On March 18, 2026, the French Corporate Community (FCC), convened networking breakfast entitled "IA : l’envers du décor" (AI: Behind the Scenes) at Artefact France Headquarters.
Imagine a CFO reviewing the quarterly cloud spend. The AI team presents a compelling chart: per-token inference costs have dropped 75% year-over-year. The models are faster, the APIs are cheaper, and the vendor is offering volume discounts. Everything points toward savings. Then the actual invoice arrives, and the total is higher than last quarter.
Artefact’s new ebook, Scaling Data Collaboration in the AI Era, explores how organizations can unlock greater value by breaking down data silos and enabling seamless collaboration across teams. As AI adoption accelerates, success depends not just on technology, but on the ability to connect data, people, and processes effectively. The ebook highlights how modern approaches to data collaboration empower faster insights, stronger governance, and more impactful AI-driven outcomes, turning data into a true strategic asset.
Human Resources is undergoing a fundamental shift from a reactive cost center to a proactive driver of value. Yet many organizations remain anchored in a minimalist approach to people analytics. While generative AI and autonomous agents are gaining traction across the enterprise, HR’s use of data is still often limited to basic turnover prediction.
For decades, placemaking in Britain has been governed as much by judgment as by methodology. Practitioners speak of "character", "vibrancy", and the elusive "feel" of a streetscape; qualities refined through experience, human use, and professional instinct rather than formalised metrics. The accomplished practitioner was often the one who had seen enough places to recognise what worked, even when the causal mechanisms remained partly intangible.
AI is reshaping professional practice across the built environment, and the surveying profession is no exception. With the Royal Institution of Chartered Surveyors (RICS) having published its first Responsible Use of Artificial Intelligence in Surveying Practice professional standard (effective from 9 March 2026) the question for many firms is no longer whether to engage with AI, but how to do so in a way that is compliant, considered, and professionally defensible.
Red Hat built a $34 billion business on Linux. IBM bought it. What the deal validated was a hypothesis that had held for four decades: that companies extracting enormous value from shared code would, in self-interest, keep funding the projects they depended on. That hypothesis is now under stress. Not because anyone decided to stop funding open source. Because the industry that funded it most — SaaS — is being dismantled by the industry that depends on it most — AI.
In a previous piece, I explored how eight independent research teams converged on the same insight: instead of building memory systems around the model, train the model itself to manage memory as a learned skill. Post-memory training — using reinforcement learning in the post-training phase — produces agents that decide what to store, delete, consolidate, and retrieve, all optimized against task completion.
Post-memory training has been a hyperfocus of mine over the past few months. If you have followed my recent writing on context management, memory architectures, and the recurring question of why agents degrade after turn 50, this article is where those threads converge. The original pattern was clear enough. Eight independent research teams arrived at the same conclusion: stop building memory systems around the model and train the model itself to manage memory as a learned skill. That convergence was significant.
