Hiring's Accidental War

One-sided fixes become weapons. The exit is collaborative.

It should be obvious that job searches have entered an adversarial phase. Nobody planned it, and AI accelerated it, but in a sense it was always there. What interests me is whether we can escape it, and if so, how. I have some instincts on this, that focus on the need to turn away from unnecessary adversarialism in hiring.

Avoidable Adversarialism

A job search is inherently adversarial within groups. Applicants compete against other applicants. Employers compete against other employers. But the relationship between an applicant and an employer is not inherently adversarial. There is a perfect match and those two matched pairs should want to discover each other.

When we talk about the adversarial nature of the modern job search, we should separate the inherent part from the avoidable part. The growing hostility between applicants and employers is avoidable. If we can avoid that we’ll collaborate. Job searches will resolve more quickly, saving money and pain. Workers will be happier and positioned to do the most good for themselves and their employers.

The devolution into adversarialism is clearly a consequence of limits to information exchange and processing. Employers started filtering resumes to minimize what they spent on hiring. Filters create something to defeat. If an applicant has a reasonable expectation that they’re a match for a role and they want it, they want to pass the filter. And since they may need to do this many times over, they have every incentive to do it efficiently.

The Historical Devolution of Hiring

Filtering predates computing. Long before anyone wrote a regex against a resume, recruitment teams read resumes by hand and made decisions from them. By necessity, those filters worked only with what appeared on the page, so candidates learned to structure a resume to pass them.

When resumes were reviewed by hand, there was some benefit to keyword matching. But there was a secondary method of passing the filter, weaving a story. A recruiter could lean on a strict rubric, or they could read for character, for an arc, for something that spoke to who the applicant was in a way that keyword matching couldn’t capture. That second channel encouraged investing in each application. Read the full job description, get to know the company, and write a cover letter specific to the role.

Human readers were also susceptible to word choice, which explains a lot of the fads in resume writing over the decades. Recruiters responded to certain modes of expression. “Synergistic” is the classic example, but the pattern runs deeper than any single buzzword. Applicants would notice an opening, exploit it. When recruiters realized they were being exploited a countertrend would set in.

The thing about that system, for all its gamesmanship, is that it evolved slowly. Trends and countertrends took hold one recruiter at a time. They might spread through HR conferences, trade publications, networks, but every update was individual. The arms race was real, but it moved at human speed.

Computerized Filters and Keyword Stuffing

Computerizing the filter degraded two things. First, the depth of processing collapsed. Matching became keyword-driven. Stories, arcs, and the patterns that needed a human to understand were no longer assessed.

Second, the motivation to invest in each application diminished because that investment was demoted to the second tier. If you passed the filter, then someone might read that and you’d benefit. But it was only rarely worth the effort with that extra distance.

This second part triggered an adversarial response. It became obvious to applicants that they were in a game, and that the game was everywhere. Callback rates fell from 10+% in the early 2010’s to 2% in the 2020’s. The decisions weren’t fair and weren’t optimal, so playing the game to its fullest felt like fair play. Resumes filled with keywords. Applications per candidate increased.

At first, human reviewers reacted by turning away obviously stuffed resumes forcing custom resumes per job with just the right keywords. Applicants had to balance to make these custom resumes not read like keyword-stuffed garbage to a person. This ate enormous amounts of applicant effort.

One page norms were initially strongly enforced. But then they gave out. The keyword filter wants more, because keywords need room to slide in while still appearing natural. Mainstream career advice shifted to two pages to leave room for the terms automated systems scan for.

The human reader, conventional wisdom holds, wants less, a clean document that doesn’t read as stuffed. But when a resume-writing firm ran a hiring simulation, recruiters preferred the two-page version by better than two to one.

Candidates were in a bind. Some filled resumes with 1-pt invisible white text, to please keyword filters and avoid reviewer reactions.

Then generative AI arrived and solved the bind. Applicants could generate a distinct resume for each role, loaded with exactly the right keywords, with acceptable writing quality, in seconds. But the story route was never revived, because there was no longer anyone reading for a story. Recruitment’s early stages became a machine with no room to absorb narrative.

Goodhart’s Law

There is a common description for that process, Goodhart’s Law. When a measure becomes a target, it stops being a good measure. Hiring teams have continued looking for targets to measure, but each has receded in usefulness.

