Mistral raises €3B

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.

Consumer rights - 14 day cooling off period for online purchases

Sale of goods - 2 year seller liability for defective goods

Right to repair - likely doesn’t need explanation, require manufacturers to offer repairs and replacement parts

Unfair commercial practices - prevent misleading or deceptive advertising

Unfair contract terms directive - prevents disproportionately one-sided provisions in T&Cs (think Disney giving themselves legal immunity for a free Disney+ trial)

The EU’s problem seems to be in communicating its benefits to its citizens.

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!)

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.

I'd be curious to hear about specific examples of AI regulation that affect small businesses without a good reason.

Edit: forgot, they also call for banning Chinese models and open weights models...

We need a lot more hardware for universal LLMs (probably 10x, if not more), and we need it to be cheaper.

If anything, I would fund a DRAM maker in Europe.

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.

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.

Even those old llama models were ok for coding

Yes yes they won't be like Claude's fire and forget (until you see how many tokens you burned to write "Hello World")

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.

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.

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.

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.

-2013 DRM/always online drama (reversed pre launch)

-2023 shutdown of its physical release division, which is what triggered shift to digital only titles we are seeing now

Different year, different mechanism, different reception and different press coverage

*Please verify results with human based weather perception

I think that there's a real second mover advantage in letting others waste money on researching ultra oversized models while creating smaller and cheaper models from their learnings.

But for the vast majority of usage GLM 5.3 or Deepseek V4 is enough.

Even people who do need frontier model will soon restrict it to the use cases that really need it and switch to cheaper models for the rest. It has already started.

Anthropic and OpenAI will never get enough customers paying top dollar to deliver on the revenue they need to offset their investments.

Note that I'm not singling out Chinese models here. The same question and concern should apply to any model you use - in security critical scenarios, you need to ask yourself if you can trust the provider, since the artifact, the model itself, is far too complex to audit and trust.

Given most AI companies' ties to the weapons industry and spying apparatus of their host countries, it would be absurd to think that burying some kind of malicious payload in the models has not at least been contemplated, and I would bet it has been at least attempted. We know retrospectively this was the case with many previous technologies (remember the backdoored encryption that the NSA tried for so long to push), so it would be naive to think it's not at least plausible with AI.

Now, that is quite a lot of people. But not nearly enough to reverse decades of brain drain.

Salaries are calculated differently in France, Germany, US etc. For example working in Germany you're likely to fork a huge amount monthly to your health insurance. It actually makes it difficult to negotiate comp across borders.

$55k USD brut in France is around 2.8k EUR / month after taxes in France, which counts as the upper bracket of Middle Class in most of the country outside of Paris. Paltry when you're used to tech comps but decent for the vast majority of people here.

On the whole I agree that it's not competitive regionally or globally, and as for myself I'd leave for Switzerland or Australia if I had the opportunity.

Then we got a PM, who's father in law is the founder of Infosys, who changed the law to favour the likes of Infosys (by making it too risky for smaller players to hire contractors). Scandalous corruption

Contracting was going to be my plan for buying a better house and early retirement, but that's gone now. Thanks Rishi

The others are valid though.

Its usually 15 days paid time off, and unlimited unpaid.

https://www.levels.fyi/benefits/PTO-Vacation-Personal-Days/#...

Most companies start at 3 weeks + 12 bank-ish holidays (or you get unlimited - which you typically have zero issues taking 5+ weeks if you're competent and your boss isn't a dickbag). Most places also give you more PTO with tenure - Amazon does, Meta doesn't.

OpenAI, Anthropic, China

> So it's not a skill issue?

I meant by offering sovereign cloud/inference, not for training. But even if it was for training, Chinese labs have limited supply of GPUs, look what they've done. So it is a skill issue.

Also to clarify, I didn't mean European engineers' skills, I meant "you get what you pay for" as a company, that's why I hope they start offering better compensation to retain talent.

I know AI has more regulations here in the EU, but what I don't see clearly explained is what impact that has on pure research: creating new comparable models. So I'm suspicious that it's really impactful. On market deployment, maybe - that's where regulations lie. On internal company research?

But the US and China are incredibly lenient on enforcing even existing laws against their AI companies. In the US the standard seems to be "no instructions to make bombs or drugs, and the ability to make porn of living people has to be restricted to paid users". Laws around cyber security and copyright that used to be heavily enforced are seemingly suspended for AI companies, or settled out of court. No company that tries to actually follow the law can compete with that

Multiple US labs were taken to court over copyright infringment for their training data. Mistral did the same data mining, but actually the copyright situation is weaker here so it's harder to build a case against them.

I think the capital situation in EU is just worse. A lot of is tied up in more conservative businesses that are reluctant to bet the house on some shiny new thing.

Is that not self evident by the insane revenue from frontier labs?

You might as well pretend there's no money in search or social media.

1999. That Google, it has no serious business model, it'll never make real money. Hello GoTo.

2006. That Facebook, it has no serious business model, it'll never make real money.

One more: 2020. That Uber, I could clone an MVP and kill them over the weekend if I really wanted to. That's a side project at best. They're bleeding to death anyway, losing money hand over fist, they'll never make it. They have no moat at all.

We're also seeing behemoths like Google and Meta both fail to keep up with OpenAI and Anthropic. Microsoft has given up on SOTA training. Loads of once promising LLM training companies are no longer relevant at the SOTA stage such as Cohere, Mistral.

I've said this countless times but SOTA LLM market looks like a classic monopoly/duopoly market over time. Each generation requires magnitudes more resources to train and you can only compete if you've made enough money on your previous generation.