Batch-level trend analysis
Breaks down 793 YC companies across five batches and summarizes the main shifts over time, including AI share, B2B share, team size, and hiring activity.
What YC Is Really Betting On
A data analysis product that examines 793 Y Combinator companies and 1,625 founder bios across five batches to surface patterns in AI depth, founder backgrounds, crowding, and batch composition. It is aimed at readers who want a structured view of YC funding trends rather than a company directory.
Overview
What YC Is Really Betting On is a data analysis product that examines 793 Y Combinator companies and 1,625 founder bios across five batches, covering Winter 2025 through Winter 2026. It organizes the cohort into views such as AI vs. non-AI, founder archetypes, competitive overlap, hiring signals, naming patterns, and partner preferences.
The homepage frames the product as a structured trend analysis rather than a directory or marketplace. Its purpose is to help readers understand what kinds of startups YC is funding, how those companies cluster, and which patterns stand out across the batches.
What the analysis surfaces
AI depth breakdown
Classifies AI companies into wrappers, applied AI, AI infrastructure, and deep tech so readers can see how much of the batch is technical depth versus thin application layers.
Founder and team profiling
Maps founder background signals such as ex-FAANG and PhD experience, plus team composition and location patterns like San Francisco concentration.
Correlation analysis
Identifies statistical relationships such as sector correlations, hiring patterns, and naming trends to highlight non-obvious patterns in the dataset.
Competitive overlap scoring
Measures competitive crowding by vertical and flags especially crowded or unusually unique companies within the batches.
Text clustering and archetypes
Groups companies into NLP clusters and pattern-based archetypes, including partner fingerprints and thematic segments like founder DNA and the archetype view.
Where it is useful
Tracking YC batch trends
Useful for people who want a compact read on what YC is funding across recent batches, especially when comparing AI, B2B, and deep-tech concentration over time.
Studying startup positioning
Helpful for founders or operators who want to see how YC companies are grouped by archetype, naming patterns, and competitive crowding before positioning a startup.
Researching cohort patterns
Relevant for investors, analysts, or journalists looking for quantitative evidence about founder backgrounds, company mix, and cluster-level patterns in YC cohorts.
Finding reference companies
Useful when you want examples of companies in crowded or unusual categories, since the analysis includes representative companies for wrappers, applied AI, infrastructure, and deep tech.
Pros and Cons
Pros
- Covers a clearly defined corpus: 793 companies and 1,625 founder bios across five batches.
- Breaks the analysis into multiple lenses, including AI depth, founder background, crowding, and naming.
- Highlights specific quantified findings, such as the share of AI companies and the decline in wrapper-style companies across batches.
- Surfaces both broad trends and example companies, which makes the analysis easier to interpret.
Cons
- The public pages do not show pricing, trial terms, or plan details.
- The pricing URL returns a 404, so there is no visible plan page to review.
- The site text does not describe integrations, exports, or a documented workflow for teams.
FAQ
What is this product for?
The site presents an analysis of 793 Y Combinator companies across five batches, from Winter 2025 through Winter 2026. The homepage does not describe a separate setup flow or login-based workflow.
What kinds of insights does it cover?
The published analysis focuses on company mix, AI depth, founder backgrounds, competitive overlap, naming patterns, hiring signals, and partner fingerprints. It is intended for readers who want a structured view of YC batch trends rather than a general company directory.
How much data is analyzed?
The homepage highlights analysis of 793 companies and 1,625 founder bios, along with NLP clustering, cross-correlations, and competitive overlap. It does not mention an export format or downloadable dataset on the page text provided.
Is pricing available?
The site text does not show pricing information, and the pricing page resolves to a 404. Based on the available evidence, pricing is not disclosed on the public pages provided.
Quick Facts
- Category
- Market research
- Scope
- 793 YC companies across 5 batches
- Founder bios analyzed
- 1,625
- Primary focus
- YC batch trend analysis and clustering
- Source domain
- yc-trends.vercel.app
- Pricing
- Not disclosed on the provided pages
What YC Is Really Betting On 替代品
Hype
Hype 是一款可依分類、時間範圍與評分模式搜尋 YouTube 熱門趨勢的網頁工具，幫助創作者掌握新興題材、查看來源影片，判斷下一步該做什麼內容。
Shengsuanyun
Shengsuanyun 提供基於雲的高效數據管理和分析解決方案。
Ranpo AI Visibility
Ranpo AI Visibility is a generative search analytics platform that checks how a brand appears in AI search systems such as ChatGPT and Gemini. It helps teams measure visibility, compare against competitors, and identify gaps that may affect inclusion in AI-generated answers.
PromptScout
PromptScout tracks how ChatGPT, Gemini, Google AI Overviews, and Perplexity mention your brand or competitors, then pairs those results with source analysis and website audits. It helps teams decide what to fix in content, positioning, or site readiness next.
SaveMRR
SaveMRR is a Stripe retention tool for SaaS teams that scans billing data for churn and MRR leaks, then automates recovery through dunning, cancel-save offers, win-back emails, and onboarding nudges. It is built for founders and bootstrapped teams using Stripe.
Sleek Analytics
Sleek Analytics is a privacy-friendly web analytics tool with real-time visitor tracking, Core Web Vitals, and revenue attribution. It helps site owners understand traffic and conversions without cookie banners or a heavy setup.