171 karma · joined December 8, 2016
You will have to wait great amount of time for great things just as you did before.
LLMs are not AGI, if so they are not foundational technology, if so we can't really built upon them - we can just use their wrappers as productivity tool, if so we will be bottleneck by what we were bottlenecked before: corporate bureaucracy, slow physical world innovation pipeline, supply chains, just a small number of actual problem solvers/risk takers around, etc.
There will be of course a lot of attach LLM wrapper and improve productivity here / there underwhelming weak fart products, ex: Meta Muse.
I find my self going opposite direction recently. I start using frontier models less and less and smaller models more and more. I recently tried Opus Opus 5 with ultra-code level of effort and I wasted like 2 days on trying to migrate some project to new libs because it spits out a huge wall of text as replies/plans and waste huge amount of time on things I didn't ask it to check and see no point in checking. At the end I just took a smaller model and used it in more controlled fashion playing the role of orchestrator/planner. Smaller models are good enough and also much faster. My collisions from that, frontier models and high reasoning effort levels are for pure vibe-coding when you don't really control or understand what is happening. My application area is not pure SWE though, I didn't churn a lot of code before and I don't now, so I have enough time on understanding/polishing my projects if needed.
Yes, It is not sufficient but I would argue it was necessary. In case of China it allowed them to have a set of domestic copycat companies like Baid, Alibaba and Tencent, that enabled high demand for local engineers to serve their needs and that allowed to have a large pool of competent domestic engineers which now can benefit other emerging sectors too. It keeps surprising how many top contributors to large OSS projects are from China.
Are/were you in a position to make such decisions or it is a guess? I'm not but given certain evidence I doubt that few percent will cut it. I know some of the richest companies on the planet from SF Bay Area who won't give lunch for free to their engineers. So I'm not sure about "few percent" :-D 10x we were promised, now that is more interesting but we all know that 10x engineers is nonsense.
It is a honeymoon still, enshittification is coming, who knows how that will look like given how much more expensive to run LLMs backed user experience. Some back of the envelope calculations: 300 million US users * 20$ a month * 12 months = 72 billion $ a year. 72B$ is some spare change for AI labs. That assuming entire US will be subs which is unlikely and outside of the US there are not many rich countries consuming it, India is the next market, then Brazil and Philippines I think, not super rich counties to say the least. I believe total revenue to just pay for the capex build out by the end 2027 should be on the scale of hundreds of billions a year.
> That's a baseless claim. For example, Germany has invested massively in solar but its electricity prices went up, not down.
> This price of electricity is high because gas, which is still expensive, sets the price of electricity most of the time [1].
1) Am I missing something or the link you provided contradicts your claim? On a chart #2 in the article you can see Germany has only 24% of the times price of electricity set by gas, 24% is not "most of the time", right?
2) Even if that is true, and electricity prices in Germany are set by the price of gas most of the time, it seems like you didn't address the point in a previous message. If the goal is to have lower prices and the goal(outcome) is what really matters not just the process and renewables is the path to get there then why Germany electricity prices rose at the end? And were not all of those because of this ... because of that ... supposed to be a part of that path(plan)? They did the thing, invested in solar, what have they missed and why?
Is not Germany an example of the opposite? I mean their decision to shut down nuclear plants while getting dependant on Russian supply of energy sources?
Many of those points assume LLMs will become amazing in many things very quickly like in a quantum leap, it doesn't seem reasonable to assume that imo. We are actually seeing a confirmation of that atm, LLMs's capability of finding zero days are growing across few months/years, and as you can see concerns are raised about that, that feedback will be taken into account. Well, if AI labs start to hide frontier models or/and lobotomize them for external users then we might be in trouble at some point but I'm not sure if that is possible. They are under pressure to release them due to money incentives, lobotomizing while preserving usefulness for customers might be impossible, hiding internally might spill out in different ways such as Hugging Face incident so not sure hiding is possible neither.
1 what is tesla cybercab plan to address legal implications of accident that will happen? who is going to be responsible for them when they happen? are they covered by tesla insurance or some other insurance? are there any official plan/statements around that?
2 what was the name of the experiment they started in san antonio tx when some cars didn't have a driver? what was the results of it? did they expand the operations? it was much smaller than waymo, is it growing? how it is related to robotaxi?
It is not able to connect the dots that I keep asking about Tesla in 2nd prompt and spit out some unrelated stuff. Really? How it can be that bad? Gemini 3.1 Pro model works fine in this case btw. I thought maybe it is about knowledge cut over date and it doesn't know about those events from 2025 but it seems it has the knowledge up to March 2025. Top 10 in Intelligence on artificialanalysis ladies and gentlemen.
