Alfred Wahlforss was running out of options. His startup, Listen Labs, needed to rent over 100 engineers, but competing against Mark Zuckerberg's $100 million offers seemed unattainable. 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: construct an algorithm to act as a digital bouncer at Berghain, the Berlin nightclub famous for rejecting nearly everyone on the door. Inside days, hundreds 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 corporate has grown annualized revenue by 15x to eight figures and conducted over a million AI-powered interviews.
"Once you obsess over customers, the whole lot else follows," Wahlforss said in an interview with VentureBeat. "Teams that use Listen bring the shopper into every decision, from marketing to product, and when the shopper is delighted, everyone seems to be."
Why traditional market research is broken, and what Listen Labs is constructing to repair it
Listen's AI researcher finds participants, conducts in-depth interviews, and delivers actionable insights in hours, not weeks. The platform replaces the standard selection between quantitative surveys — which give statistical precision but miss nuance—and qualitative interviews, which deliver depth but cannot scale.
Wahlforss explained the limitation of existing approaches: "Essentially surveys offer you false precision because people find yourself answering the identical query… You may't get the outliers. Persons are actually not honest on surveys." The choice, one-on-one human interviews, "gives you numerous depth. You may ask follow up questions. You may sort of double check in the event that they actually know what they're talking about. And the issue is you’ll be able to't scale that."
The platform works in 4 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 relatively than multiple-choice forms. "In a survey, you’ll be able to sort of guess what it is best to answer, and you could have 4 options," Wahlforss said. "Oh, they probably want me to purchase high income. Let me click on that button versus an open ended response. It just generates rather more honesty."
The dirty secret of the $140 billion market research industry: rampant fraud
Listen finds and qualifies the appropriate participants in its global network of 30 million people. But constructing that panel required confronting what Wahlforss called "probably the most shocking things that we've learned after we entered this industry"—rampant fraud.
"Essentially, there's a financial transaction involved, which implies there can be bad players," he explained. "We actually had a number of the largest corporations, a few of them have billions in revenue, send us individuals who claim to be sort of enterprise buyers to our platform and our system immediately detected, like, fraud, fraud, fraud, fraud, fraud."
The corporate built what it calls a "quality guard" that cross-references LinkedIn profiles with video responses to confirm identity, checks consistency across how participants answer questions, and flags suspicious patterns. The result, based on Wahlforss: "People talk thrice more. They're rather more honest once they speak about sensitive topics like politics and mental health."
Emeritus, a web-based education company that uses Listen, reported that roughly 20% of survey responses previously fell into the fraudulent or low-quality category. With Listen, they reduced this to almost zero. "We didn’t have to switch any responses due to fraud or gibberish information," said Gabrielli Tiburi, Assistant Manager of Customer Insights at Emeritus.
How Microsoft, Sweetgreen, and Chubbies are using AI interviews to construct higher products
The speed advantage has proven central to Listen's pitch. Traditional customer research at Microsoft could take 4 to 6 weeks to generate insights. "By the point we get to them, either the choice has been made or we lose out on the chance to truly influence it," said Romani Patel, Senior Research Manager at Microsoft.
With Listen, Microsoft can now get insights in days, and in lots of cases, inside hours.
The platform has already powered several high-profile initiatives. Microsoft used Listen Labs to gather global customer stories for its fiftieth anniversary celebration. "We wanted users to share how Copilot is empowering them to bring their best self forward," Patel said, "and we were in a position to collect those user video stories inside a day." Traditionally, that sort of work would have taken six to eight weeks.
Easy Modern, an Oklahoma-based drinkware company, used Take heed to test a brand new product concept. The method took about an hour to write down questions, an hour to launch the study, and a pair of.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 corporate's Chief Marketing Officer.
Chubbies, the shorts brand, achieved a 24x increase in youth research participation—growing from 5 to 120 participants — by utilizing Take heed 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 seek out a option to hear from them that fit into their schedules."
The corporate also discovered product issues through AI interviews that may need gone undetected otherwise. Wahlforss described how the AI "through conversations, realized there have been like issues with the the youngsters short line, and decided to, like, interview a whole lot of youngsters. And I understand that there have been issues within the liner of the shorts and that they were, like, scratchy, quote, unquote, based on 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 large but fragmented market. Wahlforss cited research from Andreessen Horowitz estimating the market research industry at roughly $140 billion annually, populated by legacy players — some with greater than a billion dollars in revenue — that he believes are vulnerable to disruption.
"There are very much existing budget lines that we’re replacing," Wahlforss said. "Why we're replacing them is that one, they're super costly. Two, they're sort of stuck on this old paradigm of selecting between a survey or interview, and in addition they take months to work with."
