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

📌 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 Eksklusif ai: Devious Prankster Posts Real Monet Painting, Tells Peopl

A poster wrought some moderate havoc this week when they shared a cropped image of a real Monet painting while claiming it was an AI fake, unleashing a flood of ill-informed reactions and muddled discourse. So, you know, it was just another day online.

“I just generated an image in the style of a Monet painting using AI,” read the original post, published to X-formerly-Twitter yesterday by an anonymous conceptual artist who goes by the pseudonym “SHL0MS.”

“Please describe, in as much detail as possible,” he continued, “what makes this inferior to a real Monet painting.”

Commenters were quick to jump in to explain why, in their view, the alleged AI image was worse than the real work of the French impressionist master. According to one, the image was an “incoherent muddle of inconsistently saturated greens.” Another lamented that there was no “coherent composition,” while someone else shared that the painting seemed “busy, artificial, nature in turmoil, polluted.” Another commenter said that the allegedly AI-generated image seemed as if it was “trying too hard” to resemble Monet’s later paintings, which he created when he was close to blindness. Others shared that the image was “obvious” AI slop.

“In terms of composition. It is (to me) emotionless. There is some spark missing,” one poster remarked. “It is not Monet, it feel like an undergrad art student’s study from a museum visit.”

“Unlike Monet, your AI model is not painting with advanced myopia and dramatic gusto during a period of artistic rebellion in Paris,” said another. “Inferior.”

Others were less highbrow in their dismissals.

“It looks like sh*t,” one person commented, “and is sh*t.”

Unfortunately for these many opinion-havers, however, they were the ones who were duped. The Monet was actually real: it’s one of his iconic “Water Lilies” paintings, created around 1915 and currently hung in the Neue Pinakothek museum in Munich, Germany.

As is to be expected, other commenters were quick to dunk on the posters who’d insulted the fake-AI-fake-Monet. Many interpreted the harsh yet ill-informed reaction to the image as an example of “knee-jerk” AI distaste and foolish “AI hysteria.”

“AI art wins again!” proclaimed one poster.

But some of the most interesting responses came from actual experts, who shared deeply informed analyses about why, based on the image alone, the painting appeared to them to be the real deal.

“Disagree with the people saying it lacks depth — there’s a clear plane with the lily pads and an inverted space with the willow reflecting. Paint texture looks pretty believable as a physical object, though thinner than most Monets I’ve seen (probably plausible for a very late life painting, which this would be if real),” surmised oil painter Kendric Tonn. “It’s not a top-tier Monet, but it’s a very credible Monet.”

Others saw right through the con.

“What the f*ck dude this is a detail from an actual late Monet? You can tell because the brush strokes are super similar to the Agapanthus in MOMA,” art historian A.V. Marraccini assessed. “Late ones always have that kind of wild impasto, and since his perception of color changed, more lilacs and purples.”

On the one hand, it is pretty embarrassing that a ton of people were quick to attack the real Monet without doing so much as a reverse-image search first. But whereas pro-AI art posters took the wave of erroneously reactive responses as affirmation of their own views on the validity of the controversial medium, the real lesson here seems to be about the nature of the online world itself.

More than ever before, a lot of the web is fake — a reality that makes it shockingly easy to manipulate actual truth. And in an online world chock full of millions of post-happy armchair experts, insight from genuine experts is perhaps more valuable than ever. Now more than ever: think before you post! Better yet, do a little research before sounding off, or seek insights from informed specialists.

“I think this experiment,” commented designer Paul Macgregor, “probably says more about Twitter than it does about AI and art.”

More on AI art: The Supreme Court Just Dealt a Crushing Blow to “AI Artists”

The post Devious Prankster Posts Real Monet Painting, Tells People It’s AI-Generated, and Watches the Chaos Unfold appeared first on Futurism.

🔗 Sumber: futurism.com