That was my insight also. As a manager, you already have the headcount approved, and your people just allegedly got some significant percentage more productive. The first thought shouldn't be, great let's cut costs, it should be great now we finally have the bandwidth to deliver faster.

On a macro level, if you were in a rising economic tide, you would still be hiring, and turning those productivity gains into more business.

I wonder what the parallels are to past automations. When part producing companies moved from manual mills to CNC mills, did they fire a bunch of people or did they make more parts?

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NathanielK 12 hours ago | parent | next [-]

CNC machines drove down operator wages. Its similar to the translator example where the machine code is written by someone else, but the person running the machine still needs to understand. Simple pushing the go button is dangerous, being able to adapt is critical.

Jobs where a machinist is in charge of large chunks of the process are rarer. Large shop will have one person setting up many machines to maximize throughput.

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superfrank 16 hours ago | parent | prev | next [-]

I'm an EM as well and I've been telling my teams for a while now that I think they really only need to start worrying once our backlog starts going down instead of up. Generally, I still agree with that (and your) sentiment when you look at the long term, but in the short term, I think all of the following arguments can be made in favor of layoffs:

- AI tools are expensive so until the increased productivity translates to increased revenue we need to make room in the budget

- We expect the bottlenecks in our org to move from writing code to something else (PM or design or something) so we're cutting SWEs in anticipation of needing to move that budget elsewhere.

- We anticipate the skillsets needed by developers in the AI world to be fundamentally different from what they are now that it's cheaper to just lay people off, run as lean as possible, and rehire people with the skills we want in a year or two than it is to try and retrain.

I don't necessarily agree with those arguments (especially the last one), but I think they're somewhat valid arguments

	▲	throwaw12 15 hours ago \| parent [-]
		I see similar arguments and I don't agree as well, here is why: > rehire people with the skills we want in a year or two than it is to try and retrain. before that future comes your company might become obsolete already, because you have lost your market share to new entrants > We expect the bottlenecks in our org to move from writing code to something else I would love to tell them, hey lets leverage current momentum and build, when those times come, we offer existing people with accumulated knowledge to retrain to a new type of work, if they think they're not good fit, they can leave, if they're willing, give them a chance, invest in people, make them feel safe and earn trust and loyalty from them > AI tools are expensive so until the increased productivity translates to increased revenue we need to make room in the budget 1. Its not that expensive: 150$/seat/month -> 5 lunches? or maybe squeeze it from Sales personnel traveling with Business class? 2. By the time increased productivity is realized by others, company who resisted could be so far behind, that they won't be able to afford hiring engineers with those skillsets, if they think 150$ is expensive now, I am sure they will say "What??? 350k$ for this engineer?, no way, I will instead hire contractors"

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anthonypasq 7 hours ago | parent | prev [-]

business success does not scale at the speed of increased profits from layoffs.