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

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

Resources and Links

All previous episodes of Early Adoptr can be found here or via your podcast player of choice: https://shows.acast.com/early-adoptr

Timestamps:

00:00 What We've Been Up to This Week 04:14 One Year of Early Adoptr: What We Got Right (and Wrong) 06:27 The Model Landscape: Why ChatGPT Lost the Top Spot 11:01 AI Pricing Is Changing — and Your Bill Is Going Up 16:20 Context Windows: From Headache to Non-Issue 20:03 When Smaller Is Better: The Case for Specialist AI Tools 20:35 Use Cases for Small Language Models 22:53 Agents: From Conference Buzzword to Actually Useful 29:02 What MCP Did for the Agent Ecosystem 31:29 Agentic Commerce and the Two-Tier Internet 37:55 How Brands Are Using AI for Discovery Without Losing the Customer 39:05 AI Security: How the Risk Picture Changed When Agents Got Real Access 44:17 Prompt Injection, Data Leaks, and What to Actually Watch For 48:26 From Subscriptions to Outcome-Based Pricing 50:22 AI Regulation, Memory, and the GDPR Question Nobody's Asking Yet 52:20 The Agent Debt Problem 56:12 What's Worth Watching: AI Predictions for the Next 12 Months

Get in Touch: [email protected] TikTok: @early_adoptr Instagram: @early_adoptr YouTube: @early_adoptr Get in touch with Early Adoptr: [email protected]

Follow Us on Socials & Resources:

IG: https://instagram.com/early_adoptr TikTok: https://tiktok.com/@early_adoptr YouTube: https://www.youtube.com/@early_adoptr Substack: https://substack.com/@earlyadoptrpod Hosted on Acast. See acast.com/privacy for more information.