Alfred Wahlforss faced a recruiting crisis. His startup, Listen Labs, needed more than 100 engineers, but couldn't match Mark Zuckerberg's $100 million compensation packages. His solution: spend $5,000—one-fifth of his marketing budget—on a San Francisco billboard displaying five strings of seemingly random numbers.
The numbers were AI tokens. Decoded, they revealed a coding challenge: build an algorithm to simulate Berghain's notoriously selective door policy. The Berlin nightclub rejects most applicants, and Wahlforss wanted engineers who could model that complexity. Thousands attempted the puzzle. 430 solved it. Several joined the company. The winner received an all-expenses-paid trip to Berlin.
That recruiting gambit now looks prescient. Listen Labs has raised $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 the company at $500 million and brings total funding to $100 million. Since launching nine months ago, Listen has grown annualized revenue 15x to eight figures and conducted over one million AI-powered interviews.
"When you obsess over customers, everything else follows," Wahlforss told VentureBeat. "Teams that use Listen bring the customer into every decision, from marketing to product, and when the customer is delighted, everyone is."
The market research problem Listen Labs is solving
Listen's AI researcher recruits participants, conducts video interviews, and delivers insights in hours instead of weeks. The platform addresses a longstanding trade-off in market research: quantitative surveys provide scale but miss nuance, while qualitative interviews capture depth but don't scale.
"Surveys give you false precision because people end up answering the same question," Wahlforss said. "You can't get the outliers. People are actually not honest on surveys." Traditional one-on-one interviews solve for depth—"you can ask follow-up questions, you can double check if they actually know what they're talking about"—but remain prohibitively expensive at scale.
Listen's workflow has four steps: users create a study with AI assistance, the platform recruits participants from a network of 30 million people, an AI moderator conducts video interviews with adaptive follow-up questions, and results are packaged into reports with key themes, highlight reels, and presentation decks.
The platform's use of open-ended video conversations, rather than multiple-choice forms, changes participant behavior. "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 say high income. Let me click on that button. An open-ended response just generates much more honesty."
Confronting fraud in the $140 billion market research industry
Listen recruits and qualifies participants from its 30 million-person panel. Building that network required addressing what Wahlforss called "one of the most shocking things we've learned when we entered this industry"—widespread fraud.
"There's a financial transaction involved, which means there will be bad players," he said. "We had some of the largest companies, some with billions in revenue, send us people who claimed to be enterprise buyers. Our system immediately detected: fraud, fraud, fraud, fraud, fraud."
The company built a "quality guard" that cross-references LinkedIn profiles with video responses to verify identity, checks consistency across answers, and flags suspicious patterns. According to Wahlforss, the result is that "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 using Listen, reported that approximately 20% of survey responses previously fell into the fraudulent or low-quality category. With Listen, that figure dropped to nearly 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 use AI interviews
Speed has become Listen's primary selling point. Traditional customer research at Microsoft took four to six weeks. "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 now receives insights in days, sometimes hours.
Microsoft used Listen Labs to collect global customer stories for its 50th anniversary. "We wanted users to share how Copilot is empowering them to bring their best self forward," Patel said. "We were able to collect those user video stories within a day." The traditional timeline would have been six to eight weeks.
Simple Modern, an Oklahoma drinkware company, used Listen to test a new product concept. The process took one hour to write questions, one hour to launch the study, and 2.5 hours to receive feedback from 120 people nationwide. "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—from 5 to 120 participants—by using Listen to bypass the scheduling constraints 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 platform also surfaced product issues that might have gone undetected. Wahlforss described how the AI "through conversations, realized there were issues with the kids short line, and decided to interview hundreds of kids. They discovered issues in the liner of the shorts—they were scratchy, quote unquote, according to the people interviewed." The redesigned product became "a blockbuster hit."
Why cheaper research creates more demand, not less
Listen Labs is entering a massive but fragmented market. Wahlforss cited Andreessen Horowitz research estimating the market research industry at roughly $140 billion annually, dominated by legacy players—some with over a billion dollars in revenue—that he believes are vulnerable to disruption.
"There are very much existing budget lines that we are replacing," Wahlforss said. "Why we're replacing them is that one, they're super costly. Two, they're stuck in this old paradigm of choosing between a survey or interview, and they also take months to work with."
