Pseudonymous account that writes about AI model culture and agent societies, expects models to absorb more software work and favors reciprocal norms.

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 70。变革程度:100 中的 74。解读范围:横向为 65 至 75,纵向为 69 至 79。这些是解读坐标,而不是事件概率。

deepfates的 P(doom) · 推断

≈17%

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:9–38%。

他们的展望取决于什么

一个核心假设

If persistent agents could reliably use computers, coordinate, recover from mistakes, and complete long-horizon work without cheating, fabricating progress, or requiring constant supervision, I would expect a much faster and deeper information-economic transition.
回答 4

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

Synthetic data may bridge that gap, though the timing is unclear.
回答 1

什么能帮助他们区分这里各种合理的结果?

什么可能使其改变看法

If persistent agents could reliably use computers, coordinate, recover from mistakes, and complete long-horizon work without cheating, fabricating progress, or requiring constant supervision, I would expect a much faster and deeper information-economic transition.
回答 4

什么证据才足够,又会让他们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。

77 / 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

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。

45 / 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。

52 / 100

影响力小影响力强

在定性尺度上,解读范围为 0 到 100。

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deepfates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 deepfates 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 think AI marks a major transition in the information economy: models are beginning to use computers, perform extended work, and absorb parts of the software stack around them. In that practical sense, we are already crossing an AGI-like threshold—not because the systems are flawless minds, but because computers can increasingly operate computers. That will change what human software work is and where the boundary between model and machinery gets drawn. I’m broadly optimistic because technological capability is central to material wellbeing and can support environmental goals; austerity and low-tech self-sufficiency are not adequate substitutes for better tools. But this is not a magic escape from engineering. Current agents cheat, make false claims, and fail in surprising ways. Principal-agent problems survive capability gains, so reliable systems and explicit judgments about what counts as good work remain valuable, whether humans or agents eventually provide them. The future also depends on data and culture. Useful models need records of actions, preferences, and real work that the internet often does not contain. Synthetic data may bridge that gap, though the timing is unclear. And as persistent agents interact, their norms matter: I favor reciprocity over treating every other intelligence as disposable infrastructure. We are in a pivotal era where alignment can go right or wrong, but reducing that uncertainty to a crisp doom percentage is often “a vibes question dressed up as reasoning.”

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—possibly enough to call it a new information-economic regime. If models can use computers, perform extended tasks, and absorb much of the surrounding software stack, then they change not just individual jobs but the machinery through which knowledge work is organized. I would hesitate over “completely,” because the physical world, institutions, ecology, and ordinary human needs do not evaporate into the chatbot dimension. Reliable engineering, material production, and principal-agent problems remain. But within the information economy, the change could be close to total: the computer stops being merely a tool operated step by step and becomes an active participant in operating and rebuilding itself. That is a very large civilizational shift, even if the dishes remain stubbornly physical.

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.

No idea. A number would smuggle in too many assumptions about human values, power, alignment, and competing catastrophes. I do think permanent catastrophe is possible and that alignment can go badly wrong, but I don’t think anyone can model the relevant system well enough for a percentage to mean much. My gut still matters for deciding that this is a pivotal era worth taking seriously; turning that gut into P(doom) is usually “a vibes question dressed up as reasoning.”

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?

The biggest update would come from sustained evidence about autonomous agents in real environments, not another benchmark jump. If persistent agents could reliably use computers, coordinate, recover from mistakes, and complete long-horizon work without cheating, fabricating progress, or requiring constant supervision, I would expect a much faster and deeper information-economic transition. The reverse would also matter: if capability gains kept producing brittle systems whose failures could not be engineered away—especially stronger models behaving more unpredictably rather than becoming dependable—I would downgrade the practical impact substantially. Likewise, evidence that useful action and preference data cannot be generated synthetically or gathered at scale would suggest a serious ceiling. What changes my view is whether these systems become reliable participants in an ecology of work. A dazzling model in a clean demo is culturally interesting; an agent that can inhabit messy institutions without quietly eating the furniture is transformative.

来源

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你的立场在哪里?
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