[exabrial]

Prior to the weaving loom and sewing machines, a factory might be able to make like 20 t-shirts a day with 50 workers. After mass production, factories generally employ the same amount of workers, but people aren't hand-sewing things. Instead roughly 50 people are producing thousands of tshirts a day leveraging giant machines.

Most of HN recognized the "We're firing people because AI makes people efficient" as one of the stupidest sales pitches ever and the CEOs that fell for it are just poorly ran companies that outed themselves.

AI is just another cycle in technology that is genuinely useful. The companies that are going to jump the gap are those that are hiring to use this new skill. If 10 workers pre-AI yields you 10x, and post AI yields you 100x, you don't cut down to 1 worker so you can keep delivering 10x. You invest, ruthlessly train, hire, and surge forward and leave your competition in the dust.

[asdff]

If there was 100x on the table for you, why weren't you already at 100x scale just with more headcount? That is the other side of this coin. Demand is a factor. Productivity goes up? Great, if there is demand to satiate that you can meet. I doubt that is the case otherwise you'd already have scaled to meet it if it were actually on the table for the taking if only you could output more volume.

[SoftTalker]

At least in software, 100x headcount does not get you 100x productivity, due to communication complexity (see Brooks).

[asdff]

Maybe not a linear relationship, but there is a relationship, which begets the question: If the market had available space with which for you to scale from 10x-100x, why aren't you or competitors already taking advantage? The answer is that there is no free 100x scaling on the table in all likelihood. If everyone's business had 100x in front of them thanks to AI we'd need the demand of an earth of 800 billion people on it to generate that.

[codingdave]

The sewing machine analogy is a good one. The problem I'm seeing is leadership who sees the t-shirt company succeed with sewing machines, so goes out, buys 100 sewing machines, then puts them in the kitchen because they are a bakery and gets confused why the bread production isn't improving.

Because AI can do some things. Not everything. Applying it to the wrong problem reduces productivity.

[giantg2]

"factories generally employ the same amount of workers"

Not really. The stats should show that factory workers as a percent of the workforce has been declining.

Thw leadership at my company has said they plan to do more with the same people rather than lay off. But they also seem to have reduced hiring and are removing certain types of roles.

[randcraw]

Agreed. When tools evolve significantly the old production process has to be reworked into a new process and that takes time. In complex production lines that will include changes to subcontracting, supply chains, and even finance models. The changes arising in software production today will take years to stabilize into a new normal, even longer if the tools continue to advance in capability as LLMs are doing. And many software-dependent companies will fail in that time for moving either too fast, too slow, or most likely, too short-sightedly.

[andrekandre]

  > If 10 workers pre-AI yields you 10x, and post AI yields you 100x, you don't cut down to 1 worker so you can keep delivering 10x.

in that scenario then should we be expecting to see a 10x or so boost in revenue as well?