OpenAI has quietly launched "skills" for its models, following the same open standard as Anthropic's Claude. This suggests a future where AI agent capabilities are reusable and interoperable across different platforms, making them significantly more powerful and easier to develop for.

While ChatGPT and Gemini chase mass adoption, Claude focuses on a "hyper-technical" user base. Features like Artifacts and Skills, while too complex for casual consumers, create a deep moat with engineers and prosumers who are willing to invest time in building complex workflows.

AI platforms using the same base model (e.g., Claude) can produce vastly different results. The key differentiator is the proprietary 'agent' layer built on top, which gives the model specific tools to interact with code (read, write, edit files). A superior agent leads to superior performance.

Claude Code can take a high-level goal, ask clarifying questions, and then independently work for over an hour to generate code and deploy a working website. This signals a shift from AI as a simple tool to AI as an autonomous agent capable of complex, multi-step projects.

Craig Hewitt argues ChatGPT is a consumer product. For serious business tasks, agentic AI tools like Manus (built on Claude) are superior, offering web browsing, data aggregation, and code generation that go far beyond a simple chat interface.

When testing models on the GDPVal benchmark, Artificial Analysis's simple agent harness allowed models like Claude to outperform their official web chatbot counterparts. This implies that bespoke chatbot environments are often constrained for cost or safety, limiting a model's full agentic capabilities which developers can unlock with custom tooling.

Block's CTO believes the key to building complex applications with AI isn't a single, powerful model. Instead, he predicts a future of "swarm intelligence"—where hundreds of smaller, cheaper, open-source agents work collaboratively, with their collective capability surpassing any individual large model.

While the U.S. leads in closed, proprietary AI models like OpenAI's, Chinese companies now dominate the leaderboards for open-source models. Because they are cheaper and easier to deploy, these Chinese models are seeing rapid global uptake, challenging the U.S.'s perceived lead in AI through wider diffusion and application.

The idea that one company will achieve AGI and dominate is challenged by current trends. The proliferation of powerful, specialized open-source models from global players suggests a future where AI technology is diverse and dispersed, not hoarded by a single entity.