Despite the flashy title that's the first "sober" analysis from a CEO I read about the technology. While not even really news, it's also worth mentioning that the energy requirements are impossible to fulfill
Also now using ChatGPT intensely since months for all kinds of tasks and having tried Claude etc. None of this is on par with a human. The code snippets are straight out of Stackoverflow...
Take this "sober" analysis with a big pinch of salt.
IBM have totally missed the AI boat, and a large chunk of their revenue comes from selling expensive consultants to clients who do not have the expertise to do IT work themselves - this business model is at a high risk of being disrupted by those clients just using AI agents instead of paying $2-5000/day for a team of 20 barely-qualified new-grads in some far-off country.
IBM have an incentive to try and pour water on the AI fire to try and sustain their business.
Asking because the biggest IT consulting branch of IBM, Global Technology Services (GTS), was spun off into Kyndryl back in 2021[0]. Same goes for some premier software products (including one I consulted for) back in 2019[1]. Anecdotal evidence suggests the consulting part of IBM was already significantly smaller than in the past.
It's worth noting that IBM may view these AI companies as competitors to it's Watson AI tech[2]. It already existed before the GPU crunch and hyperscaler boom - runs on proprietary IBM hardware.
I know people who still work there and are doing consultancy work for clients.
I am a former IBMer myself but my memory is hazy. IIRC there was 2 arms of the consultants - one was the boring day to day stuff, and the other was "innovation services" or something. Maybe the spun out the drudgery GTS and kept the "innovation" service? No idea.
My go-to analysis for these sorts of places is net income per employee. Back in the day, IBM was hovering around $5,000. Today, Kyndryl is still around $5,000 (2025). But the parent company seems to be now at $22,000 (2024). For comparison: Meta is at $800,000, Apple is at $675,000, and Alphabet is at $525,000. And Wal-Mart, the nation's largest private employer, is around $9,250.
Now, probably part of that is just that those other companies hire contractors so their employment figure is lower than reality. But even if you cut the numbers in half, neither side of that spin off is looking amazing.
The part that was spun off was "Infrastructure Services" (from the Wiki article.) Outsourcing and operations, not the business consulting organization that provides high level strategy to coding services.
Missed the boat? Have you been living under a rock? Watson AI advertising has been everywhere for years.
It’s not that they aren't in the AI space, it’s that the CEO has a shockingly sober take on it. Probably because they’ve been doing AI for 30+ years combined with the fact they don’t have endless money with nowhere to invest it like Google.
Advertising for it has been everywhere, but it's never seemed like it's at the forefront of anything. It certainly wasn't competitive with ChatGPT and they haven't managed to catch back up in the way Google have.
It was competitive before ChatGPT existed, and IMHO that gives them a special insight that people miss to consider in this context.
They know what revenue streams existed and how damn hard it was to sell it, considering IBM Watson probably had the option of 100% on-prem services for healthcare, if they failed to sell that will a privacy violation system like ChatGPT,etc have a chance to penetrate the field?
Because however good ChatGPT, Claude,etc are, the _insane_ amounts of money they're given to play with implies that they will then emerge as winners in a future with revenue streams to match the spending that has been happening.
> Missed the boat? […] Watson AI advertising has been everywhere for years.
They were ahead of the game with their original Watson tech, but pretty slow to join and try get up to speed with the current GenAI families of tech.
The meaning of “AI” has shifted to mean “generative AI like what ChatGPT does” in the eyes of most so you need to account for this. When people talk about AI, even though it is a fairly wide field, they are generally referring to a limited subset of it.
The death of IBM’s vision to own AI with Watson was never due to an inability to transition to the right tech. In fact, it was never about tech at all. As an entirely B2B company with a large revenue stream to defend, IBM was never going to go and scrape the entirety of the Internet. Especially not after the huge backlash they ignited with their customers over data rights and data ownership in trying to pitch the Watson they had.
> IBM have an incentive to try and pour water on the AI fire to try and sustain their business.
IBM has faced multiple lawsuits over the years. From age discrimination cases to various tactics allegedly used to push employees out, such as requiring them to relocate to states with more employer friendly laws only to terminate them afterward.
IBM is one of the clearest examples of a company that, if given the opportunity to replace human workers with AI, would not hesitate to do so. Assume therefore, the AI does not work for such a purpose...
If they could use THEIR AI to replace human workers, they would. If they learned that Claude or ChatGPT was better than an IBM consultant, they'd probably keep that to themselves.
