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
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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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“If you can’t measure it, you can’t manage it” goes an adage that is often recited as if it were some kind of profound truth (misattributed to Deming). And of course there is some logic here. Let’s say you own a website and you want to grow it. Measuring traffic to the site will help you figure out whether you are succeeding with that. But even this seemingly trivial example helps us understand a crucial point which could be summarized as “You manage to what you measure.”
Suppose that page view is your traffic measurement, now you can immediately see that there are many different ways of growing that which will have widely different implications. You could buy random traffic. You could split up articles into multiple sections each requiring a click and hence a separate page view. You could publish salacious nonsense to increase social sharing. And so on.
We of course recognize many of these as the very aberrations that plague so much of the web. This is just one example for a general problem: quantity is easy to measure, quality is difficult to measure. Pretty much all the other metrics that people have introduced in the web world (visits, visitors, time, etc.) are all attempts to capture some aspect of quality.
There are situations in which quality is so difficult to measure and simultaneously so important that any measurement of quantity becomes dangerous. Fundamental research is one example. In a misguided application of the adage from above, funding for fundamental research has been increasingly allocated based on quantity metrics, such as number of publications and citations. The results from this approach have been horrendous. For example in theoretical physics an area such as string theory has received massive funding (based on these metrics), while disruptive new approaches such as constructor theory struggle to attract resources.
Venture capital is a lot closer to fundamental research than it is to web traffic. Assessing the deep quality of a startup is hard. As with research often a great deal of time has to elapse (years, possibly a decade) before you can tell which companies are truly important. It is therefore at best a distraction to try and build a successful portfolio by keeping track of such statistics as how many startups we met with last quarter. At worst over time all the resource allocation of the firm will go towards those metrics and none of it towards thinking, which is crucial for actually figuring out new things.
At USV we spend a lot of our time thinking and none of our time tracking quantity or other superficial aspects of deal flow. Of course we do track the outcome of our process over time, but knowing that a lot of time needs to expire for those outcomes to mean something. As we have written recently we will add diversity of the founders we back to that.
Now you might say but how will you ever improve racial diversity of founders in your portfolio if you don’t measure it in your process? The answer is by changing the process. So much of the venture capital process revolves around networks. As a result you can have a big impact on outcomes by changing which networks you are connected to. We did this successfully a few years back when we recognized that we had not backed enough female founders. We will be doing this now.
Finally, measurement also relates to the question whether USV is doing enough about the climate crisis. As I have stated on several occasions, including in this interview with Jason Jacobs, I believe that the climate crisis cannot be solved without getting out of the industrial age. I have written a whole book about how we might accomplish getting to the knowledge age, see World After Capital. It is a mistake to believe that there is some easy measurement that can be applied to USV’s portfolio that would – quarter by quarter – tell us whether or not we are helping with that transition. Only time will tell.
“If you can’t measure it, you can’t manage it” goes an adage that is often recited as if it were some kind of profound truth (misattributed to Deming). And of course there is some logic here. Let’s say you own a website and you want to grow it. Measuring traffic to the site will help you figure out whether you are succeeding with that. But even this seemingly trivial example helps us understand a crucial point which could be summarized as “You manage to what you measure.”
Suppose that page view is your traffic measurement, now you can immediately see that there are many different ways of growing that which will have widely different implications. You could buy random traffic. You could split up articles into multiple sections each requiring a click and hence a separate page view. You could publish salacious nonsense to increase social sharing. And so on.
We of course recognize many of these as the very aberrations that plague so much of the web. This is just one example for a general problem: quantity is easy to measure, quality is difficult to measure. Pretty much all the other metrics that people have introduced in the web world (visits, visitors, time, etc.) are all attempts to capture some aspect of quality.
There are situations in which quality is so difficult to measure and simultaneously so important that any measurement of quantity becomes dangerous. Fundamental research is one example. In a misguided application of the adage from above, funding for fundamental research has been increasingly allocated based on quantity metrics, such as number of publications and citations. The results from this approach have been horrendous. For example in theoretical physics an area such as string theory has received massive funding (based on these metrics), while disruptive new approaches such as constructor theory struggle to attract resources.
Venture capital is a lot closer to fundamental research than it is to web traffic. Assessing the deep quality of a startup is hard. As with research often a great deal of time has to elapse (years, possibly a decade) before you can tell which companies are truly important. It is therefore at best a distraction to try and build a successful portfolio by keeping track of such statistics as how many startups we met with last quarter. At worst over time all the resource allocation of the firm will go towards those metrics and none of it towards thinking, which is crucial for actually figuring out new things.
At USV we spend a lot of our time thinking and none of our time tracking quantity or other superficial aspects of deal flow. Of course we do track the outcome of our process over time, but knowing that a lot of time needs to expire for those outcomes to mean something. As we have written recently we will add diversity of the founders we back to that.
Now you might say but how will you ever improve racial diversity of founders in your portfolio if you don’t measure it in your process? The answer is by changing the process. So much of the venture capital process revolves around networks. As a result you can have a big impact on outcomes by changing which networks you are connected to. We did this successfully a few years back when we recognized that we had not backed enough female founders. We will be doing this now.
Finally, measurement also relates to the question whether USV is doing enough about the climate crisis. As I have stated on several occasions, including in this interview with Jason Jacobs, I believe that the climate crisis cannot be solved without getting out of the industrial age. I have written a whole book about how we might accomplish getting to the knowledge age, see World After Capital. It is a mistake to believe that there is some easy measurement that can be applied to USV’s portfolio that would – quarter by quarter – tell us whether or not we are helping with that transition. Only time will tell.
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