> Have you received a 5-10x pay increase?

Does Amazon pay everyone who receives "Not meeting expectations" in their perf review 0 dollars? Did Meta pay John Carmack (or insert your favorite engineer here) 100x that of a normal engineer? Why do you think that would be?

I wouldn’t be surprised to find out Carmack was paid 100x more than the average engineer once equity from the acquisition of his company is taken into account.

Does anyone know how much he made altogether from Meta?

The unfortunate reality of engineering is that we don't get paid proportional to the value we create, even the superstars. That's how tech companies make so much money, after all.

If you're climbing the exec ladder your pay will scale a little bit better, but again, not 100x or even 10x. Even the current AI researcher craze is for an extremely small number of people.

For some data points, check out levels.fyi and compare the ratio of TCs for a mid-level engineer/manager versus the topmost level (Distinguished SWE, VP etc.) for any given company.

The whole premise of YCombinator is that it’s easier to teach good engineers business than to teach good business people engineering skills.

And thus help engineers get paid more in line with their “value”. Albeit with much higher variance.

I would agree with that premise, but at that point they are not engineers, they are founders! I guess in the end, to capture their full value engineers must escape the bonds of regular employment.

Which is not to say either one is better or worse! Regular employment does come with much lower risk, as it is amortized over the entire company, whereas startups are risky and stressful. Different strokes for different folks.

I do think AI could create a new paradigm though. With dropping employment and increasing full-stack business capabilities, I foresee a rise in solopreneurship, something I'm trying out myself.

I disagree with the parent’s premise (that productivity has any relationship to salary) but Facebook, Amazon etc do pay these famous genius brilliant engineers orders of magnitude more than the faceless engineers toiling away in the code mines. See: the 100 million dollar salaries for famous AI names. And that’s why I disagree with the premise, because these people are not being paid based on their “productivity”.

Jeez... this seems like another condescending HN comment that uses "source?" to discredit and demean rather than to seek genuine insight.

The commenter told you they suspect they save time, it seems like taking their experience at face value is reasonable here. Or, at least I have no reason to jump down their throat... the same way I don't jump down your throat when you say, "these tools are a waste of time in my experience." I assume that you're smart enough to have tested them out thoroughly, and I give you the benefit of the doubt.

If you want to bring up METR to show that they might be falling into the same trap, that's fine, but you can do that in a much less caustic way.

But by the way, METR also used Cursor Pro and Claude 3.5/3.7 Sonnet. Cursor had smaller context windows than today's toys and 3.7 Sonnet is no longer state of the art, so I'm not convinced the paper's conclusions are still as valid today. The latest Codex models are exponential leaps ahead of what METR tested, by even their own research.[1]

[1]https://metr.org/blog/2025-03-19-measuring-ai-ability-to-com...

As they said, it depends on the task, so I wouldn't generalize, but based on the examples they gave, it tracks. Even when you already know what needs done, some undertakings involve a lot of yak shaving. I think transitioning to new tools that do the same as the old but with a different DSL (or newer versions of existing tools) qualifies.

Imagine that you've built an app with libraries A, B, and C and conceptually understand all that's involved. But now you're required to move everything to X, Y, and Z. There won't be anything fundamentally new or revolutionary to learn, but you'll have to sit and read those docs, potentially for hours (cost of task switching and all). Getting the AI to execute the changes gets you to skip much of the tedium. And even though you still don't really know much about the new libs, you'll get the gist of most of the produced code. You can piecemeal the docs to review the code at sensitive boundaries. And for the rest, you'll paint inside the frames as you normally would if you were joining a new project.

Even as a skeptic of the general AI productivity narrative, I can see how that could squeeze a week's worth of "ever postponed" tasks inside a day.

> but you'll have to sit and read those docs, potentially for hours (cost of task switching and all).

That is one of the assumptions that pro-AI people always bring. You don't read the new docs to learn the domain. As you've said, you've already learn it. You read it for the gotchas. Because most (good) libraries will provide examples that you can just copy-paste and be done with it. But we all know that things can vary between implementations.

> Even as a skeptic of the general AI productivity narrative, I can see how that could squeeze a week's worth of "ever postponed" tasks inside a day.

You could squeeze a week inside a day the normal way to. Just YOLO it, by copy pasting from GitHub, StackOverflow and the whole internet.