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Timestamps are as accurate as they can be but may be slightly off. We encourage you to listen to the full context.
This episode features three prominent VCs - Patrick Salyer from Mayfield, Tony Wong from 500 Global, and Nastasia Myers from Felicis Ventures - discussing what it takes to go from seed to Series A in today's AI-driven venture landscape. (00:36) The panel explores current AI trends, with particular focus on AI voice technology and agent-based workflows that are creating entirely new markets worth trillions of dollars in potential value.
Partner at Mayfield, a 55-year-old early-stage venture capital firm that invests in seed through Series B rounds ranging from $2-15 million. Patrick focuses specifically on AI middleware and applications, with particular interest in AI agents and AI-enabled service companies across various verticals and horizontals.
Managing Partner at 500 Global, a pre-seed and seed investor with $2.1 billion in assets under management and a 15-year track record. Tony led early investments in major successes like Canva, Talkdesk, and Intercom, and more recently AI companies including Sakana AI, DeepInfra, and PlayAI (now part of Meta's Superintelligence Lab).
AI-focused General Partner at Felicis Ventures, an early-stage VC fund investing across inception through Series B with check sizes between $1-40 million. She has been part of the investment journeys for hyper-growth companies including Merkor, Canva, Notion, Runway, and Supabase, focusing on AI across the entire technology stack from infrastructure to applications.
AI voice is emerging as one of the most compelling investment themes due to its immediate practical applications and superior user experience. (03:06) Nastasia explains that AI voice technology is replacing manual call center operations with automated solutions that often provide better Net Promoter Scores than human interactions. The ROI for Gen AI voice applications is incredibly high, with customers eager to adopt these solutions immediately. This represents a shift from painful, minimally automated processes to seamless, AI-driven experiences that fundamentally improve business operations.
The traditional "triple, triple, double, double" growth model has been replaced by exponential expectations, with founders expected to reach $1M ARR for dev tools in 2-3 quarters instead of a year, and $2-3M ARR for Gen AI applications. (11:33) As Nastasia notes, this compressed timeline reflects the greenfield nature of AI markets where there's no existing software to replace - just latent demand waiting to be captured. However, investors are taking a "fine tooth comb" to revenue definitions, scrutinizing whether reported ARR represents real recurring revenue versus credits, pilots, or other lightweight customer engagements.
Patrick emphasizes that founders should take time to find genuine product-market fit rather than artificially driving to revenue milestones. (14:36) In AI markets, if you truly hit demand, customers will pull you forward rapidly - but this requires waiting for authentic market signals rather than pushing sales. The advice is to focus on areas where customers demonstrate latent demand and let that demand drive natural acceleration, as opposed to the previous era where you could "strong arm" your way to $1M ARR and secure the next round.
The most successful AI companies combine deep technical understanding with specific domain expertise and complex integration capabilities. (23:39) Patrick notes that while cloud providers are ahead in infrastructure this time (unlike the previous cloud era), the application layer offers tremendous opportunity for entrepreneurs who understand specific workflows, buyers, and integration challenges. Companies operating in esoteric spaces with legacy system integrations create technical moats that model companies or cloud providers are unlikely to replicate, especially when automating traditionally manual processes with high switching costs.
In an environment where anyone can build anything with AI tools, unique market insight becomes the primary differentiator. (35:38) Patrick stresses the importance of founders who have spoken with hundreds of customers rather than just a handful, developing deep texture and understanding of their market. This customer insight becomes the intellectual property that gets embedded into products that anyone could technically build. As Nastasia adds, learning velocity is crucial because AI operates at "warp speed" with constant change, making adaptability and inquisitiveness essential founder traits.