本文作者Ege Erdil对人工智能领域中普遍存在的短期时间线观点提出了质疑。他认为，当前趋势并不支持在未来两三年内实现人工智能全面自动化的预测。Erdil不认同仅凭软件就能实现技术奇点的观点，并强调了实验计算和现实世界数据的重要性。此外，他还预计莫拉维克悖论将在人工智能代理发展中产生显著影响，这意味着AI在处理复杂任务时速度会变慢且成本更高。因此，他主张采用更长的时间线来评估AI的发展，并重视包括政治建模在内的更广泛因素。

📈Ege Erdil反对人工智能将在未来2-3年内实现全面自动化的观点，他认为现有趋势并不支持如此短的时间线。

💻Ege Erdil不相信仅凭软件就能实现技术奇点，他更看重实验计算和现实世界数据所带来的瓶颈。他认为，仅仅依靠软件很难使AI在现实世界的影响力以数量级的速度突然加速。

🤖Ege Erdil预计莫拉维克悖论将在人工智能代理发展中产生重要影响。这意味着随着AI系统被要求执行更具代理性、多模态和长上下文的任务时，它们的速度会变慢，成本也会增加。

Published on April 27, 2025 3:31 PM GMT

So this post is an argument that multi-decade timelines are reasonable, and the key cruxes that Ege Erdil has with most AI safety people who believe in short timelines are due to the following set of beliefs:

Ege Erdil don't believe that trends exist that require AI to automate everything in only 2-3 years.Ege Erdil doesn't believe that the software-only singularity is likely to happen, and this is perhaps the most important crux he has with AI people like @Daniel Kokotajlo who believe that a software-only singularity is likely.Ege Erdil expects Moravec's paradox to bite hard once AI agents are made in a big way.

This is a pretty important crux, because if this is true, a lot more serial research agendas like Infra-Bayes research, Natural Abstractions work, and human intelligence augmentation can work more often, and also it means that political modeling (like is the US economy going to be stable long-term) matter a great deal more than is recognized in the LW community.

Here's a quote from the article:

I don’t see the trends that one would extrapolate in order to arrive at very short timelines on the order of a few years. The obvious trend extrapolations for AI’s economic impact give timelines to full remote work automation of around a decade, and I expect these trends to slow down by default.I don’t buy the software-only singularity as a plausible mechanism for how existing rates of growth in AI’s real-world impact could suddenly and dramatically accelerate by an order of magnitude, mostly because I put much more weight on bottlenecks coming from experimental compute and real-world data. This kind of speedup is essential to popular accounts of why we should expect timelines much shorter than 10 years to remote work automation.I think intuitions for how fast AI systems would be able to think and how many of them we would be able to deploy that come from narrow writing, coding, or reasoning tasks are very misguided due to Moravec’s paradox. In practice, I expect AI systems to become slower and more expensive as we ask them to perform agentic, multimodal, and long-context tasks. This has already been happening with the rise of AI agents, and I expect this trend to continue in the future.

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