Hey {{First name|there}}! It’s Aaron.

For decades, creative software rewarded people who learned the tools.

What happens when the tools start learning you instead?

Last week, I was listening to an episode of The AI Report featuring Adobe Express VP Ian Wang. While the conversation covered Adobe's AI roadmap, one particular idea stayed with me long after the podcast ended.

Ian spoke about a future where creative software becomes fully conversational. Instead of learning complex interfaces, users simply describe what they want, and the software handles much of the heavy lifting behind the scenes.

At first, that sounds like another AI product announcement. Every week seems to bring a new model, feature, or assistant promising to make work faster.

But the more I thought about it, the more I felt the real story wasn't about conversational interfaces at all.

It was about context.

For years, software has expected us to adapt to its way of working. If you wanted to create a design in Photoshop or edit a video in Premiere Pro, you needed to learn the tool. The better you understood the software, the better your results.

AI is beginning to reverse that relationship. Instead of people learning software, software is starting to learn people. That shift is showing up everywhere.

A few weeks ago, I wrote about ChatGPT's memory upgrade and how the next AI battle may be personal. Adobe's announcement feels like the same shift appearing in a different part of the creative stack.

ChatGPT remembers previous conversations and scheduled tasks. Adobe is embedding AI assistants directly into creative applications. Meta is experimenting with AI-powered search experiences that understand what users are looking for rather than simply presenting a list of links.

These may look like separate product updates, but they all point in the same direction.

The companies building the next generation of AI tools are trying to understand more about their users. Not just what question they are asking today, but how they work, what they create, and what they are trying to achieve.

That's a strange thing for Adobe to want.

Adobe built its business on creative professionals investing years mastering Photoshop, Premiere Pro, and Illustrator. The learning curve wasn't just a barrier to entry. It was also a form of retention. Once you've invested years developing expertise in a platform, switching becomes difficult.

A conversational interface reduces that advantage. So why would Adobe move in that direction?

Because if they don't, someone else will.

If AI can generate a design, edit a video, or build a presentation through conversation alone, the value shifts away from knowing where every button lives. Adobe would rather lead that transition than risk becoming the company protecting an old workflow while competitors build a new one.

For creators, context may become the new competitive advantage.

An AI that understands your audience, brand assets, content strategy, and previous work will usually outperform an AI starting from a blank slate. The model matters, but context increasingly shapes the quality of the output.

There's a catch.

The more context you provide, the more useful AI becomes—and the harder it becomes to leave the platform holding that context.

This isn't unique to Adobe.

It's the same trade-off we are beginning to see with ChatGPT's memory, Claude's projects, and every AI assistant trying to become a permanent part of our workflow.

The more useful these systems become, the more important it is to think about where that knowledge lives—and what happens if you ever want to leave.

That doesn't mean creators should avoid these tools. The productivity gains are real and likely to increase over time.

But it does mean the conversation is becoming bigger than prompts, benchmarks, and model rankings.

The real competition may be over who owns the context layer.

Because once an AI understands your audience, your workflow, your assets, and your preferences, switching to another platform becomes much harder than simply opening a different app.

The first generation of creative software became indispensable because creators invested years learning the tools.

The next generation may become indispensable because the tools spend years learning the creator.

See you in the next one,

I hope you found value in today’s read. If you enjoy the content and want to support me, consider checking out today’s sponsor or buy me a coffee. It helps me keep creating great content for you.

Check out my previous posts here