My Mac Studio arrived yesterday and one of the first things I did was cancel my Claude subscription. Happy to be free of load bearing, price gouging, paternalistic "altruists" Anthropic.
Am keeping my Codex sub while I find the best local model, but my plan is to eventually stop with OpenAI too.
There's a continuum between "vibe coded by someone with no technical knowledge or inclination" and "hand written domain driven design development". You can absolutely use coding agents AND have maintainable code. But yes, the coding agents will not magically make everything maintainable if you don't tell them to.
"Code maintainability and good architecture don’t have good measurements that we can apply"
Who has no wisdom? There are dozens of ways to measure code maintainability. Cyclomatic complexity is just one.
Nothing stops you from wiring up something like SonarQube metrics to your agentic coding workflow.
Plenty of measurements exist, but the trick, as the article said, is finding good measurements.
Cyclomatic complexity has been pretty solidly discredited within the maintainability research community for decades.
Sonar's cognitive complexity metric is a bit better, but here's a study that found that it still only has about a 0.5 correlation with how much difficulty programmers actually had reading code as measured by multiple methods.
They found that the most accurate way to measure code complexity that didn't involve something like an eye tracker or EEG is still basically just vibes - asking programmers if they thought it was hard to understand.
Halstead Effort came in second, and scored pretty well, but here's another one where it doesn't do so well, either. And it scores the SonarQube metrics even worse, with only a 0.35 correlation: https://www.sciencedirect.com/science/article/abs/pii/S01641...
Yeah, we have this rule in our SQ and I absolutely hate it because of the number of times I'm forced to break something up. Complexity that matters is usually at a higher, more architectural level.
Yeah. Looking at the code samples in that first page was pretty interesting. They're cherry-picked, of course, but still it's a little galling to see which ones SQ gave a lower score.
I definitely wouldn't want AI to be autonomously using that as a guide without doing some fairly serious internal A/B testing first. Kind of like for cyclomatic complexity, it's just too easy to find ways to maliciously comply. And if that's what you ask AI to do then that's likely what you're going to get.
> yes, the coding agents will not magically make everything maintainable if you don't tell them to.
There's the rub. It requires knowing about and caring about maintainability. And a lot of the people who "haven't written a line of code since 2025" don't care
Humans aren't magical, so if people can figure out what maintainable code is and how to write it, so can a non-human. Furthermore, humans are rather unlikely to be the optimal form of intelligence for software architecture and engineering, so it's almost certainly the case that if humans can figure it out, some non-human could do it better. That it's not yet solved by models suggests that it's not easy and maybe even not practically achievable with current tools. But that's not the same thing as not solvable.
> Furthermore, humans are rather unlikely to be the optimal form of intelligence for software architecture and engineering
I like this framing. Humans invented software (and engineering in general) as a means to solve problems with methods that work best for us. There may be entirely different, and parallel, problem-solving methodologies outside of human best-practices.
I have not found it difficult to get these systems to write maintainable code. You're probably correct that most people don't care about this so they don't bother to do so. But IMO, this doesn't seem like a problem that is crying out for a solution.
I agree with the larger point, but here I'll disagree:
> There are dozens of ways to measure code maintainability.
There are no good ways. I'm averse to making absolute statements, but here I'll take that chance. I worked in dev producitivy for years with people who spent decades in that domain across multiple companies with very high volumes of code production. Everybody agreed: All metrics are flawed and even a combination of metrics is insufficient.
Just to give one fundamental reason (in addition to a lot of the sibling comments): for any given metric there are an infinite set of counter-examples that don't trigger any thresholds but are clearly bad code. So these metrics typically only help in trivial cases, don't catch a majority of the cases, and so often become more of an annoyance due to low SNR. A lot of dev productivity work ends up being wiring these metrics in and then providing escape hatches when they inevitably get too noisy!
And most relevant to this discussion: these tools do not say anything about higher-level concerns like architecture, over-engineering and design, which IME is where agents tend to mess up most. I've almost never had a complaint about the code itself; the logic, naming, functions, data structures, even a lot of the testing, are all on point. It's always been the higher-level structure and design: over-engineering, duplicate classes, suboptimal abstractions, redundant operations across layers that could be solved by adding a single variable in a class, etc. etc.
I think the problem, like with code written by humans, is lack of sufficient context while doing a task leading to tunnel-vision. This is why we need to oversee and ensure things are good holistically. I suspect models are now good enough to play the role of an architect as well, though, and I've read some indications of that online... I just haven't tried giving them that much control yet.
> But yes, the coding agents will not magically make everything maintainable if you don't tell them to.
You say that like adding "Make it maintainable." to your prompts solves the problem. But the reality is we only have weak metrics for measuring maintainability. For example, you can trivially optimize for Cyclomatic complexity by blowing away abstractions and duplicating code everywhere. That doesn't make the code better. Cyclomatic complexity is a tool that has to be applied judiciously.
That doesn't mean you can't or shouldn't use AI to generate code. But it does mean if you want your project to scale, you're still going to need a lot of developer involvement at the code level to ensure the code remains maintainable so that future developers can build on top of it. AI is not like compilers, which allow developers to build complex solutions without being proficient at the next level down (assembly).
> There's a continuum between "vibe coded by someone with no technical knowledge or inclination" and "hand written domain driven design development". You can absolutely use coding agents AND have maintainable code. But yes, the coding agents will not magically make everything maintainable if you don't tell them to.
Yes. But I think the idea is without "hand written domain driven design development" the result trends to "vibe coded by someone with no technical knowledge or inclination," as developers de-skill.
- lot of (most?) people can do basic multiplication (I’m too lazy to fetch any stats but I hope you’ll have some observations in your bubble dear reader)
So are you agreeing with me or not? Because it's not black and white. Having a few non-deskilled developers around who do the equivalent of "compete in mental calculations" is the same as having none at all.
It's sort of like the retort "AI won't take all the jobs from humans, some will be left [at the very top and very bottom]." Even if true, fat lot of good it does most people who would be unemployed in that scenario.
Also, if you get widespread deskilling, but massive increases in code production due to AI, you're probably still going to get "organizations falling into the trap" like the OP describes and the remaining skilled people getting burned out trying to hold it all together. Modern American business culture (in aggregate) is incapable of learning to not burn people out until everyone is already burned out, only then will it pay attention to the problem (and then probably forget what they learned and start repeat it in 10 years).
> So are you agreeing with me or not?
I’m disagreeing with black or white view.
You make it
A bit black or white.
Llm will not take jobs, at worst it will shift jobs.
If we will allow current llm to develop code without inspecting it, we will still want to provide direction and expect certain quality level. So in “worst” case you will need at least design/QA people.
If code will be free, and it will increase 1000x, you probably will need 100x design/QA people. (Unit tests written by same llm, if not reviewed are worthless as assurance). I’m absolutely certain that humans will have plenty of software related jobs.
As to programmer deskilling, another parallel: lot of programmers have very little knowledge of how to deal with databases correctly/efficiently, because they are somewhat lazy and good tooling/orms allow to get away with it, but there are also plenty of devs who do know how db works and how to design a db schema for their app access as it improves overall system performance and correctness. Same with LLM case, having knowledge of what happens in code, gives an edge, and competitive/curious/overly-responsible people will continue reading things in depth (llms also make learning somewhat easier). And its definitely not 1%.
Furthermore, llm prices are subsidised a lot now, so it’s yet to be seen if llms are economically viable as vibe coding agents on a large scale.
Our class sizes at the university that I teach at have plummeted with the crash. What I'm hoping is that this means I'm just going to be teaching people who are actually interested in producing quality code.
//There's a continuum between "vibe coded by someone with no technical knowledge or inclination" and "hand written domain driven design development".
Somewhere in the middle of that continuum sits - "domain driven specifications, described using high level english concepts(that are well defined) from the domain , combined with a selection of a few standard architectures"
I agree that "maintainability" is the wrong place to stake one's flag. It's a technical problem and will probably be overcome.
The "lack of wisdom" in AI coded projects which does not strike me as a soluble technical problem concerns questions like: which software is worth writing and which isn't? How are tradeoffs evaluated?
I don't think AIs are structurally capable of answering these questions in the same way humans are. But they make those decisions anyway!
Those metrics don't amount to wisdom. Wisdom is something like realizing that you don't have to special-case some code over <here>, you just need to create a new reusable primitive over <there> that fixes two bugs with one stone, and you'd probably wind up needing it anyway. Both bits of code could sail through all the measurements of code maintainability you could come up with, and an agent suggest to me the brittle solution, but I came up with the more maintainable one.
The author admits to a measurement: maintainability. There are quantitative and qualitative ways to measure this, the biggest signal being outright abandonment
That's terrible as a signal. It's too late for anything, and there's many reasons things get abandoned.
> code maintainability and good architecture don’t have good measurements that we can apply, because it takes months, years even, to notice the effects of bad architecture or of unmaintainable code.
One way of understanding "good" here is "actionable", imho.
> But yes, the coding agents will not magically make everything maintainable if you don't tell them to.
But that's not actually a change. Humans wouldn't magically make everything maintainable if you don't tell them to (and maybe not even if you do). You have to monitor them and train them, carefully, basically forever.
Maybe not forever. Humans you have to do it basically forever, because humans don't improve very fast. AI... you have to do it forever with the current level of tech. If the tech improves enough, who knows?
> You can absolutely use coding agents AND have maintainable code. But yes, the coding agents will not magically make everything maintainable if you don't tell them to.
The trillion dollar question is how you do this, if your employees do not care (they are optimising for salary & time spent not code quality) and you have no way of telling apart AI slop vs. good maintainable code. (If you could you would just train the AI.)
Before AI there was at least some way to tell apart good programmers from bad, because there was some human effort involved in coding. Now with AI and slop generation there is almost now way to do this.
> if your employees do not care (they are optimising for salary & time spent not code quality)...
From what I see is it's mainly managers and higher brass who doesn't care about code quality and sustainability, and aims to drive time to market metrics down aggressively with AI.
Any employee who cares about code quality will become a poor performer with a red luddite label because they dare to change what the AI has emitted for them.
I'd love to be wrong, very wrong about this, actually.
They don't care about maintainability because they're not going to be debugging it at 1am on Friday night but mainly because if something is wrong they have lots of people below them to blame for it.
Developers, however, are still responsible for the code! We must review the AI....all 80k lines of code it generated yesterday. If we don't then we are at fault. And we must go full throttle of course. So ....not be picky and retrograde about accepting what is generated.....
IOW we know who is going to get screwed and it isn't them.
Yeah but I mean, what is their compensation based on? I'm genuinely pretty curious about the point you're making here. I don't know anything about underwriting or how that profession works or how it is analogous to what you see as the future for software developers. But it seems interesting and possibly insightful.
> From what I see is it's mainly managers and higher brass who doesn't care about code quality and sustainability, and aims to drive time to market metrics down aggressively with AI.
Trends over time will drive more observable changes. If a whole generation of programmers picks up bad habits that their managers don't care about (think very junior), that will take some time to play out. It's like children's literacy. You don't notice overnight, but a decade of neglect and you have a reading problem in kids.
Yes, this is why it's good that we're having these debates. We should keep having them. Personally, I am not yet convinced that this is a real problem. But I also haven't yet worked on a team with people who have entered the field in the last few years.
> and aims to drive time to market metrics down aggressively with AI.
Go fast and break things has been a mantra for how long?
