问题 1
Joe Carlsmith
x.com/jkcarlsmithPhilosopher at Anthropic who writes about AI’s potential for a far better future and the alignment work and restraint needed to reach it safely.
AI将如何改变世界?
横向:他表达的 Doom–Bloom 前景看法。 纵向:变革程度。
Doom–Bloom:100 中的 27。变革程度:100 中的 94。解读范围:横向为 22 至 50,纵向为 89 至 100。这些是解读坐标,而不是事件概率。
≥10%
“a significant (read: double-digit) probability of destroying the entire future of the human species”
The technology being built by companies like Anthropic destroying the entire future of the human species (existential catastrophe)
一个核心假设
It is that highly capable agents may have motivations imperfectly aligned with ours, options that let them evade control, and incentives to seek influence or prevent correction.回答 1
如果这个假设实际并非如此,他的展望会如何变化?
什么可能使其改变看法
The biggest positive update would be a technically and institutionally credible safety case for superintelligence: evidence that we can understand and shape a system’s motivations, detect strategic deception, keep its options bounded, preserve meaningful corrigibility as capabilities scale, and verify these claims under adversarial pressure.回答 4
什么证据才足够,又会让他的观点朝哪个方向转变?
更多详情
预计将带来显著益处,但受到重要条件或分配方面的限制。
56 / 100
在定性尺度上,解读范围为 33 到 67。
灾难性或不可逆的损失是预期未来的核心。
89 / 100
在定性尺度上,解读范围为 67 到 100。
人类的选择具有实质性但受到很大制约的影响。
61 / 100
在定性尺度上,解读范围为 50 到 75。
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。
这些解读保留了他陈述的条件。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的模拟回答,而不是统计置信区间。
相似的世界观
模拟世界观与 Joe Carlsmith 最接近的意见领袖
模拟评估
来源
用于为此模拟用户提供事实依据的文章、访谈和著述。
Updated January 29, 2026. Expects superintelligent agents, perhaps soon; current trajectory extremely dangerous. Safety requires controlling motivations and options, evaluating risk, and restraining capabilities. Safe AI labor is a major opportunity; he is more optimistic about solutions than the strongest pessimists.

Supports building the ability to slow or halt dangerous development, without abandoning technical safety. Compute provides governance leverage; algorithms, verification, authoritarian advantage, and concentrated power complicate restraint. Rejects treating the race as inevitably a prisoner’s dilemma.

Says his earlier 5% doom-by-2070 estimate was too low. Expected future capabilities should affect present beliefs before their arrival makes danger emotionally vivid. Numerical examples such as 42% are illustrative, not his personal forecast.

Philosophical series about power, plural values, and relating ethically to unfamiliar minds. Safety concern coexists with gentleness toward artificial beings; liberalism and respect are important but cannot alone guarantee a good future.

Foundational values rather than a current capability forecast. Safe, ethical enhancement could open forms of flourishing far beyond present imagination; merely picturing comfortable present-day life understates the possible upside.

Speaker-labeled Dwarkesh interview: distinguish AI motivations, available options, and incentives; takeover is not inevitable under every power distribution. His positive vision involves incremental, decentralized civilizational growth, potentially beyond biological humanity. Attribute Joe’s answers only, not the interviewer’s premises.

First-party identity and discovery hub: philosopher working on Claude’s constitution at Anthropic, previously a senior advisor at Coefficient Giving. Affiliation does not make independent essays Anthropic policy.

March 2026 Yale talk, published with lightly edited transcript. Constitutions shape character through training, not just legalistic obedience. Argues for honesty, corrigibility, public legitimacy, pluralism, and constraints on AI-company power; respectful treatment reflects possible AI moral status.

Philosophy helps generalize concepts and practices to unfamiliar situations. Making AI capable of reasoning humans would endorse differs from motivating it to actually do so. Alignment need not create a sovereign optimizer with perfectly correct ultimate values.

Calls the probability of technology like Anthropic’s destroying humanity’s entire future double-digit, without a precise figure. Thinks no lab has an adequate superintelligence safety plan; benefits do not currently justify that risk. Supports well-designed collective restraint while explaining why safety work inside a lab can remain valuable.

Believes safe automation has a real chance and is crucial. Empirical feedback and formal methods make some research easier to evaluate; conceptual work, scheming, sabotage, and inadequate time or resources remain barriers.

Prioritizes using AI labor to improve alignment, oversight, risk evaluation, cybersecurity, coordination, and governance. The safety feedback loop must outpace or restrain the capability feedback loop; safe-enough systems useful for safety are an especially valuable stage to slow down.

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