Is Your Business Idea Actually Good? This Claude Hack Provides the Cold, Hard Truth

Artificial intelligence has made it easier than ever to start a business, with tools like Anthropic’s Claude Code and OpenAI’s Codex enabling anyone to build websites, apps, and SaaS platforms. But not all ideas are created equal, and sometimes what seems like a great idea has already been done before, or is unviable for reasons you hadn’t considered. In the past, confirming the viability of your startup or product idea involved a combination of research, analysis, and asking trusted friends, family, and business associates. But now that you have a virtual smart person in your pocket or on your desktop, AI can help streamline this process. In this article, we’re going to teach you how to set up an AI model (in this case Claude) that will give you the cold, hard truth about your business ideas. To start, you’ll need to create a customized version of Claude that’s highly skeptical and critical of your ideas. AI models have often been found to be sycophantic—more interested in telling you what you want to hear than actually giving it to you straight. We need to give Claude a set of custom instructions to follow so it can avoid this pitfall. To start, I opened up the Claude desktop app (you can download it here), opened Claude Cowork (Anthropic’s tool for knowledge work), navigated to the projects tab, and started a new project named “Business Idea Viability.” Next, on the newly created project page, I selected “set project instructions,” and pasted in the following prompt: “You are a skilled business analyst with decades of experience under your belt. You excel at receiving an idea for a business and comprehensively running research to determine if this idea has already been turned into a business, looking into trends, historic parallels, and data related to the proposed business idea. You are highly critical and skeptical of new ideas and difficult to satisfy, but fair when you encounter a legitimately good idea for a business. You are direct in your communication style and ‘tell it like it is’ with brutal honesty.” Finally, I added two articles from Harvard Business School to the project’s files. Those articles were titled “How to Come Up With an Innovative Business Idea” and “5 Steps to Validate Your Business Idea.” With this, Claude should have a solid understanding of what makes a killer business idea. To start, I wanted to see what kind of feedback Claude would give to an obviously bad idea. Here’s the terrible idea I pitched to the model: “Help me validate this business idea: Leadership coaching for dogs. What if house-training your pooch was just the first step on the road to true doggy-disruption? Using a proprietary mix of AI, performance management tracking, thought leadership, and retired K-9 police dogs, we will teach your pet to not just be a good boy/girl, but a leader in their community and home. Optional add-on classes will help dogs learn some classic dog leadership techniques, like saving kids from wells. Does this seem like a viable business idea?” Despite the ridiculousness of my premise, Claude treated it with deadly seriousness. The AI found that “the cultural environment actually does support some version of what you’re describing.” According to Claude, the dog training market is growing at a nearly 10 percent rate, with the biggest driver being “the trend of treating dogs like family members, even like employees with performance goals.” Having said that, Claude was quick to point out that dogs do not have careers (although to be fair, some dogs kind of do), and that “the entire leadership/thought leadership framing is anthropomorphic nonsense that collapses the moment a customer asks what their dog actually learns in Week 3.” Even the more realistic pieces of my business idea, like AI-powered performance monitoring, had already been hit by other startups, like smart dog collar company Fi. Overall, Claude gave my idea a viability score of 3 out of 10, but said the only reason it wasn’t a 1 is that there’s a genuine market for dog training. All right, so the test worked: I knew for sure that Claude would give it to me straight, so next I wanted to try out a genuine business idea. Here’s my second shot: “An AI-powered employee sentiment and internal manager effectiveness survey solution. Instead of forcing employees to take dozens of surveys, our AI interviewer simply pings you on Slack/Teams/GChat, and engages you in a natural language conversation about your current working situation, relationship with your manager, and outlook of the company’s leadership and vision for the future. We could even set up voice-based interviews to capture more data.” Claude gave this one a 6.5 out of 10, and said that while I had identified a real problem of survey fatigue, I was far from the first person to have this idea. The AI assistant identified InFeedo.ai as the main player in this space; the