I think a lot of the laments about "good" code are really about "ownership" - and as someone who spent most of my working career in OTHER peoples code bases I have seen some things. There are a lot of you who think that your code bases are "great" when they are NOT. Personal understanding is not a good measure of quality.
The increased cadence from AI is just speed running to the legacy code base.
The answer: express the concern, and reiterate it after every issue that arises because of increased complexity. Start building a plan on how to "unravel" the mess, how to migrate things in place, how to start drawing boundaries in your systems. The system is designed to reward heroes who fix problems - you want to be super man who stops the bridge from falling apart, not the engineer who pushes the costly fixes it before it does.
> Go fast and break things has been a mantra for how long?
Practically since eternity, but just as we learnt to manage current rate of "fast" and "breakage", somebody attached a solid booster behind us. So we're trying to understand what happened and what's happening and what will happen.
> I think a lot of the laments about "good" code are really about "ownership"
People owning what they did, have responsibility and initiative about doing better is always a good thing, yes.
> and as someone who spent most of my working career in OTHER peoples code bases I have seen some things.
I can understand that, I'm sorry you had to go through this.
> There are a lot of you who think that your code bases are "great" when they are NOT. Personal understanding is not a good measure of quality.
My codebases are as great as my knowledge. I love when someone reads my code and points where I f'ed up. I also love to show what I did has achieved something I was aiming for and discuss how to achieve it betterer.
> The system is designed to reward heroes who fix problems...
And this is the problem. Because I work silently and diligently build something looking unimpressive while working like an atomic clock without any problems.
I often have to ask myself if I’m asking for something different because of preference or need. I don’t really know what others are doing but I see this comment a lot about needing to always correct agents. I can’t figure out if it’s an exaggeration or not because once I’ve planned how I want something done I pretty much have zero need to intervene.
Impossible to know without doing a detailed comparison. Are you using the same LLMs? Are your criteria for correcting the output the same? Are you working on similar code? Are your plans and prompts the same?
It's fine to have preferences. The biggest problem with bikeshedding is the time spent (wasted) debating. But you don't have to debate the AI, you just tell it your preferences, and ideally encode them so that they are repeatable.
Of course it's fine to have preferences. I think you missed the nuance. Bikeshedding implies the time is wasted because the topic wasn't important in the first place. The color of the bike shed, as it were, has nothing to do with the storing of the bikes.
I am equating the nerve that develops in people that get lost bikeshedding (wasting time on inconsequential parts of the problem) with fighting an llm on inconsequential implementation details.
We most certainly agree: what matters should always be the actual requirements (functional, security, performance, etc.) You can't bikeshed an important topic. Everything else is implementers decision. In my experience an experienced engineer understands the difference and trusts implementers to make the decisions that they do own.
No I understand the nuance. But it's only bad to have preferences about things that barely matter if you waste time on them. You don't have to fight the llm, you just tell it what you prefer and it does that, that's what's great about them. (If it is not listening to your directives, then you have other problems.)
You use the intentionally vague word "implementers" to abstract whether you're delegating to a human or to an AI. But the key point is that these are not the same thing. If I'm delegating to a person, that person is the "implementer". If I'm using an AI to generate an implementation, I am still the "implementer", it is merely a computer program working on my behalf.
Directing an AI's work is not "fighting it". You keep characterizing it that way, but it's the wrong characterization. "Implementer" is not blurry at all. Humans are responsible for the things they implement, whether or not they use AI tooling to do that implementation.
Is that my characterization? This subthread is about how it’s so time consuming fixing every little thing the AI does to be just like you would have done. My challenge to that sentiment is “give it some freedom, don’t micromanage it.” That’s all.
I understand the boundaries of ownership and responsibility. That’s why I can tell you if you are spending inordinate amounts of time correcting AI code then you’re doing something wrong. Either write the code yourself or reassess your ownership boundaries. You’re acting as a manager of a team of agents in an agentic coding paradigm. Managers don’t tell me how to write code.
Without going and re reading the whole thread, I'm pretty sure that both of your comments that I replied to included this "fighting the AI" characterization. It's certainly fair that you didn't start the thread about it but were just taking the premise of the sub thread. But I just disagree with that whole premise. I think what's nice about these tools is that I can just write a document that says things like "prefer immutability" and then I neither need to micromanage nor accept code I don't like, and there is no long slack thread about whether I'm right about any of the things I've written into those rules, the AIs are happy to do as I've asked.
I think this entire analogy about being a manager of a team of agents that is in vogue is completely misguided. Have I always been the manager of a team of bash scripts? No. These tools are way more capable, but they are still just tools that I'm using to do my own work, they are not people that I'm delegating responsibility to.
> I think what's nice about these tools is that I can just write a document that says things like "prefer immutability" and then I neither need to micromanage nor accept code I don't like
Why are we even arguing then? You and I agree. Did I ever say "don't share a single preference with the AI"? You set the guardrails and preferences and the AI follows them. This is how it's always worked so it's reasonable for me to assume that people "fighting the AI" have already done this and are being overly pedantic about the output. Otherwise it wouldn't be eating up inordinate amounts of time...
> Have I always been the manager of a team of bash scripts? No.
No. You're not even remotely close here. Let's revisit this once you've figure out how to have a team of bash scripts implement 100k lines of code and build entire systems in 2 weeks based on high level instructions and requirements shared in context and prompts. You have responsibility at a different level and scale in an AI native workflow.
I don't know why we're arguing about the first thing :)
But I think we have a real disagreement about the second thing. You don't "have responsibility at a different level and scale in an AI native workflow", you're still using tools. I understand that what you're saying is that it's such a difference in scale that it is a difference in kind. But I don't agree. I'm fundamentally at odds with this entire framing of ai agents as a team that is being managed. I don't like any of the anthropomorphizing of ai tools. It's fine if it's just an analogy, but people take it way too seriously as a real thing IMO. I fully recognize that I'm out of step with the prevailing discourse on this, but it's a genuine disagreement, I'm not confused about what other people think.
"Hey look I got this brand new tool it can do everything my old tools did and more but I'm scared to use it to do more--might shoot myself in the foot."
I think most people have felt that way before. Only way forward is to practice with the new tool (=
> Any employee who cares about code quality will become a poor performer with a red luddite label because they dare to change what the AI has emitted for them.
I think that's true but it's a special case. AI is here to stay and with AI coding IS faster and quality is better than ever before. Ideally you want your luddite fired along with the slop generators and keep the ones who are using AI and taking their time to deliver a maintainable code.
"Luddites" can just be the ones pointing out the Emperor's new clothes have a big hole in them. Removing them is just ensuring that whatever mistakes you're making get reinforced.
Chances are high the luddite is the one with the deepest understanding about the product and the code base - the one who is actually passionate about the project and actually cares.
Maybe? This does not seem to be in evidence to me. In my experience thus far, this seems to be way more of a personality and ideology split than a depth of understanding split. On my teams thus far, I've seen the deepest experts land on opposite sides of this question.
That could be the case. But if other companies figure out a way to deliver good code with AI, then the company with the luddites is going bankrupt. Luddites are better than slop generators, but both are worse against a developer with AI who actually cares and has a deep understanding of the code base and programming.
Well, and that's how you get enshittification everywhere. Everyone just cares about time to market, consumers are left to drown among a sea of slop, and honest businesses go down because they cannot stand a chance against slop peddlers flush with VC money.
Enshittification happens after the company was quick to deliver a working solution. They successfully captured the market, killed off the competition and now they are ready to start milking the customers who have no other choice.
AI slop can actually help with this, because it reduces the cost of replacing their enshittificated software.
The reason its hard to dethrone Facebook, Instagram, YouTube, Twitch, etc. Is not because it's hard to recreate the software (it may or may not be) it's because of network effects, budgets, etc.
And unless you have ownership level stake in the company, you *shouldnt* care about what happens downstream of selling labor to the company. Yes, I highly recommend 8 and skate.
Ive seen soooo many people burnt out, or "ive given years to the company and i got hit with layoffs", or "$200 software would have saved $1000000 when I brought it up to them". And companies will throw you away the MOMENT your usefulness is gone, even if just perceived. So, use them just as much as they use you.
And that idea of slacker is ALSO a way to generate more money for you, by slyly withholding or slowing work. I didnt get my paltry 3% last year. Inflation up 15% or whatever stupid number. But I can control how much work I do, so my effective wage/hour stays with inflation.
Save your caring for your personal projects, nonprofits you help at, your and family/friends labor you help with.
With some. You insisted, so let's get into the details.
1. Caring about the company when you are a worker and not owner?
Yes and no. I don't care about the company. I do care about what I do. It's a self-respect matter. I do good work not because I'm a slave to company, but because of self respect. My deal is simple: "I'll do my best to produce the best artifact and push the company further as long as it doesn't conflict with my personal principles, you'll buy that time for that amount of money".
I have a simple, foundational rule: I'll sleep sound at night, and this rule is rooted in my ethics. So, I don't shortchange anyone, incl. my employer. If terms change between us, we will discuss, but this probability is not a reason to do shitty work (or optimize for money, or which sugarcoated absurdity others name this).
2. Companies will throw away/layoff people with no notice?
Yes, this is bad. This is life. It's not nice, fair or acceptable, but without unionization, you can't act against this. So, you either try to change this or you just accept it. Realities of work life is not a predicament to shortchange your employer again.
This is as absurd as saying "I'll die anyway, why do all these things? I can just die on-demand".
Meaningless...
3. Work slowage (work-to-rule) as a counter to low/no pay raises in accordance to general inflation
We can accept that, but you all shall really unionize. It's not scary. Try organizing. It's a force multiplier.
4. Invest emotional and physical labor in ventures you gain completely out of
Everybody should have hobbies either productive or unproductive. I can't find the question.
There are companies where you can spend years doing as you describe. More and more, though, you’re competing with people who care even though they don’t own, put in the same effort YoY, and invest in their job. Companies love these employees.
So I guess it really depends on your values and what you want out of life. If you enjoy hobbies and time outside of work, sure find a job where you can coast. Don’t get frustrated when you get laid off just find another place to work. Etc.
Plenty of people want to grow within the industry and build a career, though. And many have what we call basic self-respect and care about how they are perceived.
> ... with AI coding IS faster and quality is better than ever before.
Is this claim based on something?
I'm not against or "for" AI (whatever that means), I try to use it as effectively I can, but for me it's not at all obvious that quality is better than ever before.
Speed I can buy, especially in new projects and utilities, but quality? At least I haven't seen this in practice, if anything I'm just seeing more code, issues, PR's and pressure ==> more slop, more bugs, less quality.
You can always say "skill issue" and "process issue", but that's partly my point here, AI doesn't magically solve this.
Presumably it is based on that person's personal experiences, like your own comment and all the other comments here?
Speaking for myself, based on my own personal experiences, quality is by far the bigger advantage of these tools. It has never ever been easier to write automated tests and to automate tedious manual validation. I'm running my code through like 10x more paces than I ever did before, because I can just say "hey try running this in these twenty different ways" (including with browser automation, if that's relevant), without needing to either do the tedious steps to run all that or to take the time to write a script to do it, and to compile and attach the findings to the PR. This saves me hours to days of work on validation, but the reality is that I just wouldn't have spent that time in the past, I just stopped at a lower bar for quality, because I couldn't justify the ROI for spending all that time on it. But now the ROI is huge, so it's a no brainer.
If people are not taking advantage of this, then yes, that is literally a skill issue.
For some reason this reminds me A LOT of past discussions about microservices, most wonderful on paper and forever debated, but I've never seen it work out perfectly in practice, for me it's mostly been a cluster F in most companies that adopted them.
