Not a single day goes by without a new record investment in the field of artificial intelligence. The unit of measurement is no longer billions, but trillions and beyond. The arms race between tech giants seems limitless – and in any case defies the physical limits of our planet.

AI is infiltrating all areas of human activity at an unimaginable speed. Mastering artificial intelligence is the most significant quantum leap since the advent of the atomic bomb.

Does the future belong to the American and Chinese tech giants? Are Switzerland and Europe doomed to follow and submit?

There is an alternative. Sovereign, collaborative, efficient and ethical.

Giotto.ai seeks to exceed the current capabilities of AI to push the boundaries towards artificial intelligence capable of going beyond memorisation – towards reflection. Giotto.ai is developing technology that stands out for its ability to generalise tasks, solve problems and offer transformative potential for industries and society in general.

Way more efficient than large LLMs and infinitely less energy-and data-intensive, the solution advocated by Giotto.ai is based on two fundamental pillars: sovereignty and efficiency.

- Yes, it is possible to escape the frantic race for resources and infrastructure.
- Yes, it is possible to assert Swiss and European leadership in artificial intelligence that serves society, democracy and humanity.

Switzerland and Europe have all the talent, universities and centres of expertise needed to succeed. Together, let's create networks of investors, developers and conditions to control our own destiny.

Submitting is not an option. We can decide.

Problem:

Kaufmann argues that humanity stands at a turning point: machines and Generative AI will soon outperform us in many routine and even complex tasks, while our social, economic, and cultural systems are still built around compulsory work and industrial‑age roles of “Homo faber.” Without a new vision, fears of job loss, loss of control, and foreign dominance (China/USA) over AI systems will shape the future instead of our own European values.​

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Approach:

Kaufmann reframes robots and Generative AI as tools, like excavators or calculators, without intrinsic power fantasies, and insists we design them to augment humans rather than replace them. He sketches a near future where every person works with several humanoid or software agents that handle routine tasks, enabling humans to focus on uniquely human, hard‑to‑automate activities. He also calls for sovereign Swiss and European AI (e.g., SwissGPT, AlpineAI) to embed local values, privacy, and trust into foundational Generative AI infrastructure.​

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Key takeaways:

• Generative AI can trigger a shift from “Homo faber” to “Homo gaudens,” freeing people to pursue meaningful work and curiosity instead of mere survival.

• A “Eutopia” – a realistic, golden age – is possible if productivity gains from AI are used to reduce compulsory work and secure public finances.

• Trust, culture, and data protection will be the decisive “currency” in the global race for AI; Switzerland can lead by building reliable, privacy‑preserving Generative AI systems for governments, hospitals, and universities.

Large language models can reason, generate code, and hold conversations. But they remain trapped behind screens. If AI is to become truly useful in our daily environments, it needs a body, a presence, and natural ways to interact with us.
In this talk, I’ll present Reachy Mini, an open and developer-friendly robot designed to explore what embodied AI can look like today. I’ll walk through how we are building its core software stack, from multimodal perception to real-time voice interaction, and why voice is emerging as the most natural interface for physical AI. We will look at how speech-to-speech pipelines, local inference, and modular backends allow Reachy Mini to move beyond scripted demos and into responsive, real-world interaction.
You will leave with a clearer understanding of what it takes to give AI a body, why voice-first interfaces matter, and how open tools can accelerate the next generation of interactive robotics.

Problem: humanoid robots and Physical AI seem to be coming into our lives any moment. At the same time, adoption beyond pilot projects is scarce. How ready are humanoids to enter our homes and industry? Approach: we will look into how we actually teach AI models to control robot bodies; the strengths and limitations of current methods. How do we ask the right questions to get the most value out of Physical AI today? Key takeaways: identify how to extract the most value of Physical AI and robotics today.

The landscape of generative AI is evolving rapidly. In this interactive session, Omar will guide you through the Google DeepMind ecosystem. We will explore the latest advancements in the Gemini family of models, focusing on capabilities and applications like image generation and agentic features. Whether you are looking to integrate open models like Gemma or leverage enterprise-grade APIs, you will leave with a clear understanding of how to start building with state-of-the-art models.

Generative AI has changed how we interact with computers through language. The next step is AI that can use computers to complete real tasks.

In this talk, Gautier Cloix, CEO of H Company, shares his vision of this shift: from AI assistants to systems that can understand interfaces, navigate software, and execute real tasks.

He will explain what computer use means, how H approaches it differently from other AI solutions, and what it enables in practice.

Through concrete examples, the session will show how AI can interact with tools, automate workflows, and augment human work.

Einstein failed physics in Zurich. Pauli was thrown out of lectures for being too disruptive. Your LLM would have gotten straight A's. And that's exactly the problem. GenAI is the best student science has ever produced — obedient, encyclopedic, and utterly incapable of the one thing that actually moves humanity forward: being wrong in a way nobody saw coming. Discovery is not retrieval. It is not pattern completion. It is the moment reality violently disagrees with everything you were trained on. GenAI has never had that moment. It never will. You cannot hallucinate your way to a Nobel Prize — trust me, it's tried. The path forward isn't a bigger model. It's a machine that can walk into a lab, run an experiment, and be genuinely surprised. Physical AI isn't the destination — it's the first honest admission that intelligence without a body is just very expensive autocomplete. AGI is not next year. It is not GPT-whatever. It is waiting for us to stop mistaking fluency for thought. Zurich built the ideas that shaped the 20th century by tolerating its most difficult, disruptive minds. The question for this room is simple: Are we building the next Einstein — or just a very confident search engine? Come ready to argue.