OpenAI's team found that as code generation speed approaches real-time, the new constraint is the human capacity to verify correctness. The challenge shifts from creating code to reviewing and testing the massive output to ensure it's bug-free and meets requirements.

Snyk founder's new venture, TESOL, posits that AI will make code disposable. Instead of code being the source of truth, a durable, versioned 'spec' document defining requirements will become the core asset. AI agents will generate the implementation, fundamentally changing software development.

Interacting with powerful coding agents requires a new skill: specifying requirements with extreme clarity. The creative process will be driven less by writing code line-by-line and more by crafting unambiguous natural language prompts. This elevates clear specification as a core competency for software engineers.

Current AI coding assistants still require engineers to verify correctness. The future involves moving from this 'vibe coding' to a system where developers specify requirements in natural language. An AI, likely an EBM, would then generate formally verified code that is guaranteed to be logically compatible with the existing codebase.

The endgame for software development isn't just code completion, but an "AI factory." A chain of specialized agents will handle design, coding, review, and security. This requires an interoperable platform where different models can check each other's work, with humans as "agent managers."

Formal verification, the process of mathematically proving software correctness, has been too complex for widespread use. New AI models can now automate this, allowing developers to build systems with mathematical guarantees against certain bugs—a huge step for creating trust in high-stakes financial software.

The role of a senior developer is evolving. They now focus on defining outcomes by writing tests that a piece of code must accomplish. The AI then generates the actual implementation, allowing small teams to build complex systems in a fraction of the traditional time.

A new paradigm for AI-driven development is emerging where developers shift from meticulously reviewing every line of generated code to trusting robust systems they've built. By focusing on automated testing and review loops, they manage outcomes rather than micromanaging implementation.

A powerful technique for creating robust software plans is to use AI as an adversarial partner. After drafting a specification, prompt an AI to "tear it apart" by identifying underspecified or inconsistent points. Iterate on this process until the AI's feedback becomes niche, indicating a solid spec.