While enterprises slowly adopt AI for workflow automation within existing structures, the frontier has moved to a new paradigm of on-demand capability creation via code generation. This isn't a difference in speed but in direction. The gap is no longer linear but compounding, as the two models of operation are fundamentally decoupling.
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.
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.
Dan Shipper proposes a practical, economic definition for AGI that sidesteps philosophical debates. We will have AGI when AI agents are so capable at continuous learning, memory management, and proactive work that the cognitive and economic cost of restarting them for each task outweighs the benefit of turning them off.
Modern AI coding agents allow non-technical and technical users alike to rapidly translate business problems into functional software. This shift means the primary question is no longer 'What tool can I use?' but 'Can I build a custom solution for this right now?' This dramatically shortens the cycle from idea to execution for everyone.
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.