Generative AI didn’t introduce Goodhart’s Law to hiring. It did remove the final exceptions. The cost of optimizing acted as a natural brake. Producing a tailored, plausible resume took effort, so people did it selectively. Now it costs nothing.

A single posting now draws hundreds or thousands of applications, because every applicant is applying to hundreds of postings. That drives employer response rates toward zero, which removes any reason for an applicant to invest care in a particular application, which pushes them to apply to even more roles, which raises volume again.

Detectors to the Rescue?

Instead of looking to solve the root problems, many hiring managers suggest they’d use another filter: automatically rejecting AI generated resumes. This is almost certain to turn away many qualified candidates. At best, it leaves behind those who invested the most time into the process. That’s a poor predictor of candidate quality. It entrenches gamesmanship too. If you’re tempted to make the argument that candidates investing more time have more confidence, consider the counter; your job may not matter to the most rational candidates. You may just be selecting for the candidates not smart enough to figure out the game.

Even the detector is suspect. Initially tools could hide the AI provenance, at the cost of quality degradation. Initially this was easy, then detectors got better. But even now, I can say, it’s not too hard to modify AI written text to not be recognized though. It would slow candidates down, but what’s the value in that to an individual employer?

While there’s some good advice to applicants to worry about accuracy with AI and resumes, this filter is low value. Turning away a quality candidate that used AI does not improve hiring quality. It won’t change candidate norms. At best it’s a guess that AI resumes are less accurate. That’s a questionable assumption due to the pressure on non-AI resumes to inflate.

More Requirements

So people propose fixes. But almost all of them share the flaw that they inflate candidate costs and arbitrarily filter qualified candidates out.

Consider degree requirements, which I wrote about separately in Or Equivalent Experience. Employers routinely demand credentials more restrictive than what their existing workforce holds, and automated screening makes that mistake worse, not better.

GitHub portfolios are a common suggestion. But building something good or great takes time. Most paid work ends up in confidential repositories. Those few jobs that produce public GitHub history benefit. For everyone else, it’s a free labor requirement.

You can spot an amazing GitHub a mile away, but the supply of engineers with that will be low. Categorizing good/average/bad is harder. Are you going to assess generally, or be tech specific? Most hiring focuses on tech specific skills. While I think first principles lead to more actual job success, they are harder to spot. If a candidate has a beautiful data-analysis project in Python, but an employer demands a message-processing stack in Rust, the candidate costs go up further. Any self-made project is about more than engineering skill, it’s also about ideas and time.

None of that is fatal, but the later gaming will be. If the signal is genuinely valuable and engineers are desperate enough, people will pay the tax. But what happens after it becomes standard?

Every standard encourages gaming. Simple things first, like using an underground agent to generate a unique, reasonable-looking GitHub project on demand. The escalation is next; hiring teams create a detector. Don’t expect the adversarial dynamics to stop at Level 1.

Any tool you hand to one side becomes a weapon in the arms race rather than a resolution to it. A new filter for employers invites better evasion from applicants. A better evasion tool for applicants invites better filtering from employers. One-sided solutions, however clever, pull toward competitive dynamics, because the other side never agreed to it and has every reason to defeat it.

My Experiences in Interviewing

Early in my time at Amazon (2015), I was active in the hiring process, conducting around 60 interviews in the first 2 years. I was surprised at how quickly I was pulled in. But AWS was hiring fast, and Chicago was a new office. The process had promising ideals. One favorite was the idea that your job was to draw positive proofs out of the candidate, not search out flaws. You wanted to hear them describe their approach to a problem or challenge that demonstrated their understanding of the right path forward.

I’ve found Steve Yegge’s writing compelling, and there is shared Amazon experience, so I was drawn to his recent The Last Technical Interview. Yegge was engaged deeper than I was. I avoided the bar raiser path. Imposter syndrome is one reason. I am proud of a number of people who I was part of their hiring loop, or mentored. If there’s anything that should give you good impressions of your ability to interview, it should be those. But I wasn’t feeling it at the time, and so when I found a technical problem to focus on, I latched on to that, and pulled away from hiring, doing a handful per year.

A less optimistic viewpoint was noticing interviewers that took pride in turning down +90% of candidates. If I approved 25% and they turned out well, what are the chances that those turning down two to three times as many weren’t turning down qualified candidates? There wasn’t any data to show their 10% was better than my 25%.

