We’re making the same mistake with AI that early car manufacturers made with automobiles – we’re building rehashed versions of existing applications instead of reimagining what’s possible.
As Pete Koomen points out in his essay AI Horseless Carriages, today’s AI software too often consists of gluing AI features onto existing UI and UX paradigms rather than thinking about new ways to use these capabilities. We’re treating AI like it needs to be used everywhere, regardless of suitability.
When all you have is a hammer, everything looks like a nail.
This isn’t just a historical parallel, but an inflection point in software development. Early cars were called “horseless carriages” because people couldn’t imagine transportation without horses. Today, we’re struggling to imagine software without traditional interfaces and workflows.
We’re trying to understand what it means to have AI deeply embedded in our work processes, but we’re still seeing through the lens of what we know.
The trap of relying on yesterday
The problem with our current approach is that we’re treating AI like a feature rather than a fundamental shift in how software works. Simple AI features like “summarize this” work well for basic tasks but fall short for complex analysis without proper context.
A true AI-first approach will create a persistent intelligence and orchestration layer to follow users across all their tools, adapting to their needs and preferences.
Future software will upgrade that annoying animated paperclip from late 90s Microsoft Word to a personal assistant that actually understands your work. Instead of building AI features into every application, we need to build a context-aware layer that enhances every tool you use.
The traditional approach embeds intelligence in each feature, while the new approach makes intelligence available across your entire toolset.
Setting a new user context
It’s now possible to create a fabric of knowledge and context that follows you across tools and helps you to excel wherever you are. Comic book fans might think of this as the amplification that a Kryptonian gets when irradiated by a yellow sun. The things you can do become superhuman (and not just in email).
The next big opportunity in software isn’t about building better features. It’s about creating a system where default functionality meets personalized context. Every user gets the same core capabilities, but how those capabilities are used and combined becomes unique to each person’s workflow and preferences.
The key difference? AI creates a fabric of capabilities that spans all software instantly. Users don’t need to learn every feature of every application. They need to establish clear boundaries and policies for how their AI assistant should operate. The focus shifts from learning software to teaching your AI how to work with you.
Where we’re going, we don’t need roads
As Pete Koomen writes, “When I use AI to build software I feel like I can create almost anything I can imagine very quickly. AI feels like a power tool. It’s a lot of fun. Many AI apps don’t feel like that. Their AI features feel tacked-on and useless, even counter-productive.”
Future interfaces will look like a combination of “standard features” and “configurable features”. A standard feature will behave as expected while a configurable feature will take inputs from the user’s AI tool context.
This personalization could take many forms. Some users might prefer voice commands, others typing. Some might work best in a notebook-style interface, others directly in the final application. The key is that users won’t just use software – they’ll shape it to their needs.
In short, the same way people interact with applications today is going to be prevalent in the future. The difference is that it’s the user creating an application fabric that establishes an AI and UX context for them across tools, rather than only the product team thinking about that process.
One unexpected outcome: some users will fulfill the product manager role by creating user contexts that other teams can use.
The Path Forward
Moving beyond horseless applications requires three fundamental shifts in how we think about software:
- Question Everything
- What assumptions about interfaces and workflows are holding us back?
- Which patterns are we copying without questioning their relevance?
- What constraints can AI help us eliminate?
- Start with First Principles
- What are we trying to accomplish?
- What’s the simplest way to get there?
- How can AI help us rethink the entire approach?
- Embrace the Unfamiliar
- Be willing to explore completely new interaction patterns
- Accept that some experiments will fail
- Look for opportunities to transform workflows across tools
We need to imagine what becomes possible to build when we are co-creating our application experience and building it across tools.
A Call to Action
The age of AI provides an opportunity to rethink how we build and use software.
The question isn’t “how can we add AI to our applications?” It’s “what kind of applications become possible in the age of AI?”
Let’s stop building horseless carriages and start imagining what transportation could be.
What’s the takeaway? Instead of adding AI features to existing applications, we need to build persistent intelligence layers that follow users across tools, adapting to their needs and enabling fundamentally new ways of working. That’s the future of software in the age of AI.
