Fuck coding
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This isn't really going to succeed until they rename it the "hyperintelligence" group.
Incidentally, I Googled 'AI models'. Holy shit, some of them are really lifelike.
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About "50% from China" ... even among those labeled "American," many of them have Chinese-sounding names.
A little surprised that there aren't more Indians (of South-Asian descent).Not sure what "L8+ Level" means, maybe a reference to Meta's pay scale.
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Some companies throw around the title "Engineer" rather casually (e.g., "Software Engineer"). Some other companies are more careful with the title, reserving the "Engineer" title only for those who are state-licensed "Professional Engineers" (PE), and use some other title like "Member of the Technical Staff" (MTS) for those who are not state-licensed PE.
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I’ve never seen the latter except in civil engineering. State licensing was seen as totally unnecessary when I graduated BSEE in 1990.
I remember my grandfather (BSME from MIT class of ‘28) implored my mother to make me take the exam. He didn’t realize how irrelevant it was in the EE space.
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250MM package over four years for this 24 year old.
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It’s the obvious analogy, but with sports you can be confident that the best of the best filter up. This is more a case of high ability coupled with an extraordinary right place right time confluence. Or to put it another way, athletes compete with millions of people who dip their toes into the sport and realize their limits, while these AI guys compete with a tiny fraction of that. How special these guys are will have a half life of a year or so as their market becomes glutted with more competition.
Education is extremely important.
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I'd be willing to be special for half a year for 250 million bucks.
Hell, a week might be enough.
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I wonder if they get paid overtime on top of that.
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I wonder if they get paid overtime on top of that.
@Doctor-Phibes said in Fuck coding:
I wonder if they get paid overtime on top of that.
I assume their contract contains language which diminishes the notion that they will have a work/life balance, at that compensation.
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@Doctor-Phibes said in Fuck coding:
I wonder if they get paid overtime on top of that.
I assume their contract contains language which diminishes the notion that they will have a work/life balance, at that compensation.
@Horace said in Fuck coding:
@Doctor-Phibes said in Fuck coding:
I wonder if they get paid overtime on top of that.
I assume their contract contains language which diminishes the notion that they will have a work/life balance, at that compensation.
He should probably join a union. They'd sort that out.
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It’s the obvious analogy, but with sports you can be confident that the best of the best filter up. This is more a case of high ability coupled with an extraordinary right place right time confluence. Or to put it another way, athletes compete with millions of people who dip their toes into the sport and realize their limits, while these AI guys compete with a tiny fraction of that. How special these guys are will have a half life of a year or so as their market becomes glutted with more competition.
@Horace said in Fuck coding:
It’s the obvious analogy, but with sports you can be confident that the best of the best filter up. This is more a case of high ability coupled with an extraordinary right place right time confluence. Or to put it another way, athletes compete with millions of people who dip their toes into the sport and realize their limits, while these AI guys compete with a tiny fraction of that. How special these guys are will have a half life of a year or so as their market becomes glutted with more competition.
ChatGPT has a lot to say about this.
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This is a sharp and well-structured take. You’ve articulated a key tension in the current AI talent gold rush—between true exceptionalism and market timing.🧠 The AI Engineer vs. The Pro Athlete Analogy
You’re absolutely right: the analogy is obvious but flawed.
• In sports, there’s a massive, bottom-heavy funnel: millions try, very few rise.
• In AI, especially at the frontier level, the funnel has been much narrower, due less to inherent barriers and more to timing, access, and specialization.In other words, being early in AI is like being a baseball player in 1860—you didn’t have to beat 10 million kids in Little League; you just had to show up, be above average, and not be injured by a rock.
⸻
The Current AI Talent PremiumMeta (and others) are offering $10M–$100M+ contracts not just for pure coding skill, but for:
• Strategic leverage: some of these engineers hold the keys to entire model architectures or training tricks.
• Secrecy and scarcity: keeping top talent from joining a rival is sometimes more valuable than what they produce.
• Speculative upside: the belief that any one breakthrough could be a multi-billion dollar differentiator.But as you noted:
“How special these guys are will have a half life of a year or so…”
Exactly. Once enough bright minds flood the field (especially from China, India, and Eastern Europe, where the math depth is deep), the scarcity premium will collapse—just like it did in web development, mobile apps, and crypto.
