The Y Combinator W23 Generative AI Landscape

Some personal musings around the YC W23 Generative AI Landscape

There are 36 AI/ML-related startups in this YC Winter Batch out of the 201 startups accepted in total.

Oliver Molander
3 min readFeb 23


This represents ca 18% of the current cohort being some kind of Generative AI startups.

Is 18% a lot or not?

Let’s compare this with the Y Combinator Winter & Summer Batch from ’21 when we were at peak Crypto & Web3 hype:

  • The ’21 YC Winter Batch had in total 333 startups with 9 Crypto / Web3 ones: representing ca 2.5% of the cohort
  • The ’21 YC Summer Batch had in total 391 startups with 15 Crypto / Web3 ones: representing ca 4% of the cohort

📈 So ca 3% were Crypto & Web3 startups in the 2021 YC batches compared to ca 18% being Generative AI startups in the 2023 Winter Batch.

Should concerned conclusions be made?

I wouldn’t personally make too many conclusions from this data, albeit I believe that the success rate in Generative AI investing will in the short- to medium-term be quite low as many tourist VCs with no background in the space are jumping on the hype bandwagon, throwing too much money at some startups/founding teams (when they should still iterate on their approaches).

📚 I wrote a few weeks back a Medium article for Better Programming, where I among others discuss the concerns about whether one can build a defensible business on top of any of the foundation model platforms:

The risk with any hype cycle is that things get overheated and we might in a worst-case scenario experience another AI Winter. A certain level of hype can’t be maintained — and at some point, the industry starts underdelivering. AI/ML turns out to be surprisingly fail-ridden. Companies and people that try using it to solve everyday problems discover it’s prone to errors, often quite mundane ones.

📚 Clive Thompson (e.g. writer at The New York Times/WIRED) published yesterday a good Medium article about this:

We’re not heading toward another AI Winter but we need to be cautious

Personally — I’ve been very bullish about the AI/ML transformation since 2015/16 and remember how jaw-dropped I was when testing out Transformer based text-models such as BERT in 2018.

However, we know that AI/ML has become truly big when it becomes a boring and standardized component of workflows & infrastructure, just like e.g. relational databases.

Gergely Orosz noted recently on Twitter how he never believed crypto would be anything meaningful innovation-wise because it was a unregulated finance pretending to be a technological breakthrough (it was not).

Gergely — among many others — sees AI/ML as a technology breakthrough that can (and most likely will) change a large variety of industries.

I agree.

Do you?

PS. 65 startups out of 201 are now AI/ML related if categories below are used for filtering YC W23 startups:

So suddenly, 32% of the startups in the YC W23 batch are AI/ML related across the following sub-categories:

→ AI
→ Artificial Intelligence
→ Generative AI
→ ML
→ Machine Learning
→ Chatbots
→ Conversational AI
→ Reinforcement Learning



Oliver Molander

Preaching about the realities and possibilities of data and machine learning. Founder & investor.