Are you suggesting IBM made up the numbers? Or that CAPEX is a pre-GAI measure and is useless in guiding decision making?
IBM may have a vested interest in calming (or even extinguishing) the AI fire, but they're not the first to point out the numbers look a little wobbly.
And why should I believe OpenAI or Alphabet/Gemini when they say AI will be the royal road to future value? Don't they have a vested interest in making AI investments look attractive?
> a high risk of being disrupted by those clients just using AI agents instead of paying $2-5000/day for a team of 20 barely-qualified new-grads in some far-off country
Is there any concrete evidence of that risk being high? That doesn't come from people whose job is to sell AI?
they have incentive but what's the sustainable, actually-pays-for-itself-and-generates-profit cost of AI? We have no idea. Everything is so heavily subsidized by burning investor capital for heat with the hope that they'll pull an amazon and make it impossible to do business on the internet without paying an AI firm. Maybe the 20 juniors will turn out to be cheaper. Maybe they'll turn out to be slightly better. Maybe they'll be loosely equivalent and the ability to automate mediocrity will drive down the cost of human mediocrity. We don't know and everyone seems to be betting heavily on the most optimistic case, so it makes an awful lot of sense to take the other side of that bet.
20 juniors become some % of 20 seniors. and some % of that principals. Even if it lives up to the claims you’re still destroying the pipeline for creating experienced people. It is incredibly short sighted.
Do you expect Sam Altman to come on stage and tell you the whole thing is a giant house of cards when the entire western economy seems to be propped up by AI? I wonder whose "sober" analysis you would accept, because surely the people that are making money hand over fist will never admit it.
Seems to me like any criticism of AI is always handwaved away with the same arguments. Either it's companies who missed the AI wave, or the models are improving incredibly quickly so if it's shit today you just have to wait one more year, or if you're not seeing 100x improvements in productivity you must be using it wrong.
IBM was ahead of the boat! They had Watson on Jeopardy years ago! /s
I think you make a fair point about the potential disruption for their consulting business but didn't they try to de-risk a bit with the Kyndryl spinout?
I am a senior engineer, I use cursor a lot in my day to day. I find I can code longer and typically faster than without. Is it on par with human? It’s getting pretty darn close to be honest, I am sure the “10x” engineers of the world would disagree but it definitely has surpassed a junior engineer. We all have our anecdotes but I am inclined to believe on average there is net value.
I think surpassed is not the right word because it doesn't create/ideate. However it is incredibly resourceful. Maybe like having a jr engineer to do your bidding without thinking or growing.
Surpassed is probably the wrong word but the intent is more that it can comprehend quite complicated algorithms and patterns and apply them to your problem space. So yea it’s not a human but I don’t think saying subpar to a human is the right comparison either. In many ways it’s much better, I can run N parallel revisions and have the best implementation picked for review. This all happens in seconds.
Yes, this. Creating multiple iterations in parallel allows much more meaningful exploration of the solution space. Create a branch for each framework and try them all, compare them directly in praxis not just in theory. My brother is doing this to great effect as a solopreneur, and having the time of his life.
Largely agree. Anything that is just a multi-file edit, like an interface change, it can do. Maybe not immediately, but you can have it iterate, and it doesn't eat up your attention.
It is without a doubt worth more than the 200 bucks a month I spend on it.
I will go as far as to say it has decent ideas. Vanilla ideas, but it has them. I've actually gotten it to come up with algorithms that I thought were industry secrets. Minor secrets, sure. But things that you don't just come across. I'm in the trading business, so you don't really expect a lot of public information to be in the dataset.
i'm also a senior engineer and I use codex a lot. It has reduced many of the typical coding tasks to simply writing really good AC. I still have to write good AC, but I'm starting to see the velocity change from using good AI in a smart way.
Senior engineer here as well. I would say Opus 4.5 is easily a mid-level engineer. It's a substantial improvement over Sonnet 4.5, which required a lot more hand-holding and interventions.
I've found Claude's usefulness is highly variable, though somewhat predictable. It can write `jq` filters flawlessly every time, whereas I would normally spend 30 minutes scanning docs because nobody memorizes `jq` syntax. And it can comb through server logs in every pod of my k8s clusters extremely fast. But it often struggles making quality code changes in a large codebase, or writing good documentation that isn't just an English translation of the code it's documenting.
