Minimum Lovable Product Replaces Minimum Viable Product

The shift from optimization to innovation in AI-driven growth requires a fundamentally different approach to product development and go-to-market strategy. Elena Verna's experience at Lovable reveals how traditional growth playbooks are being rewritten in the era of AI products.

The Minimum Lovable Product (MLP) Framework

• "Viability is left back in the 2010s. Now it's minimum lovable product that's the only thing that matters"
• The goal is to create experiences that "blow people's socks off" rather than just solving a functional need
• Product quality must trigger an emotional response strong enough to drive word-of-mouth sharing
• Success depends on creating moments where users feel "I have superpowers now and I can't wait to tell others"

Core Growth Levers for AI Products

1. Continuous Innovation Over Optimization

• "I usually spend maybe 5% innovating on growth in my previous roles. Right now I'm spending 95% innovating on growth and only 5% on optimization"
• Traditional growth teams focus on optimizing existing funnels and journeys
• In AI companies, growth teams must constantly create new growth loops and features
• The goal is reinvention of solutions, not optimization of problems

2. Building in Public

• Maintain "noise in the market" through constant shipping and transparent communication
• Founders and employees should regularly share progress on social media
• Works as both resurrection and re-engagement strategy
• "People are literally logging into their social to see 'what has Lovable shipped now?'"
• Creates a perception that the product is alive and constantly evolving

3. Strategic Product Giveaways

• "This is part of our growth secret sauce. You have to remove the barrier of entry"
• Treat LLM costs on freemium and giveaways as marketing costs, not margin reducers
• Sponsor hackathons, events, and user initiatives with free credits
• Empower evangelists: "If somebody wants to do all of the marketing and activating for us, why would we prevent them from using us?"
• Particularly effective for mind-blowing products in competitive markets

4. Community Building

• Create spaces where users can explore capabilities together and help each other
• Community amplifies word-of-mouth, social posting, and retention
• Ambassador programs extend reach and credibility
• Focus on making software feel more human through community connections

The Product-Market Fit Treadmill

• Product-market fit is no longer a one-time achievement but requires constant recapture
• "Every three months I feel like we have to recapture our product-market fit"
• Two driving factors:
  1. LLM capabilities change rapidly with each model release
  2. Consumer expectations evolve at unprecedented speed
• Companies must build ahead of capability curves, betting on where LLMs will improve
• Even at $200M ARR, focus remains on product innovation rather than just marketing/sales
• Teams must be capable of both finding and scaling product-market fit simultaneously

Organizational Implications

Role Evolution

• Growth teams now build core product features rather than just optimizing surfaces
• Marketing shifts from SEO to social-driven organic strategy
• Engineers become "product engineers" with marketing responsibilities
• New roles emerge (like "vibe coders") that blend technical and non-technical skills

Hiring Priorities

• Look for high agency and high autonomy in candidates
• Prioritize passion and fire over traditional credentials
• AI-native new graduates have advantages over experienced professionals without AI skills
• Ex-founders are in high demand for their entrepreneurial mindset and self-direction

Team Culture

• "Velocity of shipping is our number one core value"
• Empower teams to make decisions without extensive approval processes
• Focus on outcomes rather than perfect processes
• Create space for work-life boundaries while maintaining high velocity