Business

Google Meet exec on the knowledge engine hiding in your calendar: meetings become IP

Published

2 hours ago

January 16, 2026

Meetings are the dark energy of business: common, powerful, and largely invisible. The reasoning and judgments that shape a company’s direction happen in meetings, but then disappear. Email turned situational communication into organizational working memory; AI is now doing the same for meetings, making conversations usable beyond the meeting itself. It turns meetings from moments in time to a new kind of organizational asset.

Beyond the Transcript

Meetings contain information that rarely makes it into a formal system: how a leader weighs trade-offs, why a decision went one way instead of another, who deferred to whom, and how objections were resolved. Companies treat strategy documents as critical intellectual property, but the central decision-making process is just as valuable, and harder to capture. Leaders often try to approximate this in writing, but it rarely captures how decisions actually unfold.

The IP of leadership happens in the meeting room. And then it’s gone.

There is also the simple loss we already feel: decisions made but not recorded. Commitments that fade. Debates rehashed because people forget, not just what was decided, but how. Manual meeting notes are subjective and record only a small portion of this. Even complete transcripts aren’t very useful in capturing meeting meaning; the important information is rarely synthesized, analyzed, or distributed.

Many people today use AI to summarize meeting recordings and generate (and sometimes share) useful meeting data: who’s on the hook for what, where the disagreements were, who had convincing arguments, and what the agenda should include in the next meeting. This, however, is just the beginning of AI’s application to meetings. All this information can benefit a business on a much larger scale.

Business Value

The information in unrecorded meetings begins melting once the meeting ends. AI extraction, though, solidifies it into foundational data. Decisions, rationales, and patterns become durable. Their knowledge value stacks like bricks, not ice cubes. For the first time, what gets said in meetings becomes knowledge that an organization can actually build on – just as email made everyday communication archival and durable.

We can combine this data with the rest of what an organization knows: its documents, CRM, email, and contracts. This joining is where new value surfaces. When meeting intelligence flows into the same corpus as everything else, its signals become amplified.

Some organizations are doing this today. At a large payments and financial services company, leaders aggregate team and customer meeting recordings to analyze patterns across conversations, to surface emerging needs and product ideas that would be difficult or impossible to spot meeting by meeting. Instead of relying on anecdotal recollection, leadership looks across interactions to understand shifting customer signals to inform product development.

A recurring executive decision-making meeting can become a living archive not just of what decisions are made, but also of how they are made: what kind of arguments and data factor in. It can lead to smoother and more aligned decisions across the company. As organizations begin systematically capturing meeting intelligence, team dynamics will shift. At first, decision re-litigation decreases, because the capability preserves reasoning. Onboarding accelerates, because new hires can see how the team actually thinks, not just the written policies.

Individuals adopt AI meeting transcribers because they make their meetings better. But the business value is much larger than better meetings; it is organizational knowledge that we can build on.

What Leaders Should Do Now

Every durable knowledge system eventually reshapes how decisions are made and how work gets done. Consider how email changed the shape of work. AI meeting intelligence will follow the same path, and leaders can accelerate the transition.

Left to individual adoption, meeting intelligence can stay fragmented. The value, while significant, remains confined to productivity, short of business transformation. The strategic payoff doesn’t materialize.

To unlock the business value of meeting intelligence, leaders need to treat meeting capture as infrastructure, not as a tool some employees happen to use. When capture, recap, and sharing normalize across the organization, early benefits follow: more transparent accountability, less rework, and better decision follow-through.

The transformation starts by providing AI meeting tools, explaining their direct benefits to employees, and encouraging their use. At first, the benefits of widespread AI use for capture, recap, and sharing should lead to a growing accountability culture and other employee benefits, such as reduced rework and easier follow-through.

Treating meeting capture as infrastructure means more than using it to improve meeting productivity for individuals and teams. When meeting intelligence is captured consistently, organizations can apply AI analysis to that data to discern business cause-and-effect across decisions, debates, and trade-offs.

This kind of synthesis needs a shared, reliable base of meeting data to draw on. The companies that start capturing meeting data now will be positioned to use advanced analysis as tools mature; those that leave meeting AI tools to individuals and teams won’t have the shared context to build on.

Second, we need to decide how meeting information should be shared and used. We have navigated similar questions with email and documents: as soon as a new form of knowledge becomes durable, norms and governance follow. The same will happen here.

Meeting data will only become an enterprise asset if people understand how it is being used and can see benefits for themselves. We must be thoughtful about what’s being captured, how it’s retained, and how we manage access. When these choices are clear, meeting intelligence is more likely to strengthen collaboration and analysis, rather than create friction.

Finally, leaders need to be deliberate about building processes that visibly leverage this new asset. The opportunity is not just capturing conversations, it’s building new rhythms that convert into reusable guidance, behaviors, and institutional memory.

