AI research bucks the trend of the decline of corporate labs as a place where globally important, cutting edge work is done. (The decline of science in corporate R&D) If you squint, Organizations like Facebook AI Research - FAIR, Google Brain, DeepMind, and possibly OpenAI ^1 bear strong resemblance to Bell Labs, Xerox PARC, and other legendary Corporate R&D orgs (I’ll just refer to these as Legendary CR&D) in their heydays.
The parallels manifest in several ways. It’s worth speculating on why they exist in these labs but not elsewhere to understand why the dynamics may or may not be replicable in more areas.
AI research (can) require massive resources - in this case it’s just thousands of dollars of compute for training models and the datasets to train on. These resource requirements mean that there is exploratory research that people just can’t do with the resources available to most labs at universities. You saw a similar effect pulling professors away from universities and into corporate labs in the first half of the 20th century.
At the same time, corporations running AI labs reasonably expect the research to create value commiserate to the costs of this research. Machine learning can directly improve the core product lines of all the companies listed above, in the same way that Bell Labs’ work directly improved “The System” of AT&T. Innovation orgs need to be aligned with their money factory. Additionally, AI promises massive value over long but not infinite timescales. OpenAI’s business model is implicitly based on the assumption that they will create literally infinite value in the not unforeseeable future.
This alignment between the research and the money factory allow modern AI labs to do highly regarded work and collaborate closely with academia, without seeming like a waste of money. Bell Labs was the home for the work that led to nine Nobel prizes. Today, AI conferences are dominated by work from corporate AI labs, not just in quantity but quality as well.
Like the electronics work at Bell Labs, AI research benefits from having multiple disciplines in the same place and the people who will eventually produce it. When you’re creating “AI for X”, it’s generally helpful to have someone who is an expert in X around. Similarly Business and finance people are utilities. Training and implementing ML algorithms at scale requires a different skillset than creating and prototyping the algorithms. Corporate AI labs have the budget to hire research engineers and also make it easy for researchers to talk to some of the best infrastructure and production engineers in the world. This dynamic roughly parallels the close ties between Bell Labs and Western Electric. Prototyping needs manufacturing in the room.
The perhaps uncomfortable parallel is between AT&T’s status as a high-margin, cash-printing monopoly and Google/Facebook’s similar situation. AI labs suggest that successful corporate labs can only exist in domains where there are large corporations with monopoly-like power. The decline of corporate labs in chemistry and physics-related domains may have been caused by commoditization of those products. GE, Dupont, Kodak, and others’ share prices suggest that they at least are no longer perceived as monopolies. However, monopoly profits -> high quality corporate research isn’t the entire story because you don’t see a lot of high-quality non-product research coming out of Boeing and Amazon^2 ::are there other examples::? Perhaps the missing difference is whether or not the monopoly perceives that it will benefit from significantly better technology. AT+T benefitted significantly from creating better technology because they had an effective pact with the government at as long as they kept making the system better they wouldn’t be broken up. I wonder what the counterfactual is - being left as a monopoly without the pact. While Google and Facebook have no (publicly known) agreement with the government, I believe Peter Thiel? Made the argument that at least Google’s monopoly does depend on them continuing to have the best search engine - people have no loyalty to google beyond the quality of their searches. Similarly one could make an argument that advertisers will abandon Facebook as soon as they have worse ad targeting or the kids move to another platform like TikTok or Snap.
Microsoft is an interesting beast to examine through this lens. It’s unconfirmed, but arguably Microsoft Research was started as a public offering in the same way Bell Labs was. However, it both explicitly set out to do more product-focused work than PARC and Microsoft’s cash cow was Windows and the Office Suite, both of which didn’t benefit much from MSR work. MSR was subsequently pretty neglected and used for stunts like China expansion. However, Microsoft’s core business has begun to shift towards cloud services, which do benefit from AI research and it’s becoming clear with things like GPT-3 that AI research can augment Office Suite products that are now actually under threat. This shift coincides with apartnership with OpenAI that looks a bit like the relationship between AT&T and Bell Labs if you squint. Amazon remains a challenge to this just-so story because it also has a massive cloud business but is not (publicly) doing as much speculative AI research.
If it’s accurate, this narrative suggests an uncomfortable truth. Instead of declining because of “corporate short-termism,” speculative corporate R&D might depend on companies with monopoly-level margins perceiving their fate being intimately tied to a high-potential technology. This trifecta (monopoly + clearly high potential technology + that technology being tied to the company’s core business) can’t be created by policy or even a cultural shift. Healthy corporate R+D requires a trifecta of conditions. The trifecta is the most straightforward answer to the question Why hasn’t Bell Labs been replicated? and suggests that attempts to “create a new Bell Labs” are doomed to failure if they try to follow the playbook too closely. At the same time, I strongly suspect there are incredibly valuable technologies that don’t fit the criteria of the trifecta (clearly high potential to solve an existential threat for a monopoly rentee) but do need the environment and resources that were once provided by healthy CR&D labs to realize that value. This is whyWe need new institutional structures to fill the role that corporate labs once occupied.