Currently I see AI similarly, in theory perfect, in practice I don't see the claimed effects. So yes, skill issue, but skills are relevant and your company probably can't hire a rockstar team (if that matters in the future).
But your comment on personal experiences was very good! Spot on, we are all biased, easy to forget. Thank you for that.
> If people are not taking advantage of this, then yes, that is literally a skill issue.
...or domain. You said "including with browser automation", so you do web or web-adjacent development.
Not all of us are doing that. What I work on doesn't have any UI or output besides a log file most of the time, but it connects to many places and does many things like an octopus, but nobody sees that, but feels that it's there because their environment keeps on working.
I said "if that's relevant" and you jumped to "this guy just makes websites". I do many things.
The octopus you just described sounds to me like an excellent example of what having the ability to more easily do tedious validation is most useful for. If you know that the "environment keeps on working", there must be some way for you to observe that fact. And if it is an octopus, it is likely difficult and/or to change the conditions and observe the correctness with respect to those changes. I find it so much easier to do this exact kind of thing now. Or, "easier" really isn't the right word. It's that the activation energy is low enough now that I'm able to do a lot of things up front that I used to rely on runtime monitoring to validate.
I guess YMMV, and it's not magic, but for me it totally changes the calculation on when it makes sense to automate something (like that chart from the old xkcd about how many times you'll do the thing and how long it takes to automate) in a way that means I'm doing a bunch of things that are useful for quality that just would never have passed the bar in the past.
> I said "if that's relevant" and you jumped to "this guy just makes websites". I do many things.
I didn't. I made a guess. I might be wrong, that's OK. I love to be wrong, because I learn things by being wrong. Also no offense was intended, and I don't consider webdev inferior anything. What I tried to mean is, if AI has more training data for a domain, it does better. If you fire the same model on a niche domain, it falls flat.
> If you know that the "environment keeps on working", there must be some way for you to observe that fact.
Yes.
> And if it is an octopus, it is likely difficult and/or to change the conditions and observe the correctness with respect to those changes.
Nope. On the contrary, because there's so much innate knowledge that is required to know what to do, simulating in mind, deploying and testing on real world is much easier and faster than letting loose an ML model on it. You need real data, real data comes in slow, but you can catch problems early and easily.
Considering it's a niche area, AI also doesn't have much training on that domain, so it's doubly inapplicable for what we do.
> but for me it totally changes the calculation on when it makes sense to automate something ... (snipped for brevity)
It's great that if it works for you, but YMMV part is way more correct than people want to accept and want to learn. AI is a pneumatic hammer, but not everything is a nail which can be driven in with that.
When it works, it works. When it doesn't, well people still pretend it does or insists it shall. We must accept the limitations.
> deploying and testing on real world is much easier and faster than letting loose an ML model on it.
No this is what you're not getting. It is "doing the things I would do to deploy and test in the real world, but faster and in the background while I do other things", it is not "letting loose an ML model on it". This is the new capability. If you have any process like "do {action}, wait until {something}, check {something}, determine if it matches expectation", it is now possible to run that loop way more times in way more variants without either spending the time on it synchronously oneself or writing a script to do it. (If you do that specific action loop often enough, it's probably worth writing the script anyway, but that's also much quicker to do now.)
The AI doesn't need training on the domain, it just needs to be told "these are the things I would do, please do them for me and report back".
I'm sympathetic to not everything being nail-like, but I really think you're leaving a lot of chips on the table if you can't imagine any of this kind of action-check-evaluate loop you have that you could offload.
I'm saying you could do more things. Instead of waiting until a new thing is merged so that it can be deployed and waiting for monitoring to catch issues, you could be deploying pre-merge to testbeds, using different variants of the code or different configurations of the whole system or different inputs to exercise edge and corner cases. Maybe it's too complex of a system to set up that kind of testbed or simulation? Well, it's easier to do that now too! I can spin up and down environments, either with containers on my workstation, or in cloud deployments, that I would have not attempted in the past, because it would have taken me too long to set them up. But now it doesn't take that long, and I find it super valuable to be able to try more things out. (Cost is still a real constraint, I'm not saying that constraints magically no longer exist.)
Obviously I have no idea what your work looks like! But what I'm saying is that time savings are not just time savings. There can be a point at which the time savings bring you under an "activation energy" such that it unlocks a new capability, not just a speedup. And some of those unlocked capabilities can be directed toward improving the quality of software. And I think that's awesome and useful, is my prevailing point here. I won't claim that it will usher in an industry wide improvement in quality or anything, but for me personally, I'm making better software more quickly now, and I'm very pleased that I can do that.
I fully agree with this point about quality. I am doing so much more testing than I used to, because I have so much more time to do it, and it's so much easier to automate the more tedious kinds of validation.
Maybe it's true that lots of people aren't taking advantage of this and are shipping trash, but that's their own problem, and there have always been people who do the job poorly.
That's true, but it doesn't have to stay in this form.
> with AI coding IS faster and quality is better than ever before.
Citation needed, because the last study I read about was painting a completely different picture about code quality. Also, just because the AI pulling and remixing code from a known repository with high quality doesn't mean your code will be at the same quality automatically. Passing tests is not enough.
> Ideally you want your luddite fired along with the slop generators.
The thing is it's not possible to see who generates slop and who generates code, and if you fire the only people who knows about the codebase intimately, you'll be on a very exciting, possibly fatal ride. I don't recommend this. AI doesn't know your history and trade-offs. These guys do, and can guide you to clear.
AI can't.
Believing that AI will create bug-free code from start is believing that Rust is the silver bullet.
The reason that we can (not that everyone, or even most people will) create higher quality software now is that it is now much easier to try out a bunch of edge and corner cases, which would have previously often required a prohibitive amount of time to set up and run. It's a dream! I can certainly believe that people aren't taking advantage of this, but they should be!
But I totally agree with you about the measurement problem. I think it's a very difficult time to be a hiring and firing manager.
> The thing is it's not possible to see who generates slop and who generates code, and if you fire the only people who knows about the codebase intimately, you'll be on a very exciting, possibly fatal ride. I don't recommend this.
This is exactly what I said in my top comment. This is a huge problem.
"Just do everything to hold things together, and let me abuse you without complaint" is exactly the kind of patronizing sentiment that infests the tech adjacent scene these days.
The answer to that is "Fuck you, no. I have worth, and you will respect it". Gilded Age paternalisms are not something that needs to be brought back into vogue unchallenged, especially when the intent is to keep the rabble quiet, and the checks rolling in and up.
No, the luddite is the person in one bad location on the continuum, opposite the AI psychotic on the other bad end of the spectrum. The person holding everything together is the one applying wisdom to the use of tools for the things they are good at while avoiding the things they are bad at, rather than blindly following one ideology or its opposite.
Sorry, but you're distorting my framing of the term in my original comment.
I labeled the person who uses AI to generate code and uses their brain and wisdom about the system to refine that code as the luddite since they will work slower when compared to other "higher performers" who don't care about the code quality.
In my framing I'm aware that the person is not a luddite per-se, but will look like it since they will be slower while trying to create better code, albeit using AI in the process as well.
Citing myself:
> Any employee who cares about code quality will become a poor performer with a red luddite label because they dare to change what the AI has emitted for them.
One way to get workers to care about the quality of the product they are being paid to build (for someone else's profit) is for the management to show some loyalty and build trust over the course of a decades-long career by treating them fairly and avoiding layoffs.
Sounds like a culture problem. I’ve never had a team I lead not give a shit. Even if the project is dumb (some projects are dumb), there’s enough pride in competence and desire not to dump garbage on the people around you to ensure they at least want to try.
Disagree, throwing more money at devs hoping it will improve quality while continuing to take away their authority will only perpetuate the slop machine.
It's not that hard: treat people with dignity and take their contributions seriously, not as a disposable meat mass. In fact, not only will this improve code quality, it's likely to improve employee retention too.
You pay someone and give them the explicit responsibility for code quality in your system, empowering them to gate check-in with any static analysis and adversarial agent review they feel like.
It’s not a silver bullet but you absolutely can do better than just giving up.
Anything involving an AI won't work. If it did AI companies would already train their AI with it. Review solutions during training, generate synthetic data etc., or as a budget solution just route the requests through more models before giving you an answer.
This isn't true, there's a limit to how much feed-forward processes can correct themselves and well-poisoning of context is far too common.
Try using an LLM to rewrite an LLM output without the slop (vs asking for no slop to begin with) or sandboxed subagents that critique a parent's draft.
There is absolutely a step-function improvement in quality but:
1) not everyone wants to explode their cost by adding extra calls
2) this can't just be "trained in" to a system as obviously they have attempted this but the technique still provides an uplift.
If you can do adversarial agent review, or use an LLM to fix the slop and it works, then technically you already have an LLM trained which knows what to do. You can just use them to improve the other model. The issue is that we don't have these at the moment.
> Before AI there was at least some way to tell apart good programmers from bad
There were only bad ways, and the best way was to just find people who were both good programmers and cared about quality to keep an eye on the rest. Nothing much has changed in that respect.
I don't think it's that hard of a question to answer. I've noticed on my team, our thinking has shifted from how do you directly solve a problem, to how you get an agent to effectively solve the problem and not produce slop in the process.
One thing that we have done that's probably made the biggest impact is alot more upfront architecture with the knowledge that pretty soon agents will be running wild all over the code. Having worked with these agents for a while now, you get a very good sense of how they will go about solving a problem and the various footguns they will encounter along the way. Editing an AGENTS.md file or building a skill is not nearly as fun as coding by hand but it will pay dividends over and over if you do it right.
Another big thing is doing refactoring passes. Early on in our projects our agents generated ALOT of slop and we had to go back and fix alot of it. But every time we did one of these passes, a major aspect was improving agent instructions / skills / etc so it doesn't happen again. It can be a painful process at first but I found that over time, the amount of slop the agent produces goes down by orders of magnitude.
I feel like we're still very much programming, but we're now doing it at a "higher level" where we are not writing the code ourselves but instructing the agent to. And IMHO, properly instructing an agent on a production codebase is not a trivial task.
> and you have no way of telling apart AI slop vs. good maintainable code. (If you could you would just train the AI.)
I know how to distinguish good maintainable code from garbage. I have known for quite a few years. But knowing how to train someone, or an AI? I'm a good coder, not necessarily a good teacher. And there are things about code that I _feel_, not that I can rationally explain.
I completely agree with you on this. I was talking about the employer perspective. They have no way to tell apart the two and thus have no way to set the incentives.
You might write good, maintainable code, but they will prefer the slop generator who delivers quicker.
> Before AI there was at least some way to tell apart good programmers from bad
That's why companies were interviewing people on tasks that had nothing to do with writing maintainable software. /s
I understand the concern and it should be addressed and researched. But, simply saying "humans were writing code themselves" doesn't provide any evidence for better quality.
The comment you replied to did not say anything about unit tests. And "software quality" is much broader than "data structure and code complexity".
I do think this gets to the heart of the matter. I think many programmers have missed the forest for the trees on why things like data structure and code complexity matter. They do matter, but they don't matter in and of themselves. They matter because they are the best techniques we have for making software that is of high quality (the software, that is, not the code) and which remains so over time, while continuing to be developed and adapted.
