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...
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.
[0] https://en.wikipedia.org/wiki/Kyndryl
[1] https://www.prnewswire.com/news-releases/hcl-technologies-to...
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.
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.
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.
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.
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.
Hey have you seen my tulip collection?
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...
It wasn't very promising when it came to benchmarks though, go figure: https://artificialanalysis.ai/leaderboards/models
The numbers are staggering.
Both top line and bottom line numbers are staggering. Nobody knows. Let's not try to convince people otherwise.
Is there any concrete evidence of that risk being high? That doesn't come from people whose job is to sell AI?
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?
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.
It's an example of alternative cost or Copernicus-Gresham's law, rather than some axiom.
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?
Another empty suit.
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.
Or Stackoverflow is really good.
I’m producing multiple projects per week that are weeks of work each.
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?
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.
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.
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,...
Down with force-multiplying abstractions! Down with intermediate languages and CPU agnostic binaries! Down with libraries!
Same thing here. You're dismissing a flood because it rains.
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.
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'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.
Hobby Lobby has US$723m in annual revenue.
I don't make "products" I solve problems
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.
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.
But I also have it document and summarise its own work.
Could you share some of your prompts or CLAUDE.md? I'm still learning what works.
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.
Solving the problems of the business that isn’t a software business.
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.
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 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.
take care
> 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.
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.
Building something that works ? Not so easy
Pushing that thing in production ? That the hardest part
Because vibing the air about those gains without any evidence looks too shilly.
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.
You might not go for a run when the socks are not there, but I don't think you would start questioning your ability to run.
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.
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.
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.
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.
In that case, are you really producing multiple projects per week? If you've never looked at the code, have you verified that they work?
the list could go on
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.
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.
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.)
(It's the training, not the inference, that's the biggest energy usage.)
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 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.
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.
Looks very playable to me.
It's just an expensive card, but if the market is flooded with them, they can be used in gaming AND in local LLMs.
So it can push the fall of server-side AI even further.
These cards are 400 USD for reference, so if more and more are sold, we can imagine them getting down to 100 USD or so.
(and then similar for A100, H100, etc)
My main concern is the noise because I have seen datacenter hardware and it is crazy. Of course it's not ideal but there is something to do with it.
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.
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
Didn't IBM just sign quite a big deal with Groq?
the facts though, read like an endorsement not a criticism