MAROKO133 Eksklusif ai: Listen Labs raises $69M after viral billboard hiring stunt to scal

📌 MAROKO133 Eksklusif ai: Listen Labs raises $69M after viral billboard hiring stu

Alfred Wahlforss was running out of options. His startup, Listen Labs, needed to hire over 100 engineers, but competing against Mark Zuckerberg's $100 million offers seemed impossible. So he spent $5,000 — a fifth of his marketing budget — on a billboard in San Francisco displaying what looked like gibberish: five strings of random numbers.

The numbers were actually AI tokens. Decoded, they led to a coding challenge: build an algorithm to act as a digital bouncer at Berghain, the Berlin nightclub famous for rejecting nearly everyone at the door. Within days, thousands attempted the puzzle. 430 cracked it. Some got hired. The winner flew to Berlin, all expenses paid.

That unconventional approach has now attracted $69 million in Series B funding, led by Ribbit Capital with participation from Evantic and existing investors Sequoia Capital, Conviction, and Pear VC. The round values Listen Labs at $500 million and brings its total capital to $100 million. In nine months since launch, the company has grown annualized revenue by 15x to eight figures and conducted over one million AI-powered interviews.

"When you obsess over customers, everything else follows," Wahlforss said in an interview with VentureBeat. "Teams that use Listen bring the customer into every decision, from marketing to product, and when the customer is delighted, everyone is."

Why traditional market research is broken, and what Listen Labs is building to fix it

Listen's AI researcher finds participants, conducts in-depth interviews, and delivers actionable insights in hours, not weeks. The platform replaces the traditional choice between quantitative surveys — which provide statistical precision but miss nuance—and qualitative interviews, which deliver depth but cannot scale.

Wahlforss explained the limitation of existing approaches: "Essentially surveys give you false precision because people end up answering the same question… You can't get the outliers. People are actually not honest on surveys." The alternative, one-on-one human interviews, "gives you a lot of depth. You can ask follow up questions. You can kind of double check if they actually know what they're talking about. And the problem is you can't scale that."

The platform works in four steps: users create a study with AI assistance, Listen recruits participants from its global network of 30 million people, an AI moderator conducts in-depth interviews with follow-up questions, and results are packaged into executive-ready reports including key themes, highlight reels, and slide decks.

What distinguishes Listen's approach is its use of open-ended video conversations rather than multiple-choice forms. "In a survey, you can kind of guess what you should answer, and you have four options," Wahlforss said. "Oh, they probably want me to buy high income. Let me click on that button versus an open ended response. It just generates much more honesty."

The dirty secret of the $140 billion market research industry: rampant fraud

Listen finds and qualifies the right participants in its global network of 30 million people. But building that panel required confronting what Wahlforss called "one of the most shocking things that we've learned when we entered this industry"—rampant fraud.

"Essentially, there's a financial transaction involved, which means there will be bad players," he explained. "We actually had some of the largest companies, some of them have billions in revenue, send us people who claim to be kind of enterprise buyers to our platform and our system immediately detected, like, fraud, fraud, fraud, fraud, fraud."

The company built what it calls a "quality guard" that cross-references LinkedIn profiles with video responses to verify identity, checks consistency across how participants answer questions, and flags suspicious patterns. The result, according to Wahlforss: "People talk three times more. They're much more honest when they talk about sensitive topics like politics and mental health."

Emeritus, an online education company that uses Listen, reported that approximately 20% of survey responses previously fell into the fraudulent or low-quality category. With Listen, they reduced this to almost zero. "We did not have to replace any responses because of fraud or gibberish information," said Gabrielli Tiburi, Assistant Manager of Customer Insights at Emeritus.

How Microsoft, Sweetgreen, and Chubbies are using AI interviews to build better products

The speed advantage has proven central to Listen's pitch. Traditional customer research at Microsoft could take four to six weeks to generate insights. "By the time we get to them, either the decision has been made or we lose out on the opportunity to actually influence it," said Romani Patel, Senior Research Manager at Microsoft.

With Listen, Microsoft can now get insights in days, and in many cases, within hours.

The platform has already powered several high-profile initiatives. Microsoft used Listen Labs to collect global customer stories for its 50th anniversary celebration. "We wanted users to share how Copilot is empowering them to bring their best self forward," Patel said, "and we were able to collect those user video stories within a day." Traditionally, that kind of work would have taken six to eight weeks.