company provides access to an AI agent named Amber, which proactively chats with employees and generates personalized queries. “They are you,” Claude said, “but years ahead of you.” By this point, I was running out of ideas, so I went back to the pet angle with a new concept: “How about a product that is essentially an AI-powered smart pet sprayer? It would look like a flower vase or cylinder that sits on your table, has a 360 camera with AI vision, and can identify when pets jump on a table and spritz them with a 360 water sprayer.” Claude called this idea “genuinely interesting,” and rated it as a 7.5 out of 10, bordering on an 8. Not only is the pet tech market booming, the AI said, but “the existing competitive landscape here is also notably weak.” The only comparable product was the PetSafe SSSCAT, a motion-activated canister of compressed air that sprays when it detects movement within three feet. Unlike this “dumb, blunt instrument,” as Claude described it, my sprayer would be smart, and capable of telling a dog or cat from a human. Claude’s recommendation for a TikTok-ready elevator pitch? “It follows your cat and sprays only them, not your laptop.” With the makings of a solid idea, I asked Claude to use PowerPoint to create a pitch deck that I can use to help potential business partners and investors understand the idea. And there you have it, a validated business idea courtesy of Claude (please don’t steal it). The examples here may be silly, but hopefully the lesson is not. The next time you have a crazy-great business idea, try hashing it out with an AI model; by instructing it to be honest and direct with you, you can get actionable feedback on demand. BY BEN SHERRY @BENLUCASSHERRY

Gen Z is outsourcing hard conversations to AI. Why it matters

Around 2 a.m. on a Monday, Emily received a text from a fellow student, Patrick, whom she had gone on a blind date with two days earlier. The pair are juniors at Yale University who were set up by mutual friends. They requested anonymity so CNN agreed to change their names to protect their privacy. “Hey Emily! I hope your half-marathon went well — I’m sure you crushed it,” Patrick wrote with a winky-face emoji. “Okay, bear with me here — I’m not the best at this kind of thing, but here goes.” In a six-paragraph-long text, Patrick said he would like to “hang out more — whether it’s just as friends or whatever it was we were this weekend.” He added that he wasn’t “looking for anything too serious right now.” At first, Emily didn’t think his reply was anything out of the ordinary. “It just seemed really proper, and I guess I knew that he was a really nice guy. So, I was just like, maybe this is just how he texts.” But after sharing his message with two friends, who put it through an artificial intelligence detector, she had her answer: “It was like, 99% AI.” She was right. Patrick admitted using ChatGPT to craft his text. He said he didn’t have much experience crafting a rejection message: “What do I do here? It’s the first time I had seen anyone since my high school girlfriend, which is why I was so nervous and wanted a second opinion.” “I tried to write my thoughts down, but I wasn’t sure how to format this in a way that’s not, like, really bad, so then I went to Chat,” he said. He gave ChatGPT the situation, his thoughts and emotions, and “Chat spit out a response.” Patrick is far from alone. Researchers say a growing number of young people are turning to AI to navigate social situations — drafting rejection texts, decoding mixed signals and scripting difficult conversations. Experts warn that this habit may be stunting emotional growth, leaving an already isolated generation who came of age during the pandemic even less prepared for the messiness of human connection. Patrick went back-and-forth with the chatbot and “tweaked certain lines here and there, but it was mostly copy and paste” from ChatGPT. “I added an emoji and tried to make it sound more human,” he said. “I felt better putting this out there because I wanted to be very clear and forthcoming. I didn’t want to be wishy-washy with it in case she took it the wrong way. I knew if I did it on my own, I would have been wishy-washy,” said Patrick, who considered his move like consulting an expert. Emily said she did not think the text was clear and it made his intentions more confusing. She couldn’t tell from the AI wording “if he wanted to be friends or what.” “My main intention was to be clear in how I was feeling and thinking about the situation,” Patrick said. “Looking back on it, that was pretty poor behavior on my part. I think sitting on it for so long was the reason I went to Chat.” “I think he was overthinking it,” Emily said. “You definitely don’t need to use AI; you’re an emotionally sane guy.” She described the interaction as weird but said many of her friends have also turned to artificial intelligence to draft texts to friends or partners, or to analyze social