Opening Keynotes

AI systems are evolving at breakneck speed: new architectures, model weights, and deployment frameworks appear weekly. But which innovations actually move the needle for developers?

Artificial Analysis will go beyond leaderboard hype to unpack the real technical trade-offs shaping today’s AI frontier: reasoning efficiency vs. latency, cost vs. intelligence, open vs. closed weights, and throughput vs. accuracy.

Drawing on months of live benchmarking across models, frameworks, and chips, they’ll share data-driven insights on how these forces interact and what it means for anyone building or scaling AI-powered systems in production.

Expect benchmarks, performance graphs, and a candid look at where the frontier actually stands.

Master Stage

Benchmarking the frontier: what really matters in the new AI stack

Gain deep insights into their multi-agent AI architecture, observing how the system evaluates dynamic combat situations, formulates tactics, and executes maneuvers. The session will explicitly address the immense engineering complexities of bridging the gap from high-fidelity simulation to live flight operations. A technical leap akin to DeepMind's transition from virtual to physical agent control, but within the stringent demands of aviation safety and performance. Prepare for a technical deep-dive into real-life AI agent deployment at its most critical.

The first AI flying a production fighter jet

As AI agents move from experimentation to production, the real challenge isn't building a single agent BUT orchestrating fleets of specialized agents that collaborate with each other and humans. Dust, is building the infrastructure layer for this new paradigm.

In this talk, you'll explore the practical architecture decisions behind production agentic systems: how to pragmatically combine RAG, MCP, and context windows; manage permissions across high-stakes and low-risk workflows; design multi-agent architectures where agents coordinate to solve complex tasks; and tackle the thorny problem of evaluation when traditional benchmarks don't capture operational reality. 

Discover Dust's learnings about enabling agent-to-agent-to-human collaboration at scale.

From single agents to agent fleets: lessons from building Dust's agentic platform

As AI models scale, so does their hidden cost: CO₂.

While few teams have the tools or methodology to accurately measure the carbon impact of inference, new frameworks are emerging to make energy use visible and actionable.

In this session, Salesforce and Scaleway will share how they’re building the foundations for transparent and standardized measurement of AI’s environmental footprint:

Salesforce will present the AI Energy Score, a standardized method developed with Hugging Face to evaluate model energy efficiency.

Scaleway will show how it translates hardware and GPU utilization data into carbon metrics through its Environmental Footprint Calculator.

A practical deep dive for ML and infra engineers who want to make “energy-efficient AI” measurable, not mythical.

Beyond FLOPs: Engineering transparency into AI’s carbon footprint

In this talk, Bart Butler, CTO of Proton, will explore how Proton brings artificial‑intelligence capabilities to its privacy‑focused ecosystem while staying true to its zero‑access encryption principles. Drawing on the development of Lumo, the company’s conversational AI assistant, Bart will walk through the real‑world compromises and engineering decisions that make privacy‑preserving AI feasible at scale. Expect a deep dive into the challenges of combining security, scalability, and intelligence and how AI can be both powerful and privacy-first.

AI and Privacy: Building secure intelligence at scale

Steeve Morin of ZML will walk through how to build inference systems that run efficiently across a variety of hardware — from edge devices to servers — without sacrificing latency, throughput, or reliability. He will dive into architectural decisions, hardware constraints, trade-offs between generality and specialization, and strategies to make inference flexible, portable, and performant. Expect real insights from his work at ZML and lessons learned from putting inference into production at scale.

Inference Everywhere: optimizing performance

At Photoroom, building AI for creative tools means constantly experimenting and sometimes (pretty often actually) rebuilding from scratch. In this talk, Jon Almazan, Machine Learning Researcher at Photoroom, takes you behind the scenes of the team’s latest open-source text-to-image model, designed to push the limits of visual fidelity, controllability, and open reproducibility. He’ll share how the team iterated on training pipelines, data composition, and architectural tweaks to make the model both high-performing and publicly usable  and why open research remains essential for innovation in production environments. 

