Kolibri and Europe’s Emerging Industrial AI Ecosystem

Yesterday, German AI company Aleph Alpha released Kolibri, a new German-English language and reasoning model.

At first sight, this looks like another entry in an increasingly crowded field. Kolibri has 78 billion parameters in a Mixture-of-Experts architecture, with around 3.5 billion active for each token. It can reason, use tools and work with very long contexts. Its weights have been released under the Apache 2.0 licence, allowing organisations to run and modify the model themselves.

I don't know whether Kolibri will turn out to be an exceptional model. It may not beat the best models from OpenAI, Google or Anthropic across every benchmark.

But I increasingly think that is the wrong comparison.

Because Kolibri arrived just as I have been trying to understand something else: how research and development actually turns into technological capability.

And viewed through that lens, the model itself may not be the most interesting part.

The ability to make another one

I recently wrote that R&D was there all along, after realising that I had spent years looking at innovation largely through its outputs.

A company. A product. A technology. A successful scale-up.

But R&D doesn't only produce things. It produces capabilities.

A research programme that results in a new battery chemistry has obviously produced knowledge about that particular chemistry. But it has also produced researchers, methods, equipment, experimental data and an organisation that has learned how to investigate the next problem.

Kolibri can be viewed in much the same way.

Aleph Alpha describes the infrastructure behind it as a model factory: a reusable system for building and specialising models. Training pipelines have been automated. Evaluations run continuously. Experiments can be repeated. Hardware failures can be recovered from. The company says it moved from a 30-billion-parameter precursor to Kolibri in about three months.

That changes how I look at the release.

The interesting capability isn't merely that Aleph Alpha has produced this particular model. Models improve and become obsolete extraordinarily quickly.

It is that the people and organisations around it have accumulated knowledge, methods and infrastructure for building them.

And that capability did not emerge inside one company in isolation. Aleph Alpha participated in OpenGPT-X, a publicly funded research programme involving Fraunhofer institutes and other organisations. Its current development sits within another network of investors, infrastructure providers, researchers, suppliers and customers.

Already, the borders become harder to draw.

Then there is Lidl

There was another reason Kolibri caught my attention.

Last year I wrote about STACKIT, the cloud provider belonging to Schwarz Group, the company behind Lidl and Kaufland.

I hadn't expected to find a serious cloud infrastructure company behind a supermarket chain. But the more I looked at Schwarz, the less strange it became.

This is an enormous industrial organisation. Its businesses span retail, logistics, food production, recycling and digital services across many countries. Building substantial technological capabilities of its own starts to make sense at that scale.

Schwarz is also one of the major investors behind Aleph Alpha.

And suddenly some familiar pieces reconnect.

There is industrial capital. There is AI research and model development. There is STACKIT providing cloud infrastructure. And there is a huge collection of operating companies in which digital technology can actually be used.

The connections now stretch considerably further.

Earlier this year, Aleph Alpha and Canadian AI company Cohere announced plans to combine, with Schwarz Group committing €500 million in structured financing. In September, the companies signed the definitive combination agreement. Subject to regulatory approval, the combined company will operate globally as Cohere while retaining headquarters, R&D centres and leadership in both Canada and Germany.

STACKIT is intended to provide infrastructure for part of that offering.

This makes it difficult to tell a simple national story about Kolibri.

It was developed by a company based in Germany, with roots in German and European research programmes and substantial backing from Schwarz. But the emerging system around it crosses companies, countries and institutional boundaries.

That isn't a complication around the edges of the story.

It may be the story.

An LLM is only one piece

There is also a temptation to look at Kolibri and ask whether European industry now has another answer to ChatGPT.

I don't think that is particularly useful.

A language model isn't going to run a chemical plant, optimise a power grid or solve every engineering problem inside a car factory. Industrial AI includes computer vision, optimisation, simulation, control systems, predictive models, robotics and increasingly specialised scientific models.

An LLM is one component in a much larger technological environment.

But it could become an important one.

Industrial companies have accumulated enormous amounts of knowledge: engineering drawings, research results, maintenance histories, technical documentation, patents, contracts, software and decades of correspondence and decisions.

Increasingly, language models provide a way of working with that material and connecting it to other systems.

They can interpret documentation, call tools, interact with software, combine information from different sources and provide a natural-language layer over increasingly complicated technical environments.

The LLM is not the industrial intelligence.

It can become part of the machinery through which that intelligence is made usable.

The knowledge is somewhere else

For consumers, the calculation is fairly simple. If ChatGPT or Gemini gives the better answer, there may be little reason to care that another model can be run on infrastructure somewhere in Europe.

For an industrial company, the calculation can be different.

The valuable asset may not be the language model at all.

It may be thirty years of engineering knowledge.

That connects to another argument I have been making recently: knowledge is becoming infrastructure. AI becomes much more interesting inside an organisation when it can work with accumulated organisational knowledge rather than merely with what happened to be contained in its training data.

But that knowledge does not need to live inside the LLM. In fact, I have argued almost the opposite: the company does not live in the LLM.

The knowledge, models, operational systems and AI can remain distinct components.

The question is how they connect.

For an industrial company, that creates a different technological problem from choosing the best consumer chatbot. How do you combine increasingly powerful AI with proprietary knowledge, specialist models and operational systems while retaining enough control over the environment in which they come together?

An open-weight model that can be modified and deployed on infrastructure chosen by the organisation provides one possible component of that architecture.

Not the whole answer.

But a piece that can be adapted, operated and, importantly, replaced.

A pattern is appearing

Kolibri also joins a collection of European developments I have been following.

Switzerland's Apertus is an openly developed multilingual foundation model trained using Swiss supercomputing infrastructure. In the Netherlands, GPT-NL and investments in AI infrastructure represent a rather different attempt to preserve and develop AI capability. Aleph Alpha, Kolibri, Schwarz and STACKIT reveal yet another configuration.

I don't think these add up to a coherent European AI strategy.

They involve universities, governments, research institutes, commercial companies, industrial groups and international partnerships. Their motivations differ. Their technologies differ. Even the geographical boundaries become fuzzy as soon as you look closely.

That matters because I have become increasingly uncomfortable with treating technological sovereignty as a question of national ownership.

When I looked at Apertus, I initially found the cleanliness of the Swiss stack attractive. But I eventually arrived at a different idea: perhaps sovereignty is less about owning every component and more about optionality.

Can you switch models?

Can workloads move?

Does expertise exist in more than one organisation?

Can infrastructure be changed?

If one supplier disappears, does the rest of the system continue to work?

Kolibri reinforces that thought.

Its significance is not that "Germany now has an LLM". The model itself depends on a much wider technological world, while the organisations around it are becoming more internationally connected rather than less.

The more interesting question is what capabilities are accumulating inside that network.

What does an industrial economy need?

That leaves me with a question I find more useful than asking whether Europe has its own OpenAI:

What technological capabilities does an industrial economy need when AI becomes a general-purpose technology?

The answer is unlikely to be a single European foundation model.

It probably includes compute, cloud infrastructure, models, research capability, specialist AI, deployment expertise and the ability to combine all of those with existing industrial knowledge.

It also requires relationships between them: researchers moving between institutions, companies investing in technology, infrastructure being shared, knowledge travelling from research into industry and industrial problems finding their way back into research.

Some of the initiatives I am following will fail. Some models will disappear. Commercial relationships will change. Kolibri itself may turn out to be a relatively short-lived artefact in a field that moves extraordinarily quickly.

That is precisely why I find the capability underneath it more interesting.

Kolibri makes part of an emerging industrial AI ecosystem visible.

The model will be replaced.

What the network around it learns how to do may prove much more durable.