The cutting edge of LLM-based software engineering seems to be all about how to harness the "good enough" pseudo-intelligence of consumer-level affordable models into achieving practical results, through iterations, tests, harnesses, etc. And these models are getting smarter every month, including open-weight models people can run on their own machines and servers. We're not seeing the kind of leaps as often as before, but it hasn't plateau'ed yet, the models are getting better all the time.

It implies that eventually open-weight models like DeepSeek, which are self-hostable locally or on premises, will become good enough for more people and businesses, in terms of productivity gains versus cost. Consumer hardware will adapt to that demand, making it even more affordable and within reach.

Not sure how that speculation fits with the billions of dollars of investment that AI companies will need to convert to profit somehow.