Last fall, Google Translate rolled out a new-and-improved artificial intelligence translation engine that it claimed was, at times, “nearly indistinguishable” from human translation. Jost Zetzsche could only roll his eyes. The German native had been working as a professional translator for 20 years, and he’d heard time and time again that his industry would be threatened by advances in automation. Every time, he’d found, the hype was overblown—and Google Translate’s makeover was no exception. It certainly wasn’t the key to translation, he thought.

Miranda Katz is an associate editor at Backchannel. Sign up to get Backchannel's weekly newsletter, and follow us on Facebook, Twitter, and Instagram.

But it was remarkably good. Google had spent the better part of 2016 reworking its translation tool to be powered by AI—and in doing so, it had created something unnervingly powerful. Google Translate, once known for producing stilted but passable translations, had begun producing fluid, highly accurate prose. The kind of output that, to the untrained eye, was nearly indistinguishable from human translation. A 15,000-word New York Times story hailed it as “the great AI awakening.” The engine quickly began learning new tricks, figuring out how to translate language pairs it hadn’t encountered before: If it could do English to Japanese and English to Korean, it could figure out Korean to Japanese. At last month’s Pixel 2 launch, Google took its ambitious agenda a step further, introducing wireless headphones that it promised could translate 40 languages in real-time.

Since IBM debuted its pioneering machine translation system in 1954, the notion of a flawless machine translator has captured the imagination of programmers and the public alike. Science fiction writers have seized upon the idea, serving up utopian visions ranging from Star Trek’s Universal Translator to The Hitchhiker’s Guide to the Galaxy’s Babel Fish. Human-level translation—fluent prose that captures the meaning of the source text—is a holy grail of machine learning: one of the “AI-complete” challenges that, if conquered, would indicate that a machine had reached a human level of intelligence. The fanfare around Google’s advances in neural machine translation implied that the grail was within reach—and, along with it, the moment when human workers become obsolete.

But translators have long been on the frontlines of AI-induced job panic, and they aren’t worried. In fact, some are delighted. For those that have seized on the potential of AI tools, productivity has skyrocketed, along with demand for their work.

Think of them as the canary in the white-collar coal mine. At the moment, they’re still singing. As deep learning burgeons, many industries are coming to grips with the fact that AI is indeed capable of tasks that were once regarded as deeply human. Unlike drivers and warehouse employees, knowledge workers aren’t in immediate danger of being displaced. But as AI becomes an essential part of their workflow, their jobs are changing—and there’s no guarantee that today’s helpful AI tools won’t become a threat in the future. This presents workers with a choice: Set aside your ego and embrace your new AI coworker, or get left behind.

We’re not living in the golden age of AI, but we are living in the golden age of AI-enhanced productivity. Call it the First Pass Era. AI is now powerful enough to make a solid first attempt at countless complex tasks, but it’s not so powerful that it seems threatening. For more thought-intensive, subjective work, we still need humans.

That labor shift is unfolding across industries. The Washington Post’s in-house AI, Heliograf, published some 850 stories last year, with human reporters and editors adding analysis and colorful details. In graphic design, AI tools can now generate a first pass at designs, leaving the final execution to human designers. In film and publishing, new tools promise to weed through slush piles in search of the next great hit, freeing up editors from the never-ending submissions queue. These AI tools are like plucky young assistants on steroids: They’re highly competent and prolific, but still need a seasoned manager to do the heavy intellectual lifting. And, of course, that manager has to get on board with working alongside machines to reap the benefits.