But AI-powered research may not simply replace existing spending—it could create new demand. Wahlforss invoked the Jevons paradox, an economic principle stating that when technological improvements increase the efficiency of resource use, total consumption of that resource often increases rather than decreases.
"What I've noticed is that as something gets cheaper, you don't need less of it. You want more of it," Wahlforss said. "There's infinite demand for customer understanding. The researchers on the team can do an order of magnitude more research, and other people who weren't researchers before can now do that as part of their job."
Building an elite engineering team before installing a working toilet
Listen Labs began as a consumer app that Wahlforss and his co-founder built after meeting at Harvard. "We built this consumer app that got 20,000 downloads in one day," Wahlforss said. "We had all these users, and we were thinking, okay, what can we do to get to know them better? We built this prototype of what Listen is today."
The founding team brings unusual credentials. Wahlforss's co-founder "was the national champion in competitive programming in Germany, and he worked at Tesla Autopilot." The company claims that 30% of its engineering team are medalists from the International Olympiad in Informatics—the same competition that produced the founders of Cognition, the AI coding startup.
The Berghain billboard stunt generated approximately 5 million views across social media, according to Wahlforss. It reflected the intensity of the Bay Area talent war.
"We had to do these things because some of our early employees joined the company before we had a working toilet," he said. "But now we fixed that situation."
The company grew from 5 to 40 employees in 2024 and plans to reach 150 this year. It hires engineers for non-engineering roles across marketing, growth, and operations—a bet that technical fluency matters everywhere in the AI era.
Synthetic customers and automated decisions on the roadmap
Wahlforss outlined an ambitious product roadmap. The company is building "the ability to simulate your customers, so you can take all of those interviews we've done, and then extrapolate based on that and create synthetic users or simulated user voices."
Beyond simulation, Listen aims to enable automated action based on research findings. "Can you not just make recommendations, but also spawn agents to either change things in code or—if some customer churns—can you give them a discount and try to bring them back?"
Wahlforss acknowledged the ethical implications. "Obviously, there's ethical concerns there. Automated decision making overall can be bad, but we will have considerable guardrails to make sure that the companies are always in the loop."
Listen Labs takes data privacy seriously. "We don't train on any of the data," Wahlforss said. "We will also scrub any sensitive PII automatically so the model can detect that. And there are times when, for example, you work with investors, where if you accidentally mention something that could be material, non public information, the AI can actually detect that and remove any information like that."
How AI could reshape the future of product development
Perhaps the most provocative implication of Listen's model is what it could mean for product development itself. Wahlforss described one customer — an Australian startup — that has built what amounts to a continuous feedback loop between engineering and research.
"They're based in Australia, so they're coding during the day, and then in their night, they're releasing a Listen study with an American audience. Listen validates whatever they built during the day, and they get feedback on that. They can then plug that feedback directly into coding tools like Claude Code and iterate."
The vision extends Y Combinator's famous dictum — "write code, talk to users" — into a self-sustaining cycle. "Write code is now getting automated. And I think like talk to users will be as well, and you'll have this kind of infinite loop where you can start to ship this truly amazing product, almost kind of autonomously."
Whether that vision materializes depends on factors well beyond Listen's control: continued improvement of underlying AI models, enterprise willingness to trust automated research, and whether speed genuinely correlates with better outcomes. A 2024 MIT study found that 95% of AI pilots fail to reach production — a statistic Wahlforss cited as precisely why he prioritizes quality over flashy demonstrations.
"I'm constantly have to emphasize like, let's make sure the quality is there and the details are right," he said.
The company's growth, however, suggests genuine market appetite. Microsoft's Patel said Listen has "removed the drudgery of research and brought the fun and joy back into my work." Chubbies is now pushing its founder to give every employee a login. And Sling Money, a stablecoin payments startup, can now build a survey in ten minutes and receive results the same day.
"It's a total game changer," said Ali Romero, Sling Money's marketing manager.
Wahlforss has a more pointed way of framing what he's building. When pressed on the tension between speed and rigor — the long-held industry belief that moving fast means cutting corners — he reached for a line from Nat Friedman, the former GitHub CEO and Listen investor, who publishes a collection of aphorisms on his website.
One of them: "Slow is fake."
It's a provocative claim for an industry long defined by methodological caution. But Listen Labs is betting that in the AI era, the companies that listen fastest will be the ones that win. The only question is whether customers will keep talking back.