I was expecting a GitHub repository with the data and processes. Instead, I am seeing this long text, (re)phrased by ChatGPT.

So here is a GPT summary of that long text

The text categorizes Y Combinator (YC) startups into three areas:

1. *Driving Efficiencies*: These startups improve existing markets with better, tech-enabled solutions, often disrupting current players.

2. *Removing Limitations*: These startups serve underserved communities or address new problems using existing technologies, such as FinTech in developing regions.

3. *Advancing Technology*: These startups push the boundaries of innovation with new technologies that transform industries, offering high rewards despite high risks.

The author critiques venture capital for being risk-averse and suggests a more proactive approach to nurturing deep tech and ambitious funding models.

This was (partially) researched and written by ChatGPT, which probably explains why it contains no actual data or examples. The analysis is interesting, but entirely abstract.

> The most common mechanism for creating a venture-backed business is by bringing efficiency to existing markets. This approach is the simplest and least risky because the demand for the product already exists. The promise is to deliver a product that is quantitatively better than what currently exists. There is often little technological uncertainty, as existing technology is applied to a new domain (low R&D).

Great insight from the pile of data. I know a lot of fellow founders failed miserably to conclude on this same lessons.

Self-fulfilling prophecy.

YC startups get pre-seed funding, ridiculously good deal on their seed, access to the alumni to market, free promotion on HN etc.

Which means they are going to have a 100x better chance of surviving until PMF versus someone who is bootstrapped or has limited access to capital.

So really what you're measuring isn't what makes a good startup but rather what type of startups get you into YC. And that has changed significantly pre and post Garry Tan taking over as CEO.

Now the statistics show you want to be based in SF, team of 2-3, 30 and under and building something involving LLMs. Which is kind of understandable given that we are in a gold rush period.

Would love to see more of the output data. - How many win/loss clusters did the data produce? - In vector space what was the seperation between B2B and B2C companies? - Did you normalize against size of win/market-cap or anything else?

The point about not being able to create demand is absolutely vital to understand.

This analysis sounds very "smart" but in my opinion, contains little of substantial value.

Just make something a lot of people want, but can't currently get. That's the recipe.