There is an insightful literature that documents and tries to explain why large incumbent tech firms fail to invest appropriately in disruptive technologies, even when they played an important role in its invention. I speculatively think this sheds some light on why we see new firms such as OpenAI rather than incumbents such as Google and Meta leading the deployment of recent innovations in AI, notably LLMs.
Disruptive technologies—technologies that initially fail to satisfy existing demands but later surpass the dominant technology—are often underinvested in by incumbents, even when these incumbents played a major role in their invention. Henderson and Clark, 1990 discuss examples of this phenomenon, such as Xerox’s failure to exploit their technology and transition from larger to smaller copiers:
Xerox, the pioneer of plain-paper copiers, was confronted in the mid-1970s with competitors offering copiers that were much smaller and more reliable than the traditional product. The new products required little new scientific or engineering knowledge, but despite the fact that Xerox had invented the core technologies and had enormous experience in the industry, it took the company almost eight years of missteps and false starts to introduce a competitive product into the market. In that time Xerox lost half of its market share and suffered serious financial problems
and RCA’s failure to embrace the small transistorized radio during the 1950s:
In the mid-1950s engineers at RCA’s corporate research and development center developed a prototype of a portable, transistorized radio receiver. The new product used technology in which RCA was accomplished (transistors, radio circuits, speakers, tuning devices), but RCA saw little reason to pursue such an apparently inferior technology. In contrast, Sony, a small, relatively new company, used the small transistorized radio to gain entry into the US, market. Even after Sony’s success was apparent, RCA remained a follower in the market as Sony introduced successive models with improved sound quality and FM capability. The irony of the situation was not lost on the R&D engineers: for many years Sony’s radios were produced with technology licensed from RCA, yet RCA had great difficulty matching Sony’s product in the marketplace
A few explanations of this “Innovator’s curse” are given in the literature:
Incumbents focus on innovations that address existing customer needs rather than serving small markets. Customer bases usually ask for incremental improvements rather than radical innovations.
Disruptive products are simpler and cheaper; they generally promise lower margins, not greater profits
Incumbents’ most important customers usually don’t want radically new technologies, as they can’t immediately use these
Reinganum (1983) shows that under conditions of uncertainty, incumbent monopolists will rationally invest less in innovation than entrants will, for fear of cannibalizing the stream of rents from their existing products
Leonard-Barton (1992) suggests that the same competencies that have driven incumbent’s commercial success may produce ‘competency traps’ (engrained habits, procedures, equipment or expertise that make change difficult); see also Henderson, 2006
Henderson, 1993 highlights that entrants have greater strategic incentives to invest in radical innovation, and incumbents fall prey to inertia and complacency
After skimming a few papers on this, I’m inclined to draw an analogue here for AI: Google produced the Transformer; labs at Google, Meta, and Microsoft, have long been key players in AI research, and yet, the creation of explicitly disruptive LLM products that aim to do much more than existing technologies has been led mostly by relative new-comers (such as OpenAI, Anthropic, and Cohere for LLMs and StabilityAI for generative image models).
The same literature also suggests how to avoid the “innovator curse”, such as through establishing independent sub-organizations focused on disruptive innovations (see Christensen ,1997 and Christensen, 2003), which is clearly what companies like Google have done, as its AI labs have a large degree of independence. And yet this seems not to seem to have been sufficient to establish the dominance of these firms when it comes to the frontiers of LLMs and the like.
I suspect that from inside it seems like the company uses various metrics to evaluate its employees, and the new inventions usually do not look good from this perspective. Like, when you start your own company, you can accept that during the first year or two you will only eat ramen, if it means than in five or ten years you have a chance to become rich. In someone else’s company, this simply means that your KPIs suck, so the project will get cancelled, or a new manager will be assigned, who will change the original idea into something that seems good in short term.
Another reason would be company politics and bureaucracy. Like, you cannot use the best tools for the job, but instead what the rest of the company is using, even if your needs are different… and in the worst case the company standard will be some internally developed tool with lots of bugs and no documentation that no one can complain about because the person who developed it 5 or 10 year ago is currently too high in the company hierarchy.
(That is basically what you said, the first is the “incremental improvements, immediate use”, the second is the “engrained habits and procedures”. I guess my point is that from near mode it will appear much less rational than the abstract scientific descriptions.)
There is an insightful literature that documents and tries to explain why large incumbent tech firms fail to invest appropriately in disruptive technologies, even when they played an important role in its invention. I speculatively think this sheds some light on why we see new firms such as OpenAI rather than incumbents such as Google and Meta leading the deployment of recent innovations in AI, notably LLMs.
Disruptive technologies—technologies that initially fail to satisfy existing demands but later surpass the dominant technology—are often underinvested in by incumbents, even when these incumbents played a major role in their invention. Henderson and Clark, 1990 discuss examples of this phenomenon, such as Xerox’s failure to exploit their technology and transition from larger to smaller copiers:
and RCA’s failure to embrace the small transistorized radio during the 1950s:
A few explanations of this “Innovator’s curse” are given in the literature:
Christensen (1997) suggests this is due to, among other things:
Incumbents focus on innovations that address existing customer needs rather than serving small markets. Customer bases usually ask for incremental improvements rather than radical innovations.
Disruptive products are simpler and cheaper; they generally promise lower margins, not greater profits
Incumbents’ most important customers usually don’t want radically new technologies, as they can’t immediately use these
Reinganum (1983) shows that under conditions of uncertainty, incumbent monopolists will rationally invest less in innovation than entrants will, for fear of cannibalizing the stream of rents from their existing products
Leonard-Barton (1992) suggests that the same competencies that have driven incumbent’s commercial success may produce ‘competency traps’ (engrained habits, procedures, equipment or expertise that make change difficult); see also Henderson, 2006
Henderson, 1993 highlights that entrants have greater strategic incentives to invest in radical innovation, and incumbents fall prey to inertia and complacency
After skimming a few papers on this, I’m inclined to draw an analogue here for AI: Google produced the Transformer; labs at Google, Meta, and Microsoft, have long been key players in AI research, and yet, the creation of explicitly disruptive LLM products that aim to do much more than existing technologies has been led mostly by relative new-comers (such as OpenAI, Anthropic, and Cohere for LLMs and StabilityAI for generative image models).
The same literature also suggests how to avoid the “innovator curse”, such as through establishing independent sub-organizations focused on disruptive innovations (see Christensen ,1997 and Christensen, 2003), which is clearly what companies like Google have done, as its AI labs have a large degree of independence. And yet this seems not to seem to have been sufficient to establish the dominance of these firms when it comes to the frontiers of LLMs and the like.
I suspect that from inside it seems like the company uses various metrics to evaluate its employees, and the new inventions usually do not look good from this perspective. Like, when you start your own company, you can accept that during the first year or two you will only eat ramen, if it means than in five or ten years you have a chance to become rich. In someone else’s company, this simply means that your KPIs suck, so the project will get cancelled, or a new manager will be assigned, who will change the original idea into something that seems good in short term.
Another reason would be company politics and bureaucracy. Like, you cannot use the best tools for the job, but instead what the rest of the company is using, even if your needs are different… and in the worst case the company standard will be some internally developed tool with lots of bugs and no documentation that no one can complain about because the person who developed it 5 or 10 year ago is currently too high in the company hierarchy.
(That is basically what you said, the first is the “incremental improvements, immediate use”, the second is the “engrained habits and procedures”. I guess my point is that from near mode it will appear much less rational than the abstract scientific descriptions.)