Topic
14 facts from 1 episode, in date order. These are what speakers said on the shows, each linked to the moment it was said. They aren't independently verified.
1990s
Sadun says computers and software in the 1990s changed the task content of jobs, replacing some tasks and complementing others.
“With the introduction of computers and software in the nineties, these technologies were changing the task content of jobs”
1990s
Sadun found that information technology helped some firms more than others depending on their ability to reallocate tasks, hire different workers, or reorganize work.
“it all came down to their ability to adopt this technology, which in turn came down to their ability to reallocate tasks or hire different types of workers, or change the ways in which work was organized.”
2020s
Sadun says tight labor markets after the COVID-19 pandemic led companies to rethink reskilling.
“after COVID, tightening labor markets really scared companies, so they started thinking about reskilling in a different way.”
Undated
Raffaella Sadun's first doctoral dissertation paper examined why computer-related capital helped some firms far more than others.
“my first paper of my doctoral dissertation was all about this.”
Undated
Sadun says data link the emergence of superstar firms and widening disparities in outcomes across firms to differences in the ability to implement new technology.
“there is data out there that traces back the emergence of superstar firms and this disparity in outcomes across firms at the macro level.”
Undated
Sadun speculates, based on very preliminary evidence, that new organizations may use AI better than older firms that must retrofit their operations.
“a new organization that is born today would be better equipped at using this technology and getting exponential returns from these technologies than organizations that have to retrofit what they do to adopt the technology.”
Undated
In Sadun's ongoing study of AI for blue-collar workers solving machinery problems, job satisfaction was increasing at the halfway point.
“We are halfway through the study, and it seems like job satisfaction is increasing.”
Undated
Sadun's experiment on AI and teams was run with a consumer packaging company and involved research and development and marketing staff.
“In particular, in this experiment, we have R and D and marketing. So we worked with one company. This is a consumer packaging company.”
Undated
The experiment compared individuals alone, individuals with an AI persona acting as their counterpart function, teams without AI, and teams with AI.
“One is individuals working on their own, the second is an individual working with AI, and the role of AI is to be a persona.”
Undated
The researchers ran the experiment as a hackathon designed to mimic the company's normal innovation process.
“In the context of a hackathon, we tried to mimic as much as possible what the company does normally.”
Undated
In the experiment, individuals working with AI produced ideas whose average quality matched that of teams and teams with AI.
“The quality of their output is on average as good as the quality that comes out of teams and teams with AI.”
Undated
Without AI, participants' ideas reflected their own function, with R&D people proposing technical ideas and marketing people proposing commercial ones.
“when people work by themselves without AI, their ideas pretty much mimic their functional expertise. If you're an R and D person, you're going to propose a very technical proposal.”
Undated
Teams with AI were disproportionately likely to place ideas in the top decile of submissions in Sadun's experiment.
“we find that teams with AI are disproportionately better at placing these ideas on the top decile of the submission.”
Undated
Participants using AI, alone or in teams, produced better solutions on average in a fraction of the time.
“The individuals plus AI were, and also the teams with AI were able to, on average, come up with better solutions, but also at a fraction of the time.”
Episodes