Expect a technical deep dive into what worked, what didn’t, and what it really means to build a new model when starting from zero.

Inside Photoroom’s Open-Source T2I model

In this session, Enrico Bertino, Chief AI Officer of Indigo.ai — the company behind Italy’s DistilBERTino and advanced conversational systems — will discuss how European teams are translating cutting-edge research into usable, scalable AI products.

From lab to product with European LLMs

MCP has grown to be a key component for AI agents connecting AI with the world. 

We will dive into the latest development of the stack including MCP, ACP, and ChatGPT apps… Then explore best practices and tips on how to build, deploy and monetize an MCP server in production.

Central room

A deep dive into MCP and ChatGPT apps

AI for video now spans two radically different architectures: 

Between them, broadcasters like France Télévisions must guarantee reliability, content provenance, and metadata integrity under production constraints.

This panel breaks down the engineering behind each layer. Inference pipelines, asynchronous rendering, edge vs cloud trade-offs, and how standards are reshaping video AI at scale.

A technical deep dive into how video AI systems are actually built, deployed, and trusted in production.

From edge inference to generative pipelines: building video AI that works

Pharmaceutical R&D is being reshaped by AI. Not through buzzwords, but through tangible engineering breakthroughs.

Biolevate and Owkin have built AI pipelines that accelerate molecular discovery, predict biological activity, and optimize clinical research at scale. These systems blend multimodal data (genomic, molecular, imaging) with large-scale model architectures to guide scientists toward more effective treatments.

This session explores how these AI models are deployed in real research environments:

Moderated by Sophie Monnier (InstaDeep), this discussion will go under the hood of AI-powered drug discovery: the data, the models, and the systems making faster, smarter R&D possible.

From molecules to models: How AI is redesigning pharmaceutical R&D

When you think cooling, you often think fans and CRAC units. But for end-to-end AI stacks: from chip through server to data center, the thermal challenge spans multiple layers. 

In this session we’ll map the full spectrum: 

We’ll bring case studies, benchmark data, and methodology insight from companies owning each layer of the chain. 

Cooling the AI stack from chip to cloud

In this hands-on workshop, participants will learn how to transform a simple photo of a handwritten restaurant menu into a structured, machine-readable inventory — the secret behind the flawless organisation of the event’s fictional coffee shop, Café de l’AI.

The session walks through each layer of a vision-driven workflow:

By the end of the workshop, attendees will have built a fully functional pipeline that turns an image into a restaurant inventory.

This structured menu will then serve as the knowledge base for the follow-up workshop, where participants build a voice-first AI agent capable of taking customer orders through WhatsApp.

Founders Café

Build the OCR-powered menu inventory for “Café de l’AI”

Inference speed is the bottleneck holding back the next generation of AI applications. Agentic workflows, real-time creation, and test-time compute scaling all demand much faster token generation at runtime.

This talk reveals Kog's path to 100x faster AI inference through systematic GPU optimization and novel Transformer architectural innovations that circumvent hardware constraints. When AI operates at 10,000 or 100,000 tokens per second, computers stop being static rule-execution engines and become smart, real-time adaptive platforms. Programming in natural language becomes practical. Complex games and applications generate themselves dynamically. Agents and Deep Research are instant.

Gaël, founder and CEO of Kog, will share the unique research insights and GPU engineering breakthroughs used to build the future of real-time AI computing

Real-time AI: building 100x faster inference

Learn how to run AI on truly green infrastructure and why sustainability starts with accurately measuring impact.

Discover practical methods to track energy usage, present transparent impact data, and engage stakeholders effectively.

Explore tools, architectures and user education strategies that make AI systems measurably greener and more responsible.

