This week’s podcast is about SenseTime, which is a leader in computer vision in China / Asia. It's a fascinating company for looking at the evolving business models of AI software. The two big questions are:

• What are the unit economics of large AI companies?
• What types of scale advantages and network effects do large AI companies?

You can listen to this podcast here or at iTunes and Google Podcasts.

Here is my new book:

• Moats and Marathons (Part 1): How to Build and Measure Competitive Advantage in Digital Businesses Kindle Edition

I break the network effects question into three sub-questions:

• Do existing AI models get better the more they are used?
  • How much? Does it flatline? Does it keep increasing?
• Do existing AI models make each other smarter and better?
• Do existing AI models and the data they collect make additional AI models smarter? Do they make them faster and cheaper to train?

My working for conclusions are:

• This is mostly an integrated software bundle going for global scale.
• It's a learning platform with a new type of network effect.
• It is building an innovation platform, with another network effect.

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Related articles:

• SenseTime and an Introduction to AI Software Economics (1 of 3) (Asia Tech Strategy – Daily Article)
• 3 Lessons in China AI/ML from Artefact (Data Consultants and Digital Marketers)
• Adobe Inc. and the Power of Old School Software Economics (Asia Tech Strategy – Podcast 81)

From the Concept Library, concepts for this article are:

• Learning Platforms
• Integrated Bundles
• Artificial Intelligence
• Computer Vision

From the Company Library, companies for this article are:

• SenseTime

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I write and speak about digital competition and China / Asia’s leading tech companies.

This content (articles, podcasts, website info) is not investment advice. The information and opinions from me and any guests may be incorrect. The numbers and information may be wrong. The views expressed may no longer be relevant or accurate. Investing is risky. Do your own research.

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