Reads like aimlessly burning whatever they felt they were able to burn without existential risk on some "also ran" LLM sinkhole.
In other words: nothing to see here, same as everybody else.
To do nothing is to die. I've been a JetBrains subscriber for over a decade. Nobody is going to use their tools in five years. I certainly don't need my subscription anymore.
> on some "also ran" LLM sinkhole.
They can't do Junie. It's a dead end. They can't do the same thing everyone else is doing either, or they're exactly as you put it: an "also ran" in a very crowded field.
The only way for JetBrains to survive is to figure out their Garmin play. They either find some niche within the existing product or - maybe (and very improbably) - they can innovate something wild nobody else has figured out yet that provides a path to new fertile pasture. But that's more the startup path than the thriving incumbent facing innovator's dilemma path.
But no matter what, if they stay the course, they're dead. Just like half the people expecting to still be writing code by hand.
I know this is not your main point, but common.
If this is true, AI would be the first tech impossible to learn 5 years after it appeared.
How many people out there can do hand lithography to make an 8086 to bootstrap computing? One?
What does THAT have to do with whether people who dont use AI today will die soon? What does "hand lithography" have to do with literally anything here?
VSCode has been eating at their users for years now. Jetbrains tools still were much better in my opinion but they are competing with “free” and “good enough”. With AI getting better, the main advantages of Jetbrains are gone.
I don’t need a massive IDE anymore. I still need an editor, but there’s hundreds of better tools for quick edits than a massive IDE.
It’s a company at the end of the day, but I hope they find a niche or pull a Garmin play like you said, otherwise I don’t see them being relevant.
What will replace it? The dumpster fire called Eclipse, certainly not. The even larger dumpster fire that is Electron based VS Code, also not. And "vibe coding", "agentic AI" etc. will also be a thing of the past once the VC money spigot selling tokens for far below actual cost dries up.
The JetBrains portfolio is here to stay.
(One might even see a total ban for American products in the EU or a significant push in sovereign technology on the horizon, and JB stands to profit from both options!)
In the devops/sysadmin world my stack would be common, and mostly people pick an editor and stick to it. I've been using Emacs for the past 20+ years at this point, and the last time I used Microsoft's IDE was back in the day when I was writing desktop apps using MFC (which probably dates me pretty well!) with visual studio.
I've seen colleagues use anything from atom, vim, emacs, notepad++, and other generic editors whos name I didn't recognize. There are one or two instances of VS Code I see now and again, but they're the outlier rather than the norm in my professional life.
They should stay in where they started, dev experience and IDEs custom for each language. Entering the AI coding was a mistake in my opinion, I’m sure one can argue that AI coding would have better tooling without their IDEs, but AI coders (Cc and codex) don’t use IDEs anymore so it’s a lost battle before the start
Sure. I still use mine though; privately and at work.
> Nobody is going to use their tools in five years.
> they're dead. Just like half the people expecting to still be writing code by hand.
Hyperbole much. Leave it on LinkedIn man.
Who knows?
5 years is a long time.
They're trying to navigate the AI era just like everyone else.
You know what we got told at work? “Scrappy AI startups are snapping at our heels, we need to move fast so the competition doesn't over take us”
…but if you ask: what startups? Silence. If you ask, why are we scared of some teenager vibe coding a platform and stealing our customers? Silence.
Dont question the narrative.
Of course AI is an existential threat. Of course there are vibed startups trying to eat our market.
Have you not seen our share price?
Such hyperbolic BS. Ffs. Calm down.
I could not care less about "new paradigm" editors, UI reworks and AI lock-in offerings, the only AI I want is let me bring my own local model, or interface with claude / codex / ...
Integration with other developer tools, code reviews etc. are better than ever. Ideavim is probably the best vim plugin and it gets new features every month. They are also investing a lot in the AI.
I would bet they will survive just fine unless we would write software in slack only using emoji :-)
Can't agents just use a language server? There are already a bunch of projects that provide this, e.g.: https://www.agent-lsp.com/
Doesn't seem like a huge differentiator anymore?
I don't really know much about their current users, but from a business perspective, post-tokenmaxxing and them having an IDE, it seems like the most efficient environment for having a human in the loop is something they could tackle?
