I’ve been casually reading Arthur Koestler’s memoir Arrow in the Blue and found this observation about ‘moneymaking’ in relation to his father’s business pursuits. The passage happens right after several pages of description of various quixotic business attempts his father made while Arthur was young:

“The truly remarkable thing about all this seems to me that with a mentality as described, my father was, during long stretches, a highly successful businessman. As I grew up I became more and more puzzled by the paradox that a person with such a gullible, and indeed childlike, character could be capable of extracting money from the hard world of commerce. Much later, when I became acquainted with some really big money-makers, the paradox became even more pronounced. The financial heavyweights who have crossed my path - publishers, art-dealers, bankers, movie producers - have been without exception idiosyncratic, eccentric, irrational, and basically naive individuals; almost the exact opposite of the popular image of the hard, shrewd businessman. Apparently, the shrewd, cold, calculating type is mainly to be foundin the light and middle-weight categories of business; while moneymaking on a truly large scale is a special talent, unrelated to intelligence, like playing the trombone or roller skating. And alas, it is not hereditary.”

I really enjoyed this conversation with Jason Knight on the One Knight in Product podcast. Some of the topics we got into:

• Data is a representation of a part of the world where you’re trying to add value; recognize where your data is a good representative and where it’s not (and possibly doesn’t need to be anyway).

• If you can’t represent your value proposition to a user in as concise a format as a simple message (e.g. a notification), then you’re missing out on a major opportunity to add value at less cost (in terms of users’ attention).

• If you’re a data scientist trying to build a business, don’t try to build it out of a model or algorithm. Embed your data science in a product that adds value and that customers can use.

Plus I got to talk a little more about what we’re building at Aampe, and that’s always my favorite part.

Episode 74 - Taking Data Science from Academia to the Heart of your Product (with Paul Meinshausen)

I find there’s a pretty big gap in high quality stories and analysis of the tech ecosystem and startup world in southeast Asia and India. There’s so much happening in this part of the world and so much skill being deployed to solve such a diverse variety of problems. But unless you’re in the middle of it, and even if you are, there aren’t too many places to hear or read about it. And no, funding announcements and press releases don’t cut it. We need more thoughtful podcasts and long-form writing!

So I’ve been really glad to see what Michael Waitze is doing with his (many!) podcasts, and was glad to have an opportunity to chat with him for an episode of the Asia Tech Podcast. Our conversation was wide-ranging, and somehow managed to cover the weird split in my career between the work I did early on within US Defense and Intelligence (first 18 minutes) and the last 8 years I’ve spent in tech/startups in India and Singapore (from ~19:00 on). My favorite part was how well he managed to capture and describe the problem we’re solving at Aampe.

Asia Tech Podcast – Episode 128 – Paul Meinshausen – Co-Founder at Aampe – Get the Information That Unlocks Value

Many of the early-stage founders I meet like to tell stories that are uniformly optimistic. No aspect of the journey they describe, and nothing I ask about either, worries them. Their market is big, their product is just what customers need and it’s all driven by AI. Their sales next quarter are expected to take off and grow tremendously. Just last week they actually had this incredible partnership meeting and a very large/well-known enterprise is all set to become a customer. Of course the partnership will open all kinds of doors, so the future is truly bright.

I co-authored a Medium post about exploratory work we’re doing at Montane Ventures to apply data science to early-stage Venture Capital investing. Sanjiv Soni, a data scientist and colleague of mine at the fund, was my co-author and the post is written from his perspective. You can see the post here.

I was asked for recommendations for non-technical books for data scientists, so I answered. My list didn’t fit in a comment, so I put it into a post. You can see it here

I co-authored a Medium post about a series of Executive Data Science Workshops I developed with GreyAtom, an edtech startup in Mumbai that I advise. Shweta Doshi, one of GreyAtom’s co-founders, was my co-author. You can see the post here.