by lincoln20xx | 2025-06-02 17:19:36+00:00
I have a non-zero number of industrial process patents under my belt. Allegedly, that means that I had ideas that had not previously been recorded. Once I wrote them down, paid some lawyers a bunch of money, and did some paperwork, I have the right to pay lawyers more money to make someone's life difficult if I think that someone ever tries to do something with the same thoughts, regardless of if they had those thoughts before, after, or independently of me.

In my opinion, there is a very valid argument that the vast majority of things that are patented are not "new" things, because everything builds on something else that came before it.

The things that are seen as "new" are not infrequently something where someone in field A sees something in field B, ponders it for a minute, and goes "hey, if we take that idea from field B, twist it clockwise a bit, and bolt it onto the other thing we already use, it would make our lives easier over in this nasty corner of field A." Congratulations! "New" idea, and the patent lawyers and finance wonks rejoice.

LLMs may not be able to truly "invent" "new" things, depending on where you place those particular goalposts.

However, even a year or two ago - well before Deep Research et al - they could be shockingly useful for drawing connections between disparate fields and applications. I was working through a "try to sort out the design space of a chemical process" type exercise, and decided to ask whichever GPT was available and free at the time about analogous applications and processes in various industries.

After a bit of prodding it made some suggestions that I definitely could have come up on my own if I had the requisite domain knowledge, but would almost certainly never have managed on my own. It also caused me to make a connection between a few things that I don't think I would have stumbled upon otherwise.

I checked with my chemist friends, and they said the resulting ideas were worth testing. After much iteration, one of the suggested compounds/approaches ended up generating the least bad result from that set of experiments.

I've previously sketched out a framework for using these tools (combined with other similar machine learning/AI/simulation tools) to massively improve the energy consumption of industrial chemical processes. It seems to me that that type of application is one where the LLM's environmental cost could be very much offset by the advances it provides.

The social cost is a completely different question though, and I think a very valid one. I also don't think our economic system is structured in such a way that the social costs will ever be mitigated.

Where am I going with this? I'm not sure.

Is there a "ghost in the machine"? I wouldn't place a bet on yes, at least not today. But I think that there is a fair bit of something there. Utility, if nothing else. They seem like a force multiplier to me, and I think that with proper guidance, that force multiplier could be applied to basic research, material science, economics, and "inventions".

Right now, it does seem that it takes someone with a lot of knowledge about the specific area, process, or task to get really good results out of LLMs.

Will that always be true? I don't know. I think there's at least one piece of the puzzle we don't have sorted out yet, and that the utility of the existing models/architectures will ride the s-curve up a bit longer but ultimately flatten out.

I'm also wrong a LOT, so I wouldn't bet a shiny nickel on that.