But until language servers, IDEs with built-in code analysis had a huge advantage. This stopped once LSP was designed and some language servers got mature. LSP was developed originally by VS Code and it is easy to see why - it allowed VS Code to compete with IDEs without writing their own code analysis by letting language developers write a standard server. This made VS Code (and others like Zed) so powerful, that they had already replaced IDEs for many users (maybe outside Java). LLMs are just another nail in the coffin.
If you use ideavim you can enable feature which shows you action ID you have currently triggered (Ideavim: Track action IDs). If you click on anything it will show you name of that action and you can setup vim shortcut for that. This way, you can use almost anything by just typing a few letters. But that is just a start.
Now imagine you can use this to create "macro" which will create something like skill for LLM. So in a few seconds to minutes you can create "skills"/runbooks for major refactorings, project updates, git bisections, project deployments, log analysis or whatever you can think of. You will just click on 20 features and it will record what you did with some additional context (files, connected services, build systems etc.). This is the strength of integrated tools.
Of course this is just my imagination but I am not the smartest person in the world and I am pretty sure/hope that someone in jetbrains or in other IDE company is thinking this way.
I think that world is farther off than you think, especially in critical line of business software. You can YOLO it with your internal dashboard or your hipster coffee review site, but in my 2M-line highly regulated banking system, humans sign off on the code - even if LLMs do most of the typing.
No, it didn't stop. In my experience, LSPs are still incredibly poor and comparatively primitive vs the actual analysis that JetBrains have implemented. It's night-and-day different in terms of the supported refactorings, actual understanding of the types and the codebase etc.
Also, LSP is just poorly designed and inefficient. JSON-RPC was an awful choice, the specification text is not well-written and the whole thing feels like a Microsoft pet project for VS Code than an open specification that everyone agrees works well.
Not many did that because JetBrains never cared much to properly document this path, and so using LSPs is a better paved cowpath. But that's because IDEs aren't a real business for Microsoft, whereas they are for JetBrains, and we know where that leads - the landscape is filled with the skeletons of dead IDEs that were just loss making corporate side projects, defunded and "donated" to some foundation once the executive sponsors moved on. NetBeans and Eclipse are two of the most obvious but there have been others.
The risk with VS Code is it goes the same way. Eventually Microsoft needs to cut back, perhaps due to needing more capital for AI or due to AI related losses, and in the general layoffs that follow VS Code gets cut back to a skeleton crew.
And yes, there are people who use vim in a screen session, but I don't fit into that category. I like my GoLand IDE.
And while there are a ton of IDEs out there, I use what I'm used to.
Yeah, no, vast majority just want to pick a AI vendor and integrate it into their IDEA or CLion or whatever other editor they were already using. Pushing into entirely different subscription was dead on arrival.
They figured that out and started integrating it into main tooling (https://plugins.jetbrains.com/plugin/33314-air), but they definitely wasted a bunch of resources on something nobody really wanted.
Maybe people smarter than me know better but couldn't there be a middle ground where an IDE/Editor has an embedded engine (doesn't need to be a full-on LLM) that doesn't require external tool calls and token spend?
If an organization is paying $2400/year per developer for tokens and a highly intelligent editor/IDE comes around that charges $1000/yr and gets more output at a fixed cost, its a no-brainer of a decision.
I'm using the word understanding loosely there, but I couldn't think of another word.
It just so happens that there's no framework (outside silos) for many specific tasks, and AI is the workaround to surface those patterns.
Intellij when I used to use it had a lot great features like refactoring, extracting part of code as a function, renaming and creating empty classes/boilerplate but that's it
In current job I can order LLM to take data sink from other endpoint and write new with given URL. It will fetch from endpoint, check what it gives, compare with other and write new sink. Then it needs polishing because it always create something as awful as possible with cloning data all around but the most boring and soul sucking part is done
They can typically explain the history of something faster than any human
But finding out what an LLM needs to understand from a business side to write your code good, is an otpimzation which no one cares currently.
I'm pretty sure we either stay on big full frontier models for a long time, just use them for everything or we will start to see more and more people doing finetuning/project specific training like java + german + english + business contxt xy;
It will be an indicator for the whole industry.
Qwen3.8-Flash-Next - relatively small, it runs on 6 6 year old GPUs on my home PC happily running 5 simultaneous 262k sessions with additional 10 cached in RAM (bought back when you didn't have to remortgage your house for Ram) and it has been the first local model that is not a toy.
But there is a class of problems where I still reach for Anthropic's fable...
However, I have a hunch bordering with certainty Anthropic is achieving such great results by doing a lot of harness tricks.
