Comment by blablabla123
4 months ago
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.
Is this true in 2025?
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.
[0] https://en.wikipedia.org/wiki/Kyndryl
[1] https://www.prnewswire.com/news-releases/hcl-technologies-to...
[2] https://en.wikipedia.org/wiki/IBM_Watson
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.
https://www.ibm.com/consulting
Yes. GTS was infrastructure services and was spun off. What's left is the old GBS - business services and systems implementation 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.
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> 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.
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The only similarity of Watson-style “AI” and generative “AI” is the name.
> 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.
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I would any day take chatGPT/Claude over an IBM consultant. I worked at IBM.
I'd rather be slapped in the face than kicked by a horse, but that doesn't mean either is a good thing
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You could have both worlds: an LLM model by IBM https://huggingface.co/ibm-granite/granite-4.0-h-small
It wasn't very promising when it came to benchmarks though, go figure: https://artificialanalysis.ai/leaderboards/models
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.
Isn't he one of the first ass clowns to start laying people off, replaced by AI.
Another empty suit.
How do you see the math working out?
The numbers are staggering.
The fair answer is that nobody knows. Even Ilya answered he does not know on his latest podcast with Dwarkesh.
Both top line and bottom line numbers are staggering. Nobody knows. Let's not try to convince people otherwise.
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.
> entire western economy seems to be propped up by AI?
It's an example of alternative cost or Copernicus-Gresham's law, rather than some axiom.
Right. Just like Intel completely shitting the bed on GPUs then their CEO tweeted a prayer. Old tech companies are going to be left behind.
IBM was founded in 1911 and it survived many things...
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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.
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I use AI tools extensively. I have seen it come up with truly novel solutions.
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.
A lot of time vanilla ideas and established, well proven patterns are just what the customer ordered. And AI code tools are great at this now.
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 think less. not sure if that's a good thing. but small little bugs and improvements get cleared so quickly now.
it surpassed 30 junior engineers
Your assessment of Claude simply isn’t true.
Or Stackoverflow is really good.
I’m producing multiple projects per week that are weeks of work each.
Would you mind sharing some of these projects?
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.
It is always "I'm producing 300 projects in a nanosecond" but it's almost never about sharing or actually deploying these ;)
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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.
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Not the OP you're replying to, but I've put together quite a few projects using only LLMs, no hand crafted code anywhere (I couldn't do it!)
https://dnbfamily.com
https://eventme.app
https://blazingbanana.com
https://play.google.com/store/apps/details?id=com.blazingban...
https://play.google.com/store/apps/details?id=com.blazingban...
https://play.google.com/store/apps/details?id=com.blazingban...
Are they perfect? No probably not, but I wouldn't have been able to make any of these without LLMs. The last app was originally built with GPT-3.5.
There is a whole host of other non-public projects I've built with LLMs, these are just a few of the public ones.
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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.
https://revise.io
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.
https://github.com/lawless-m
You can see by Contributors which ones Claude has done.
I have no idea if the code is any good, I’ve never looked at it and I have no idea how to code in Rust or Racket or Erlang anyway.
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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.
At the end of this week we are releasing https://github.com/eqtylab/cupcake
You can see all of Claude’s commits.
I’ve shipped so much with ai.
Favorite has been metrics dashboards of various kinds - across life and business.
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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.
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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.
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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.
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Sure but these are likely just variations of existing things. And yet the quality is still behind the original
I produce a lot of shit every week too, but I don’t brag about my digestive system on “Hacker” “News.”
You are so bitter. Take a moment to ponder why you are that way.
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I'll do one better: I poop every day in the water closet!
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.
65% of people in the world earn less than 3000 euros/year.
Getting 65% of the population to spend 1% of their income on some new digital toy forever does not seem so far fetched.
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The power distribution goes the other way too. There are outliers that will spend much more per capita.
Lol. If you ballpark numbers like that probably anything is doable!
$10/head x $8bn people is easier said than done - only your major enterprises like Google or Amazon can. But AI even if just LLMs may be there.
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.
Most of the energy for AI does not go into chatbots. Using Gemini is not remotely close to driving a car for 2 hours. If a prompt is 0.3 Wh (https://cloud.google.com/blog/products/infrastructure/measur..., https://andymasley.substack.com/p/a-cheat-sheet-for-conversa...), each prompt is closer to using an e-bike for 50 metres.
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.)
I think it's also worth comparing to the CO2 impact of consuming meat, especially beef, which is pretty high.
(It's the training, not the inference, that's the biggest energy usage.)
+1 interesting
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)
A100’s are not suitable for gaming.
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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
I've had decent results hackin', wackin' and smashin'.
> Despite the flashy title that's the first "sober" analysis from a CEO I read about the technology.
Didn't IBM just sign quite a big deal with Groq?
> Also now using ChatGPT intensely since months for all kinds of tasks and having tried Claude etc.
the facts though, read like an endorsement not a criticism
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