Ben Lorica and Gabriela de Queiroz, director of AI at Microsoft, talk about startups: specifically, AI startups. How do you get noticed? How do you generate real traction? What are startups doing with agents and with protocols like MCP and A2A? And which security issues should startups watch for, especially if they’re using open weights models?
Points of Interest
- 0:30: You work with a lot of startups and founders. How have the opportunities for startups in generative AI changed? Are the opportunities expanding?
- 0:56: Absolutely. The entry barrier for founders and developers is much lower. Startups are exploding—not just the amount but also the interesting things they are doing.
- 1:19: You catch startups when they’re still exploring, trying to build their MVP. So startups need to be more persistent in trying to find differentiation. If anyone can build an MVP, how do you distinguish yourself?
- 1:46: At Microsoft, I drive several strategic initiatives to help growth-stage startups. I also guide them in solving real pain points using our stacks. I’ve designed programs to spotlight founders.
- 3:08: I do a lot of engagement where I help startups go from the prototype or MVP to impact. An MVP is not enough. I need to see a real use case and I need to see some traction. When they have real customers, we see whether their MVP is working.
- 3:49: Are you starting to see patterns for gaining traction? Are they focusing on a specific domain? Or do they have a good dataset?
- 4:02: If they are solving a real use case in a specific domain or niche, this is where we see them succeed. They are solving a real pain, not building something generic.
- 4:27: We’re both in San Francisco, and solving a specific pain or finding a specific domain means something different. Techie founders can build something that’s used by their friends, but there’s no revenue.
- 5:03: This happens everywhere, but there’s a bigger culture around that here. I tell founders, “You need to show me traction.” We have several companies that started as open source, then they built a paid layer on top of the open source project.
- 5:34: You work with the folks at Azure, so presumably you know what actual enterprises are doing with generative AI. Can you give us an idea of what enterprises are starting to deploy? What is the level of comfort of enterprise with these technologies?
- 6:06: Enterprises are a little bit behind startups. Startups are building agents. Enterprises are not there yet. There’s a lot of heavy lifting on the data infrastructure that they need to have in place. And their use cases are complex. It’s similar to Big Data, where the enterprise took longer to optimize their stack.
- 7:19: Can you describe why enterprises need to modernize their data stack?
- 7:42: Reality isn’t magic. There’s a lot of complexity in data and how data is handled. There is a lot of data security and privacy that startups aren’t aware of but are important to enterprises. Even the kinds of data—the data isn’t well organized, there are different teams using different data sources.
- 8:28: Is RAG now a well-established pattern in the enterprise?
- 8:44: It is. RAG is part of everybody’s workflow.
- 8:51: The common use cases that seem to be further along are customer support, coding—what other buckets can you add?
- 9:07: Customer support and tickets are among the main pains and use cases. And they are very expensive. So it’s an easy win for enterprises when they move to GenAI or AI agents.
- 9:48: Are you saying that the tool builders are ahead of the tool buyers?
- 10:05: You’re right. I talk a lot with startups building agents. We discuss where the industry is heading and what the challenges are. If you think we are close to AGI, try to build an agent and you’ll see how far we are from AGI. When you want to scale, there’s another level of difficulty. When I ask for real examples and customers, the majority are not there yet.
