前工程师 Lauren Tan 认为,AI 智能体让每位工程师都能成为"编码机器",因此产出量(PR 数、代码行数)重新成为衡量生产力的重要指标。她指出,过去只有分布两端的极值者才看量,现在前沿模型写代码比人更快更好;若 PR 数不变就该反思。这套做法需要更多 token,但按"每单位智能成本"算,单名工程师配智能体可完成数十甚至数百人的工作。
volume does matter
look, i get it. if you were an engineer before agents came along, you're rightfully skeptical about lines of code and number of PRs being used as a metric for any kind of productivity. and i agree! or at least, i used to. for ease of explanation i will talk about this from the perspective as being an engineer on a large team of at least 50.
volume didn't matter before because we focused on impact. you could have high impact even with a few PRs: for example you could make a one line config change and save the company millions of dollars.
but before agents, the times where i saw volume come into the conversation were for the outliers. on the negative end you might have someone who is struggling to perform, on the other end you might have someone who was a "coding machine" archetype. a coding machine was someone who could just lock themselves in a room and emerge with a stack of PRs that solve a huge number of problems. so volume did matter, but only at the tail ends of the distribution, and combined with impact.
if you've ever worked at a large tech company before, you'll get what i mean. the thing about agents is that big tech co problems are now small-medium co problems as well.
how do agents change this calculus? well, everyone now has the ability to become a coding machine. you have incredibly capable frontier models that can write code better than any of us, at a rate much faster than we can. in this world, if you're still producing the same number of PRs as before, why wouldn't you pause to wonder why you're not being more productive? alien intelligence is here, and yet you can't outship a human coding machine?
this brings me back to why i keep talking about trust (https://x.com/poteto/status/2102050467505430555). if you haven't put in the work to trust your agent's output, it is very difficult to scale up your productivity.
and yes, this approach does require more tokens, but i think about this in terms of cost per intelligence. before agents, this cost was very high - you needed to hire many software engineers with high 6 figure salaries to do the work. tokens are still expensive today, but are likely to get cheaper over time (https://x.com/theo/status/2107924138094703084) factoring in the cost of intelligence. what looks exorbitant today will likely be affordable in 6 moths to a year. a single engineer with agents can do the work of tens if not hundreds of engineers, at a fraction of the cost.
the reality that i don't think has hit yet is that job of the software engineer has truly changed. our job is not merely to produce software any longer (well tbh it was never only about the code, but bear with me for the sake of the explanation), but the machine that writes the software. this idea of a software factory, or a Michelin kitchen as i like to call it, is still a topic of research. and it's the type of stuff i like to share, not to flex, but to show you what's possible when you put a lot of rigor into using agents at scale
Can someone help me understand how shipping 100s PRs a day makes sense? Why do you need to ship so many PRs in the first place? I might just not be AI-pilled enough to understand the need for it yet, so I’m genuinely trying to understand it.在 X 查看被引用的帖子
来源:lauren · x.com