How many apps did you open today? Now think about how many of those you used just to move information from one place to another.
There's a growing argument among engineers and product thinkers that the traditional "app" - a self-contained box with its own UI, login, and data silo - is a transitional form. Not the final shape of how we'll interact with software, but a workaround we've accepted because nothing better existed.
The logic goes like this: most of us still treat AI as a peripheral. You ask ChatGPT to draft an email, then copy-paste it into Gmail. You use Claude to analyze a spreadsheet, then manually update your project management tool with the findings. The AI does the thinking, but you do the plumbing between systems.
The Copy-Paste Tax
That manual shuttle between AI output and your actual systems of record is a tax on every interaction. And it's one that increasingly feels unnecessary.
Consider what happens when an AI model can directly read your calendar, pull context from your CRM, check your project board, and draft a response in your email client - all without you switching tabs. The individual apps don't disappear, but they stop being the thing you interact with. They become backends.
This is already happening in small ways. Microsoft Copilot reaches across Office apps. ChatGPT's plugin system (now custom GPTs) connects to third-party services. Zapier and n8n wire tools together with AI as the orchestration layer. Each of these chips away at the idea that you need to consciously navigate between separate applications.
What Actually Changes for Daily Users
For people who spend their days in productivity tools - marketers juggling Hootsuite, HubSpot, and Google Analytics, or freelancers bouncing between Notion, Calendly, and their invoicing app - the practical shift is this: the AI becomes the interface, and the apps become data sources.
Instead of opening Asana to check your tasks, you ask your AI assistant what needs attention today. Instead of logging into three analytics dashboards, you get a morning summary that pulls from all of them. The information finds you, shaped by context, rather than you hunting for it across tabs.
This isn't science fiction. Pieces of it work today. But the complete version requires something the industry hasn't solved: a universal data layer that lets AI read and write across all your tools with proper permissions and without each vendor building walls around their data.
The Practical Blocker
Here's the honest reality: every SaaS company's business model depends on you opening their app. Engagement metrics, upsell opportunities, and ad revenue all require eyeballs on their interface. An AI layer that abstracts away the app threatens that model directly.
So while the technology to dissolve app boundaries is arriving fast, the business incentives push the opposite direction. Expect a messy middle period where some tools embrace being AI-accessible backends (Notion's API, Linear's integrations) while others fight to keep you inside their walled garden.
For now, the most productive move is to lean into tools that play well with automation and AI orchestration. The apps that survive the transition will be the ones that are useful even when nobody's looking at their UI.