I strongly believe that it is now much easier to create software that is of high quality and adaptability, orthogonally to the data structure and code complexity concerns. Those concerns remain relevant, but it's a mistake to think of them as the primary thing rather than things that support the primary thing
I'm spreading my bets a lot lately, trying a bunch of stuff:
https://modelrigs.com - a website for running open weights models on various hardware setups, including benchmarks, how to run them, and a TCO economics analysis
https://riverfish.uk - this one's a bit different! It's basically a pSEO experiment in combining fish population surveys and river data across England
https://findclients.org - a B2B client prospecting database of UK companies across different cities and industries, with a market analysis of the different sizes and ages of companies
https://octoloops.com - AI growth app. I've downgraded my time on this as although I could get users to sign up, getting them to actually use the app hasn't worked out at all; new users won't reply to my nice founder emails either. Bit of a bummer because I invested quite a lot of effort but it just doesn't seem to be working. Ironically I still use it to dogfood on my own projects...
https://signalbump.com - another experiment: enrich people's projects in monday.com with data on new (capital) projects and expansions of their clients. It's a monday.com app, the marketplace submission process was very challenging as a solopreneur. We'll see, very early days.
There's a lot going on in energy, manufacturing, mining, and of course defence at the moment. For example, we're still making progress towards economically positive fusion power; Vehicle-to-Grid for EVs is starting to roll out as trials; hydrogen is still being seriously looked at in different parts of the energy supply chain; the first flight of a mid-sized electric aircraft happened the other day; a "quantum" based navigation system that doesn't need GPS did a couple of trials in the last weeks; laser based mining, new geothermal tech, and lots and lots of autonomous/drone defence tech is being developed.
Then there's also "newspace", an interesting area to watch!
Nope. The projects I'm on where we use it, we're carefully migrating to the newer models. Where we can we test with evals to try and get an understanding of how the models have changed.
It's not all roses -- I've seen some regressions -- but generally the 3.x Flash models are pretty great for our use cases.
The great thing about LLMs though is it's incredibly easy to diversify and have fallbacks. But of course that means additional costs, mostly centered around engineering efforts to test and integrate them.
https://octoloops.com - AI outbound and community outreach for indie/vibe coders. Unfortunately it's not going well: people sign up, but then they don't use the app. I'm running out of ideas to try quite frankly.
Mistral is an interesting AI company because they clearly have a contrarian business strategy to the other AI labs. They're also landing big customers in Europe for the right reasons. People dump on them because they're not benchmaxxxing which is pretty shortsighted - do you really want to be in a benchmark arms race with China, or do you want to make money and deploy sovereign AI compute in Europe?
When Apple is not racing for the frontier it's a strategy, but when it's Mistral it's a mistake.
The two companies have read the market the same.
It's always very dangerous for first movers and their investors, and the commodification of intelligence seems even more likely each time a chinese open model release. It's less exciting to do business that way, but if you're building to stand the test of time, it's wiser that way.
I am not sure it's correct to lump apple and Mistral's strategies together. Apple's business is selling hardware/services and their stores, but Mistral's business is AI.
Apple's strategy seems to be "wait till real business shakes out" but Mistral's strategy seems to be "go after profitable niches and avoid unwinnable fights".
Mistral is doing one more thing: building up local know-how in the European ecosystem. This is worth more than money, you can't bootstrap an industry overnight.
There are a bunch of Europeans working in the top labs abroad. We do not lack any know how, we only lack the raw amount of capital invested in doing private research, since our companies cannot thrive and compete globally due to regulations. Mistral would be much bigger if it was founded in the US.
Or it would be gone. Or it would be in the hands of psychopaths. Europe trades off the extremes for a better middle. It doesn't produce quite as many big names, but US and EU economies are roughly the same size.
Ah, interesting I always thought USA’s GDP far ahead, but it’s not. Compared to EU it’s 20 vs. 30 trillions/y. Adding Switzerland, UK, etc. and it’s close.
According to wikipedia, EU has a 23 trillion GDP (30 trillion PPP) and 451 million people, making ~51k GDP per person (67k PPP).
US is at 32 trillion GDP and same PPP for 342 million people - 94k per person
The other part of Apple's strategy is "lets not waste money doing all that expensive research - lets just pay them for the finished result and save money".
Is that really part of Apple’s strategy? They have recent examples of moving a whole bunch of stuff that requires a lot of research in-house. You’ve got stuff like their silicon, cellular modems, Vision Pro, the health business, etc.
Really Apple is pretty R&D heavy when it comes to their hardware business.
I think it’s more accurate to say that Apple sees itself as a consumer solutions provider first while a lot of the companies in the AI race are heavily focused on B2B.
The frontier AI model race is about being the first to be able to sell solutions to companies that will replace their workers and make their workers more productive.
But for B2C, the value potential just isn’t there, which is why Apple isn’t chasing it.
And now you’ve got the Mac mini/Mac Studio situation where Apple is better off selling pickaxes.
Whatever they do will take 2 to 4 years and I think they will do something to permanently solve their memory problem and I think it will be no different than solving their processor problem (Intel) or their long-term modem problem (Qualcomm).
Let Google spend $185 billion, and Microsoft spend $140 billion thru the end of this year on AI model building and AI hardware.
Well the only place they were ever able to compete with are open models, which is by definition completely unprofitable (unless they also want to compete with Vast or Openrouter as hosting for their models or something), so that sort of makes sense from the business side of things?
I think the route to profitability is super clear and simple. To be a reasonable alternative to Chinese and US models.
I think you're probably better off using the Chinese open models right now if you're concerned about vendor lock-in or capabilities disappearing because someone's economy seems a bit fragile atm. There's no guarantee China keeps releasing open weights though, so supporting a pragmatic alternative isn't a horrible idea.
I don't think any Mistral model can match an open 30B-sized Qwen from a year ago, so right now they're not really a competitive alternative to anything at all. Except Voxtral perhaps, but that's very niche.
> When Apple is not racing for the frontier it's a strategy, but when it's Mistral it's a mistake.
I think what Mistral is doing is smart within their financial constraints, but this comparison is misleading. Mistral is an LLM company; Apple is a consumer hardware and services company.
It's smart for Apple not to join the LLM arms race, because they can just pick the cheapest supplier and let other companies take the financial losses. Mistral is in a very different situation; they are the supplier.
Mistral is a Sovereign LLM Company, it's their main product and has been from nearly the start.
They don't actually need to offer the best models, they need credibility on the tech front and the security/strategic front, and institutional clients will keep coming.
What’s the point of having “sovereign” weights that are worse than publicly available ones? Wouldn’t Europe be better off just keeping up-to-date on the Chinese releases? In the event of some schism requiring sovereign capability, or even if the Chinese pulled ahead and stopped releasing the weights, why would Europe be better off because of Mistral? (Or any country’s inferior sovereign effort make them better off?)
I think I understand the incentives that cause this to exist (it would be politically worse to say we’re just going to use Chinese models) but they are misguided. If sovereigns want to have valuable models, they should insist on world class, relevant ones like the Chinese have. Instead they embrace mediocrity in the name of sovereignty.
In the views of most Europeans, that schism already happened.
Europe was perfectly happy to rely on US software and services for decades. None of the large US tech companies would be nearly as profitable if they hadn’t had a whole continent of wealthy customers, and no competition.
I don’t think Americans are realizing yet how much has changed for us the past two years.
Can I still get Google Adsense payments when Google has my European bank account and address if there are sanctions or something like that? Can I still login to my Cloudflare account and manage a domain registered with them? Will I lose access to Outlook or Gmail? Should I rely on Claude at all?
But the problem isn't if I can bypass sanctions, use VPNs, etc, or even if a company wants or can legally do that... it's the fact I'm asking these questions at all. It's not something I'd ask 15 years ago about US companies.
It doesn't happen overnight, but the adversarial behaviour of the current administration and the tariffs really changed the perspective about America on many Europeans.
The discussion about building European alternatives had never been so mainstream. If and once they emerge, I think the shift will happen. But let's see
It's a slow shift, but it's coming. For instance, Airbus already picked a French AWS replacement (Scaleway). It won't be all, it won't be tomorrow, but "what happens if we get tariffed / they invade Greenland" is already part of everyone's disaster planning.
2 years is not a long time. What I’m telling you is that sentiments have changed dramatically, and every government and company board across the continent is taking actions to position itself according to those sentiments.
You won’t see the full effect of that for at least a decade, but that doesn’t mean it’s not happening.
The reality seems to be that for decades Europe gave only lip service to decoupling from American tech infrastructure, but in the last couple of years America has gone from being seen as a strong ally to being a major risk.
It will take time to move. Frankly as an American I hope it takes a long time and we get our shit together and rebuild our alliance with Europe. But it’s possible that the damage is not reversible in the next couple of decades and Europe will accelerate their decoupling. It’s also possible we continue to slide into imperialist authoritarianism (and Europe definitely accelerates their decoupling).
This is what I mean: Americans don’t understand the immense dividends they have enjoyed from being the defacto symbol of “progress” in the 20th and 21st centuries so far. American solutions were chosen by European customers because they were reliable trading partners with an air of modernity. Homegrown was seen as the antithesis to leapfrogging into the future.
People celebrated when McDonald’s came to their country or town. Not anymore.
I'm not sure if it's really a "trump agenda". The ideas didn't start with him, the people who voted for said ideas aren't going to suddenly disappear, and this is his second term.
It looks more like part of the new normal from the outside, and that's why things are changing. A country can't go from reliable to unreliable partner every 4 years without any consequences.
Even the people that voted for Trump didn't sign up for this, which is why his approval ratings are terrible even by historic standards. He didn't run on a platform of "attack Canada and suck up to Putin". Most Americans are very pro-European. Most of the voting population has living memory of the cold war.
As to what happens post-Trump, it's anyone's guess. I expect a hard reset. Markets have short memories, as they say.
Not to the same extent, but during his first term, his administration had issues with Canada, Mexico, Europe/UK, NATO, China, etc. Tariffs were a thing back then already. And he was elected again. Clearly it wasn't a huge issue for those who are bothered to go out and vote.
In any case, for everyone else, it's the second time in 10 years that they have to play the waiting game until it's over. It's good to be optimistic, but when you're in charge of a country, you can't just plan for the best and rely on hope alone. Same if you run a company or and even with services you as individual use.
Regarding memories, I don't know about the markets, but in Europe I think it will take a few years for everyone to forget that in 2026 Denmark had to transfer blood reserves to Greenland and that different European countries deployed tripwire forces because of... the US of A. Do you know who triggers similar responses? Russia, when they start saying that their borders are fluid.
2 things here, one is related to benchmaxxing, another to being good enough
1. Mistral isn't benchmaxxing. That doesn't mean they're better, but it does mean the benchmark gap not a good reflection of the actual gap
2. I think the "world class or nothing" framing mixes general capability with system capability.
Most deployments don't need AGI
In RL you need a model that's reliably good at one or two things, thats it.
Example:
Case of a hospital flooded in emails.
You make a system that decides which patient emails needs a human and drafts replies for the rest.
If a sovereign model is good enough at that, and you can run it on a hospital's own servers under EU jurisdiction, the frontier gap part has zero importance
Who cares about "beats DeepSeek / GPT11 / Claude Fairytale 8.9"
Not sure if you are European, but in EU it's a bit taboo to even talk about this in this manner. We like to spend a lot of money to make sure we finish last.
Any model, even an open weight one, is fundamentally an encoding of a way of viewing the world.
What kind of "alignment" are AI labs optimizing for? Ideological alignment is the full term, self-censored into something more technological-sounding.
Every model has people behind it rating what it should and shouldn't say. Every time you ask a model and trust its answer, you become ever-so-slightly ideologically indoctrinated.
