At Panoramai's Real World AI panel, Vincent Favrat, Founder and Manager of Scale-Up-Factory, delivered the session's most visionary and strategically ambitious presentation. Drawing on philosophical concepts of planetary intelligence and leveraging Switzerland's precision innovation heritage, Favrat outlined a compelling blueprint for European AI competitiveness through revolutionary inference infrastructure that challenges conventional scaling assumptions.

The Philosophical Framework: Noosphere and Planetary Intelligence

The moderator positioned Favrat's vision within Pierre Teilhard de Chardin's concept of the noosphere—« a concept of Chardin and Warnowski that thematized that earth would be kind of visualized as three layers. One layer that is inert, that is the soil, the earth. One layer that is life, where we stand as humans. And the third layer that would be all encompassing and connecting billions of people and devices in a new type of intelligence. »

This philosophical foundation informed Favrat's practical approach to AI infrastructure, positioning inference capabilities not merely as technical solutions but as foundational elements for humanity's next evolutionary layer of awareness and connectivity.

The Strategic Question: Europe's AI Infrastructure Reality

Favrat opened his analysis with fundamental questions about European positioning in global AI competition. « When I was told to speak about real world AI, I thought I don't want to speak about humanoid robots or something like that, right? I want to think about something that worries me and something that gives me hope at the same time, » he explained, framing his presentation around both concerns and opportunities.

His core inquiry focused on enablers: « What is the enabler of real-world AI? And the answer to that for me is inference AI factories. » The follow-up question revealed his strategic ambition: « Can Switzerland play a role in this particular section and can it lead in Europe on this market that is the inference factories? »

The Investment Misalignment: Training vs. Inference

Favrat's most compelling analysis revealed a critical misalignment between AI investment patterns and real-world value creation. While acknowledging data quality and AI training importance, he identified inference as the overlooked critical component. « If you look at the life cycle of AI and what you invest in AI, actually 10% only is on the AI training and 90% is on adapting these models to the real life. 90%, not less than that, » he emphasized.

This insight reframes current European strategy. While the United States invests heavily in training infrastructure like Stargate and Europe commits 200 billion euros to five gigafactories, Favrat questioned the strategic focus. « The question that we can ask ourselves is what type of factories is that? Are they training factories or are they inference factories? And the answer is that it's mostly training factories. »

The strategic vulnerability becomes apparent when considering that earlier speakers had declared the race for LLMs over. Favrat's response: « Houston, we have a problem, right? Because the future is in this segment as well. »

The Swiss Solution: Precision Over Scale

Rather than competing with trillion-dollar US investments through scale, Favrat proposed leveraging Switzerland's traditional strength in precision innovation. « What I think we can do, that's a question mark, is revolutionize the hardware setup of inference. And the way I see it is that we can take 0.005% of what is spent in the U.S. on this large Stargate project and invest it in a very cutting-edge type of new factories, inference factories. »

The scale comparison illustrates Swiss competitive advantages: « If you have acres for Stargate, right, we need 25 square meters to build such a factory. So Pascal, I don't know if you have 25 square meters in the basement, but I guess it's manageable. »

The Technology Trinity: Canton Vaud Innovation

Favrat identified three complementary technologies from Canton Vaud that could be packaged into revolutionary inference infrastructure. Cerebras, founded by EPFL's Jean-Philippe Fricker, produces wafer-scale processors representing « the best performing HPC solution today for this type of applications for inference factories. » Despite raising 750 million and achieving breakthrough performance, it became a US company—« We are used to the story. »

Advanced cooling technology from EPFL addresses energy consumption challenges in massive data centers, while Exergo's waste heat recycling systems convert data center thermal output to heat neighborhoods and cities. Combined, these three technologies could create « the most powerful, greenest and most sovereign inference factory in the world. »

Performance Differential: Quantified Superiority

Favrat presented compelling performance data comparing current Azure capabilities to Cerebras systems for Llama4 processing. « 38, so we're speaking about on tokens per second, right? For Llama4 specifically, to 2,749. So it's not like it's not close raised, right? We speak about 20 times, 50 times, 100 times faster. »

This dramatic performance advantage positions Swiss technology not as incremental improvement but as transformational capability that « comes from here and we can do that here. »

Strategic Applications: Swiss Excellence Sectors

Favrat identified three sectors where Switzerland's expertise demands high-performance inference capabilities. Pharmaceutical research could achieve « up to 80 times faster drug discovery. » Financial services, particularly Geneva's high-frequency trading sector, could boost performance « with a factor 15 times. » Scientific research supporting climate modeling, genomics, and computational research at institutions like EPFL and ETH Zurich represents the third critical application area.

« Why? Because we have Lucerne, we have ETH, Zurich, we have EPFL, and many other institutions that are needing this type of powers, » Favrat explained, positioning academic excellence as both market demand and competitive advantage.

The Capital Allocation Challenge

Favrat's most sophisticated insight addressed European capital allocation rather than scarcity. « In Switzerland and Europe generally, we don't have a problem of money because we are extremely wealthy and there is a lot of capital. The problem is, it's another of another nature, is the misallocation of capital. »

The misallocation manifests in pension funds « building up piles of money and basically powering the US economy, the big techs of California. That means the money of all of you goes to power Meta and SpaceX and so on. » This creates a strategic vulnerability where European wealth finances American technological dominance.

The Scaling Vision: Network of Micro Factories

Favrat's ultimate vision transcends single facility deployment. « Can we do a blueprint of this technology here and then scale it in Europe to make a network of micro factories that are mighty powerful? » he challenged, positioning Switzerland's traditional approach of being « little and innovative » as a competitive advantage for distributed inference infrastructure.

The European scaling potential addresses sovereignty concerns while leveraging Swiss precision. « If we don't manage to do that, we will fail to become innovation deficiency drug for Europe. And that would be a shame of course. We have been inventing antidepressants and many other things. »

Sovereignty and Independence: The UBS Example

When addressing infrastructure dependence concerns, Favrat posed a provocative question about financial sector sovereignty. « Do you want like UBS to run, it's like taking platform GPT, I mean open AI for the banking system. Is that the Swiss sovereignty in terms of data protection or do we need something else? »

This example illustrates broader implications for European technological independence, positioning inference infrastructure as critical national security and economic sovereignty infrastructure rather than merely commercial advantage.

The Agentic Workflow Reality

Favrat grounded his vision in immediate market transformation. « We have to be consistent with the facts. The fact is that we are entering the agentic workflow era, and the power need for compute is going to explode. This is a fact, this is not a question. »

The choice becomes binary: « Either you build some European sovereign tech stack and factories or you outsource that to the US or other parts of the world, which is the case already very largely for companies of big size. »

Innovation Heritage: From Antidepressants to AI

Favrat concluded by connecting his vision to Switzerland's innovation heritage. « So it's better to pump up good energy and to solve real life problems in the real world AI, » he declared, positioning inference factories as continuing Switzerland's tradition of breakthrough innovations that solve global challenges.

The discussion was further enriched by practical considerations about infrastructure requirements. The moderator highlighted the explosive growth in token requirements for reasoning models, noting that companies with 200 developers using AI coding assistance would require 16 H100 GPUs, while current Swiss infrastructure provides only 4-8 GPUs, underscoring the infrastructure gap Favrat's vision aims to address.

Key Achievement: Favrat demonstrated how Switzerland can leverage its precision innovation heritage, strategic geographic positioning, and existing technological capabilities to create competitive advantages in AI inference infrastructure that address both immediate business needs and long-term European technological sovereignty while requiring minimal capital compared to competing approaches, positioning small-scale precision as superior to large-scale centralization for the distributed intelligence future.