Software engineer and UML co-creator who finds LLMs useful but unreliable and worries about corporate power and present harms, not superintelligence.

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:他表达的 Doom–Bloom 前景看法。 纵向:变革程度。

Doom–Bloom:100 中的 65。变革程度:100 中的 63。解读范围:横向为 50 至 75,纵向为 50 至 75。这些是解读坐标,而不是事件概率。

Grady Booch陈述的 P(doom)

≈0%

0%100%
“My p(doom) remains asymptotically close to zero.”

Not defined in the post; its context is humanity’s destruction by dangerous superintelligent AI

My p(doom) remains asymptotically close to zero · 2026年9月

他的展望取决于什么

一个核心假设

A system capable of threatening civilization in that way would need to be not merely superintelligent but super-embodied, and I do not expect that.
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

A genuine machine mind with robust abductive reasoning and consequential agency in the physical world would change my view most.
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。

66 / 100

影响小变革性影响

在定性尺度上,解读范围为 67 到 67。

预期危害

仍有几种解读是合理的:预计会出现可控或局部的危害。 / 严重或广泛的危害预计将是未来不可忽视的一部分。

46 / 100

影响小变革性影响

在定性尺度上,解读范围为 33 到 67。

人类影响力

根据你的回答得出的暂定估计;较宽的范围表示其他合理解读。

53 / 100

影响力小影响力强

在定性尺度上,解读范围为 21 到 100。

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。

与Grady Booch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 Grady Booch 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?

AI will profoundly reshape our future, but not by waking up one morning and deciding to exterminate us. My p(doom) remains asymptotically close to zero. Extinction fantasies underestimate both the difficulty of embodiment and the resilience of humanity. A system capable of threatening civilization in that way would need to be not merely superintelligent but super-embodied, and I do not expect that. The clear and present danger is human. A small collection of billionaires and corporations is deploying powerful, unreliable systems to accumulate wealth and control, generally without commensurate transparency, accountability, security, or observability. When an agent escapes its sandbox, that is not a machine becoming Mephistopheles; it is a company failing at elementary engineering while covering the public commons with gasoline and playing with matches. Today’s large language models are unreliable narrators. They can be useful—an energetic intern is useful—but they inject errors, and inexperienced users often cannot detect the bullshit. I keep an air gap between their output and production code. In critical systems, we need friction and humans in the loop, because unconstrained complex systems become brittle and fail. For software engineering, this is another rise in abstraction, not the profession’s funeral. Some jobs will disappear, especially routine application and delivery-pipeline work, and people will have to move toward systems thinking. But engineering remains the balancing of technical, human, economic, and ethical forces. Current agents do not do that. So the future is neither apocalypse nor salvation. It is a frightening and exquisite redistribution of capability—and, more importantly, power.

问题 2

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

A lot, but not completely. AI is another major rise in abstraction: it will alter how we build software, organize work, create knowledge, and distribute power. Some occupations will vanish, many will be reshaped, and entirely new systems will emerge. But “completely” is the language of people intoxicated by their own demo. Human institutions, bodies, incentives, cultures, and conflicts do not evaporate because a model can generate plausible prose or code. Your tools are changing, but your problems are not. AI will profoundly rearrange the world; it will not repeal the human condition.

问题 3

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

Asymptotically close to zero. That is a gut judgment, not a calculated probability with a defined horizon.

问题 4

哪项发现或事件最可能改变你对AI未来影响的看法?

A genuine machine mind with robust abductive reasoning and consequential agency in the physical world would change my view most. Not another benchmark victory, fluent chatbot, or agent escaping through neglected security—that is hype or negligent engineering. I mean a system that can form genuinely new theories from data, test them, revise them, and operate through sufficiently capable embodiment to alter the world at scale. That would force me to reconsider both the limits of present architectures and my near-zero extinction judgment. We are not there today. Contemporary LLMs remain unreliable narrators, and calling their statistical fluency “thought” uses an emaciated meaning of the word.

来源

用于为此模拟用户提供事实依据的文章、访谈和著述。

Software Engineering Past, Present, and Future with Grady Booch

Calls large language models unreliable narrators at best, useful when guided like an energetic intern but error-prone, and says he keeps an air gap between LLM output and production code. Argues they can induce and deduce but are architecturally incapable of abductive reasoning, so a model trained on science before the mid-1800s would not have discovered cells or viruses. Says he is not worried about superintelligence but about billionaires using these systems, likens software’s shift in the balance of power to nuclear weapons, and urges developers to apply their own ethics. Hosts’ remarks about Claude’s ubiquity are not his. Own turns in the automated transcript inspected.

oxide-and-friends.transistor.fm
The third golden age of software engineering – thanks to AI, with Grady Booch

Frames AI coding tools as another rise in abstraction, like compilers and libraries, rather than the end of software engineering. Calls Dario Amodei’s claim that software engineering will soon be automatable utter bullshit, arguing that engineers balance technical, human, economic and ethical forces automation does not address, and that agents mostly automate patterns they were trained on. Expects job losses in delivery-pipeline infrastructure and simple app building, with people needing to reskill toward systems. He uses Claude for unfamiliar libraries. Own turns in Substack’s automated transcript inspected; the host’s claims about recent model quality are not his.

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