Over the past 18 months, there has been a rise in AI-enabled cheating services for technical interviews, such as Clu, which raised $5.3M and nears $1M ARR.
Success rates on LeetCode-style coding challenges are extremely high (up to 93%) among candidates interviewing at companies like Google and Meta.
About one in three interviews now involve candidates using AI assistants.
The current interview process often assesses who has the best AI assistant, not who has the best engineering skills.
Industry leaders note the shift: Sam Altman advocates learning AI tools, and Salesforce reported a 30% productivity boost after replacing some engineers with AI (though Glenfield questions the data).
Job competition is no longer only about pay but also about company brand, prestige, and stability, especially amidst frequent layoffs.
Top candidates are more likely to choose major tech divisions over startups due to perceived security and reputation.
Ideal candidates for AI development are creative, collaborative, and adept at working with AI, not just skilled at coding challenges.
These candidates build AI tools, use AI libraries, contribute to open source, and understand business impacts rather than focusing solely on coding puzzles.
LeetCode and similar approaches no longer measure the most relevant skills for actual job performance.
Glenfield suggests observing candidates’ collaboration with AI on real-world business scenarios, rather than on abstract algorithm puzzles.
New assessment methods could include evaluating delegation, ambiguity management, and adaptability to changing requirements.
Demonstrating a company's engineering culture and simulating real work environments during interviews can help assess better fit.
At DevDay, the approach emphasizes workplace simulations where candidates work alongside diverse AI agents (e.g., perfectionists, pragmatists, security experts, juniors needing mentorship).
These simulations place candidates in authentic settings, requiring day-to-day tradeoffs relevant to the company’s business domain.
Evaluation focuses on collaboration with AI, communication (e.g., pull requests, tickets), decision-making, mentoring, and adaptability.
Large companies like Google or Meta can "brute force" hiring, screening many candidates and offering high salaries to a select few.
Smaller companies face high costs for bad hires, with losses ranging from $20K to $60K, and cannot afford to hire engineers who can’t ship AI products.
AI may soon handle most mid-level engineering work, with reports suggesting entry-level roles are being eliminated.
While the nature of engineering jobs is changing, there will remain demand for roles emphasizing creativity, collaboration, business judgment, and working with AI.
DevDay collaborates with design partners interested in evolving their hiring practices and invites further discussion on this approach.