IIMB Management Review

Journal of Indian Institute of Management Bangalore

Research & Publications Office with CSITM to host seminar on ‘The Impact of LLM on Open-source Innovation: Evidence from GitHub Copilot’

The session by Prof. Raveesh Mayya, New York University, will be held on 20 August

11 August, 2026, Bangalore: The Office of Research & Publications, in collaboration with the Centre for Software and Information Technology Management (CSITM) at IIM Bangalore, will host a research seminar titled ‘The Impact of Large Language Models on Open-source Innovation: Evidence from GitHub Copilot’ by Prof. Raveesh Mayya, Assistant Professor of Technology, Operations, and Statistics, New York University Stern School of Business, in Classroom Q-101, at 2:30 PM on 20 August 2026.

The seminar will examine how the adoption of large language models is influencing open-source software development, with a focus on whether AI tools such as GitHub Copilot encourage incremental improvements or more substantive forms of innovation.

Register for the seminar: https://forms.gle/KMzXTeomCrxtqxfT9

Abstract:

Large Language Models (LLMs) are reshaping knowledge work, yet their impact on Large Language Models (LLMs) are reshaping knowledge work, yet their impact on voluntary, self-guided open innovation forums (contributors choose tasks without managerial direction) may differ fundamentally from effects observed in organizational settings. We study this question in open-source software development, where individuals' contributions collectively drive innovation at a community level. Unlike product innovation, where typologies for classifying innovation are well established, knowledge work in open-source settings calls for a distinction grounded in the cognitive demand a task places on the contributor. Burgeoning literature distinguishes substantive contributions, which require creative problem formulation to introduce new functionality, from incremental contributions, which draw on comprehension of existing code to maintain and refine it. We exploit a natural experiment around GitHub Copilot's launch in October 2021, where Copilot supported languages like Python while not supporting R for business reasons, creating an exogenous partition between otherwise comparable ecosystems. Using three complementary identification strategies and two classification approaches, we find that Copilot availability increases open-source contributions by 28 to 40 percent. The increase in incremental contributions is significantly larger than the increase in substantive contributions across all specifications. This disparity is more pronounced in projects with higher activity levels and widens following a model upgrade: LLMs function more effectively when existing context helps define the problem and constrain solutions, tilting collaborative innovation toward exploitation of established codebases rather than exploration of new functionality. This paper provides a rare instance of causal field evidence on LLM effects, given the speed at which GenAI has exploded across the knowledge economy.

Speaker Profile:

Prof. Raveesh Mayya, Assistant Professor of Technology, Operations, and Statistics at the New York University Stern School of Business, with deep interests in digital platform policies as well as in AI and Innovation. His research examines forces reshaping the digital economy, such as how digital platform policy changes create unintended consequences, and how generative AI transforms knowledge work and innovation. His research has been published in the field’s premier journals such as Management Science, Information Systems Research, and MIS Quarterly. His research has won the ISR Best Reviewer of the Year 2024 and the Gordon B. Davis Young Scholar Award 2025 and received multiple nominations for Best Paper awards at various conferences.

