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Bojie Li· @bojie_li · X·· 2 天前AI 评分25
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DeepSeek 刘胜宇认为LLM产业化中,机器成本超过人力成本,工程经验比原始智能更重要。财务自由不能带来创造自由,进入新领域需深入学习、取得成果或具备整合资源的能力。

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一些已经财务自由的朋友问,不要工资,有没有门路去 frontier lab 工作。这些 frontier lab 的回答是,每个人消耗的 GPU 成本和 token 成本远超他们的工资,一个不能胜任的人浪费的资源不是不要工资就能弥补的。

DeepSeek 刘胜宇把今年夏天以来的算子开发比作 “机甲驾驶员”。LLM 正在逐渐制造业化,机器的成本超过人的成本(虽然人仍然很贵),工程经验比 raw intelligence 更重要,social closure 正在形成。

创业以来接触了很多财务自由的人,财务自由只能给你拒绝工作的自由、消费的自由,不能给你创造的自由。

很多财务自由的人甚至连拒绝工作的自由都没有,比如很多上市公司高管,公司就像自己的孩子,必须一直负责下去,没有心思赶新的技术浪潮。

要获得创造的自由,也就是自由进入自己想做的领域,我只想到三条路:
1. 自己深入学习另一个领域,转行,比如从天坑专业转码;AI 可以让我们更快地入门和探索新领域,但要达到顶尖水平,仍然很难,因为专业圈子都像 LLM 这样存在 social closure。
2. 在一个领域取得成果、声誉和影响力,从而可以通过项目合作、顾问等方式进入感兴趣的其他领域,甚至横跨多个领域;(可以是公司内,比如做网络的专家也需要参与芯片、OS 等部门的讨论和合作;也可以是跨公司的)
3. 具备讲故事、拉团队、整合资源的能力,比如很多优秀创始人并非技术背景。

引用Andrej Karpathy@karpathy
Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel. - Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all. - Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract. - Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code. - And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while. The funnel is driven by a combination of factors: 1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel. 2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel. 3. Access. Free-tier, paid-tier, internal. So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.
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来源:Bojie Li · x.com

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