While global discourse fixates on Generative AI capabilities, Silicon Valley faces a different challenge: commoditization. The barrier to entry for building software has collapsed, shifting the primary value driver from engineering complexity to market distribution. In an environment where technical differentiation is fleeting, the definition of a winning startup has evolved.
Synthesizing insights from the San Francisco panel, hosted by UAtech – featuring investors from Rain Capital and Dnipro VC alongside founders from Fuelfinance and Connection Silicon Valley – we outline the practical adjustments required to survive.
Go-to-Market is the Primary Differentiator
Since engineering resources are accessible to everyone, the product itself is rarely a sufficient competitive advantage. Success now depends on distribution. Alyona Mysko (CEO, Fuelfinance) identifies Go-to-Market (GTM) as the new "moat" for startups. A common error is delaying sales until the product is finalized. Alyona Mysko advises building sales channels months or years before the product is ready, as securing distribution is significantly harder than writing code:
"Start selling today, not in 6 months. You can build anything for one day, but it will take months or years for you to figure out how to find scalable Go-to-Market channels."
Agentic AI and the Liability Problem
The industry is moving from generative AI to "Agentic AI" – systems designed to execute workflows autonomously. Chenxi Wang (Founder & GP, Rain Capital) identifies this as the dominant trend in the Enterprise sector but warns of a "trust gap". The unresolved issue is liability: legal frameworks are not ready to handle scenarios where an AI agent makes a mistake on behalf of a human. Additionally, Joanne Fedeyko (Founder & CEO, Connection Silicon Valley) warns that deepfakes necessitate a stricter focus on security, as scammers "just have to be right once".
"How do we trust an AI acting on behalf of a human? ... I think we are not ready because the liability side, the protection side, and the legal side are not yet ready for AIs to make mistakes on my behalf."
Stability in "Non-Sexy" Industries
While the tech sector is volatile, traditional industries remain underserved and operationally inefficient. Alyona Mysko (CEO, Fuelfinance) suggests targeting "non-sexy" sectors such as insurance, telecommunications, and HVAC, which offer better retention rates than tech clients. Chenxi Wang (Founder & GP, Rain Capital) supports this with the example of public utility companies, where teams still manually compile SEC reports – a high-value automation case often overlooked by founders.
"Take the most out of AI... but go to companies NOT in tech. ... Find these industries... and you will be surprised you can build a billion [dollar] company." "I sit on the board of a public energy utility company... We have a team of 5 people, full-time job, just do SEC reporting... I look at them like... 'Mmm, shouldn't we do some AI?' And they look at me sideways."
The Changing Role of the Engineer
Automation tools like "Vibe Coding" handle routine tasks, but this does not eliminate the need for talent. The role is evolving from writing syntax to system design. Alyona Mysko (CEO, Fuelfinance) argues that engineers now need a "philosophical background" to teach AI, requiring a broader skillset than just technical coding.
"When you build AI you also need to have some philosophical background. You need to be smarter. Maybe the role will be evolved, so you need much more than just coding now." "If you're not using the tools, you are doing yourself a disservice. Your competitors will be using them."
Conclusion
The narrative has shifted from discovery to infrastructure. As Chenxi Wang notes, the consistent gains in such cycles go to those selling "picks and shovels" – the essential tools everyone needs. For founders, this means trading the hype of pure technology for the discipline of sales execution and targeting the unglamorous, manual workflows of the physical economy.
Co-Authors: Yuliya Dmytryshyn, Strategic Advisor at UAtech, and Erika Danaikanych
