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.
"For the analysis, I used an LLM to embed the companies' value propositions in a vector space, ran a clustering algorithm on top of the embeddings, manually analyzed the results, and used ChatGPT to edit the draft."
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 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.
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.
I’ve never understood the notion that YC is a good deal on the seed. Traditional series a VC, which I did, was far more capital for the same eventual percentage. You’re going to do a venture round. If 125k or whatever is significant to you, I guess it makes sense.
That's far beyond what YC companies are able to achieve in a few months.
So they are raising at a Seed level which YC companies get a good deal on because YC is effectively running their fundraising for them e.g. Demo Day, vetted investors, optimised process etc.
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.
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[ 2.5 ms ] story [ 49.2 ms ] threadI'd legitimately pay money for a similar productivity tool.
Just make something a lot of people want, but can't currently get. That's the recipe.
Great insight from the pile of data. I know a lot of fellow founders failed miserably to conclude on this same lessons.
take an existing budget line item
do not try and combine line items
do not cross operational boundaries in the customer
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.
That's far beyond what YC companies are able to achieve in a few months.
So they are raising at a Seed level which YC companies get a good deal on because YC is effectively running their fundraising for them e.g. Demo Day, vetted investors, optimised process etc.
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.
2. Removing Limitations: Blue Ocean - Sequioa Arc's "Hard Fact of Life" - Symbol.vc's "Pre-Consensus Markets"
3. Advancing Technology: Sequioa Arc's "Future Vision" - Symbol.vc's "Non-Consensus Markets"
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The Arc Product-Market Fit Framework
https://www.sequoiacap.com/article/pmf-framework/
Introducing Symbol: not seeking consensus
https://medium.com/symbol-vc/introducing-symbol-not-seeking-...