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If AI is so good at what it does, then it shouldn't matter if you fall behind in adopting it... it should be able to pick up from where you need it. And if it's not mature, there's an equally valid argument to be made for not even STARTING adoption until it IS - early adopters always pay the most.

There's practically no situation where rushing now makes sense, even if the tech eventually DOES deliver on the promise.

I think that's called a cargo cult. Just because something is a tech gadget doesn't mean it's going to change the world.

Basically, the question is this: If you were to adopt it late and it became a hit, could you emulate the technology with what you have in the brief window between when your business partners and customers start expecting it and when you have adapted your workflow to include it?

For computers, the answer was no. You had to get ahead of it so companies with computers could communicate with your computer faster than with any comptetitors.

But e-mail is just a cheaper fax machine. And for office work, mobile phones are just digital secretaries+desk phones. Mobile phones were critical on the move, though.

Even if LLMs were profitable, it's not going to be better at talking to LLMs than humans are. Put two LLMs together and they tend to enter hallucinatory death spirals, lose their sense of identity, and other failure modes. Computers could rely on a communicable standards, but LLMs fundamentally don't have standards. There is no API, no consistent internal data structure.

If you put in the labor to make a LLM play nice with another LLM, you just end up with a standard API. And yes, it's possible that this ends up being cheaper than humans, but it does mean you lose out on nothing by adapting late when all the kinks have been worked out and protocols have been established. Just hire some LLM experts to do the transfer right the first time.

"But the fact that some geniuses were laughed at does not imply that all who are laughed at are geniuses. They laughed at Columbus, they laughed at Fulton, they laughed at the Wright brothers. But they also laughed at Bozo the Clown."

— Carl Sagan

We just need another billion parameters bro. Surely if we just gave the LLMs another billion parameters it would solve the problem…

I realized the fundamental limitation of the current generation of AI: it's not afraid of fucking up. The fear of losing your job is a powerful source of motivation to actually get things right the first time.

And this isn't meant to glorify toxic working environments or anything like that; even in the most open and collaborative team that never tries to place blame on anyone, in general, no one likes fucking up.

So you double check your work, you try to be reasonably confident in your answers, and you make sure your code actually does what it's supposed to do. You take responsibility for your work, maybe even take pride in it.

Even now we're still having to lean on that, but we're putting all the responsibility and blame on the shoulders of the gatekeeper, not the creator. We're shooting a gun at a bulletproof vest and going "look, it's completely safe!"

something i keep thinking about: is the electricity and water usage actually cheaper than a human? i feel like once the vc money dries up the whole thing will be incredibly unsustainable.

We'll be in this state until actually intelligent AI comes along. Some evolution of machine learning beyond LLMs.

Yep. The methodology of LLMs is effectively an evolution of Markov chains. If someone hadn't recently change the definition of AI to include "the illusion of intelligence" we wouldn't be calling this AI. It's just algorithmic with a few extra steps to try keep the algorithm on-topic.

These types.of things, we have all the time in generative algorithms. I think LLMs being more publicly seen is why someone started calling it AI now.

So we've basically hit the ceiling straight out of the gate and progress is not quicker or slower. We'll have another step forward in predictive algorithms in the future, but not now. It's usually a once a decade thing and varies in advancement.

Edit: I have to point out that I initially had hope that this current iteration of "genAI" would be a very useful tool in advancing us to actual AI faster, but, no. It seems the issues of "hallucination"—which are a built-in unavoidable issue with predictive algorithms trained on unfiltered mass—is not very capable. The university I work at, we've been trying different things for the past two years, and so far there seems to be no hope. However, genAI is good at summarising mass outputs of our normal AI, which can produce a lot to comb through, but anything the genAI interpretats still needs double-checked despite closed off training.

It's been unsurprisingly disappointing.

We're still at a point where logic is done with the same old method of mass iterations. Training is slow and complex. genAI relies on being taught logic that already exists, not being able to thoroughly learn it's own. There is no logic in predictive algorithms outside of the algorithm itself, and they're very logically closed and defined.

bright white teeth are highly overrated, glow in the dark teeth, well…wouldn’t a cheap little night light work even better than a radioactive mouth?

I also remember the Internet being fiddly as fuck and questionably useful during the dialup days.

AI is improving a lot faster than Internet did. It was like a decade before we got broadband and another before we had wifi.

By that logic, people shitting on AI will look very quaint in a decade or so.

This isn't the "very beginning", that was either 70 or 120 years ago, depending on whether you're counting from the formalization of "AI" as an academic discipline with the advent of the Markov Decision Process or the earlier foundational work on Markov Chains.

Chatbots are old-hat, I was playing around with Eliza back in the 90's. Hell, even Large Language Models aren't new, the transformer architecture they're based on is almost 10 years old and itself merely a minor evolution of earlier statistical and recurrent neural network language processing models. By the time big tech started ramping up the "AI" bubble in 2024, I had already been bored with LLMs for two years.

There's no "early adaptation" here, just a rushed and wildly excessive implementation of a very interesting but fundamentally untrustworthy tech with no practical value proposition for the people it is nevertheless being sold to.

Not realizing that should be an instant firing. The dev didn't even glance a look at the unit tests...

Trust with verification. I've had it do everything right, I've had it do thing so incredibly stupid that even a cursory glance at the could would me more than enough to /clear and start back over.

claude code is capable of producing code and unit tests, but it doesn't always get it right. It's smart enough that it will keep trying until it gets the result, but if you start running low on context it'll start getting worse at it.

I wouldn't have it contribute a lot of code AND unit tests in the same session. new session, read this code and make unit tests. new session read these unit tests, give me advice on any problems or edge cases that might be missed.

To be fair, if you're not reading what it's doing and guiding it, you're fucking up.

I think it's better as a second set of eyes than a software architect.

My company is pushing LLM code assistants REALLY hard (like, you WILL use it but we're supposedly not flagging you for termination if you don't... yet). My experience is the same as yours - unit tests are one of the places where it actually seems to do pretty good. It's definitely not 100%, but in general it's not bad and does seem to save some time in this particular area.

That said, I did just remove a test that it created that verified that IMPORTED_CONSTANT is equal to localUnitTestConstantWithSameHardcodedValueAsImportedConstant. It passed ; )