How to do a startup, according to YC data

🌈 Abstract

The article discusses the various approaches and strategies for starting a successful venture-backed business, based on an analysis of Y Combinator (YC) companies. It covers the common mechanisms for creating a venture-backed business, the distinctions between B2B and B2C startups, and the different categories of startups that aim to drive efficiency, serve underserved communities, solve emerging problems using data, and advance technology.

🙋 Q&A

[01] Driving Efficiency

1. What are the key distinctions between B2B and B2C startups that aim to drive efficiency?

• In B2B, efficiencies are geared towards improving or reducing the workforce, with a focus on sales and reducing transaction costs through automation.
• In B2C, efficiency is passed down to consumers by aggregating demand or creating a marketplace, which requires significant capital investment and can result in winner-take-all markets.

2. What are the potential downsides of startups that drive efficiency?

• There is no guarantee that the efficiencies will be passed on to customers indefinitely.
• Companies are exposed to the risk of being replaced by new paradigms that drive greater efficiencies.
• Growth often comes at the expense of competition, which can create moral dilemmas.
• There is an inherent contradiction between the startup's desire to grow and the customer's desire to maximize efficiency.

[02] Serving Underserved Communities

1. What are the key reasons that facilitate the establishment of businesses catering to underserved communities?

• Incumbents may have been sluggish in expanding into adjacent markets.
• Margins may not be lucrative enough to incentivize market leaders.
• Regulatory or regional challenges may necessitate significant modifications to existing solutions.

2. What are the advantages and challenges of startups that serve underserved communities?

• They possess greater technological defensibility compared to efficiency-driven startups.
• Their valuation is constrained by market size, and estimating demand can be more challenging.
• There are contrarian incentives at play, as removing limitations can render the startup redundant.

[03] Solving Emerging Problems with Data

1. How do startups solve emerging problems using data?

• As companies expand, new challenges arise that require solutions that can be replicated and automated using data insights.
• Data provides the insights to enable the replication of solutions that were previously feasible only through manual efforts.
• Data fosters network effects, creating a moat around the solution that improves its effectiveness with each new customer.

2. What are the advantages of this business model?

• With continued growth, complexity increases, leading to greater rewards.
• Data fosters network effects, bringing processing power and creating a moat around the solution.

[04] Advancing Technology

1. What are the three broad categories of startups that aim to advance technology?

• Healthcare (biotech)
• Industrial (including climate)
• AI (until a couple of years ago, with the commoditization of Generative AI potentially shifting the focus towards robotics)

2. What are the author's thoughts on the venture capital industry's approach to funding deep tech startups?

• The author feels that the venture capital industry's aversion to risk and lack of innovation in its business model are limiting the scope and ambition of deep tech startups.
• The author suggests that investors could potentially do a better job at allocating the brightest minds to the most pressing societal problems by being more willing to take on risk and incubate technology over a longer time frame.