Something that I have realized recently is that it has become so easy to get an answer to almost any question with the help of chatbots that its almost unnecessary to spend any effort thinking about the problem or the solution. I feel like before when I had to spend time researching a problem to find an answer I learned so many things around the topic itself which helped me understand the problem itself better and gained a deeper understanding. Today it feels like you can have an answer to the most complex questions you might have, yet you gain a superficial understanding of the topic and might forget about it quickly.
IME this works until it does not. This approach works well at the beginning of a greenfield project, but at the same time because it is so easy to add features, you will likely ship something that is way too over engineered. And that complexity will not amortize over next increments and will more likely lead to the entire project being a black box only fully understood by AI. However a more careful use of AI for targeted surgical changes is far more ”productive” in the long term IMO.
I disagree, the approach works well in a legacy project, since there are structures and standards that already exist, which them model can draw from (if you aren't more explicit about it in AGENTS.md)
This! I don't think folks understand how easy it is to go from greenfield to brownfield with these tools, esp if your organization is only valuing velocity. Meaning your doing full agentic development on large features, barely reviewing any code, and shipping without much refinement. It's insane, but this appears to be the status quo in SF startups.
Something isn't clear about the size of your codebase here and the level of reliability your customers expect, as a reader of your comments. Clarity there will help.
My observation has been:
- Initial greenfield work by an LLM is fast and very effective with minimal or no human oversight.
- Subsequent work ends up being over engineered and very verbose. Assumptions are made that aren't suited to the problem at hand (for example I find Fable is extremely regex happy where structured data would work much better from a readability perspective.)
- Once code bloats beyond a certain point due to unguided LLM usage, complexity is high enough that only LLMs can operate on the codebase with any economical amount of time.
- Rinse repeat and your code ends up unclear about any state that's not explicitly being tested and verified in QA loops
For some of our products this has been fine, for others it's been problematic. An understanding of your size and reliability requirements will help make the conversation more productive.
Why aren't you guiding your LLM usage? Is that what I said - to spam it and not guide anything? Or to have a careful workflow where you agree on design and maximize your human judgement/leverage?
> any state that's not explicitly being tested and verified in QA loops
As opposed to before, when engineers perfectly reasoned about code behavior from first principals and QA was unnecessary?
> Why aren't you guiding your LLM usage? Is that what I said - to spam it and not guide anything? Or to have a careful workflow where you agree on design and maximize your human judgement/leverage?
You didn't say anything positively or negatively regarding this so I made an assumption that you were using the LLM relatively unguided (e.g. a bit of oversight, not the kind of thing that heavy code reviews used to involve pre-agents.) Feel free to add clarity on your actual usage loop.
> As opposed to before, when engineers perfectly reasoned about code behavior from first principals and QA was unnecessary?
In my experience, most engineers are quite good at reasoning about code behavior for non-QAed code paths. Obviously things fall through the cracks. But I've been in the ground floor of plenty of Big Techs in their early stages before agents and, yes, a lot of initial development had spotty test coverage and yet most of the engineers had good mental models of what was happening. It used to be a very valuable skill to wrap your head around a torrid piece of code with few or no tests but was nonetheless a core piece of your application. Conversely, agentic development can bring cognitive debt [1].
===
This isn't a fight. We aren't sparring over what's right and wrong. I'm just curious how other people use agents in their work as someone who is also now in a startup that uses LLM agents heavily and has no limitations on spend.
> You didn't say anything positively or negatively regarding this so I made an assumption that you were using the LLM relatively unguided
I feel like this statement betrays your lack of advanced experience coding with LLMs.
OP's elaboration of the steps they are going through (planning, agreeing on plan, getting one LLM to draft execution plan, approving it, then executing with a separate LLM, then reviewing/testing) made it super obvious to me that they are guiding their LLMs quite considerably as part of their work.
Anyone making blanket statements about LLMs producing garbage is just telling on themselves about not having proper SDLC practices in place.
Planning, agreeing on a plan, separating planning and implementation LLM, using separate review LLMs, these are all table stakes. This isn't "guidance" if you're getting paid to write software. If you think "unguided" means "I typed a prompt into claude code and waited yolo" I don't know what to say but, you have a very different idea of what professionals do than I do.
