The following is an analysis of seven prominent AI analogies: aliens, the brain, climate change, electricity, the Industrial Revolution, the neocortex, & nuclear fission. You can find longer versions of these as separate blogposts on my substack.
AI analogies have a real-world impact
For better or worse, analogies play a prominent role in the public debate about the long-term trajectory and impacts of AI.
Analogies can be misleading
Every individual analogy is imperfect. AI is its own thing, and there is simply no precedent that would closely match the characteristics of AI across 50+ governance-relevant dimensions.
Overly relying on a single analogy without considering differences and other analogies can lead to blind spots, overconfidence, and overfitting reality to a preconceived pattern.
Analogies can be useful
When facing a complex, open-ended challenge, we do not start with a system model. It is not clear which domain logic, questions, scenarios, risks, or opportunities we should pay attention to. Analogies can be a tool to explore such a future with deep uncertainty.
Analogies can be an instrumental tool in advocacy to communicate complex concepts in a digestible and intuitively appealing way.
My analysis is written in the spirit of exploration without prescribing or proscribing any specific analogy. At the same time, as a repository, it may still be of interest to policy advocates.
Superhuman power potential: Technologically mature extraterrestrials would likely be either far less advanced than us or significantly more advanced, comparable to our potential future digital superintelligence.
Digital life: Popular culture often envisions aliens as evolved humans, but mature aliens are likely digital beings due to the advantages of digital intelligence over biological constraints and because digital beings can be more easily transported across space. The closest Earthly equivalent to these digital aliens is artificial intelligence.
Terraforming: Humans shape their environment for biological needs, while terraforming by digital aliens would require habitats like electricity grids and data centers, which is very similar to a rapid build-out of AI infrastructure. Pathogens from digital aliens are unlikely to affect humans directly but could impact our information technology.
Non-anthropomorphic minds: AI and aliens encompass a vast range of possible minds shaped by different environments and selection pressures than human minds. AI can develop non-human strategies, especially when trained with reinforcement learning. AI can have non-human failure modes such as through adversarial attacks. Future AI may have modular and superhuman bandwidth of sensors and effectors.
“First contact”: A first contact with aliens would likely involve centralized, limited communication for containment / diplomatic representation purposes. AI-human interaction is decentralized and happens at high speed and volume over millions of devices.
Basic neuron logic: The McCulloch-Pitts model (1943) conceptualized artificial neurons based on simple logical operations, forming the foundation of artificial neural networks. Basic inhibition and excitation: Biological neurons use neurotransmitters like glutamate (excitatory) and GABA (inhibitory). Artificial neurons use positive or negative weights to simulate this effect.
Access to connectome: We have access to the full connectome of AI models. For comparison, the first (and, so far, only) fully reconstructed connectome of a biological neural network belongs to the roundworm C. elegans.
Ownership and distribution: Brains are “owned” by individual humans. The infrastructure of artificial neural networks is owned by the tech giants, such as Amazon, Microsoft, and Google. There are no “brain billionaires” that have more neocortex than entire countries.
The default assumption for the future pre-1970 seemed to be artificial climate control with relatively little concern about inadvertent climate change
Today the default vision is a that natural climate change is an existential threat and there is a strong ideological opposition to solar geoengineering
Cross-industry applications, complements, productivity: Both electricity and AI have some of the classic hallmarks of general-purpose technologies, meaning they have widespread applications across numerous industries, they have innovational complements, and we expect them to boost productivity.
Switch from in-house capacity to an outsourced service: Before widespread electrification, power was primarily generated in-house. After 1900, the availability of cheaper, centrally produced power led to a shift towards outsourcing power production, adopting an electricity-as-a-service model.
Large cloud providers, who own significant AI hardware, offer AI compute as a service, allowing companies to use AI capabilities without owning the hardware. This trend could lead to a decrease in in-house intellectual labor and an increase in the use of flexible, outsourced AI intelligence, as “AI remote workers” or “exocortex” of companies.
No new transmission infrastructure: Electrification was in large parts about building a new transmission network that connects all homes (others are water & telecommunications). AI does not require any new transmission network. Rather AI is distributed over the existing data networks as part of Internet traffic
Local vs global market: Electricity is location-dependent due to transmission losses, leading to varying costs and no global market. AI can function globally without transmission losses, resembling the internet's integrated market but must comply with local laws.
Degree of commodification: Electricity has a uniform quality. AI models differ in significant ways so that AI tokens are not equally commodified. No one will ever run a medical device on electricity from one power plant and then from another power plant just to see if it reaches the same conclusion. In contrast, it is reasonable to ask for a second or even third opinion on medical diagnosis from different AI doctors.
Labor substitution: Electrification was not a labor substitution. In factories it was a transition from one artificial form of energy to another. As such, it has never caused any significant worries about massive job losses. This stands in contrast to the First Industrial Revolution in which many laborers lost their jobs. In terms of labor turnover and related societal unrest and pushback, the First Industrial Revolution is a better fit to AI than electrification.
Interpretability, agency, autonomy: Electric current as it comes out of your socket is a controlled and understood physical phenomenon with no cognition, goals, or agency. We understood how electricity functions at the time of electrification. In contrast, the inner working of large neural networks are still poorly understood. AI-companies are not just building general-purpose tools, but general-purpose agents that can follow instructions with many intermediate steps and use tools themselves, and we should expect these to get more and more autonomy over time.
However, much like saying humans are water (humans are literally 60% water by weight), this low level of analysis is inadequate for a meaningful analysis.
New knowledge access institutions: The Industrial Revolution saw the emergence of institutions that significantly increased the availability and accessibility of knowledge (e.g., Royal Society & various scientific societies). Similarly, the AI Revolution coincides with increased knowledge accessibility through the Internet and future AI could evolve into advanced personal tutors.
Distribution and variety of exocortex: The distribution of neocortex among humans is fairly even. There are no “brain billionaires”. In contrast, computing power for the exocortex is distributed unequally within and between countries.
There is a popular claim that Szilard’s fight for changing publication norms has led to Fermi’s self-censorship, which in turn led Germany to cripple their program by choosing heavy water over graphite as moderator. This originates from Rhodes 1986 book on the US nuclear program.
In 1989 Mark Walker wrote a book on the German program based on original German sources. Bothe’s efforts to evaluate graphite as a moderator did reach misleading results due to lack of purity. However, Hanle realized that this was due to pollution and informed the Heereswaffenamt, incl. with instructions for how to create sufficiently pure graphite. Their decision to go with heavy water rather than pure graphite as a moderator was primarily based on economic considerations.