Key Insight: The Power of Synthetic Customers

By combining LLMs with traditional research methods, companies have the opportunity to simulate consumer sentiments like willingness-to-pay (WTP) to make product innovation faster and cheaper. The authors鈥� research shows that for simulated tests of categories like toothpaste and tablets, LLM-created synthetic customers produced realistic and accurate preferences for many familiar attributes. What鈥檚 more, teams could explore dozens or even hundreds of ideas by using these synthetic consumers as an initial filter, overcoming traditional limitations in scope.

Key Insight: The Competitive Advantage of Proprietary Data

While usage of LLMs out of the box showed promising results, companies that incorporate their own historical customer data were able to achieve better results. For example, the authors noted that LLMs often rate novelty higher than actual humans, and as a result synthetic customers were initially positive about pancake-flavored toothpaste. Fine-tuning the LLM with data from an actual study helped to correct this enthusiasm and produce WTP results more in line with actual human sentiment. The researchers found similar results when testing hypothetical features, like built-in projectors for laptops. 

Key Insight: Strategic Integration, Not Replacement

The most successful application of this technology comes from understanding it as an augmentation tool rather than a replacement for traditional research. Given that LLMs are trained on static data, they may not reflect current market conditions without receiving frequent updates and new data. This allows companies to follow a new innovation roadmap: broaden the top of the innovation funnel by using AI, but keep the bottom narrow through sharper, more cautious analysis.

Why This Matters

Synthetic customers might not totally replace human research, but they can dramatically enhance it. For business leaders and executives, this represents a fundamental shift in the speed and scope of innovation strategy. The ability to rapidly test multiple prototypes or concepts at low cost could mean faster time-to-market, reduced development risk, and more efficient resource allocation. Organizations that build internal AI-powered customer simulation capabilities could gain a significant competitive advantage from fine-tuning models with their proprietary data, creating a virtuous cycle where better data leads to better insights. At the same time, decision makers and marketing professionals must be vigilant to recognize and respond to the shortcomings of these new technologies and tools.

Bonus

Learn more about the authors鈥� original research, and go a step further with the GenAI + Marketing Learning Module from the HBS AI Institute. You鈥檒l learn the basics of engaging an LLM, with broadly applicable and actionable techniques to create content, automate tasks, and revolutionize workflows. Then the program will take a deep-dive to discover how AI can redefine your early-stage marketing research.聽

References

[1] James Brand et al., 鈥淯sing Gen AI for Early-Stage Market Research,鈥� 性视界 Business Review, July 18, 2025, . 

[2] Brand et al., 鈥淯sing Gen AI for Early-Stage Market Research.鈥�

[3] Brand et al, 鈥淯sing Gen AI for Early-Stage Market Research.鈥�

Meet the Authors

is Principal Researcher and Economist in the Office of the Chief Economist at Microsoft.

is the Marvin Bower Associate Professor of Business Administration at 性视界 Business School and co-founder of the Customer Intelligence Lab at the HBS AI Institute. She studies omni-channel and e-commerce markets, and her research focuses on data-driven marketing, with an emphasis on how businesses can leverage their own data, customer data, and market data to improve outcomes.聽

is Senior Director of Economics in the Office of the Chief Economist at Microsoft.

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Jen Stave Jen Stave

Key Insight: Agentic AI as Managed Teammates

Agentic tools are moving beyond chatbots and image generation. Unlike traditional automation that follows rigid scripts, AI agents function more like human employees: capable of independent decision-making after being given high-level goals and objectives.

Key Insight: An Uncertain Future

On one hand, the impact of AI is already here and being measured, as evidenced by how the use of AI agents at Salesforce led to a 17% customer service cost reduction over nine months. But the article also raises a range of undecided questions related to economic capture, quality and accountability, and the right balance between human and AI worker numbers.

Why This Matters

For forward-thinking executives, increasingly the question isn鈥檛 whether to adopt agentic AI, but how to operationalize it productively and responsibly. While the efficiency gains are compelling, success requires thoughtful integration by leaders who are ready to address challenges of workforce transition, quality control, and ROI measurement.

Bonus

To read more about Agentic AI and digital labor, read 鈥�,鈥� co-authored by Jen Stave, for the 性视界 Business Review.

