As we move into the era of agentic AI, what kind of influence will this emerging technology have on democracy and misinformation? In the new Science paper 鈥�,鈥� , Assistant Professor of Business Administration at 性视界 Business School and Faculty PI of the at the 性视界 Business School AI Institute, and an international, multi-disciplinary group of co-authors argue that we鈥檙e entering a phase where 鈥渕alicious AI swarms鈥� could use multi-agent systems to infiltrate communities, mimic human social behavior, and iteratively refine persuasion tactics in real time. By expanding misinformation into persistent manipulation, these systems threaten the information ecosystem that democratic societies depend on, but Goldenberg and his co-authors also outline technical, economic, and institutional measures that could meaningfully defend against this new danger.

Key Insight: AI Swarms Operate Like Digital Societies

Unlike earlier botnets, which relied on centralized control, rigid scripts, and human labor, AI swarms combine LLM reasoning with multi-agent architectures to function more like adaptive digital societies. The authors define malicious AI swarms as systems of persistent agents that coordinate toward shared objectives, adapt in real time to engagement and platform cues, and operate with minimal human oversight across platforms. Five capabilities make these systems especially potent. (1) Swarms replace centralized command with fluid coordination, allowing thousands of AI personas to locally adapt while periodically synchronizing narratives. (2) They can map social networks to identify and infiltrate vulnerable communities with tailored appeals. (3) Human-level linguistic mimicry and irregular behavior patterns help them evade detection. (4) Continuous, automated A/B testing enables rapid optimization of persuasive content. (5) Finally, their always-on persistence allows influence to accumulate gradually, embedding itself within communities over time and subtly reshaping norms, language, and identity. As the article notes, recent elections in Taiwan and India already saw a proliferation of AI-generated propaganda and synthetic media outlets, meaning that this threat is already here and poised to expand in the future.

Key Insight: The Harm Cascade

Goldenberg and his team argue that AI swarms could trigger a 鈥榗ascade鈥� of harms by systematically distorting the information ecosystem. By engineering 鈥榮ynthetic consensus鈥� and targeting different misinformation to different communities, these agents would have the power to undermine the independent thought essential for collective intelligence while simultaneously fragmenting the public sphere. This manipulation, together with coordinated synthetic harassment campaigns, could create a hostile environment that drives journalists and citizens into silence. The damage would compound as swarms 鈥榩oison鈥� the web with fabricated content that contaminates future AI training data. Ultimately, this sustained erosion of trust could corrode institutional legitimacy, rendering democratic safeguards vulnerable to collapse.

Key Insight: A Layered Defense Strategy

Rather than a single fix, the authors argue for a layered defense strategy designed to raise the cost, complexity, and visibility of swarm-based manipulation. The first layer is always-on detection: continuous monitoring systems that identify statistically anomalous coordination patterns in real time, paired with public audits and transparency to reduce misuse. Because attackers will adapt, detection alone is insufficient. A second layer involves simulation and stress-testing. Agent-based simulations can replicate platform dynamics and recommender systems, allowing researchers and platforms to probe how swarms might evolve to recalibrate defenses before major elections or crises. Third, the authors emphasize empowering users through optional 鈥淎I shields,鈥� tools that flag likely swarm activity, allowing individuals to recognize suspicious content. Finally, the paper highlights governance and economic levers as essential. Proposals include standardized persuasion-risk evaluations for frontier models, mandatory disclosure of automated identities, stronger provenance infrastructure, and a distributed AI Influence Observatory to coordinate evidence across platforms, researchers, and civil society. Crucially, the authors argue that disrupting the commercial market for manipulation may be among the most effective ways to reduce large-scale abuse.

Why This Matters

For business leaders and professionals, this study reveals a threat that extends beyond electoral politics into the fundamental information ecosystem that underpins market confidence, consumer behavior, and corporate reputation. The same AI swarm technologies that  manipulate political discourse could target brand perception, financial markets, or industry narratives just as easily. The defense strategy outlined by the authors can similarly provide a roadmap for corporate action: implementing detection systems for monitoring threats to brand reputation, advocating for industry standards around AI transparency, and supporting governance initiatives that protect the broader information ecosystem. Executives who treat information integrity as core infrastructure will be better positioned to protect stakeholder trust, decision quality, and long-term resilience in an era of AI-enabled influence operations.