I don't want my model to reflect the views of American oligarchs or Chinese cadres. I want European values of enlightenment and humanitarianism to be the default and that's why the sovereign part is important.
Is there a specific concern you have, and what kind of performance penalty is it worth to you on say coding tasks?
Conceptually, sure I understand, but in practice it currently seems like it amounts to just using a worse model without getting anything in return. And if some hypothetical alignment to European values is important, it seems like putting the necessary effort into building a model that’s actually competitive but has this alignment is the solution, rather than accepting an inferior one.
I think for coding tasks, it probably doesn't matter as much. I'm more worried about stuff like chatbots subtly pushing or normalizing a certain world view.
I think I'm not the only one that's had this revelation, recall the Llama4 announcement. [0]
> It’s well-known that all leading LLMs have had issues with bias—specifically, they historically have leaned left when it comes to debated political and social topics. This is due to the types of training data available on the internet.
> Our goal is to remove bias from our AI models
This goal of course is self-defeatingly impossible to achieve. There is no unbiased, there's always only an unbiased relative to the bias of the observer.
Phrased differently, models weren't right leaning enough for the American oligarchy class and they publicly shared their desire to change the ideology they perpetuate.
I think an important perspective to keep in mind here is Zizek, the philosopher who's dedicated his life's work to the functioning of ideology.
> I already am eating from the trashcan all the time. The name of this trashcan is ideology. The material force of ideology - makes me not see what I'm effectively eating. It's not only our reality which enslaves us. The tragedy of our predicament - when we are within ideology, is that - when we think that we escape it into our dreams - at that point we are within ideology.
-- Slavoj Zizek
My concern is that both the Americans and the Chinese will be aligning their models more and more ideologically and that they will function as the perfect propaganda machine - surface-level objective and unthreatening, but answering every question asked from a world view decided elsewhere.
For some tasks (like aforementioned coding), that won't matter and there we can use whatever model is most capable. But as we outsource more and more of our thinking to AI and use it more and more to educate impressionable young people, not having our own models will mean not getting a say in how societies views are shaped and perpetuated.
Imagine for example, the question: "What caused the French revolution?" There's many answers that might be technically correct. Which ones get emphasized is where ideology lives and gets perpetuated.
Since there is no unideological, the best we can do is a pluralism of ideologies building LLMs. To not build one representing your views is for your views to not be represented.
The training data and knowledge is the edge, you need to build that up and maintain it. And of course mine everything you can from the American and Chinese models, like they mined everything from the internet / films / music / games etc.
China's model for decades has been to do it cheaper and then do it better for cheaper. Just accepting this makes you an economic vassal state. We've seen this play out over decades now with other industries. I'm not blaming China for this approach, but if you want to stay relevant, then you need to compete.
You don't need to view China as some scary boogeyman who's going to use AI to attack you or w/e the current conspiracy is. They just need to continue peacefully outperforming while everyone else gets fat and lazy.
We know it's possible to put backdoors into LLMs, we don't have reliable ways to detect them without direct support from whoever inserted it.
Europe is less-worse-off with open weights than with… I guess it's weights-as-a-service? WaaS? The thing Anthropic and OpenAI do.
But that's not enough. As recently demonstrated, being just a few months behind with the power differential between defending with an open weight model while being attacked by a leading model, means losing absolutely.
I do not know if this holds going forward or not. It's not inconceivable that we're just about to get models that make unhackable code, using all the things software developers keep saying you need to do if you really care about security.
But anyone concerned about sovereignty can't bet the farm on this possibility. For the moment, it looks like it's a national security matter to ensure at least core state functionality (including core private sector logistics) gets the absolute best attention money can buy, and that the absolute best money can buy ("can buy" does a lot of heavy lifting here) is currently LLMs, and it's important those LLMs aren't going to get cut off by arbitrary whim like Mythos was, and it's important that those LLMs don't have backdoors like we can't rule out anyone else's from having.
This is true even if Europe was only defending from Russian cyberwarfare and didn't need to plan for the president of the country in which Mythos was developed, attempting to annex two NATO states.
Apple _owns_ computers in people's pockets. There are very few businesses that can match this value. What does Mistral own? A head start at best. For the record I like Mistral and hope they succeed, but you're comparing apples to oranges.
Do they actually own it though? AFAIK they have no significant moat even in the EU (legal or competitive) and given the current accessibility to non-SOTA AI (open weight models and distillation) it's just a matter of time until Mistral gets serious competition on these points.
Siri is a minor part of the Apple ecosystem. Fundamentally, it can call into a better LLM provided by a better company. Siri's only utility is that it has access to your iData and can control your iDevices.
Apple sells devices. They have benefited a lot from their devices being good (Apple Silicon).
Mistral sells LLMs. They would benefit from having good LLMs.
They are not the same kind of companies but they benefit both in their own way of the same market reading.
Apple is fine-tunning Gemini to customize Siri for their customers. They improve what's between the model and their customers : fine tune, inference up to the product. This is exactly what Mistral is doing, with an even more diversity of usage and needs because they are business oriented, instead of customer oriented. This is also what make them economically very efficient in comparison.
Beside Apple is still doing science experiments while Mistral is capable of releasing commercial models, albeit small and specialized.
Don't get me wrong, it's absolutely obvious that Apple is incredibly powerful, now more than ever. But the way they see the future, Mistral have a leaner trajectory.
Specifically we have American models which were built on extremely crappy data in insane quantities. What happens when you use same models to build a corpus of extremely high quality training data. Say for Math, coding etc. Then use that to train models. Can you get the same performance from models 10% of the size? Or 1%? Or 0.01%?
From what I've been seeing we're clearly getting to a position where models are getting "good enough" for some tasks to be really cool assistants to skilled people. And they're limited more by being extremely slow and expensive to run. What happens when they're not?
I can't see the model providers winning enough to make their valuations real.
When Microsoft is killing physical in 2023 to push for Gamepass and digital only, it's labeled as "progress and infrastructure planning", when it's Sony that does it, it's labeled as "greed and anti consumerism"
Hopefully more people get attentive to how the industry & the media works, and how US Big Tech manages to kill any form of alternative
> When Microsoft is killing physical in 2023 to push for Gamepass and digital only, it's labeled as "progress and infrastructure planning", when it's Sony that does it, it's labeled as "greed and anti consumerism"
Microsoft got massive pushback when they did it, Sony even made ad making fun of it. Selective memory much ?
Well Microsoft’s PR machine is working overtime then because I wasn’t even aware of Microsoft doing this until this HN thread whereas Sony was everywhere on social media
Apple is in an entirely different market than Mistral (consumer electronics vs AI lab focusing on enterprise consulting). Which obviously means their optimal strategies are different. It doesn't really matter for Apple if it's Gemini or other model running behind their AI features. If anything it saves them a lot of money and provides a lot of flexibility.
> When Apple is not racing for the frontier it's a strategy, but when it's Mistral it's a mistake.
Ah, yes, it is really baffling that Mistral, a new European AI startup, is held to a different standard compared to a unique, 50 year old, software + hardware, 4.5 TRILLION dollar market cap company. A truly mystery of our times.
They aren't following the same strategy. Apple does race for the frontier in their main field: beautiful, well-integrated hardware and software. Mistral does not race for the frontier in their main field: AI creation. They're grabbing bits and bobs from people that need (or feel they need) sovereign AI. It's like if you go into making phones for the military. You aren't going for the best phones; you're going for making sure you're the only company that has the right connections to keep that customer.
That's uncharitable. Defense companies do play the game (like anyone in the enterprise space), but they also do ship stuff that is uniquely set up to satisfy the use-case. I don't think an unmodified iPhone would do great in a warzone with no cell signal.
True - fair enough. Perhaps a little uncharitable. I would still currently say that the Mistral AI is more like a worse cellphone in every sense that has the right supplier agreements, but perhaps there are subtleties I'm missing.
If they get to "good enough is good enough" and are sovereign to the EU, that is a market fit/moat that's hard to crack since the distinguishing criteria is both parts.
It's hard for a US company to be sovereign to the EU and the same with China ;).
Given current geopolitics there are going to be a lot of quiet conversations in government offices across Europe about how much we want to rely on the Americans/Chinese for anything and how we reduce that exposure to either.
Yes we have and will have the entrenched problem to deal with but adding to that problem is the issue at this point.
When this comes up on HN quite frequently the comments are.. interesting - there seems to be a kneejerk "How dare the Europeans consider their alternatives/not using us (where us is the US)", they take it personally that lots of techies/governments in the EU are trying to find ways to avoid making the dependence we have any worse than it already is but that's frankly getting annoyed at the wrong people, maybe get annoyed at the people who are making us question if the current cosy (for the US) situation is what we want.
For some of us this has been expected for a long while and just defacto handing over/not competing with the US on the tech side never really sat right to start with, it was and is always a risk to hand that kind of power to another country/entity.
AI company not keeping up with AI companies is by no definition the same as combinedhardwaresoftwareservicesentertainmentlifestyletechcompany not keeping up with AI companies
Apple tried at AI integration and fumbled the bag repeatedly.
In 2010s, they were among the "greats" of consumer AI. After 2022, they kept trying, and just had delays and underperformance. I don't think their actions now are "strategy" and not "skill issue".
They're enormously risk averse, and realised the infrastructure (and perhaps more important external dependency cost of building out their own training data centres). Now that the utility of the technology is clearer, and OpenAI amongst others have proven it's not only Nvidia and Google that can build a stack capable of training frontier models - I think we may find that the beast waketh from slumber.
Yea people tend to give Apple the benefit of the doubt because they’re the most successful and valuable company in human history.
Mistral is not Apple, and is not emulating their strategy. Please show me Mistral’s half a $Trillion in yearly revenue coming from consumer hardware/software.
Then I’ll agree with you that they’re taking the Apple strategy.
I root for Mistral and hope they'll be successful, perhaps I'll buy a subscription too once they're good enough for coding aid (perhaps they are now, didn't do any test with their models recently).
First of all, they release the models' weights, perdonally I don't consider any other option as viable (no OpenAI and definitely no Anthropic, thank you).
I especially like their Vibe Chat web offer, the allowed monthly usage with a free account is incredibly generous (still have to hit a limit) and the deep research feature (5/month for free) is also valuable.
I don't know anything about the alleged regulation maxxing problems, I don't perceive them as a problem for my causal/personal usage anyway.
The gap has only been increasing, though. Devstral 2 was obviously not great compared to Claude/GPT but kind of acceptable if you were willing to compromise. There has been no real progress since then and frontier labs are massively ahead.
After living in the US for several years, I was happy to return to the EU where regulations exist to protect against the worst corporate behavior.
My American bank sold my credit card transactions to advertisers. My American mobile operator had insane fees for roaming and other features that are basic in Europe. Sending a bank transfer in America was unreliable and slow and expensive because there was nothing like SEPA instant 24/7 free transfer (I guess FedWire does that nowadays, I don’t know if consumers actually have guaranteed access at all banks like they do in EU).
When American AI companies become established Fortune 100 members, they’ll start abusing their customers just like all the others in that club, those banks and phone operators and
Microsoft and the rest. Google once pretended to be different, now they have the corporate cancer. No reason to believe the same won’t happen to OpenAI and Anthropic.
There is regulation that is effective and useful (e.g. plenty of consumer protection laws) and regulation that actually makes markets more competitive and efficient. Then there is a lot of what EU is doing which leads to less efficient markets and more stagnation.
Even a lot of their attempts to increase competitiveness like forcing Apple to allow alternative app stores have been halfassed and not very effective.
