Andrew Ng: Building Faster with AI

Andrew Ng, speaking at a startup school event, emphasized the importance of execution speed for startup success, particularly in the rapidly evolving AI landscape. He highlighted the role of AI in accelerating development processes, allowing for faster iteration and testing of ideas. Ng discussed the significance of concrete ideas that can be quickly executed and iterated upon, stressing the need for rapid feedback mechanisms to refine products. He also addressed the hype around AI, advocating for a realistic approach to AI's capabilities and potential risks. Ng encouraged startups to focus on building products that users love while being mindful of ethical considerations.

• AI Fund is a venture studio that co-founds startups, focusing on execution speed as a critical predictor of success.
• The studio is actively involved in the startup process, including coding, customer interaction, feature design, and pricing.
• New AI technologies are enabling startups to accelerate their processes, with best practices evolving every few months.

"AI Fund's a venture studio and we build an average of about one startup per month."

• AI Fund is highly productive, creating new startups frequently, which provides valuable insights into effective startup practices.

• The AI stack consists of layers: semiconductors, cloud hyperscalers, AI foundation models, and the application layer.
• The application layer presents the largest opportunities for startups, despite less media attention compared to technology layers.

"Almost by definition, the biggest opportunities have to be at the application layer."

• The application layer is crucial for generating revenue to support the underlying technology layers.

• Agentic AI is an emerging trend, allowing AI systems to engage in iterative workflows rather than linear output.
• These workflows involve outlining, researching, drafting, critiquing, and revising, leading to higher-quality outcomes.

"With agentic workflows, we can go to an AI system and ask it to please first write an essay outline, then do some web research if it needs to, and fetch some web pages to put in their own context."

• Agentic AI enables AI systems to produce better work through iterative processes, which is beneficial for complex tasks like compliance and medical diagnosis.

• Startups should focus on concrete ideas that are specific enough for engineers to build quickly.
• Vague ideas, while often praised, lack the specificity needed for rapid execution and validation.

"Concreteness buys you speed, and the deceptive thing for a lot of entrepreneurs is the vague ideas tend to get a lot of kudos."

• Concrete ideas allow for quick validation or falsification, which is critical for startup success.

• Subject matter experts, with their deep understanding and intuition, can make quick and effective decisions.
• Relying solely on data for decision-making can be slow; expert intuition often provides a faster alternative.

"A subject matter expert with a good gut is often a much better mechanism for making a speedy decision."

• Expertise and intuition are valuable for navigating the startup landscape and making rapid progress.

• Building startups involves rapid iteration, with frequent feedback from users to refine products and achieve product-market fit.
• AI coding assistance is enhancing the speed and reducing the cost of engineering, transforming the startup development process.

"The speed of engineering is going up rapidly, and the cost of engineering is also going down rapidly."

• AI tools enable quicker prototyping and development, significantly accelerating the startup feedback loop.

• Two main types of software development: quick prototypes for idea testing and production-quality code for long-term use.
• AI systems drastically increase the speed of prototyping, potentially by more than ten times.

"In terms of building quick and dirty prototypes, we're not 50% faster; I think we're easily 10 times faster, maybe much more than 10 times."

• Rapid prototyping with AI allows startups to test ideas swiftly and efficiently, facilitating faster innovation and iteration.

• Standalone prototypes are quicker to build due to reduced integration with existing systems and lower requirements for reliability, scalability, and security.
• Insecure code is sometimes permissible during initial testing phases when software is only run on a developer's personal machine, but security must be ensured before distribution.
• Startups often create numerous prototypes to explore viable ideas, accepting that many will not reach production.
• The philosophy of "move fast and break things" has evolved into moving quickly while maintaining responsibility.

"Go ahead, write insecure code. Because if this software is only going to run on your laptop and you don't plan to maliciously hack your own laptop, it's fine to have insecure code."

• Encourages initial rapid development with less concern for security, emphasizing the need for eventual security measures before release.

"I tend to tell my teams to move fast and be responsible."

• Advocates for rapid development paired with a sense of responsibility to ensure quality and safety.

• Recent advancements in AI coding assistants have significantly boosted developer productivity.
• Staying updated with the latest tools is crucial as even a half-generation gap can make a substantial difference.
• The decreasing cost of software engineering has shifted the perception of code from a valuable artifact to a more disposable resource.

"Cloud codeex this is a new generation of highly agentic coding assistance that is making developer productivity keep on growing."

• Highlights the role of advanced AI tools in enhancing coding efficiency and productivity.

"I'm on teams where you know we've completely rebuilt a codebase three times the last month."

• Illustrates the reduced cost and increased flexibility in software development, allowing frequent reengineering.

