I'm on Substack, for now, to read. I wish more writers would do this. They would not only use this as a guideline, but review it on a regular period to see if they've lost their way or need to bring order and clarity to the new directions they are urged to go toward.
I've no portfolio to speak, and no direct investments in China. However I appreciate the evidence and hypothesis on how the world and in particular China work.
Enjoyed reading your articles since some time! Thank you! I'm local Chinese located in Shanghai, now also into personal investments, may I invite you for coffee at your convenience!
Hi, I sent you "chat" but couldn't manage to invoke "direct" (substack is not accessible here normally and my crappy free vpn is with its best efforts already). You may also drop me a line with my email (in profile). Looking forward to the coffee meet.
this is substack, where people write to get paid, and others pay to read.
you dont have to tell your readers who you are and who you are not.
no amount of user manuals will be enough to filter out the "wrong readers".
and no matter how many times you tell them "this is not investment advice", if you write about companies and stocks publicly, someone is bound to take that as investment advice.
heck if you write publicly, stocks or whatnot, you are gonna get responses. you are gonna get likes and hates.
even if you write the investment bible, you are still gonna get wrong readers. not everybody like to read the bible.
just like im gonna tell you i rather read about PDD and DIDI and BIDU and AMAP and Tencent and AI Capex, than a user manual for how to read your sub.
Hi there, I really enjoyed this summation of why / how you write. For the past year I’ve had to learn a lot about food delivery and other quick commerce businesses and I realized after a while that no one seems to talk about the IP of these companies, their algorithms. How do they build them, does one of these platforms really have a killer algorithm? Which based on my basic understanding of the business would simply be a model with better factor weights, lower error terms, or at least lower correlation in error terms. So far, I have not found any good writing on this topic. It came up because I wanted to quantify how important daily order volume vs value per order etc really is. What makes an optimal model? What started me on all that is surveying drivers here in the UAE where I’m based about their delivery experiences. It was fascinating and super variable it seems. Our data isn’t enough really for statistical significance but it really made me wonder - how much more could you pay a driver if orders were optimally stacked - and then what does that really mean? Should the platforms incentivize customers to preorder to enhance predictability? What are the trade offs. I would love it if you would write about any of these topics in the China context! Thanks!
I'm on Substack, for now, to read. I wish more writers would do this. They would not only use this as a guideline, but review it on a regular period to see if they've lost their way or need to bring order and clarity to the new directions they are urged to go toward.
I've no portfolio to speak, and no direct investments in China. However I appreciate the evidence and hypothesis on how the world and in particular China work.
We plan to visit Shanghai next spring and would be delighted to receive an invitation for coffee.
Enjoyed reading your articles since some time! Thank you! I'm local Chinese located in Shanghai, now also into personal investments, may I invite you for coffee at your convenience!
Sure! You can send me a direct message.
Hi, I sent you "chat" but couldn't manage to invoke "direct" (substack is not accessible here normally and my crappy free vpn is with its best efforts already). You may also drop me a line with my email (in profile). Looking forward to the coffee meet.
you have a paywall, you write to get paid.
this is substack, where people write to get paid, and others pay to read.
you dont have to tell your readers who you are and who you are not.
no amount of user manuals will be enough to filter out the "wrong readers".
and no matter how many times you tell them "this is not investment advice", if you write about companies and stocks publicly, someone is bound to take that as investment advice.
heck if you write publicly, stocks or whatnot, you are gonna get responses. you are gonna get likes and hates.
even if you write the investment bible, you are still gonna get wrong readers. not everybody like to read the bible.
just like im gonna tell you i rather read about PDD and DIDI and BIDU and AMAP and Tencent and AI Capex, than a user manual for how to read your sub.
Big fan of your writing! At the same time, I agree that we need to come to our own conclusions -- that's how you become a better investor.
Maybe it's Graphomania...(?)
Hi there, I really enjoyed this summation of why / how you write. For the past year I’ve had to learn a lot about food delivery and other quick commerce businesses and I realized after a while that no one seems to talk about the IP of these companies, their algorithms. How do they build them, does one of these platforms really have a killer algorithm? Which based on my basic understanding of the business would simply be a model with better factor weights, lower error terms, or at least lower correlation in error terms. So far, I have not found any good writing on this topic. It came up because I wanted to quantify how important daily order volume vs value per order etc really is. What makes an optimal model? What started me on all that is surveying drivers here in the UAE where I’m based about their delivery experiences. It was fascinating and super variable it seems. Our data isn’t enough really for statistical significance but it really made me wonder - how much more could you pay a driver if orders were optimally stacked - and then what does that really mean? Should the platforms incentivize customers to preorder to enhance predictability? What are the trade offs. I would love it if you would write about any of these topics in the China context! Thanks!