> How would a Post-Capitalist Silicon Valley be different from today?
What's interesting is that I don't think the day-to-day of it would look very different. Extremely hierarchical corporate structures don't work very well in small scale unless you have phenomenally charismatic (or even rarer, actual top-of-field domain expert) leaders. As an incentive to bring folks into the space, varying degrees of group ownership and power are offered to the founding set (and founding investors, which are often an integral part of the early business).
In a very real sense but perhaps to a lesser total degree, this is (as the syndicalists love to put it) "the workers owning the mill they operate."
What's also interesting is that as the company scales, this approach is considered increasingly untenable (mostly from a profit-generation standpoint) and a more absolutely hierarchical system emerges. But nearly everyone has the opinion that these corporate structures are awful, inefficient beasts that create incentives for parasitic management, outsized rewards for C-sets who may or may not provide actual value, and a constant conflict within the workforce to avoid being trivialized by the desire to increase efficiency over product quality or market share.
There was a pair of papers recently that I really enjoyed investigating what "fractal" community (and by obvious extension, corporate) structures could look like by De Florio & then Pajaziti:
These delve into how one can build service organizations around non-traditional (e.g., not-straight-line-to-the-top-always-centralized) structures and how these can actually have deceptively better efficiency even if their outcomes are only statistically predictable.
It's worth noting that these sorts of organization styles are not untested in the valley. The biggest success for such an organization I can think of off the top of my head is Google's technical support and loaner laptop system, which is semi-fractal and horizontal and despite looking like it'd be a huge money pit (from the traditional "control outcomes and limit loss" school of IT management), is actually dramatically cheaper than traditional IT departments at its scale. Data driven organizations often naturally push towards open and relatively flat org structures, with the understanding that the overhead of abuse of such system's leniency is much less expensive than the total expense of minimizing said abuse.
This isn't isn't exactly new, we see echos of the "actually the chaotic version of this over resources that are infinite in principle but constrained in a time domain is not much worse than the perfectly scheduled system" principle in lots of fields. Market economics likes this outcome (although not when you start making them own "externalities" like environmental damage). Network engineers have long ago made their peace with this truth, as well. A randomly scheduled network is only about 1/3 worse than an optimally scheduled network!
What's interesting is that I don't think the day-to-day of it would look very different. Extremely hierarchical corporate structures don't work very well in small scale unless you have phenomenally charismatic (or even rarer, actual top-of-field domain expert) leaders. As an incentive to bring folks into the space, varying degrees of group ownership and power are offered to the founding set (and founding investors, which are often an integral part of the early business).
In a very real sense but perhaps to a lesser total degree, this is (as the syndicalists love to put it) "the workers owning the mill they operate."
What's also interesting is that as the company scales, this approach is considered increasingly untenable (mostly from a profit-generation standpoint) and a more absolutely hierarchical system emerges. But nearly everyone has the opinion that these corporate structures are awful, inefficient beasts that create incentives for parasitic management, outsized rewards for C-sets who may or may not provide actual value, and a constant conflict within the workforce to avoid being trivialized by the desire to increase efficiency over product quality or market share.
There was a pair of papers recently that I really enjoyed investigating what "fractal" community (and by obvious extension, corporate) structures could look like by De Florio & then Pajaziti:
https://onlinelibrary.wiley.com/doi/pdf/10.1002/sres.2242
https://arxiv.org/abs/1509.05112
These delve into how one can build service organizations around non-traditional (e.g., not-straight-line-to-the-top-always-centralized) structures and how these can actually have deceptively better efficiency even if their outcomes are only statistically predictable.
It's worth noting that these sorts of organization styles are not untested in the valley. The biggest success for such an organization I can think of off the top of my head is Google's technical support and loaner laptop system, which is semi-fractal and horizontal and despite looking like it'd be a huge money pit (from the traditional "control outcomes and limit loss" school of IT management), is actually dramatically cheaper than traditional IT departments at its scale. Data driven organizations often naturally push towards open and relatively flat org structures, with the understanding that the overhead of abuse of such system's leniency is much less expensive than the total expense of minimizing said abuse.
This isn't isn't exactly new, we see echos of the "actually the chaotic version of this over resources that are infinite in principle but constrained in a time domain is not much worse than the perfectly scheduled system" principle in lots of fields. Market economics likes this outcome (although not when you start making them own "externalities" like environmental damage). Network engineers have long ago made their peace with this truth, as well. A randomly scheduled network is only about 1/3 worse than an optimally scheduled network!