WHY REMOTE WORK COULD LEAD TO LESS INNOVATION - Kanebridge News
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WHY REMOTE WORK COULD LEAD TO LESS INNOVATION

Do chance encounters among employees of different Silicon Valley companies in coffee shops, restaurants and other public places lead to innovation? The answer is yes, say researchers who examined such “knowledge spillovers” in a study that may have implications for today’s work-from-home culture.

The researchers—Keith Chen of the University of California, Los Angeles, and David Atkin and Anton Popov of the Massachusetts Institute of Technology—tracked the locations of 425,000 phones using commercially available cellphone-location data. Though the data is anonymous and linked only to the unique ID number of each phone, the researchers surmised where the phone owners worked by looking at where the phones spent large parts of the workday, using a map of buildings occupied by Silicon Valley companies that have filed patents.

Examining instances where phone owners went outside the office and ended up near someone from another Silicon Valley company, they found 218 million episodes in which two workers from different companies were in the same place between September 2016 and November 2017.

For their study, they considered only situations in which both people were near each other for at least a half-hour, and used a probability technique to eliminate meetings that might have been arranged in advance. They also assumed that many of these people bumped into someone they already knew, such as a former colleague.

Sharing knowledge
Such chance meetings “may spark a conversation that leads to a transfer of knowledge or a collaboration,” the researchers wrote.

Next, the research team pulled up patent applications filed by the companies of the employees. Such applications list relevant patents from other companies in so-called patent citations. Patent citations are “one measure of which firms are influencing each other and how firms are sharing ideas,” says Prof. Chen, who studies behavioral economics and strategy at UCLA’s Anderson School of Management.

The researchers then worked backward in time. They looked for places where employees of a patent-filing company may have crossed paths with workers from companies cited in the patent application.

“We rewind the clock to a year before when they would have been developing this technology,” says Prof. Chen. “What school were they dropping their kids off at, what mall were they shopping at, what bar do they frequent. And you infer who was at that bar when they were there,” based on the phone-location data.

The goal, Prof. Chen says, is “to connect workers of the firm that is going to file the patent, at the establishment where we infer that patent was innovated, with what other workers they were interacting with.”

Next, the researchers calculated the overall number of such citations that appear to have been linked to unplanned encounters. The upshot: The researchers say that without these encounters, there would have been about 8% fewer cross-firm patent citations in the period covered by the phone-location data.

“There is a tremendous correlation between my workers’ meeting a lot with your workers, and my workers’ citing your workers’ patent,” says Prof. Chen.

The innovation boost from the encounters, by the team’s calculations, is about twice as large as a similar effect found by other research that looked for knowledge transfer based on whether two companies’ offices are near each other, Prof. Chen says.

Their study comes with some caveats. The researchers don’t know whether these employees actually spoke when they were in the same location, or, if they spoke, what they talked about. And they don’t know whether the workers’ jobs would have facilitated a tech discussion—they might have involved a Google HR staffer and an Apple maintenance person.

Still, the report shines a light on what some experts have long suspected: that random conversations involving people in similar industries can increase innovation.

Enrico Moretti, an economics professor at the University of California, Berkeley, says the study “significantly advances our understanding of knowledge spillovers and how they shape the geography of innovation.” Prof. Moretti, who says he has been working on the topic for 25 years, says, “I find this paper to be one of the most direct and convincing pieces of evidence on this question. It provides important insights into why Silicon Valley-style clusters of innovation exist.”

Remote work’s impact
Though the study involved cellphone data from before Covid, the researchers say it has implications for an era when many people work all or part of the time from home.

The researchers looked at people who occasionally worked from home in the study period, based on where their phones were located during daytime hours, and then at how that affected their probability of attending planned or serendipitous meetings with someone from another company who didn’t work from home, Prof. Chen says.

Looking at two hypothetical companies, the researchers extrapolated that if one-half of employees at each business work from home, their meetings of all types—serendipitous and planned—would fall 35% and patent citations between the companies would decline almost 12%.

“We think this means information exchange between firms is decreasing,” Prof. Chen says. “It is worrying. These businesses co-locate for a reason. If they can’t learn from each other, we think that is a big deal.”

“Presumably,” he adds, “an even bigger effect is the harm that it does to serendipity and flow of information and innovation within the firm.”

By BART ZIEGLER
Wed, May 17, 2023 4:31pmGrey Clock 3 min

Do chance encounters among employees of different Silicon Valley companies in coffee shops, restaurants and other public places lead to innovation? The answer is yes, say researchers who examined such “knowledge spillovers” in a study that may have implications for today’s work-from-home culture.