I find for my own work that I need to read the diff the LLM produces then offer feedback on the diff in its own loop before I am satisfied, and this is after all the unattended QA steps through Codex Computer or Claude MCPs happen. Then auto reviewers come in and then reviewers come in. Of course, at our stage, we rarely have this luxury and it's only reserved for the very core of our codebase.
This is still much less guidance than we used to do for code before agents became popular. Even at Series A companies, before agents, we used to socialize tech specs, get buy-in from multiple engineers, create test plans, etc etc.
> Anyone making blanket statements about LLMs producing garbage is just telling on themselves about not having proper SDLC practices in place.
> I feel like this statement betrays your lack of advanced experience coding with LLMs.
Are we in school debate club? I don't know what's going on lol, I'm just curious how people are using LLMs! Is it just that irresistable to take a cheap shot at each other?
Not that I know of but that's the conclusion I drew from your statement.
It's not a cheap shot unless you took it personally?
I suppose I could have said "the fact that OP's explanation of how they work did not lead you to conclude they were in fact guiding their LLM usage quite a bit tells me that perhaps you have not been working with LLMs in any advanced capacity".
For the SDLC comment I admit it was a broader statement (based on observing people generalizing that "LLMs produce bad outputs") and not specifically aimed at you, and I didn't make that clear, so my bad.
> If you think "unguided" means "I typed a prompt into claude code and waited yolo" I don't know what to say but, you have a very different idea of what professionals do than I do.
Not having human input in the loop, i.e. allowing agents to act without guidance. I understand the idea of having agents guide agents, but really how much do we gain when Sol scolds Fable?
I use it. We have more workstreams than people and so we have to rely on this pattern for most of our new products, but I still find the code doesn't meet my bar. To me it produces passable but not good, maintainable code. Good enough for our experimental products with the cognitive debt associated with heavy agentic development. We take this calculated tradeoff of velocity for cognitive debt and middling quality because we're a startup and we feel that velocity matters more than correctness, but that is just a bet as the nature of startups go.
AI is an accelerate tool for any organizations, management thinks it'll solve their organization issue because it accelerates it. Most often, it accelerates toward a wall.
Design is too expensive, we do agile.
QA too expensive, we fire all of them, and claim devops is the now, which allows us to fire the Ops team too, 100% ownership from deisng to ops on devs.
One person with an agent can replace all these teams. Yeah mo profits.
What is the point of working at a startup if you’re dealing with millions of lines of legacy code ? Isn’t the whole point of startups to create & innovate with a clean slate and modern tools?
How long has your startup been around? I’ve worked at plenty of startups over the past 20 years. Including one that was still calling themselves a startup 10 years out. The org I work at now was a startup before my tech giant employer acquired them. We have a very bloated and very profitable 8 year old codebase that is barely 500k LOC.
I’ve never seen a startup with a multi million line legacy codebase.
In response to a question about a legacy codebase at a startup. That implies that they think whatever they are doing is common. And forking a multi million line codebase and heavily developing it isn’t common for startups.
I don't know if that's the whole point, but I agree with the sentiment, why would a startup be working in legacy code and where would that code come from if this is truly the start of something.
OP might just be working at a small software company or for one that broke from a bigger one and is now "startup" like?
You'd be surprised. I met a guy last week who was proud to tell me he had vibe coded an almost 2 million line code base. The app did not sound that complicated, so I'm assuming it's full of copy-pasta flavored slop.
"startup" and "legacy codebase" are diametrically opposed concepts.
And if you're saying (based on your other comments) that a 6 month window is enough to create a legacy codebase...that indicates a serious lack of experience or understanding as to what a legacy codebase is, or why they exist.
Man, so many people in this thread just arguing pointless semantics, making weirdo absolutist (and incorrect) statements.
Accept that other people may ascribe different meanings/interpretations to words than you, and that if your reading of their statement doesn't make sense to you, perhaps you are simply reading it wrong.
Trying to hold someone else to your definition of words suits what purpose exactly? Are you just trying to "win" ?