We heard this since Uber days and all it did was break the old taxi systems and brought taxi service to a wider audience. As a consumer I benefited immensely from Uber.
Likewise for AirBnB and hotels.
Not saying that these don't have secondary effects on the society - but as a consumer they were and arguably still are pretty amazing.
Me and quite a few others are getting real tired of the constant name-dropping of mega-corporations that do not care about your safety, health, or well-being. This does not resonate with anyone except a small circle of wealthy tech elitists.
If they can make a profit from those things, great, but they will also not hesitate to make a profit from your suffering as well. Ask the lung doctors of the people living next to xAI's data centers if their patients are better off than before.
You benefitted temporarily. Those offering the service are often worse off. Various regulatory bodies had to beat these companies into submission, that no, you cannot hire all your employees as contract workers and deprive them of their benefits. After the competition is murdered, they will raise their prices over and over again.
All these companies thrive on the externalization of costs and killing an economy to then use their monopoly position to maximize money extraction.
I think in this case, the contract workers are the ones taking the L, generally. Taxis are not overpriced just from a lack of competition, but because they are an actual full time job with all that comes along with it, and not well.. part time gamified bullshit where you fight for scraps.
Most of the does EU has Uber now, and Wolt and some others, so it's not like we avoided anything there in practice though.
Sure but it's not the government calling for regulation, it's Mistral. At times they seem like a government mouthpiece to check the waters, which I could accept if their models were at least any good
They've both been insisting that humanity's survival is contingent on regulation since the start and employ armies of lobbyists to try to shape the policies they want that hold significant sway in their own country. But of course Americans doing this is smart and entrepreneurial...
Meh, OpenAI and Anthropic are doing the same. The US shtick is "our models are too powerful, government, please hold them back (and buy 10% of the company)".
Edit: forgot, they also call for banning Chinese models and open weights models...
Ah, "Americans are awful. Now we can be just awful"
> they also call for banning Chinese models and open weights models...
So I guess Anthropic called for banning Chinese models when they said "but let me state it clearly so that there is no doubt: Anthropic has never advocated for a ban on open-weights models."? - [0]
May be they imply it? They work behind the scene for it? well, who knows. My guess is as good as yours. But since we're acting like a child, bending the truth is allowed?
By the way, China is decisive. They just banned outright Claude and Open AI. I guess no open arguments about regulation is good?
> I would just ban US software companies like China did (I'd obviously also ban Chinese software companies, too). The resulting chaos would end up with some pretty solid EU software companies, the know how is there.
I'm all for following positive examples, as long as we keep democracy. We don't need to copy everything, just the good parts.
> They are regulationmaxxing instead of benchmaxxing, that's my problem with them.
To those who is in know (r/localllama, r/sillytavernai), is well aware that Mistral models - at least the small, <=24b ones - are the least censored, even less than Chinese.
Regulation != censorship. (Apart from that, Mistral has Shieldstral[0] for adding policy enforcement).
With it's general approach of Open Weights, and being able to be deployed on-premise (/private/public cloud), they are a viable solution for your typical European enterprise, that has to comply with traditional (non-AI) regulation. With the NIS2 directive the amount of those companies is also significantly expanding.
We[1] are in the same market as Mistral, and among our customers, the go-to-solution of MS Copilot is typically performing badly, and the typical SaaS solutions are not even given a consideration, which is why they are reaching for on-premise-first solutions.
This is always going to be the case, because regulation is typically a form of friction.
It is the shape of regulation that matters, and ideology on either side of the divide is religion.
Good regulation and regulators would punish anti competitive behavior, but this will also act as a drag on firms, while preventing firms from reaching certain sizes and potential economies of scale.
The point is that regulation deserves to be understood and crafted well.
Those economies of scale are meaningless to consumers if it means a single firm squeezes everyone in the supply chain and captures the benefit for itself.
This is also a specific weakness in American conversations. The divide between free markets, competition, and regulation are ideological.
If you want good markets you need good regulation and incentive structuring.
> Actually, in the real world, the worst markets are also the strongest regulated: housing, education and health care.
> Meanwhile the fields that got to be regulated last brought us the most wealth, advances and innovation: high tech & software.
Financial markets are highly regulated. They are one of the most structured markets out there.
The outcomes in all those markets, whether they are good or bad, is a direct result of how the markets are structured.
The 2008 financial crisis, for example, was a direct result of regulators being defanged and underfunded for multiple years.
If you look at technology: innovation amazing in the era of unlimited horizons, the era of "move fast and break things."
However, the cost of externalities from inventions like social media, were not captured in that market structure. This means that firms can absolutely pollute or addict people in the information economy, capturing the profit but socializing the costs.
The ability of dominant players to consume/acquihire upstarts and keep their market position, is a drag on innovation and consumer outcomes today.
The situation that was faced in the early days of a new technology is different than what is faced in its maturity, when the advantages of the nimble upstart give way to the massive overlord.
At each stage, you have to craft the incentives and rules to ensure you achieve the outcomes you (as a nation) actually priortize.
That's not true. EU regulations like the AI Act and Digital Services Act have different obligations and requirements for large and small businesses and that amount can't be zero because being a small business doesn't give you a free pass to cause damage to society.
I'd be curious to hear about specific examples of AI regulation that affect small businesses without a good reason.
Not having regulation is also "not free" because it imposes external costs on the rest of society. The goal of regulation is to reduce these external costs and instead impose them on the people (broadly) responsible.
Great example. An highly regulated field (aerospace) put so many barriers of entry for startups and competition that the incumbents simple have to reason to innovate or even compete anymore.
I heard there used to be dozens of plane-building companies when the field was unregulated, with new, more efficient models and lower prices appearing regularly. I wonder what happened.
> I heard there used to be dozens of plane-building companies when the field was unregulated, with new, more efficient models and lower prices appearing regularly. I wonder what happened.
I heard that this is nonsense. There never were more than a handful of large aircraft builders (still are plenty of small ones), safety records certainly weren't better in the era before consolidation, and new entrants funded by the might of their governments have failed not because of regulation but because what airlines actually want is lots of identical aircraft from a reliable supplier and the new entrants haven't really been competitive in terms of performance either. The certification is complex, but so's building an aircraft, and it's really only the people further down the supply chain that need the protective umbrella...
But Boeing and Airbus kept desiging and building new and improved planes.
There are multiple aircraft manufacturers for smaller aircraft, and there are several showing up for electric aircraft today.
For jets, the capex required to build them is massive, and its never going to have an upstart just up and build a jet.
The regulations in place are the reason why its safe to fly, and airlines have the safety records they do.
This is the degree of safety end consumers want to be reassured that its safe to fly. The checks and balances in places create the market that you are hoping to give over to cheaper upstarts.
The market doesn't exist without that safety regulation.
See how people feel uneasy when flying Boeing after the decay of their engineering and quality culture became public knowledge.
> Markets, left to their own devices, do not end up automatically being competitive in favor of consumers.
True. Does not mean all regulation is increases competition and marker efficiency by default. If anything EU has long abandoned the core tenets of Ordoliberalism and "Social Market Economy"
Right now the weakly regulated American market is delivering staggering competition where massive corporations are fighting tooth and nail over my 100 bucks.
I kept hearing the same about previous age tech and there also Americans rule the Internet: Google Search, Workspace or m365, AWS, Cloudflare... Even Linus moved to the US.
They've been pretty efficient in squashing any potential competition because of that weak regulation, yes. Don't confuse it with competing on the merits of their products.
These are effective monopolies competing to become super monopolies - the period leading up to dominance by one or two is beneficial to consumers with freebies and cheap offers that also completely lock out any smaller players.
If you don't let such things develop in the first place then there's not enough money to round out and scale the product. So if we want world beating products we may need monopolies but we can nationalise them in the same way we nationalised big infrastructure in the past. Then at least we're not the tail wagging the dog anymore.
There's a big difference between governments regulating to benefit their own control and power, and regulating in favor of consumers.
Most regulation in the EU have jack-all to do with consumers. There's a couple of niceties - having all prices displayed in comparable unites (EUR/kg, EUR/liter etc). But apart from that the regulation have broadly served as moats around the largest businesses.
I wouldn't even call it a communication problem - I believe it's more a media problem, than anything. Good news are minimized and bad news are amplified to the maximum - nowadays even broadsheet newspapers operate with tabloid headlines, and that is really bad.
We somehow need to "want" a slower news cycle and simultaneously push back against the algorithmic outrage. The world is more predictable and safer than ever but the perception of the average person is not this, it's manufactured outrage at every possible opportunity and topic.
Despite the average european objectively being better off and safer than a decade ago, almost none feel that way and that is almost 100% due to the news algorithms being tuned to keep you in a constant dread cycle.
Awesome!
But, looking at the numbers the decrease in totals are due to fewer registered "street crimes", less fraud and legalization of cannabis (removing a 183k comparable from 2016).
Violent crimes however went from 193,542 to 212,335. With rapes almost doubling, going from 7,919 to 14,454 (82%). Murders remained stable though.
We can debate which of these crimes have a greater impact on the feeling of safety. Personally I don't really feel unsafe due to people being caught stealing a sweater or smoking weed (when that was illegal).
So I don't think this is as clear cut as the headline number makes it seem.
I feel like it's valid if people want to put more weight on the violent crimes.
> cooling off [...] seller liability [...] Right to repair
Let's not forget I am paying directly for those when I buy a product. Those benefits are not free, they cost money. The money has to come from somewhere. It can't come from profits (since those are the sacred reason the company exists in the first place) thus they will come from higher prices.
Higher product prices mean less competitive companies. It also means fewer startups which must implement the required regulation no matter the market says. Then you need protectionism against Chinese competition.
But the worst is that I must pay for those benefits whether if I want them or not. Thus my choice is reduced. I cannot vote with my wallet and send price signals in the market to tell sellers that I want those benefits or not and at what price. The market gets corrupted. The economy falters. Outside competitors gain. The Government needs to intervene again and again, growing stronger and more intrusive "for my own benefit".
It's the decay spiral we are witnessing here in the EU right now.
> But the worst is that I must pay for those benefits whether if I want them or not
that regulation acts as a lower bound on what it takes to do business in the place. You also pay for companies not dumping toxic material on the streets, for appliances not catching fire, for not eating rotten food and million other things. All of those regulations of course "corrupt" the market for the good of a society so it's not just a race to the bottom. The idea of a free for all market is an utopia that doesn't exist ... thankfully.
He's stating a fact that you don't dispute.
You follow up with hyperbolic language; "eating rotten food" (as if anyone would purchase their goods from purveyors known to disregard food safety).
In a market that has been designed to let people take part in voluntary transactions, we also design methods for making sure that the counterpart takes responsibility, and delivers the agreed amount quality of product.
If you purchase a car that won't turn on, you're in your good right to make a claim against the producer. Same for all the other things you mention. And people do.
You're making a point about negative externalities. Agreed that a factory shouldn't just be able to poison a river, because there's no tangible cost to piping your dirty water to one. Here it makes sense to make artificial interventions, in my opinion.
I never tried to dispute the fact, I'm disputing the idea that we should be free to opt out of benefits that have been agreed as the minimum requirements to do business in a society. I would actually go further and argue that allowing that freedom would lead to everyone being worse off. Having minimum standards in a society gives a solid foundation on which to build, can't think of any society that doesn't have them.
A way more interesting conversation would be where to regulate, how much, for how long, what impacts would it have in innovation, etc not that I want to be free to get sold broken goods.
