The candidate's portfolio looked flawless. Clean code, comprehensive documentation, sophisticated architecture. Their resume hit every keyword. They articulated solutions beautifully on paper. But when I asked them to explain a basic design decision from their own project, they couldn't.

Welcome to hiring in 2025, where AI makes everyone look like a senior engineer.

Let me be clear: we should all be using AI in our workflows. As recruiters and founders, we're leveraging AI to scan resumes, draft job descriptions, and streamline our processes. The candidates we're evaluating should be using AI too—it's becoming table stakes for productivity.

But here's the challenge: when everyone's using AI to optimize their presentation, traditional evaluation signals break down. With 63% of developers now using AI tools in their development process, we need to evolve our evaluation methods. Your startup needs people who can think alongside AI, not just people who can prompt it effectively.

The goal isn't detecting AI use—it's identifying genuine capability and sound judgment underneath the AI-enhanced presentation. When everyone's resume looks professional, we need to get better at parsing signal from noise.

Look beyond the polished presentation. Focus on signals of intentionality and authentic experience when reviewing resumes and GitHub profiles.

Evidence of Intentional Effort Applications personalized to your specific company and role. Clear demonstration of research about your technical challenges. Writing that shows personality while remaining professional—they used AI to enhance their unique voice, not replace it.

Lived-In GitHub Presence Regular contribution patterns beyond just personal projects. Engagement with the broader developer community through issues, pull requests, and code reviews. Projects that evolved over time with visible iteration and improvement cycles.

Specific Impact Over Generic Claims Concrete metrics and business outcomes rather than abstract improvements. Technical challenges described with specific context about constraints and trade-offs. Clear connection between their actions and measurable results that actually matter.

The Copy-Paste Resume Epidemic You'll start seeing the same bullet points across multiple candidates. AI generates similar language, so you get identical phrases like "Spearheaded cross-functional initiatives that optimized performance metrics" appearing verbatim on different resumes. When everyone sounds the same, nobody stands out.

Missing Personal Voice Cover letters that could have been written by anyone for any company. It's not that candidates can't use AI to help write—it's that they're not adding anything unique to make themselves memorable. Generic applications that show zero research about your specific company or role.

Performative GitHub Activity Repositories that feel like they were built to showcase skills rather than solve real problems—projects that appear fully-formed without evidence of iteration, debugging, or learning from mistakes. GitHub profiles that look like they were assembled specifically for job applications rather than reflecting genuine building over time.

Buzzword Overload Without Substance Technical accomplishments described in abstract terms that could apply to any role at any company. "Enhanced system performance" without specific metrics. Industry jargon that doesn't connect to concrete examples of impact.

This is where AI-enhanced resumes meet human conversation. Time to verify whether the polished application reflects genuine capability.

Authentic Technical Depth Can walk through their debugging process with specific examples and tools. Explains technical decisions with clear reasoning about trade-offs and constraints. Shares specific frustrations they've encountered with technologies. Shows learning evolution: "I used to think X, but now I understand Y."

Real Problem-Solving Experience Provides concrete examples of approaching unfamiliar problems. Can discuss what didn't work and how they adapted their approach. Shows systematic thinking about complex technical challenges. Demonstrates understanding of business context behind technical decisions.

Effective AI Integration Can explain how they use AI tools as part of their workflow. Shows examples of improving upon or correcting AI-generated solutions. Understands limitations of AI tools and when to rely on human judgment. Demonstrates thoughtful validation processes for AI-assisted work.

Genuine Curiosity and Growth Can articulate specific things they've learned recently and how they learned them. Shows curiosity about your technical challenges and asks thoughtful follow-up questions. Admits knowledge gaps honestly and explains how they would research solutions.

Surface-Level Technical Understanding They can describe what they built but not why they made specific architectural choices. Unable to discuss trade-offs between different approaches. Cannot explain what would break if certain assumptions changed. Lacks strong opinions about tools and technologies they claim expertise in.

Disconnection Between Written and Verbal Skills Cannot elaborate on resume accomplishments when asked for specifics. Struggles to explain basic technical decisions from their own projects. Perfect written communication but difficulty articulating ideas in real-time conversation.

AI-Coached Response Patterns Responses that sound overly polished for casual conversation. Struggle when you ask follow-up questions that go off-script. Cannot explain their thought process in real-time. Defensive or evasive when asked about specific implementation details.

Generic Problem-Solving Stories Challenge stories that sound rehearsed or template-like. Cannot provide specific details when you dig deeper into their problem-solving process. No examples of failed approaches or lessons learned from mistakes.

The goal isn't to eliminate candidates who use AI—it's to identify candidates who use AI thoughtfully alongside strong human judgment. The strongest engineers I've hired recently didn't avoid AI—they used it more effectively than anyone else. They could explain when to rely on AI versus when to think from first principles, improved AI output with domain knowledge, and caught errors others missed.

Teams that master human-AI collaboration will significantly outperform those that don't. The most valuable hires in 2025 combine AI efficiency with critical thinking, domain expertise, and genuine technical curiosity.

As talent professionals, we need to model this behavior: use AI to enhance our processes while getting better at evaluating human judgment, problem-solving ability, and collaborative skills. When you focus on authentic experience signals in applications and validate real understanding through conversation, you'll identify the builders who can actually drive your company forward.

The companies that figure this out first will have significant competitive advantage over those still hiring based on pre-AI signals. While your competitors are looking for perfect portfolios, you're finding the people who can think, build, and adapt in an AI-augmented world.