This is an ultra cop out. There are standards in language that are not all “left means right for me so you cannot assume when i say right it is right and not left”
This whole thread around loc is depressing. It speaks volumes of some peoples inexperience working on actual legacy code. Legacy code is not just age or size but that the technical foundation is dated in a fundamental way. A giant monolith running on a now defunk framework using a database only one guy in canada knows about.
Case and point in my day job. The org that owns XMM development does not know how to recover a physical bench that is bricked because everyone who knew how has left. So now they just use simulators…
Interestingly, AI figured out some of this pretty easily for me. But the org has the exact same AI as i do. At the same time another org is close to a year into a greenfield rewrite that has been developed via agentic swarms. Absolute trainwreck.
AI doesnt make bad engineers good. Anyone who says they are doing 4 eng work likely would be without ai too. Those that claim otherwise, are the bad engineers.
> There are standards in language that are not all “left means right for me so you cannot assume when i say right it is right and not left”
Yes of course there are words that have very clear meaning, and that's not the case here.
"startup" and "legacy" - used in the context of this discussion - are not words like "left" and "right" where everyone has a universal agreed-upon understanding of their meaning.
Yes lol. Of all things people are getting on me for it's the number of LoC x Years In Business of this startup. I don't fucking know, I didn't start the company and I wasn't here for several of those industrious years. Looking now it looks like we have slightly fewer LoC than that, I was counting some of the generated stuff.
But who cares? The point is any codebase over a few years old with lots of customers and a big surface area has lots of code, much of it "legacy" from the standpoint of a guy in 2026.
I’ve worked at many startups and consulted at many more over the last 20 years. I’ve never seen 2 million lines of code happen that fast at a small startup.
15 devs putting out 400k LOC a year into the same codebase is not normal at all. I’ve never seen anything close to that kind of rate of growth across that number of people.
And 50 engineers is not even remotely close to a small startup. I have worked at a startup that had 50 engineers after a few years, but it was a multi billion dollar unicorn.
exactly. Usually legacy code forms when people lose context and confidence in parts of the codebase due to staff turnover etc and ppl avoid touching or enhancing those parts for long periods. Six months is a short time to accrue that much tech debt, its enough time where most of the people who created that "legacy" are probably still around. As you said indicates bigger problems.
One advantage that I see in models that are implemented as code is that they can quickly and cheaply be modified using LoRAs. What would the equivalent be in hardware? Another piece of hardware you would attach like adding a graphics card to a computer?
Sort of but Adapters allow for multiple weight adjustments (think loras) for specific skills so it is more like extra optimized mixture of experts or multi agent approach. They have a slide with adapters listed like summarization, prioritization, tone (happy, business, etc), editor, etc) -- this is not to be mixed up with Intents which is how on device apps publish their capabilities to the Intelligence system for real npu os level multi agent tool use.
While fundamentals are important to learn, there is also a huge benefit in learning specific tools and frameworks. There is no ”one size fits all” when it comes to software and more often than not, you need very customized solution for optimal performance. Moreover, learning to master a tool often means you are also able to improve it, which is part of the reason open-source tools usually improve with the loyal contributers!
> This ‘goal drift’ means that agents, or tasks done in a sequence with iteration, get less reliable. It ‘forgets’ where to focus, because its attention is not selective nor dynamic.
I don't know if I agree with this. The attention module is specifically designed to be selective and dynamic, otherwise it would not be much different than a word embedding (look up "soft" weights vs "hard" weights [1]).
I think deep learning should not be confused with deep RL. LLMs are autoregressive models which means that they are trained to predict the next token and that is all they do. The next token is not necessarily the most reasonable (this is why datasets are super important for better performance). Deep RL models on the other hand, seem to be excellent at agency and decision making (although in restricted environment), because they are trained to do so.
LLMs are fine-tuned with RL too. They are NOT simply next token predictors. RLHF uses whole answers at once to generate gradients, so it is looking further into the future. This might not be perfect but it is clearly more than focusing just 1 token ahead.
In the future the RL part of LLM training will increase a lot. Why am I saying this? There are two sources for learning - the past and the present. Training on human text is using past data, that is off-policy. But training on interactive data is on-policy. There is nothing we know that doesn't come from the environment. What is not written in any books must be learned from outside.
