Do we know this? Smaller more carefully curated training sets are proving to be valuable and gaining traction. It seems like the strategy of throwing huge amounts of data at LLMs is specific to companies that are attempting to dominate this space regardless of cost. It may turn out that more modest and better optimized methodologies will end up winning this race, much like WebVan flamed out taking huge amounts of investment money with them but now Instacart serves the same sector in a way that actually works robustly and profitably.
It seems like what you are perceiving is a common market delusion. An unfortunate fact of hiring is those workers who are not employed and satisfied are often less experienced and skilled than those who are well placed and not looking. The same logic applies the other way around to companies. Those who are looking to hire juniors who haven't yet found their way are often companies that lack a solid center and just want to squeeze some money out of whatever customers they can find using whatever tool is at hand.
With the current state of things if your needs are truly modest then there is a good chance that you can get by with some independent offering. Find something you are interested in and make it work for someone willing to pay for it. Make sure to lean more into sales and actually making things work for customers than the engineer tendency to envision mechanisms and focus entirely on that. This way you can set the balance for yourself, and I can absolutely guarantee that you will experience the realities of growth or death up close, though in a more personal way that you can take control of and manage for yourself using criteria that have meaning for you.
Lots of applications have a simple structure of collecting and operating data with fairly well documented business logic tying everything together. Coding outside of that is going to be more tricky.
And if agentic coding is so great then why are there so still so many awful spreadsheets that can't compete with Excel? Something isn't adding up quite as well as some seem to expect.
Well, there's your problem. Here is a potentially interesting math paper that comes to very different conclusions: https://www.fooledbyrandomness.com/BTC-QF.pdf
Even with strong adoption it may take many years for LLMs available now to reach their potential utility in the economy. This should moderate the outlook for future changes, but instead we have a situation where the speculative MIT study that predicted "AI" could perform 12% of the work in the economy is widely considered to not only be accurate, but inevitable in the short term. How much time is needed dramatically changes calculations of potential and what might be considered waste.
Also worth keeping in mind that the Y2K tech bust left behind excess network capacity that ended up being useful later, but the LLM boom can be expected to leave behind poorly considered data centers full of burned out chips which is a very different legacy.