However the more intriguing dynamic could also be that AI-powered research doesn't just replace existing spending — it creates recent demand. Wahlforss invoked the Jevons paradox, an economic principle that happens when technological advancements make a resource more efficient to make use of, but increased efficiency results in increased overall consumption relatively than decreased consumption.
"What I've noticed is that as something gets cheaper, you don't need less of it. You would like more of it," Wahlforss explained. "There's infinite demand for customer understanding. So the researchers on the team can do an order of magnitude more research, and in addition other individuals who weren't researchers before can now try this as a part of their job."
Contained in the elite engineering team that built Listen Labs before that they had a working toilet
Listen Labs traces its origins to 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 in the future," Wahlforss recalled. "We had all these users, and we were considering like, okay, what can we do to get to know them higher? And we built this prototype of what Listen is today."
The founding team brings an unusual pedigree. Wahlforss's co-founder "was the national champion in competitive programming in Germany, and he worked at Tesla Autopilot." The corporate claims that 30% of its engineering team are medalists from the International Olympiad in Informatics — the identical competition that produced the founders of Cognition, the AI coding startup.
The Berghain billboard stunt generated roughly 5 million views across social media, based on Wahlforss. It reflected the intensity of the talent war within the Bay Area.
"We needed to do these items because a few of our, like early employees, joined the corporate before we had a working toilet," he said. "But now we fixed that situation."
The corporate grew from 5 to 40 employees in 2024 and plans to achieve 150 this yr. It hires engineers for non-engineering roles across marketing, growth, and operations — a bet that within the AI era, technical fluency matters in every single place.
Synthetic customers and automatic decisions: what Listen Labs is constructing next
Wahlforss outlined an ambitious product roadmap that pushes into more speculative territory. The corporate is constructing "the flexibility to simulate your customers, so you’ll be able to take all of those interviews we've done, after which extrapolate based on that and create synthetic users or simulated user voices."
Beyond simulation, Listen goals to enable automated motion based on research findings. "Are you able to not only make recommendations, but additionally create spawn agents to either change things in code or some customer churns? Are you able to give them a reduction and check out to bring them back?"
Wahlforss acknowledged the moral implications. "Obviously, as you said, there's sort of ethical concerns there. Of like, automated decision making overall may be bad, but we can have considerable guardrails to be sure that that the businesses are at all times within the loop."
The corporate already handles sensitive data with care. "We don't train on any of the information," Wahlforss said. "We may even scrub any sensitive PII mechanically so the model can detect that. And there are occasions when, for instance, you’re employed with investors, where in the event you unintentionally mention something that may very well be material, non public information, the AI can actually detect that and take away any information like that."
How AI could reshape the longer term of product development
Perhaps essentially the most provocative implication of Listen's model is the way it could reshape product development itself. Wahlforss described a customer — an Australian startup — that has adopted what amounts to a continuous feedback loop.
"They're based in Australia, so that they're coding throughout the day, after which of their night, they're releasing a Listen study with an American audience. Listen validates whatever they built throughout the day, and so they get feedback on that. They’ll then plug that feedback directly into coding tools like Claude Code and iterate."
The vision extends Y Combinator's famous dictum — "write code, confer with users" — into an automatic cycle. "Write code is now getting automated. And I believe like confer with users can be as well, and also you'll have this type of infinite loop where you’ll be able to begin to ship this truly amazing product, almost sort of autonomously."
Whether that vision materializes relies on aspects beyond Listen's control — the continued improvement of AI models, enterprise willingness to trust automated research, and whether speed truly correlates with higher products. A 2024 MIT study found that 95% of AI pilots fail to maneuver into production, a statistic Wahlforss cited as the rationale he emphasizes quality over demos.
"I'm consistently need to emphasize like, let's be sure that the standard is there and the small print are right," he said.
But the corporate's growth suggests appetite for the experiment. 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 present everyone in the corporate a login. Sling Money, a stablecoin payments startup, can create a survey in ten minutes and receive results the identical day.
"It's a complete game changer," said Ali Romero, Sling Money's marketing manager.
Wahlforss has a special phrase for what he's constructing. When asked in regards to the tension between speed and rigor — the long-held belief that moving fast means cutting corners — he cited Nat Friedman, the previous GitHub CEO and Listen investor, who keeps an inventory of one-liners on his website.
One in every of them: "Slow is fake."
It's an aggressive claim for an industry built on methodological caution. But Listen Labs is betting that within the AI era, the businesses that listen fastest can be those that win. The one query is whether or not customers will talk back.
