Andrew Wilkinson argues that advanced AI models have achieved AGI-like capabilities in programming. He quotes Anthropic's CEO, suggesting that the role of a programmer is shifting to that of an architect, and many current programmers are in denial because their paycheck depends on not understanding this shift.

In a world where anyone can generate software to solve a problem, the primary constraint on progress is no longer engineering capacity ('who can code'). Instead, competitive advantage shifts to creativity, judgment, and the quality of ideas. This fundamentally breaks traditional organizational structures built around resource allocation for execution.

As AI agents handle technical execution, the most valuable human skill becomes ideation. Replit CEO Amjad Massad predicts this will dissolve rigid corporate hierarchies in favor of adaptable teams of generalists who collaborate with autonomous AI tools to bring ideas to life.

The ability to code is not just another domain for AI; it's a meta-skill. An AI that can program can build tools on demand to solve problems in nearly any digital domain, effectively simulating general competence. This makes mastery of code a form of instrumental, functional AGI for most economically valuable work.

The next major advance for AI in software development is not just completing tasks, but deeply understanding entire codebases. This capability aims to "mind meld" the human with the AI, enabling them to collaboratively tackle problems that neither could solve alone.

Moving away from abstract definitions, Sequoia Capital's Pat Grady and Sonia Huang propose a functional definition of AGI: the ability to figure things out. This involves combining baseline knowledge (pre-training) with reasoning and the capacity to iterate over long horizons to solve a problem without a predefined script, as seen in emerging coding agents.

The evolution from AI autocomplete to chat is reaching its next phase: parallel agents. Replit's CEO Amjad Masad argues the next major productivity gain will come not from a single, better agent, but from environments where a developer manages tens of agents working simultaneously on different features.

Current AI progress isn't true, scalable intelligence but a 'brute force' effort. Amjad Masad contends models improve via massive, manual data labeling and contrived RL environments for specific tasks, a method he calls 'functional AGI,' not a fundamental crack in understanding intelligence.

To effectively interact with the world and use a computer, an AI is most powerful when it can write code. OpenAI's thesis is that even agents for non-technical users will be "coding agents" under the hood, as code is the most robust and versatile way for AI to perform tasks.