Ex-Founders Are Hot Commodity for AI Companies

The traditional growth playbook is largely obsolete in AI companies, requiring constant innovation rather than optimization. At Lovable, Elena Verna finds herself spending 95% of her time innovating on growth versus just 5% on optimization—a complete reversal from her previous roles at companies like Miro, Dropbox, and SurveyMonkey.

In this new AI landscape, the pace of change is relentless. What worked three months ago may no longer apply today. This requires a fundamental shift in how growth teams operate—they must dive deeper into core product functionality rather than just focusing on surface-level optimizations. Growth teams at Lovable are building new features, creating integrations, and even working on agent instructions rather than merely tweaking existing user journeys.

The most effective growth levers for AI companies are dramatically different from traditional software. Building in public has become essential—constantly shipping and talking about new features creates market noise that drives resurrection and re-engagement. This approach requires everyone to have "a little bit of marketer within them," with engineers announcing their own shipped features rather than funneling everything through marketing.

Another counterintuitive strategy is giving the product away liberally. While AI products have significant per-use costs, Lovable treats these giveaways as marketing expenses rather than margin deterioration. When someone wants to host a hackathon using Lovable, the company provides free credits generously, recognizing that these users become powerful advocates who activate others.

For leaders, this means rethinking team composition. The most valuable hires are often those with high agency and autonomy—particularly ex-founders who can operate with minimal supervision. Additionally, AI-native new graduates bring fresh perspectives unburdened by traditional approaches. This creates an environment where the "old guard" can learn from the "new guard" about what's possible.

The concept of product-market fit has also fundamentally changed. Rather than achieving it once and scaling for years, AI companies must recapture product-market fit every three months as both technology capabilities and consumer expectations evolve at unprecedented speeds. This creates a constant tension between scaling existing offerings and reinventing for the next wave.

For individual contributors, this environment requires comfort with chaos and the ability to create clarity without waiting for it to be provided. Success depends on leveraging AI tools extensively in your own work while maintaining strong personal boundaries to prevent burnout. The most effective people don't think in terms of work-life balance but rather make in-the-moment decisions about priorities based on where they're most needed.

Building for Growth in AI Companies

The traditional growth frameworks focused on optimization now represent only 30-40% of what drives success. Instead of A/B testing minor UI elements, growth teams should:

• Launch entirely new features and growth loops
• Enable community-building to amplify word-of-mouth
• Give the product away generously to remove barriers to entry
• Create "minimum lovable products" rather than minimum viable ones
• Build humanity and personality into every interaction

This approach requires a different mindset—one that values innovation over optimization, velocity over perfection, and emotional connection over mere utility. As Elena puts it, "The only way to create a word-of-mouth loop is just to blow their socks off."

The New Marketing Landscape

Marketing has shifted dramatically from search-focused strategies to social-driven approaches. The most effective organic marketing now happens through:

• Founder and employee social sharing
• Customer word-of-mouth on social platforms
• Influencer marketing (which drives 10x more results than paid social for Lovable)

This requires authentic, human communication rather than corporate messaging. People want to rally behind teams they want to see win, which means showing personality and vulnerability in communications.

For AI companies, the goal isn't just to optimize existing channels but to create experiences worth talking about—products that make users feel they have "superpowers" they can't wait to share with others.

The Product-Market Fit Treadmill

Perhaps most significantly, AI companies exist on what Elena calls a "product-market fit treadmill." Every three months, both product capabilities and market expectations shift dramatically, requiring companies to essentially recapture product-market fit continuously.

This creates a strange dynamic where even at $200M ARR, a company can't focus solely on scaling through marketing and sales—it must simultaneously reinvent its core offering. This requires building teams capable of both finding product-market fit and scaling it, a combination rarely needed in traditional software companies.

For those considering joining AI companies, this environment offers unprecedented learning opportunities but demands comfort with ambiguity and rapid change. The right candidates aren't necessarily those with the most impressive resumes, but those with passion, high agency, and the ability to thrive amid constant reinvention.