In this article, we break down the top 5 keynotes & insights from Friends of Search 2026, highlighting the most actionable takeaways for navigating the evolving SEO and AI-driven landscape.
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.
In the age of AI, the ultimate competitive advantage isn't technological fluency, it's human connection. Drawing from the recent Women@Artefact panel, this article explores how professionals can become "AI-proof" by leveraging inclusive tech like Fierté AI, freeing up time for strategic thinking, embracing authentic career journeys, and shifting from traditional mentoring to active sponsorship.
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.
As we look back on 2025, one thing is clear: artificial intelligence and data are no longer experimental tools sitting at the edges of organizations. They have moved decisively into the core of how businesses operate, compete, and create value. The pace of adoption tells the story. By the end of 2025, roughly one in six people worldwide had used generative AI tools, according to Microsoft’s AI Diffusion Report. In enterprises, momentum was even stronger, with nearly 70% of global organizations deploying generative AI in at least one business function by mid-year. What began as isolated pilots has rapidly evolved into embedded capabilities affecting decision-making, customer engagement, and operational efficiency.
In the high-stakes aviation landscape, On-Time Performance (OTP) is one of the primary levers for operational profitability. For a Tier-1 carrier, the financial impact of operational slippage has reached a critical threshold. As of 2024/2025, a single minute of delay costs an average of $100.76 in direct operating expenses.
