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Decades of software development created established patterns and best practices. Steve Newman argues AI invalidates many of them. The most valuable engineers now are not those who know the old rulebook, but those who are comfortable with ambiguity, can think outside the box, and can invent new methods on the fly in a world without a map.
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With AI automating routine coding, the value of junior developers as inexpensive labor for simple tasks is diminishing. Companies will now hire juniors based on their creative problem-solving abilities and learning mindset, as they transition from being 'coders' to 'problem solvers who talk to computers.'
With AI trivializing the mechanical act of writing code, the most valuable traits for emerging engineers are no longer just technical proficiency. Instead, employers will seek demonstrated agency (the drive to build), taste (knowing what to build), and a commitment to quality.
Theoretical knowledge is now just a prerequisite, not the key to getting hired in AI. Companies demand candidates who can demonstrate practical, day-one skills in building, deploying, and maintaining real, scalable AI systems. The ability to build is the new currency.
Traditional software engineering valued meticulous upfront planning to avoid costly coding and debugging cycles. Newman argues that with AI agents, the cost of building and iterating is so low that the old "measure twice, cut once" philosophy is obsolete. The superior modern approach is to build quickly, even incorrectly, and rapidly iterate.
For roles leveraging new technologies like AI, where tools are nascent and constantly changing, competency is a fleeting metric. Instead, hire for curiosity. A curious mind will adapt, learn, and master new tools as they emerge, making them a more valuable long-term asset.
Many software development conventions, like 'clean code' rules, are unproven beliefs, not empirical facts. AI interacts with code differently, so engineers must have the humility to question these foundational principles, as what's 'good code' for an LLM may differ from what's good for a human.
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.
Top engineers are no longer just coding specialists. They are hybrids who cross disciplines—combining product sense, infrastructure knowledge, design skills, and user empathy. AI handles the specialized coding, elevating the value of broad, system-level thinking.
In a paradigm shift like AI, an experienced hire's knowledge can become obsolete. It's often better to hire a hungry junior employee. Their lack of preconceived notions, combined with a high learning velocity powered by AI tools, allows them to surpass seasoned professionals who must unlearn outdated workflows.