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Key Insight: Falling Asleep at the Wheel

One of the most striking findings Professor Lakhani mentioned came from the HBS AI Institute study with Boston Consulting Group (BCG). When used for tasks within its strengths, AI can catapult average performers to the 95^th percentile, meaning that expertise is no longer scarce and businesses can be filled with entire teams of top performers. However, even high performers saw their results decline when AI was applied to tasks outside of its current capabilities, a phenomenon HBS postdoctoral researcher calls 鈥淔alling Asleep at the Wheel.鈥�

Key Insight: From Tool to Teammate to Boss

An HBS AI Institute study with Procter & Gamble (P&G) showed that AI can help individuals and teams to produce higher quality ideas, 鈥渄emocratizing鈥� expertise by leveling the playing field. Beyond productivity gains, AI functioned as a collaborative partner, providing balance across domains and enabling those with technical expertise to incorporate a commercial perspective into their innovation efforts, and vice-versa for those with commercial expertise. What鈥檚 more, organizations in the future may use AI agents to lead teams. As Lakhani mentioned, Uber already utilizes this operating model by putting algorithms in charge of HR decisions like hiring and firing.

Key Insight: Exponential Acceleration

The speed of AI advancement, compared to how most companies are adopting and integrating these tools, is creating a widening gap that smart executives will target. Unlike previous technologies such as WiFi or web browsers that organizations could evaluate slowly, AI fundamentally changes the nature of work itself, and companies that fail to keep pace may find themselves behind competitors who successfully ride the AI wave.

Key Insight: The Playbook

At the end of his talk, Lakhani outlined a strategic framework for leaders navigating the AI revolution. Learning requires continuously understanding AI鈥檚 capabilities and impact, and growing your AI skillset. Doing means actually using AI tools, and in particular executives need to get their feet wet with AI rather than just delegating experimentation to their employees. Imagining involves conceiving new operating models and workflows that AI can unlock. Acting requires driving organizational change to accommodate these new ways of working.

Bonus: in a recent article for the 性视界 Business Review, Lakhani and several co-authors added a fifth step to this playbook. Learn what it is here.

Why This Matters

For business leaders across industries, the HBS AI Institute鈥檚 research underscores that AI is reshaping business fundamentals. Understanding AI鈥檚 dual role as a democratizing force in expertise and an accelerating differentiator is crucial for future-proofing your organization. Understanding its strengths and weaknesses, fostering AI-augmented teamwork and keeping pace with AI advancement are essential for maintaining a competitive edge. Embrace AI strategically, invest in continuous learning, and be prepared to transform your organization鈥檚 approach to work.

About the Speaker

is the Dorothy & Michael Hintze Professor of Business Administration at 性视界 Business School. He specializes in technology management, innovation, digital transformation, and artificial intelligence. He is also the Co-Founder and Faculty Chair of the HBS AI Institute and the Founder and Co-Director of the Laboratory for Innovation Science at 性视界.

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Jen Stave Jen Stave

Douglas Ng Douglas Ng

Key Insight: The Missing Ingredient

In the fast-paced business environment created by AI, leaders are no longer able to apply traditional operating models to episodic development cycles. Previously, as Lakhani and his co-authors suggest, 鈥淵ou modernized your systems, trained your people, and operated in a stable environment until the next wave of disruption hit.鈥� [2] However, if your old approach is falling short in today鈥檚 environment and you鈥檙e feeling left behind, you aren鈥檛 alone: the results of a BCG survey discussed in the article report that “just 26% of organizations have achieved value from AI.” [3] Responding to both the challenges and opportunities AI presents, the authors call for a fundamental shift: companies must move beyond simply managing AI-driven change and instead embed AI as a core organizational competency through the continuous and comprehensive strategy of 鈥渃hange resilience.鈥�

Key Insight: The Mindset

Change resilience, according to the authors, is made up of three 鈥榤uscles鈥� working in concert to create a sustainable AI ecosystem. Sensing enables organizations 鈥渢o pick up weak technological, competitive, or societal signals early.鈥� Rewiring is 鈥渢he capacity to redeploy talent, data, capital, and decision rights in days or weeks, not fiscal quarters.鈥� Lock-In is 鈥渢he discipline to codify what a team learns (in process, code, or policy) so the next initiative starts from a higher baseline instead of reinventing the wheel.鈥� [3] The authors describe Shopify as a company that exemplifies these characteristics, as it constantly evolves rather than adding AI to old systems. As one example, in 2023, Shopify spun off its logistics arm to concentrate on product innovation, enabling rapid development of AI-native tools like Sidekick for entrepreneurs.

