Cloudlflare builds OAuth with Claude and publishes all the prompts

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.

Experts understand orbital mechanics pretty well. If experts say an asteroid in the next 5 years it's pretty similar to saying that a rock dropped from the top of a skyscraper will hit the ground. It happens billions of times every day, we know the cause and effect.

With AI, there's no real expertise involved in saying "well, it was very stupid 5 years ago, now it's starting to seem smart, if we extrapolate it's going to be smarter than me in 5 years." But no one really knows what level of effort is required to make it smarter than me. No one is an expert in something that doesn't exist yet.

> increased familiarity with the tooling you use

In general I agree, but sometimes you want something that you haven't done in years but vaguely remember.

~20 years ago I worked with ffmpeg and vlc extensively in an IPTV project. It took me months to RTFM, implement stuff, test and so on. Documentation was king, and really the only thing I could use. Old-school. But after that project I moved on.

In 2018 I worked on a ML - CV project. I knew vlc / ffmpeg could do everything that I needed, but I had forgotten most of everything by then. So I googled/so/random-blogs, plus a bit of RTFM where things didn't match. But it still took a few days to cobble together the thing I needed.

Now I just ask, and the perfect one-liner pops-up, I run it, check that it does what I need it to, and go on my merry way. Verification is much faster than context changing, searching, reading, understanding, testing it out, using a work-around for the features that ffmpeg supports but not that python wrapper, and so on.

> The big question is: is it useful enough to justify the cost when the VC subsidies go away?

I won't claim local LLMs as nearly as good as various top models behind paid subscriptions/APIs, but I'm certain I'd be able find a way (for me) of working with them well enough, if the entire paid/hosted ecosystem disappeared over night. Even with models released today.

I think the VC subsidies probably "make stuff happen" faster, and without it we'd see slower progress, but I don't think 100% of the ecosystem would disappear even if 100% of VC funding disappeared. We're bound for another AI winter at one point, and some will surely survive even that :)

Inference is actually quite cheap. Like, a highly competitive LLM can cost 1/25th of a search query. And it is not due to inference being subsidized by VC money.

It's also getting cheaper all the time. Something like 1000x cheaper in the last two years at the same quality level, and there's not yet any sign of a plateau.

So it'd be quite surprising if the only long-term business model turned out to be subscriptions.