Bonus

For a look at how efforts to align AI systems with human preferences can unintentionally undermine trustworthiness itself, check out 鈥�AI Alignment: The Hidden Costs of Trustworthiness.鈥�&苍产蝉辫;

References

[1] Daniel Thilo Schroeder et al., 鈥淗ow Malicious AI Swarms Can Threaten Democracy,鈥� Science (391) (2026): 354.  

[2] Schroeder et al., 鈥淗ow Malicious AI Swarms Can Threaten Democracy,鈥� 355.

[3] Schroeder et al., 鈥淗ow Malicious AI Swarms Can Threaten Democracy,鈥� 357.

Meet the Authors

is an assistant professor in the Negotiation Organization & Markets unit at 性视界 Business School, an affiliate with 性视界鈥檚 Department of Psychology, and a faculty principal investigator in the HBS AI Institute’s Digital Emotions Lab.

Additional Authors: Daniel Thilo Schroeder, Meeyoung Cha, Andrea Baronchelli, Nick Bostrom, Nicholas A. Christakis, David Garcia, Yara Kyrychenko, Kevin Leyton-Brown, Nina Lutz, Gary Marcus, Filippo Menczer, Gordon Pennycook, David G. Rand, Maria Ressa, Frank Schweitzer, Dawn Song, Christopher Summerfield, Audrey Tang, Jay Van Bavel, Sander van der Linden, and Jonas R. Kunst

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Key Insight: Teaching AI to Distinguish Opportunity from Noise

The paper tackles a basic but stubborn problem: while climate risks are increasingly measured (emissions, carbon pricing exposure), there is no standardized way to track climate opportunities, the products and services that help others decarbonize. Companies don鈥檛 have to disclose climate solution revenues, and when they do, it鈥檚 often in voluntary, non-standardized formats. To fill this gap, the team fine-tuned a GPT model on 3,508 carefully labeled sentences to distinguish genuine climate solutions from generic climate-related discussion. For example, a company that manufactures electric vehicles is pursuing climate solutions; a company that uses electric vehicles in its corporate fleet is not. Then they turned the GPT on the business descriptions within 10-K filings. This is critical because it describes what a company actually sells, and unlike a press release, it is scrutinized by auditors and carries legal liability for misrepresentation. 

Key Insight: AI Reveals Which Climate Technologies Actually Drive Growth

The AI鈥檚 ability to categorize sentences into specific technology topics enabled granular analysis. And once you can measure climate solutions systematically, you can ask questions impossible to answer before, such as, 鈥楧o these opportunities actually translate to business performance?鈥� AI-derived metrics revealed that firms with a one standard deviation higher climate solutions measure experienced 2% higher revenue growth. However, the positive association is statistically significant primarily in industries where innovation is protected by patents. This suggests that commercializing climate solutions is not just about having a good idea, it鈥檚 about possessing the intellectual property to defend it. Furthermore, the AI model allowed the researchers to categorize the type of technology being discussed. They found that revenue growth was stronger for technologies with high abatement potential, solutions that can significantly reduce emissions, indicating that the market is effectively rewarding technologies that solve the biggest chunks of the climate problem.

Key Insight: The Great Convergence

Perhaps the most striking discovery emerged when researchers , representations of how similar different climate solution sentences are to each other. By plotting these embeddings, the researchers could analyze not just how much individual firms disclose about climate solutions, but how entire industries cluster around shared technology narratives. For example, electric vehicle topics clustered around both automobiles and capital goods (equipment manufacturing), revealing the emerging battery and electric powertrain value chain. This is more than just linguistic similarity: the study found that industry pairs with higher topic similarity exhibited higher stock return synchronicity. Essentially, if you talk about the same climate tech, your stock prices start moving together because your economic fundamentals are aligning. 