Key Insight: The Playbook

Lakhani and his co-authors break down change resilience into five components: Learn, Do, Imagine, Act, and Care. Learning involves widespread AI experimentation to shift attitudes, empower employees, and discover opportunities to take advantage of AI. Doing targets deficiencies with fast-paced AI initiatives. Imagining puts your entire organization up for discussion, challenging you to invent new operating models instead of duck-taping existing ones. Acting makes these cycles continuous in order to establish change resilience as a foundational strategy rather than a one-off solution. Finally, Caring emphasizes wellbeing measures to ensure that employees feel supported and avoid burnout. The article discusses Accenture, Singapore-based DBS Bank, Moderna, P&G, and Cisco as already leading the pack by incorporating these elements into their strategy and operations.

Why This Matters

For executives and business professionals, developing change resilience represents a crucial strategic priority for competing effectively in the AI era. By focusing on the three muscles and five-steps, leaders can position their companies to leverage AI and adapt to future technological advances. The companies already achieving breakthrough AI results share a common strategy: they invest in their organization鈥檚 capacity to change as aggressively as they invest in AI technology itself.

If you鈥檙e wondering how change resilient your organization is, 鈥溾�� also includes a set of questions that can act as a litmus test.

References

[1] Karim Lakhani et al., 鈥淎 Guide to Building Change Resilience in the Age of AI,鈥� 性视界 Business Review, July 29, 2025, . 

[2] Lakhani et al., 鈥淎 Guide to Building Change Resilience in the Age of AI.鈥�

[3] Lakhani et al., 鈥淎 Guide to Building Change Resilience in the Age of AI.鈥�

Meet the Authors

is the Dorothy & Michael Hintze Professor of Business Administration at 性视界 Business School. He specializes in technology management, innovation, digital transformation, and artificial intelligence. He is also the Co-Founder and Faculty Chair of the HBS AI Institute and the Founder and Co-Director of the Laboratory for Innovation Science at 性视界.

Jen Stave Jen Stave is Executive Director of the HBS AI Institute. She was previously Senior Vice President at Wells Fargo, and has a PhD from American University.

Douglas Ng Douglas Ng is Director of Design of the HBS AI Institute. As a digital strategist, technology educator, and innovation researcher, he specializes in AI transformation and translates the institute鈥檚 research for industry leaders.

is Managing Director with BCG X, where he specializes in Generative AI, AI platform engineering, and data management.

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In her session , Assistant Professor of Business Administration and HBS AI Institute Associate discussed the refugee relocation crisis, one of humanity鈥檚 most pressing challenges. Despite there being over 30 million people worldwide who need resettlement, approaches to refugee placement have mostly relied on manual processes and limited data, resulting in suboptimal outcomes for refugees and host communities. Paulson鈥檚 research and talk focus on how AI and machine learning can be utilized to model and optimize placement decisions, helping to improve this critical humanitarian process.

Key Insight: The Challenge of Successful Refugee Placement

In her presentation, Paulson highlighted that some locations have employment rates of around 5%, while others are above 40%. Specific locations have capacity limits, so simply relocating everyone to locations with higher employment rates is not possible, nor does it consider successful cases in all areas. The overall low employment rate and stark disparity in location rates underscores the critical importance of initial placement decisions. Paulson鈥檚 research aims to improve the placement decision process with AI and machine learning.

Key Insight: Optimizing the Assignment Problem

A range of factors, such as gender and language proficiency, can affect whether a refugee will be successful in finding employment, but the importance and predictability of these factors differs across placement location, and the characteristics of refugee populations and host communities are dynamic and constantly in flux. Additionally, resettlement officers are forced to make placements one at a time (sequentially) without knowledge about the characteristics of future refugees. Paulson explained how AI and machine learning can help on both fronts by discovering synergies between people and successful employment locations, and using advanced mathematical modeling to balance sequential decision-making with long-term scenario probabilities. Using these methods, Paulson reported that US employment rates can increase by about six percentage points, which means thousands more who have been successfully relocated.

