Strategic thinking is powerful -- it can often enable you to play chess when everyone else is playing checkers. Take Tesla’s Master Plan for example, “The strategy of Tesla is to enter at the high end of the market, where customers are prepared to pay a premium, and then drive down market as fast as possible to higher unit volume and lower prices with each successive model.” Tesla’s strategy enabled widespread adoption for the electric vehicle, where other OEMs with much more resources had failed.
Another great, easy to understand piece :) With regards to exercise 2, is it more of an assumption exercise or there's any process to go about (in)validating whether the conditions are true?
Typically once you decompose a hypothesis into conditions, you can evaluate the likelihood of each one. e.g. with drones, you might look at Condition #2 and say something like, "well, the energy density of batteries have been linearly (rather than exponentially) increasingly over time, so we should plan for a much longer time scale."
If there's a specific hypothesis you're referring to in your own work, feel free to share so we can talk about it more concretely.
Thanks for another thoughtful piece. curious to hear more about "If you can set up a process so that you are systematically consuming lots of data and analyzing it from non-obvious perspectives, you can design around this trap."
Specifically things like, what does this process look like? How to analyze it from non-obvious perspectives?
Hi Kaye, Good question. I like to put myself in a position where I am systematically consuming a lot of data by:
* Subscribing to newsletters and podcasts that are non-obvious
* Working with raw data
* Talking to people in various industries
* Giving myself mini-homeworks to analyze and backup ambitious theses like with the exercises above
For example, when I wrote a recent thesis on how self-driving cars will impact retail (https://www.perell.com/fellowship/self-driving-cars-and-the-future-of-retail), instead of listening to obvious podcasts like a16z or NPR, I tuned into podcasts with industry veterans talking about the logistics and warehousing. I also worked with raw data, where I looked at numbers that broke down the costs of trucking operations, and reached out to old colleagues who had worked in retail for decades.
This is more useful from a consulting perspective than a PM pure execution perspective, to identify where new market opportunities might lie.
Another great, easy to understand piece :) With regards to exercise 2, is it more of an assumption exercise or there's any process to go about (in)validating whether the conditions are true?
Thanks, Natalia!
Typically once you decompose a hypothesis into conditions, you can evaluate the likelihood of each one. e.g. with drones, you might look at Condition #2 and say something like, "well, the energy density of batteries have been linearly (rather than exponentially) increasingly over time, so we should plan for a much longer time scale."
If there's a specific hypothesis you're referring to in your own work, feel free to share so we can talk about it more concretely.
Thanks for another thoughtful piece. curious to hear more about "If you can set up a process so that you are systematically consuming lots of data and analyzing it from non-obvious perspectives, you can design around this trap."
Specifically things like, what does this process look like? How to analyze it from non-obvious perspectives?
Hi Kaye, Good question. I like to put myself in a position where I am systematically consuming a lot of data by:
* Subscribing to newsletters and podcasts that are non-obvious
* Working with raw data
* Talking to people in various industries
* Giving myself mini-homeworks to analyze and backup ambitious theses like with the exercises above
For example, when I wrote a recent thesis on how self-driving cars will impact retail (https://www.perell.com/fellowship/self-driving-cars-and-the-future-of-retail), instead of listening to obvious podcasts like a16z or NPR, I tuned into podcasts with industry veterans talking about the logistics and warehousing. I also worked with raw data, where I looked at numbers that broke down the costs of trucking operations, and reached out to old colleagues who had worked in retail for decades.
This is more useful from a consulting perspective than a PM pure execution perspective, to identify where new market opportunities might lie.
- Adrienne
Your thesis is simply amazing!
Thanks for your support Neeraj!
Hey Adrienne - I am super curious about where you sourced your raw data from? Thanks for the great article and essay!
Impressive outline on the process. Well done
Thx for this! Is there a book you recommend to develop second/third order thinking?
A fun counter-thinking: Unsystematized Substack noted that we should focus on the counterfactual.