That is why I think supervised pre-training from human text is just half the story and RL based agent learning, interactivity in other words, is the next step. The two feed on which intelligence stands are language (past experience) and environment (present experience). We can't get ahead without both of them.
AlphaZero showed what an agent can learn from an environment alone, and LLMs show what they can learn from humans. But the world is big, there are plenty of environments that can provide learning signal, in other words feedback to LLMs.
> The facilities will reportedly consume as much as 13 percent of the plant's output.
Why are AI products being shipped so aggressively despite being so inefficient? Is code autocompletion and generating random images really worth so much electricity? Shouldn’t we wait until the research has created an efficient architecture that is easily scalable first?
What a bizarre question. Me and many others are paying $20+ for these services. The electricity cost is already priced in and subscribers looks at the price and are fine with that price. If the price comes from dev costs, electricity, capital expenses etc. is irrelevant. There are really no alternatives either (well, I could go to 99Designs, pay $100 and wait days). People pay a lot for this and every day new use cases get discovered, so clearly more services like this get built.
Edit: " Shouldn’t we wait until the research has created an efficient architecture that is easily scalable first?"
Who is "we"? If I think I can offer a product that uses AI and after paying API requests to OpenAI I can turn a profit, I'll build it. API requests will only get cheaper, but why should I wait for that if I can turn a profit now?
Between my flatrate usage and companies paying for additional API quota, I'd expect them to at least be in a position where they could be profitable if they slowed down new trading. When if that wasn't the case though it just shifts the obvious answer to the expectation of future profitability. There is clearly massive demand for this tech.
You've somehow missed all the "enshitification" processes Cory Doctorow and others have been documenting?
"Here is how platforms die: first, they are good to their users; then they abuse their users to make things better for their business customers; finally, they abuse those business customers to claw back all the value for themselves. Then, they die. I call this enshittification."
OpenAI (et al.) are all deeply in the "First, they are good to their users" stage of their business plan, but they WILL. NOT. STAY. THERE. Whatever product relying on OpenAI you can offer/build/grow into a business, sooner or later you're going to be squeezed for every cent in profit, in the same way as companies who were convinced Facebook was a great alternative to running their own web presence are these days - having to pay to ensure content that people who've like and subscribed to their pages end up seeing anything they post.
Your $20 subscription is not even close to covering electricity costs - never mind covering capitalisation costs for $1.6 billion data centres with another $1.6 billion dollars worth of GPUs to put in them. (Think about it. Even if that 3.2 billion dollar datacenter was funded borrowing at only 1% - you'd need over 130,000 paying subscribers at $20/month just to cover the interest on the loans.)
So what's your solution? Sit on your hands and build nothing, leaving potentially millions or billions of dollars on the table? Whine about things that haven't happened, may never happen, and could be worked around if they did happen? Google is successful and it charges most people nothing. So it is possible to make a successful business out of providing a service at low(ish) cost.
Of course you wouldn't build a multi-billion dollar data center for a single application bringing in a pittance. But that's not what anyone is doing. Demand is high and data centers can be shared between many different companies.
Or it could wind up being a pretty big waste of resources. On the one hand you have the ability to extract "apparent knowledge" in a conversational form. On the other hand you are killing the ability to create new knowledge and as an added bonus consolidate power into service providers that work worse than ever.
This "conversational knowledge" stuff is probably just a primitive and inefficient form of AI that may be superceded by other innovations. It need not have any real impact on new knowledge, any more than billions of ordinary people babbeling for hundreds of years. The service provider concern is understandable, but I think competition may keep this tech available to everyone who can reasonably afford it. We still have private computers and software despite the possibility for everything to be run from data centers and using proprietary subscription-based software.
"You've somehow missed all the "enshitification" processes Cory Doctorow and others have been documenting?"
I read about it, but don't believe it's a new law now that affects everything with no exceptions. Even is OpenAI wants to go from explore and expand into extract mode, open-source models are getting better every day and will create a decent floor.
> you'd need over 130,000 paying subscribers at $20/month just to cover the interest on the loans.
