Which is probably why now is a safe time to release employees into the labour pool i.e. conduct layoffs. There’s a low risk those people will be absorbed into a competitor, until money gets cheap again.
I've spent a lot of time with LLM tools this year, mainly ChatGPT and copilot, I have found them to be incredible useful but with clear limitations.
It always strikes me as obtuse when I hear about the job-displacement potential of AI within software engineering or 'programming', and I try to understand where this chorus is coming from. To me it's clear that there is this specific animosity towards engineering and related skill-based professions within the business world, and a ravenous desire to replace the skillset en masse (no doubt because of how expensive it is).
It's important to recognise why good SWEs are so expensive (emphasis on good), and I think it in large part simply boils down to how integral the discipline is to just about every facet of the economy, the value creation potential that it has (demand), and the fact that to do it well is challenging, cognitively discerning and demands constant upskilling/learning of complex technical concepts (supply).
There is a big difference between being a 'coder' who can print hello world, and performing at the level which is required to maintain a high-paying role at the likes of big-tech. In any corner of the world outside of the population-skewed tech hubs, you could walk into a room full of people (the masses), everyone of which would be a consumer of tech products in some way, and almost none of which can/will perform the technical roles required to build them.
With the AI tools, I've found chatGPT particularly useful as a supplement to reading technical documentation due to the conversational format, and copilot as a shotgun-approach which sometimes sparks inspiration and gets me to a solution faster. However, the achilles heel of both is the distinct lack of comprehension for what is being output, especially as they lack longitudinal awareness of the desired output. To me this is best illustrated when trying to solve complex leetcode-style questions which are novel or deviate from existing solutions and require the solver to think through the problem at a macro/conceptual scale before authoring a line-by-line solution.
Is it possible that AI can overcome these limitations? Probably. It remains to be seen how far the current GPT paradigms can be pushed, or if further breakthroughs are needed to reach AGI performance (or at there very least, a version of intelligence which more closely resembles human capabilities). It could be as close as the OpenAI optimism suggests, or much further out as experts like Andrew Ng say.
One thing I feel more confident about however, is that when the time comes that it can truely replace a highly-competent SWE, it can also replace the overwhelming majority of professionals, including traditional middle-managers and their ineffable 'leadership' skillsets. I do not see a version of the future where engineering is uniquely displaced while all other professions remained unaffected. I think it's much more likely that engineering is amongst the last to go.
Ultimately, I think the collective attitude should be that we are all in this together, and we have to seriously architect a socio-economic future for humanity where we can all thrive in a world where the concept of employment is a relic of the distant past.