Economics has a concept of the market for lemons about information asymmetry. In a market like used cars the seller has more information about the quality than the buyers. In these markets, the price converges to the average price, essentially making all lemons (bad cars) too expensive, but all normal cars too cheap (at least for the seller) as the market prices on expected value. The canonical approach to increase efficiency is to add various 3rd party inspections for quality or to have the seller provide some warranties or guarantees on quality.

How is this relevant to tech recruiting? In the past, prior to DOJ anti-trust action and rise of levels.fyi and teamblind, the information asymmetry favored employers, who understood economic value of a great engineer. But now, candidates have the information asymmetry. They know their performance reviews, promo path, and track record. They know if they are using AI to cheat during coding screens. They know (deep down or not) how they compare to peers, and with internet research they know exactly how much Two Sigma or Google pays.

To mitigate this information asymmetry big-tech uses 3rd party quality measures (hiring from same ~20 universities/PHD programs, hiring from alums of other FAANGs), developing similar behavioral and technical screens as each other, and trusting leveling of their peers. While this dramatically limits pool of potential candidates, it reduces ‘lemon’ risk.

Where does that leave other employers? If they recruit from the same pool of applicants, and follow the same processes, they are likely to compete for the same candidates. Which means they are much more likely to end with an adverse selection of candidates who don’t get perceived better offers (proverbially ‘lemons’) and turn out to be net negative. What can they do?

  1. Find a different pool: target a more diverse pool that has high quality candidates not served. Recruit outside SF, go on-campus outside of usual suspects, adjust requirements (years of experience, degree requirements - e.g accept industry experience instead of a PHD for an applied science role), find industry or role switchers (think finance/consulting to tech or PM to engineer). And after joining, diverse teams will bring more perspectives to the table.

  2. Find false negatives in the pool: find candidates that will perform the job better than FAANG thinks they will from interviews. Can approach this with more pleasant interview process for people intimidated in confrontational settings, or to offer a take-home or consulting project that mimics real work instead of the contrived settings available in most interviews. Most importantly must fit your actual culture.

  3. Win competitive candidates: make a compelling pitch to candidates that get competitive offers. Best way to do this is with a truly compelling mission candidates will believe in. Both directly and for peers - working with cynical or disinterested coworkers can be frustrating! And to offer more autonomy and impact. Lastly, should work to build the brand via contributing to engineering communities and conferences.

Overall, to compete for talent with FAANG best way is to do all three. Start with a compelling mission, search for diverse talent pools, and build alternative ways to evaluate talent.