Maybe I live in a bubble (I definitely do), but that subscriber number seems quite reasonable and of course omits additional revenue from their APIs. Also if OpenAI wants to lose money, be my guest. I definitely enjoyed my VC-subsidized Uber rides of the past.
It's not a property of the company. It's a property of the funding mechanism. Anything getting its funds from VC or hedge-funds will eventually succumb to this - it's the capital speaking.
Ah, a true capitalist mind. The only consideration is the almighty dollar!
Perhaps it would be better for it to be efficient because it uses less energy and in general is a good thing to do for the world. I understand there aren't financial incentives around that so it's probably difficult for you to agree with.
Edit: It seems simply suggesting we do better is enough for people to lose their minds.
I was going to respond in sarcasm but realized your comment does not even deserve that.
Governments of any shape are unable to accurately pick which industry to allow or prevent. Even harder would be creating legislation that carves out specific industry.
You can tax the externalities of the use of electricity. Please don’t create pandering comments with zero substance.
I don’t like bitcoin but who am I to tell people what to spend money on.
> Governments of any shape are unable to accurately pick which industry to allow or prevent.
i would say that some gov'ts in the world does do this - famously the chinese CCP.
They dictate by providing state funding for industries they deem important (for whatever reason - geopolitically strategic, military, or welfare, etc). Whether it's the most "efficient" form of capital allocation is irrelevant (for them).
The people do not have a say, and in fact, cannot be doing any form of criticism - they may have feedback, but only in so far as the party allows said feedback. It's implicit that the CCP is picking for the "greater good" - but who is the judge of that? CCP itself is!
So by an act of tautology, they are doing it most efficiently!
Governments certainly try. The US does the similar efforts through subsidy type programs, I don't think the Chinese government are able to accurately pick industry any better. They have more force to put behind their decisions, like building ultra cheap real estate, roads and general infrastructure but I am not sure they have the ability to pick a specific piece of industry any better.
Ah yes, the one who over-allocated so much capital into real estate that it has more unused apartments than the rest of the world combined, 5 times over
With the greatest housing bubble has ever seen, and a deflationary spiral around the corner.
Why is using less energy good thing for the world? Using energy in itself is fine, you can use all the solar you want and nobody is going to complain. Nuclear is marginally less harmless, but still quite fine. Don't use coal or gas.
A person with this kind of mindset will tell less-developed nations to stop developing fossil fuel infrastructure because any more impact on the planet is unjustified.
I don't think that has to be the case for anyone in this chain. I am very conscious of my use of resources. I also realize that there is massive green washing of things. Lots of things marketed as the "green" way are also not that great for the planet, sometimes a sheet of cling wrap or a ziploc bag is ok to use. It also is true that often people love to project their feelings for saving the planet but are living a lifestyle that says otherwise.
When it comes to energy use, I don't believe anyone should have the power to say what should be allowed or not. That will stifle innovation. For every innovation there must have been thousands if not more failures.
The hypocrisy is as good a reason as any to keep it real. But the real problem is people so brainwashed by green propaganda that they think no human activity is worthwhile. This AI stuff for example might appear wasteful, but it is very promising as a means to improve efficiency in many industries and improve people's quality of life, which may in fact reduce pollution. Even if it increased pollution somehow, that has to be weighed against all other outcomes. You can't just look at any old use of resources and declare that it isn't worth it and expect everyone to agree with you. Different people value different things in life, and that's ok.
I didn't suggest otherwise. The person I replied to said it was a bizarre question, which I disagree with. Their are obvious benefits outside of money.
I don’t think you would want to live in the world we’d have if we rolled back all of the energy expenditure done in the name of profit over the past 200 years since the discovery of oil. You wouldn’t have anything. You wouldn’t be able to cross the Atlantic lest you burn coal to produce steam.
As a sailor, who crossed oceans, on nothing but wind power (and solar), I resent that - but I totally get what you’re saying. The world would be, different, to say the least.
