Cool analogy, but there's another possibility: software is uniquely suited for being boosted by LLM-based coding agents. That's not to say LLMs are useless in other domains, but they may not yield as much benefit.

Software creation tools have been worked on for decades to keep fallible human coders out of trouble as much as possible. Starting with compilers that enforce or at least encourage coding style humans can understand. Compilers embody formal definitions of language syntax. Lint and other static analysis technologies contribute by identifying problematic patterns in code. Leak detectors identify memory management problems. Profilers identify performance problems.

Few other endeavors have this much tooling that serendipitously happens to help help LLM-based agents be more useful and less error prone.

A lot of discussion around AI is very high-level and abstract. It helps to dig in and identify the characteristics that actually make a domain more or less amenable to being boosted by AI.

And whole repositories with commit history that provides step by step training on how to code.

AI could probably write decently if every scraped book had all the revisions to create it embedded within or art had every brush stroke documented. As it stands programming is the first to be trained. The rest will take some more time.

I can't speak for everywhere else, but here in the Bay Area (California), pretty much all hospitals use AI for transcription, summarizing, and some other stuff that I'm not fully aware of (I should really listen to what my Nurses say before agreeing to being recorded).

> As an example, take a factory that was built around hyrdo-power like a grain mill. You can't just take an electric motor and drop it in in place of the water wheel.

Your chosen example is rather more fitting for the opposite case. Dropping an electric motor is pretty easy, you just attach it to the other side of the axle. But there is really no point in doing that. The sole benefit for that mill, will be that there is the cheap power from the water. It's also the sole reason for the current position of the mill, for the electric motor, you hence need to add long cables first. The size and operation mode of the mill is also not fit at all for the electric age. There is really no point, you are better of just building a new mill in a completely different location.

But you can do that independently of the old mill, since there is hardly any capital to be taken from the old to the new mill, short of maybe the office supply. So you just build a new mill, completely independently. But there is also no reason to decommission the old at the same time, the only reason is to shut it down when is no longer economically viable. But that also might be at a different point in time than building the new mill.

I think you are missing the other side of the coin where it is actually government regulations holding adoption back in many sectors. Ie you are only beginning to see benefit in driving now because regulation has been forced enough to change to let the pilot projects happen. The millions of drivers who should have been fired by now almost all still have jobs and will ibto the medium term

The issue is that software is almost unique it the sense that you can use reinforcement learning to get the desired outcome - does it compile or not? A large amount of automated trial-and-error runs do that.

It's not true elsewhere. The definition of "is this a good outcome" is pretty nebulous in other areas.

"Is this what the law says?"

Yes, but that's not how the judge will interpret it, etc.

But do you really see a change in the technology sector? I don’t. If you plot operating profit per employee for Apple and Alphabet, it’ll show a trend up but since COVID. If you do the same for MS, it’ll show a steady trend since Nadella became the CEO. Amazon is flat, so is Tesla. Can’t really see a change caused by AI.

most lawyers haven't tried fable and astra ultrafast yet.

those who did are rather convinced it's over, for juniors at least.

Look at how many tech or tech-adjacent companies are in the S&P: https://en.wikipedia.org/wiki/List_of_S&P_500_companies

Like %40 of the companies in the index are tech or IT. SO how does the supposed flat line across all those tech companies fit your thesis?

> You can see a similar history with electrification.

It took a massive amount of new infrastructure for electricity to benefit people. Lines had to be run, houses and businesses had to be wired, new light fixtures and appliances had to be purchased and installed.

LLMs can currently be accessed and used everywhere instantly by everybody right now and at any moment day or night. Rich and poor alike can take advantage of it. There's nothing holding back anybody. If everybody without electricity could have pulled a small rectangle out of their pocket and pressed a button to instantly have electric lights and power, the positive impact of electrification would have been immediate. This is not even close to a "same thing now" situation.

I'm not an cliometrician, but my impression is that it took about 4 decades for electricity and steam to do much for productivity.

And it was in 1987 that Nobel laureate economist Robert Solow famously said “You can see the computer age everywhere but in the productivity statistics”

I mean, maybe AI will be faster, because it's especially easy to apply compared to generally applicable technologies. There was the Bank of Korea study showing this spring that did show productivity gains with single proprietors--folks who don't have to reorg employees and processes to do something with more abilty or saved time.

But, generally, I don't know that there was an reason to expect that corporate America generally was going to see an AI payoff so soon.

Maybe you could give an example of a Fortune 500 business that is too capital constrained to take advantage of AI. It seems far more likely to me that the issue is that of the skill sets important to these businesses, these agents are only really good at coding, and they still are very jagged even in that.

