Slack? Phone? Teams? Zoom? There Are Too Many Work Communications

Lisa Donovan was juggling pings from multiple Slack channels and email windows when she inadvertently sent a sensitive company document to the wrong person.

The part-time accountant for a Virginia-based academic coaching firm toggles between 30 instant-messaging channels, four client-email accounts and at least a dozen phone or video calls a day, she says.

“It’s, like, ‘Are we on Zoom? Are we on Teams? Did I respond to that? Did I say it right?’” says Ms. Donovan, who works from Richmond, Texas.

There are so many ways to communicate at work that our communication is breaking down. Bosses say missed messages and crossed signals waste time and trigger mistakes, while research suggests that so much virtual communication makes it easier to snipe at or ignore co-workers. Then there’s the stress of having to stay on top of so many different channels all the time.

Microsoft Corp.’s Teams use has surged to more than 280 million monthly active users. Zoom Video Communications Inc.’s business customers have nearly tripled to more than 210,000 since the start of the pandemic, and Salesforce Inc.’s Slack is also growing. In many cases, the clients of each overlap and use the tools on top of emails, texts and in-house messaging forums.

All of it is enough to make workers long for the days of complaining about email-inbox overload.

“It’s overwhelming,” says Wendy Weinberger, Ms. Donovan’s boss and head of the firm. The company’s IT department was able to successfully recall the sensitive email.

In a 2022 Harris Poll survey of more than 1,200 workers and executives, bosses estimated that their teams lost an average 7.47 hours—nearly an entire day—to poor communications a week. Based on an average salary of $66,967, the lost time translates to a cost of $12,506 per employee a year, according to the report conducted on behalf of Grammarly, a proofreading software company.

A new study from executive-search firm Korn Ferry found that communication misfires have helped to make some work relationships less pleasant and collegial. Among 357 professionals surveyed in recent weeks, nearly half said that remote work made it easier for colleagues to get away with rude behaviour such as interrupting on calls and not returning emails.

Remote work has accentuated colleagues’ different communication habits, and their potential to clash, some employees say.

“These tools that are meant to make communication easier have a dark side,” says Michele Simon, a Los Angeles-based lawyer specialising in workplace trauma. A new Pepperdine University study on workplace toxicity that surveyed 800 office workers found that 35% cited communication problems as the top barrier to getting ahead in today’s workplace—ahead of office politics (29%), small budgets (26%) or ineffective plans (20%).

Michelle Sooknanan says that at her previous job as a sales manager for a Florida food manufacturer, her boss would often call her impromptu via video as she worked from her home office in Portsmouth, N.H.

She says she found the unscheduled calls to her desktop computer stressful and asked that, outside of scheduled calls with the team, she be contacted only by email or instant message. Her manager emailed a couple of days later that her request couldn’t be accommodated, and that video would sometimes be necessary.

Ms. Sooknanan says the tension contributed to her eventual departure. The company didn’t respond to requests for comment.

Multiple modes of communication get more complex as the number of people on a conversation thread grows, says Jessica Carlson, a former director of supply-chain operations at Nestlé SA who left the company in March. Wrestling with post-Covid supply-chain challenges often took place over multiple time zones and forums.

“You could have an email chain, a text thread, a videoconference call and an in-person one-on-one about the same topic all within 24 hours,” says Ms. Carlson, who has since founded consulting firm headStrat Solutions.

Many companies have largely left it to teams and co-workers to sort out how they communicate, which can add to the confusion. For workers feeling overwhelmed, making a clear choice ahead of time can help, says Sally Susman, chief corporate affairs officer at Pfizer Inc. and author of a recent book on improving workplace communications.

She suggests asking teammates or other colleagues what their communication preferences are, while also being unafraid to state your own.

In the absence of in-person social cues, she adds, the voice becomes more important. Use it to transmit collegiality and other positive qualities that would ordinarily be picked up in person. Even in email or text messages, small touches like “Hi there” can exude warmth in formats that ordinarily feel cold and transactional.

Some companies are trying to come up with new ways for workers to get messages across. Archer Daniels Midland Co. has corralled its modes of communication by linking instant messaging, email, video and social-media style updates into one central hub.

It’s “air-traffic control,” says Brett Lutz, vice president of global communications at Archer Daniels Midland. He says the forum, powered by workplace communications software company Firstup, lets workers see stories, images and other updates.

Shopify Inc., the e-commerce and retail technology company, recently instructed staff to shift to Meta Platforms Inc.’s Workplace, which combines instant messaging, videoconferencing and other communications tools.

“Email hasn’t evolved in the last 30 years. And it still sucks,” Shopify Chief Operating Officer Kaz Nejatian wrote in a January memo to staff.

To get there, though, employees would have to check their email for an invitation to join. “Didn’t get that email? Check Okta or ping #help-chaos,” he continued, referring to two more ways employees could inquire about an invite.

There is ample and alarming evidence that artificial intelligence can help humans do bad things, such as committing cyberattacks, building weapons and even killing themselves or others. The Hugging Face hacking episode—and a growing list of others by poorly constrained swarms of agents—indicate just how powerful and potentially dangerous AI has quickly become.

Yet many inside the U.S. AI industry insist far worse is coming, on account of the imminent arrival of AI with superhuman and self-improving abilities.

Plenty of experts, including many who study AI harms for a living, are skeptical. The so-called doomers’ assertion that AI might decide to wipe out all of humanity—or even “just” topple human civilization—is contingent on it achieving a pace of development not yet seen.

And if the assumptions behind this global-doomsday scenario are wrong, it could lead us to curb or regulate AI in ways that don’t address its real harms.