How to build a sustainable AI infrastructure

Designing a printed circuit board (PCB) is a classic example of a complex combinatorial problem: hundreds of components need to be connected without overlaps, while minimizing path lengths and respecting physical constraints. The number of possible configurations quickly becomes astronomical, making traditional approaches inefficient.

In this 15-minute demo, InstaDeep will showcase how Reinforcement Learning (RL), combined with their MCP framework, can tackle PCB routing by learning to “play the game” of placing and connecting components. Beyond PCB design, this approach illustrates how RL can be applied to real-world optimization problems in industries such as logistics, infrastructure, and even biology.

Solving complex combinatorial challenges with Reinforcement Learning: the PCB routing demo

In this talk by Dr. Antoine Jardin, researcher at CNRS and CTO of Arlequin, we explore what it means for information to be true and authentic in the age of generative AI. Fake news and misinformation is an ever-increasing threat. Defining safety and authenticity requires deep understanding of how models are trained and developed, and even the smartest or safest of models may still lack the ability to differentiate truth from fiction. Using a combination of unsupervised learning and multi-modal inference, Arlequin helps cut through the noise and identify narratives without inferring bias from the analysis. We will explore how combining methodologies will lead to a safer, more auditable and understandable superintelligence.

Inductive learning, AI safety, and superintelligence in the age of disinformation

In this hands-on session, participants will learn how to build a fully functional audio agent capable of taking customer orders orally through WhatsApp, for the event’s fictional restaurant "Café de l’AI"

The workshop will walk you through each layer of the system:

By the end of the session, attendees will have built a voice-first AI interface combining speech recognition, natural language understanding, and automation.

Build the audio agent for "Café de l'AI"

In this hands-on workshop, Adam Marszowski (Solutions Architect at SOFTIQ) will present a use case based on a real-world production deployment for an e-commerce client, showcasing how Computer-Using Agents can autonomously search the web, interact with diverse real-world interfaces, and generate qualified B2B leads without human intervention.

The session will demonstrate how to run such agents on Scaleway infrastructure, highlighting best practices for reliability, security, and performance at scale. Attendees will gain both a high-level understanding of how these systems operate and expert implementation tips drawn from a production-grade deployment.

Building computer-using agents for AI lead generation

For 30 years, video creation meant tools like Adobe or Canva: powerful, but manual. Then came AI prompting: you could just type what you wanted. But without structure, results often fell short.

Today, Aive takes us into a third era: video built from a brief, not just a prompt.

Their proprietary Multimodal Generative Technology (MGT) combines multiple AI models and meta-learning, framed by data.

After 6 years of R&D, MGT is here and it works! Join Alexandre Thomas, VP of Engineering at Aive, to open the hood of MGT and explore how this proprietary technology really works — a deep dive for experts eager to understand the mechanics behind next-gen video creation.

Decoding Aive's technology behind next-gen video transcreation: MGT

While generative AI has revolutionized how we work with unstructured data like text, images, and audio, the most important business assets remain in structured formats like tables and spreadsheets. These datasets are central to decision-making, powering predictive tasks that optimize processes across the entire business chain, from sales and marketing to product and operations.

Yet, data science teams handling these tasks still rely on traditional machine learning (ML) methods, often spending weeks, or even months, developing, training, and maintaining models. This can significantly slow down collaboration with business teams and delay crucial decisions that depend on the models’ outputs.

In this talk, Alexandre Pasquiou, co-founder of Neuralk AI (Future 40, Station F) will present a new generation of frontier models designed to accelerate the data science life cycle: tabular foundation models (TFMs). He will reveal NICL 2.0, Neuralk’s powerful new tabular foundation model, capable of generating instant predictions for any ML task without the need for retraining, cutting weeks off the typical pipeline. He will break down the innovative principle of in-context learning, which allows NICL to adapt on the fly to any new dataset or task using examples (“context”) provided at inference time, similar to how LLMs respond to prompts.

Tabular Foundation Models: the new frontier of data science

Agenda 2025

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