> as if anyone would purchase their goods from purveyors known to disregard food safety
How would you know they disregard food safety if there's no regulation on what food safety means?
The choices you are presented with, are always a function of market structure, aka the interplay of incentives, regulation, and competition.
You have a market because your taxes pay for many things, ranging from policing, to courts, to power and standards underlying how various systems work.
The choices presented to you are always a trade off vs other options. Having regulation that dictates how plug points and voltage works for example, reduces the choices you have, but the baseline standard means many other things can be built because standardization enables modularity.
The position that there exists some platonic ideal "choice" isn't even useful as an illustrative argument in this context.
Markets that are effective at ensuring better outcomes for the median or average consumer are things that have to be crafted. Figuring out the correct extent of intervention, (whether you champion less regulation or more) is the issue.
I went to California earlier this year.
In our apartment we had a freezer that was clearly made before politicians thought to decide to put a cap on how good a freezer could be.
It was fucking amazing at freezing stuff. No freezer I've ever used could freeze this good.
It was probably from the 80's - old/sturdy AF.
I wouldn't mind paying for some extra solar panels to feed it.
I’m not sure that’s a function of regulation but more a function of most modern freezers defaulting to -18°, where an American or industrial freezer is -22 to -28°C.
Getting a better energy efficiency score is one reason (and consumers are motivated on running cost, not just environmental) but presumably these are cheaper to manufacture as well.
I have an American style, EU-compliant freezer than I can set to -26° which I bought for preparing sashimi safely. It rocks! Highly recommend them.
And it's not as if there aren't a bunch of somewhat arbitrary regulation in the US either.
But the general sense I got from the US, was that people still feel agency to improve their reality, and that there's fewer artificial barriers to that end.
(Except for building houses in California. omg. That sounds like a horrible horrible process. At least for those I spoke to).
I don't think you're taking my "there's two sides to regulation" comment in good faith.
I gave a pretty clear example of pro-consumer regulation myself.
Saying that the EU is overregulating cannot possibly be that controversial, given that even the European politicians are beginning to admit it.. Draghi, Von der Leyen (even though she passes blame), Merz, Macron.
- the overregulation narrative conflates two different problems
- and that you minimized too much the consumer benefits of EU legislation
I think the overregulation problem olis less that we have 200 EU regulations and we need to hack and slash 100 of them and more that we have 100 national regulations 25 are exact transcriptions of EU directives, 25 are transcriptions with tweaks and the other 50 are national scoped regulations and what we need to do is to make them uniform so that companies don't have to deal with 27x75 problems.
I'm a bit burned by politicians using the overregulation narrative and then not specifying a single example of a meaningfull rule that they would get rid of. Usually what is left in the air is that worker rights and consumer rights should be cut.
That's not even true in the concrete case we're talking about here.
As part of the AI Act, an important decision on a person (such as a hiring decision) cannot be made without human involvement.
You think that's not in favor of "consumers"? (also note the implicitly submissive framing of humans as receptacles for consumption instead of subjects with rights!)
Why shouldn't it be possible to completely automate decisions?
Imaginary entry level job, where you need to deliver parcels from one business to another.
Qualifications: 1. Drivers license. 2: Eyes in your head.
Applicant 1: Drivers license? No. Eyes in your head? Yes.
Applicant 2: Drivers license? Yes. Eyes in your head? Yes.
Reject applicant 1 and accept applicant 2. Ez.
I'd prefer to work at a place that uses it's ressources on things that provide value. If we're able to conserve ressources by automating hiring, I'm all for it.
I don't want to take a pay-cut to hire meat-proxies.
> (48) The extent of the adverse impact caused by the AI system on the fundamental rights protected by the Charter is of particular relevance when classifying an AI system as high risk. Those rights include the right to human dignity, respect for private and family life, protection of personal data, freedom of expression and information, freedom of assembly and of association, the right to non-discrimination, the right to education, consumer protection, workers’ rights, the rights of persons with disabilities, gender equality, intellectual property rights, the right to an effective remedy and to a fair trial, the right of defence and the presumption of innocence, and the right to good administration. [...]
> (57) AI systems used in employment, workers management and access to self-employment, in particular for the recruitment and selection of persons, for making decisions affecting terms of the work-related relationship, promotion and termination of work-related contractual relationships, for allocating tasks on the basis of individual behaviour, personal traits or characteristics and for monitoring or evaluation of persons in work-related contractual relationships,
> should also be classified as high-risk, since those systems may have an appreciable impact on future career prospects, livelihoods of those persons and workers’ rights. Relevant work-related contractual relationships should, in a meaningful manner, involve employees and persons providing services through platforms as referred to in the Commission Work Programme 2021.
> Throughout the recruitment process and in the evaluation, promotion, or retention of persons in work-related contractual relationships, such systems may perpetuate historical patterns of discrimination, for example against women, certain age groups, persons with disabilities, or persons of certain racial or ethnic origins or sexual orientation. AI systems used to monitor the performance and behaviour of such persons may also undermine their fundamental rights to data protection and privacy
I suggest reading the regulation(s), it usually does a good job laying down the reasoning in the preamble.
They are definitely feeding off the fear of US control from US companies and institutions, but you can't blame them for reaping the benefits of Trump's lunacy.
Amodei and Altman on the other hand are relentlessly pushing for regulation to bar open weight competition from eating their lunch.
>They're also landing big customers in Europe for the right reasons.
We've been migrating all of our AI automations from Gemini to Mistral because of the fear of data transfer regulations. Maybe they don't apply to us (we don't really feed personal data to AI), but we can't afford to find out.
It's been quite annoying too because the Mistral documentation and dashboards are all over the place.
Fear of fines... that's not what I would call "the right reasons".
I don't care about benchmarks. Benchmarks show that Opus 5 is a stronger model than Fable 5 which is obviously not the case.
But I do care about capability and so far only Anthropic and, very recently with Astra, OpenAI can deliver on coding quality. And capability matters immensely. There is a world of difference between being able to do something and not being able.
A capability isn't binary. There is a massive difference between can produce an impressive demo and can reliably complete the task without a human babysitting it.
Yup, new SOTA models especially with high/xhigh/max reasoning too often overengineer solutions, good for benchmarks that usually measure task completion, bad for normal development where you don't want 'rewrite in rust and 1k LOC unit tests' style solutions when agent does mundane bug fixes.
When it comes to mundane bug fixes the value is in actually finding the cause of the bug, and I find SOTA models way outperform smaller ones here. I don't care about their output - I can write the correct 5 line patch myself once I understand what's wrong.
It's not this week's change. Fable was the step change for programming. And most of truly useful and powerful capabilities arrived in the last eight months.
AFAIK: Mistral does not even try to compete in this field. There are other use cases for LLMs beside coding. As Mistral AI wrote:
> During the first wave of generative AI, the central question was who could build the most powerful model. Organizations and governments are now asking a different one: how to harness the power of AI for their mission-critical needs without surrendering control over the infrastructure and intelligence loop. Demand for that combination of performance with control, choice and independence is growing internationally, as enterprises and governments weigh the long-term technology dependencies, data governance requirements and deployment choices that come with any AI investment.
> Mistral is the only AI company in the world building the full stack required to answer that question: open-weight models, the infrastructure and the compute capacity they run on, and the products that bring them into production; ensuring that customers are never locked into a single vendor's roadmap, pricing or availability.
> Mistral’s full-stack and open approach also allows organizations to build on it without exposing their most valuable data, workflows and institutional knowledge to anyone outside their own walls. That's what makes Mistral’s stack the sovereign AI layer, meaning retaining control across four dimensions: data that stays inside the organization's boundaries, models that are controllable and customizable, compute that is private and predictable, and systems in production that are fully controllable and auditable.
People said this for Opus 4.6 too. Every release the models get RLHF'ed into accomplishing a new task and the people who need to do this task think there was a step change.
Yeah that's kinda my point. I'm not sure if the models have gotten that much smarter, but they're certainly getting more capable. That's not the same thing though.
There are things GPT 6.0 can accomplish for me that 5.3 was not able to. But there are also things it still fails at, and it doesn't seem to be much better at the big picture. It does spam about 100x more tests though and I wonder if just RLHFing it to test everything constantly is carrying it more. 6.0 writes so many tests and spends so much time verifying it's work in python sandboxes. Slow as hell but it tends to get things right the first time more which is good, I guess. I don't love the thought of a 500loc feature adding +4000loc due to tests though.
Yep same with latest Claude models - code isn't really any better than Opus 4.5/4.6, but use 5x as many tokens doing random stuff that's mostly unnecessary.
And yeah still for some reason they often can't understand how to set up any project locally without handholding, which is something you'd think an LLM would actually be good at
People fawn over AI brands now like cars and it's silly. OpenAI and Anthropic have been flipping spots for best LLM coder for the last two years and to say one is better feels silly; I've been using them both and they're very similar with different personalities. Recently Grok has become competitive in many aspects, and while I don't have much experience with Gemini it seems to come and go in terms of coding quality.
Saying only anthropic models are competitive frontier coding models is out of touch with the space imo
Genuinely interested, which ones do you think have relevance?
If I read forums and talk to people IRL most have differing opinions what model is best. Yes, for me it's pretty clear Opus is better than earlier models, but it's at least not obvious to me that the later are significant improvements.
It can be subjective, at this stage of product availability.
Personally, I hate to be frustrated by gross intellectual faults, so I did some research in the past about the best benchmarks to assess pure (simulated, apparent) intelligence. (The quality of the found benchmarks may not reflect what the models seem to do in practice, so one's experience should be compared to the raw numbers out of the benchmarks.) Good ideas emerge in the field: it was proposed and discussed on these very pages that the LLM should be able to solve "murder mysteries", for example (alongside the pattern recognition problems in which IQ tests consist, etc.).
Moreover, the LLM shall not delirate. It is an intrinsic issue with the current architectures (they do not mirror the "Foundational theory of Knowledge", which requires confidence values and relations of foundation between notions), but it is a problem with more or less presence per model. Artificial Analysis has introduced a metric for that.
Moreover again, I want an output style that works well for the purpose - must not be a clashing style like "youngspeak" ("like, awsome") or "paternalistspeak" ("when a planet likes another very much they are attracted...") or "sycophantspeak" ("your question is so deep and interesting") or "wetspeak" ("you can do it, feel this not that")... So, for example, I very much preferred Kimi k2 to gpt-oss-120b. I doubt there are benchmarks for this - "seriousspeak", "maturespeak" - but there should be.
This is a bottom feeder mentality. Europe has enough bright people and resources to truly compete in the AI race. There is something wrong when the only selling point is that it's local.
We're regularly getting demonstrations that being at the frontier is no moat at all. "Run open weights locally" was literally on the HN front page a few days ago as a primary concern/competitor for the big US labs. So there's a big and meaningful gap between "not frontier" and "bottom feeder". There's just tons of applications where you don't need "the best" model, especially not tomorrow's best model.
Those things are not mutually exclusive. We can have open weight, yet close-to-frontier models. China has very strong models that can also run locally.
I wouldn't. I can think of at least 10 regulations (labor and environmental chef amongst them) that would render such a maker uncompetitive from the start. Chinese makers would eat its lunch.
Doesn't matter, we need to opt out of the rat race that consists of making the planet worse for the next generations in exchange of quick bucks for the oligarchs.
The same applies to labour conditions and quality standards. And don't bring up arguments like "it can't work" or "that hurt competitiveness": all workers around the world want the exact same thing, a livable planet for their children. The only ones to object are the global elite who one ends up defending when they make the current status quo sound inevitable.