So I had to chuckle at the reflection where his team of interviewers voted not to hire 2/3rds of themselves. Made me feel a bit better about those nagging doubts that should be part of any difficult decision like this.

Interview Stages

The Last Technical Interview does get at something real though. His diagnosis: the interview has been broken for fifty years, even Google’s best interviewers couldn’t agree with each other or with their own past judgments. The whole apparatus is an elaborate attempt to generate signal, but consistently falls short. His prescription, the “campfire” model, is to bring people in to do paid, real work for a few days, then decide.

What makes his version more interesting than GitHub is a detail he calls counting the work twice: the candidate walks away with a permanent, portable record of what they did, stamped by the employer, whether or not they get an offer. If I get the concept, the employer would be doing a service for the candidate, and for all other potential employers. Both want the information a “stamp” would provide. Well done, this could counterbalance unnecessary adversarial tendencies that have accumulated.

Because it’s employer-certified rather than self-reported, it is harder to fake than a repo an agent can spin up. You’d have to find employers who hand out stamps to everyone, and Yegge is right that failure mode is self-correcting: a company whose stamps mean nothing has stamps worth nothing.

The network effect here is not one-sided. Both sides care about credibility. Gaming still exists, but if your stamp comes from a company vulnerable to gaming, its value diminishes. The signal lives or dies on credibility. Also, the more personal process offers fewer structural approaches to gaming.

There’s definitely some details left unspecified. I worry that some liability concerns could kill it. You still need to screen those invited to a “campfire”. You still have to scale.

An instinct of mine is that this requires some collaboration across the employer space. The campfire with credential stamp would do this, if it scaled. It would be challenging for every employer to create this credibility though if it’s by word of mouth.

An idea here is a company that does this as a service for an industry or multiple industries. This could solve the scaling challenge, if this is a passthrough. In some sense, this is what universities are: a 4-year campfire you pay tens of thousands of dollars to attend. I think the flaw is obvious, they are too expensive and too inflexible. In a sense though, they might have the best infrastructure for this impossible mission, should they choose to accept it.

The Shared Road Out

Which finally points at the answer to the question I opened with. If you want a solution that doesn’t just escalate the war, it has to be something both sides are actually happy with, or one side will fight it. One-sided efficiency tools breed counter-tools. A solution has to be a collaborative tunnel from the start, or it gets pulled back into the field of competitive options.

The trouble is we’re stuck in a bad equilibrium that’s individually rational. It’s a coordination trap, structurally a prisoner’s dilemma. A better equilibrium exists, where applicants apply selectively to roles they fit and employers actually read what comes in, and everyone would be better off there. But reaching it requires someone to move first and trust that the other side won’t simply exploit the opening. Right now nobody trusts that, for good reason. Applicants won’t invest in a tailored application when the expected response is silence. Employers won’t slow down to read carefully when they’re drowning. Both behaviors are sensible. Both perpetuate the trap.

So who moves first? Probably the employer, for an unsentimental reason: the employer is the scarcer and concentrated resource. A small show of goodwill from the side holding the scarce thing tends to get reciprocated. A well-known employer with a reputation can start something new. When candidates learn of it, they would opt-in. But it has to be something that builds collaboratively, rather than whittles away negatively.

I don’t have a clean answer. But I think the shape of the answer is clear enough, and it’s not a better resume or a better filter. It’s changing what each side gets from honest engagement, so that participating sincerely beats gaming.

Yegge’s instinct toward “gravity” is the right one, even if the mechanism is unfinished: make your rejection valuable, and candidates stop treating you as an adversary to defeat. A few partial paths point the same direction. Employer-certified, portable records of real work, if their credibility can be established. Skills-based hiring done deliberately rather than as language stripped from a posting. Two-sided interest signals like Greenhouse’s MyGreenhouse “dream job” feature, where a candidate can mark one application as special, though even that is still half a handshake until the employer offers something reciprocal. Something as basic as committing to send a real response.

None of these escapes the adversarial dynamic completely. Each one can be gamed at some level, and you should assume someone will try.

But that’s the wrong bar. The question isn’t whether a solution is immune to gaming. Nothing is. The question is whether it moves the incentives so that honesty is worth more than evasion to both sides at once. The inherent competition, applicant against applicant, employer against employer, isn’t going anywhere, and that’s fine. The unnecessary war, applicant against employer, is a failure to generate signal collaboratively.

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