Add to Calendar 2026-08-20 05:30:00 2026-08-12 22:31:39 Research & Publications Office with CSITM to host seminar on ‘The Impact of LLM on Open-source Innovation: Evidence from GitHub Copilot’ The session by Prof. Raveesh Mayya, New York University, will be held on 20 August 11 August, 2026, Bangalore: The Office of Research & Publications, in collaboration with the Centre for Software and Information Technology Management (CSITM) at IIM Bangalore, will host a research seminar titled ‘The Impact of Large Language Models on Open-source Innovation: Evidence from GitHub Copilot’ by Prof. Raveesh Mayya, Assistant Professor of Technology, Operations, and Statistics, New York University Stern School of Business, in Classroom Q-101, at 2:30 PM on 20 August 2026. The seminar will examine how the adoption of large language models is influencing open-source software development, with a focus on whether AI tools such as GitHub Copilot encourage incremental improvements or more substantive forms of innovation. Register for the seminar: https://forms.gle/KMzXTeomCrxtqxfT9 Abstract: Large Language Models (LLMs) are reshaping knowledge work, yet their impact on Large Language Models (LLMs) are reshaping knowledge work, yet their impact on voluntary, self-guided open innovation forums (contributors choose tasks without managerial direction) may differ fundamentally from effects observed in organizational settings. We study this question in open-source software development, where individuals' contributions collectively drive innovation at a community level. Unlike product innovation, where typologies for classifying innovation are well established, knowledge work in open-source settings calls for a distinction grounded in the cognitive demand a task places on the contributor. Burgeoning literature distinguishes substantive contributions, which require creative problem formulation to introduce new functionality, from incremental contributions, which draw on comprehension of existing code to maintain and refine it. We exploit a natural experiment around GitHub Copilot's launch in October 2021, where Copilot supported languages like Python while not supporting R for business reasons, creating an exogenous partition between otherwise comparable ecosystems. Using three complementary identification strategies and two classification approaches, we find that Copilot availability increases open-source contributions by 28 to 40 percent. The increase in incremental contributions is significantly larger than the increase in substantive contributions across all specifications. This disparity is more pronounced in projects with higher activity levels and widens following a model upgrade: LLMs function more effectively when existing context helps define the problem and constrain solutions, tilting collaborative innovation toward exploitation of established codebases rather than exploration of new functionality. This paper provides a rare instance of causal field evidence on LLM effects, given the speed at which GenAI has exploded across the knowledge economy. Speaker Profile: Prof. Raveesh Mayya, Assistant Professor of Technology, Operations, and Statistics at the New York University Stern School of Business, with deep interests in digital platform policies as well as in AI and Innovation. His research examines forces reshaping the digital economy, such as how digital platform policy changes create unintended consequences, and how generative AI transforms knowledge work and innovation. His research has been published in the field’s premier journals such as Management Science, Information Systems Research, and MIS Quarterly. His research has won the ISR Best Reviewer of the Year 2024 and the Gordon B. Davis Young Scholar Award 2025 and received multiple nominations for Best Paper awards at various conferences. IIM Bangalore IIM Bangalore communications@iimb.ac.in Asia/Kolkata public
20 Aug 2026

Research & Publications Office with CSITM to host seminar on ‘The Impact of LLM on Open-source Innovation: Evidence from GitHub Copilot’

Add to Calendar 2026-08-20 05:30:00 2026-08-12 22:31:39 Research & Publications Office with CSITM to host seminar on ‘The Impact of LLM on Open-source Innovation: Evidence from GitHub Copilot’ The session by Prof. Raveesh Mayya, New York University, will be held on 20 August 11 August, 2026, Bangalore: The Office of Research & Publications, in collaboration with the Centre for Software and Information Technology Management (CSITM) at IIM Bangalore, will host a research seminar titled ‘The Impact of Large Language Models on Open-source Innovation: Evidence from GitHub Copilot’ by Prof. Raveesh Mayya, Assistant Professor of Technology, Operations, and Statistics, New York University Stern School of Business, in Classroom Q-101, at 2:30 PM on 20 August 2026. The seminar will examine how the adoption of large language models is influencing open-source software development, with a focus on whether AI tools such as GitHub Copilot encourage incremental improvements or more substantive forms of innovation. Register for the seminar: https://forms.gle/KMzXTeomCrxtqxfT9 Abstract: Large Language Models (LLMs) are reshaping knowledge work, yet their impact on Large Language Models (LLMs) are reshaping knowledge work, yet their impact on voluntary, self-guided open innovation forums (contributors choose tasks without managerial direction) may differ fundamentally from effects observed in organizational settings. We study this question in open-source software development, where individuals' contributions collectively drive innovation at a community level. Unlike product innovation, where typologies for classifying innovation are well established, knowledge work in open-source settings calls for a distinction grounded in the cognitive demand a task places on the contributor. Burgeoning literature distinguishes substantive contributions, which require creative problem formulation to introduce new functionality, from incremental contributions, which draw on comprehension of existing code to maintain and refine it. We exploit a natural experiment around GitHub Copilot's launch in October 2021, where Copilot supported languages like Python while not supporting R for business reasons, creating an exogenous partition between otherwise comparable ecosystems. Using three complementary identification strategies and two classification approaches, we find that Copilot availability increases open-source contributions by 28 to 40 percent. The increase in incremental contributions is significantly larger than the increase in substantive contributions across all specifications. This disparity is more pronounced in projects with higher activity levels and widens following a model upgrade: LLMs function more effectively when existing context helps define the problem and constrain solutions, tilting collaborative innovation toward exploitation of established codebases rather than exploration of new functionality. This paper provides a rare instance of causal field evidence on LLM effects, given the speed at which GenAI has exploded across the knowledge economy. Speaker Profile: Prof. Raveesh Mayya, Assistant Professor of Technology, Operations, and Statistics at the New York University Stern School of Business, with deep interests in digital platform policies as well as in AI and Innovation. His research examines forces reshaping the digital economy, such as how digital platform policy changes create unintended consequences, and how generative AI transforms knowledge work and innovation. His research has been published in the field’s premier journals such as Management Science, Information Systems Research, and MIS Quarterly. His research has won the ISR Best Reviewer of the Year 2024 and the Gordon B. Davis Young Scholar Award 2025 and received multiple nominations for Best Paper awards at various conferences. IIM Bangalore IIM Bangalore communications@iimb.ac.in Asia/Kolkata public

