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The 2026 AI shift isn't what you think 🤖 Start documenting your processes NOW before this changes everything #AI #TechTrends #2026 #BusinessTips #AIRevolution
When ChatGPT was first released, there was this hope that we could just keep throwing more data, more compute at the problem and the models would just keep getting smarter and smarter and smarter. And while that's true to some extent, it hasn't happened nearly as fast as a lot of people hoped.
Then of course we got reasoning model. We could just throw more time at the problem and the models did keep getting smarter. And then we got agents and multiple agent architectures and models continue to get smarter and smarter and capabilities in each of those three areas will continue to advance.
And even though progress has slowed in each area, they all tend to compound. So we still see meaningful advances every couple months.
However, as we go into 2026, I think the biggest advancements we're gonna see are on the software layer. So far, these AI models have some specific limitations that don't seem to be going away anytime soon. These are mostly context limitations.
They just can't see the big picture the way a human can. And these are really hard problems to solve, but they're not gonna be solved by smarter AI models. We're gonna need really good software that bridges this gap for the AI.
This doesn't actually require any new technology. It's been possible all these years. It's just really hard to do.
But the more we learn about AI and these limitations, the more incentives there are to put in that hard work. And so now there are hundreds, thousands, maybe tens of thousands of startups working on these problems. And that's why I think in 2026 we're gonna start to see some really powerful software that effectively bridges this gap.
That being said, this software is not going to be plug and play. The gap this software is going to build is going to allow you to provide that context to the AI. And while the software will probably make it easier to provide that context, you still need that information.
This is why I think the best thing you can be doing right now is collecting a lot of data and writing really good SOPs. Identify all that important context that currently just lives in the heads of your team members. Get all those data ideas and processes out of your heads, document it, label it.
And then when the software does arrive, you'll be ready.
AI visibility work depends on matching the right model, search surface, or answer format to the business outcome being optimized.
Map each AI/search surface to a task: answer extraction, public copy, citation mining, checkout readiness, media generation, or customer-intent capture.
This source is connected to AI visibility and answer readiness.
When ChatGPT was first released, there was this hope that we could just keep throwing more data, more compute at the problem and the models would just keep getting smarter and smarter and smarter.