Why This Matters

For business leaders, investors, and strategists, this research underscores how AI can change how we understand markets. By converting dense, regulated text into structured intelligence, AI can reveal patterns that are otherwise invisible, whether related to climate technologies, shifting consumer needs, emerging value chains, or entirely different domains. Even companies that operate far from climate-related sectors can benefit from the underlying method: using AI to mine filings, contracts, product documentation, customer feedback, and market reports for indicators of strategic change and future direction. Ultimately, this research demonstrates the potential for AI to handle sophisticated, complex analysis, and for organizations to evaluate whether their own narrative matches their strategy and competitive aspirations.

Footnote

1. The data generated from the study is publicly available at . 

References

[1] Shirley Lu et al., 鈥淭racking opportunities for climate solutions using AI in regulated accounting reports,鈥� Nat Commun 16, 9769 (2025): 2. DOI:  

[2] Lu et al., 鈥淭racking opportunities for climate solutions using AI in regulated accounting reports,鈥� 6. 

[3] Lu et al., 鈥淭racking opportunities for climate solutions using AI in regulated accounting reports,鈥� 12.

Meet the Authors

is Assistant Professor of Business Administration in the Accounting and Management Unit at 性视界 Business School. She is faculty within the Climate and Sustainability Impact Lab at the HBS AI Institute.

is Charles M. Williams Professor of Business Administration at 性视界 Business School. He co-leads the Climate and Sustainability Impact Lab at HBS AI Institute.

is a Postdoctoral Fellow at the Climate and Sustainability Impact Lab at the HBS AI Institute.聽

is Chief Innovation and Digital Strategy Officer at Brown Capital Management, Senior Lecturer at 性视界 Extension School, and was formerly Head of Research and Data Science at the HBS AI Institute.聽

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Key Insight: Mapping AI Resistance

Participants rated a sample from 940 occupations twice: first under current AI capabilities, then imagining AI that can exceed humans at the job while doing so at a lower cost. The researchers also developed and validated a new scale meant to measure moral repugnance towards AI, the perception that using AI in certain contexts is inherently wrong, irrespective of benefits. This scale thereby taps into fundamental concerns about human dignity, betrayal, and categorical prohibitions that no amount of engineering can overcome. As a result, the researchers came to distinguish two fundamentally different sources of resistance to AI: performance-based concerns and principle-based objections.

Key Insight: Performance Concerns Fade Fast

The researchers identify performance-based resistance to AI as opposition due to AI鈥檚 current technical capabilities, including factors such as accuracy, reliability, cost, and speed. We might expect this type of resistance to recede as AI technology becomes more capable and cost-effective over time, a result backed up by the study. This was especially true for occupations that were deemed morally permissible for AI help (augmentation) and replacement (automation) like clerks, transportation planners, and data entry keyers. 

Key Insight: The Principle Line

Other occupations, including clergy, childcare workers, and therapists, fall into the category of principle-based resistance towards AI. In these cases, AI faces complete rejection that doesn鈥檛 budge even when it鈥檚 positioned as better, faster, and cheaper. The use of AI in these roles is deemed morally repugnant regardless of capability. What makes these occupations special? They share common threads of caregiving, emotional labor, public speaking, and spiritual leadership. The researchers highlight that the dynamic between AI capabilities and human repugnance creates 鈥渕oral friction zones鈥� where capability meets rejection (e.g. school psychologists and fraud examiners) and 鈥渓atent zones鈥� where acceptance is actually ahead of current ability (e.g. cashiers, conveyor operators). [4]

Why This Matters

For business leaders and executives, this research is both liberating and sobering. Liberating, because a large share of public hesitation is performance-based: as your models improve, acceptance will follow in line. Sobering, because a line remains where AI is judged intrinsically inappropriate. The strategic response isn鈥檛 abandoning AI, but designing hybrid solutions that preserve human touchpoints in morally sensitive tasks, carefully framing AI as augmentation rather than replacement, and investing in transparency and ethics communication. 

Bonus

Just as this research shows that better AI doesn鈥檛 guarantee broader acceptance, earlier HBS AI Institute work revealed that improving AI capabilities can actually reverse inequality effects in unexpected ways. For more on how AI鈥檚 relationship with workers shifts as technology advances, check out Who Benefits When Bots Get Better?

