MAROKO133 Update ai: Listen Labs raises $69M after viral billboard hiring stunt to scale A

📌 MAROKO133 Update ai: Listen Labs raises $69M after viral billboard hiring stunt

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…

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🔗 Sumber: venturebeat.com

📌 MAROKO133 Breaking ai: Cereal-box-sized NASA spacecraft sends first UV images of

NASA has received the first images from a tiny spacecraft designed to study the stars that host distant planets. The mission, called the Star-Planet Activity Research CubeSat, or SPARCS, will track how stellar activity affects the habitability of nearby worlds.

The first images confirm the spacecraft’s instruments operate correctly in space. Scientists can now begin studying ultraviolet radiation from some of the galaxy’s most common stars.

SPARCS launched on Jan. 11. The spacecraft transmitted its first images on Feb. 6 after engineers completed early checks.

The data has since been processed, marking a key milestone known as “first light.” The event confirms the telescope and detectors work properly in orbit.

First light signals that a spacecraft’s instruments function correctly in space. For SPARCS, the test matters because the mission depends on precise ultraviolet measurements.

The spacecraft is roughly the size of a large cereal box. Despite its small size, it will study a key group of stars.

These low-mass stars contain about 30 percent to 70 percent of the Sun’s mass.

They are among the most common stars in the Milky Way and host many rocky planets.

Watching active stars

SPARCS will study about 20 low-mass stars during its one-year mission. The spacecraft will observe each target for five to 45 days.

Scientists want to understand how often these stars flare and how intense those eruptions become. Stellar flares release radiation that can affect nearby planetary atmospheres.

Although these stars appear cooler and dimmer than the Sun, they flare far more frequently.

Understanding the host star helps scientists judge whether surrounding planets could remain habitable.

New ultraviolet technology

SPARCS also tests advanced ultraviolet detector technology developed at NASA’s Jet Propulsion Laboratory in Southern California.

The mission uses a camera called SPARCam. Engineers built it with specialized filters placed directly on sensitive ultraviolet detectors.

“I am so excited that we are on the brink of learning about exoplanets’ host stars and the effect of their activities on the planets’ potential habitability,” said Shouleh Nikzad, the lead developer of the SPARCS camera (dubbed SPARCam) and the chief technologist at NASA‘s Jet Propulsion Laboratory in Southern California.

“We took silicon-based detectors — the same technology as in your smartphone camera — and we created a high-sensitivity UV imager. Then we integrated filters into the detector to reject the unwanted light.”

“That is a huge leap forward to doing big science in small packages,” Nikzad said, “and SPARCS serves to demonstrate their long-term performance in space.”

“The SPARCS mission brings all of these pieces together — focused science, cutting-edge detectors, and intelligent onboard processing — to deepen our understanding of the stars that most planets in the galaxy call home,” said David Ardila, SPARCS instrument scientist at JPL.

“By watching these stars in ultraviolet light in a way we’ve never done before, we’re not just studying flares.” He added that the observations will help scientists interpret the habitability of distant planets.

🔗 Sumber: interestingengineering.com