That's a bit easy to say this. "We progressed during capitalism hence capitalism allowed the progress" but you don't know at all. You also don't know how will the next 1000 years look if we continue to do things in the name of profit. I think that making hypothetical branches like that is just useless because there are so many more factors that just "capitalism or communism"
Maybe anyone with a conscience, nuclear energy should be going to decarbonisation of the existing economy not to expanding a new industry en masse.
People are funny on this forum, when it was Bitcoin it was the devil and now it’s helping us complete our lines of code it’s ok to require a nuclear power plant to run such a system? I guess whatever benefits the individual is ok ? Silicone valley types are funny too because it was all about developing fusion to save humanity now it’s about developing fusion to cat gifs?
These products need to be taxed to price in these externalities we can’t keep this up, it’s ridiculous.
"These products need to be taxed to price in these externalities we can’t keep this up, it’s ridiculous."
The negative externality you are worried about isn't AI, but pollution/carbon emissions. Tax pollution instead of deciding for other people what use case is worth it.
Edit: To be clear, I am all for this. I'd love to see a massive(!) carbon tax.
Bitcoin doesn’t have any real utility besides being a toy or committing crimes.
Machine learning has utility, not just in theory but to plenty of people, including me. It has helped me do my job faster.
You can put tax on energy from carbon, fine. There’s no justification for taxing energy intensive things by themselves because you don’t like using energy. Human energy usage is and should be growing exponentially (and not an S curve) besides occasional pauses when efficiencies are found (like LED lighting using something like 90% less energy than incandescent).
Fossil fuels are clearly going away anyway, not because of ideals either, solar is just way cheaper now.
There's no good reason for BTC to be set up to use that much energy. There are viable, proven alternatives like proof of stake. I'd also be more forgiving if BTC only required a given amount of power, but no, the system is designed such the power requirement is related to the cost of flops/watt - it's specifically designed such that the system will use more power as power becomes cheaper.
No but as others are arguing in this thread, “power is fungible” etc etc, “it’s not up to you to decide where the energy goes”. Double standards in my opinion.
The difference is that, by design, the best (cheapest!) Bitcoin you can buy is the Bitcoin you buy today. It only gets more expensive as more people use it and the supply of new Bitcoin decreases.
By contrast, the ai we have today is the worst ai it will ever be. Quality will go up with time, and price will go down, as incentives drive the development of better, cheaper models.
Look at how neural voice synthesis has evolved over the last five years. It's gone from science fiction to extremely expensive to 'can run in real time on a phone' thanks to a combination of hardware and algorithmic improvements... But mostly algorithmic improvements.
Algorithmic improvements are coming quickly in generative ml. We're still in very early days.
"Is code autocompletion and generating random images really worth so much electricity?"
Yes. Very much so.
Some back of enveloping. The worldwide labor market is USD$70trillion. If you save 1% of the time of the worldwide labor market (that's quite reasonable for autocomplete etc right?) that's USD$700billion. A quick Google says the global electricity revenues are USD$255billion.
So if you have a way to save %1 of human labor it's economically worth tripling power generation to make use of it.
Where did that $70T come from? The most obvious objection is that most labor is not desk jobs, so 1% from autocomplete seems very unreasonable to me, but I don’t know how it breaks out in dollars. Maybe your source says that?
It's admittedly super rough. I googled worldwide labor market and got 3.5billion workers. I then Googled average global earnings, $20k. However i'll admit to a mistake. The second number appears incorrect and it seems it should have been ~$10k based on the following: https://www.zippia.com/advice/average-income-worldwide/#:~:t....
Still i think the point should stand no matter how precise we get here. The cost of labor absolutely dwarfs the cost of electricity. That was what i wanted to hit on while completely acknowledging the numbers are rough.
Not sure that llm autocomplete is so much better than autocomplete using more energy efficient algorithms to make a noticeable difference in productivity.
Hmm... so it's so bad that you might as well just use old-school autocorrect, and so good that you have to use it to remain competitive, at the same time?
But that is exactly what GP is criticizing: just looking at the $$$. All of us here know that the environmental impact of electric energy generation is externalized and global. If we were to price that in fairly, the labor might well be cheaper!
The jaquard loom and steam engine can't do those things either. A revolution occurs when a technology saves even a fraction of worldwide labor costs. That's all it needs to do to be literally revolutionary.