For example opus 4.8, is not much better on coding than before mentioned Qwen model, but gets amazing results on factual knowledge stuff (the knowing all works of Shakespeare thing). How hard would it be to add a general knowledge RAG to requests that contain relevant questions and beat all benchmarks like that? Not very hard.
So I think there is big innovation to be had in harnesses, routers, inference and so on.
As to money spent on AI per developer my current client (a fortune 200 software company) spends $500 per month. That is $6k a year. A lot more than your examples. And many people run out of their quota pretty quickly.
That's what, the same as a Mac studio?
I do not enjoy the fact those cards cost more than their msrp 6 years later, but there is a much more important consideration than money (which also makes sense, but about that later). It is the fact soon people will not be able to do my job without AI at all. Even now if I didn't use it I think I'd be out competed very quickly.
And having the ability to run it locally, using a really useful, not toy model is very useful. It makes you independent from Anthropic deciding to ban your account for example.
As for money, it is an open secret the biggest cost of coding agents use is input tokens not generation. I tend to use about 1.3B input tokens per week on claude code with only 7-8M out. Out if this 80% is cached. And the cache is pretty restrictive. You have 5min cache and 1h cache. If you don't keep reading over that time your cache expires on the cloud. Then your 500k context counts as 500k input in its entirety.
And the numbers I mentioned would cost thousands of USD a week at API prices.
But when you control inference, you can keep your cache for as long as you want and save it to disk.
I tend to have up to 10 coding agent sessions open at a time. Some are used once a week. I never use more than 5 at the same moment. Having 15 full contexts cached in RAM basically moves my local cache utilisation to 95%+
Basically I think the AI companies will soon require us to pay the real price for the inference. I prefer to be ready.
This 6 GPU setup will probably outspend OpenRouter on electricity alone.
If all those nvidia gpus the hyperscalers bought were online the cost to rent a single b200 wouldn't be $50 and hour and you could buy those 6 year old gpus I use for $200 each. Not almost $2k a pop they sold for now.
A 180gb b200 on runpod is $6.8/hr. Nowhere near $50/hr.
The _most expensive_ on-demand b200 rental I can find is $11.2/hr. There's a number of websites around these days that track GPU rental prices across different services: gpus.io, gpu.watchworks.dev (vast and runpod only), gputracker.net (not free, lol), gpurentalprices.com, priceofcompute.com, rentgpu.org, etc (I'm only looking at the first page of search results for GPU rental prices tracker search). Most of these don't include vast or runpod, but all of them show prices under $10, mostly around $6/hr for b200.
I have a home network consisting of multiple buildings, a server room with a k8 cluster and various devices, some Cctv cameras, redundant fiber links between buildings, ftth Internet and lte backup, and so on. Not a simple network. All on Mikrotik switches using a lot of modern features like L3 in hardware routing. All properly designed, servers are multi homed with 10G DAC cables between switched.
Occasionally I'd notice few second drops when observing Cctv from my cameras on the monitor attached to my pc. Pc on the 10G.
Also occasionally I'd get random devices (android TV) decide "it has no Internet) for a minute at a time.
Also occasionally I'd have my AI take 20s to answer when I know for a fact it is doing absolutely nothing.
First I looked into it myself and I found nothing. No misconfiguration etc. Then I used opus with it. It found no network drops on any interfaces etc. But it wrote a script that opened 10k connections, kept them open and sent traffic through them. This was tried to all my k8 nodes and one node would occasionally refuse to open 0.1% of these connections.
Opus decided it must be a network card or a DAC cable. It took ages to identify which. I replaced both, problem went away for weeks each time and then returned every time.
Opus had a bajillion ideas to mess with my network config. Thankfully I know enough about networking not to let it take me on a wild goose chase.
Then I used Fable. Fable took 5 minutes and found the server grade Nic card's driver I use has a known rare problem where in certain configuration the default memory buffers for some hardware offload feature are too small for it and when connections get opened rapidly it sometimes chokes.
But there are two nodes with exact same config. Why only one was affected? Actually both were affected, but on one it was so rare I had to run the testing for hours to notice it.
The memory was increased and the problem... Became a lot rarer. Not resolved completely.
Fable again. This time it came up with an idea there must be a bug in the active/standby part of the driver where it occasionally let's some traffic through the standby interface. Which makes the switches ARP learn the standby as the correct path to that mac and send a portion of that traffic there, but the standby couldn't receive traffic.
