Japanese companies are developing artificial intelligence tools to protect call center workers from "customer harassment," or unreasonable complaints and disruptive behavior by customers. AI tools, such as those that can modify callers' voices and help train call center workers to deal effectively with angry customers, are expected to help prevent call center workers from quitting. One reason for the industry's high turnover rate is believed to be customers escalating over the phone. SoftBank and the University of Tokyo have jointly developed technology that can change the tone of an angry caller's voice to reduce its "scariness." AI trained with data from tens of thousands of voices identifies "scary voices" based on volume and intonation.

To many people, chatbots and other technology feel like a ticking time bomb, sure to explode their work. But to some, the threat is already here.

"Now I have enabled the accent translation," he says. It's the same person, but he sounds completely different: loud and slightly nasal, impossible to distinguish from the accents of my friends in Brooklyn. Only after he had spoken a few more sentences did I notice a hint of the software changing his voice: it rendered the word "technology" with an unnatural cadence and stress on the wrong syllable. Still, it was hard not to be impressed – and disturbed. The man calling me was a product manager from Sanas, a Silicon Valley startup that's building real-time voice-altering technology that aims to help call center workers around the world sound like westerners.

A startup company based out of Silicon Valley called Sanas plans to use artificial intelligence (AI) to modify the voices of workers in call centers to remove their accents. The company's demo features the voice of a man with an Indian accent, reading through a call center script in a simulated customer interaction. Enabling the slider on screen to use Sanas' technology seamlessly switches from obviously human audio to a processed version that finds itself in the uncanny valley. The voice is still noticeably synthesized, but the Indian accent is gone and replaced with a more Americanized or "white" accent. Sanas launched in August 2021 and has already received large amounts of funding, with $32 million in funding secured during a Series A funding round in June 2022. The company's founders, three former students of Stanford University, claim the funding is the largest amount ever put towards a speech technology service.

"Now I have enabled the accent translation," he says. It's the same person, but he sounds completely different: loud and slightly nasal, impossible to distinguish from the accents of my friends in Brooklyn. Only after he had spoken a few more sentences did I notice a hint of the software changing his voice: it rendered the word "technology" with an unnatural cadence and stress on the wrong syllable. Still, it was hard not to be impressed – and disturbed. The man calling me was a product manager from Sanas, a Silicon Valley startup that's building real-time voice-altering technology that aims to help call center workers around the world sound like westerners.

Will robots take my job? This question has inspired numerous memes and websites and was even a topic in the U.S. presidential campaign. It is something I think about often as a technology entrepreneur and as someone who leads a team of remarkable people. Progress is an unstoppable force, but how can we ensure that people don't become casualties of it? In January 2019, global leaders gathered in Davos for the World Economic Forum (WEF).

On conference stages and at campaign rallies, tech executives and politicians warn of a looming automation crisis -- one where workers are gradually, then all at once, replaced by intelligent machines. But their warnings mask the fact that an automation crisis has already arrived. The robots are here, they're working in management, and they're grinding workers into the ground. The robots are watching over hotel housekeepers, telling them which room to clean and tracking how quickly they do it. They're managing software developers, monitoring their clicks and scrolls and docking their pay if they work too slowly. They're listening to call center workers, telling them what to say, how to say it, and keeping them constantly, maximally busy. While we've been watching the horizon for the self-driving trucks, perpetually five years away, the robots arrived in the form of the supervisor, the foreman, the middle manager. These automated systems can detect inefficiencies that a human manager never would -- a moment's downtime between calls, a habit of lingering at the coffee machine after finishing a task, a new route that, if all goes perfectly, could get a few more packages delivered in a day. But for workers, what look like inefficiencies to an algorithm were their last reserves of respite and autonomy, and as these little breaks and minor freedoms get optimized out, their jobs are becoming more intense, stressful, and dangerous. Over the last several months, I've spoken with more than 20 workers in six countries. For many of them, their greatest fear isn't that robots might come for their jobs: it's that robots have already become their boss. In few sectors are the perils of automated management more apparent than at Amazon.

You know when you're listening to a podcast interview and the guest says something and you literally smack your head, pause the podcast, and start tweeting? Happened to me last week. Andrew Yang, founder of "Venture for America" (and a long-shot candidate for president in 2020) said "Google recently demonstrated software that can do the job of an average call center worker … that's going to result in hundreds of thousands of jobs lost". Now, I grant some leniency for people outside our industry not getting some details right. But this thought is so wrong -- and, sadly, growing in popularity -- that it really needs correcting.

In this approach, technology is used to perform a job that used to be the primary activity of a person. Examples: replacing bank tellers with automated teller machines or call center workers with interactive voice response systems. This approach involves atomizing, or breaking up, a job into pieces, and automating as much as possible, leaving humans to do the non-automatable pieces and possibly supervise the automation. The transition of artisans to assembly line workers is an example of this. Relying on machine language translation and leaving professionals to "clean up" the results is another.