The problem I had that the larger your project gets, the more mistakes Claude makes. I (not a parent commenter) started with a basic CRUD web app and was blown away by how detailed it was, new CSS, good error handling, good selection and use of libraries, it could even write the terminal commands for package management and building. As the project grew to something larger Claude started forgetting that some code already existed in the project and started repeating itself, and worse still when I asked for new features it would pick a copy at random leaving them out of sync with eachother. Moving forward I've been alternating between writing stuff with AI, then rewriting it myself.
> The problem I had that the larger your project gets, the more mistakes Claude makes
I think the reason for this is because these systems get all their coding and design expertise from training, and while there is lots of training data available for small scale software (individual functions, small projects), there is much less for large projects (mostly commercial and private, aside from a few large open source projects).
Designing large software systems, both to meet initial requirements, and to be maintainable and extensible over time, is a different skill than writing small software projects, which is why design of these systems is done by senior developers and systems architects. It's perhaps a bit like the difference between designing a city and designing a single building - there are different considerations and decisions being made. A city is not just a big building, or a collection of buildings, and large software system is not just a large function or collection of functions.
Here's mine fully deployed, https://hackernewsanalyzer.com/. I use it daily and have some users. ~99.7% LLM code. About 1 hour to first working prototype then another 40 hours to get it polished and complete to current state.
It shows, quite an interesting wrapper over GPT with unauthorized access to prompting it you assembled there ;) Very much liked the part where it makes 1000 requests pulling 1000 comments from the firebase to the client and then shoots them back to GPT via supabase
41 hours total of prompting, looking at code diffs, reverting, reprompting, and occasional direct code commits. I do review the full code changes nearly every step of the way and often iterate numerous times until I'm satisfied with the resulting code approach.
Have you tried to go back to the old way, maybe just as an experiment, to see how much time you are actually saving? You might be a little surprised! Significant "reprompting" time to me indicates maybe a little too much relying on it rather than leading by example. Things are much faster in general if you find the right loop of maybe using Claude for like 15%-20% of stuff instead of 99.7%. You wouldn't give your junior 99.7% ownership of the app unless they were your only person, right? I find spending time thinking through certain things by hand will make you so much more productive, and the code will generally be much better quality.
I get that like 3 years ago we were all just essentially proving points building apps completely with prompts, and they make good blog subjects maybe, but in practice they end up being either fragile novelties or bloated rat's nests that end up taking more time not less.
I’ve done things in days that in the before times would have took me months. I don’t see how you can make that time difference up.
I have at least one project where I can make that direct comparison - I spent three months writing something in the language I’ve done most of my professional career in, then as a weekend project I got ChatGPT to write it from scratch in a different language I had never used before. That was pre-agentic tools - it could probably be done in an afternoon now.
I'm not a fulltime developer, but manage a large dev team now. So, this project is basically beyond my abilities to code myself by hand. Pre llm, I would expect in neighborhood of 1.5-2 months for a capable dev on my team to produce this and replicate all the features.
If you haunt the pull requests of projects you use I bet you'll find there's a new species of PR:
> I'm not an expert in this language or this project but I used AI to add a feature and I think its pretty good. Do you want to use it?
I find myself writing these and bumping into others doing the same thing. It's exciting, projects that were stagnant are getting new attention.
I understand that a maintainer may not want to take responsibility for new features of this sort, but its easier than ever to fork the project and merge them yourself.
I noticed this most recently in https://github.com/andyk/ht/pulls which has two open (one draft) PRs of that sort, plus several closed ones.
Issues that have been stale for years are getting traction, and if you look at the commit messages, it's AI tooling doing the work.
People feel more capable to attempt contributions which they'd otherwise have to wait for a specialist for. We do need to be careful not to overwhelm the specialists with such things, as some of them are of low quality, but on the whole it's a really good thing.
If you're not noticing it, I suggests hanging out in places where people actually share code, rather than here where we often instead brag about unshared code.
> People feel more capable to attempt contributions
That does not mean that they are more capable, and that's the problem.
> We do need to be careful not to overwhelm the specialists with such things, as some of them are of low quality, but on the whole it's a really good thing.
That's not what the specialists who have to deal with this slop say. There have been articles about this discussed here already.
At this point my prior is that all these 300/ns projects are some kind of internal tools, with very narrow scope and many just for a one-off use.
Which is also fine and great and very useful and I am also making those, but it probably does not generalize to projects that require higher quality standards and actual maintenance.
Places that aren't software businesses are usually the inverse. The software is extremely sticky and will be around for ages, and will also bloat to 4x the features it was originally supposed to have.
