The New AI Stack: Why Models, Agents, and Skills Are Reshaping Software

In the span of two years, we've witnessed the birth of an entirely new computing paradigm. Just as the personal computer created a three-layer stack (hardware, operating system, applications), AI is crystallizing into its own fundamental layers.

This isn't just a metaphor—it's a practical framework for understanding where to invest your engineering effort, where value will accumulate, and how to build defensible AI products.

The Stack Defined

Layer 1: Models as Chips

Think of foundation models—GPT-4, Claude, Gemini—as the CPUs of the AI era. They're the raw computational substrate upon which everything else is built.

Microsoft CEO Satya Nadella captured this perfectly: "As AI accelerates, any advantage in model quality disappears fast, prices collapse." The implication is profound: raw model superiority no longer creates durable competitive advantage.

Just like Intel and AMD compete on incremental performance gains while most value flows to software, foundation models are becoming commoditized infrastructure. You don't build apps at the chip level—you build them on top.

Key insight: Don't compete at the model layer unless you have $100M+ in compute budget. Focus on layers above.

Layer 2: Agents as Operating Systems

If models are chips, agents are the operating systems that orchestrate them. ChatGPT, Claude Code, and Google's NotebookLM exemplify this—they provide unified interfaces through which users interact with AI capabilities.

These platforms:

• Manage context (the new memory management)
• Orchestrate tool calls (the new system APIs)
• Handle user experience (the new GUI)
• Control distribution (the new app store)

Just as Windows and iOS became the gatekeepers of software distribution, agent platforms will control access to AI capabilities. They take their cut, set the rules, and determine what gets promoted.

Key insight: Agent platforms are where distribution power concentrates. Partner strategically.

Layer 3: Skills as Killer Apps

Skills are where value is captured. Like the killer apps that drove PC adoption (spreadsheets, word processors, games), AI skills are modular capabilities that extend agent functionality for specific domains.

A skill might:

• Fill out PDF forms automatically
• Analyze legal documents for risks
• Process expense reports
• Generate code following your team's conventions

Skills are designed to be plugged into agents dynamically. They're the "apps" of the AI era—focused, valuable, and shareable.

Key insight: This is where you should build. Domain expertise + modular packaging = defensible value.

Why This Matters for You

For Developers

1. Stop building infrastructure. Unless you're Anthropic or OpenAI, you're not going to out-model them. Focus on what you know that they don't—your domain, your users, your workflows.

2. Master the agent layer. Understand how Claude Code, ChatGPT, and other agents work. Learn their skill formats, their limitations, their distribution mechanisms.

3. Ship skills 60x faster. With the right agent platform, you can go from idea to deployed capability in hours, not months. The iteration speed is unprecedented.

For Businesses

The economic implications are profound:

• Build vs. Buy calculus changes. Why build custom AI when you can compose existing skills?
• Talent requirements shift. You need people who understand skill composition, not just ML engineering.
• Competitive dynamics accelerate. First-mover advantage matters more when iteration is 60x faster.

The Historical Parallel

The framework mirrors the desktop-to-mobile transition. Remember when Apple's App Store launched? Within a decade, it had paid developers over $320 billion.

But Apple also:

• Took 30% of every transaction
• Changed rules that killed some apps overnight
• Promoted apps that served its interests

The same dynamics will play out in AI. Agent platforms will be gatekeepers. Skills that play by the rules will thrive. Those that don't will be deprecated.

The lesson: Build on platforms, but don't depend on them entirely. Cultivate direct relationships with users. Own your data advantage.

What to Build Now

If you're convinced this framework is right, here's where to start:

1. Pick a domain you know deeply. Legal? Healthcare? Finance? Developer tools? Your expertise is your moat.

2. Build a skill, not an app. Package your domain knowledge as a modular capability that can plug into existing agents.

3. Start with Claude Code. It has the most mature skill ecosystem, the best documentation, and the most developer-friendly approach.

4. Ship fast, iterate faster. The skill format is simple. You can go from idea to published skill in a day.

5. Build defensibility through data. The skill itself might be copied, but your user data, your iteration insights, your community—those compound.

Conclusion

The New AI Stack isn't just a metaphor—it's a map. Models are commoditizing. Agents are consolidating. Skills are where value will be captured.

The builders who understand this early will have a two-year head start. The ones who don't will be building at the wrong layer, competing on the wrong dimensions, and wondering why their AI products aren't gaining traction.

The question isn't whether to adopt this framework. It's how fast you can execute on it.

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