How tool use became a substitute for solving hard problems
When OpenAI released o1 in 2024 and called it a "reasoning model," the industry celebrated a breakthrough. Finally, AI that could think step-by-step, solve complex problems, handle graduate-level mathematics.
But look closer at what's actually happening under the hood. When you ask o1 to multiply two large numbers, it doesn't calculate. It generates Python code, executes it in a sandbox, and returns the result. Unlike GPT-3, which at least attempted arithmetic internally (and often failed), o1 explicitly delegates computation to external tools.
This pattern extends everywhere. The autonomy in agentic AI? Chained tool calls like web searches, API invocations, database queries. The breakthrough isn't in the model's intelligence. It's in the orchestration layer coordinating external systems. Everything from reasoning to agentic AI is just a sophisticated application of code generation. These are not model improvements. They're engineering workarounds for models that stopped improving.
) depends on continued model improvement. What we're getting instead is increasingly elaborate plumbing for fundamentally stagnant foundations. This matters because the entire AI industry (from unicorn valuations to trillion-dollar GDP projections
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