Powerful AI models for biology exist, but the industry lacks a breakthrough user interface—a "ChatGPT for science"—that makes them accessible, trustworthy, and integrated into wet lab scientists' workflows. This adoption and translation problem is the biggest hurdle, not the raw capability of the AI models themselves.

OpenAI learned from its "Plugins" product that developers need control over their brand and user experience. The new Apps SDK allows custom UI components inside ChatGPT, a direct response to feedback that Plugins offered too little control, binding developers too tightly to the standard chat interface.

Browser-based ChatGPT cannot execute code or connect to external APIs, limiting its power. The Codex CLI unlocks these agentic capabilities, allowing it to interact with local files, run scripts, and connect to databases, making it a far more powerful tool for real-world tasks.

Contrary to popular narrative, Google's AI products have likely surpassed OpenAI in monthly users. By bundling AI into its existing ecosystem (2B users for AI Overviews, 650M for the Gemini app), Google leverages its massive distribution to win consumer adoption, even if user intent is less direct than visiting ChatGPT.

The best UI for an AI tool is a direct function of the underlying model's power. A more capable model unlocks more autonomous 'form factors.' For example, the sudden rise of CLI agents was only possible once models like Claude 3 became capable enough to reliably handle multi-step tasks.

The terminal-first interface of Claude Code wasn't a deliberate design choice. It emerged organically from prototyping an API client in the terminal, which unexpectedly revealed the power of giving an AI model direct access to the same tools (like bash) that a developer uses.

The history of AI tools shows that products launching with fewer restrictions to empower individual developers (e.g., Stable Diffusion) tend to capture mindshare and adoption faster than cautious, locked-down competitors (e.g., DALL-E). Early-stage velocity trumps enterprise-grade caution.

AI chat interfaces are often mistaken for simple, accessible tools. In reality, they are power-user interfaces that expose the raw capabilities of the underlying model. Achieving great results requires skill and virtuosity, much like mastering a complex tool.

AI development has evolved to where models can be directed using human-like language. Instead of complex prompt engineering or fine-tuning, developers can provide instructions, documentation, and context in plain English to guide the AI's behavior, democratizing access to sophisticated outcomes.