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.

Contrary to fears of job replacement, AI coding systems expand what software can achieve, fueling a surge in project complexity and ambition. This trend increases the overall volume of code and the need for high-level human oversight, resulting in continued growth for developer roles rather than a reduction.

The key to AI's economic disruption is its "task horizon"—how long an agent can work autonomously before failing. This metric is reportedly doubling every 4-7 months. As the horizon extends from minutes (code completion) to hours (module refactoring) and eventually days (full audits), AI agents unlock progressively larger portions of the information work economy.

Increased developer productivity from AI won't lead to fewer jobs. Instead, it mirrors the Jevons paradox seen with electricity: as building software becomes cheaper and faster, the demand for it will dramatically increase. This boosts investment in new projects and ultimately grows the entire software engineering industry.

The current disruptions and workflow changes in software engineering, such as AI's impact on business models like Tailwind's and tools like Stack Overflow, offer a direct preview of what other knowledge-based professions will experience. Observing how developers adapt provides a six-month glimpse into the future of AI-driven change across all industries.

Leading LLMs can now replicate a two-hour human software engineering task with 50% accuracy. This capability is doubling every seven months, signaling an urgent need for organizations to adapt their data infrastructure, security, and governance to leverage this exponential growth.

Instead of fearing job loss, focus on skills in industries with elastic demand. When AI makes workers 10x more productive in these fields (e.g., software), the market will demand 100x more output, increasing the need for skilled humans who can leverage AI.

The primary impact of AI coding tools is enabling non-coders to perform complex development tasks. For example, a hedge fund analyst can now build sophisticated financial models simply by describing the goal, democratizing software creation for domain experts without coding skills.

Experience alone no longer determines engineering productivity. An engineer's value is now a function of their experience plus their fluency with AI tools. Experienced coders who haven't adapted are now less valuable than AI-native recent graduates, who are in high demand.