It setup tcpdump on both the standby and live and proved indeed it was happening. I do not remember how this was resolved, but it was and for last 4 weeks the problem is gone.
I have actual programming problems too. 4 of which I turned into a personal Ai benchmark. Only fable solves all 4.
IDEA already have small LLM for one line code completion IIRC.
But the gain people want from LLM is generally "here, add this entire feature" or "here, go thru every dependency's changelog and update code to work with latest version". Those are not small LLM tasks
Would there be problem domains in which the more educated LLM would perform better? Are your names directly related to concepts from said domain,
LLM comments: "I think it may be a potential bug that the sum VATAddedTax gets added to the TaxFreeItems".
I mean it seems a bit unnecessary but also maybe it can help in unexpected ways.
But I’m sure we can have a small model that’s really strong at programming concepts, JavaScript syntax, and that’s about it. You’d interact with it differently, at specific seams in your code base - review a PR, merge two functions together, investigate these logs.
Or maybe I’m just not adequately absorbing the bitter lesson. Idk
1. Storing Shakespeare's work costs almost no $ in regards to disk space.
2. If the prompt doesn't include "Shakespeare" or relevant terms then no regression is performed for that topic and therefore there is no effective token cost.
Someone may correct me, but I think it's not a big $ win to exclude relevant topics from the models' overall capabilities. Instead you'd tune weights so that #2 better identifies what is or isn't among the relevant terms on which to run regressions.
Meanwhile, a template (maybe a couple KB) costs less than a couple cents to store and run. Large Languages Models are not really interesting, (smaller) LLMs that only contain "what you need" are.
I don't see it. In every case my time is more valuable than the cost of the prompt (so far) so the higher dollar cost, one shot, "getter done" model is the better net value option.
This is especially true when taking into account that the crow doesn't just fail on a single prompt, it does something much worse. It creates new problems that need to be undone afterwards. It confidently duplicates, triplicates, etc. a damaging work output that then needs more and more work to clean up before starting over.
That's a trivial number for sharing between a small user base of, in my case, 150 employees.
What am I missing? You're talking about maybe $60k retail cost in high read speed SANs that are likely already in place for a business of this size anyway? (Probably purchased a few years ago for under $30k. At least mine are.) In a US data center I'm paying a flat rate for rack space, so power consumption isn't a consideration anyway, but honestly it really isn't that much power even if I was being billed for it.
It's really nothing. An overlooked line item on a budget sheet.
So the real cost if I were to self host is video cards. But as mentioned previously, that's not reduced by smaller data sets, it's reduced by better weighted models, right? Let me know if I'm mistaken, please.
I'm very interested to be persuaded otherwise as a decision maker. Thanks for your insight and ideas.
Considering all of the above, I'm currently of the mind that a lower cost model that makes more mistakes is much more expensive in net, actually. So I prefer the most accurate, better weighted model, not the slimmer data set.
If the implementation brief says "attempting a reconnect in this handler would be a wild goose chase", the model needs to know enough Shakespeare, at least indirectly, to understand that expression...
I don't think LLM needs to know Shakespeare to understand: "attempting a reconnect in this handler would be a wild goose chase." I would even bet that most people learned of this expression before reading Romeo and Julie.
func randomName () { desired machine physics }
Everything around "desired machine physics" is superfluous wank; historically a biz case stored as code when some UI could feed biz case params go a function generator
Come on we know what we use computers for; media consumption and 2D data entry/review. Locally we just need a core engine for geometric transforms of visual state. What all these languages give us ability to create such a generic VM filled with customized semantics that mean nothing to solving the problem but plenty to a clever coder.
Kind of like Unicode we need distilled geometry primitives like "teapot for text" and desktop metaphors and to let people put the superfluous wank at the presentation layer
Which text used to be so making UI out of layers of text, OOP, and such made sense for decades
But we're just engaged in bloating system state through def jargon_to_encapsulate { desired machine physics } when we already know it's going to be simulated 3D or 2D visual transforms. We don't need to capture all those states in code verbatim.
Things like Jev are the future of models. Fine tuned on transforms given a context. "So you want to replicate GTA5? Here's a data set of geometric shapes and gradients constraints from all observed xyz" pipe that into your local renderer
We're entering the phase of software engineering (and engineering generally) where we realized we been dramatically over playing the song and can strip out entire asides and digressions, circumlocutions of provenance, to tighten up pacing and improve enjoyment of the outcomes. Hopefully. Or we kill ourselves. Through social squabbles (political, economic, religious, whatever) due to laziness to learn etiquette, and environmental destruction.
