You’re an aspiring founder with a brilliant idea for an AI-powered application. You’ve got the vision, the passion, and maybe even a small team ready to build. But here’s the catch – AI is moving so fast that by the time you launch, the landscape might have already shifted. New models, new tools, and new competitors seem to pop up every week. How do you keep up? More importantly, how do you outpace the competition and build something that lasts?
If you’ve ever felt overwhelmed by the speed of AI innovation, you’re not alone. The truth is, in today’s AI-driven world, execution speed is one of the strongest predictors of a startup’s success. But speed isn’t just about coding faster or launching sooner—it’s about making the right decisions quickly, iterating based on real feedback, and staying agile in a field where the rules are constantly changing.
In a recent talk at a startup school event, AI pioneer Andrew Ng shared invaluable lessons from his experience at AI Fund, a venture studio that builds about one startup per month. Having co-founded numerous AI ventures and worked closely with entrepreneurs, Ng has a front-row seat to what makes AI startups succeed—or fail. His insights revolve around one central theme: speed. But not just any speed—smart, strategic speed that leverages the latest AI tools and workflows to outmaneuver the competition.
In this post, we’ll dive deep into Ng’s key lessons, explore the evolving AI landscape, and uncover actionable strategies you can use to build faster, smarter, and more responsibly. Whether you’re a founder, a developer, or simply an AI enthusiast, these insights will help you navigate the complexities of building in the AI era.
The AI Stack: Where the Real Opportunities Lie
Before we get into the nitty-gritty of speed, let’s set the stage by understanding the broader AI ecosystem. Ng describes what he calls the AI stack, a layered framework that helps us see where opportunities for startups exist:
- Semiconductor Companies – The foundation, providing the hardware that powers AI.
- Cloud Providers (Hyperscalers) – Built on top of semiconductors, offering scalable computing resources.
- Foundation Model Companies – Developing large language models (LLMs) and other AI models.
- Application Layer – Where AI is applied to solve real-world problems, from healthcare to education.
While much of the media hype focuses on the lower layers—especially foundation models—Ng argues that the biggest opportunities are actually at the application layer. Why? Because applications generate the revenue that fuels the entire stack. Without valuable applications, there’s no demand for the underlying technology.
For aspiring founders, this is crucial. While building a new foundation model might seem glamorous, the real impact (and profit) often comes from applying AI to specific problems. Think of it this way: the foundation models are like the engines, but the applications are the cars that people actually drive.
If you’re looking to start an AI company, focus on the application layer. Identify a specific problem that AI can solve better than existing solutions, and build a product that delivers tangible value to users.
The Rise of Agentic AI: A Game-Changer for Startups
One of the most significant shifts in AI over the past year is the rise of agentic AI. But what exactly is it, and why does it matter?
In simple terms, agentic AI refers to systems that can perform tasks iteratively, improving their output over time. Unlike traditional AI models that generate a single response to a prompt, agentic workflows allow AI to think, research, revise, and refine—much like a human would.
Ng uses a relatable example: writing an essay. If you ask a human to write an essay, they don’t just start typing from the first word to the last without stopping. They outline, draft, revise, and polish. Similarly, agentic AI can be prompted to create an outline, conduct research, draft content, and then critique and improve its own work. This iterative process leads to higher-quality outputs, especially for complex tasks like legal analysis, medical diagnosis, or compliance documentation.
For startups, this opens up a world of possibilities. Agentic workflows can automate intricate processes that previously required human expertise, making it easier to build sophisticated applications without a massive team.
But here’s the catch: While agentic AI is powerful, it’s also slower and more resource-intensive. Each iteration adds time and cost, so founders need to balance quality with efficiency.
Actionable Insight: When building your AI application, consider whether an agentic workflow is necessary. For simple tasks, a single prompt might suffice. But for complex, high-stakes applications, investing in an iterative approach can be the difference between success and failure.
Speed as a Competitive Advantage: Lessons from AI Fund
At AI Fund, Ng and his team have built dozens of startups, and one consistent theme emerges: execution speed is critical. But speed doesn’t mean rushing blindly—it means making fast, informed decisions and iterating quickly based on feedback.
Here are the key strategies Ng shared for moving faster:
1. Work on Concrete Ideas
One of the biggest mistakes founders make is chasing vague, grandiose ideas. Ng emphasizes the importance of concrete ideas—concepts that are specific enough for an engineer to build immediately.
- Vague Idea: “Let’s use AI to optimize healthcare.”
- Concrete Idea: “Let’s build software that allows patients to book MRI machine slots online to maximize usage.”
The concrete idea is actionable. It gives your team clear direction, allowing them to build, test, and iterate quickly. Vague ideas, while they might sound impressive, often lead to confusion and wasted time.
Actionable Insight: Before you start building, refine your idea until it’s concrete. Ask yourself: “Can an engineer build this today?” If not, keep refining.