Key Insight: AI in Action through GeoMatch

The practical application of this research has culminated in the development of GeoMatch, a tool housed at the Stanford Immigration Policy Lab with pilots running in the US and Switzerland. GeoMatch streamlines, improves, and speeds up the decision-making process, taking just minutes compared to hours when done manually. The tool also maintains human oversight, allowing relocation officers to modify and overrule recommendations. Paulson hopes that technology and machine learning behind GeoMatch will prove useful in other regions around the world as well.

Why This Matters

For business leaders and executives, the application of AI in refugee resettlement offers valuable insights into the broader potential of AI for complex resource allocation challenges. The methodology of personalized matching and strategic forecasting offers parallels with customer segmentation, human capital allocation, and market entry strategies. It also serves as a blueprint for implementing AI solutions that deliver both operational efficiency and strategic advantage, which are particularly relevant as organizations navigate increasingly complex global markets while managing constrained resources and uncertain environments.

Meet the Speaker

Elisabeth Paulson is an Assistant Professor of Business Administration in the Technology and Operations Management Unit at 性视界 Business School. Her research is in the area of operations for social good. In particular, she designs analytical methods and algorithms for allocating scarce resources efficiently and fairly to improve social outcomes. Much of her work draws on tools from optimization, machine learning, mathematical modeling, and statistics. She received her PhD in Operations Research from MIT.

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For the session , Bobby Yerramilli-Rao, Chief Strategy Officer at Microsoft, and HBS AI Institute co-founder Karim Lakhani discussed the far-reaching implications of AI on business operations, organizational structures, and strategic planning. Their insights and research offer a compelling vision of how companies must adapt to thrive in an era of proliferating access to expertise.

Key Insight: Expertise is No Longer Scarce, it鈥檚 Scalable

The most immediate impact of AI is appearing in productivity and performance, with gains that defy traditional economic expectations. AI is effectively raising the floor of competency on difficult tasks that once required years of specialized training across a wide range of fields. Expertise, which used to be a key driver of competitive advantage, is now democratized, and the implications are seismic.

Key Insight: You are More Than an Individual

Yerramilli-Rao and Lakhani discussed a future where employees regularly incorporate their own AI agents into their work, and even bring them along across jobs and educational experiences. According to Yerramilli-Rao and Lakhani, companies will need to integrate these AI agents into their systems while maintaining control, governance, and security. For hiring purposes they will need to identify individuals who can effectively collaborate with human-AI teams. The outcome will be flatter structures and less-siloed employees compared to traditional departmental architecture. One vivid example the speakers gave was Focus Fuel, a startup launched by three friends working part-time using GPT tools to develop, market, and scale a new consumer product, all without prior Consumer Packaged Goods (CPG) experience.

Key Insight: Know Your Core Value Proposition

The competitive landscape may be entering a phase of continuous acceleration where companies must simultaneously leverage AI while preparing for advances in AI to match and then exceed their current capabilities. If AI levels the playing field, companies must clarify what truly sets them apart. What are you uniquely good at, and what expertise is replicable by AI or your competitors using AI?

Why This Matters

For business leaders, these insights signal the beginning of a new era where strategic value comes from focus, speed, and broad AI implementation. Those who treat this as a technology upgrade rather than a fundamental shift risk being outpaced. The question is no longer whether AI will transform your industry, but whether your organization will lead or scramble to catch up. Embracing these changes and proactively reshaping your organization around AI capabilities may be the key to unlocking previously unheard of levels of innovation, efficiency, and success in the years to come.

Read their article .

Meet the Speakers

is Chief Strategy Officer at Microsoft. He has co-founded several companies, and has served at organizations including Vodafone and McKinsey. He holds an MA from the University of Cambridge and a PhD from the University of Oxford.

is the Dorothy & Michael Hintze Professor of Business Administration at 性视界 Business School. He specializes in technology management, innovation, digital transformation, and artificial intelligence. He is the Co-Founder and Chair of the HBS AI Institute and the Founder and Co-Director of the Laboratory for Innovation Science at 性视界.

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In the session , H.E. Omar Sultan Al Olama, the world鈥檚 first Minister of State for Artificial Intelligence, sat down with HBS AI Institute co-founder Karim Lakhani for a fireside chat to discuss the UAE鈥檚 strategic approach to AI integration and its impact on governance, growth, and quality of life.