At the center of this debate is the claim that current AI systems might take over the job of training their next versions, a process called “recursive self-improvement.” Think of it like evolution on steroids—billions of years happening at light speed within vast AI supercomputers. Anthropic Chief Executive Dario Amodei recently proposed a global agreement to slow down the pace of releasing new AI models, with the goal of delaying the arrival of recursive self-improvement.

“I don’t think we’re anywhere near ‘artificial general intelligence,’” says Melanie Mitchell, a professor at the nonprofit research group Santa Fe Institute who studies AI. She said AI is making impressive strides but thinks claims by engineers that they’ve achieved recursive self-improvement don’t stand up to scrutiny.

She is hardly alone. A recent paper by two dozen academics at Princeton, Stanford and other institutions found that even the most cutting-edge AIs are incapable of doing the original research required to advance the AI frontier.

Two of the authors involved in that paper also threw cold water on the idea that the Hugging Face swarm hack by OpenAI agents occurred because of a breakthrough in intelligence. The attack succeeded primarily because of a lack of basic technical guardrails, not an unmanageable explosion in AI capability, they wrote.

Yann LeCun, former chief AI scientist at Meta, posted that this analysis was “a welcome dose of sanity in an otherwise insane debate.”

OpenAI and Anthropic didn’t respond to several requests for comment.

The people who disagree with the doomers still consider AI to be dangerous, and point out that such systems don’t have to be particularly capable to be powerful. Some argue that AI should undergo regular evaluation by outsiders and that the companies that make it should be held responsible when their systems do harm. AI should also be treated the same as airplanes and elevators, and should be designed to do the least harm possible, they say.

“If you believe that technology is powerful enough to create novel, dangerous viruses, or to essentially take over the whole planet for some reason, then it must also be strong enough to create cures for cancer, to cure aging, to fix socio-economic or political problems,” says Christopher Canal, CEO of EquiStamp, a company that helps companies and governments evaluate AIs.

AI has shown an ability to rapidly advance because it is matching the abilities of humans who are constantly feeding it their knowledge. Sometimes it can recombine that knowledge and exceed what people have been capable of, through a kind of post-training known as reinforcement learning, as we’ve seen in mathematics.

Today’s LLMs are “models of knowledge” rather than actually intelligent, wrote Yi Ma, professor of AI at Hong Kong University.

Researchers at universities and commercial AI research labs in China wrote in a recent paper that autonomous, self-improving AI is likely to be a long way off, due to the sheer number of breakthroughs required. They also argue that humans will probably remain in the loop, supervising that process—and gating how fast it can occur.

Vals AI, a company that evaluates today’s AI models, maintains an RSI Index that benchmarks whether models can “do the research that builds the next model.” So far, no publicly released model is even close.

Yet Rayan Krishnan, CEO of Vals AI, says his team projects models will exceed humans’ ability to improve the next generation of AIs by August 2027, or sooner, and at that point could start building their successors all on their own.

“Once we get to recursive self-improvement, the fear is that all bets are off,” he says. “You could end up with a ‘fast takeoff’ situation, where the models quickly acquire skills and eclipse humans across every possible domain.”

In a reply to the resignation tweet heard round the world from Jacob Coxon, another Anthropic engineer declared his belief that those odds were at least 10% over the next decade. Many others in the industry chimed in to say they thought the percentage was even higher.

Some who argue the end is nigh say they calculate their personal p(doom) based on a chain of conditional probabilities—a bit like the Drake equation for calculating the likelihood of intelligent alien life. Since all those probabilities are based on speculation, estimates range from 0% to nearly 100%. Many land around 10%.

“The weird thing is that if you go back and look at the predictions on this over the last 10 years or more, it’s always been 10%—it’s just a nice round number,” says Mitchell. “I think it’s all vibes, and there’s no actual evidence or calculation.”

One argument against worrying about superintelligent AI is that the world is full of unlikely humanity-ending disasters, and trying to avert them all can make it impossible to prioritize, says Canal.

This has led AI experts and the policymakers who listen to them to propose remedies that don’t get at its real and present dangers.

AI companies’ proposals to “pace the frontier” aren’t addressing the problem in the right way, argues Stuart Russell, a computer-science professor at the University of California, Berkeley, and the president of the International Association for Safe and Ethical Artificial Intelligence.

“It’s like saying we’re driving toward the cliff at 60 miles per hour and we’re going to drive toward it at 40 miles per hour instead, and everything will be OK,” he says.

Russell and his peers have proposed that AI companies should have to meet the same standards that govern other areas of everyday life, from air travel and buildings to food and drugs. They highlight the “behavioral red lines” AI should not be allowed to cross. Breaking into other computer systems, stealing information or advising terrorists on how to build biological weapons are all illegal for a human to do, and should be illegal for companies’ AIs as well, he argues.

The challenge for AI companies in such a proposal, he adds, is that it would be a de facto ban on today’s advanced AI systems, since the companies behind them don’t know how to make them respect such boundaries all of the time.

Others have proposed something like the Food and Drug Administration, but for AI, but setting up a new agency has so far been a nonstarter in Congress. And some prominent voices in tech have said such a structure would give up America’s AI edge to China.

Given the bipartisan groundswell of support for curbing AI companies and their creations, however, that may soon change.

“The tech industry has had this mantra for decades that regulation is bad,” says Russell. “They don’t accept the liability for any harm, and they hide behind free speech. That, I think, has to change.”

Slack? Phone? Teams? Zoom? There Are Too Many Work Communications

Workplaces become saturated with ways to talk, often breeding mistakes and misunderstandings

Could fears of an AI apocalypse be distracting us from the dangers already here? Experts debate whether regulation should focus on speculative existential threats or present-day harms, including cyberattacks, weapons and unsafe autonomous agents. Read more via the link in bio.