Jared Bauer summarizes results of a study I suggested this spring. 202 developers were randomly assigned GitHub Copilot, while the others were instructed not to use AI tools. The participants were asked to complete a coding task. Developers with GitHub Copilot had 56% greater likelihood of passing all unit tests. Other developers evaluated code to assess quality and readability. Code from developers with GitHub Copilot was rated better on readability, maintainability, and conciseness. All these differences were statistically significant.
The Effect of Microsoft Copilot in a Multi-lingual Context with Donald Ngwe
We tested Microsoft Copilot in multilingual contexts, examining how Copilot can facilitate collaboration between colleagues with different native languages.
First, we asked 77 native Japanese speakers to review a meeting recorded in English. Half the participants had to watch and listen to the video. The other half could use Copilot Meeting Recap, which gave them an AI meeting summary as well as a chatbot to answer questions about the meeting.
Then, we asked 83 other native Japanese speakers to review a similar meeting, following the same script, but this time held in Japanese by native Japanese speakers. Again, half of participants had access to Copilot.
For the meeting in English, participants with Copilot answered 16.4% more multiple-choice questions about the meeting correctly, and they were more than twice as likely to get a perfect score. Moreover, in comparing accuracy between the two scenarios, people listening to a meeting in English with Copilot achieved 97.5% accuracy, slightly more accurate than people listening to a meeting in their native Japanese using standard tools (94.8%). This is a statistically significant difference (p<.05). The changes are small in percentage point terms because the baseline accuracy is so high, but Copilot closed 38.5% of the gap to perfect accuracy for those working in their native language (p<0.10) and closed 84.6% of the gap for those working in (non-native) English (p<.05).
Summary from Jaffe et al, Generative AI in Real-World Workplaces, July 2024.
Impact of M365 Copilot on Legal Work at Microsoft
Teams at Microsoft often reflect on how Copilot helps. I try to help these teams both by measuring Copilot usage in the field (as they do their ordinary work) and in lab experiments (idealized versions of their tasks in environments where I can better isolate cause and effect). This month I ran an experiment with CELA, Microsoft’s in-house legal department. Hossein Nowbar, Chief Legal Officer and Corporate Vice President, summarized the findings in a post at LinkedIn:
Recently, we ran a controlled experiment with Microsoft’s Office of the Chief Economist, and the results are groundbreaking. In this experiment, we asked legal professional volunteers on our team to complete three realistic legal tasks and randomly granted Copilot to some participants. Individuals with Copilot completed the tasks 32% faster and with 20.3% greater accuracy!
Copilot isn’t just a tool; it’s a game-changer, empowering our team to focus on what truly matters by enhancing productivity, elevating work quality, and, most importantly, reclaiming time.
All findings statistically significant at P<0.05.
Early LLM-based Tools for Enterprise Information Workers Likely Provide Meaningful Boosts to Productivity
Early LLM-based Tools for Enterprise Information Workers Likely Provide Meaningful Boosts to Productivity. Microsoft Research Report – AI and Productivity Team. With Alexia Cambon, Brent Hecht, Donald Ngwe, Sonia Jaffe, Amy Heger, Mihaela Vorvoreanu, Sida Peng, Jake Hofman, Alex Farach, Margarita Bermejo-Cano, Eric Knudsen, James Bono, Hardik Sanghavi, Sofia Spatharioti, David Rothschild, Daniel G. Goldstein, Eirini Kalliamvakou, Peter Cihon, Mert Demirer, Michael Schwarz, and Jaime Teevan.
This report presents the initial findings of Microsoft’s research initiative on “AI and Productivity”, which seeks to measure and accelerate the productivity gains created by LLM-powered productivity tools like Microsoft’s Copilot. The many studies summarized in this report, the initiative’s first, focus on common enterprise information worker tasks for which LLMs are most likely to provide significant value. Results from the studies support the hypothesis that the first versions of Copilot tools substantially increase productivity on these tasks. This productivity boost usually appeared in the studies as a meaningful increase in speed of execution without a significant decrease in quality. Furthermore, we observed that the willingness-to-pay for LLM-based tools is higher for people who have used the tools than those who have not, suggesting that the tools provide value above initial expectations. The report also highlights future directions for the AI and Productivity initiative, including an emphasis on approaches that capture a wider range of tasks and roles.
Studies I led that are included within this report:
Randomized Controlled Trials for Microsoft Copilot for Security with James Bono, Sida Peng, Roberto Rodriguez, and Sandra Ho. updated March 29, 2024.
Randomized Controlled Trials for Microsoft Copilot for Security. SSRN Working Paper 4648700. With James Bono, Sida Peng, Roberto Rodriguez, and Sandra Ho.
We conducted randomized controlled trials (RCTs) to measure the efficiency gains from using Security Copilot, including speed and quality improvements. External experimental subjects logged into a M365 Defender instance created for this experiment and performed four tasks: Incident Summarization, Script Analyzer, Incident Report, and Guided Response. We found that Security Copilot delivered large improvements on both speed and accuracy. Copilot brought improvements for both novices and security professionals.
(Also summarized in What Can Copilot’s Earliest Users Teach Us About Generative AI at Work? at “Role-specific pain points and opportunities: Security.” Also summarized in AI and Productivity Report at “M365 Defender Security Copilot study.”)
