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

Happy Birthday to Us: The AI Landscape Changed Completely in a Year, Here's What You Missed

Show Notes

A year in AI doesn't feel like a normal year, it feels like about five.

In honour of Early Adoptr's first anniversary, we thought it was worth stopping to take stock of what's changed in the last year.

When we launched Early Adoptr, there was a pretty clear hierarchy, with ChatGPT was at the top, everyone else was catching up, and agents were something people talked about at conferences without anyone being entirely sure what they meant. Twelve months later, almost none of that is still true.

In this episode, we take a proper look back at what actually changed. From the model landscape and the slow collapse of the "ChatGPT is the Google of AI" era, to reasoning becoming the baseline rather than the premium tier, to context windows going from a genuine operational headache to essentially a non-issue, the foundations shifted faster than most businesses could keep up with. We also get into what happened with agents once MCP — Model Context Protocol — gave them a shared language to work with, why agentic commerce looks completely different to how we predicted it would, what Answer Engine Optimisation means for any business that needs to be found online, and what has changed with the security picture once agents got access to real tools.

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
What the two-tier internet looks like in practice and why it matters
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