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No Priors Ep. 128 | With Andrew Ng, Managing General Partner at AI Fund

Andrew Ng has always been at the bleeding edge of fast-evolving AI technologies, founding companies and projects like Google Brain, AI Fund, and DeepLearning.AI. So he knows better than anyone that founders who operate the same way in 2025 as they did in 2022 are doing it wrong. Sarah Guo and Elad Gil sit down with Andrew Ng, the godfather of the AI revolution, to discuss the rise of agentic AI, and how the technology has changed everything from what makes a successful founder to the value of small teams. They talk about where future capability growth may come from, the potential for models to bootstrap themselves, and why Andrew doesn’t like the term “vibe coding.” Also, Andrew makes the case for why everybody in an organization—not just the engineers—should learn to code. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @AndrewYNg Chapters: 00:00 – Andrew Ng Introduction 00:32 – The Next Frontier for Capability Growth 01:29 – Andrew’s Definition of Agentic AI 02:44 – Obstacles to Building True Agents 06:09 – The Bleeding Edge of Agentic AI 08:12 – Will Models Bootstrap Themselves? 09:05 – Vibe Coding vs. AI Assisted Coding 09:56 – Is Vibe Coding Changing the Nature of Startups? 11:35 – Speeding Up Project Management 12:55 – The Evolution of the Successful Founder Profile 19:23 – Finding Great Product People 21:14 – Building for One User Profile vs. Many 22:47 – Requisites for Leaders and Teams in the AI Age 28:21 – The Value of Keeping Teams Small 32:13 – The Next Industry Transformations 34:04 – Future of Automation in Investing Firms and Incubators 37:39 – Technical People as First Time Founders 41:08– Broad Impact of AI Over the Next 5 Years 41:49 – Conclusion

Sarah GuohostAndrew NgguestElad Gilhost
Aug 20, 202542mWatch on YouTube ↗

At a glance

WHAT IT’S REALLY ABOUT

Andrew Ng on Agentic AI, Rapid Engineering, and Founder Mindsets

  1. Andrew Ng discusses where future AI capabilities will come from, arguing that while scale still helps, real progress now depends on agentic workflows, better tooling, and disciplined engineering rather than sheer model size.
  2. He defines “agentic AI” as a spectrum of systems with varying degrees of autonomy, with coding agents currently being the most successful and economically valuable examples.
  3. Ng explains how AI-assisted coding is radically shrinking engineering headcount needs and shifting the bottleneck from software development to product management, thereby raising the bar for technical, high-empathy founders and product leaders.
  4. He also forecasts that individuals who aggressively adopt AI tools across professions will be dramatically more capable than their peers, and that small, highly skilled, AI-leveraged teams will increasingly outperform large, traditional organizations.

IDEAS WORTH REMEMBERING

5 ideas

Agentic AI is a spectrum, not a binary category.

Ng coined “agentic AI” to capture varying degrees of autonomy—from simple LLM-driven steps to multi-step planning agents—so teams stop arguing about definitions and instead focus on building useful workflows.

The main bottleneck to agentic AI adoption is talent and disciplined engineering, not core model capability.

Teams that systematically use evals and error analysis to refine agents vastly outperform those tinkering randomly, and there aren’t yet enough people with these skills.

Coding agents are currently the most valuable and mature form of agentic AI.

Tools like Claude Code can autonomously plan and execute multi-step coding tasks, creating huge productivity gains, while more general “computer use” agents remain mostly demo-grade.

AI-assisted coding is shifting the startup bottleneck from engineering to product management.

Because one engineer can now do what used to take a small team, the real constraint becomes deciding what to build, how to prioritize, and how quickly to iterate based on customer insight.

Technical, AI-native founders have a decisive advantage in this era.

Ng believes founders who deeply understand fast-evolving AI capabilities will out-execute more traditional, business-only leaders, especially during periods of rapid technological disruption.

WORDS WORTH SAVING

5 quotes

The single biggest barrier to getting more agentic AI workflows implemented is actually talent.

— Andrew Ng

Vibe coding makes people think it's easier than it is. After a day of AI-assisted coding, I'm exhausted mentally.

— Andrew Ng

Today, the bottleneck is deciding what we actually want to build.

— Andrew Ng

In moments of technological disruption, having a good feel for what this technology can and cannot do is the real knowledge.

— Andrew Ng

People who embrace this will be so much more powerful and so much more capable than they probably can imagine.

— Andrew Ng

Definition and evolution of agentic AI and its marketing hypeCurrent obstacles to deploying real-world AI agents (talent, evals, tooling)Coding agents as the leading practical example of high-agency AIAI-assisted coding, rapid engineering, and their impact on startupsChanging founder profile: technical depth, work ethic, and product intuitionProduct management as the new bottleneck and the role of user empathyFuture of work: small AI-augmented teams, hiring, and cross-industry disruption

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