• The concept of "two-way doors" versus "one-way doors" in decision-making is evolving, with software architecture choices becoming more reversible.
• The reduction in software engineering costs allows for more frequent changes in tech stacks and database schemas.

"Choosing the software architecture of your tech stack used to be a one-way door."

• Describes how decisions that were once difficult to reverse are now more flexible due to reduced costs and increased agility in software development.

• The notion that AI will replace the need for coding is challenged; as tools improve, more individuals should learn to code.
• Learning to code is valuable for all job roles, enhancing productivity and job performance.
• Understanding how to effectively communicate with computers is a crucial skill for the future.

"I think actually it's time for everyone of every job role to learn to code."

• Advocates for widespread coding literacy across all professions to improve job performance and productivity.

"One of the most important skills of the future is the ability to tell a computer exactly what you want."

• Emphasizes the importance of precise communication with computers, facilitated by coding knowledge.

• The speed of product management and user feedback processes is becoming a bottleneck due to faster engineering cycles.
• Traditional ratios of product managers to engineers are shifting, with proposals for more product managers per engineer.
• Rapid feedback is essential for shaping product development, with various tactics ranging from personal intuition to large-scale user testing.

"I still don't know if this proposal I heard yes is a good idea but I think it's a sign of where the world is going."

• Reflects on the changing dynamics in team composition and the increasing need for product management resources.

"The fastest tactic for getting feedback is look at the product yourself and just go by your gut."

• Suggests that personal intuition can be a surprisingly effective method for initial product feedback, especially for experts.

• Continuous improvement of mental models is crucial for making better product decisions.
• Analyzing data from various feedback tactics helps refine instincts and improve decision-making speed and quality.

"Often sit down and think, gee, I thought, you know, this product name will work better than her product name."

• Stresses the importance of reflecting on feedback data to update and refine mental models for better future decisions.

• Understanding AI provides a competitive advantage due to its emerging nature, unlike mature technologies where knowledge is widespread.
• Correct technical decisions in AI can drastically reduce problem-solving time, while incorrect decisions can lead to significant delays.
• Technical judgment is crucial for startups to accelerate their development processes.

"So things like what accuracy can you get for a customer service chatbot? You know, should you prom fine tune a workflow? Um how do you get a voice out to low latency?"

• These are examples of technical decisions in AI that require precise judgment to optimize processes.

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

• The right technical decision in AI can significantly speed up problem-solving, whereas the wrong one can lead to wasted time.

• The development of numerous generative AI tools and building blocks has opened new opportunities for creating innovative software.
• Combining multiple AI building blocks exponentially increases the potential for developing complex applications.
• Continuous learning and acquiring new AI skills enhance the ability to create unique software solutions.

"Over the last two years we have just had a ton of wonderful genai tools or genai building blocks... that can quickly combine to build software that no one on the planet could have built, you know, even a year ago."

• The rapid development of AI tools allows for the creation of unprecedented software solutions.

"If you acquire a blue building brick as well, you can build something even more interesting."

• Acquiring new AI skills and tools enables the development of more sophisticated applications.

• The ability to execute quickly is a critical factor in the success of startups, especially in AI development.
• Rapid iteration and user feedback are essential for refining product decisions.
• Staying updated with technological advancements is crucial for maintaining speed in development.

"I find that the management team's ability to execute at speed is highly correlated with its odds of success."

• Speed in execution is directly linked to the success of startups.

"Rapid entrying with AI coding assistance makes you go much faster but that shifts the bottleneck to getting user feedback on the product decisions."

• While AI can speed up coding, the challenge shifts to obtaining quick user feedback.

• Developing skills to use AI tools effectively is more critical than creating the tools themselves.
• Understanding how to leverage AI to accomplish tasks increases one's power and relevance in the workforce.
• Concerns about AI replacing human jobs are largely overhyped.

"The people that are most powerful are the people that can make computers do exactly what you want it to do."

• Mastering the use of AI tools is essential for maintaining influence and capability in an AI-driven world.

"People that know how to use AI to get computers to do what you want it to do will be much more powerful."

• The ability to use AI effectively distinguishes individuals in the workforce.

• Many narratives about AI, such as its potential to cause human extinction, are exaggerated for promotional purposes.
• AI's power and impact are often overstated, leading to misconceptions about its capabilities and risks.
• Responsible application of AI, rather than the technology itself, determines its safety and ethical implications.

"AI is so powerful, we might accidentally lead to human extinction. That's just ridiculous."

• Exaggerated narratives about AI's risks are often used for promotional benefits.

"AI is neither safe nor unsafe. It is how you apply it that makes it safe or unsafe."

• The application of AI, not the technology itself, determines its safety and ethical impact.

• Building a product that users love is the primary concern for startups, despite the ease of replication by competitors.
• The focus should be on creating unique value and user satisfaction rather than worrying about potential disruptions.
• Successful businesses continuously adapt and innovate to maintain a competitive edge.

