On the freakishly strong YC W26 batch

Now that Rebel Fund has invested in nearly 300 top Y Combinator startups, I’m often asked by our LPs and other investors what I think about the latest YC batch. Since I’ve been to every YC Demo Day since my own in 2013, I could answer this question subjectively, but I prefer to answer it with data —and the data shows the YC Winter 2026 (W26) batch is freakishly strong.

As my long-time readers know, Rebel has invested millions of dollars into developing the world’s most sophisticated ML/AI algorithm for predicting YC startup success, Rebel Theorem 4.0, which we use not only to accurately assess the quality of individual YC startups, but also entire YC batches.

To illustrate the strength of W26, this chart shows the percentage of startups in each YC batch over the past few years that are “top 20% companies” as scored by our algorithm (benchmarked to pre-W26 data). A whopping 35% of W26 startups meet this threshold, far more than any other YC batch.

In case you’re wondering if there’s just a small group of top startups holding up W26 batch quality, that’s apparently not the case. As you’ll see in the charts below, the entire distribution curve of the batch has shifted in the right direction, with significantly more startups predicted to be successful, and significantly less predicted to die or become zombies, simultaneously at the 25th, 50th, and 75th percentiles.

This begs the question “What makes the W26 batch so strong?” Of course my crack Rebel data science team has some answers.

The main thing is that W26 batch founders demonstrate more of the characteristics that have historically predicted strong YC startup outcomes. This is probably no coincidence, since as I mentioned in my previous post On the ‘new’ Combinator, YC under Garry Tan’s leadership has generally refocused on all the things that made it great in the first place.

For example, we’ve seen in our data that younger founders are generally more likely to be successful, and W26 founders are much younger and fresher out of school than their predecessors.

As another example, we’ve seen that Bay Area companies are more likely to be successful than, say, European companies, and sure enough, W26 companies are much more concentrated in the Bay Area.

Looking at industry-related features, we’ve seen that consumer companies are less likely to be successful than, say, industrials companies, and W26 has much less consumer companies and more industrials companies.

To be clear, I’m greatly over-simplifying the examples above, since these are just a few of the hundreds of features that Rebel Theorem 4.0 assesses in parallel for each startup, and the interaction between features matters much more than any feature alone. In other words, while younger founders generally have higher odds of success, that’s not true in every scenario. The same goes for Bay Area companies, industrials companies, etc.

There are many more ways that W26 founders and companies more closely reflect the historical ideal of what a top YC startup should look like, from founder pedigree, to prior founder accomplishments, to founder-product fit, and some ways they don’t. This post just focuses on the macro trend.

The billion dollar question on my mind and presumably the YC partners’ is whether in today’s world of Claude Code, Codex, etc if the fundamentals of building and managing a startup have changed so dramatically that the technical pedigree, educational and work background, skills, personality traits, etc that have historically predicted YC founder success still apply. I don’t have a confident answer to that yet, but since Rebel Fund tracks hundreds of characteristics and outcomes of every YC company and founder in nearly real-time, once the data is in, we’ll be the first to know.