Thesis-Driven Investing in AI

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Jeremy Kaufmann has a wide variety of experiences. He started his career as a statistician and economist at the New York Federal Reserve and became very interested in looking at healthcare outcomes research. So he took his love of data, gained SAS experience at Salesforce, and then took that today to Scale Venture Partners, where he has focused his last three and a half years in the world of AI and machine learning.

Episode Links:  

Jeremy Kaufmann’s LinkedIn: https://www.linkedin.com/in/jeremy-kaufmann-42171370/ 

Jeremy Kaufmann’s Twitter: https://twitter.com/jkauf_mann?s=20 

Jeremy Kaufmann’s Website: https://scalestudio.vc/ 

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Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(02:39) – KeepTrucking integrates the mobile phone into the trucker workflow

(04:26) – Cognata: a machine to  train the vehicles 

(05:34) – Investing in the broader world of AI is all about understanding timing risk

(07:30) – Cognata corner cases: making a left-hand turn, an attempt to derive pedestrians’ intents, when to slam the breaks

(09:23) – Solvvy,  a company in the conversational AI space not only answering questions, but automating actions.  

(11:51) – Deflecting questions at the origin to reduce costs to respond to questions, and increase the percentage of times that a given action is taken

(15:16) – TechSee, an example of international investment. Self-serve and installation, is going to be the future

(18:54) – Look for verticals and industries where the promise is highest: customer pain points and ROI

(20:14) – AI is fundamentally a probabilistic technology and not deterministic, meaning it's going to make errors and business buyers aren't necessarily comfortable with buying a product that's going to make errors

(21:02) –  Proprietary data advantage and building a sustainable data moat. Talent as a differentiator in some of these companies.

(24:14) – The world of AI to date and deep learning is all about massive quantities of data

(26:01) – Overcoming Cold Start: Beging with SAS, then go to AI, publicly scraping data, offering deals and price discounts

(28:19) – The real world is full of  these human complexities around gathering data. So the ability to simulate it is going to be one of the major trends for 2019 and 2020

(31:43) – It's all about the business case and the economics, not only about the AI

(36:05) – There are many cool technologies and robots can do different things, but it's really about where are the robots going to be most reasonable and cost-saving and business productivity driving

(36:53) – The sales process in selling an AI product is hard because AI is somewhat of a black box. It's not very explainable

(38:27) –  AI data moats and data network effects are not always going to drive long-term success of a business

(43:59) – Conversational AI: improvements in natural language understanding and the ability to handle multi-step conversations while maintaining state