Building Faster with AI: Andrew Ng's Startup Speed Manifesto

Andrew Ng, founder of AI Fund and former Stanford AI Professor, recently delivered a masterclass at Y Combinator's Startup School that every entrepreneur should watch. His central message? AI isn't just changing what's possible—it's fundamentally accelerating how fast we can build it.

Drawing from AI Fund's experience of building an average of one startup per month, Ng shared battle-tested insights on leveraging AI to achieve unprecedented execution speed. As someone who's been in the trenches writing code, talking to customers, and determining pricing for dozens of AI startups, his advice carries unique weight.

🎯 The Speed Imperative

Ng's core thesis is simple but profound: execution speed is the strongest predictor of startup success. In the AI era, this advantage is becoming even more pronounced as new technologies enable unprecedented development velocity.

"I have a lot of respect for entrepreneurs and executives that can just do things really quickly, and new AI technology is enabling startups to go much faster."

🚀 The Rise of Agentic AI

One of Ng's most important insights centers on agentic AI—a paradigm shift from simple prompt-response interactions to iterative, multi-step workflows. This isn't just marketing hype; it's a fundamental change in how we can leverage AI systems.

Traditional LLM Usage vs. Agentic Workflows

This iterative approach has proven transformative across AI Fund's portfolio, from complex compliance document processing to medical diagnosis and legal document analysis. The difference between "working" and "not working" often comes down to implementing these agentic workflows.

💡 The Concrete Ideas Framework

Perhaps Ng's most actionable advice revolves around the concept of "concrete ideas"—product concepts specified in enough detail that an engineer can immediately build them.

The key insight? Vague ideas get applause, concrete ideas get built. When you're vague, you're almost always "right" because everyone can project their own interpretation. When you're concrete, you might be wrong—but you'll discover that quickly, which is exactly what you want for speed.

⚡ The New Engineering Reality

AI coding assistance is fundamentally changing the development landscape, but not uniformly across all types of work:

The Permission to Move Fast (Responsibly)

Ng advocates for a nuanced approach to development speed. His teams systematically build 20+ prototypes to validate ideas because the cost has dropped so dramatically. The key principle? "Move fast and be responsible"—not the problematic "move fast and break things."

"Go ahead, write insecure code—if it's only running on your laptop and you don't plan to hack yourself. Just make it secure before you ship it."

🔄 The Shifting Bottlenecks

As engineering velocity increases, AI Fund has observed a fascinating shift in team dynamics and bottlenecks:

Product management, user feedback, and feature decisions are increasingly becoming the limiting factors. This creates new opportunities for PMs who can code and engineers with strong product instincts.

📊 The Feedback Velocity Hierarchy

With engineering accelerating, getting rapid user feedback becomes critical. Ng provides a hierarchy of feedback mechanisms, ranked by speed vs. accuracy:

The counterintuitive insight? A/B testing, often considered the gold standard, is now one of the slowest feedback mechanisms. Expert intuition, when properly calibrated through experience, can be surprisingly effective for rapid decision-making.

The Calibration Loop

Ng emphasizes using slower, more accurate feedback methods to calibrate and improve your faster instincts. When A/B test results contradict your intuition, take time to understand why your mental model was wrong and update accordingly.

🧠 The AI Knowledge Advantage

Understanding AI deeply provides startups with a significant competitive advantage, but not in the way you might expect. It's less about the technology itself and more about making the right architectural decisions quickly.

"If you make the wrong technical decision, you could chase a blind alley for three months. If you make the right one, you can solve the problem in a couple of days."

The Lego Block Effect

Ng uses a compelling analogy: AI building blocks are like Lego pieces. With one block (white), you can build something cool. With two blocks (white + black), you can build something more interesting. But as you acquire more building blocks—prompting, RAG, fine-tuning, voice APIs, guardrails, evals—the number of possible combinations grows exponentially.

🔮 The Changing Nature of Code

One of Ng's most surprising insights concerns how we should think about code itself. As the cost of software engineering plummets, code becomes less valuable as an asset.

From One-Way to Two-Way Doors

Using Jeff Bezos's framework of reversible vs. irreversible decisions, Ng points out that many traditionally "one-way door" decisions are becoming "two-way doors":

• Tech stack selection: Used to lock you in; now his teams regularly rebuild entire codebases from scratch
• Database schema design: Previously required careful upfront planning; now more easily changed
• Architecture decisions: Can be reconsidered and implemented rapidly

This shift enables a fundamentally different approach to software development—one focused on rapid experimentation rather than extensive upfront planning.

👨‍💻 The Universal Coding Future

Ng makes a controversial but compelling case: everyone should learn to code. Not because everyone needs to be a programmer, but because the ability to precisely communicate with computers will become increasingly valuable across all roles.

The Art History Analogy

Ng illustrates this with a perfect example: When creating course materials, his team member with art history knowledge could prompt Midjourney with specific genres, palettes, and artistic inspirations, generating far superior images than Ng's generic "make pretty pictures of robots" prompts.

"One of the most important skills of the future is the ability to tell a computer exactly what you want so they'll do it for you."

⚖️ Responsible AI in Practice

Throughout his talk, Ng emphasizes responsible development without falling into AI doomerism. His framework focuses on practical responsibility rather than hypothetical dangers:

Fighting Misguided Regulation

Ng warns against regulatory approaches that sound safety-focused but actually serve to create gatekeepers and stifle innovation. He specifically mentions opposition to California's SB 1047, which would have imposed burdensome requirements on open-source AI development.

Practical Guidelines

• Look in your heart: If you don't think your product will make people better off, don't build it
• Kill projects on ethical grounds: AI Fund has terminated financially viable projects for ethical reasons
• Think application-level safety: Safety isn't inherent to the technology—it's how you apply it
• Protect open source: Maintain the freedom for startups to innovate with open-weight models

🎯 Key Takeaways for Entrepreneurs

🚀 The Future is Being Built Now

Andrew Ng's insights reveal that we're living through a unique moment in technology history. The application layer opportunities are vast, the tools are rapidly improving, and the knowledge needed to leverage them effectively is still concentrated among a relatively small group.

For entrepreneurs willing to master these new building blocks and embrace the pace of change, the next few years present an unprecedented opportunity to build faster, more effectively, and with greater impact than ever before.

The question isn't whether AI will transform how we build startups—it already is. The question is whether you'll be among those who master these new capabilities or those who get left behind.

"The number of opportunities seems much greater than the number of people with the skill to build them. Focus on building a product that people love, and figure out the rest along the way."

Want to dive deeper? Watch the full Y Combinator talk and consider taking courses from DeepLearning.AI to master more AI building blocks. The future belongs to those who can move fast and build responsibly.