The article argues that AI is shrinking the value of credentials, so students should avoid resume-stuffing and focus on durable signal. In a world of scarce attention, clear positioning often beats more APs. It highlights two skills. Students need to state how they create value with proof, and ask sharp questions that reveal where opportunities are forming.

For the first, students pick an area, learn the basics, ship a small project, and share their work in a consistent public narrative that cuts through AI noise. For the second, they talk to practitioners, track where startups are hiring, and reach founders before roles hit public job boards and AI filters. The piece urges a few focused hours each week that compound over time, while noting that schools and policymakers still bear responsibility for the wider labor market shock.

Most large companies have formal policies, enterprise licenses, internal copilots, or approved tool stacks. In many sectors, AI is already embedded in day-to-day work. If adoption alone were the constraint, we should already see it in the productivity data.

And yet, the aggregate numbers remain underwhelming.

This tension is often framed as disappointment or hype fatigue. I think it is better understood as a timing and measurement problem. In this post, we will suggest a different, slightly more uncomfortable explanation to the fears of an AI bubble.