Sound Like Me: Findings from a Randomized Experiment with Donald Ngwe
Sound Like Me: Findings from a Randomized Experiment. SSRN Working Paper 4648689. With Donald Ngwe.
A new version of Copilot for Microsoft 365 includes a feature to let Outlook draft messages that “Sound Like Me” (SLM) based on training from messages in a user’s Sent Items folder. We sought to evaluate whether SLM lives up to its name. We find that it does, and more. Users widely and systematically praise SLM-generated messages as being more clear, more concise, and more “couldn’t have said it better myself”. When presented with a human-written message versus a SLM rewrite, users say they’d rather receive the SLM rewrite. All these findings are statistically significant. Furthermore, when presented with human and SLM messages, users struggle to tell the difference, in one specification doing worse than random.
(Also summarized in What Can Copilot’s Earliest Users Teach Us About Generative AI at Work? at “Email effectiveness.” Also summarized in AI and Productivity Report at “Outlook Email Study.”)
Measuring the Impact of AI on Information Worker Productivity with Donald Ngwe and Sida Peng
Measuring the Impact of AI on Information Worker Productivity. SSRN Working Paper 4648686. With Donald Ngwe and Sida Peng.
This paper reports the results of two randomized controlled trials evaluating the performance and user satisfaction of a new AI product in the context of common information worker tasks. We designed workplace scenarios to test common information worker tasks: retrieving information from files, emails, and calendar; catching up after a missed online meeting; and drafting prose. We assigned these tasks to 310 subjects tasked to find relevant information, answer multiple choice questions about what they found, and write marketing content. In both studies, users with the AI tool were statistically significantly faster, a difference that holds both on its own and when controlling for accuracy/quality. Furthermore, users who tried the AI tool reported higher willingness to pay relative to users who merely heard about it but didn’t get to try it, indicating that the product exceeded expectations.
(Also summarized in What Can Copilot’s Earliest Users Teach Us About Generative AI at Work? at “A day in the life” and “The strain of searching.” Also summarized in AI and Productivity Report at “Copilot Common Tasks Study” and “Copilot Information Retrieval Study.”)
An Introduction to the Competition Law and Economics of “Free” with Damien Geradin
Benjamin Edelman and Damien Geradin. An Introduction to the Competition Law and Economics of ‘Free’. Antitrust Chronicle, Competition Policy International. August 2018.
Many of the largest and most successful businesses today rely on providing services at no charge to at least a portion of their users. Consider companies as diverse as Dropbox, Facebook, Google, LinkedIn, The Guardian, Wikipedia, and the Yellow Pages.
For consumers, it is easy to celebrate free service. At least in the short term, free services are often high quality, and users find a zero price virtually irresistible.
But long-term assessments could differ, particularly if the free service reduces quality and consumer choice. In this short paper, we examine these concerns. Some highlights:
First, “free” service tends to be free only in terms of currency. Consumers typically pay in other ways, such as seeing advertising and providing data, though these payments tend to be more difficult to measure.
Second, free service sometimes exacerbates market concentration. Most notably, free service impedes a natural strategy for entrants: offer a similar product or service at a lower price. Entrants usually can’t pay users to accept their service. (That would tend to attract undesirable users who might even discard the product without trying it.) As a result, prices are stuck at zero, entry may be more difficult, effectively shielding incumbents from entry.
In this short paper, we examine the competition economics of “free” — how competition works in affected markets, what role competition policy might have and what approach it should take, and finally how competitors and prospective competitors can compete with “free.” Our bottom line: While free service has undeniable appeal for consumers, it can also impede competition, and especially entry. Competition authorities should be correspondingly attuned to allegations arising out of “free” service and should, at least, enforce existing doctrines strictly in affected markets.
In Accusing Microsoft, Google Doth Protest Too Much
In Accusing Microsoft, Google Doth Protest Too Much. HBR Online. February 3, 2011.
Google this week sparked a media uproar by alleging that Microsoft Bing “copies” Google results. But is that actually the best characterization of what happened? In fact Google’s engineers intentionally clicked bogus listings they had previously inserted into Google’s results, and they did this on computers where they had specifically authorized Microsoft to examine their browsing in order to improve Bing.
Strikingly, Google’s own Matt Cutts previously endorsed the use of Toolbar and similar data to improve search results — calling this approach “a good idea.” And Google’s own Toolbar Privacy Policy allows Google to perform the same analysis Bing used. So I don’t have much sympathy for Google’s allegations of impropriety. Quite the contrary: With Bing’s small market share, this data is important in improving Bing search results and building a viable competitor to Google’s dominant search offering.
Details, including what exactly happened, Google’s prior statements, and Google’s widespread use of others’ intellectual property:
Windows Vista (teaching materials)
Edelman, Benjamin. “Windows Vista.” Harvard Business School Case 909-038, February 2009. (Revised December 2010.) (educator access at HBP. request a courtesy copy.)
Microsoft designs, modifies, publicizes, and distributes Windows Vistaagainst a backdrop of consumers already largely satisfied with their existing Windows XP systems. Microsoft must decide what features to include and what to drop, how to compete with its own installed base, and how to mobilize partners to offer Vista-compatible systems.