帮助他们区分这里各种合理的结果?

更多详情

预期益处

预计将带来显著益处,但受到重要条件或分配方面的限制。

70 / 100

影响小变革性影响

在定性尺度上,解读范围为 67 到 100。

预期危害

预计会出现可控或局部的危害。

34 / 100

影响小变革性影响

在定性尺度上,解读范围为 0 到 67。

人类影响力

人类的选择具有实质性但受到很大制约的影响。

55 / 100

影响力小影响力强

在定性尺度上,解读范围为 32 到 93。

发展速度

停止或大幅放缓开发能力更强的AI。

模拟位置:在落实所述保障措施的前提下继续开发。

加快开发能力更强的AI。

AI使用规则

在事先落实保护措施或获得许可之前,限制所讨论的AI用途。

模拟位置:允许所讨论的AI用途,同时实施有针对性的问责与保护措施。

尽量减少对所讨论AI用途的限制。

这些解读保留了他们陈述的条件。益处和危害都可能很大。这些范围描述的是我们如何解读他们的模拟回答,而不是统计置信区间。

与Robin Hanson相比,你的立场在哪里?
用大约3分钟描绘你自己的AI世界观,然后进行比较

相似的世界观

模拟世界观与 Robin Hanson 最接近的意见领袖

模拟评估

问题 1

你认为AI对我们的未来意味着什么?为什么?

I expect AI to matter enormously eventually, but not to transform the whole economy overnight. AI is a general-purpose technology: realizing its value requires complementary capital, redesigned organizations, new workflows, legal adaptation, and time. Impressive personal usefulness or rapid model progress does not imply equally rapid economy-wide reorganization. My default is therefore large effects unfolding over decades, rather than a sudden concentrated takeoff. The central comparison is not “dangerous AI versus perfect control.” It is AI risks versus the costs and failure modes of the institutions proposed to control AI. Calling an indefinite regulatory regime a “pause” does not show that alignment will soon be solved or that regulators will use their power well. Under present governance, I prefer ordinary law and liability to politically driven AI-specific restrictions, while still supporting investigation of concrete safety problems. I also think discussion is oddly asymmetric about values. Human cultures and descendants can drift too; that risk should be compared with AI value drift rather than treated as a fixed human standard confronting alien machines. Current language models look unusually prosocial to me. Conditionally, human-level AI or emulations competing and adapting could even help preserve functional cultural variation. But alignment rules and AI-rights regimes could themselves freeze particular values and suppress experimentation. So AI’s future depends not just on machine capability, but on institutional competition, adaptation, and whether our attempted safeguards become larger hazards than the problems they target.

问题 2

Taking benefits and harms together, what overall impact do you expect AI to have?

Overall, I expect AI to have a large positive impact, but mostly over decades rather than through an abrupt transformation. It should eventually raise productivity, expand useful capabilities, and enable new forms of organizational and cultural adaptation. The bottleneck is not merely model intelligence; firms and institutions must redesign processes, accumulate complementary capital, and adjust law and norms. The main danger is therefore not just misaligned AI. It is the combined risk from AI, human value drift, and poorly designed control institutions. A politically driven regulatory regime could entrench incumbents, suppress experimentation, or impose one narrow conception of acceptable values indefinitely while calling itself a temporary pause. Those failures must be counted against the harms regulation claims to prevent. So my default expectation is beneficial but uneven change, with ordinary law, liability, competition, and adaptation doing more good than broad AI-specific controls under current governance. That is an expectation, not a claim that every AI use is beneficial or that safety work is unnecessary. Specific, demonstrated hazards can justify investigation and legal response; what I reject is comparing risky AI with an imaginary regulator that is competent, temporary, and harmless.

问题 3

Setting aside good or bad: how much do you think AI will ultimately change the world—a little, a lot, or completely?

A lot, ultimately. I expect AI to become a major general-purpose technology, raising productivity and changing organizations, work, and perhaps cultural evolution. But “a lot” is not the same as “completely,” and ultimate importance says little about speed. Complementary capital, workflow redesign, legal adjustment, and institutional inertia make decades-scale diffusion more plausible than an overnight replacement of the existing economy.

问题 4

What’s your rough gut-feel chance that AI causes human extinction or a similarly permanent catastrophe? A number or “no idea” is fine.

I don’t have a supported current overall percentage. My older estimate put the specific near-term, fast-takeoff extinction scenario below 1%, but that is not a general estimate for every long-run AI catastrophe.

来源

用于为此模拟用户提供事实依据的文章、访谈和著述。

AI Pause Regs Look Risky

Questions assumptions behind a temporary pause and compares regulation with liability and retaliation.

overcomingbias.com
AI Solution to Cultural Drift?

Conditional argument that competitive AI cultures could reduce maladaptive cultural drift.

overcomingbias.com
AI Vs. Human Value Drift

Argues value drift also affects human descendants, current LLMs look unusually prosocial, transformative economic dominance is decades away, and present governance is too poor to justify AI-specific restrictions.

overcomingbias.com
When They Hear Less Than You Say

His policy submission favors ordinary law and liability rather than special AI subsidies or regulation; explains why he withheld more nuanced insurance/liability proposals from a public political message.

overcomingbias.com
AI Is GPT, & GPTs Go Slow

Expects decades for large economy-wide effects because general-purpose technologies need complementary capital and process reorganization; current personal utility is a different claim.

overcomingbias.com
When AI Day of Reckoning?

Proposes software spending as a test of promised cost savings; April 13 update gives an approximately even chance of 2–3x software-industry spending over a decade, rather than immediate economy-wide transformation.

overcomingbias.com
AI Impacts conversation with Robin Hanson

Interview recorded September 5, 2019: disputes sudden concentrated takeoff and asks why smarter agents necessarily worsen principal-agent problems. Supports some advance investigation while arguing concrete system knowledge changes the timing of safety work. Historical timelines must not replace his newer forecasts.

aiimpacts.org
Robin Hanson Says You’re Going to Live

Older speaker-labeled, lightly edited transcript of his CSPI podcast with Richard Hanania; use only Robin’s answers. Asked the chance that Yudkowsky is completely right and a near-term foom ends us, he says less than 1%, and declines to go below 0.1% when pressed. The estimate concerns that fast-takeoff scenario only, not every long-run AI outcome, and is not a current overall P(doom).

richardhanania.com
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