Introducing the Pegacorn Club.
By Kenn So and Ben Lorica.
[Also see our followup post: The Data Pegacorns.]
Nearly 600 companies became unicorns in 2021. It used to be a status that meant that a startup has graduated from being a startup and into a mature company worth being listed on the public stock market backed by revenue. In today’s climate becoming a unicorn is increasingly a signal of investor enthusiasm than how mature a company is, with companies with $1-5 million in revenue being awarded the status. The irony is 75% of VCs think unicorns are significantly overvalued.
Consequently, we are creating and maintaining a list of flying unicorns (Pegacorns1) – AI companies that have reached the $100M revenue milestone and have graduated from startups to mature companies. $100M is the traditional benchmark of becoming a unicorn back when multiples were still 10x revenue. But reaching $100M in revenue also implies real scale that few startups ever achieve. An investor can value any business at $1 billion but only the best businesses can convince enough customers to pay for solutions and achieve the $100M revenue scale.
How to contribute
We’d love for you to add any companies we missed! You can either submit using the form below, or submit a pull request to this public GitHub repo where we maintain the list of AI pegacorn companies.
1. Most pegacorn companies sell applications. The reason for this is that there are more use cases in the application layer than in the infrastructure layer where a single company can serve the same use case across companies, such as processing big data pipelines.
2. As the “oldest” data types, structured & semi-structured data has more varied use cases compared to newer data types like computer vision and language.
- Computer vision companies are almost exclusively self-driving companies: Zoox (acquired by Amazon), Cruise, Argo, and Pony.ai
- NLP companies primarily provide enterprise marketing and sales software for companies to increase revenue. For example, Gong records, transcribes, and summarizes sales calls.
3. US and Chinese companies dominate this space. This is not a surprise since both countries and the EU+UK get 66% of the global AI research paper citations (see the Stanford HAI report). We didn’t find many European companies and we speculate that the region’s strict and fragmented data laws likely make it harder for AI startups to grow. But if you know of any European companies we have missed, please tell us via the form!
4. Founder Profiles: We looked at founders’ Linkedin profiles to see what skills they list, and deep expertise in building large-scale systems stood out. Yes, AI related skills are needed (e.g. machine learning), but the top five technical skills emphasize prowess in building large-scale applications rather than machine learning models.
By creating the list of AI pegacorn startups, we hope to highlight the best companies that have achieved something truly remarkable. That’s not to say the other AI unicorns are not noteworthy. In fact, there are a lot of companies we’re both excited about but just need more time to get to $100 million. We hope this list becomes a resource to anyone interested in pre-IPO companies, from investors to job seekers.
Thanks for reading. To stay up to date, subscribe to the Gradient Flow newsletter. We are working on similar lists for Data and Web3.
Kenn So is an investor at Shasta Ventures, an early-stage VC, and was previously a data scientist. Shasta Ventures is an investor in Highspot. Opinions expressed here are solely his own.
Ben Lorica helps organize the Data+AI Summit and the Ray Summit, is co-chair of the NLP Summit, and principal at Gradient Flow. He is an advisor to Databricks and other startups.
Appendix: List of Companies
[Note: This is a snapshot as of 2022-04-11. For the most up-to-date list of AI pegacorn companies, see our GitHub page.]
- 🇺🇸 6sense (“Helps B2B organizations achieve predictable revenue growth”)
- 🇺🇸 Dialpad (“One workspace for team and customer communications”)
- 🇺🇸 eightfold (“AI talent management”)
- 🇺🇸🇮🇱 Gong (“Visibility into all deals, team performance, and market changes”)
- 🇺🇸 Highspot (“Improve the performance of your sales team”)
- 🇺🇸 Outreach (“Helps teams prospect more effectively”)
- 🇺🇸 ThoughtSpot (“Modern analytics cloud”)
- 🇮🇱 Verbit (“Professional AI-based transcriptioning”)
- 🇺🇸 Innovacer (“Healthcare data platform”)
- 🇺🇸 Komodo Health (“Connected Insights Drive Success”)
- 🇺🇸 Olive AI (” … the automation company creating the internet of healthcare”)
- 🇺🇸 Tempus (“Data-driven precision medicine”)
Media / Security / IoT
- 🇨🇳 ByteDance (“Inspire Creativity, Enrich Life”)
- 🇺🇸 Exabeam (“Eliminate your blindspots and respond to threats faster and more accurately”)
- 🇨🇳 Megvii (“Create machines that can see and think”)
- 🇺🇸 Argo AI (“We’re building self-driving cars and services to make the world’s streets and roadways safe and accessible”)
- 🇺🇸 Cruise (“We’re building self-driving vehicles to improve life in our cities”)
- 🇺🇸 KeepTruckin (“Everything you need to manage your fleet. All in one place.”)
- 🇨🇳 Pony.ai (“Autonomous Mobility Everywhere”)
- 🇺🇸 Zoox (“The future is for riders”)
- 🇺🇸 Databricks (“All your data, analytics and AI on one platform”)
- 🇺🇸🇫🇷 Dataiku (“Everyday AI, Extraordinary People”)
- 🇺🇸 DataRobot (“AI Cloud is a new approach built for the demands, challenges and opportunities of AI today”)
- 🇺🇸 Scale (“Better Data. Better AI.”)
- 🇬🇧 Graphcore (“World’s most advanced AI system for performance & power efficiency at scale”)
- 🇺🇸 Lambda (“GPU compute built for deep learning”)
- 🇨🇳 Squirrel AI (“AI-powered K-12 adaptive instructional system and services”)
- 🇺🇸 Uptake (“Industrial Intelligence that works for you”)
- The Data Pegacorns
- Distributed Computing for AI: A Status Report
- Resurgence of Conversational AI
- Data Quality Unpacked
- What is Graph Intelligence?
 A Pegacorn is a flying (winged) unicorn.