Christine Käser-Chen

Researcher at @GoogleDeepMind, working on efficient long context models and agentic memory. All opinions are my own. 🏠: 🗽 and 🇨🇭

[Nando de Freitas @NandoDF]

No one should be surprised by this. The USA is doing what any self-interested nation state would do.

The real question is why are Europe, Canada, Australia, Korea, Japan and UK not able to compete seriously. That is the question everyone in government needs to answer.

And no, having a couple of startups that have raised $1B or $2B is FAR from enough to compete with $100B American companies. The scale matters. Imagine your sword’s length is 1cm and your rival’s 1m — no match.

Here is the harsh math (thanks to a poor version of Claude):

•10,000 GB200 superchips ≈ ~278 NVL72 racks.
•Each NVL72 rack costs roughly $3M–$3.5M.
•That puts the full-system total around $830M–$970M, before networking, power, cooling, and datacenter buildout.

That would enable you to train a model that was Sota 2 years ago. You need about 5 to 7 times this to compete today.
So the starting bill is $5B, but even if you have this, here is the reality: there’s no available chips. So when you hear someone raised $1B, remember this is going back to American compute, and is simply not enough.

The other two ingredients for AI are data and people.

American startups pay better than European ones, so the people vote with their feet so they can pay their mortgage and send kids to school. An experienced AI engineer makes double the salary in Europe by working for an American startup (like Anthropic) than a European one, and about ten times more if they work for a USA corporation. There are however amazing European startups, but the money and ambition is lacking.

The USA is far more relaxed with data and fair use - Canada is good too and @cohere is doing fine thanks to this. So American companies have a strong advantage over European ones. Brussels and the UK think they can hold the world to their questionable “ethical” views on data but they are just destroying the local AI industry, and in the process falling into a very precarious situation. They are partly responsible. Only the French minister has stood by their local LLM @MistralAI … and I guess more recently Germany has started to wake up.

The hope is of course LLM startups like @MistralAI and @cohere which are a year or so behind but can provide personalised services, and amazing startups like @cusp_ai @IneffableLabs @nscale @Orbital_Ind @bfl_ai and a few others. But for all these, it’s incredibly hard to compete.

[Christine Käser-Chen @kaeserchen]

I don't post often, but as a researcher with strong Europe ties, this sobering to read and to reflect on. There is a strong AI research community in Europe and many I know are ambitious and willing to build. But we need still 10x the ambition and 10x the investment, if not 100x.

[Michiel Bakker @bakkermichiel]

"What will happen to Europe if it keeps ignoring AI?"

Three American labs each (!!) operate more AI compute than all of Europe combined. Today we're launching Europe 2031: a story of what might happen if that doesn't change.

[Christine Käser-Chen @kaeserchen]

Our only co-authored publication: https://t.co/0cVCNgnScl

[Christine Käser-Chen @kaeserchen]

I had the privilege of working with @FelixHill84 at DeepMind. Despite me being a n00b when I first joined, he gave me the warmest welcome and invited me to join his group meetings and his projects. I learned so much from him. I wish we could have worked together more. RIP Felix.