Artificial General Software

For decades, scientists have dreamed of artificial general intelligence (AGI)—AI that matches human thinking across any task. While we debate when AGI might arrive, something equally transformative is happening right now: the rise of artificial general software. As AI language models grow more powerful, we’re approaching a tipping point where one-size-fits-all AI agents could replace the specialized apps we use today.

The Problem with Today’s Software

Think about your typical workday. You probably jump between dozens of apps—Slack for messages, Google Docs for writing, Asana for tasks, Figma for design. Each app does one thing well, but switching between them is exhausting. Every time you switch, you lose focus and waste time remembering how each one works.

This fragmentation isn’t just annoying—it limits what we can accomplish. We spend more time managing our tools than using them to create and solve problems.

There’s a principle in machine learning called the “bitter lesson”: simple, flexible approaches eventually beat specialized solutions. The same might be true for software itself. Just as general AI promises to outperform narrow AI, general software could make today’s specialized apps obsolete.

From Clicking to Talking

Traditional software makes you learn its language. Want to edit a photo? Learn where Photoshop hides its tools. Need to manage a project? Master your project management app’s workflows. We adapt to software, not the other way around.

AI changes this completely. Instead of clicking through menus, you just say what you want: “Remove the background from this image” or “Set up a marketing project with our usual timeline.” The AI figures out how to make it happen.

This isn’t a small change—it’s revolutionary. When you can accomplish any task by describing it in plain language, traditional interfaces become unnecessary. Why learn complex software when you can just talk to it naturally?

What This Means for Software Companies

This shift threatens the business models of today’s software giants. Companies like Adobe and Microsoft stay dominant partly because their software is hard to replace—users invest years learning their tools and building workflows around them.

But when any AI can understand and execute your requests, these advantages disappear. Why stick with expensive, complex software when a general AI agent can do the same work through simple conversation?

The value in software will shift from building specific features to creating AI that truly understands what users want and delivers results reliably.

The Transition Period

This change won’t happen overnight. Here’s how it will likely unfold:

  1. First: AI assistants appear within existing apps (like GitHub Copilot for coding)
  2. Next: These assistants handle more tasks, making traditional features less important
  3. Eventually: The AI becomes the main interface, with traditional menus fading away
  4. Finally: Specialized apps disappear, replaced by general AI agents

We’re already seeing early examples. ChatGPT started as a chatbot but now handles everything from writing to coding to analysis. This is just the beginning.

New Challenges Ahead

While natural language interfaces are easier to use, they create new problems:

Unclear Boundaries: With traditional software, you can see all available options in menus. With AI, you’re never quite sure what’s possible. When you ask an AI to “improve this document,” what exactly will it do?

Building Trust: Users need to understand what AI agents can and can’t do, without overwhelming them with technical details.

Safety Concerns: When AI can perform any action based on your words, preventing mistakes becomes crucial. What if you accidentally ask for something harmful?

What Developers Will Do

As software becomes more general, the role of developers will change dramatically. Instead of building specific features, they’ll focus on:

  • Teaching AI to understand different domains better
  • Making AI responses more accurate and reliable
  • Building safety measures to prevent harmful actions
  • Creating ways for AI to explain what it’s doing

It’s similar to how machine learning shifted from manual feature engineering to designing better architectures—the work becomes more about shaping intelligence than coding specific behaviors.

Key Questions for the Future

As we move toward general software, several crucial questions emerge:

  1. Visual Interfaces: Will screens disappear entirely, or evolve into dynamic displays that adapt to each conversation?

  2. Measuring Capabilities: How do we describe what an AI can do when its abilities change based on context?

  3. Security: How do we protect data when AI agents work across all your information seamlessly?

  4. Business Models: What will software companies sell when anyone can build anything with AI?

  5. Education: How should we teach people to work with AI partners instead of traditional tools?

The Path Forward

The rise of artificial general software isn’t just another tech trend—it’s a fundamental shift in how humans and computers work together. We’re moving from a world of specialized tools to one of intelligent partners that understand our intentions and help us achieve them.

This transition will bring challenges. Some jobs will change dramatically. Privacy and security will need rethinking. We’ll need new ways to ensure AI acts according to human values.

But the potential is enormous. Imagine focusing entirely on what you want to accomplish, without worrying about how to use your tools. Imagine software that truly understands you and grows more helpful over time.

The age of clicking through menus and learning complex interfaces is ending. The age of simply saying what we want—and having it happen—is just beginning.