References

[1] Simon Friis and James W. Riley, 鈥淧erformance or Principle: Resistance to Artificial Intelligence in the U.S. Labor Market,鈥� 性视界 Business School Working Paper No. 26-017 (October 6, 2025): 6, . 

[2] Friis and Riley, 鈥淧erformance or Principle,鈥� 5.

[3] Friis and Riley, 鈥淧erformance or Principle,鈥� 5.

[4] Friis and Riley, 鈥淧erformance or Principle,鈥� 16.

Meet the Authors

is a postdoctoral fellow at the Laboratory for Innovation Science at 性视界 (LISH), part of the HBS AI Institute. His research focuses on the social and economic impacts of generative AI.

is an Assistant Professor of Business Administration in the Organizational Behavior unit at 性视界 Business School. He is an economic sociologist, conducting ethnographic research to produce qualitative studies on the role of status, norms, social valuations, and organizational culture within innovation-driven organizations, creative industries, and cultural markets.

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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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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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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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Key Insight: Governance Changes Shift Incentives Rather Than Create Them

The study鈥檚 primary focus is PyTorch鈥檚 shift from strategic and technical governance by Meta, to a collective model of governance via the independent, nonprofit Linux Foundation in 2022. Yue and Nagle found that when PyTorch transitioned to a collective governance model, the shift resulted in an increase in external company contributions of approximately 25.7%. However, this increase was largely offset by Meta’s reduced involvement. The authors concluded that changes in governance structure do not necessarily create new incentives, but rather redistribute existing incentives among participants.

Key Insight: Governance Changes Drive Different Levels of Participation

Yue and Nagle’s research revealed that not all external firms respond equally to changes in project governance. Following the PyTorch transition, they found that chip manufacturers (such as NVIDIA, Intel, and AMD), which they classify as “complementors,” increased their contributions to PyTorch by 47.1%, significantly more than other types of firms, which they call 鈥渦sers,鈥� including application developers (such as Microsoft, Amazon, and Alibaba) and cloud providers (such as OpenAI, Hugging Face, and Spotify).

The researchers attribute this difference to varying levels of dependency on the project. Chip manufacturers that rely on interoperability are more susceptible to potential “hold-up” problems if a single company controls the project’s direction. Application developers and cloud providers are less affected by the governance change because they primarily derive value from using the technology rather than integrating with it.

Key Insight: Control Rights and Strategic Openness Require Careful Consideration

The authors define 鈥渃ontrol rights鈥� as the power to make decisions about how the technology develops in the future. Yue and Nagle suggest that control rights are one reason why companies open their technologies to external participants, especially in industries experiencing fast-paced innovation and where the ability to capture value is unclear, such as artificial intelligence and machine learning. Maintaining control rights can be an important advantage for certain companies, but they do not benefit from external innovations. Alternatively, giving up control rights through open collaboration can encourage growth.

Yue and Nagle define 鈥渙pen collaboration鈥� as projects that involve more than two or three participants. Their research focused on 鈥渇irm-sponsored鈥� collaboration, where a single (focal) company releases a technology to spur innovation by other companies. The PyTorch case illustrates the potential downside of open collaboration, where the focal firm (Meta) found a reduced incentive to invest in the project. If a focal firm reduces its investment and cooperation with other participants, users can still benefit from access to the open-source technology interface, but complementors鈥� options become limited because they rely on interoperability with the technology, which might create a hold-up if it changes.

Why This Matters

This study suggests that companies should weigh the benefits of maintaining tight control over their open-source projects versus participating in collaborative governance models. For business executives and policy makers, this research offers insights into the strategic considerations surrounding collaboration and governance:

• For executives, starting open-source projects can help their companies obtain and increase control rights over future technology innovations. However, establishing control rights can be expensive. And going forward, retaining control rights can limit participation and collaboration with external companies.
• Policy makers must also recognize the trade-offs between retaining control rights and inviting open collaboration. Mandating open-source technology might not be appropriate in all cases. While regulations can help to prevent focal firms from taking advantage of control rights to increase prices after external participants invest, they should also consider the 鈥渙verall welfare鈥� of these projects, including incentives for focal companies to continue to participate.

