Few understand how to anticipate major technology shifts in the enterprise better than today's guest, Ed Sim. Ed is a pioneer in the world of venture capital, specifically focusing on enterprise software and infrastructure since 1996. He founded Boldstart in 2010 to invest at the earliest stages of enterprise software companies, growing the firm from $1M to around $375M today.
So where does an experienced investor who has seen countless tech waves come and go place his bets in this new AI-first future? That’s the key topic we dive into today.
While AI forms a core part of our dialogue, Ed emphasizes that he doesn’t look at pitches and go “Oh, AI, I need to invest in that.” Rather, he tries to see if founders have identified a real pain point, have a unique approach to solving it, and can clearly articulate how they will provide a significant improvement over status quo. AI is an important component, of course, but it isn’t a reason to invest alone.
With that framing in mind, Ed shares where he is most excited to invest in light of recent generative AI breakthroughs. Unsurprisingly, AI security ranks high on his list given enterprises’ skittishness around adopting any technology that could compromise sensitive data or infrastructure. Ed saw this need early, backing a startup called Protect AI in March 2022 that focuses specifically on monitoring and certifying the security of AI systems.
The implications of AI have branched into virtually every sector, but Ed reminds us that as investors and builders, we must stay grounded in solving real problems vs just chasing the shiny new thing.
Key Points:
- Ed Sim started Boldstart Ventures in 2010 to provide early stage funding for enterprise startups, writing smaller checks than typical VC firms. The firm now manages a nearly $200 million main fund and a $175 million opportunity fund.
- Generative AI is an exciting new technology, but the key is backing founders who are solving real problems for end users in a unique way that is 10x better than current solutions. AI is just the underlying technology.
- AI security is critical for enterprise adoption. Ed invested early in Protect AI, which helps monitor AI models for security, privacy, and compliance issues. AI security will be key to scale adoption.
- There are still open questions around data governance with large language models that access sensitive company data. Approaches that check governance policies before providing answers are the safest for now.
- Factors like inference cost, subscription fatigue, and proving ROI will impact how quickly some of the consumer generative AI applications gain traction. Creative solutions around caching, pricing models, and hybrid human+AI loops can help.
- There will be opportunities related to embedding expertise into systems to empower junior and senior employees. Tools like GitHub Copilot show potential to augment technical skills.
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