While large tech companies are shedding over-hired staff, the broader tech industry and non-tech sectors are aggressively hiring engineers. The net effect is an increase in engineering jobs, though demand has shifted towards full-stack and data-focused roles across a wider range of industries.

Short-term, AI amplifies senior engineers who can validate its output. Long-term, as AI tools improve and coding becomes a commodity, the advantage will shift. Junior developers who are native to AI tooling and don't have to "unlearn" old habits will become highly valuable, especially given their lower cost.

AI lowers the economic bar for building software, increasing the total market for development. Companies will need more high-leverage engineers to compete, creating a schism between those who adopt AI tools and those who fall behind and become obsolete.

Despite powerful new models, enterprises struggle to integrate them. OpenAI is hiring hundreds of 'forward-deployed engineers' to help corporations customize models and automate tasks. This highlights that human expertise is still critical for unlocking the business value of advanced AI, creating a new wave of high-skill jobs.

Contrary to fears that AI replaces entry-level jobs, companies will increasingly seek 'AI-native' young talent. These employees grew up with the technology and can apply it with a fluency their older peers lack. This makes them highly valuable 'super producers,' reversing the assumption that junior roles are at risk.

The 'cracked engineer' archetype is a direct response to AI's growing capabilities. As AI automates the work of average engineers, the value of human engineers shifts to exceptional tasks. Companies now prioritize hiring these highly productive superstars who can supervise multiple AI instances, as AI itself can handle the rest.

With AI handling much of the coding, the most valuable engineers are no longer just prolific coders. Companies now prioritize platform engineers who can make deep architectural choices and product engineers who can embed with customers to excel at requirements gathering, which becomes the new bottleneck.

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.

Contrary to popular belief, AI's initial disruptive impact is not on low-level clerical work but on the core tasks of average software developers. While elite AI talent commands unprecedented salaries, the broad base of software engineering is becoming a commoditized skill.