The AI Coding Crisis Series

AI coding tools are increasing output while hiding codebase drift, review overload, and lost mentorship. Bruce Tate argues teams should reinvest the time AI gives them in training and craft.

Bruce Tate traces how AI coding tools moved from helpful completion to unchecked delegation, leaving teams with faster output, weaker design, and less room for judgment to grow.

Bruce Tate argues the AI coding crisis is not a junior talent problem but a training failure, and outlines the concrete skills teams need to teach explicitly now.

Bruce Tate argues that code is no longer the core asset – judgment is. The organizations that win will protect senior craft, measure mentorship, and reinvest AI's dividend in training juniors into future seniors.

Bruce Tate argues that AI did not eliminate junior developers. It moved their work toward judgment, verification, architecture boundaries, and training that grows future seniors.

Bruce Tate argues that AI has not removed coding from junior development. It has made every keystroke an act of teaching, making planning, review, prompt discipline, and mentorship the new training ground.

Bruce Tate lays out Groxio's position on AI: use it where it strengthens human judgment, pay the full costs it creates, compensate the people whose work trained it, and reinvest the dividend in juniors and craft.

Bruce Tate argues that AI changes how we should choose programming languages: not by GitHub stars, but by problem fit, operational characteristics, abstractions, and training data consistency.

Bruce Tate argues that cheap AI coding tools are hiding two bills: the real cost of tokens and the maintenance cost of code built without human guidance.

Bruce Tate explains how AI is reshaping software training, why small teams need ongoing guidance, and how prompt architecture, Git, and human oversight keep agentic coding on track.

Bruce Tate explains why agentic coding fails when model priors, codebase evidence, and correction channels break down—and why explicit systems are easier to verify.

Bruce Tate examines the costs hidden behind AI's low price: data centers, uncompensated creative work, changes to human work, and concentrated wealth and power.