20VC Scale Founder Alex Wang on How To Hire Incredible Talent Before You Are A Hot Company, Why Beating Competition Is Not As Clear Cut As Investors Believe & Why AI Is UnderHyped Today In Terms of Total Impact

In this episode of "20 Minutes VC," host Harry Stebbings interviews Alexander Wang, the 22-year-old founder and CEO of Scale AI, a data platform for artificial intelligence that recently reached a $1 billion valuation with its $100 million Series C funding. Wang shares his journey from tech lead at Quora to dropping out of MIT to start Scale AI, emphasizing the importance of taking risks and the minimal downside for young entrepreneurs. He discusses the hiring process, advising to focus on candidates comfortable with startup risks and to offer significant equity to early employees. Wang also addresses the hype around AI, believing it's either appropriately hyped or underhyped, and critiques the overvaluation of large datasets without considering edge cases. He emphasizes the need for comprehensive, high-quality data to improve AI model reliability. Lastly, Wang reflects on investor relationships, stressing the importance of choosing investors who are long-term believers in the company's mission.

• Harry Stebbings hosts the 20 minutes VC and Founders Friday.
• Listeners can provide feedback to Harry Stebbings via Instagram.
• The episode features Alexander Wang, founder and CEO of Scale.
• Scale is a data platform for AI that surpassed a $1 billion valuation.
• Alexander Wang is one of the youngest founders to reach this milestone.
• Scale has raised over $123 million from notable investors.
• Alexander Wang has a background as a tech lead at Quora and a software engineer at Adipa.
• Mike Volpe from Index Ventures contributed question suggestions for the episode.
• Activecampaign and Atom Finance are mentioned as sponsors.

"This is the 20 minutes VC and founders Friday with me, Harry Stebbings."

This quote introduces the show and its host, Harry Stebbings.

"Alexander Wang found and CEO at scale scale, the data platform for AI, providing high-quality training and validation data for AI applications."

This quote introduces the guest, Alexander Wang, and his company, Scale, which provides data services for AI applications.

• Alexander Wang grew up in New Mexico and worked in Silicon Valley before returning to school at MIT.
• He dropped out of MIT to start Scale, which has become a unicorn company.
• Wang's background includes working at Quora as a tech lead and at Adipa as a software engineer.

"Yeah, so I had a very lucky path. I grew up in New Mexico, and then after high school, I actually came out here to the valley to work."

Alexander Wang describes his fortunate career path, beginning in New Mexico and leading to Silicon Valley.

"I went back to MIT, doing mostly deep learning and AI, and then dropped out to start scale."

This quote explains Wang's educational background in deep learning and AI at MIT and his decision to drop out to start his company, Scale.

• Alexander Wang views starting a company as low risk, especially for young individuals.
• He believes that failure in starting a company is not a significantly negative experience.
• Wang advises young entrepreneurs to first join a small startup to gain experience before starting their own company.

"The reality is that there's not actually that much risk when you start a company."

Wang shares his perspective on the perceived versus actual risk of starting a company.

"I think a lot of people overweight risk, particularly when it comes to taking a job at a small startup or starting a company, et cetera."

This quote reflects Wang's belief that people often overestimate the risks associated with joining or starting a startup.

• Wang suggests joining a small startup to learn good habits and culture.
• He believes that prior experience in a startup environment can make one more effective when starting their own company.

"I think the thing that I would do first is join a small startup."

Alexander Wang advises that gaining experience in a small startup is a beneficial first step for aspiring entrepreneurs.

"The reason I wouldn't start a company straight out of the gate is that I do think if you've kind of seen an organization run once before and have sort of like, learned from that experience, you're going to be a lot more effective when starting your own company."

Wang explains why it is advantageous to have prior experience in an existing company before founding a new one.

• Wang grew up in a scientific community in Los Alamos, New Mexico, which was not focused on business or commerciality.
• His upbringing emphasized knowledge and learning, influencing his approach to building company culture.

"Growing up, I almost never thought about business and almost never thought about being commercial or capitalist at all."

Alexander Wang describes how his upbringing in a scientific community shaped his early perspectives on business.

"I think being a part of that kind of culture, growing up really influenced how to build that kind of culture at my company today."

Wang reflects on how his childhood environment influenced his current leadership and company culture.

• The episode intends to discuss the investment in AI and whether it is overhyped.
• Alexander Wang believes that AI is either appropriately hyped or possibly underhyped.

"So I actually have this belief that AI is either appropriately hyped or even maybe."

This incomplete quote indicates that Wang was about to share his thoughts on the current hype surrounding AI before being cut off.

• AI and machine learning are viewed as the next significant wave in technology, following the Internet and mobile.
• The impact of AI and machine learning will be massive as they are applied to a broad range of businesses and processes.
• Some aspects of AI, such as Artificial General Intelligence (AGI), are considered overhyped.
• There is skepticism about the timeline for AI to surpass human abilities in all areas.
• Despite overhype in some areas, AI is already solving simple problems with significant impact.
• The assumption is that even without technological advances, AI's current capabilities justify the excitement.

"If you were to think about technology in terms of these giant waves, I think there's the Internet, there's mobile, and now the next wave is really about AI and machine learning."

