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 sport, data and AI are primarily associated with on-field performance: player analytics, tactical modelling and injury prevention. Technology has expanded the boundaries of athletic achievement, enabling athletes to push beyond previous limits. So why aren’t more sports organisations applying the same thinking to the business of sport? Sport is an intensely competitive entertainment industry where marginal gains in areas such as fan engagement, content, operations and commercial decision-making can matter as much as results.

The technology is real but immature. The trajectory is clear but the timeline is not. Most organizations deploying long-running AI in 2026 will learn expensive lessons. A few will gain genuine advantages. The difference will come down to three things: where they deploy, how they govern, and whether they understand what "autonomous" actually means in practice.

In late 2025, a technique with the absurd name "Ralph Wiggum" went viral among practitioners. The name comes from a Simpsons character—deliberately silly, because the core idea is almost embarrassingly simple.

In March 2025, a research organization called METR published a finding that got less attention than it deserved. They had been measuring something unfashionable: how long AI systems could work on tasks before they broke down. Not what they could do in a single interaction. METR wanted to know how long they could sustain coherent, useful effort.

We are living through a period of immense technological possibilities. Across industries, leaders are being inspired by what AI can achieve, yet realizing that value requires more than just installing new software. To bridge the gap between a successful proof-of-concept and scalable business value, we must embrace a spirit of process reinvention. The projects that succeed are those that treat AI not just as a tool, but as a catalyst for cultural evolution.