The researchers—Keith Chen of the University of California, Los Angeles, and David Atkin and Anton Popov of the Massachusetts Institute of Technology—tracked the locations of 425,000 phones using commercially available cellphone-location data. Though the data is anonymous and linked only to the unique ID number of each phone, the researchers surmised where the phone owners worked by looking at where the phones spent large parts of the workday, using a map of buildings occupied by Silicon Valley companies that have filed patents.

Examining instances where phone owners went outside the office and ended up near someone from another Silicon Valley company, they found 218 million episodes in which two workers from different companies were in the same place between September 2016 and November 2017.

For their study, they considered only situations in which both people were near each other for at least a half-hour, and used a probability technique to eliminate meetings that might have been arranged in advance. They also assumed that many of these people bumped into someone they already knew, such as a former colleague.

Sharing knowledge

Such chance meetings “may spark a conversation that leads to a transfer of knowledge or a collaboration,” the researchers wrote.

Next, the research team pulled up patent applications filed by the companies of the employees. Such applications list relevant patents from other companies in so-called patent citations. Patent citations are “one measure of which firms are influencing each other and how firms are sharing ideas,” says Prof. Chen, who studies behavioral economics and strategy at UCLA’s Anderson School of Management.

The researchers then worked backward in time. They looked for places where employees of a patent-filing company may have crossed paths with workers from companies cited in the patent application.

“We rewind the clock to a year before when they would have been developing this technology,” says Prof. Chen. “What school were they dropping their kids off at, what mall were they shopping at, what bar do they frequent. And you infer who was at that bar when they were there,” based on the phone-location data.

The goal, Prof. Chen says, is “to connect workers of the firm that is going to file the patent, at the establishment where we infer that patent was innovated, with what other workers they were interacting with.”

Next, the researchers calculated the overall number of such citations that appear to have been linked to unplanned encounters. The upshot: The researchers say that without these encounters, there would have been about 8% fewer cross-firm patent citations in the period covered by the phone-location data.

“There is a tremendous correlation between my workers’ meeting a lot with your workers, and my workers’ citing your workers’ patent,” says Prof. Chen.

The innovation boost from the encounters, by the team’s calculations, is about twice as large as a similar effect found by other research that looked for knowledge transfer based on whether two companies’ offices are near each other, Prof. Chen says.

Their study comes with some caveats. The researchers don’t know whether these employees actually spoke when they were in the same location, or, if they spoke, what they talked about. And they don’t know whether the workers’ jobs would have facilitated a tech discussion—they might have involved a Google HR staffer and an Apple maintenance person.

Still, the report shines a light on what some experts have long suspected: that random conversations involving people in similar industries can increase innovation.

Enrico Moretti, an economics professor at the University of California, Berkeley, says the study “significantly advances our understanding of knowledge spillovers and how they shape the geography of innovation.” Prof. Moretti, who says he has been working on the topic for 25 years, says, “I find this paper to be one of the most direct and convincing pieces of evidence on this question. It provides important insights into why Silicon Valley-style clusters of innovation exist.”

Remote work’s impact

Though the study involved cellphone data from before Covid, the researchers say it has implications for an era when many people work all or part of the time from home.

The researchers looked at people who occasionally worked from home in the study period, based on where their phones were located during daytime hours, and then at how that affected their probability of attending planned or serendipitous meetings with someone from another company who didn’t work from home, Prof. Chen says.

Looking at two hypothetical companies, the researchers extrapolated that if one-half of employees at each business work from home, their meetings of all types—serendipitous and planned—would fall 35% and patent citations between the companies would decline almost 12%.

“We think this means information exchange between firms is decreasing,” Prof. Chen says. “It is worrying. These businesses co-locate for a reason. If they can’t learn from each other, we think that is a big deal.”

“Presumably,” he adds, “an even bigger effect is the harm that it does to serendipity and flow of information and innovation within the firm.”



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Nvidia’s earnings will test Wall Street’s confidence in the AI boom.

By David Uberti and Krystal Hur
Mon, Aug 24, 2026 3 min

Chip makers are fighting to assure investors that the artificial-intelligence boom is racing forward. Wall Street might not believe it until Nvidia’s NVDA -0.98%decrease; down pointing triangle Jensen Huang says so.