I worked at an insurance company a decade ago and the majority of their software was ancient. There were a couple desktops in the datacenter lab running Windows NT for something that had never been ported. They'd spent the past decade trying to get off the mainframe and a majority of requests still hit the mainframe at some point. We kept versions of Java and IBM WebSphere on NFS shares because Oracle or IBM (or both) wouldn't even let us download versions that old and insecure.
Software businesses are way more willing to continually rebuild an app every year.
There's a massive incentive not to share them. If I wrote a project using AI I'd be reluctant to publish it at all because of the backlash I've seen people get for it.
People are and always were reluctant to share their own code just the same. There is nothing to be gained, the chances of getting positive reviews from fellow engineers are slim to none. We are a critical and somewhat hypocritical bunch on average.
Claude has taught me so much about how to use jq better. And really, way more efficient ways of using the command line in general. It's great. Ironically, the more I learn the less I want to ask it to do things.
Maybe the most depressing part of all this is if people start thinking they would not have been able to do things without the LLM. Of course they would have, it's not like LLMs can do anything that you cannot. Maybe it would have taken more time at least the first time and you would have learned a few things in the process.
Sure, I can write all of it. But I simply won’t. I have Claude generated Avalonia C# applications and there is no way I would have written the thousands of lines of xaml they needed for the layouts. I would just have done it as a console app with flags.
But reducing friction, eliminating the barrier to entry, is of fundamental importance. It's human psychology; putting running socks next to your bed at night makes it like 95% more likely you'll actually go for a run in the morning.
I understand the point, and to some degree agree. For myself, I really couldn't (not to say it wouldn't have been possible). I tried many many times over so many years and just didn't have the mental stamina for it, it would never "click" like infra/networking/hardware does etc and I would always end up frustrated.
I have learnt so much in this process, nowhere near as much as someone that wrote every line (which is why I think being a good developer will be a hot commodity) but I have had so much fun and enjoyment, alongside actually seeing tangible stuff get created, at the end of the day, that's what it's all about.
I have a finite amount of time to do things, I already want to do more than I can fit into that time, LLMs help me achieve some of them.
This is a "scratch an itch" project I initially started to write manually in the past, but never finishing. I then used claude to do it basically on the side while watching the world series http://nixpkgs-pr-explorer.s3-website-us-west-2.amazonaws.co...
It’s not just good for small code bases. In the last six months I’ve built a collaborative word processor with its own editor engine and canvas renderer using Claude, mostly Opus. It’s practically a mini Google Docs, but with better document history and an AI agent built in. I could never have built this in 6 months by myself without Claude Code.
I think if you stick with a project for a while, keep code organized well, and most importantly prioritize having an excellent test suite, you can go very far with these tools. I am still developing this at a high pace every single day using these tools. It’s night and day to me, and I say that as someone who solo founded and was acquired once before, 10 years ago.
yes, I am using my voice agent, my head tracker, my sql writer, my odbc client, my shopping list, my sharepoint file uploader, my Timberborn map generator, my wireguard routing, my oxygen not included launch scripts, my i3wm config, my rust ATA over Ethernet with Content Addressable storage
The former tasks are directly from the training material, directly embedded into the model. For the latter task, it needs a context window and intelligence.
It'll be a common paradigm. Some agents support the coding agent discover relevant context for a plan, others will help the agent stay on track and ensure no rules break.
They really should have been supplying at least a week worth of readymade "projects" to every freelance AI promoter out there to demonstrate x9000 AI productivity gains for the skeptics.
Because vibing the air about those gains without any evidence looks too shilly.
Pointing out the where the burden of proof lies is not an ad hominem. Calling it such is in fact a good example of poisoning the well. all the fan girls have to do is post links to code they have vibe coded. some people have even done that in this thread. it's not an unreasonable standard.
I'm just as much of an avid llm code generator fan as you may be but I do wonder about the practicality of spending time making projects anymore.
Why build them if other can just generate them too, where is the value of making so many projects?
If the value is in who can sell it the best to people who can't generate it, isn't it just a matter of time before someone else will generate one and they may become better than you at selling it?
> Why build them if other can just generate them too, where is the value of making so many projects?
No offence to anyone but these generated projects are nothing ground-breaking. As soon as you venture outside the usual CRUD apps where novelty and serious engineering is necessary, the value proposition of LLMs drops considerably.
