Season 1 · Episode 53 · May 20, 2026 · 58 min

A Year in AI: What Changed, What We Got Wrong, and What's Coming Next

Show Notes

A year in AI doesn't feel like a normal year, it feels like about five. The AI landscape has shifted faster in the last twelve months than most businesses could track, let alone act on. For Early Adoptr's first anniversary, we're taking the opportunity to map what actually changed: the model landscape, the rise of MCP, what happened to agents, and what's worth paying attention to in the next twelve months.

We close out with a look ahead at what's actually worth paying attention to: outcome-based pricing, orchestrated multi-agent systems reaching smaller businesses, and what we're calling agent debt, the accumulating consequences of workflows that were built in a hurry and haven't been stress-tested yet.

Thanks for being with us for the last year, and here's to the next 12 months!

What You'll Learn

Why reasoning models went from a premium add-on to the default , and what that shift enabled for agents and complex workflows
How context windows grew from a operational constraint to a non-issue, and what that unlocks for businesses working with large volumes of documents, contracts, or correspondence
Why smaller, more focused AI tools regularly outperform general-purpose models on the tasks they're built for, and what that means for how you structure your own stack
What MCP actually solved and why it's the reason agents went from demo-quality to deployable for non-technical teams
hat the two-tier internet is and why it decides whether AI recommends your business or your competitor's.
Why ChatGPT's instant checkout failed commercially and what it tells us about how brands are learning to use AI for discovery
What AEO — Answer Engine Optimisation — means for any business that needs to be found online
How the security risk picture changed once agents got real access to real tools via MCP
What agent debt is
What outcome-based pricing means