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Belgium 10-09-2026 Squad Only Physical english

Building the Infrastructure AI Needs: From computer and data access to scalability, cost control, and operational readiness.

AI is moving from pilots into operational use, and the infrastructure question is becoming more concrete. The challenge is deciding what needs to be built, expanded, controlled, or delayed as usage grows. Three pressure points usually appear first. - Compute capacity requires careful planning: teams need enough GPU, cloud, or specialised processing power, while keeping utilisation and cost under control. - Data movement becomes more demanding: AI needs access to data across systems, with latency, security, integration, and quality managed from the start. - Operational control becomes essential: monitoring, access rights, resilience, cost visibility, and ownership need to scale with usage. The working question is simple: how do we support AI at scale while keeping infrastructure efficient, governed, and financially sustainable? If you are working through these choices, let’s compare approaches with others facing similar constraints.

Belgium 15-09-2026 Squad Only Virtual english

Turning AI Pilots into Daily Workflows: How to move from experimentation to adoption by redesigning work, ownership, controls, and habits.

AI pilots are easier to launch than to embed into daily operations. The challenge is changing how work happens once AI becomes part of decisions, tasks, handovers, and controls. Three pressure points tend to decide whether adoption holds. - Workflow fit matters: AI has to enter the flow of work as part of the process. - Ownership needs to be clear: someone must own the process, the controls, the exceptions, and the outcome. - User trust has to be built: people need to know when to rely on AI, when to challenge it, and when to escalate. The working question is simple: how do we move AI from promising pilots into daily work while keeping clarity, accountability, and risk control? If this is part of your current reality, let’s compare choices, constraints, and lessons learned.

Belgium 17-09-2026 Country Members Physical french

Les Rencontres: CTO: Le Saint Graal?

Pendant longtemps, le TCO donnait une impression de contrôle. On investissait, on amortissait, on optimisait. Les coûts étaient visibles, relativement prévisibles, et structurés autour d’une logique claire entre CAPEX et OPEX. Ce modèle ne suffit plus à expliquer où va réellement l’argent. Aujourd’hui, une part croissante des dépenses IT ne repose plus sur ce que l’on possède, mais sur ce que l’on consomme: des tokens générés par l’IA des agents qui exécutent des actions en continu des outils SaaS qui se multiplient dans les équipes du cloud qui s’adapte en permanence à l’usage Le coût ne disparaît pas, mais il devient plus diffus, plus dynamique, et souvent plus difficile à attribuer, à expliquer, et à maîtriser. C’est là que des approches comme le Technology Business Management, ou TBM, redeviennent particulièrement utiles: non pas comme un exercice de reporting supplémentaire, mais comme un moyen de relier concrètement les dépenses aux services, aux usages, et aux décisions. Et c’est là aussi que la question du “Saint Graal” revient, mais sous une autre forme. Vous vous demandez peut-être: Comment garder de la maîtrise quand la dépense dépend du comportement, des usages, et parfois même de systèmes autonomes ? Comment piloter l’équilibre CAPEX/OPEX quand le cloud, le SaaS et l’IA déplacent progressivement les coûts vers des modèles variables ? Quels coûts restent encore trop souvent hors radar: ressources métier, formation, support, intégration, sécurité, gouvernance ? Et surtout, qu’est-ce qu’un “bon TCO” veut encore dire quand il faut le mettre en regard d’objectifs parfois plus stratégiques, plus longs à mesurer, ou plus difficiles à quantifier: qualité de service, résilience, agilité, expérience utilisateur, capacité d’innovation ? Ce sont précisément ces questions qui seront au cœur de notre rencontre: une discussion ouverte entre pairs, ancrée dans la réalité du terrain, pas pour débattre de modèles idéaux, mais pour comprendre comment chacun tente, concrètement, de mesurer, d’attribuer et de reprendre le contrôle, sans réduire la valeur d’un choix technologique à son seul coût.

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Anthropic trains LLMs and powers their language model assistant with Google Cloud infrastructure.

Anthropic Forges Partnership With Google Cloud to Help Deliver Reliable and Responsible AI

Leading AI startup leverages the power of Google Cloud TPUs and GPUs to accelerate safe, cutting-edge AI

SUNNYVALE, Calif., Feb. 3, 2023 /PRNewswire/ -- Google Cloud today announced a new partnership with Anthropic, an AI startup focused on safety and research. Anthropic has selected Google Cloud as its preferred cloud provider, giving it the compute power necessary to build reliable and trustworthy AI systems. Additionally, Google Cloud intends to build large-scale, next-generation TPU and GPU clusters that Anthropic plans to use to train and deploy its cutting-edge AI systems.

"We're thrilled to be able to partner with Google Cloud on training and deploying useful AI systems," said Anthropic CEO Dario Amodei. "We've been impressed with Google Cloud's open and flexible infrastructure. We are excited to partner with Google to scale-up our systems in the coming months."

Anthropic selected Google Cloud to benefit from the company's deep expertise in large-scale systems for machine learning, and as a partner with shared values around safe and beneficial development of AI. By leveraging Google's custom-developed machine learning systems designed to run computationally-intensive workloads, Anthropic will continue to conduct breakthrough AI research on the same infrastructure that powers Google Search and YouTube.

"At Google, we believe it is imperative to pursue AI boldly and responsibly," said James Manyika, SVP of Technology and Society, Google. "We are committed to developing and delivering useful and beneficial applications, applying responsible principles grounded in human values and safety, and evolving our approaches as we learn from research, experience, users, and the wider community. Our partnership with Anthropic is aligned with that philosophy."

"AI has evolved from academic research to become one of the biggest drivers of technological change, creating new opportunities for growth and improved services across all industries," said Thomas Kurian, CEO, Google Cloud. "Google Cloud is providing open infrastructure for the next generation of AI startups, and our partnership with Anthropic is a great example of how we're helping users and businesses tap into the power of reliable and responsible AI."

Anthropic was founded in January 2021 by the team behind AI breakthroughs such as GPT-3 and Reinforcement Learning from Human Feedback (RLHF). Since its founding, Anthropic has published 14 research papers showing how to build language models that are reliable and controllable.

In 2023, Anthropic began deploying its technology publicly, starting off with a language model assistant named Claude. Anthropic is working with a broad range of early partners on deploying Claude and will be expanding access to the assistant in the coming months. Claude pairs RLHF with a variety of safety techniques built by Anthropic to make AI systems that are predictable, steerable, and easier to interpret. Claude runs on Google Cloud as will other systems developed by Anthropic.

Additional Resources

Keep up with the latest Google Cloud news on our newsroom and blog.
Learn more about Google Cloud TPUs here.

About Google Cloud
Google Cloud accelerates every organization's ability to digitally transform its business. We deliver enterprise-grade solutions that leverage Google's cutting-edge technology – all on the cleanest cloud in the industry. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

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