From Ephemeral to Enduring

Managing cumulative meeting intelligence is new territory. There’s no established playbook for how to synthesize it or build on it over time. The organizations that figure this out first will compound their advantage, just as the companies that quickly transitioned to email did. Those that don’t will keep losing the same knowledge they’ve always lost – just with better transcripts.

This transition won’t happen on its own. It requires leaders who recognize that meetings have crossed from ephemeral to durable, and who are ready to build the systems and culture to capture that value.

The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of Fortune.

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Business

Shark Tank’s Kevin O’Leary warns job seekers he’ll throw your resume ‘straight in the garbage’ if you have bad WiFi

Published

20 minutes ago

January 16, 2026

Jace Porter

We’ve all been there: midway through a video call, the audio freezes. Faces stop moving. A moment later, the dreaded message appears: Your connection is unstable.

For years, those glitches have been shrugged off as an unavoidable reality of remote work. But according to Shark Tank star Kevin O’Leary, that grace period is officially over.

More than five years after the pandemic pushed millions of workers onto Zoom calls, “Mr. Wonderful” now said spotty internet is no longer an inconvenience—it’s a red flag, especially for someone looking for a job.

“In a hybrid world, your internet connection tells me everything,” O’Leary said on Instagram.

“If your audio cuts out, your video freezes, or you don’t care enough to fix it…you’re telling me you’re not serious about business,” the 71-year-old added. “That résumé goes straight in the garbage.”

The message may sound harsh—especially from a business leader who shows up to meetings in pink pajama pants and flip-flops. But for O’Leary, the issue is more than just professionalism for its own sake—it’s about efficiency.

After all, what he values the most is time. And time, in his view, is money.

Workers need to ditch job-hopping—or face not landing another role again

A strong internet connection isn’t the only bar O’Leary sets for prospective hires. Before a candidate ever reaches the interview stage, he wants proof of something else: execution—and loyalty.

“What I can’t stand is seeing a résumé where every six months they job hop. To me that means they couldn’t execute anything, and I take that résumé into the garbage,” O’Leary said in a video posted to his social media last year. “If I see anything that’s less than two [years], that’s a red flag for me.

Rather than constantly chasing the next opportunity, O’Leary encouraged young workers to embed themselves in a role, deliver results, and prove their value over time.

“Show me you had a mandate and delivered on it over two years or more, that’s gold,” he added. “Discipline, focus, and results matter; that’s how I decide who gets hired.”

It’s not just the résumé—what you say in the interview can be a make-or-break

O’Leary isn’t alone in setting firm—and sometimes unforgiving—expectations for job candidates. For many top executives, the interview itself offers a clearer signal than anything written on a résumé.

For Twilio’s CEO Khozema Shipchandler, it often comes down to what happens at the very end of the conversation.

“The number one red flag for me is when someone doesn’t ask questions toward the end of an interview,” Shipchandler previously told Fortune. “That’s a pretty significant mark against them being curious about what they’re interviewing, the company, the way we might work together, chemistry, culture, all of those things.”

Denny’s CEO Kelli Valade has echoed a similar view, saying that the specific question matters less than the act of asking one at all. To her, it signals preparation, genuine interest, and that a candidate has done their homework.

General Motors CEO Mary Barra, who previously headed the automaker’s human resources department, looks for something more subtle: language.

The 64-year-old said she pays attention to how often people talk about GM using the pronoun “we” instead of “you” or “they”—an indication as to whether someone already sees themselves as part of the organization.

“Jump in the boat, own the problem, and be part of it,” she said at the Wharton People Analytics Conference in 2018. “You can almost tell in an interview when they interview like they’re already at the company—but in a respectful way where they’re not over assuming anything.”

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Business

‘We are Jerome Powell’: Gen Z finds an unlikely meme hero in the Fed chair via AI songs and fan edits

Published

52 minutes ago

January 16, 2026

Jace Porter

Memes tend to gravitate toward pop stars, politicians and villains. But this week, the internet found a central banker.

Jerome Powell, the 72-year-old chair of the Federal Reserve, is not the kind of guy you’d expect to see flashing across Instagram and Tiktok to the tune of a high-saturated techno remix. Yet, his image has broken containment over the last few days, as Gen Z has turned the famously taciturn technocrat into a symbol of defiance of the second Trump era, clad with reverent edits usually reserved for K-Pop stars.

It’s been quite a development for the central banker that Trump initially chose as the replacement for Janet Yellen, who would go on to become Joe Biden’s Treasury Secretary. Trump was reported back in 2017 to appreciate that Powell had a “central casting” air to him, but the longtime Washingtonian surprised onlookers over the next several years by maintaining and even extending Yellen’s focus on the “full employment” side of the Fed’s dual mandate.

