Hiten Shah on How AI is Breaking Hiring: The Shift from Performance to Proof

In a recent LinkedIn post, Hiten Shah discusses a significant shift in the hiring landscape, arguing that artificial intelligence is fundamentally altering how candidates present themselves and challenging traditional evaluation methods. Shah highlights how AI tools enable applicants to appear qualified, making it increasingly difficult for companies to discern genuine ability from polished, AI-generated content.

“AI gave every applicant the ability to look qualified before they have proved anything. Resumes are AI-polished. Writing samples are AI-written. Coding tests get solved with quiet copilots. Behavioral interviews turn into scripted performances shaped by tools that know your question bank.”

Shah contends that this new reality presents a stark contrast to previous hiring eras, where human intuition and the observable ‘texture’ of work provided more reliable signals. He explains that while these older methods had limitations, they reflected genuine reality. AI, in his view, has flattened these distinctions, making it harder to differentiate between average and top performers.

The Illusion of Performance in the Age of AI

The core of Shah’s argument centers on the idea that AI creates a ‘simulation’ in the hiring funnel, where interviews become ‘theater’ and assessments become ‘artifacts.’ He emphasizes that the goal isn’t to penalize AI usage but to recognize that the early indicators are no longer trustworthy.

“The point is not to punish AI use. The point is to recognize that the early signals no longer tell you what you think they tell you,” Shah writes.

This phenomenon, according to Shah, means that average candidates can now appear as top performers, while genuine top talent might get lost in the noise. The traditional interview and assessment processes are becoming less effective at revealing the true capabilities of candidates.

Companies Adapting to the New Hiring Paradigm

Shah points to forward-thinking companies that are already adapting their hiring strategies to address these challenges. He notes that teams closest to the problem are moving away from traditional methods.

Moving Towards Reliable Evaluation Methods

Examples cited include:

• Trials revealing unassisted capability: Companies are increasingly using trials to see what candidates can do without AI assistance in real conditions.
• Paid sprints and contract trials: Firms like Linear use short, paid sprints, while Automattic employs longer contract trials to assess candidates over time.
• Trusted networks: Companies such as Stripe and Facebook lean on their existing networks, where the environment itself can recognize and validate talent.

Shah highlights Cursor as an example of a company taking this a step further by focusing on individuals rather than just roles. When a promising candidate emerges, the team engages them in live conversations and assigns small, immediate projects, allowing for rapid assessment of real collaboration and problem-solving skills.

“Real collaboration exposes what polished artifacts hide.”

The Future of Hiring: Clarity Through Action and Vouching

Looking ahead, Shah posits that hiring will move away from larger funnels towards ‘clearer loops’ and environments that quickly reveal a candidate’s suitability. He proposes a provocative question for businesses to consider:

“What would happen if you hired the one person each team member would vouch for without thinking.”

Shah suggests a practical first step: ask each team member for a single name and then involve that person in a small, real work task. This approach, he argues, allows for proof over mere performance. The most reliable hiring loops, according to Shah, are those that cannot be rehearsed – such as trusted introductions, genuine collaboration, and experiencing the actual work environment.

“If you want clarity, put people in motion and pay attention,” Shah concludes, advocating for active observation and engagement as the path to effective hiring in the AI era.