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You’re already behind if you’re still vetting engineers like it’s 2021. We’re living through what I believe will be the most transformative technological shift of our lifetime, even bigger than the Internet.
The AI revolution is accelerating at a pace most of us can’t even fathom. It’s not hype. It’s a recalibration of what it means to build, create and work. Founders who prepare now will lead in what comes next. Those who don’t will find themselves outpaced by five-person AI-native startups that operate with 10X the speed and precision.
So, how do you hire developers in this era of acceleration?
You don’t screen them for how well they write code. You screen them to see how well they orchestrate it. Let me explain.
AI fluency is actually the new literacy
Every founder wants an “AI developer.” But that term can mean many things. Are you looking for someone to build large language models (LLMs) in Python? Or someone skilled at leveraging AI tools to boost velocity and reduce bugs?
Most companies need the second. But they don’t always know how to ask for it. That’s why AI fluency, or how well a developer can navigate and leverage a wide range of AI tools, is becoming as critical as knowing a specific language or framework.
The tooling will keep changing. But the meta-skill of learning how to use new AI assistants, evaluate their output, and incorporate that into your workflow? That’s the durable advantage.
What’s an AI-orchestrator, and why do you need one?
An AI orchestrator is today’s essential developer archetype. They don’t manually write every line of code — they prompt, critique, debug and refactor AI-generated output. They understand when to delegate to machines and when to apply their judgment. And they know how to communicate with AI agents like coworkers.
At the same time, while AI is fast, it’s not always right. And it certainly doesn’t know your company’s specific needs. So the traits you’ll want to prioritize in hiring are:
- Architecture — The ability to zoom out and design systems at a high level.
- Critical thinking — Evaluating trade-offs, making good decisions and choosing the right tools for the job.
- Communication — This is the big one. How well can you explain your thinking to a robot? AI doesn’t do heuristics. You won’t get what you need if you can’t articulate what you want.
Just like we didn’t stop teaching math because calculators exist, we can’t abandon foundational programming skills just because AI writes code. We need developers who understand the architecture, know when to trust AI and know when to step in and fix what’s broken.
4 ways to assess an engineer’s AI competency
In response to the proliferation of AI tools, my company has overhauled how we screen technical talent. The traditional process of technical interviews, algorithm challenges and language-specific coding tests just doesn’t cut it anymore.
Here’s what to do instead:
- Simulate real-world problem-solving. Ask candidates to build a feature or debug an issue, but don’t allow them to write any code themselves. Instead, require them to use tools like ChatGPT or Claude, sharing their screen the whole time so you can observe how they interact with the AI.
- Assess prompting. You’re not just looking for the right answer. You want to see how candidates frame the problem, prompt the AI and refine and iterate on its output. This exercise is more about determining a candidate’s clarity of thought and communication over syntax mastery.
- Verify authenticity. Yes, people will try to cheat by sharing screens with someone else, having someone impersonate them or resorting to deepfakes. That’s why you’ll want to insist upon full-screen sharing and having their camera turned on. Let developers know you’re not trying to pull a “gotcha” on them; you want to understand how they work with AI day-to-day.
- Test judgment. It’s easy to get working code from AI. The harder skill is knowing whether it’s good code, fits the system architecture, and is the right solution for the problem. Throughout all these steps, you’ll want to see if they can clear the bar of critical thinking over simple copy-pasting.
What to be mindful of amid AI adoption
My team used to assume that senior developers would get more out of AI. But what we found surprised us. In a series of surveys, junior developers reported high productivity gains from AI, but often lacked the judgment to catch flawed output. Senior developers, by contrast, were skeptical or cautious, which led to lower short-term gains.
So, we built training for each experience level. For juniors, it’s about slowing them down, helping them see where AI is steering them wrong. For seniors, it’s about educating them on integrating AI without losing control. In both cases, the goal is to unlock real productivity without compromising quality.
Accept that change creates opportunity
Yes, this transition to AI is scary. And yes, there will be turbulence. There will be jobs that fade and new ones that rise. But those who learn to screen, train and build teams around AI-enabled talent will write the future.
If you’re still hiring engineers for what they can do alone, you’re missing the point. Start hiring them based on how well they work with machines.
The future isn’t AI versus humans. It’s AI with humans, and those who adapt the fastest will win.
Jacqueline Samira is the founder and CEO of Howdy.com, which builds and manages elite software engineering teams across Latin America.