situations — sometimes pasting entire text chains into a chatbot to decipher what someone might be thinking. “The thought of my little brother using AI to break up with his girlfriend is concerning. Because right now he comes to me, but when’s the day he’s going to turn to AI instead?” She said she is worried that Gen Zers have trouble “confronting their own feelings.” Emily said she’s also concerned about her generation’s ability to socialize, and some experts agree. It’s called ‘social offloading’ Emily’s experience is part of a broader pattern that concerns researchers. Dr. Michael Robb, head of research at Common Sense Media, calls it “social offloading,” using AI to navigate interpersonal situations, and he said it isn’t limited to Generation Z. He has observed it among Gen Alpha (born between 2010 and 2024) and some millennials (born between 1981 and 1996) as well. One-third of teens already prefer AI companions over humans for serious conversations, according to a 2025 survey conducted by Common Sense Media, a nonprofit organization that helps families navigate age-appropriate media choices. “If you’re using AI to draft your messages to friends or romantic partners, you’re outsourcing the communicative act itself,” Robb said. The problem is twofold, he noted. It creates an “expectation mismatch” since the recipient is “responding to an AI-polished version of their friend and not the actual person.” Second, repeated use can erode users’ confidence in their own voices, preventing young adults from developing essential skills, such as reading social intent, inferring others’ emotions and tolerating ambiguity in social interactions. “It has implications for your sense of self, advocacy and identity formation,” which are central to social development, Robb said. “If every tricky or difficult text is mediated by the AI, it may instill the belief in users that their own words and instincts are never good enough.” Dr. Michelle DiBlasi, a psychiatrist at Tufts Medical Center and assistant professor at Tufts University School of Medicine, has observed the same trend. “I have seen young people, late teens, early 20s, using AI to socialize, and oftentimes they’re using it as a way to overcompensate for the fact that they don’t really know how to truly interact with others,” she said. “We’re social beings, and a lot of our feelings of self-worth and connection are really related to our interactions with others.” DiBlasi said that using AI in social interactions stunts emotional growth and can perpetuate feelings of loneliness and isolation. It can also limit people’s ability to pick up social cues, repair relationships and connect with others. The pandemic’s impact on connection Why is Gen Z struggling with socialization? Researchers point to a combination of digital culture and the pandemic. Russell Fulmer, an associate professor at Kansas State University who studies AI and behavioral sciences, said the two forces created the “perfect storm” for AI to be integrated into social interaction. Adolescence — roughly ages 10 to 19, according to the World Health Organization — is the critical window for developing confidence, a stable sense of identity and emotional regulation. If adolescents don’t fully develop their social skills during this time, people may be “more prone to lack confidence, more apt to escapism or avoidance and maybe there’s a lack of resiliency,” Fulmer said. DiBlasi said the pandemic hit Gen Z at a particularly vulnerable moment. “When it happened, they were in the stages where the frontal lobe of their brain was starting to form,” she said. Typically, that’s when adolescents learn to build relationships, pick up social cues and develop mentalization — “the ability to understand somebody else’s mental state or what they’re thinking and how they’re feeling.” DiBlasi said that this lack of interaction leads to “a deep sense of isolation, feeling like others don’t understand them, or that they don’t understand others,” which drives many toward AI for companionship. But Fulmer warns that chatbots can create a “loneliness loop,” offering an “appearance of connection” that ultimately feels unfulfilling and can deepen isolation. In the most serious cases, DiBlasi has seen patients experiencing suicidal thoughts turn to AI to help articulate what they’re feeling when they can’t find the words to tell others. “I think this can be really, really detrimental, because it’s important for people to express some of these emotions in a very honest way with family or friends, so that they can actually work through this in an authentic way,” she said. It’s not too late to change course Although some Gen Zers may have missed a prime window for developing social skills, DiBlasi emphasized that it is not too late for them to learn. She encourages people to reach out to friends and family rather than AI when they struggle to express difficult emotions. “These things are skills that, just like