Key Insight: History Driving AI Adoption

Al Olama drew an important parallel between today鈥檚 AI hesitation and the Middle East鈥檚 historic decision to ban the printing press, which sent the region away from global knowledge leadership hundreds of years ago. Concerns about misinformation, loss of control over knowledge production, and fear of unknown consequences – what Al Olama terms 鈥榠gnorance-based decisions鈥� – are top of mind now because of the uncertainty around AI, but in this case the UAE is aggressively leaning into the new technology, such as by appointing a Minister of State for Artificial Intelligence, and launching more than 147 different applications of AI within the government.

Key Insight: A Dual Track for National Development

This dual approach has been central to the UAE鈥檚 growth strategy over the past five decades, with learning and upskilling in AI as only the latest step. For example, over 377 senior government officials recently completed an intensive AI training program, and 2.1 million UAE citizens engaged in prompt engineering for UAE Codes day.

Key Insight: AI for Quality of Life

Al Olama stressed that AI should be used to enhance people鈥檚 quality of life. For example, in Abu Dhabi, traffic lights are connected to an AI hub that optimizes flow, ensuring that the existing infrastructure can maintain efficiency even with population growth. Another example is the use of AI technology in airports, where facial recognition technology allows for a quicker and more seamless experience reducing lengthy waits at checkpoints prevalent elsewhere.

Why This Matters

Al Olama and Lakhani鈥檚 conversation provides executives with examples and a strategy for approaching AI adoption and transformation that extends beyond traditional models. The UAE鈥檚 experience demonstrates that successful AI implementation requires organizational forethought and commitment, balanced investment in both human and technological capital, and a fundamental reorientation towards human-centered outcomes. By fostering an AI-ready populace, the UAE demonstrates how government, business, and society at large can collaborate to prioritize meaningful outcomes. The UAE鈥檚 AI mandate is clear: invest with purpose, lead with clarity, and deploy with empathy.

Meet the Speakers

is the UAE Minister of State for Artificial Intelligence, Digital Economy and Remote Work Applications. He is also Director General of the Prime Minister鈥檚 Office at the Ministry of Cabinet Affairs.

is the Dorothy & Michael Hintze Professor of Business Administration at 性视界 Business School. He specializes in technology management, innovation, digital transformation, and artificial intelligence. He is the Co-Founder and Chair of the HBS AI Institute and the Founder and Co-Director of the Laboratory for Innovation Science at 性视界.

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Recent research explores a new framework for machine unlearning in the article, “,” by , 性视界 Business School Assistant Professor, Faculty Affiliate, and Principal Investigator at the 性视界 Business School AI Institute Trustworthy AI Lab; , PhD student in Computer Science at the 性视界 John A. Paulson School of Engineering and Applied Sciences (co-advised with Seth Neel); , PhD candidate at MIT鈥檚 Electrical Engineering & Computer Science (EECS) Department; , Postdoctoral Scholar in Computer Science at Stanford University; , PhD student at Stanford University; , Stein Fellow at Stanford University; and , Cadence Design Systems Professor at MIT鈥檚 EECS Department.

Key Insight: The Growing Need for Machine Unlearning

The research emphasizes how regulatory pressures, like the EU’s Right to Be Forgotten, and practical needs鈥攕uch as mitigating the effects of poisoned, toxic, or outdated data and resolving copyright infringement issues in generative AI models鈥攁re driving the demand for machine unlearning. The authors demonstrate how machine unlearning can address these challenges by enabling models to function as though specific data points (the 鈥渇orget set鈥�) were never part of the training process.

Key Insight: A Breakthrough Framework鈥擠atamodel Matching

The authors introduce DMM, a novel approach that links machine unlearning to data attribution. Unlike traditional retraining methods that can be computationally expensive, DMM employs data attribution to predict a model鈥檚 output as if it were retrained without the forget-set data and fine-tunes the data to match these predicted outputs. 

Key concepts:

• Data attribution: A framework within machine learning that connects specific training data samples to the predictions made by a trained model. This concept focuses on understanding and quantifying the influence of individual training data points on a model’s behavior and predicting how changes to the training dataset, such as adding or removing data points, would affect a model’s outputs.
• Oracle Matching (OM): A hypothetical and idealized approach to machine unlearning where a model is fine-tuned to match the outputs of an oracle model. The oracle model represents a machine learning model that has been retrained from scratch on the dataset excluding the data points to be unlearned (the forget set). 
• Fine-tuning: A process in which an already-trained machine learning model is updated to achieve a specific objective by making small adjustments to its parameters. In the context of machine unlearning, fine-tuning is used to modify a model so it behaves as though the forget-set data were never part of the original training process. The fine-tuned model鈥檚 behavior should be statistically indistinguishable from the oracle model on both the forget set and the retained data.