In August 2020, Powell revealed that the Fed had revised its monetary policy framework to emphasize the “broad-based and inclusive goal” of maximum employment, running the economy as hot as it took to get all Americans back to work. Critics soon pounced, warning of the risk of higher inflation, and Powell’s series of aggressive rate hikes in 2022 and 2023 made this policy a close-but-distant memory. Still, during the period known as “the Great Resignation,” when labor had the most leverage to command salary hikes in a generation, Jerome Powell was a millennial-era hero.

It looks like Gen Z is discovering what their older siblings did, half a decade ago.

One manifestation of the trend began with a video made by Democratic strategist and popular YouTuber Keith Edwards. Riffing on the “We are Charlie Kirk” song that conservatives championed after the death of the right-wing activist, Edwards decided to flip the script and make it “We are Jerome Powell.”

“We are Jerome Powell, we carry the line,” the voice of a man wistfully moans. “Not to a man – but to law and time.”

Edwards said he used AI to generate both the lyrics and the video itself.

“I personally believe if you look at the memes from 2016, they were very liberal-coded,” Edwards told Fortune. “I think that’s flipped. Conservative ideas travel faster on the internet now.”

For Edwards, the Powell meme is a tactical necessity in what he describes as a literal “information war.”

“We are at war,” Edwards said. “When you’re in war, you grab the biggest weapon you can and you fire it. I’m going to pull every single grenade I can and throw it.”

In this context, Powell is the “grenade.”

After Powell released a rare video statement confirming that the Justice Department had subpoenaed him over Federal Reserve office renovations, and explicitly framed the inquiry as political pressure tied to his refusal to cut interest rates faster, he emerged online as an unlikely symbol of resistance.

Edwards explained that, to him, Powell represents a vanishing archetype: the technocratic figure who still believes in institutional norms and does things “by the book.” It’s a similar and yet different Powell boomlet to the pandemic “maximum employment” era, when the Georgetown figurehead was arguably woke in his commitment to getting every American back to work.

The internet—or more specifically, Gen Z—decided that Edwards’ video “went hard,” as it were. They’ve now taken to making fancams with videos of Powell looking tough; him posing in a sleek suit, him giving Trump a dirty look as they both stand around in hardware hats. This recalls another #resistance hero who took on an almost Marlboro man-style American toughness in meme world: the former FBI chief and special counsel, Robert Mueller.

According to Aiden Walker, a researcher who specializes in internet culture, the appeal is more that Powell doesn’t look cool. He suggested the “alchemy” lies in the contrast: Powell is both “venerable” and “unassuming,” and placing that persona into a fan cam typically reserved for K-Pop idols or action stars has a “gently subversive irony” to it.

Powell is also very “authentic to himself,” Walker said, and Gen Z loves authenticity (or, like Trump, they love the central casting aspect of the gray-haired politico).

“He’s an old banker, he’s been around the block,” Walker said. As an example, he pointed out the moment of Powell and Trump in their construction hats as they argue over the renovation numbers to the building.

“It’s his posture there,” Walker said. “He’s clearly not a guy who wears construction hats, but that’s what they’re doing, and he’s very true to himself, and I think people online love that in a figure.”

But there is also a deeper shift in how the public relates to the Fed. We are no longer in an era where the Federal Reserve is a black box to everyone but Wall Street. Commission-free apps like Robinhood and the exploding popularity of pandemic-era “meme stocks” and spaces like r/WallStreetBets on Reddit have made something of a culture around retail investing in the 2020s.

The numbers back that up. Prior to the pandemic, retail order flow rarely exceeded 10% of daily U.S. equity trading. By contrast, J.P. Morgan reports that retail activity reached an all-time-high of 36% of total order flow on April 29, 2025.

“There are so many more retail investors today,” Walker noted. “Twenty-somethings own a couple of stocks on Robinhood. They feel much closer to the market.”

The result is a new kind of familiarity with figures like Powell, even among left-leaning Gen-Zers who might otherwise distrust the Federal Reserve.

“There’s a fandom logic now,” Walker said. “And he’s kind of a fun, ironic figure, because he clearly doesn’t want to be famous necessarily. It’s just kind of been forced.”

AI and accelerationism

In 2016–a time on many people’s minds as the internet celebrates the origin of a slower internet culture—a political meme might have taken days or weeks to saturate the culture. In 2026, AI-generated content has compressed that cycle into hours.

“AI generation makes it a lot easier and faster to make your Jerome Powell edit,” Walker said. “You can watch a clip of Powell, and within two hours, have your edit responding to it.” This speed doesn’t just accelerate the meme, but it changes its nature and the nature of its subject, where news events become absurd spectacles of participation.