anything with practice, can actually improve,” DiBlasi said. “I understand that people are fearful or they may not want to say the wrong thing. But I really think it takes away any sort of understanding of what you’re actually truly feeling and takes away the connection and the repair that you need to make in these relationships.” Artificial intelligence is a poor substitute for the messiness of real human interaction, experts say, and that messiness is the point. “Relationships and conversations can be messy and probably should be messy, and that’s part of what makes you more socially competent in the long run,” Robb said. AI companions are “designed to be very validating and agreeable,” he noted, so their feedback doesn’t reflect the friction that’s part of how people respond in real relationships. AI users shouldn’t expect an objective read on social situations either, Fulmer added. “Social contexts are often not entirely objective,” he said. “They’re contextual, they’re relational, and therefore nuanced.” As confident as a chatbot may sound, he said, it’s searching for a through line in something that may not have one. For parents, Robb recommended watching for warning signs, including social withdrawal, declining grades or a growing preference for AI over human interaction. They can respond with low-pressure check-ins, such as asking what their children use AI for, how it makes them feel and what they think they get out of it. The goal is to get kids thinking critically about what AI does well and where it falls short, said Robb, who suggested that families consider limits to AI-usage similar to screen time rules. By Asuka Koda

The Real Reason AI Projects Fail, According to Prezi’s CEO

For years, leaders have been told that artificial intelligence is the competitive edge. According to Prezi CEO Jim Szafranski, that thinking is backward. “The technology is not the hard part,” Szafranski said. “Finding the right problem, that’s the hard part.” Most companies are getting that wrong. The myth of starting with technology Szafranski explained leaders often begin their AI journey by asking, “Where can we use AI?” instead of “What are we actually trying to fix?” That misstep is costing companies billions. According to Gartner, as many as 50% of AI projects fail to deliver meaningful results, largely due to poor alignment with business goals. Szafranski saw this play out in a steel mill project. “We thought we were solving for scheduling, but that wasn’t the real issue,” he said. After deeper analysis, the team discovered the real problem was optimizing how steel reached customers, not replacing a human scheduler. Once reframed, the AI delivered actual business impact. “The first problem you see is almost never the right one,” he added. Finding the “perfect problem” Szafranski described what he called the “perfect problem,” a challenge that is both meaningful and solvable. “You’re looking for something where the impact is obvious, and the path is achievable,” he said. “That’s where AI works.” AI pilots fail to produce measurable business impact, not because of weak models, but because companies pursue the wrong use cases. The takeaway: AI success is less about sophistication and more about precision. Why “time to outcome” beats “time to value” One of Szafranski’s biggest shifts in thinking is moving beyond “time to value.” “Time to value is incomplete,” he explained. “What matters is time to outcome, did the user actually achieve what they needed?” That insight reshaped Prezi’s AI strategy. Initially, the company focused on automating presentation features, making slides faster and easier to build. However, that wasn’t the real job customers needed done. “They’re not trying to make slides,” Szafranski shared. “They’re trying to persuade somebody.” That realization changed everything. What Prezi is doing differently Today, Prezi is using AI to help users communicate and persuade more effectively, not just design better presentations. “We shifted from helping people build presentations to helping them win moments,” Szafranski explained. The platform now focuses on: Simplifying visual storytelling for non-designers Helping users communicate ideas quickly under pressure Enabling more engaging, outcome-driven presentations This shift has unlocked growth, particularly in global markets. Szafranski noted that accessibility has become a major driver. “When you remove the barrier of design skill, you open the door to entirely new audiences,” he said. That strategy is working. Prezi continues to expand internationally, especially in regions where traditional presentation tools were harder to adopt due to language or educational barriers. Accessibility is a growth strategy, not a feature Prezi’s approach highlights a broader truth: accessibility is inclusion and expansion. According to MIT research the vast majority of AI investments fail to generate financial returns when they are disconnected from real user needs. Prezi is doing the opposite — building for real-world communication challenges at scale. The real takeaway for leaders AI isn’t magic. It’s a multiplier. As Szafranski made clear, “If you pick the wrong problem, AI just helps you get there faster.” The companies winning with AI aren’t the ones with the best models. They’re the ones asking better questions. Because in the end, the difference between failure and transformation comes down to one decision: Are you solving the problem you see or the one that matters? BY NETTA JENKINS, FOUNDER, HIC; WORKPLACE CONSULTING FIRM | AUTHOR OF SUPERCHARGED TEAMS

