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OSD 246: Eroom’s law
In his newsletter this week, former Microsoft exec Steven Sinofsky wrote a piece called “Regulating AI by Executive Order is the Real AI Risk”. It’s a long and thorough essay about President Biden’s “Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence”. The gist of it goes like this:
The underlying goal of the EO is to stop AI, and everything else flows from that. “Section I of the EO says it all right up front. This is not a document about innovation. It is about stifling innovation. It is not about fostering competition or free markets but about controlling them a priori. It is not about regulating known problems but preventing problems that don’t yet exist from existing.”
Even without #1, the EO is from a culture that wants to regulate away tech problems instead of allowing people to innovate them away. “The best, enduring, and most thoughtful [scifi] writers who most eloquently expressed the fragility and risks of technology also saw technology as the answer to forward progress. They did not seek to pre-regulate the problems but to innovate our way out of problems. In all cases, we would not have gotten to the problems on display without the optimism of innovation. “
Even without #2, the EO is unprincipled and doesn’t consider what should be regulated. Instead it grabs for whatever can be regulated. “[T]his document is the work of aggregating policy inputs from an extended committee of interested constituencies while also navigating the law — literally what is it that can be done to throttle artificial intelligence legally without passing any new laws that might throttle artificial intelligence. There is no clear owner of this document. There is no leading science consensus or direction that we can discern. It is impossible to separate out the document from the process and approach used to ‘govern’ AI innovation. Govern is quoted because it is the word used in the EO. This is so much less a document of what should be done with the potential of technology than it is a document pushing the limits of what can be done legally to slow innovation.”
This is a gun newsletter, and that was a lot of talk about AI … but was it really about AI? Replace each occurrence of “AI” in those paragraphs with “guns” and everything applies just as well to the gun-related topics we normally talk about. And as Sinofsky points out, this is really a point about technology generally:
What if at the dawn of the internet the concern over having computers connected to each other and becoming an all-knowing communications network resulted in regulations that set limits on packet size and speed, or the number of computers that could be connected to the network or to each other?
Perhaps databases were the risk you saw the most. It was decided that the risk of big databases that could contain more knowledge and retrieve it instantly better and faster than any human ever could was so great that strict limits needed to be placed on databases such that only IBM would be allowed to make databases and everyone else had to keep databases under a certain size, unless they submitted to rules and regulations that were so burdensome that no small company inventing new database techniques could build enough capability and raise enough capital to compete with IBM. And for fun, IBM created the rules for databases and was instrumental in developing the regulatory framework, so they had a head start in the whole process and knew all the players.
These are not fantasies. There were many people that would have loved to have put in place these sorts of limits. As mentioned, books were written. Movies were made. Universities debated the existential risk to humans and society because of the rise of computers, networks, and databases.
In “OSD 139: The Structure of Scientific Revolutions”, we talked about Thomas Kuhn’s book by that name. In that book Kuhn coined the term “paradigm shift” in the course of explaining how new ideas replace old ones. The truly major advances aren’t iterative. They replace the entire preexisting approach — the paradigm. From Wikipedia:
Aristotle’s Physics was astonishingly unlike Isaac Newton’s work in its concepts of matter and motion. Kuhn wrote “… as I was reading him, Aristotle appeared not only ignorant of mechanics, but a dreadfully bad physical scientist as well. About motion, in particular, his writings seemed to me full of egregious errors, both of logic and of observation.” This was in an apparent contradiction with the fact that Aristotle was a brilliant mind. While perusing Aristotle’s Physics, Kuhn formed the view that in order to properly appreciate Aristotle’s reasoning, one must be aware of the scientific conventions of the time. Kuhn concluded that Aristotle’s concepts were not “bad Newton,” just different.
A scientific paradigm isn’t just the particular measurements one takes, or the specific way some studies are designed. It’s the entire toolkit. The terminology, the history, the assumptions, the full corpus of work that everyone’s building on top of.
So, for example, you will never get to the germ theory of disease by running more and more studies on the four bodily humors. They’re different paradigms.
Paradigm shifts are contentious, because they’re inherently hostile and destructive to incumbents. So they rarely come from incumbents. Max Planck summed this up in 1950 like so:
A new scientific truth does not triumph by convincing its opponents and making them see the light, but rather because its opponents eventually die and a new generation grows up that is familiar with it.
That quote is more famous in its paraphrased form: “Science progresses one funeral at a time.”
There’s a corollary here: if paradigm shifts are how innovation happens, and if only rebels produce paradigm shifts, then any domain which snuffs out rebels will also snuff out innovation.
There’s a fun illustration of this called Eroom’s law. Well, “fun” may not be the word once you see what it is, but hey, the name is definitely pretty fun. Eroom. It’s Moore — as in Moore’s law — backwards.
Moore’s law is Gordon Moore’s observation that the number of transistors on a microchip doubles every two years — i.e. chips are getting better at O(2^n).
Eroom’s law is the observation that the cost of developing new pharmaceutical drugs has been doubling every nine years — i.e. drug discovery is getting worse at O(2^n).
2^n grows quickly. You want that curve to be working for you, not against you.
What Eroom’s law and similar patterns (e.g. this image from a post by Elad Gill) hint at is that regulation and innovation are at odds. More regulation leads to less innovation. But the corollary also holds: more innovation leads to less regulation. That’s not obvious at first. After all, it’s the incredible pace of AI innovation that’s driving the calls to squash it, and ditto for 3D printing of guns. But in all such cases, the way to protect innovation against the restrictionist impulse is to innovate even more.
Innovation creates facts on the ground, and creators can move a lot faster than restrictionists can. Uber exists today because Travis Kalanick decided not to ask taxi regulators for permission. Ditto 3D-printed guns. Ditto the AR accessory market. Ditto, perhaps, AI.
This works the other way too. Technologies that are gated on prior restraint generally eke out a meager, increasingly expensive existence. See Eroom’s law above for pharmaceuticals, or the fact that since the Nuclear Regulatory Commission was created in 1975, zero new nuclear plants have reached operation.
Decentralized gun rights are a belief that we’ll innovate our way out of problems.
This week’s links
Theory that the multi-decade drop in crime happened partially because it simply got harder to commit crimes.
Being a pentathlon, there are five events that make up the match:
The Minuteman Dash
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