If 90% of code is now AI-generated, what happens to the engineers who never had to write it themselves? The ability to review code well, to catch what the LLM missed, depends on mental models that only come from practice. That tension sits at the center of this conversation.
Rob and Hywel dig into how software engineers actually develop expertise, why knowledge transfer and real learning are not the same thing, and what it means to grow junior engineers in an era when the reps that used to build skill are disappearing. They also get into what separates engineers who get serious leverage from AI tools from those who don't, and why the answer has less to do with prompting tricks than with understanding how LLMs actually work.
Hywel Carver is the founder and CEO of Skiller Whale, a human-led technical learning platform focused on building engineering skills through coached, hands-on practice. His teams have worked with organizations including Pleo, where one cohort saw a 235% productivity improvement through structured skills development.
Topics covered:
Why knowledge transfer is not the same as learning
Bloom's taxonomy and what it takes to evaluate code effectively
How AI adoption is changing the skill development path for junior engineers
What separates high-leverage AI users from low-leverage ones
Mental models as the durable foundation for navigating constant change
Whether agile principles still hold in an AI-accelerated world
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