Broad economic impact due to Internet boom in the the US came from 2 main categories: 1) New huge internet enabled tech companies appeared(Amazon, Google, Meta etc.), all created their own products, all were build on reliable foundational technology. LLMs is not a reliable foundational technology. 2) Existing legacy companies could utilize internet to connect their teams/departments/offices though a bunch of new software which were build on reliable foundational technology. LLMs is not a reliable foundational technology.
> And remember that this is the least powerful that it will ever be.
This mantra is being repeated but "serious LLMs's issues" I described are still there and will be there because they are part of how LLMs are built and work. Without addressing those you can't build products but LLMs's capability as a personal productivity tool will keep rising, yes.
The "best" possible outcome of LLMs to a broader economy might be that a smaller group of experts will be able to do the job in companies due to boost of their personal productivity and a bunch of people will be freed up(laid off) and they will have to go and work on something else and thus a boost of productivity in the economy. But it seems it won't be any low hanging fruits(problems to solve) this time as it was during the internet era. It is not like we out of problems: new cancer treatments, self driving cars, nuclear fusion, modular nuclear reactors etc. There is a huge value to capture there, here is the thing though, those are hard problems, it is not a new TikTok, gmail, netflix etc. Yes, people will have LLMs now but can we actually start solving hard problems with them? Because it might be the case that a gravy train of the last few decades for Silicon Valley is over, no more useless internet enabled services, no more billion dollar companies built on just applying internet to yet another thing and producing another digital product. People's free time is limited, its redistribution across digital services will not grow the economy, global internet penetration is already pretty high and won't grow that much, so there might not be much value to capture there.
Yes, for a verifiable domains you can set up a harness and brute-force a search space if you have enough money for compute. Why do you think they keep coming up with those examples of impressive achievements like solving math puzzles? Why not focus on something with economic value to it? My answer is they can't, those are hard problems, those require building an actual product, those require reliability.
> EDIT: If you asked people on the street in 1990, they’d probably not consider the Internet to be in the same category as the Steam engine either. I mean, you already had phone and fax, so it wasn’t that ground breaking. And I’ve even read articles from the mid-90s declaring the Internet a temporary fad.
We are not people people on the street we are people who are directly involved in application of the technology, we posses a higher level of insight.
We are not people who write newspapers we are people who are directly involved in application of the technology, we posses a higher level of insight.
> I feel like this is incredibly simplistic, and if you changed "LLM" to "computer" or "internet" and went back decades, it's highly likely you'd read the exact same takes in the newspaper back then about those things.
Computers, the Internet, Steam engine, Rail roads are much more reliable. If you were a problem solver and entrepreneur you could go and apply those on small scale, get profits, reinvest, set up a flywheel, build a fortune on the top of those, there were a lot of low hanging fruit due to the fact that the foundation was laid out, a break-even period of James Watt steam engine was like 2-3 years I believe until everyone saw it and margins flattened. Can you do that with LLMs? It doesn't look that way.
Yes, but LLMs are not animals, animals learn from experience and LLMs don't.
Btw I meant bigger and bigger amount of data of extracted reasoning chains when you go deeper and generate more and more of them in your attempt to describe the universe, the amount of permutations explodes. And it seems LLMs can't workaround that because they don't build world model inside so they can't deduct it from pre-built world/object model, they must memorize it and look it up later.
If we talk about just LLMs, given how things have been going since ChatGPT, my bet it would not change that much. LLMs are not foundational technology such as Internet or Steam engine or Rail roads were. There are very few products that can build upon them because of reliability issues which are completely unresolvable for LLMs, chatbots is a decent product that came out of it, coding harnesses is another one, this is not even close to the impact Internet or Steam engine had. LLMs gave us nice productivity tools for highly motivated expert knowledge workers, that is all. LLMs are getting better and will get better, but it is impossible to describe the universe and compress it into few terabytes and that is what they are doing atm effectively, so all serious LLMs's issues will still be there in 2040: the lack on continues learning, hallucinations, terrible sample ratio, agent's failures on long horizon tasks, instruction following failures.
My explanation is - the technology is amazing and engineers admired it, but CEOs kept talking about an exponential curve while engineers on the ground started to notice signs of Gartner hype cycle curve.
Are you aware of any mid-large projects that went that path? That sound like an irreversible one way decision, codebase will be not suitable for humans pretty soon after which means from now one you at the mercy of LLMs.
I think it is more like top companies, not top developers, and the problem with developers in top companies was and is - absolute majority of them are not actually directly working on things that increase revenue, so companies can spend a ton of money and see barely if any changes in the product and the bottom line, so companies, at least legacy ones will be reluctant to sponsor that long term.
LLMs spared my neck of the woods for now but is this really how PR review looks like these days for an average SWE or its just an example of a Junior in a team?