Comment by 0manrho

> To be sure, I happen to believe there is a lot of mileage for LLMs even in their current state—a lot of use-cases, integration we have yet to explore. But Sutskever I assume is a researcher and not a plumber—for him the LLM was probably over.

Indeed. Humans are a sucker for a quick answer delivered confidently. And The industry coalesced around LLM's once it was able to output competent, confident, corporate (aka HR-approved) english, which for many AI/DL/ML/NN researchers was actually a bit of a bummer. Reason I say that is because that milestone suddenly made the "[AGI is] always a decade away" to seeming much more imminent. Thus the focus of investment in the space shifted from actual ML/DL/NN research to who could convert largest pile of speculatively leveraged money into pallets of GPU's and data to feed them as "throw more compute/data" at it was a quicker/more reliable way to realize performance gains than investing in research did. Yes, research would inevitably yield results, but it's incredibly hard to forceast how long it takes for research to yield tangible results and harder still to quantify that X dollars will result in Y result in Z time compared to X dollars buys Y compute deployed in Z time. With the immense speculative backed FOMO and the potential valuation/investment that could result from being "the leader" in any given regard, it's no wonder that BigTech chose to primarily invest in the latter, thus leaving to those working in the former space to start considering looking elsewhere to continue actual research.