Uber CTO Praveen Neppalli Naga Joins StrictlyVC SF: What Founders Should Expect
AI at – Uber CTO Praveen Neppalli Naga will speak at StrictlyVC San Francisco, where founders and investors will tackle building AI-ready systems at scale—alongside funding, AI coding, and media integrity.
Uber CTO set to headline StrictlyVC San Francisco on “AI at scale”
Why Praveen Neppalli Naga’s “systems at scale” matters to investors and founders
For founders. that angle lands at a key pain point: AI doesn’t just add new features. it raises the bar for engineering discipline.. Scaling “AI-ready” products typically involves data pipelines. infrastructure design. latency and reliability tradeoffs. model governance. and integration across services that already power core operations.. When someone with deep operating experience joins the stage. investors tend to listen differently too. because the talk is likely to connect technology choices to execution outcomes.
Naga’s background gives the conversation weight.. Misryoum notes that he has been with Uber since 2015—well before the recent AI boom accelerated what CTOs are expected to build and refine.. That timing matters because it suggests an engineering-first mindset formed under constraints that existed before generative tools became mainstream.. He has also focused on earnings systems for drivers and couriers across Uber’s network. a domain where software decisions show up quickly in real livelihoods. not just dashboards.
The bigger theme: how AI shifts “who builds what” inside large platforms
Misryoum also highlights a second thread in Naga’s history—work connected to LinkedIn’s early products and infrastructure.. That experience matters because it aligns with an important lesson for many startups: building durable infrastructure is often less glamorous than releasing new features. but it’s what enables teams to scale without breaking what customers rely on.
For business readers, the human impact is direct.. Earnings and platform behavior aren’t abstract.. When algorithmic systems influence how work is distributed or how pay-related logic functions. it affects day-to-day outcomes for drivers and couriers.. In other words. “AI at scale” isn’t only about technical throughput; it’s also about the stability of the mechanisms that support income and trust in the platform economy.
More than AI: physical AI. software creation. and capital strategy round out the roster
There’s also Replit co-founder and CEO Amjad Masad, set to share a look at AI-driven software development.. The timing is hard to miss.. Developers and engineers have been weighing big claims about AI coding capabilities against real-world experience—everything from code quality and test coverage to maintainability.. For founders building dev tools or platforms. this segment can sharpen expectations about what AI coding accelerates today. what still requires human review. and where teams should invest to reduce engineering risk.
Misryoum further notes that TDV Ventures’ sponsor role brings in Nicolas Sauvage. president of TDK Ventures. hosting a discussion on raising capital from strategic backers and early-stage investing topics.. That matters because AI progress hasn’t eliminated the fundamentals of fundraising: narrative clarity. traction. unit economics. governance. and the ability to translate technical roadmaps into measurable business milestones.
Media integrity and the startup community’s responsibility in the AI era
For founders building in AI-adjacent spaces, this is a reminder that growth can’t rely solely on model performance.. Trust. transparency. and safeguards are becoming part of product strategy. especially for companies whose outputs can influence how people make decisions.. Investors, too, increasingly evaluate whether teams have a plan for risk management beyond performance benchmarks.
What attendees should take away before they book their seat
If you’re a founder. investor. or operator trying to cut through AI noise. the most useful outcome from events like this usually isn’t a single “prediction.” It’s a clearer map of tradeoffs: what to build now. what to instrument for later. what to standardize early. and what types of teams investors can trust when the technology landscape moves fast.. That’s the kind of insight that can turn networking into strategy—especially in a startup ecosystem that’s re-evaluating how it scales. funds. and governs AI-enabled products.