Recap of the "Benefits and Challenges of Open Source" Conference
On September 29, 2023 NYU’s Center for the Future of Management hosted a conference focused on the pressing questions around “open source” at NYU’s Stern School of Business. The in-person conference was attended by approximately 75 participants from academia, industry, non-profit, think tanks, and government organizations. Support for the conference was generously provided by GitHub and NYU’s Stern School of Business. The listed as a conference extension to the Strategic Management Society (SMS) Annual Meeting in Toronto.
In opening remarks, Frank Nagle from Harvard Business School summarized the two main goals for the conference: to connect a community of scholars, practitioners and policy-makers around the economics of open source, AI, and other technologies, and to begin the process of developing a research and policy agenda in these areas. Areas of importance included: Measurement/Economy-wide Issues; Entrepreneurship and Innovation; Effects on Organizations; Effects on Workers.
An opening presentation by Peter Cihon, Senior Policy Manager from Github, shared details on Github’s data and analysis repository, the Github Innovation Graph, which measures the absolute number of open source software developers in an economy, their projects, organizations, programming languages, licenses and topics – helpful to understanding the popularity of open source software in various global economies.
The first panel session focused on Measurement and Economy-wide Issues. Martin Fleming from the Productivity Institute led the discussion which considered how to measure the value of open-source software that transacts at a price of zero, and calculate its value to the economy. Jim Bessen from Boston University shared that only a small number (250) of large firms account for 99% of the increase in software use since 2008, and theorized that this level of market power and inequality could slow innovation. To measure the value of open source software, Ran Zhuo from the University of Michigan explained how to calculate the price of the nearest market substitute (like the cost of a Microsoft server when considering the use of Apache open-source servers). Carol Robbins from the National Center for Science and Engineering Statistics explained how the NCSES is assessing the cost of creation: using a constructive cost model to estimate the cost of recreating open source software based on team size, skill level and lines of code. Ashish Arora from Duke University recommended drawing from the measurement of open science to measure open source.
In the second session, Sonali Shah from the University of Illinois chaired a panel on open source, entrepreneurship and innovation. Kush Varney from IBM shared how IBM cloud is creating an algorithmic fairness tool (AI Fairness 360), which it is implementing in WatsonX. James Herbsleb from Carnegie Mellon University explained that how ecosystems manage breaking changes – fixes when open code breaks – can determine the types and sizes of projects that continue and those that go dormant. Natalia Levina from NYU Stern explained the importance of considering an open source community’s health (i.e. the vibrancy of developer base, growth of code base, attention to software quality improvement), when deciding how to structure governance for open source projects – to create joint value as well as value for one firm. Nataliya Langburd-Wright from Columbia Business School showed that more contribution to open source predicts entrepreneurial founding at the country level, especially of ventures that are high-quality, mission-oriented and globally-oriented.
In a keynote address, Frank Nagle from Harvard Business School moderated a discussion with Nithya Ruff, the Head of Amazon’s Open Source Program Office, and Chair of the Board of Directors at the Linux Foundation. She explained the importance of collecting more data on open source to help increase its profile inside tech firms (for example, to increase its recognition in performance evaluation), as well as for helping foundations to educate policymakers.
In a third session, Frank Nagle from Harvard Business School chaired a panel on open source and its effects on organizations. Sherae Daniel from the University of Cincinnati shared how coders can communicate their technical skills on Github and interpersonal skills through recommendations on LinkedIn to improve their career outcomes. Abhishek Nagaraj from UC Berkeley explained why open source models are better at serving the needs of the vulnerable but less efficient at scale than private models – because if people are not bought into a project, competition pulls people away from open source. Tony Tong explained that low quality participation in open source can inhibit overall project quality. David Nalley, the Director of Open Source Strategy at Amazon AWS and President of the Apache Software Foundation, shared that open source software is ubiquitous in tech firms, which leverage this channel to test things cheaply and save cost.
The final panel, moderated by Martin Fleming from the Productivity Institute, focused on open source and its effects on workforces. Julia Lamm, a Partner in PWC Workforce Strategy shared data on what workforces want, and the particular needs of people with specialist/ technical skills, who are anxious about the changing economy, AI, and want promotions. Amanda Brock, the CEO of Open UK, explained the UK firms that build open source infrastructure are part of the global tech sector but are 'hidden' because they register and sell in the US. Milan Maric from University of Southern California explained that new tech creates both standardization effects, which enable workers to move between firms, as well as affecting individual workers’ skills.
Conference participants responded to a survey after the event to indicate additional interest in a variety of topics. The following topics were the most popular among survey respondents:
Measurement/ Economy-wide Issues: How can we best measure the effect of open source on productivity, GDP, and the economy? How do you value/price open source?
Entrepreneurship & Innovation: How can open source diversify who is involved in innovation and high growth entrepreneurship? How should we measure open source innovation?
Effects on Organizations: How do we convince/incentivize organizations (including for-profits, non-
profits, governments, etc) to support open source and encourage their employees to contribute to open source? How can organizations most effectively govern open source software communities?
Effects on Workers: What are the labor market effects of open source on developers; can we learn
anything from recent work on the effect of automation/AI? Under what conditions do developers and other open source users make themselves more productive via use of open source?