Key Insight: Addressing the Missing Targets Problem

Existing fine-tuning-based unlearning methods suffer from the “missing targets” problem, which describes the challenge of determining the precise output a model should produce after forgetting a particular data point or group of points. DMM circumvents this issue by using data attribution to estimate the target outputs of an oracle model, and then fine-tuning to match, ensuring stability and preventing overshooting or undershooting the target loss.

Key Insight: Practical Efficiency with Broad Applications

To better assess unlearning performance, the researchers propose a new evaluation metric called KL Divergence of Margins (KLoM). This metric directly measures the distributional difference between unlearned model outputs and those of models retrained without the forget set. The authors鈥� research demonstrates that DMM delivers results comparable to full retraining at a fraction of the computational cost.

Why This Matters

DMM represents a significant step forward in the machine unlearning field, offering a more reliable and efficient approach to unlearning in complex neural networks. For C-suite executives and business professionals, this research highlights the potential for improved data management practices and reduced computational costs associated with model maintenance. This approach opens new avenues for future research and offers practical solutions for addressing privacy concerns and data removal requests in real-world applications.

References

[1] Kristian Georgiev, Roy Rinberg, Sung Min Park, Shivam Garg, Andrew Ilyas, Aleksander Madry, and Seth Neel, “Attribute-to-Delete: Machine Unlearning via Datamodel Matching”, arXiv preprint arXiv:2410.23232 (October 2024): 1-47, 1.

[2] Georgiev et al., “Attribute-to-Delete: Machine Unlearning via Datamodel Matching,” 3.

[3] Georgiev et al., “Attribute-to-Delete: Machine Unlearning via Datamodel Matching,” 2.

[4] Georgiev et al., “Attribute-to-Delete: Machine Unlearning via Datamodel Matching,” 3.

Meet the Authors

is an Assistant Professor housed in the Department of Technology and Operations Management (TOM) at 性视界 Business School, and a Faculty Affiliate in Computer Science at SEAS. He is the Principal Investigator at the HBS AI Institute Trustworthy AI Lab.

is PhD student in Computer Science at the 性视界 John A. Paulson School of Engineering and Applied Sciences, and is co-advised by Seth Neel. His research interests focus on public-interest technology, with a recent focus on privacy technology.

is a PhD candidate at MIT鈥檚 Electrical Engineering & Computer Science (EECS) Department advised by Aleksander Madry. They are interested in the science of deep learning and deep learning for science.

is a Postdoctoral Scholar at Stanford working with Prof. Tatsu Hashimoto, Prof. Percy Liang, and Prof. James Zou. He received his PhD from MIT, where he was advised by Prof. Aleksander M膮dry. He is interested in understanding and improving machine learning (ML) methodology through the lens of data.

is a PhD student at Stanford, advised by Greg Valiant . His is part of the Machine Learning Group and the Theory Group at Stanford. Prior to Stanford, he worked at Microsoft Research India.

is a Stein Fellow at Stanford University. His research pursues a precise empirical understanding of the entire machine learning pipeline, with an emphasis on data. His interests span tracing predictions back to training data, identifying and alleviating data bias, and studying machine learning robustness.

is the Cadence Design Systems Professor of in the and a member of . He received his Ph.D. from in 2011. He is the Director of the MIT Center for Deployable Machine Learning and a Faculty Co-Lead of the . Prior to joining the MIT’s faculty, he spent a year as a postdoctoral researcher at .

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Key Insight: AI Enables Developers to Focus on Core Coding Tasks

The researchers found that developers with access to GitHub Copilot increased their coding activities by 5.4 percentage points (a 12.37% increase) while reducing project management activities by 10 percentage points (a 24.93% decrease). This suggests AI tools allow knowledge workers to spend more time on their primary skilled tasks by reducing the burden of auxiliary responsibilities, such as reviewing code and submitting and responding to pull requests.

Key Insight: AI Promotes More Autonomous Work Patterns

The study revealed that Copilot users engaged in more autonomous work, reducing their interactions with other developers. Specifically, developers with Copilot access worked in repositories with 17 fewer peers on average, a 79.3% reduction compared to non-users. This suggests AI tools may reduce the need for collaboration on routine tasks, allowing workers to operate more independently. Furthermore, the researchers found that a secondary effect of reduced collaboration was avoidance of the usual collaborative difficulties and transaction costs that would otherwise impede workflows.

