In July, software engineer Julian Joseph became the latest victim of the tech industry’s sweeping job cuts. Facing his second layoff in two years, he dreaded spending another couple months hunched over his laptop filling out repetitive job applications and blasting them into the void.
Joseph specializes in user interface automation and figured someone must have roboticized the unpleasant task of applying for jobs. Casting about online, he came upon a company called LazyApply. It offers an AI-powered service called Job GPT that promises to automatically apply to thousands of jobs “in a single click.” All he had to fill in was some basic information about his skills, experience, and desired position.
After Joseph paid $250 for a lifetime unlimited plan and installed LazyApply’s Chrome extension, he watched the bot zip through applications on his behalf on sites like LinkedIn and Indeed, targeting jobs that matched his criteria. Thirsting for efficiency, he installed the app on his boyfriend’s laptop too, and he went to bed with two computers furiously churning through reams of applications. By morning, the bot had applied to close to 1,000 jobs on his behalf.
The tool wasn’t perfect. It appeared to guess the answers to questions on some applications, with sometimes confused results. But in a brute force kind of way, it worked. After LazyApply completed applications for some 5,000 jobs, Joseph says he landed around 20 interviews, a hit rate of about a half percent. Compared to the 20 interviews he’d landed after manually applying to 200 to 300 jobs, the success rate was dismal. But given the time Job GPT saved, Joseph felt it was worth the investment. LazyApply didn’t respond to a question about how the service works.
Many job seekers will understand the allure of automating applications. Slogging through different applicant tracking systems to reenter the same information, knowing that you are likely to be ghosted or auto-rejected by an algorithm, is a grind, and technology hasn’t made the process quicker. The average time to make a new hire reached an all-time high of 44 days this year, according to a study across 25 countries by the talent solutions company AMS and the Josh Bersin Company, an HR advisory firm. “The fact that this tool exists suggests that something is broken in the process,” Joseph says. “I see it as taking back some of the power that’s been ceded to the companies over the years.”
Recruiters are less enamored with the idea of bots besieging their application portals. When Christine Nichlos, CEO of the talent acquisition company People Science, told her recruiting staff about the tools, the news raised a collective groan. She and some others see the use of AI as a sign that a candidate isn’t serious about a job. “It’s like asking out every woman in the bar, regardless of who they are,” says a recruiting manager at a Fortune 500 company who asked to remain anonymous because he wasn’t authorized to speak on behalf of his employer.
Other recruiters are less concerned. “I don’t really care how the résumé gets to me as long as the person is a valid person,” says Emi Dawson, who runs the tech recruiting firm NeedleFinder Recruiting. For years, some candidates have outsourced their applications to inexpensive workers in other countries. She estimates that 95 percent of the applications she gets come from totally unqualified candidates, but she says her applicant tracking software filters most of them out—perhaps the fate of some of the 99.5 percent of Joseph’s LazyApply applications that vanished into the ether.
LazyApply has plenty of competition, some of which involve humans to pick up any slack. A company called Sonara charges up to $80 per month to auto-complete as many as 420 applications and recommends jobs from a database compiled through partnerships with applicant tracking firms and companies that scrape job listings. Users can teach the algorithm about their preferences by liking and unliking jobs, and it offers to run jobs past the user before firing up its automated application filler. Human staff take over where the AI falls short, for instance, on certain free-text answers.