JetBrains reports revenue growth, net financial loss for 2025

I agree that they have gotten shockingly good. It's been a long time since I've seen them do something that is objectively wrong. Once we get closer to the "too cheap to meter" cost level things will change radically again.

I have seen millions wasted because someone trusted an ai scripts calculation of a metric from the bottom of the org that led the top of the org to make a wrong decision only to laugh about ai. There is value but ffs read the god damn code. You can have the cake and eat it too. If the volume of code is so large you cannot read it, maybe it isnt worth shipping?

Or are you one of the ones pushing the real code reading on to others which seems to be common. Yes i can have agents vibe out 10 features and have my coworkers suffer fixing it in reviews.

What IS useful are the AI reviews. They catch bugs, not all are bugs but they do catch some. It is almost like they are better at finding logical issues across millions of tokens but not good at writing streamlined logic.

The number of times ai gives me a 800 line dif only to replace it with a 5 line dif after i read it and notice it grossly overcomplicated the ask and scoped in a bunch of nonsense from training data.

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.

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!)

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 :-)

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.

I'm using the word understanding loosely there, but I couldn't think of another word.

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.

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.

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...

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:

https://paraplouis.github.io/llm-pareto-frontier/

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?

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

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

I’ve bought WebStorm every year but I’m getting a bit pissy about their focus on ‘the wrong thing’. The UI redesign feels not-quite-there-yet, DB introspection often fails, Deno support is barebones, etc.

That there is no lack of people delivering low quality using AI does not change this.

Most "developers" were busy cranking out low-end software before AI.

They never worked on large, complex software systems or in professional engineering orgs.

Lots of these people never would have made the grade, would have been cut, or forever juniors. Many were probably bitter about this.

Well now comes along AI..

And I don't use the debugger nowadays. The AI can do it much faster and add relevant instrumentation and parse logs.

You are able to do this while the output is small enough. But, it will not stay small for long. Business expectations are rising fast. To stay competitive, you have to push large amounts of code/fixes/features. There is no way to manually review it or QA it. If your org is not there yet, it will eventually get there. Or it will die out. It is only a matter of time.

The company has always been registered in Prague, Czechia.

They just moved the development center from St. Peterburg, Russia to Munich, Germany.

Of course it did. Because reviewing code thoroughly is harder than writing it.

Writing only consists of "making sure it does the task", reviewer have to understand code first, check if it does the task, then check whether it doesn't affect something else negatively (and it can be meta things like "doesn't adhere to project style", even if technical side is correct)

This doesn't say much. Plenty mobile apps, even paid ones, are of shovelware quality. And even if that's not the case, it's typically something very self-contained and of relatively low complexity.

On my personal projects I also seldom need to review code very thoroughly. But it's also not super complex stuff.

At my workplace things are not like that. It's a very complex system on a highly regulated environment. Opus 5.5 constantly get things wrong in ways that are simply not acceptable.

And I am not even an AI naysayer. I accept it as another development tool.

If that's your goal, stop using the slop machine. You will never make something actually good if you are letting it drive.

> What would you say... ya do here?

Your laughter is noted - but we have ever laughed about it.

Most code I have seen and reviewed over my 25+ year programmer life has been no worse than mediocre LLM code.

It's not a massive sample, but to me it suggests, most code programmers write, is somewhere between bad and mediocre.

And here's the appeal of the models: if what you used to produced was bad-to-mediocre, and you can now do slightly better for much cheaper, that's a huge win.

After I give feedback to the AI I then ask it if we learned something and then I ask it to store that guidance in a relevant skill file.

I have skill files for everything and anything in my project. Three just for testing - one for frontend, backend, and e2e. I have one just for frontend forms. I have one for project terms, which I ask it to read before doing anything, so it keeps the terms in the project right. I have one for doing db migrations and one for doing backend routes. I have many tens of skills that have been slowly built up like this.

That's always been my experience. Not even close. One gives you legos that lets you make a plastic toy, and the other is an industrial machine shop for code.

I thought maybe I was holding VSCode wrong, though.

I don't want to spend a lot of time finding, installing, and configuring the right combination of plugins to bring VS Code up to Webstorm level.

Also, I don't trust plugin/extension marketplaces anymore. There is lower exposure in having fewer dependencies.

I use Rider but need VS for some things.

The best thing to do would be to make it a checkbox, like IntelliJ does for almost everything else, but not that for some reason.