2. Leverage Rapid Prototyping with AI Coding Assistance
AI has revolutionized software engineering, particularly when it comes to building prototypes. With tools like GitHub Copilot, Cursor, and Claude, developers can build quick and dirty prototypes 10x faster than before.
Ng encourages teams to embrace this speed. For prototypes, don’t worry about scalability, security, or even code quality. The goal is to test ideas quickly and see what works. Once you find a promising direction, you can invest in building a production-ready version.
A Surprising Shift: Ng notes that with AI assistance, the cost of engineering has plummeted, making it easier to scrap and rebuild entire codebases. What used to be a “one-way door” decision (like choosing a tech stack) is now more of a “two-way door”—you can change your mind without catastrophic costs.
Actionable Insight: Use AI coding tools to build multiple prototypes in parallel. Test them with users, gather feedback, and double down on what works. Don’t be afraid to throw away code—it’s cheaper than ever.
3. Shift the Bottleneck: Product Management and Feedback Loops
With engineering speeding up, the new bottleneck is often product management—deciding what to build next. Ng observes that the traditional ratio of product managers to engineers (e.g., 1 PM to 4 engineers) is changing. In some cases, teams now have more PMs than engineers because the engineering work is so fast.
To keep up, startups need to get rapid feedback from users. Ng shares a portfolio of tactics for getting feedback quickly:
- Fastest: Look at the product yourself and trust your gut (if you’re a subject matter expert).
- Faster: Ask 3 friends or teammates for feedback.
- Fast: Ask 3-10 strangers (e.g., in a coffee shop or hotel lobby).
- Slower: Send prototypes to 100 testers.
- Slowest: Run A/B tests.
Pro Tip: Ng reveals that he’s made countless product decisions by respectfully asking strangers for feedback in high-traffic areas. It’s a skill every founder should develop.
Actionable Insight: Develop a feedback loop that matches your stage. Early on, rely on your gut and small groups of users. As you scale, invest in more rigorous testing. But always prioritize speed.
4. Understand AI to Make Better Decisions
In a field as dynamic as AI, technical knowledge is a superpower. Ng points out that understanding AI can save you from chasing dead ends. For example, knowing whether to use prompting, fine-tuning, or a specific model architecture can mean the difference between solving a problem in days versus months.
A Common Pitfall: Ng warns against the “one-bit” mistake. In theory, if you have two options, trying both should only slow you down by 2x. But in practice, choosing the wrong path can lead to months of wasted effort.
Actionable Insight: Invest time in learning the latest AI tools and techniques. Even if you’re not a developer, understanding the basics of AI can help you make smarter, faster decisions.
The Changing Landscape of Software Engineering
AI isn’t just changing what we build—it’s changing how we build. Ng highlights several trends that are reshaping software engineering:
- Code is Less Precious: With AI assistance, code is easier to generate, making it less of a valuable artifact. Teams can rebuild entire codebases quickly, leading to more experimentation.
- Everyone Should Code: Contrary to the narrative that AI will replace coders, Ng believes that more people should learn to code. As tools make coding easier, non-engineers can leverage AI to automate tasks and boost productivity.
- Product Management is the New Bottleneck: With engineers working faster, the challenge shifts to deciding what to build. Founders need to be adept at gathering feedback and making quick product decisions.
Actionable Insight: Encourage your entire team to learn basic coding skills. Even non-technical roles can benefit from understanding how to use AI tools to automate workflows.
Responsible AI: Cutting Through the Hype
Ng doesn’t shy away from addressing the overhyped narratives surrounding AI, particularly around safety and ethics. He argues that much of the fearmongering—such as AI leading to human extinction or mass unemployment—is exaggerated for PR and fundraising purposes.
Key Takeaway: AI is a tool, and like any tool, its impact depends on how it’s used. Ng prefers the term responsible AI over “AI safety” because safety is a function of application, not technology.
For founders, this means focusing on building products that genuinely improve lives. Ng shares that at AI Fund, they’ve killed projects not because they weren’t profitable, but because they didn’t align with their ethical standards.
Actionable Insight: Before building, ask yourself: “Does this make the world better?” If not, reconsider. And always prioritize responsible use of AI in your products.
Join the “Everything in AI” Newsletter: Stay Ahead of the Curve
If you’re serious about building in the AI space, staying updated is non-negotiable. The field moves fast, and what worked yesterday might be obsolete tomorrow. That’s why I’m excited to invite you to join the “Everything in AI” newsletter.
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Speed, Strategy, and Responsibility
Building an AI startup is no small feat, but with the right approach, it’s entirely possible to thrive in this fast-paced landscape. Andrew Ng’s lessons from AI Fund offer a roadmap for success:
- Focus on concrete ideas that you can build and test quickly.
- Leverage AI tools to prototype rapidly and iterate based on feedback.
- Understand the technology to make smarter, faster decisions.
- Build responsibly, always considering the broader impact of your work.
The AI revolution is just getting started, and the opportunities are vast—especially at the application layer. By embracing speed, staying agile, and committing to responsible innovation, you can build a startup that not only succeeds but also makes a meaningful difference.