Building and running technology at the scale Uber operates—billions of rides, millions of drivers, real-time decision-making across continents—is already mind-bending complexity. Now add artificial intelligence into the mix, and you've got one of the most pressing operational challenges facing modern business leaders. This is precisely why Uber's Chief Technology Officer Praveen Neppalli Naga will take the stage at StrictlyVC San Francisco on April 30 to share hard-won lessons from the frontlines.
For executives and product leaders watching the AI wave crash across their industries, this matters. Uber isn't a company experimenting with AI in a lab somewhere. It's deploying machine learning and AI systems that directly impact real business outcomes every single day—from routing algorithms that get drivers to passengers faster, to pricing models that balance supply and demand, to safety systems that protect millions of users. When someone leading tech at that scale talks about operating in the age of AI, people listen.
StrictlyVC San Francisco, launching TechCrunch's 2026 event calendar, is positioning itself as a gathering place for serious technologists and business leaders. The April 30 event at the Sentro Filipino Cultural Center will bring together a curated lineup of speakers tackling the intersection of venture capital, technology, and artificial intelligence. Neppalli Naga's addition signals that organizers are pulling in practitioners—not just theorists—to discuss how AI actually works in production environments serving hundreds of millions of people.
What makes this session particularly relevant is the focus on "operating at scale in the age of AI." This phrase cuts to the heart of what's keeping many executives up at night. It's one thing to build a prototype or pilot an AI system. It's entirely different to deploy AI across legacy systems, massive teams, and complex business logic while maintaining reliability, controlling costs, and ensuring ethical use. Uber's experience navigating these tradeoffs—and doing so while competing fiercely in markets worldwide—offers a masterclass most companies desperately need.
The event itself represents a broader shift in how the tech community is approaching AI conversations. Rather than abstract discussions about capabilities and risks, there's growing hunger for concrete, practical insights from leaders who've actually had to make these decisions at scale. StrictlyVC is betting that bringing together venture investors, founders, and established tech leaders in one room will generate the kind of conversations that shape how companies approach AI strategy over the next few years.
Neppalli Naga's track record supports the draw. As CTO of one of the world's most valuable private companies (before Uber's IPO), he's overseen technical strategy across ride-sharing, food delivery, freight, and autonomous vehicle initiatives. He's had to think about how AI can improve core services while also considering how to build the infrastructure and talent needed to sustain innovation. These aren't theoretical problems—they're the exact challenges facing every scaled technology company right now.
CuraFeed Take: This is a smart move by StrictlyVC to anchor their lineup with someone who actually operates at the scale where AI implementation gets messy and real. Neppalli Naga represents something increasingly valuable: a technologist who can bridge the gap between academic AI research and the grinding reality of deploying these systems across billions of transactions. For attendees, the real value won't be in polished talking points—it'll be in the operational details and trade-offs he discusses. What infrastructure decisions did Uber have to make? Where did they bet big on AI versus where did they stay conservative? How do you hire and organize teams to move fast on AI while maintaining quality? These are the questions that will matter most to executives trying to navigate their own AI strategies. Watch for whether Neppalli Naga discusses specific failures or challenges—that's where the real insights typically hide. The companies that win in AI over the next few years will be those that learn from leaders willing to share not just successes, but the messy path to getting there.