This quote emphasizes the speaker's belief that AI and machine learning represent the next transformative technological era, akin to the advent of the Internet and mobile technology.

"It feels when we think about, it's like AI can be applied in at least as many ways as the Internet, and it's going to be this incredibly impactful technology."

The speaker suggests that AI's potential applications are as vast as those of the Internet, indicating a belief in its far-reaching and profound impact on society and industry.

"But the reality is that there's a lot of very simple problems that AI is already solving and already capable of solving that are already going to be really, really impactful."

The speaker acknowledges that even though some aspects of AI are overhyped, there are already many practical and impactful applications of AI in solving real-world problems.

• There is a trend of companies loosely using the term "AI" for rebranding traditional fields like actuarial science, statistics, or big data.
• This misuse of the term AI may affect the public's perception of the technology.
• Scale works with companies genuinely using AI, machine learning, and deep learning.
• It's important to distinguish between real AI innovation and the use of AI as a buzzword.
• The industry needs to overcome the challenge of accurately representing AI's true capabilities.

"Today a lot of companies and projects really do, they're using the term AI extremely loosely."

This quote reflects the speaker's agreement with the notion that many entities are inappropriately labeling their work as AI-centric, which could lead to misconceptions about the technology.

"When you're actually able to apply deep learning to a problem, it is really, really impactful and really incredible what it can do."

The speaker contrasts the misuse of the AI label with the genuine, significant impact that true deep learning applications can have when properly implemented.

• The AI industry must address how machine learning systems handle ambiguity and novel situations.
• Current state-of-the-art systems may struggle with scenarios outside their training data.
• Improving machine learning models' reliability in the real world is crucial.
• The quality of data is paramount: accurate, comprehensive, and varied data lead to better models.
• Scale is focused on resolving data issues to enhance machine learning model performance.

"There's a lot of work that we have to do to make sure that these systems are actually performing well."

The speaker highlights the need for ongoing work to ensure that AI systems function effectively and reliably, especially when encountering new or ambiguous situations.

"Machine learning and AI really follows a kind of garbage in, garbage out kind of framework, where if the data is bad, the models are going to be bad."

This quote underlines the importance of high-quality data in the development of effective AI models, asserting that the input data's quality directly influences the output.

• There's a debate on the diminishing returns of adding more data to AI models.
• Google's research suggests that exponentially increasing data set size can lead to significant performance gains.
• The performance of AI models in real-world applications often hinges on how they handle edge cases.
• Adding data that represent edge cases can greatly improve model reliability and safety.
• The speaker acknowledges that while each additional data point may offer less value, data for edge cases is still highly valuable.

"Each time you ten x the size of your data set, you're going to keep getting bang out of your buck in terms of the performance of your model."

The speaker cites Google's research to argue that scaling up data sets exponentially can yield substantial improvements in AI model performance, countering the idea of diminishing returns.

"If you think about a self driving car right now, everybody in the industry is sort of tuning and sharpening the performance against edge cases."

The quote emphasizes the importance of edge cases in the development of reliable AI systems, such as self-driving cars, where rare and unusual situations must be accounted for to ensure safety.

• Synthetic data creation platforms are gaining attention as a potential solution for generating data for edge cases.
• The effectiveness of synthetic data in practice, especially for visual and textual data, is questioned.
• Synthetic data often contains biases or artifacts that can negatively impact machine learning models.
• The current state of synthetic data technology is not yet significantly impacting machine learning progress.

"It hasn't really worked in practice. In most situations, particularly when it comes to visual data or even textual data, it really hasn't worked."

This quote expresses skepticism about the practical effectiveness of synthetic data, noting that it often fails to meet the needs of machine learning, particularly in visual and textual applications.

• Hiring incredible people requires a significant time investment.
• Early hiring efforts at Scale involved extensive personal interactions with potential hires.
• Persistence and time are crucial when convincing talented individuals to join a startup.

"A lot of our early engineers, I probably spent north of 20 hours getting coffee with them, talking to them on the phone, chatting with them, et cetera, to be able to convince them to join the company."

The speaker reflects on the intense effort and time commitment required to recruit top talent for a growing company, emphasizing the need for a personal touch in the hiring process.

• Emphasize the excellence of current team members to attract new talent.
• Create a strong impression of the team's quality within and outside the company.
• Avoid spending time on candidates not comfortable with the risks of joining a smaller, less established company.
• Recognize that a candidate's comfort with risk can override other factors when considering joining a startup.

"So you need to make it clear that the people you've hired so far are really excellent, and then make sure that that's the impression that everybody gets when they meet people at your company, when they talk to other people about your company, et cetera."

This quote highlights the importance of promoting the quality of current team members to build a positive perception of the company, which is crucial for attracting new talent.

• Examine the range of opportunities a candidate is considering to gauge their readiness for startup life.
• Candidates interviewing with multiple small startups likely have a higher risk tolerance.
• Specific questions from candidates can indicate their comfort level with risk and the startup environment.
• Look for signs that candidates understand and accept the demands and risks of working in a startup.