For example, I'm exploring a novel design for a microkernel, and I have no need for machine generated boilerplate, as most of the hard work is not implementing yet another JSON API boilerplate, but it's thinking very hard with pen and paper about something few have thought before, and even fewer LLMs have been trained on, and have no intelligence to ponder upon the material.
To be fair, even for the most dumb side-projects, like the notes app I wrote for myself, there is still a joy in doing things by hand, because I do not care about shipping early and getting VC money.
Weird, because I've created a webcam app that does segmentation so they can delete the background and put a new background in I mean, I suppose that's not groundbreaking. But it's not just reading and writing to a database.
I've just added a ATA over Ethernet server in Rust, I thought of doing it in the car on the way home and an hour later I've got a working version.
I type this comment using a voice to text system I built, admittedly it uses Whisper as the transcriber but I've turned it into a personal assistant.
I make stuff every day I just wouldn't bother to make if I had to do it myself. and on top of that it does configuration. So I've had it build full wireguard configs that is taking on our pay addresses so that different destinations cause different routing. I don't know how to do that off the top of my head. I'm not going to spend weeks trying to find out how it works. It took me an evening of prompting.
> I make stuff every day I just wouldn't bother to make if I had to do it myself
> I'm not going to spend weeks trying to find out how it works.
Then what is the point? For some of us, programming is an art form. Creativity is an art form and an ideal to strive towards. Why have a machine to create something we wouldn’t care about?
The only result is a devaluation to zero of actual effort and passion, whose only beneficiary are those that only care about creating more “product”. Sure, you can pump out products with little effort now, all the while making a few ultrabilionaires richer. Good for you, I guess.
The value is that we need a lot more software and now, because building software has gotten so much less time consuming, you can sell software to people that could/would not have paid for it previously at a different price point.
We don’t need more software, we need the right software implemented better. That’s not something LLMs can possibly give us because they’re fucking pachinko machines.
Here’s a hint: Nobody should ever write a CRUD app, because nobody should ever have to write a CRUD app; that’s something that can be generated fully and deterministically (i.e. by a set of locally-executable heuristics, not a goddamn ocean-boiling LLM) from a sufficiently detailed model of the data involved.
In the 1970s you could wire up an OS-level forms library to your database schema and then serve literally thousands of users from a system less powerful than the CPU in modern peripheral or storage controller. And in less RAM too.
People need to take a look at what was done before in order to truly have a proper degree of shame about how things are being done now.
Most CRUD software development is not really about the CRUD part. And for most framework, you can find packages that generate the UI and the glue code that ties it to the database.
When you're doing CRUD, you're spending most of the time with the extra constraints designed by product. It's dealing with the CRUD events, the IAM system, the Notification system,...
> That’s not something LLMs can possibly give us because they’re fucking pachinko machines.
I mostly agree, but I do find them useful for fuzzing out tests and finding issues with implementations. I have moved away from larger architectural sketches using LLMs because over larger time scales I no longer find they actually save time, but I do think they're useful for finding ways to improve correctness and safety in code.
It isn't the exciting and magical thing AI platforms want people to think it is, and it isn't indispensable, but I like having it handy sometimes.
The key is that it still requires an operator who knows something is missing, or that there are still improvements to be made, and how to suss them out. This is far less likely to occur in the hands of people who don't know, in which case I agree that it's essentially a pachinko machine.
I’m with you. Anyone writing in anything higher level than assembly, with anything less than the optimization work done by the demo scene, should feel great same.
Down with force-multiplying abstractions! Down with intermediate languages and CPU agnostic binaries! Down with libraries!
An issue with the doom forecasts is most of the hypothetical $8tn hasn't happened yet. Current big tech capex is about $315bn this year, $250bn last against a pre AI level ~$100bn so ~$400bn has been spent so far on AI boom data centers. https://sherwood.news/business/amazon-plans-100-billion-spen...
The future spend is optional - AGI takeoff, you spend loads, not happening not so much.
Say it levels of at $800bn. The world's population is ~8bn so $100 a head so you'd need to be making $10 or $20 per head per year. Quite possibly doable.
That seems super far fetched given that 37%[1] of the world's population does not have internet access. You could reasonably restrict further to populations that speak languages that are even passably represented in LLMs.
Even disregarding that, if you're making <3000 euros a year, I really don't think you'd be willing or able to spend that much money to let your computer gaslight you.