"The number thing I worry about is are you building a product that users love?"

• Ensuring user satisfaction is the key focus for startups to succeed amidst rapid technological changes.

• The primary focus for businesses should be on creating products that users genuinely desire. Without this, building a valuable business is challenging.
• After establishing a product that users want, other business considerations like customer channels, pricing, and moats become relevant.
• Moats are often overemphasized; many businesses start with a product and develop a moat over time. Consumer product brands can be more defensible due to momentum, while enterprise products may require more strategic channel considerations.

"If I were to have a singular focus on one thing, it is: are you building a product that users really want? Until you solve that, it is very difficult to build a valuable business."

• The speaker emphasizes the importance of focusing on user-desired products as the foundation of a successful business.

"I find that moats tend to be overhyped. Actually, I find that more businesses tend to start off with a product and then evolve eventually into a moat."

• The speaker suggests that moats are often overvalued and that a strong product can naturally develop a moat over time.

• There is a significant amount of untapped potential in technology, with many opportunities for innovation that have not yet been explored.
• The application layer presents a lot of "white space" for new developments, indicating vast possibilities for building unique products.
• Building a product that people love should be the priority, and other elements can be figured out subsequently.

"The number of opportunities, meaning the amount of stuff that is possible that no one's built yet in the world, seems much greater than the number of people with the skill to build them."

• The speaker highlights the abundance of opportunities in the tech space relative to the available skilled workforce.

• Current AI tools are viewed as foundational elements (like bricks) that can be built upon for greater functionality.
• The integration of AI tools often involves dynamic challenges, such as token costs and time overhead, which differ from static engineering.
• Developers are advised not to overly concern themselves with token costs initially, as only a few startups encounter significant issues with it.

"My most common advice to developers is to first approximation just don't worry about how much tokens cost."

• The speaker advises developers to focus on product development rather than worrying prematurely about token costs.

"I've definitely been on a bunch of teams where the cost, you know, users like our product, and we started to look at our bills, and it was definitely climbing in a way that really became a problem."

• The speaker acknowledges that while token costs can become an issue, it is typically a sign of success that can be managed with engineering solutions.

• It is beneficial to design software that allows easy switching between different AI building block providers to maintain flexibility and speed in development.
• The speaker's team often uses evaluation (evals) to determine the best model to use, switching models as needed based on performance.

"I will often architect my software to make switching between different building block providers relatively easy."

• The speaker emphasizes the importance of flexibility in software architecture to facilitate easier transitions between AI models.

"The switching cost for foundation models is relatively low, and we often architect our software."

• The speaker notes the low cost of switching between foundation models, which supports maintaining flexibility in product development.

• AI in education is poised for significant change, with potential roles in automating tasks and providing personalized tutoring.
• There is a lot of experimentation in educational technology, but the final state of AI's role in education is not yet clear.
• AI's potential to hyperpersonalize education is acknowledged, but the exact implementation is still under development.

"I think everyone feels like a change is coming in edtech, but I don't think the disruption is here yet."

• The speaker suggests that while changes in educational technology are anticipated, they have not yet fully materialized.

"I do think education will be hyperpersonalized. But that workflow is an avatar, is a text chatbot, what's the workflow?"

• The speaker envisions a future where education is highly personalized, but the exact methods and workflows are still being explored.

• AI developers should balance product development with potential societal downsides, such as economic inequality.
• The speaker's organization has terminated projects on ethical grounds despite strong economic cases.
• Empowering all individuals to build with AI, not just engineers, is crucial for inclusive progress.

"Look in your heart, and if fundamentally what you're building, if you don't think it'll make people at large better off, don't do it."

• The speaker stresses the importance of ethical considerations in AI development, advocating for products that positively impact society.

"AI fund, we've killed multiple projects, projects not on financial grounds but on ethical grounds."

• The speaker shares that their organization has made decisions to halt projects based on ethical concerns, prioritizing societal well-being over financial gain.

• Educating the general public about AI is important to bridge the gap between AI capabilities and public perception.
• The diffusion of AI knowledge is essential, and open-source models play a critical role in democratizing AI development.
• The speaker warns against regulatory measures that could stifle innovation and create gatekeepers in the AI ecosystem.

"As AI becomes more powerful and widespread, there seems to be a growing gap between what AI can actually do and what people perceive it."

• The speaker highlights the need for public education to align perceptions of AI with its actual capabilities.

"One of the dangers to inequality as well is if these regulatory proposals succeed and end up siphoning regulations leaving us with a small number of gatekeepers."

• The speaker warns of the risks associated with regulatory proposals that could limit innovation and create monopolistic control over AI technology.