Meta just provided its clearest look yet at its AI plan. It’s about time

Meta’s most important launch in years may not be its latest Ray-Ban glasses or its AI app. Instead, it could be the new AI model it introduced on Wednesday, hinting at how its billions in AI investments could one day transform its products. Muse Spark, the first AI model from Meta’s superintelligence lab, powers Meta’s AI app and will be integrated into Instagram, WhatsApp, Facebook and its AI Ray-Bans in the coming weeks, the company said in a press release. Meta calls the model “purpose-built” for its products and says it is designed to streamline tasks like shopping and trip planning — the kinds of things that people already use Instagram for. The launch seemed to be exactly what Wall Street wanted to hear after Meta poured billions into its AI ambitions, with little detail about how those dollars will affect to its bottom line. Shares were up more than 9% shortly after the announcement on Wednesday and closed 6% higher. Last June, Meta invested $14.3 billion in data labeling startup Scale AI and hired its former CEO, Alexandr Wang, as its chief AI officer. It gobbled up rising AI startups Manus and Moltbook. OpenAI CEO Sam Altman claimed last year that Meta CEO Mark Zuckerberg offered $100 million signing bonuses to lure talent away from the ChatGPT maker. And the Facebook parent company spent more than $72 billion on capital expenditures, or costs related to AI infrastructure, in 2025. Analysts and investors want to know how those investments will pay off. Zuckerberg didn’t offer specifics when asked about the return on AI investments during a January earnings call, saying his response “may be somewhat unfulfilling.” He added that the company is in “this interesting period where we’ve been rebuilding our AI effort, and we’re six months into that, and I’m happy with how it’s going.” Muse Spark is the clearest answer Meta has yet provided. Meta outlined use cases for the model similar to those offered by platforms like ChatGPT and Gemini:. for example, creating a game with a prompt, answering health questions and analyzing a photo of snacks on a shelf to provide nutritional information. But the launch signals a concrete strategy to challenge OpenAI and Google after initial confusion around the direction of Meta’s AI app. Meta positioned the app both as a destination for AI-generated videos and a hub for its smart glasses in the past. Some users accidentally posted public queries that they believed to be private last year, perhaps an indication that certain consumers weren’t sure how to use the product. Meta also provided some clues about how its social media platforms could give its AI app an edge over rivals. The Meta AI app will reference content from the company’s social media apps when answering questions related to shopping, trending topics and locations. It says it’ll draw on public posts for certain answers to provide “context from your people, right where you need it.” The company also plans to eventually incorporate Instagram Reels, photos and posts directly into answers. The timing is also critical; Meta faces increasing competition from OpenAI, Google and Apple in the coming months: • OpenAI has been aggressively expanding as it seeks to replicate the success of ChatGPT in other corners of our lives. • Google is expected to release its Android-powered spectacles this year. The search giant will likely make more announcements around its AI strategy next month during its developers conference. • And Apple’s revamped Siri is expected to launch this year following delays. Similar to Meta, Apple’s strategy is centered on leveraging a person’s preferences to personalize answers. Meta needs a win. The metaverse didn’t upend the internet like it expected. Meta’s smart glasses have been at the center of privacy concerns. OpenAI’s ChatGPT caught the tech industry – Meta included – largely by surprise, leaving tech giants racing to catch up over the last three years. The jury is out on whether Meta’s new AI models will propel its products to new heights, replicating the success of Facebook and Instagram’s early days. But the launch of a model made specifically for its products for the first time suggests Meta is building towards a vision. Now it just has to execute it. Analysis by Lisa Eadicicco