I agree. re: energy and other resource use: the analogy I like is with driving cars: we use cars for transportation knowing the environmental costs so we don’t usually just go on two hour drives for the fun of it, rather we drive to get to work, go shopping. I use Gemini 3 but only in specific high value use cases. When I use commercial models I think a little about the societal costs.
In the USA we have lost the thread here: we don’t maximize the use of small tuned models throughout society and industry, instead we use the pursuit of advanced AI as a distraction to the reality that our economy and competitiveness are failing.
You could have your morning shower 1°C less hot and save enough energy for about 200 prompts (assuming 50 litres per shower). (Or skip the shower altogether and save thousands of prompts.)
Yesterday I was talking to coworkers about AI I mentioned that a friend of mine used ChatGPT to help him move. So a coworker said I have to test this and asked ChatGPT if he could fit a set of the largest Magnepan speakers (the wide folding older room divider style) in his Infinity QX80. The results were hilarious. It had some of the dimensions right but it then decided the QX80 is as wide as a box truck (~8-8.5 feet/2.5 m) and to align the nearly 7 foot long speakers sideways between the wheel wells. It also posted hilariously incomprehensible ASCII diagrams.
I'm not sure what you mean with the "code snippets are straight out of Stackoverflow".
That is factually incorrect just by how LLM works. By now there has been so much code ingested from all kinds of sources, including Stackoverflow LLM is able to help generate quite good code in many occasions.
My point being it is extremly useful for super popular languages and many languages where resources are more scarce for developer but because they got the code from who knows where, it can definitely give you many useful ideas.
It's not human, which I'm not sure what is supposed to actually mean. Humans make mistakes, humans make good code. AI does also both. What it definitely needs is a good programmer still on top to know what he is getting and how to improve it.
I find AI (LLM) very useful as a very good code completion and light coder where you know exactly what to do because you did it a thousand times but it's wasteful to be typing it again. Especially a lot of boilerplate code or tests.
It's also useful for agentic use cases because some things you just couldn't do before because there was nothing to understand a human voice/text input and translate that to an actual command.
But that is all far from some AGI and it all costs a lot today an average company to say that this actually provided return on the money but it definitely speeds things up.
> I'm not sure what you mean with the "code snippets are straight out of Stackoverflow". That is factually incorrect just by how LLM works.
I'm not an AI lover, but I did try Gemini for a small, well-contained algorithm for a personal project that I didn't want to spend the time looking up, and it was straight-up a StackOverflow solution. I found out because I said "hm, there has to be a more elegant solution", and quickly found the StackOverflow solution that the AI regurgitated. Another 10 or 20 minutes of hunting uncovered another StackOverflow solution with the requisite elegance.
> While not even really news, it's also worth mentioning that the energy requirements are impossible to fulfill
If you believe this, you must also believe that global warming is unstoppable. OpenAI's energy costs are large compared to the current electricity market, but not so large compared to the current energy market. Environmentalists usually suggest that electrification - converting non-electrical energy to electrical energy - and then making that electrical energy clean - is the solution to global warming. OpenAI's energy needs are something like 10% of the current worldwide electricity market but less than 1% of the current worldwide energy market.
Google recently announced to double AI data center capacity every 6 month. While both unfortunately deal with exponential growth, we are talking about 1% growth CO2 which is bad enough vs 300% effectively per year according to Google
Constraints breed innovation. Humans will continue to innovate and demand for resources will grow. it is fairly well baked into most of civilization. Will that change in the future? Perhaps but it’s not changing now.
Imagine how big pile of trash as the current generation of graphics cards used for LLM training will get outdated. It will crash the hardware market (which is a good news for gamers)
I'd rather phrase it as "code is straight out of GitHub, but tailored to match your data structures"
That's at least how I use it. If I know there's a library that can solve the issue, I know an LLM can implement the same thing for me. Often much faster than integrating the library. And hey, now it's my code. Ethical? Probably not. Useful? Sometimes.
If I know there isn't a library available, and I'm not doing the most trivial UI or data processing, well, then it can be very tough to get anything usable out of an LLM.
> it's also worth mentioning that the energy requirements are impossible to fulfill
Maybe I'm misunderstanding you but they're definitely not impossible to fulfill, in fact I'd argue the energy requirements are some of the most straightforward to fulfill. Bringing a natural gas power plant online is not the hardest part in creating AGI
Also now using ChatGPT intensely since months for all kinds of tasks and having tried Claude etc. None of this is on par with a human. The code snippets are straight out of Stackoverflow...