"Can I dive in and ask how do you determine their own risk assessment? I mean, quite literally say, are you ready for startup life and maybe a drop in salary?"

This question from the interviewer points to the challenge of assessing a candidate's true readiness for the uncertainties and potential sacrifices involved in startup life.

• Founders should be willing to offer significant equity to early employees.
• Despite larger seed rounds allowing for reasonable salaries, a willingness to take a salary cut in exchange for equity is desirable.
• Healthy equity packages align early employees with the long-term success of the company.

"I think you do really want people who are okay taking a massive salary cut and then really excited about getting more equity."

Alexander Wang emphasizes the importance of finding early employees who prioritize equity over a high salary, suggesting they are more likely to be committed to the company's long-term success.

• Beating competition involves more than just having a better product.
• Success in competition comes from excellence in multiple areas: go-to-market strategy, technology, customer service, design, and branding.
• As a company grows, it's crucial to hire top talent in every function to maintain a competitive edge.
• Investors' perception of competition may be too simplistic; startups need a well-rounded approach to outperform rivals.

"It really is not that clean cut. I mean, if you think about some of these classic examples over time, like think about Uber versus Lyft, that's just been."

Alexander Wang argues that competition in the business world is complex and multifaceted, using Uber and Lyft as examples of how various factors contribute to competitive success.

• Building relationships with investors well before fundraising can lead to quick and successful funding rounds.
• Founders should choose investors who are the biggest believers in their mission and vision.
• Long-term alignment with investors can greatly affect a company's trajectory, especially during challenging periods.

"You want to pick your biggest believers. And so usually, not always, but usually your sort of first term sheet is usually your biggest believer."

Alexander Wang conveys advice received from Nat Friedman, suggesting that founders should prioritize investors who show the strongest belief in their company, as this alignment will support the company's growth and navigation through challenges.

• Building close personal relationships with board members is crucial.
• Direct feedback from the board is essential for CEO growth and company success.
• Transparency with the board, including providing more information and access to the team, leads to more effective assistance from them.
• A strong CEO-board relationship prepares for the long-term challenges a company may face.

"I think the first thing that has been probably the first and foremost thing, and I don't know if this works for everyone, but the thing that's gone the furthest for me is just really building close personal relationships with the members of my board."

This quote emphasizes the importance of personal relationships with board members for effective board management and the direct impact it has had on the speaker's experience as a CEO.

"It's really crucial that your board is able to just give you direct feedback and not be worried about how you take it."

The quote highlights the necessity of open and honest communication between the CEO and the board members, where feedback can be given without concern for personal offense.

"You really want to clue your board members into more information, give them more access to your team, and give them as much visibility as possible."

The speaker is advocating for transparency and openness with the board, suggesting that this approach will lead to more effective support from them.

• "Seven Powers" by Hamilton Helmer is highly recommended for its analytical approach to business.
• The book provides a quantitative, game-theoretical perspective on what makes businesses successful over long periods.

"It's probably one of the best business books ever. And the reason is that it goes through what he calls these seven powers, which are these seven qualities of businesses that allow them to have differentiated returns over long periods of time."

The quote explains why "Seven Powers" is considered one of the best business books, highlighting its focus on key qualities that contribute to sustained business success.

• A shift in focus from press and hype to building solid businesses is necessary.
• The speaker suggests that hype is often counterproductive to creating a successful business.

"Being hyped very rarely ends up helping you build a great business."

This quote implies that the pursuit of media attention and hype does not correlate with the foundational work required to build a lasting business.

• Transitioning from a doer to a leader is a current focus for improvement.
• This shift is common among founders and is crucial for company growth.

"I transition away from that and focus on being more of a leader for the company."

The quote reveals the speaker's personal development goal of moving from hands-on tasks to a more strategic leadership role within the company.

• Steve Jobs is considered the gold standard for success due to his lasting impact on technology and business.

"Steve Jobs, I think, accomplished some of the most incredible feats that anybody in the world has accomplished honestly."

This quote reflects the speaker's admiration for Steve Jobs' achievements and the profound influence he had on the tech industry.

• YC showcases the abundance of business opportunities and the talented individuals pursuing them.

"It's maybe one of the few places where you can go and you can sort of really soak in how much opportunity there really is out there."

The speaker is expressing the inspirational aspect of Y Combinator, which exposes entrepreneurs to the vast potential and opportunities in the business world.

• The goal is to expand the company's AI infrastructure, tools, and platforms.
• The aim is to enable organizations of all sizes to effectively utilize machine learning for productivity.

"We want to build out all of the pieces of infrastructure, all of the tools, all of the platforms that would allow more and more organizations to be successful, effective and productive with machine learning."

This quote outlines the company's vision for the next five years, focusing on enhancing AI capabilities for a broad range of businesses.

• Appreciation for being a guest on the show and excitement for the company's future prospects.

"Thank you so much for having me. You do a great job with the show. I'm really glad to be on it for a second time."

The speaker expresses gratitude for the opportunity to participate in the show and compliments the host's work.

"Such a wonderful guest to have on the show, and I couldn't be more excited about the times ahead with scale."

The host reciprocates the sentiment, showing enthusiasm for the guest's future endeavors.