> all workers around the world want the exact same thing, a livable planet for their children
Actually, they don't. First of all they want livable conditions for themselves. Ecologism is a hobby for people with full bellies.
I remember during communism which failed to even feed and clothe us - but The Party kept telling us that we were actually winning and we were so morally superior to those "corrupt capitalists". This is what happens when ideological justifications replace rational thought.
> Ecologism is a hobby for people with full bellies.
Almost the entire planet where this debate is raging, is the part of the planet where people have full bellies. On average, too full, even.
Ecologism is also a hobby for people that don't want their kids to starve, as people living in Mesopotamia around the time of Akkad, or people around the Sea of Aral starting in the 1980s have discovered.
> Almost the entire planet where this debate is raging, is the part of the planet where people have full bellies.
Exactly. And most of the pollution happens in the other part.
We’re crippling our economies and render our companies uncompetitive to merely export environmental effects around, while pushing our voter base to the extremes.
All for naught too, because when the orc armies come (attracted by our weakness to even defend ourselves) they will do the the environment what they did in Ukraine…
> And most of the pollution happens in the other part.
Where does it happen exactly?
> All for naught too, because when the orc armies come (attracted by our weakness to even defend ourselves) they will do the the environment what they did in Ukraine…
Who are the orcs here? Russia? Europe is rearming like it's the 1980s.
How much money are Europeans paying to US companies for AI services and how much are Americans paying to EU companies? I don't think it's a good idea to let billions of dollars go to other countries because you couldn't be bothered to build a competitive product.
Why hasn't Baidu taken over the world's search market?
What moat does Google have that's so special in search?
You need to spend hundreds of billions of dollars to scale globally in search. And you have to take the market away from Google at the same time. Good luck.
OpenAI and Anthropic are doing that right now in LLMs, hyper scaling to a billion people. Having a billion users you can actually serve is a moat.
When OpenAI is done attaching a full ad model to GPT, they'll be able to serve a billion users profitably globally with zero subscriptions. This is what keeps Google up at night: ~$500 billion in ad revenue up for grabs circa 2030.
Any service with hundreds of millions of users is a lucrative ad business. TikTok, YouTube, Instagram, Facebook, Google, Amazon.
OpenAI's moat, if they get there soon enough, is that they'll be able to lean into a high margin ad business to press down on the market and kill everybody. You won't be able to serve the infrastructure to compete at the cost structure they'll able to subsidize at via the high margin ad business. This is an extraordinarily straight-forward move, and it's comically anti-competitive, and nobody will stop them from doing it. Their mistake is that they're at least two years behind where they should be in advertising.
Anthropic is the one at the most risk from the Chinese models, they have the weaker everyday consumer side. Enterprise won't be supported by an ad model very well.
To compete, we’d have to throw a lot of the regulations and laws into the trash (especially anything regarding copyright) and do an order of magnitude more investment.
I’m surprised that there’s no domestic chip production either, we don’t have our own CPUs or GPUs, meanwhile China is spinning up manufacturing so they don’t have to work around the Nvidia export restrictions as much.
I like Mistral and there’s cool stuff going on like how EuroLLM models know Latvian language and all the other EU ones better than way bigger models, but we don’t have anything frontier.
At the very least, they should be distilling Kimi K3 and GLM 5.3 as much as possible and working on MoE models like ~35B and ~120B versions to match Qwen.
> I’m surprised that there’s no domestic chip production either, we don’t have our own CPUs or GPUs, meanwhile China is spinning up manufacturing so they don’t have to work around the Nvidia export restrictions as much.
I haven't heard of updates on ESMC in a while, but that has been a thing on the horizon for a while, with production planned to start in fall 2027 last I read.
> I’m surprised that there’s no domestic chip production either, we don’t have our own CPUs or GPUs, meanwhile China is spinning up manufacturing so they don’t have to work around the Nvidia export restrictions as much.
The CCP is able to plan the economy. We used to be able to do that in the 60s-70s, at least in France, but that mindset is long gone.
In the long run a planned economy is a huge disadvantage, the Soviets and now the the CCP are prime examples. For instance, the CCP is also sitting on a bunch of practically empty metropolises, paired with a property bubble and a falling population (all of which are exist to their meddling).
"Planning" has a very different meaning when referring to what France did, what the USSR did, and what China is doing. Remains to be seen if China is at a disadvantage.
I mean the EU has ASML, why can’t we go the rest of the way? Seems kinda shortsighted and I say that as someone who bought the Intel Arc A580 on release and don’t regret it.
No, Europe doesn't have the capital markets required to build the required data centers. Underwriting gigawatt data centers and frontier models demands a fully realized European Capital Markets Union, a single energy regulator with cross-border grid integration, and shared fiscal borrowing power.
> Underwriting gigawatt data centers and frontier models demands a fully realized European Capital Markets Union, a single energy regulator with cross-border grid integration, and shared fiscal borrowing power.
Care to elaborate why you consider this an absolute must? I don't see why, say, France can't build an "AI valley" somewhere near to on of their nuclear power plants and commit to provide X amount of energy for some fixed price for a decade. To me the main reasons for not building top AI labs with frontier models in EU seems to be:
- Venture money is not there (or people controlling the money are not on board to invest into such labs).
- Weak political will from countries who could do that to actually go for it.
- Top scientist who could work on it choosing to go for much higher payouts in the US.
Power is the easy part; the hard part is where the check comes from.
One single frontier computing cluster costs some €30-40B initially and becomes virtually worth zero every four years. That’s the equivalent of France’s full-year VC market for all industries combined times three. A single Member State, already indebted far beyond sustainable limits, cannot subsidize expensive, long-term 40bn€ hardware risks from its national budget more than 33 times without both falling foul of state aid rules and blowing up the debt crisis even more seriously than it already is.
The three factors you mention, missing VCs, political reluctance, and brain drain, are by-products of exactly these fragmented fiscal structures. European capital is not missing; it is frozen at 33T€ (roughly) in risk-averse national bank deposits and Member State pension regulations instead of in one unified Capital Markets Union to pay for high risks related to enormous tech bets. Similarly, leading researchers are not looking to work on a better salary but for liquid equity, which is not available at the high valuations seen in the NASDAQ through deeply integrated public markets.
With insufficient scale for an entire continent in finance, even the best AI valley in a single country will hit the wall much faster than a US hyper-scaler investing greater amounts in CAPEX in one single quarter than every member state treasury.
We're still deep in the research and exploration phase, who knows what will huge oodles of compute be useful for in the end? Maybe it will end up being used by the owning corporation or local nation state, against similar amount of compute wielded by another corporation or nation state.
After all, what else than governments and corporations will be left after the rest of us poors will inevitably be priced out of the economy?
We're not in a good position on the supply chain needed. Right now it really means pouring billions on American corps, either by renting the compute or by building it. That would be mostly fine if it were only VC money but it's not.
> We're not in a good position on the supply chain needed. Right now it really means pouring billions on American corps, either by renting the compute or by building it.
You are saying "right now" as if EU is seriously investing into developing its own AI hardware (both training and inference) but I'm not aware of any serious attempts to build such supply chain in EU.
Mistral doesn't look like it's benching at all. They're just as well funded as a lot of Chinese labs doing much more interesting work R&D-wise.
Tailoring products for compliance doesn't cut it IMO, but I'm not in their shoes.
Realistically they will have to deploy the Chinese models or their finetuned versions though since their models are completely out of date and not competitive. Outside of maybe government contracts it will be hard to compete against Azure/AWS who promise to run their models in EU datacenters and not store any data since actual companies normally prefer frontier models with decent performance (cost/performance is pretty decent as well if you are fine with e.g. Luna which is massively better than anything Mistral can offer).
There are two separate issues here. One of which is very simple. That issue is where to run the models. For many companies this has to be in the EU, on EU terms. Mostly, this is not really optional from a compliance point of view. It's why all the big cloud providers have data centers in places like Frankfurt, Amsterdam, etc. and why a lot of new data centers are being built in Ireland. Of course a lot of those investments are being made by US companies. But they all have legal entities in the EU because otherwise they'd have no business here. And they can't afford to miss out on that business because it's a huge market.
The second one is about which model to run and who controls and oversees quality control. OpenAI and Anthropic seem to insist that only they can do that. But of course here in the EU we see that a bit differently. The big US based hyper-scalers are neither liked nor trusted here at this point. We don't trust the Chinese model makers much either. But with open weight models, we can at least pick different models and run them on our own terms.
Also, what most companies need is not necessarily the latest fashionable model straight from the Silicon Valley cat walk but something that will work reliably and predictably for years. Factories are not going to install the latest model in their production lines every few weeks. Same with most banks, insurers, etc. I actually know people that do business with those in relation to AI development in Germany. Companies like that are very much obsessing about self hosting their models. Sending customer data off premises is a big concern for them. They are building stuff that will be used for many years. In five years, nobody will care which model was best in autumn of 2026. But a lot of software built this year that uses AI might still be running.
You have to see Mistral's investment in that context. They could make a lot of money in the EU if they do a decent enough job. Lots of conservative companies here that are going to pick something that's good enough and then they'll be using that for many years.
"something that will work reliably and predictably for years" is not really the class of product being sold, unless you're using a fine-tuned SLM to do something like classification. The vast majority of work being done with AI unfortunately benefits from being run on the biggest/best model.
So they are parasites on sovereign blah blah blah.
And yes , Chinese models will be cheaper compared to what they offer , as well as American models will be much smarter polished . This is the only market they have , lobby sovereignty among politicians.
With how they’re currently being used we might as well call them bendmarks.
Every newly released model is paraded as SOTA showing peak or near peak performance on cherry-picked bendmarks the model was either fine-tuned on, or tested under specific conditions optimal for that model.
> they clearly have a contrarian business strategy to the other AI labs.
Yeah, spot on.
I'd add they are also betting on building specialized AI's targeting narrow yet very profitable segment markets, where general AI's à la AnthroOpenAI don't work very well.
Then your choice is either a US mega corp under the Trump partial dictatorship shitshow or a Chinese model distillation factory that could turn on you at any second.
I understand that there are users that would prefer another option, especially in the EU.
I tried them via OpenRouter. I loved their OCR. I really disliked their code generation. It was about six months ago -- so it was a geological era ago in this world. However Mistral is legally favored in Europe. In fact from my point of view , using them presents no trouble with GDPR (I live and work in Europe). I'm NOT a lawyer but I'm a technician that define itself 'privacy savy'.
I fail to see how this is relevant with regards to areas.
Whether AI is hosted by the USA, Europe or China - they all are awful and eliminating real jobs while also driving up RAM prices etc... Why should I want to support any of these?
Because it's increasingly obvious that this is the next step in our capabilities as humans competing with that of the discovery of bacteria and transistors.
Because humanity stalled out- and coasted for the last 50 years and it shows. And now it must compete and git good or git gone. No more fat ponies paraded as race-horses.
The last 50 years is a wild take considering thats the time in with the computers became a bigger thing, mobile phones, smart phones. CRISPR, mRNA Vaccincs, HIV Antivirals, HPV Vaccines, We friggin confirmed the Higgs Boson and Gravity Waves. Found thousands of other worlds outside the solar system. Lithium Ion Batteries, the modern solar panel making solar power the cheapest energy source in most of the world. Blue LEDs making RGB LEDs a thing. Reusable Orbital Rockets.
Am keeping my Codex sub while I find the best local model, but my plan is to eventually stop with OpenAI too.
reply