I've been conflicted on AI/ML efforts for years. On one hand, the output of locally run inference is astounding. There are plenty of models on HuggingFace that I can run on my Mac Studio and provide real value to me every single work day. On the other hand, while I have the experience to evaluate the output, some of my younger colleagues do not. They are learning, and when I have time to help them, I certainly do, but I wish they just didn't have access to LLMs. LLMs are miracle tools in the right hands. They are dangerous conveniences in the wrong hands.

Wasted money is a totally different topic. If we view LLMs as a business opportunity, they haven't yet paid off. To imply, however, that a massive investment in GPUs is a waste seems flawed. GPUs are massively parallel compute. Were the AI market to collapse, we can imagine these GPUs being sold a severe discounts which would then likely spur some other technological innovation just as the crypto market laid the groundwork for ML/AI. When a resource gets cheap, more people gain access to it and innovation occurs. Things that were previously cost prohibitive become affordable.

So, whether or not we humans achieve AGI or make tons of money off of LLMs is somewhat irrelevant. The investment is creating goods of actual value even if those goods are currently overpriced, and should the currently intended use prove to be poor, a better and more lucrative use will be found in the event of an AI market crash.

Personally, I hope that the AGI effort is successful, and that we can all have a robot house keeper for $30k. I'd gladly trade one of the cars in my household to never do dishes, laundry, lawnmowing, or household repairs again just as I paid a few hundred to never have to vacuum my floors (though I actually still do once a month when I move furniture to vacuum places the Roomba can't go, a humanoid robot could do that for me).