And have you studied anything into modern mining yourself? Mines are starting to convert fleets into self driving trucks. Capabilities like image/video recognition that LLMs can do improve their identification and alert systems.
Modern AI is a whole lot more than just shitting out spam on the internet.
To be clear we're talking about less than 200 trucks by my count, most of which were built in 2013-2016, the Baghdad Arizona mine still hasn't deployed anything yet as far as I know (I asked people in that area). None of this has anything to do with code completion or image generation and LLMs don't even do anything semi-related to vision. That's entirely beyond the scope of how they're designed.
It is not surprising to me anymore the threads that get pulled to justify this insane hype, but what does shock me is that it's going on 8 months of the same very tired and thin arguments that get smaller and smaller in their claims the more questions you ask.
Global electricity production is about 30 PWh per year and wholesale electricity costs are about $100 / MWh around the world. That’s about $3T. (Numbers are rough, don’t get pedantic)
Global electricity generation revenues are only $255 billion? Amazon/Google/Apple have greater revenue than the worldwide electricity generation sector?
I just Googled these numbers quickly but yes it seems correct.
"In 2021, the global electricity transmission and distribution market generated some 255.6 billion U.S. dollars in revenue, up from 248.5 billion U.S. dollars in the previous year"
> Shouldn't we wait until research has created an efficient architecture that is easily scalable first?
A scalable architecture has a prerequisite of known product requirements and known implementation specs. Neither is true right now as this field is evolving at a crazy rate. Approaches like what Groq has are great for increasing efficiency, but at the cost of flexibility. Early on in any technology flexibility is imperative until product/market fit is established more clearly.
Also reducing this to autocompletion/generating random images really reduces what AI actually is. Those are two use cases for a very generalizable area.
A problem is that we can't measure how efficient a computing architecture really is, we can only compare them, and not to a hypothetical machine yet to be devised.
When AWS can run their datacenter operations 20x more efficiently, which sounds "reasonable to me", they obviously will. Are you criticizing their customers? Or their customers' customers, the ones who like autocomplete? Literally the computer engineers, by whose work these applications have been enabled?
I think this is why you were perceived as calling for central planning. Ironically because you took aim at every level but the investors.
Growth is aggressive because of the huge investment in the sector currently. Literally because investors want a return. For better or worse there is nobody in the chain who interprets "worthwhile?" in the same sense, and the applications are not essentially relevant to the people in command.
Why are companies pouring money into LLMs? Well the naysayers like yourself are simply shortsighted and being negative. I think many businesses are already seeing the power of current models in specific processes. You have to assume some natural progression in the coming years to LLMs and the general ecosystem.
Since you only reference autocompletion and image gen you obviously have not worked with using LLM in business processes. Is there a lot of hype? Certainly, but there is also a lot of value in the universe today for these models.
How do you even define "efficient architecture"? Nobody can define that.
Are they? It could be a tax to offset the externalities that are currently being socialized by keeping other coal plants running to sustain existing homes, farms, factories, etc.
But all the industries that consume any electricity are already "keeping other coal plants running".
If you abitrarily decice a metal processing factory is "worth it" while an AI datacenter isn't, then yes, it's planned economy.
And yeah, I know the governments already tax different industries differently. I think these policies are hardly justified and mostly "redistribution by lobbying".
Arguably some industries make us all richer than others. So we incentivize those with more collective benefit, say manufacturing or farming, and disincentivize those with more collective costs, say gambling or tobacco.
That’s already being done, and the taxes/limits on co2 pollution will be increasing. So nothing new here.
What you’re implicitly suggesting is government allowing/banning certain uses of technology because they are deemed not worthy. That is an autocratic style of thinking.
We as a society can decide we don't want our air to become toxic to prop up convoluted gambling schemes. It's only autocratic if it's decided by a single person.
Shouldn't that be factored into the price of electricity? I think the market economy ideal is that if houses can't afford enough electricity for heating, then they need to be insulated and/or install heat pumps, etc.
Maybe it’s time to build new power plants, preferably nuclear, and continue efforts to learn about and expand efficient energy sources. No society has ever advanced without ample energy supplies.
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