A new generation of entrepreneurs is using AI to build successful startups without traditional engineering teams, with companies like Giggles attracting over 120,000 users and Base44 selling to Wix for $80 million. This “vibe coding” approach—where founders use natural language to direct AI in creating applications—is democratizing entrepreneurship but raising questions about whether these ventures can scale without deeper technical expertise.
What you should know: “Vibe coding,” a term coined by former OpenAI cofounder Andrej Karpathy, describes using AI to write code through natural language rather than traditional programming.
- Y Combinator CEO Garry Tan reports that many startups now use AI to generate up to 95% of their codebase, achieving results that once required 50-100 engineers with fewer than 10 people.
- The approach allows product managers, artists, and even high schoolers to ship products faster than ever before without technical expertise.
- GitHub CEO Thomas Dohmke warns that “a non-technical founder will find it difficult to build a startup at scale without developers,” noting that vibe coding tools don’t provide the depth needed for serious investment.
Key success stories: Two prominent examples demonstrate the potential and limitations of AI-first entrepreneurship.
- Giggles, launched by 18-year-old Justin Jin, created an AI-powered social entertainment app that attracted 120,000 waitlist sign-ups and generated 150 million impressions without venture capital or a marketing budget.
- Base44, founded by a non-technical creator, reached profitability with 300,000 users and sold to Wix for $80 million in cash within six months using fewer than 10 people.
- Jin previously founded Mediababy, which sold for $3.8 million, giving him experience in building platforms that prioritize user expression over rigid structure.
The Giggles model: The platform exemplifies how creativity can replace traditional coding in product development.
- Users interact through AI-generated content, digital collectibles, and gamified social engagements, with the system rewarding digital expression through game-like interactions.
- Co-founder Matthew Hershoff explains that “creators aren’t limited to just posting photos and videos. They can vibe code a game, develop an app, create a whole virtual world and post it on Giggles.”
- The company positions itself as an alternative to TikTok, specifically tailored for a generation increasingly disengaged from traditional social formats.
Scaling challenges: Industry experts and founders acknowledge significant limitations in the AI-first approach.
- Nigel Douglas from Cloudsmith, a software supply chain security company, warns that while DIY disasters might only mean “an ugly interface” in personal projects, “in a business setting, the wrong tool can do real damage and result in data breaches, service outages, or a compromised software supply chain.”
- Even AI-native founders recognize the need for technical depth, with Giggles co-founder Edwin Wang stating: “There’s a need to build technical depth. We know that’s important and are expanding engineering operations and bringing on advisors.”
- Wang acknowledges that “scaling creativity still requires coding discipline,” indicating the company is investing in its technical foundation despite its AI-first origins.
What they’re saying: Industry leaders emphasize the need for balance between AI capabilities and traditional engineering rigor.
- “You fully give in to the vibes, embrace exponentials and forget that the code even exists,” Karpathy tweeted about vibe coding in February.
- Reid Hoffman, LinkedIn co-founder and partner at Greylock Partners, notes that “bringing AI into your toolkit makes you enormously attractive,” but cautions that early AI advantage doesn’t equal long-term lead.
- Jin reflects on the broader challenge: “In the end, it’s not just about who can build fast. It’s about who can build something that lasts.”
The big picture: The rise of vibe coding represents a fundamental shift in entrepreneurship, but successful scaling will likely require hybrid approaches that combine AI fluency with traditional engineering expertise. As AI-generated code improves, practices around testing, review, and security become increasingly critical for turning creative visions into dependable platforms that others can rely on.
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