References

[1] Daniel Yue and Frank Nagle, 鈥淚gniting Innovation: Evidence from PyTorch on Technology Control in Open Collaboration鈥�, 性视界 Business School Strategy Unit Working Paper No. 25-013 and 性视界 Business Working Paper No. No. 25-013 (September 10, 2024): 1- 53, 21.

[2] Yue and Nagle, 鈥淚gniting Innovation: Evidence from PyTorch on Technology Control in Open Collaboration鈥�, 3.

[3] Yue and Nagle, 鈥淚gniting Innovation: Evidence from PyTorch on Technology Control in Open Collaboration鈥�, 3.

Meet The Authors

is an Assistant Professor in the  area at Georgia Tech’s . His research explores why firms openly share innovative knowledge without directly profiting, a strategy called 鈥渙pen disclosure.鈥� His projects use scientific publications and open source software in AI research as an empirical setting to develop and test new theories. 

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 . 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.

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Key Insight: Media Coverage Boosts Brand Awareness

Venture capitalists recognize that increasing a startup鈥檚 visibility in the media is essential for building brand recognition, particularly for younger companies that lack an established reputation. Media coverage allows startups to gain public recognition, which can be crucial for attracting stakeholders such as customers and suppliers. By strategically managing their portfolio companies’ media exposure, VCs help these companies break through the noise in crowded markets, enhancing their brand presence and increasing stakeholder engagement.

Key Insight: VCs Use Active and Passive Strategies to Influence Media

VCs employ both active and passive strategies to enhance media coverage for their portfolio companies. On the active side, VCs may directly engage with media by facilitating interviews, issuing press releases, or working with public relations firms to boost visibility. Passive strategies, on the other hand, leverage the reputation of the VC firm itself, with media outlets covering companies simply because they are backed by a well-known venture capitalist. This dual approach helps VCs ensure that their investments receive the media attention they need to thrive, whether through direct involvement or by reputation alone.

Key Insight: Media Coverage Varies by Company Type and Stage

The type of company and its stage of growth heavily influence how VCs allocate media resources. VCs focus more on business-to-business (B2B) companies  where media coverage is more effective, as it is targeted at specific groups, rather than the general public. Additionally, VCs are more likely to increase media coverage for early-stage companies, which typically lack the reputation and customer base of more established firms. These companies, with relatively limited public information available, experience the greatest benefits from increased positive media attention.

Key Insight: Media Exposure and Talent Acquisition

The research highlights that VCs’ efforts to increase media visibility also plays a critical role in attracting top-tier talent. By enhancing public awareness and company reputation, startups can draw higher-quality employees, which in turn significantly influences the company鈥檚 success trajectory. This underscores the multifaceted value of media in shaping both external perceptions and internal capabilities of growing startups.

Why This Matters

For business leaders and C-suite executives, understanding the role of media in shaping public perception and driving business outcomes is essential. Venture capital firms have long recognized the strategic value of media coverage, using it not just to promote their portfolio companies but also to attract talent, secure additional funding, and prepare for exits. As competition in the startup ecosystem grows, leveraging media becomes a crucial tool for gaining a competitive edge. Executives should consider how to integrate media strategy into their overall business planning, ensuring that their company is not only seen by the right people but also perceived in a way that aligns with their long-term goals.

References

[1] Brian K. Baik, Albert Shin, 鈥淚nvestor Influence on Media Coverage: Evidence from Venture Capital-Backed Startups鈥� 性视界 Business School Accounting & Management Unit Working Paper No. 24-073 (August 1, 2024), 12. 

[2] Baik and Shin, 鈥淚nvestor Influence on Media Coverage: Evidence from Venture Capital-Backed Startups鈥�, 2. 

[3] Baik and Shin, 鈥淚nvestor Influence on Media Coverage: Evidence from Venture Capital-Backed Startups鈥�, 4. 

[4] Baik and Shin, 鈥淚nvestor Influence on Media Coverage: Evidence from Venture Capital-Backed Startups鈥�, 7.