In postmodern theory, this is what is known as “accelerationism.” By feeding a stodgy institutional figure like Powell into the AI-meme deluge, the internet hijacks the Federal Reserve’s image and accelerates it past the point of professional control. The process of taking a serious person out of their serious context—what French psychoanalysts Gilles Deleuze and Felix Guattari called “deterritorialization”—plugs them into a high-speed digital world where they are fashioned into a particular vibe. In this framework, the meme is what psychoanalysts call a “hyperstition,” a digital fiction that, through the sheer power of speed and repetition, begins to dictate how we perceive the actual stability of our institutions. Philosophers sometimes use the example of cyberspace to explain superstition, pointing to how science-fiction author William Gibson’s imagined cyberpunk world shaped the ethos of what actually became the internet.

Despite the ultimate one-dimensionality or “frivolousness” of the Powell meme, Walker said he is glad that Gen Z is paying attention.

“I’d say there’s a lot of people who probably saw a reel like that, and maybe Googled who he was or what he said,” Walker said. “We are Jerome Powell, it out-ironies the ironic post because it makes it sincere again, because we enjoy him.”

This story was originally featured on Fortune.com

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Business

In the AI economy, the ‘weirdness premium’ will set you apart. Lean into it, says expert on tech change economics

Published

1 hour ago

January 16, 2026

Jace Porter

The word “weird” didn’t always mean strange. In Old English, descended from a mix of Germanic and Norse concepts, it meant something closer to “destiny” or “becoming” or even “fate.” Once upon a time, human beings in that culture thought that the way someone’s life would turn out was unseverable from the fundamental weirdness of being alive.

William Shakespeare’s MacBeth is known for its three witches, who popularized the “double, double, toil and trouble” line, often misquoted from its appearance in a Disney cartoon as “bubble, bubble.” But what’s often forgotten is that Shakespeare named these characters the “Weird Sisters,” connecting them to another mythological group of three old crones: the Norns from Scandinavian mythology, who together weaved a web of destiny called (what else?) the “wyrd,” containing every human’s life story. (J.K. Rowling later named a popular band in her Harry Potter universe “The Weird Sisters,” but that was an all-male lineup.)

The weirdest thing of all in economics, says Brandeis University Economics Professor Benjamin Shiller, is that weirdness is closely tied to fate in the age of artificial intelligence (AI). The weirder you are, he tells Fortune, the better off you’ll be.

In his new book “AI Economics: How Technology Transforms Jobs, Markets, Life, and Our Future,” Shiller, argues that the more bizarre your job, the less likely that AI will take it. A specialist in the economics of technological change—and the son of a famous economist in his own right, Yale’s Robert Shiller, the co-creator of a national home price index still in use today, Shiller tells Fortune that the future of employment is weird.

“AI models can learn stuff really well but only with a massive amount of training data as humans are much more efficient learners,” Shiller says. “If you have a niche field where there’s not a lot of data out there to train an AI model, then AI probably won’t displace your job.”

Goldman Sachs predicts that 300 million jobs in the U.S. and Europe could be susceptible to some level of change because of AI, predicting that humans could go the way of the workhorse in the modern economy. However, Shiller’s “weirdness premium” suggests a cheat code to gaming AI’s takeover: find a job that’s so complex, not even trillions of tokens of data can replace it.

AI doesn’t learn as efficiently as humans … yet

Shiller describes what Tesla CEO Elon Musk recently suggested regarding the sheer volume of information required to replace a human skill. The businessman posted in a reply on X (formerly Twitter) “Roughly 10 billion miles of training data is needed to achieve safe unsupervised self-driving.”

“If a typical American drives about 13,500 miles per year, that’s about 750,000 years of a person driving that they need for training data,” Shiller said. In contrast, it takes the average human just a few hundred driving miles and six months of practice to secure their driver’s license.

Of course, self-driving cars already exist, and can easily get people from point A to point B free of harm. Yet if it takes that much data for an AI to learn a task as simple as driving, then it could take a massive amount of data to automate niche professions, such as that of an aviation accident analyst or an industrial ride engineer. In other words, in fields where data is scarce, humans retain a comparative advantage.

Humans are better equipped at handling kangaroos

Shiller illustrates AI’s limitations with “the kangaroo example,” a cautionary tale of when Waymo tested its self-driving cars in Australia. The vehicles failed to navigate a bizarre and foreign obstacle: jumping marsupials. “They just basically kept on crashing into kangaroos because kangaroos weren’t in their training data and their movements were different [from] other animals’ movements.”

AI fails to predict the unknown, and that failure is what differentiates humans from even the most advanced machines. “For a human, we’re able to adapt and deal with these edge cases without being specifically trained to handle them,” Shiller said. We’re naturally apt at handling niche scenarios, from the unpredictability of the road to the chaos of a hospital or an investment bank.

Shiller says that modern workers—and young people contemplating a degree—should avoid being caught in a profession that everyone else is doing. “Just taking the standard classes and becoming well-versed in what you’re directly taught in these large majors is a risky strategy,” Shiller said.

In other words, your fate is certain to be weird.

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