Philosophy Mondays: Human-AI Collaboration
Today's Philosophy Monday is an important interlude. I want to reveal that I have not been writing the posts in this series entirely by myself. Instead I have been working with Claude, not just for the graphic illustrations, but also for the text. My method has been to write a rough draft and then ask Claude for improvement suggestions. I will expand this collaboration to other intelligences going forward, including open source models such as Llama and DeepSeek. I will also explore other moda...

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Web3/Crypto: Why Bother?
One thing that keeps surprising me is how quite a few people see absolutely nothing redeeming in web3 (née crypto). Maybe this is their genuine belief. Maybe it is a reaction to the extreme boosterism of some proponents who present web3 as bringing about a libertarian nirvana. From early on I have tried to provide a more rounded perspective, pointing to both the good and the bad that can come from it as in my talks at the Blockstack Summits. Today, however, I want to attempt to provide a coge...
Philosophy Mondays: Human-AI Collaboration
Today's Philosophy Monday is an important interlude. I want to reveal that I have not been writing the posts in this series entirely by myself. Instead I have been working with Claude, not just for the graphic illustrations, but also for the text. My method has been to write a rough draft and then ask Claude for improvement suggestions. I will expand this collaboration to other intelligences going forward, including open source models such as Llama and DeepSeek. I will also explore other moda...

Intent-based Collaboration Environments
AI Native IDEs for Code, Engineering, Science
Web3/Crypto: Why Bother?
One thing that keeps surprising me is how quite a few people see absolutely nothing redeeming in web3 (née crypto). Maybe this is their genuine belief. Maybe it is a reaction to the extreme boosterism of some proponents who present web3 as bringing about a libertarian nirvana. From early on I have tried to provide a more rounded perspective, pointing to both the good and the bad that can come from it as in my talks at the Blockstack Summits. Today, however, I want to attempt to provide a coge...
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This will be a bit of a wonky and short post with a longer and less technical one to follow some time soon. Google has just announced a coming update to their privacy policy which will essentially make it possible for Google to integrate all the information it has about a user across its many different services. This comes at the same time as the revelation that Larry Page apparently explicitly stated the goal of building “a single unified, ‘beautiful’ product across everything.”
While one can come up with many possible verbal explanations for why Google might want to go this direction, there is some powerful math that lies at the heart of it: supermodularity. Here is the definition:
A function
is supermodular if
for all x, y Rk, where x y denotes the componentwise maximum and x y the componentwise minimum of x and y.
If a production function is supermodular then x and y are strongly complementary. If you want to read the bible on this consult Don Topkis “Supermodularity and Complementarity.”
A firm such as Google for which the production function relies almost exclusively on information (yes, there are servers and people as well) will exhibit super modularity almost by definition. Why? Because if X and Y are different information vectors, then as long as they carry some joint signal, the inequality will be met as you can always choose to discard additional information (meaning you always have access to the component wise minimum). In plain English: if you have access to both the search history (X) and the social graph (Y) of a user, you can always “do better” than two separate services that only have access to one of these respectively.
This will be a bit of a wonky and short post with a longer and less technical one to follow some time soon. Google has just announced a coming update to their privacy policy which will essentially make it possible for Google to integrate all the information it has about a user across its many different services. This comes at the same time as the revelation that Larry Page apparently explicitly stated the goal of building “a single unified, ‘beautiful’ product across everything.”
While one can come up with many possible verbal explanations for why Google might want to go this direction, there is some powerful math that lies at the heart of it: supermodularity. Here is the definition:
A function
is supermodular if
for all x, y Rk, where x y denotes the componentwise maximum and x y the componentwise minimum of x and y.
If a production function is supermodular then x and y are strongly complementary. If you want to read the bible on this consult Don Topkis “Supermodularity and Complementarity.”
A firm such as Google for which the production function relies almost exclusively on information (yes, there are servers and people as well) will exhibit super modularity almost by definition. Why? Because if X and Y are different information vectors, then as long as they carry some joint signal, the inequality will be met as you can always choose to discard additional information (meaning you always have access to the component wise minimum). In plain English: if you have access to both the search history (X) and the social graph (Y) of a user, you can always “do better” than two separate services that only have access to one of these respectively.
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