The real losers in the team system are those who do not have the temperament for all the training and gaining qualifications.
Working in ever-larger teams, often enabled by technology, is a fundamental feature of modern life. And yet the wider social and economic implications of this trend are rarely discussed – especially when – and this will increasingly be the case – technology itself is involved in the process.
Think about business. For decades, large companies have been on the rise in the US, which means it’s increasingly likely to work in large companies that take a team approach. One effect of this is that individual performance is harder to measure. When a product is successful, it’s often not clear who should get the credit because so many people were involved in its development.
It’s difficult to recalibrate incentives to reflect this changed reality. Companies often respond by requiring higher qualifications and trying to ensure that everyone is a valuable employee. That might mean looking for an Ivy League education or a stellar GitHub profile. In any case, companies are more likely to look for ex ante signals of quality and are less likely to take risks with true outsiders, because if the outsider doesn’t pull their weight, it may not be apparent for a long time.
The opposite scenario would be with a chess player or a tennis star. They too work with their teams and thank them when they win – but when they win, the players take home a large portion of the prize money. And in those fields, there are very few qualification requirements. Magnus Carlsen was just a young guy from Norway who kept winning and got to the top. He never needed a master’s degree in chess.
The real losers in the team system are those who don’t have the temperament for all the training and gaining qualifications. Of course, these qualifications include recommendations from known contacts, so networking and socializing have become increasingly important. For most people, this is a workable situation, but for others, it is a frustrating solution.
Some recent evidence suggests that this problem is particularly severe in the world of science. The number of authors on scientific papers has risen sharply, a trend I have observed in my own field, economics. It used to be rare for a new applicant to the job market to have multiple authors in their research paper; now it is common. The paper may be great, but how can you tell how much a single author contributed? In the physical and life sciences, a paper can have dozens of co-authors.
Again, the importance of qualifications will not decrease, but rather increase. Relatively speaking, someone from MIT who is listed on a multi-author publication is more attractive than someone from Iowa State University.
The obsolescence of current incentives could have an even greater impact with the advent of AI and large language models (LLMs). LLMs read and scan large amounts of data and can be considered an extreme example of co-production. But they typically do not have access to most archival data unless it has been put online. For example, if the papers and letters of a famous scientist are held at a university, the LLMs are unlikely to have processed them.
If an enterprising historian were to write a book based on these and other papers, he or she would get a tenured position. But suppose the historian were to put these papers into a form that could be read by major AI services. Even if all the legal approval issues were skillfully handled, that researcher would not have much career prospects. After all, his or her name is not on the LLM. And yet, the LLM database, which now contains these additional papers, would be more valuable.
Also consider that the book may only cover a single substantive thesis and could cost $100 or more as an academic volume. In contrast, the newly expanded LLM could answer all possible questions about the data it included.
Current academic institutions – and, come to think of it, current societal institutions in general – under-reward people who improve the quality of LL.M.s, at least when they work outside of the big AI companies. That doesn’t seem like a big problem right now, since people aren’t used to having high-quality LL.M.s. But in the future, it could significantly slow down AI progress. Scientists and researchers don’t usually win Nobel Prizes for creating databases, even though that endeavor is extremely valuable now and will become even more valuable.
Adam Smith had an astonishing foresight regarding the division of labor in a market economy. But one aspect of modernity that he did not foresee was the trend toward greater skill demands and the increasing difficulty of rewarding co-creators.
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