Check this graph:
That's needed to interpret the 10000 monkeys typing requirements in the various corporate product roles /s
Probably not unless you're writing tooling relevant to literature or prose, but I can't imagine trusting jetbrains (or any ai studio) to curate this.
I also imagine it could be a big shakeup if all of a sudden models could run on CPU. Imagine running an Astra-level coding agent, locally on your laptop. All of a sudden GPUs wouldn’t look as valuable, if you don’t need them as much
We are still some time away from that, but it seems like progress is being made
What language do you plan on prompting it in?
That might be a bad example ... replace english with nearly anything not related to prompting/coding. For example, I bet the models have "knowledge" of biology, chemistry, etc. not exactly useful for programming a SaaS web app that say does project mgmt. I think there's opportunity for very specific tooling rather than "general" knowledge.
But even besides that... Yesterday I prompted a feature by referencing a specific Monty Python skit. Does the coding tool need to know Monty Python? Maybe I could describe the feature in other terms, but it sure was convenient to have this shared knowledge. I don't see why Shakespeare would be any different.
Turns out that actually - no. Researchers have managed to prune half the Experts in a MoE model that had a low probability of getting activated during coding tasks, resulting in a more focused model:
https://arxiv.org/abs/2607.16721
Main benefit is that it greatly reduces the amount of RAM required to run these models. Of course you could just cache those unused experts on disk instead, but the main point here is that you know which ones matter.
But aside from that recent models, like Qwen3.8-27b are reportedly more durable under heavy quantisation, e.g. 3bits or even ternary. With additional techniques like TurboQuant, you can feasibly run these models on consumer hardware - even if at 1/4th the speed you'd get from rented infrastructure.
VS Code has extensions such as Kilo Code or llama-vscode which let you work with local models much like you would with cloud based solutions.
My employer is spending $50,000/yr per employee on tokens, and they're not alone
And hopefully they are just paying that per developer, not per employee.
The same can be said about software engineers. The tricky thing is, it's hard to separate a subset of knowledge from the whole, for both human beings and LLMs
1: Local models are catching up quickly
2: Visual Studio Intellicode (not to be confused with Intellisense) was/is surprisingly good at suggestions (when it first appeared before people went over to CLI's I felt that it was almost magically good for it's time, turned off co-pilot and whatever backend for a reason of compliance for a local workspace and the suggestions are quite good still even if not on the same level as the frontier suggestions).
If it pans out, with their brand people many would probably be happy to fork over for a Jetbrains licence.
People are not going to stop using AI no matter what your concerns or feelings are.
Negative cash flow from investing means that some prior investment has been shown to be worth far less than they paid for it.
I've always been a JB fan. But the last few updates have been quite rough, and they really don't seem to have worked out the AI thing at all.
Who has?
They host a lot of models too, and that is probably where they burned the cash.
I love that I can use browse the code in it and inline my comments for my prompts and send out all my requests into a single prompt
No idea why they thought they can compete with frontier AI labs on that, it's entirely waste of money
They also tried to push AI feature set as a separate subscription which is just insane in current tooling market.
All users wanted is good integration of multi-AI providers in their base offering. I think they are slowly shifting to that but that's a lot of money wasted already
These companies see the interfaces with the customer moving into domains they lack the taste and capability to compete.
They want to own that channel even if it is to spam them with ads like BMW did with the Spider-Man “special feature.”
Or Chamberlain has (reportedly) playing with the location of buttons and ads in their “app.”
Rivian, BMW, Chamberlain have the market dominance to ignore what customers want.
Jetbrains does not but had / has to try.
...or they're bailing out of really bad investments before they become even worse ones...
You're suggesting "investing" is like R&D?
Investing is when you buy stocks or bitcoin and hope. Except for 1 year, their "investments" have always lost, last year about 50% of total assets:
-908 -4,374 3,970 -1,946 -10,273
"Investing" is traditionally how money is skimmed: something's sold to a friend at an inflated price that only becomes recognized as such years later amidst some other cover/crisis (if not after a statute of limitations). The other option is sweetheart deals - unneeded property leases, jobs for friends, etc. - but those are a bit more traceable, while investments can have multiple shell-company layers and complex derivatives.