Key Insight: Implications for Workers

The research suggested that AI had a positive impact on less skilled workers. According to the study, less experienced workers who integrated AI tools into their workflow increased their coding activities and reduced time spent on project management activities at a higher rate than that of their more skilled coworkers.

The study also showed that Copilot-eligible developers increased their exposure to new programming languages by 21.79% compared to non-users. Moreover, the languages they explored tended to be associated with 1.41% higher salaries, suggesting AI tools may facilitate valuable skill development and career advancement. Based on this finding, which shows that the cost of experimentation appears to decrease with the introduction of Copilot, the researchers suggest that, overall, the AI tool caused programmers to focus increasingly on exploration activities in their work and decreasingly on exploitation.

Key Insight: AI’s Impact is Sustained Over Time

The study tracked developers over a two-year period, finding that the effects of Copilot persisted throughout this time. While there was some initial ramp-up and later attenuation, the impact remained significant, suggesting AI tools can drive lasting changes in coding practices rather than just creating short-term productivity boosts.

Why This Matters

While this study focuses on open-source programmers, it suggests to business leaders the ways in which AI can reshape work practices in all sorts of organizations. Importantly, the study reveals that the benefits of AI are more pronounced for lower-skill workers. Executives can implement initiatives promoting the use of AI to close the gap between high-skill and low-skill workers to create more efficient work environments while also promoting upskilling and inclusivity. These findings could also encourage managers to identify areas for AI implementation, restructuring workflows to reduce collaborative friction and accommodate more autonomous work for complex projects.Finally, and perhaps most crucially for firms seeking to thrive in a business environment that is evolving at lightning speed, if in this study AI lowered the cost of exploration for coders who spent less time on their core work, CEOs should consider how their technology departments, or any departments where they implement AI, can drive innovation and experimentation with no adverse effects on exploitation of established projects.

References

[1] Manuel Hoffmann, Sam Boysel, Frank Nagle, Sida Peng, and Kevin Xu, 鈥淕enerative AI and the Nature of Work鈥�, 性视界 Business School Strategy Unit Working Paper No. 25-021 (November 1, 2024): 1-71, 29.

[2] Hoffmann et al., 鈥淕enerative AI and the Nature of Work鈥�, 23-24.

[3] Hoffmann et al., 鈥淕enerative AI and the Nature of Work鈥�, 25.

[4] Hoffmann et al., 鈥淕enerative AI and the Nature of Work鈥�, 26.

Meet the Authors

is a postdoctoral fellow at the Laboratory for Innovation Science at 性视界 (LISH). His research focuses on labor, innovation, and health economics while leveraging experimental, quasi-experimental, and structural methods to answer exciting research questions that can improve individual and social welfare.

is a postdoctoral fellow at the Laboratory for Innovation Science at 性视界. He is an applied microeconomist with research interests at the intersection of digital economics, labor and productivity, industrial organization, and socio-technical networks. Specifically, his work has centered around the private provision of public goods, productivity in open collaboration, and welfare effects within the context of open source software (OSS) ecosystems.

is an Assistant Professor in the Strategy Unit at 性视界 Business School, a faculty affiliate of the HBS AI Institute, the Managing the Future of Work Project, and LISH. He studies how competitors can collaborate on the creation of core technologies, while still competing on the products and services built on top of them. His research falls into the broader categories of the future of work, the economics of IT, and digital transformation and considers how technology is weakening firm boundaries.

is Senior Principal Economist in the Office of Chief Economist at Microsoft. His research interests include econometrics, industrial organization, machine learning and artificial intelligence. His work has been published in economics, statics and CS journals and conferences, including Biometrika, Marketing Science, Journal of Health Economics, and AISTAT. I received my Ph.D. in Economics from Cornell University in 2017. He received his M.S. in Statistics, B.S. in Mathematics and B.A. in Economics from University of Virginia in 2011.

is a software engineer at GitHub. He focuses on projects related to building trust through transparency, contributing his skills in data analysis/visualization, full stack engineering, and legal research.

