They are under no obligation to continue functioning as a bottomless free repository for text file hosting, especially now that text file creation has multiplied exponentially. They could make a few almost purely commercial changes and solve this without any major re-engineering while maintaining their status as the go-to public / open source code hosting platform.

1. Immediately increase pricing of all enterprise licenses and add super-committer overage fees.

2. Rate limit or cap commit size / frequency for public accounts.

Their service is more valuable than ever and switching is much harder if people have automation built up on their platform. Now is the time to cash in their chips.

And the positive externality of increasing commit cost would be forcing people to have some semblance of restraint for the AI content they generate.

Was very surprised to find things like “bookshelves are all men’s names”, “all names must have ä, å, or ö”, etc.

Complete coincidence that this is #2 today. Had to check that ’NaOH’ wasn’t one of my coworkers

Thus the reticence of science-oriented companies to invest heavily in these mass data-gathering exercises to feed ML. It’s damn expensive, and almost always leads you back to raw data issues, not breakthrough discovery. Doing it without a set purpose in mind is even more likely to yield garbage.

This is the problem with AI for all of science - not just drug discovery. Applied ML has spread like wildfire through academia over the past decade - this started well before the LLM hype. It’s the perfect honey trap: research is painstaking and slow, ML offered a shortcut, and best of all, it just needs data. Research produces lots and lots of data! Surely this will be a match made in heaven.

I’ve watched the same pattern play out at least four or five times now in various roles.

(1) Propose an ML-guided approach to material/chemistry discovery/optimization.

(2) Gather existing data (real, experimental data).

(3) Realize there’s less than about 50 true rows of data on the outputs of interest.

At this point, you either: (4a) revert to traditional methods but keep the veneer of using ML to save face, or (4b) pivot to computational/simulation work or a high-throughput system that’s very far removed from your original problem, but allows you to keep playing with ML toys

It’s really bad. I left the industry. I don’t know how long it will take for people doing real science to take back the reins (and the funding).

The only difference is AI isn’t regulated, so they just have a vague “ethics” department with no real teeth because it’s essentially PR and has no legal consequences to back up their stance.

LLMs have made math proofs more computable, in the sense that a computer can both generate potential solutions and check the validity of its solutions on its own, with a reasonable chance of converging on something correct. I assume this was already doable to some extent, but it seems like it’s now exponentially easier. That still doesn’t mean that all math is automatically solved.

This is somewhat similar to things like molecular dynamics or protein folding or finite element simulations, etc. Some problems that were previously intractable via computation became tractable. Others - the vast majority of other problems - remain unsolvable by these computational techniques, because the scale of compute required is beyond imagination. These are simple things like simulating the dynamics of a cubic millimeter of water molecules for 1 second. Unfathomably beyond current capabilities (and LLMs aren’t going to change that).

I think LLMs are great, I use them every day and I think they have a ton of value. But if these things were as revolutionary as people promote/fear them to be, you should immediately point them at the highest value math problems and see progress. Like the Millenium Prize problems. Haven’t seen a solution to those.

So there are limits - but we’re about to learn a lot about the new normal of what constitutes a layup math proof vs the truly difficult.

But that also makes the case for government subsidization + bailout, if you accept it as a capability critical to national security, but one that may not be profitable to run within the US on its own merits.

> Electrolyzers are located on-site. They use wind energy to split water into hydrogen and oxygen gas.

> Additionally, nitrogen gas is directly harnessed from the atmosphere using an air-separation unit.

> Hydrogen and nitrogen are combined under pressure, resulting in carbon-free ammonia fertilizer.

That sounds a lot like the Haber Bosch process to me. So they’re just saying, rather than divert excess generation to a battery, you could instead build a micro H-B plant on site to produce ammonia.

I doubt this makes any sense in the grand scheme of things as you achieve major efficiencies at scale for chemical plants. Makes more sense to just electrify existing ammonia plants and ensure they use renewable electricity sources.

The green chemistry industry is full of penny-wise pound-foolish concepts like this and it drives me absolutely nuts.

I agree with you when talking about high level software design. As you say it ultimately boils down to building something people will pay for, which is a fuzzy correctness function that is hard to measure within an agentic sandbox.

But unlike other professions, there are a lot of sub-problems within software development that are able to be fully specified and tested via text generation. And I think the developers of AI overestimate how many such problems exist for other professions. What I’m saying is most other professions tend to be “fuzzy all the way down”… which incidentally is why they select for people with fuzzier skillsets. Or in other cases, like physical engineering, the correctness is quantitative, but the necessary I/O integrations and physical automation lower the ROI of agentic workflows considerably.

- extent to which correctness of solution be easily specified and checked

- extent to which new potential solutions can be implemented as text

- extent to which prior art exists online

This basically maps to software engineering and math. I think a fair bit of AI hype comes from the fact that the very architects of AI are the people whose jobs are most easily automated by AI. They think, “if my job receives this much of a boost from AI, surely every job will be the same”. Ironically it couldn’t be further from the truth… and likewise the predictions of widespread labor obsolescence

I’m not denying that his companies are awash in zany financials, but I don’t think that’s ever been the point

Ok, deploy a local model on a lightweight edge compute device and strap it to a chassis with wheels, and attach a cheap webcam

> Then I’d want to see an embodied agent that could navigate its environment in order to survive as well as, say, a lizard can

Give the robot appendages that enable it to plug itself into a standard wall outlet, guided by a vision model plugged into its webcam. As long as it can feed itself, it can survive long enough.

> Next I would want to see an embodied agent with the same capacity to deal with novel situations as a mouse.

I think if you fed frames from the webcam into a local VLM every 5s you’d be able to assess a situation and respond with simple actions (turn, advance, retreat).

> After that I’d want to see agents whose social dynamics are as complex as those of wolves, and then agents with the tool-making abilities of chimpanzees.

Social dynamics could be implemented in many ways, maybe by transmitting tokens over RF? Idk. Then you have a scanner that picks them up, feeds them into some LLM frontend and decides whether to add them to a global context file that guides the VLM action-taker. A new action could be to broadcast a token message. Tool-making would have to be code-based. Physical tools are hard. Still unsolved.

> At that point I would want to see people successfully teaching such embodied agents how to communicate their desires

This part is relatively straightforward except for the “via nonlinguistic modality”.

Anyway. These are all engineering problems. Personally I would demand to see the AI reproduce its body under its own power and volition. That’s a pretty neat trick we’ve got going for us.

Well I’ll be..

Somewhat oversimplifying; writing software and building apps was a bottleneck - now it is not. What is the next bottleneck that LLMs can solve? Is there one? And is there enough publicly available data to solve it repeatably at scale? Or did we just automate stack overflow searches and now we’re stuck again?

Or is the endgame of this innovation cycle the complete removal of interaction with machines through code? Will we simply interact with machine coworkers purely through natural language? Can an LLM make PowerPoint slides and run a meeting? So far not seeing much progress on that.