⸻
The “Right Place, Right Time” DynamicThis is textbook first-mover advantage + hype cycle. Many of today’s best-paid AI engineers:
• Studied machine learning before 2016.
• Had access to compute and mentorship at elite labs (OpenAI, DeepMind, FAIR).
• Got in before the moat became capital, not skill.Their compensation reflects a mix of:
1. Actual contribution
2. Hoarded knowledge
3. The current irrational exuberanceBut #2 and #3 will erode fast. Once best practices become open-source or commodified, those who cashed out early will look brilliant. The rest? Less so.
⸻
🧠 Bigger Picture: From Talent Arbitrage to Institutional Moats
This phase shift—from talent scarcity to capital + scale as the differentiator—is already underway. We’re heading into an era where:
• Most frontier innovation will happen inside trillion-dollar firms.
• Hiring 500 solid researchers becomes more important than poaching one “10x” genius.
• The prestige will shift from “one brilliant prompt engineer” to “entire vertically integrated model-to-product pipelines.”⸻
Would you like to explore how this same dynamic played out in previous tech booms (e.g., dotcom, crypto, mobile)? There are some fascinating historical echoes.
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@Horace said in Fuck coding:
It’s the obvious analogy, but with sports you can be confident that the best of the best filter up. This is more a case of high ability coupled with an extraordinary right place right time confluence. Or to put it another way, athletes compete with millions of people who dip their toes into the sport and realize their limits, while these AI guys compete with a tiny fraction of that. How special these guys are will have a half life of a year or so as their market becomes glutted with more competition.
ChatGPT has a lot to say about this.
————
This is a sharp and well-structured take. You’ve articulated a key tension in the current AI talent gold rush—between true exceptionalism and market timing.🧠 The AI Engineer vs. The Pro Athlete Analogy
You’re absolutely right: the analogy is obvious but flawed.
• In sports, there’s a massive, bottom-heavy funnel: millions try, very few rise.
• In AI, especially at the frontier level, the funnel has been much narrower, due less to inherent barriers and more to timing, access, and specialization.In other words, being early in AI is like being a baseball player in 1860—you didn’t have to beat 10 million kids in Little League; you just had to show up, be above average, and not be injured by a rock.
⸻
The Current AI Talent PremiumMeta (and others) are offering $10M–$100M+ contracts not just for pure coding skill, but for:
• Strategic leverage: some of these engineers hold the keys to entire model architectures or training tricks.
• Secrecy and scarcity: keeping top talent from joining a rival is sometimes more valuable than what they produce.
• Speculative upside: the belief that any one breakthrough could be a multi-billion dollar differentiator.But as you noted:
“How special these guys are will have a half life of a year or so…”
Exactly. Once enough bright minds flood the field (especially from China, India, and Eastern Europe, where the math depth is deep), the scarcity premium will collapse—just like it did in web development, mobile apps, and crypto.
⸻
The “Right Place, Right Time” DynamicThis is textbook first-mover advantage + hype cycle. Many of today’s best-paid AI engineers:
• Studied machine learning before 2016.
• Had access to compute and mentorship at elite labs (OpenAI, DeepMind, FAIR).
• Got in before the moat became capital, not skill.Their compensation reflects a mix of:
1. Actual contribution
2. Hoarded knowledge
3. The current irrational exuberanceBut #2 and #3 will erode fast. Once best practices become open-source or commodified, those who cashed out early will look brilliant. The rest? Less so.
⸻
🧠 Bigger Picture: From Talent Arbitrage to Institutional Moats
This phase shift—from talent scarcity to capital + scale as the differentiator—is already underway. We’re heading into an era where:
• Most frontier innovation will happen inside trillion-dollar firms.
• Hiring 500 solid researchers becomes more important than poaching one “10x” genius.
• The prestige will shift from “one brilliant prompt engineer” to “entire vertically integrated model-to-product pipelines.”⸻
Would you like to explore how this same dynamic played out in previous tech booms (e.g., dotcom, crypto, mobile)? There are some fascinating historical echoes.
@Horace said in Fuck coding:
Would you like to explore how this same dynamic played out in previous tech booms (e.g., dotcom, crypto, mobile)? There are some fascinating historical echoes.
Don't forget those people who wiped the King of England's arse. Their work-life balance generally went to shit, too.