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Key Insight: The Ubiquity of Open Source Software

OSS is software with publicly available source code, allowing anyone to inspect, use, and modify it. It’s often developed collaboratively and distributed for free, making it a global public good. OSS has become foundational for most technology used today, in everything from smartphones and cars to cutting-edge technology used in AI, quantum computing, big data, and analytics.

Key Insight: Overcoming the Challenges of Assessing OSS Value

Measuring the economic value of OSS has been challenging due to its non-pecuniary nature and lack of centralized usage tracking. Hoffman鈥檚 study uses two complementary data sources, Census II of Free and Open Source Software¹, which tracks OSS embedded in commercial software products, and BuiltWith², which supplies data on OSS utilized in publicly facing websites, to estimate the supply-side value and demand-side value of OSS. 

Hoffman and his team estimate supply-side value at $4.15 billion, arriving at this number by calculating the expense involved in replicating the most commonly utilized OSS once. Demand-side value, rather, is estimated at $8.8 trillion, a value achieved by assigning a replacement value for every firm that uses the OSS and would need to build it internally if it were unavailable.

Key Insight: Concentration of Value Creation

The study reveals that OSS value is highly concentrated, with just six programming languages鈥擩avaScript, Java, Go, Typescript, C, and Python鈥攁ccounting for the vast majority of its demand-side value. Additionally, the research team finds that a small group of developers (5%) contribute disproportionately, not only to a few of the most common products but also to a substantially wider variety of products.

Key Insight: Risk of the Commons

As they consider the stakes of the research, the authors draw a parallel with the concept of “the tragedy of the commons,鈥� which dates back to Aristotle and describes a situation where individuals acting in their own self-interest deplete a shared resource, even when it is not in their long-term best interest to do so. This concept was further developed by economists like William Forster Lloyd and Garrett Hardin and, ultimately, Elinor Ostrom won the 1993 Nobel Prize in Economics for her research on avoiding the tragedy of the commons. The researchers argue that OSS faces a risk in this vein, as its widespread availability and free nature could lead to overuse and underinvestment.

Why This Matters

OSS is a critical foundation of the digital economy, yet it often goes unsupported. Policymakers should recognize OSS as a global public good that powers a vast majority of codebases and significantly reduces costs for businesses. By understanding the real value of OSS, policymakers can ensure OSS remains secure and innovative, by incentivizing corporate contributions, funding key OSS projects, and promoting public-private partnerships.

Footnotes

[1] Census II of Free and Open Source Software is a collaborative project by the Linux Foundation and the Laboratory for Innovation Science at 性视界 (LISH), which focuses on “inward-facing” OSS, meaning the software incorporated into the products that companies develop and sell. This data comes from software composition analysis (SCA) firms that scan companies’ codebases, primarily for licensing compliance purposes. As a byproduct, SCAs track the specific OSS packages used by their clients.

[2] BuiltWith collects data by scanning public websites globally, identifying both proprietary and OSS technologies used. It offers an “outward-facing” perspective on OSS, revealing the software employed in websites that consumers interact with directly.

References

[1] Manuel Hoffmann, Frank Nagle, and Yanuo Zhou, 鈥淭he Value of Open Source Software鈥�, 性视界 Business School Strategy Unit Working Paper No. 24-038 (January 1, 2024): 1-40, 2.

[2] Hoffmann, Nagle, and Zhou, 鈥淭he Value of Open Source Software鈥�, 2.

[3] Hoffmann, Nagle, and Zhou, 鈥淭he Value of Open Source Software鈥�, 21.

[4] Hoffmann, Nagle, and Zhou, 鈥淭he Value of Open Source Software鈥�, 2.

Meet the Authors

is a postdoctoral fellow at the Laboratory for Innovation Science at 性视界 (LISH). His research focuses on labor, innovation, and health economics while leveraging experimental, quasi-experimental, and structural methods to answer exciting research questions that can improve individual and social welfare.

is an Assistant Professor in the Strategy Unit at 性视界 Business School, a faculty affiliate of the HBS AI Institute, the Managing the Future of Work Project, and LISH. He studies how competitors can collaborate on the creation of core technologies, while still competing on the products and services built on top of them. His research falls into the broader categories of the future of work, the economics of IT, and digital transformation and considers how technology is weakening firm boundaries.

is a doctoral student in Strategic Management at the University of Toronto Rotman School and a research associate at 性视界 Business School. His research interests in economics include innovation, labor, economic growth.

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