Gary Marcus 回应 Rutger Bregman「该恐慌 AI」论：批评正在变得公式化

Bregman 的论据包括 OpenAI 发布超过 700 篇数学论文、菲尔兹奖得主 Hugo Duminil-Copin 表示「震惊到瘫痪」，并认为怀疑派过去几年几乎全部预测错误。Marcus 承认早期纯 LLM 确实值得批评，但指出四年来领域已演化，神经符号方法等技术超出了纯 LLM 的框架。

他强调当前系统依赖可验证性技术，这只在数学和编程上有效，缺乏通用性——是「在数学和逻辑上超强」，而非超级智能。他给出的真正风险判断是：把仍不可靠的系统用于空管等关键领域、不加以监管、赋予过多互联网访问权限。

他主张暂时召回通用 agentic AI 产品，直到问题被解决。

Getting tired of panicky doomer posts like the one below, which are becoming widespread, and starting to feel formulaic. Folks in this genre often point to unnamed critics, and ideas from years ago, and make it sound like all skeptics have exactly the same views, which is very much untrue. For example, I never called AI a con, so he goes after those that did, and not my more nuanced views.) These posts must sell a lot of subscriptions. But my advice is don’t panic, and don’t listen panicky merchants of doom. The reality is that the original pure LLMs were deeply troubled, and it was right to critique them, then. But of course the field has evolved in four years. Important some new techniques beyond pure LLMs have been developed (primarily neurosymbolic, along lines I always pushed for). But even so, what we have now is heavily dependent on verifiability—a technique that works well for math, and coding, but lacks generality. It’s not superintelligent, it’s super good at math and logic. (Pure LLMs remain poor at both, but symbolic techniques are helping pick up the slack.) The biggest problem is not that the machines are getting smarter, and that we are getting dumber. How so? By stupidly putting systems that are still unreliable in charge of things like air traffic control, and not regulating them, and giving them too much access to the internet. We can, at least for now, solve most of what’s going by demanding a temporary recall of the most egregious of the current technologies (general purpose agentic AI) until the problems are solved. Tearing our hair out and misrepresenting the work of past scholars may score cheap (and misleading) rhetorical points but it doesn’t do anything to solve the actual problems. Rutger Bregman @rutgerbregman It's time to panic about AI. For years, I listened to the reasonable people. I nodded along when critics called AI “a con,” and when they waved ChatGPT away as “spicy autocomplete.” I laughed in 2024, when the internet was mocking ChatGPT because it couldn’t count the r’s in “strawberry.” When I met young researchers who had devoted their careers to the danger of rogue AI, I thought: what a waste of talent. But at some point, I started to realize something was off. The machine kept learning things it was never supposed to learn. OpenAI recently published more than 700 papers with the solutions to hundreds of open problems in mathematics. Hugo Duminil-Copin, winner of the Fields Medal (the highest prize in mathematics), wrote: “The shock is immense. I am paralyzed.” None of this should have happened. So I went back and looked at what else the skeptics had predicted, and it dawned on me: they had been wrong about... pretty much everything. Next, I looked at the predictions of the crazy people, and realized they’ve been eerily prescient. Why then, you may wonder, does the world still feel pretty normal? Because new technology usually spreads more slowly than its inventors expect (it took factories decades to reorganize around electricity). But the scary scenario is not about what happens in the wider economy; it’s about what happens inside a handful of companies. I wrote down what changed my mind, and what we can do about it: rutgerbregman.substack.com/p/yes-you-shou… 🔗 View Quoted Tweet📊 15 ⚡

This report is categorized under AITOP's "Industry" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to AI regulation, compliance frameworks, and sovereign compute policy. Key entities and technical topics under continuous tracking include Gary Marcus, Rutger Bregman, OpenAI, AI safety.

Source attribution: curated from Gary Marcus (@GaryMarcus). Cross-source corroboration: 11 independent sources have covered this development (including IT之家, ChatGPT, 宝玉), confirming industry-wide signal strength.