Simple Modern, an Oklahoma-based drinkware company, used Listen to test a new product concept. The process took about an hour to write questions, an hour to launch the study, and 2.5 hours to receive feedback from 120 people across the country. "We went from 'Should we even have this product?' to 'How should we launch it?'" said Chris Hoyle, the company's Chief Marketing Officer.

Chubbies, the shorts brand, achieved a 24x increase in youth research participation—growing from 5 to 120 participants — by using Listen to overcome the scheduling challenges of traditional focus groups with children. "There's school, sports, dinner, and homework," explained Lauren Neville, Director of Insights and Innovation. "I had to find a way to hear from them that fit into their schedules."

The company also discovered product issues through AI interviews that might have gone undetected otherwise. Wahlforss described how the AI "through conversations, realized there were like issues with the the kids short line, and decided to, like, interview hundreds of kids. And I understand that there were issues in the liner of the shorts and that they were, like, scratchy, quote, unquote, according to the people interviewed." The redesigned product became "a blockbuster hit."

The Jevons paradox explains why cheaper research creates more demand, not less

Listen Labs is entering a massive but fragmented market. Wahlforss cited research from Andreessen Horowitz estimating the market research ind…

Konten dipersingkat otomatis.

🔗 Sumber: venturebeat.com

📌 MAROKO133 Update ai: An Analysis Just Found Something Extremely Unflattering Abo

After an analyst found that users are losing money faster on sites like Polymarket than on traditional sports gambling platforms, the US’s largest prediction market Kalshi hit back with a decidedly unusual response.

As Bloomberg reports, the betting platform told it that the analysis was part of an “extortion plot” by the startup that had collected the data, Juice Reel — before, even more puzzlingly, backing off from the stunning claim. 

The report was conducted by Jordan Bender, an equity research analyst at the bank Citizens. Using Juice Reel’s data, it found that in the first three months, users on prediction markets like Kalshi were losing more money than on traditional gambling sites like FanDuel and DraftKings, in proportion to the amount of money they wagered.

If true, the findings put a considerable dent in prediction markets’ self-styled image as a more cerebral form of betting, allowing customers to place wagers on a variety of real-world events, ranging from presidential elections to natural disasters to military interventions. And because bettors are competing against each other, instead of the house, these sites claim that means users have a more reliable way of making money.

Though they’re essentially dressed-up casinos, they also seek to be seen as an authority on the future itself. Last month, for example, Kalshi partnered with CNN to provide real time prediction data on its broadcasts. Polymarket announced a similar partnership with Dow Jones, the publisher of The Wall Street Journal, barely a week later. These add heft and credibility to their projections, even though Kalshi’s and Polymarket’s projections are entirely determined by users’ bets.

Of course, if Kalshi and similar services are just as much as a money sink as plain old gambling, then that elevated veneer vanishes. And they appear to: Bender found that the bottom quarter of users lost about 28 centers of every dollar they put on the lane, compared to just 11 cents per dollar on traditional gambling hubs.

Kalshi’s tried-and-true defense against these claims? Calling the data “flat-out wrong,” it said in a statement to Bloomberg. Its head of communications Elisabeth Diana accused Juice Reel of having a conflict of interest because it sought “investment support” from Kalshi in the past. She also claimed that Juice Reel’s founder offered to “defuse the situation” if he was given a meeting with Kalshi’s CEO.

“Please consider the source and its motives,” she told Bloomberg in an emailed statement. “This is extortion.”

Juice Reel’s CEO Ricky Gold tells it differently. “They called and messaged us, pressuring us to tell Bloomberg that our data is inaccurate,” Gold told the publication. “We stand for transparency, we stand for helping bettors, traders understand their activity across the platform, and we stand behind our data.”

Kalshi continued to dispute the findings and denied pressuring Gold, per the reporting. But it’s now changed its tune on the whole alleged blackmail angle. “We are in ongoing discussions about Juice Reel’s legal ability to obtain our data, but after further review, we don’t believe the intention was extortion,” the company said in an updated statement last week.

The touchy response from Kalshi also comes amid increasing calls to regulate prediction markets after several scandals captured national attention. The most significant came in January, when a slew of traders on Polymarket placed sizable bets on the prediction that US forces would enter Venezuela just hours before Trump launched the operation to capture the country’s leader Nicolas Maduro. One “lucky” bettor made more than $400,000.

More on gambling: Golden Globes Constantly Begs Viewers to Place Polymarket Bets

The post An Analysis Just Found Something Extremely Unflattering About What Happens to Users of Prediction Markets appeared first on Futurism.

🔗 Sumber: futurism.com