When Huang steps up to the mic for his company’s earnings call Wednesday, he will have the world’s attention. What he says about Nvidia’s present will preview the future of AI, dictate the path forward for a tech-crazed stock market and influence an American economy increasingly tethered to hopes that the boom won’t go bust.

The $5 trillion chip maker has provided the key building blocks for AI since the launch of ChatGPT in 2022 set off a race for dominance among OpenAI, Anthropic and established Silicon Valley giants. Now, as Nvidia backstops sprawling data-center projects and an exotic money pipeline to boost chip demand, the company’s influence is arguably bigger than ever.

But there are signs of trouble ahead. Political pushback to AI is growing. A bond selloff propelled borrowing costs to their highest levels in years. The hyperscalers that include some of Nvidia’s key customers—once cash-printing machines—are relying more on debt. OpenAI recently told investors its revenue rose by a tepid 18% in the second quarter while its losses deepened.

Nvidia is increasingly stepping in to shore up potential weak points across the market. Earlier this month, the company teamed up with six of Wall Street’s biggest firms on a $500 billion AI-financing plan, pledging to backstop lending to customers that can’t afford its chips otherwise. The chip maker last week also took a stake in Cloverleaf Infrastructure, which arranges power for data centers, and struck a $6 billion deal with startup Poolside aimed at developing a powerful open-weight AI model.

After watching shares in other chip makers and the so-called Magnificent Seven tech companies swing wildly in recent months, Wall Street is hoping Nvidia can beat expectations—again. The countdown is on.

“It’s kind of becoming more and more like the World Cup final than the Super Bowl at this point,” said Brian Mulberry, chief market strategist at Zacks Investment Management. “It’s just gotten to be that big.”

The company has smashed analysts’ earnings estimates for each of the 14 quarters since the AI boom kicked into high gear. Nvidia posted 210% annual growth in net income in its last three-month period, according to FactSet, making Wall Street’s 126% projection look pedestrian.

Expectations for a blowout second quarter have risen rapidly over the course of this year. All Nvidia will have to do to beat this target: outrun 95% annual earnings growth to more than $51.5 billion. Analysts project the chip maker will report record sales of $92 billion for the period, up from a forecast of $78 billion at the start of this year.

In July, big-tech earnings sparked volatility. Concerns about runaway capital spending spread across the sector after Alphabet’s and Tesla’s results, driving a $890 billion wipeout that contributed to the unwind of hedge fund Situational Awareness. Microsoft posted the largest one-day gain in market capitalization by any company, ever, after a quarter proving that it could still show investors the money. SpaceX rocketed higher after a record-breaking initial public offering, only to see $1 trillion in value evaporate.

Surging memory prices and borrowing costs have fueled fears that those and other companies will be unable to keep plowing more money into supplies including Nvidia chips. Shaia Hosseinzadeh, founder of OnyxPoint Global Management, has recently bought dips in AI-infrastructure stocks when Wall Street has strained to absorb massive debt issued by Silicon Valley.

“The macro data is really quite robust,” he said. “Of course, there’s a level at which everything breaks.”

Investors have kept pumping money into the AI trade despite concerns around chip consumers—and to the benefit of chip producers. That is why Nvidia’s outlook for semiconductor demand could send ripples through counterparts such as Micron Technology and Sandisk, developers of the data centers in which their chips reside, and a supply chain of power producers, contractors and other specialists that underpin the globe-spanning AI build-out.

“We joke internally that we’re all Nvidia analysts now,” said David Lefkowitz, head of U.S. equities at UBS Global Wealth Management.

The irony is that investors have tended to sell Nvidia stock immediately after blockbuster earnings, with shares falling each trading session after its four past quarterly reports. Some are betting that will be the case this time around, too.

The options market is pricing in a 5.3% swing, higher or lower, in Nvidia shares during the session following earnings, according to Option Research & Technology Services. That is higher than the 4.8% average move in Nvidia’s stock over the last 12 months after the company reports quarterly results.

In recent days, some of the most actively traded Nvidia options have been put contracts tied to the stock falling from its Friday value of $214.75 to $205 and $210 apiece, according to Cboe Global Markets data. Put options give the right to sell a stock by a set price and typically represent a bearish wager.

Many analysts remain optimistic. Frank Lee, global head of tech hardware and semiconductor research at HSBC Global Investment Research, recently raised his price target for Nvidia shares to $360 from $325, citing, among other things, Nvidia’s strategic partnerships with suppliers and its role as a top contributor to open-source AI.