Author Archives: Carl Shulman

Follow up to: Brain Emulation and Hard Takeoff

Suppose that Robin’s Crack of a Future Dawn scenario occurs: whole brain emulations (‘ems’) are developed, diverse producers create ems of many different human brains, which are reproduced extensively until the marginal productivity of em labor approaches marginal cost, i.e. Malthusian near-subsistence wages. Ems that hold capital could use it to increase their wealth by investing, e.g. by creating improved ems and collecting the fruits of their increased productivity, by investing in hardware to rent to ems, or otherwise. However, an em would not be able to earn higher returns on its capital than any other investor, and ems with no capital would not be able to earn more than subsistence (including rental or licensing payments). In Robin’s preferred scenario, free ems would borrow or rent bodies, devoting their wages to rental costs, and would be subject to "eviction" or "repossession" for nonpayment.

In this intensely competitive environment, even small differences in productivity between em templates will result in great differences in market share, as an em template with higher productivity can outbid less productive templates for scarce hardware resources in the rental market, resulting in their "eviction" until the new template fully supplants them in the labor market. Initially, the flow of more productive templates and competitive niche exclusion might be driven by the scanning of additional brains with varying skills, abilities, temperament, and values, but later on em education and changes in productive skill profiles would matter more.

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The construction of a working brain emulation would require, aside from brain scanning equipment and computer hardware to test and run emulations on, highly intelligent and skilled scientists and engineers to develop and improve the emulation software. How many such researchers? A billion dollar project might employ thousands, of widely varying quality and expertise, who would acquire additional expertise over the course of a successful project that results in a working prototype. Now, as Robin says:

They would try multitudes of ways to cut corners on the emulation implementation, checking to see that their bot stayed sane.  I expect several orders of magnitude of efficiency gains to be found easily at first, but that such gains would quickly get hard to find.  While a few key insights would allow large gains, most gains would come from many small improvements.   

Some project would start selling bots when their bot cost fell substantially below the (speedup-adjusted) wages of a profession with humans available to scan.  Even if this risked more leaks, the vast revenue would likely be irresistible.   

To make further improvements they would need skilled workers up-to-speed on relevant fields and the specific workings of the project’s design. But the project above can now run an emulation at a cost substantially less than the wages it can bring in. In other words, it is now cheaper for the project to run an instance of one of its brain emulation engineers than it is to hire outside staff or collaborate with competitors. This is especially so because an emulation can be run at high speeds to catch up on areas it does not know well, faster than humans could be hired and brought up to speed, and then duplicated many times. The limiting resource for further advances is no longer the supply of expert humans, but simply computing hardware on which to run emulations.

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