Mention anything about the water and electricity wastage and embrace the downvotes.

Simplified comparisons like these rarely show the full picture [0]. They focus on electricity use only, not on heating, transport, or meat production, and certainly not on the CO2 emissions associated with New York’s airports. As a rough, back-of-the-envelope estimate, a flight from Los Angeles to New York with one seat is on the order of 1,000,000 small chat queries CO2e.

Of course we should care about AI’s electricity consumption, especially when we run 100 agents in parallel simply because we can. But it’s important to keep it in perspective.

[0] https://andymasley.substack.com/p/a-cheat-sheet-for-conversa...

I spent some time thinking of a better word than "evil" before typing this comment. I can't think of one. Doing something bad that harms more than it helps for the purposes of enrichment and power is simply put: evil.

For what it is worth, I have also gone from a "this looks interesting" to "this is a regular part of my daily workflow" in the same 6 month time period.

The model providers should really start having LTS (at least 2 years) offerings that deliver consistent results regardless of load, IMO. Folks are tired of the treadmill and just want some stability here, and if the providers aren't going to offer it, llama.cpp will...

Memory costs are skyrocketing right now as everyone pivots to using hbm paired with moderate processing power. This is the perfect combination for inference. The current memory situation is obviously temporary. Factories will be built and scaled and memory is not particularly power hungry, there’s a reason you don’t really need much cooling for it. As training becomes less of a focus and inference more of a focus we will at some point be moving from the highest end nvidia cards to boxes of essentially power efficient memory hbm memory attached to smaller more efficient compute in the future.

I see a lot of commentary “ai companies are so stupid buying up all the memory” around the place atm. That memory is what’s needed to run the inference cheaply. It’s currently done on nvidia cards and apple m series cpus because those two are the first to utilise High Bandwidth Memory but the raw compute of the nvidia cards is really only useful for training, they are just using them for inference right now because there’s not much pn the market that has similar memory bandwidth. But this will be changing very soon. Everyone in the industry is coming along with their own dedicated compute using hbm memory.

In the few apps I've built, progress is initially amazing. And then you get to a certain point and things slow down. I've built a list of things that are "not quite right" and then, as I work through each one all the strange architectural decisions the AI initially made start to become obvious.

Much like any software development, you have to stop adding features and start refactoring. That's the point at which not being a good software developer will really start to bite you, because it's only experience that will point you in the right direction.

It's completely incredible what the models can do. Both in speed of building (especially credible front ends), and as sounding boards for architecture. It's definitely a productivity boost. But I think we're still a long way off non-technical people being able to develop applications.

A while ago I worked on a non-trivial no-code application. I realised then that even though there's "no code", you still needed to apply careful thought about data structures and UI and all the other things that make an application great. Otherwise it turned into a huge mess. This feels similar.

LLMs will trivialize some subfields, be nearly useless in others, but will probably help to some degree in most of them. The range of opinions online about how useful LLMs are in their work probably correlates to what subfields they work in