I think the reason is much more simple.

It's very hard to improve something that you don't understand yourself. All the tiny decisions you make as you create a product are informed by the experience you have.

So it's seems obvious to me that a product created by software engineers would be better at software engineering. A programmer knows what a programmer needs.

To improve another industry successfully, you would really need people from that industry to be involved in a deep level in the design of the product.

It will be new factories designed from the ground up around ai and its benefits and limitations that really move the needle. Yes, totally agree. But we need more. As I wrote in an essay responding to Dario’s pacing the frontier, in my view, we need a stable long term supported model that companies can build around. In the same way that Ubuntu for example has a long term supported version. This gives companies time to build and adapt. The latest models can still be the realm of the frontier but businesses, schools, countries etc need long term guarantees.

It is most impactful in tech because tech is almost nothing but languages and more so super expensive languages where one hour of an “authors” time can cost thousands with a supply demand issue so bad workers are imported from across the world.

Artists were already starving. The more physical trades are…physical not language so a plumber still plums. Maybe now they ask gemini instead of google tho.

Areas related most to…language are the most impact by large language models? The one that i think is just as impacted is education because it is also almost all language driven.

I see zero sign of any of the new mills coming around. Steel, crops, farming, none of it has changed and what little has is from…the software side or…language.

The SV world really over indexes on how much software can do.

It's far more simple. LLMs just aren't that good at stuff that isn't programming.

All businesses across sectors have been digitally transforming into software businesses over the last twenty years

"the tech sector is the most software defined"

AIs don't just make code they make words, images, and sounds. Hasn't worked in those markets either.

>but then it took like a decade of little growth before the true benefits showed up

That's the core issue apollo is talking about. Investors have been told, "Buckle up, we're getting rich in 2027." Not "Hey, after 20 years, this will probably be pretty nice, maybe." This is how bubbles pop. Everyone starts to realize they were oversold and they rush to get their money out at the top. LLMs won't go away, but the financial collapse will be devastating.

this isn't the invention of the wheel, electricity, etc. You are definitely overhyping it. LLMs hallucinate. It is an unsolvable problem defined by the very nature of the architecture itself. No amount of thinking or counter thinking or triple checking will fix it because LLMs don't understand the basis of what is right and wrong, what looks right and wrong, why saying or doing the wrong thing is bad, and why or when it should realize what it produced is wrong, and what it should do about it.

Big Tech wants to make humans the go-between, however, the thinking and problem solving wasn't the problem to begin with. Checking for mistakes, correcting them, and presenting the corrected versions is the hard part. If a human does not go through the steps of solving the problem, they can't effectively analyze or process the output, which will always lead to mistakes. The only way for LLMs to take over any jobs would be if humans were perfectionists and could check every single bit of work without fault (since LLMs will always make some mistakes: a "perfect" LLM isn't possible anymore than a perfect human being is). Humans (especially myself are disaster and mistake prone.

That's why you should not invest now unless you are short term trading, and you should get that bag of popcorn ready. Trillion dollar valuations aren't real. It's a fake number propped up by a bubble. The US is in record debt, and it will be unable to backstop the financial loss...without taxing heavily the very folks that want to be backstopped. Those folks are dimwits and are only focused on making a buck. They are assuming everything will work out, either via investors/revenue/profits or a government bailout.

Bear Stearns had the same attitude.

In 2008 the government had leeway. This time, they do not. Regardless of political party.

Disclaimer: Please note I do not have an investment in any company, not even in a 401k. I am a disabled software engineer (and manager, in some cases) who worked in/with startups with various sectors. I have knowledge about all the financial and investor nonsense that goes into startups and small businesses. As an autistic millennial, who has definitely been around the block with bubbles, I am also interested in tech and finance. I also have a decent amount of knowledge about the damn stock market. I don't and will never claim to be an expert in any of those things, however, I know enough to say that a whole bunch of folks are going to lose a TON of money at some point within the next few months/years (likely before 2030, possibly by 2028), so if you are overexposed to the stock market (pretend it drops to zero, won't happen, however, it will drop, and no stock is safe, this goes for you FIRE folks as well), you should back off your investments to an acceptable level (the most amount of money you'd be fine with losing ALL of).

Sorry for the rant. Have a good night all.

There are some things that current generation LLMs are extremely good at. They also suck for a lot of reasons and appear to be prohibitively expensive to train as "general" models.

hey are amazing tools, but every time I see a task that LLMs helped me complete in 30% less time, I see 5 more tasks that took me 30-50% longer to complete because of errors, lies, mistakes, hallucinations, context limits.

Do not tell me I'm holding it wrong.