They are killing the liquidity of the tech labor pool that made the gold rush possible.
Orgs got hyper-efficient at classifying applicants and terminating their candidacy for highly opaque and variable reasons.
Suddenly a growing subpopulation of workers exists in a survival lottery of being either unqualified for any offer or getting multiple $500k+ offers.
Outside of that zone, workers are job-hugging, under-employed are retraining, and the undergrad pipeline is getting flushed. Labor costs are not getting reduced, they are simply getting amortized like a balloon mortgage. (Rip-off your grandchildren, at scale)
Hopefully the foolishness of gold rush thinking will soon conclude, however briefly, and sober business can resume.
P.S. the navel-gazing ‘what is a good engineer’ is performative time-wasting nonsense, just stop, we have a probationary period for new-hires, use it like it’s not your first hire
Author here. I'm a VPoE and CTO Association senior member in Japan who has mentored 10+ engineers into CTO roles. This essay was triggered by watching a startup CEO publicly ask "what does a good engineer even mean in the AI age?" — two weeks after cutting short an interview with a senior engineer whose track record included 200x performance optimizations and national-scale system architecture. He didn't read the resume.
The thesis: AI didn't create the evaluation problem. It exposed it. "Writes code" was the only visible proxy non-engineers had for judging engineering talent. AI killed that proxy. Now the underlying ignorance is visible — and the people most affected are making hiring/firing decisions for the entire industry.
The data is brutal: METR's RCT found experienced devs were 19% slower with AI while believing they were 20% faster. OpenAI announced hiring freezes then doubled headcount 54 days later. Amazon mandated AI coding tools then held emergency safety meetings 90 days later. 55% of companies regret AI-driven layoffs.
Curious what HN thinks — especially from engineers who've experienced the evaluation gap firsthand.
They are killing the liquidity of the tech labor pool that made the gold rush possible.
Orgs got hyper-efficient at classifying applicants and terminating their candidacy for highly opaque and variable reasons.
Suddenly a growing subpopulation of workers exists in a survival lottery of being either unqualified for any offer or getting multiple $500k+ offers.
Outside of that zone, workers are job-hugging, under-employed are retraining, and the undergrad pipeline is getting flushed. Labor costs are not getting reduced, they are simply getting amortized like a balloon mortgage. (Rip-off your grandchildren, at scale)
Hopefully the foolishness of gold rush thinking will soon conclude, however briefly, and sober business can resume.
P.S. the navel-gazing ‘what is a good engineer’ is performative time-wasting nonsense, just stop, we have a probationary period for new-hires, use it like it’s not your first hire
Author here. I'm a VPoE and CTO Association senior member in Japan who has mentored 10+ engineers into CTO roles. This essay was triggered by watching a startup CEO publicly ask "what does a good engineer even mean in the AI age?" — two weeks after cutting short an interview with a senior engineer whose track record included 200x performance optimizations and national-scale system architecture. He didn't read the resume. The thesis: AI didn't create the evaluation problem. It exposed it. "Writes code" was the only visible proxy non-engineers had for judging engineering talent. AI killed that proxy. Now the underlying ignorance is visible — and the people most affected are making hiring/firing decisions for the entire industry. The data is brutal: METR's RCT found experienced devs were 19% slower with AI while believing they were 20% faster. OpenAI announced hiring freezes then doubled headcount 54 days later. Amazon mandated AI coding tools then held emergency safety meetings 90 days later. 55% of companies regret AI-driven layoffs. Curious what HN thinks — especially from engineers who've experienced the evaluation gap firsthand.