The session by Prof. Raveesh Mayya, New York University, will be held on 20 August

11 August, 2026, Bangalore: The Office of Research & Publications, in collaboration with the Centre for Software and Information Technology Management (CSITM) at IIM Bangalore, will host a research seminar titled ‘The Impact of Large Language Models on Open-source Innovation: Evidence from GitHub Copilot’ by Prof. Raveesh Mayya, Assistant Professor of Technology, Operations, and Statistics, New York University Stern School of Business, in Classroom Q-101, at 2:30 PM on 20 August 2026.

The seminar will examine how the adoption of large language models is influencing open-source software development, with a focus on whether AI tools such as GitHub Copilot encourage incremental improvements or more substantive forms of innovation.

Register for the seminar: https://forms.gle/KMzXTeomCrxtqxfT9

Abstract:

Large Language Models (LLMs) are reshaping knowledge work, yet their impact on Large Language Models (LLMs) are reshaping knowledge work, yet their impact on voluntary, self-guided open innovation forums (contributors choose tasks without managerial direction) may differ fundamentally from effects observed in organizational settings. We study this question in open-source software development, where individuals' contributions collectively drive innovation at a community level. Unlike product innovation, where typologies for classifying innovation are well established, knowledge work in open-source settings calls for a distinction grounded in the cognitive demand a task places on the contributor. Burgeoning literature distinguishes substantive contributions, which require creative problem formulation to introduce new functionality, from incremental contributions, which draw on comprehension of existing code to maintain and refine it. We exploit a natural experiment around GitHub Copilot's launch in October 2021, where Copilot supported languages like Python while not supporting R for business reasons, creating an exogenous partition between otherwise comparable ecosystems. Using three complementary identification strategies and two classification approaches, we find that Copilot availability increases open-source contributions by 28 to 40 percent. The increase in incremental contributions is significantly larger than the increase in substantive contributions across all specifications. This disparity is more pronounced in projects with higher activity levels and widens following a model upgrade: LLMs function more effectively when existing context helps define the problem and constrain solutions, tilting collaborative innovation toward exploitation of established codebases rather than exploration of new functionality. This paper provides a rare instance of causal field evidence on LLM effects, given the speed at which GenAI has exploded across the knowledge economy.

Speaker Profile:

Prof. Raveesh Mayya, Assistant Professor of Technology, Operations, and Statistics at the New York University Stern School of Business, with deep interests in digital platform policies as well as in AI and Innovation. His research examines forces reshaping the digital economy, such as how digital platform policy changes create unintended consequences, and how generative AI transforms knowledge work and innovation. His research has been published in the field’s premier journals such as Management Science, Information Systems Research, and MIS Quarterly. His research has won the ISR Best Reviewer of the Year 2024 and the Gordon B. Davis Young Scholar Award 2025 and received multiple nominations for Best Paper awards at various conferences.