Meet the Authors

is an assistant professor in the Accounting and Management Unit at 性视界 Business School. He teaches the Financial Reporting and Control course in the MBA required curriculum. Professor Baik studies how information, financial reporting, and corporate taxes matter for PE/VC investors or startup firms. Some of his works have focused on the role of financial statement disclosure for PE/VC investments, and whether and how private equity fund managers inflate their interim fund valuations (net asset values) during fundraising periods.

received a B.A. in Economics and Mathematics from Yale University and M.S. in Finance from MIT. Previously, he worked as an investor at Susquehanna Growth Equity and served as a fellow and mentor at the MIT Sandbox Accelerator Fund.

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Key Insight: Experiment Faster Without Compromising Statistical Validity

Traditional A/B testing, while accurate, uniformly assigns 50% of users to each treatment, regardless of how good or bad the treatments are, delaying decision-making. Multi-Armed Bandit (MAB) algorithms ¹, on the other hand, prioritize fast identification of successful strategies but sacrifice statistical depth. The MAD offers a hybrid approach, that combines any chosen MAB algorithm with a Bernoulli design ², thus, combining the quick adaptability of MABs with the rigorous inference of A/B testing. This balance allows businesses to experiment faster without losing confidence in the validity of their results.

By adjusting the weight between exploration (testing different options) and exploitation (favoring the current best option), the MAD gives managers control over the experiment鈥檚 speed and precision. This is particularly useful in environments where quick decisions are critical, but businesses cannot afford to compromise on accuracy.

Key Insight: Minimizing Business Risk Through Early Stopping

A standout feature of the MAD is the flexibility it offers to experiment design. With traditional designs, managers must commit to a fixed experiment size or duration, hence there is no function allowing them to monitor and stop harmful experiments. The MAD changes this by allowing managers to monitor results continuously and adjust the balance between exploration and exploitation. 

For businesses like e-commerce platforms or consumer-facing services, the features this approach offers are essential. The MAD can help quickly phase out underperforming variations and focus on the successful ones, protecting both the customer experience and the company鈥檚 bottom line.

Key Insight: How MAD Achieves Anytime-Valid Inferences and its Regret-Minimization Compared to Standard Bandit Designs

To get into the nitty gritty: at each timestep, the MAD assigns treatments based either on the Bernoulli design (with probability 未t ³) or the bandit algorithm (with probability 1-未t). The deterministic sequence 未t controls the balance between these two. By ensuring 未t converges to zero slower than 1/t1/4, the MAD retains the inferential power and validity similar to the Bernoulli design while still leveraging the adaptive learning capabilities of the bandit algorithm.

Meanwhile, the MAD’s regret ⁴ is a weighted sum of the regret from the underlying bandit algorithm and the regret from the Bernoulli design, with weights determined by 未t. By carefully selecting 未t, one can explicitly control the trade-off between regret minimization and statistical power. At each time point, the regret difference between the MAD and the underlying bandit algorithm decreases towards zero at a rate proportional to 未t.

Why This Matters

For modern business leaders, speed and accuracy are essential in decision-making. Whether it鈥檚 optimizing digital marketing strategies, enhancing user experience, or improving product features, the MAD provides a powerful tool for executives looking to make data-driven decisions more efficiently. In industries where rapid innovation is key, the MAD can help firms maintain their competitive edge by enabling smarter, more flexible experimentation.

Footnotes

1. A multi-armed bandit (MAB) algorithm is a type of reinforcement learning algorithm used for sequential decision-making in scenarios where there are multiple options (or “arms”) to choose from, and the goal is to maximize rewards over time. The name derives from the analogy of a gambler facing a row of slot machines (“one-armed bandits”), each with an unknown payout probability. The gambler’s objective is to figure out which machine offers the best rewards and exploit it as much as possible.

2. Bernoulli Design is a fundamental experimental design where each unit is independently assigned to either treatment or control with a fixed probability, typically 0.5. In other words, it’s like flipping a coin to determine the treatment assignment for each participant.

3. The sequence 未t is a crucial element of the Mixture Adaptive Design (MAD) proposed in the sources. It represents a deterministic, user-specified sequence of values that lie between 0 and 1 (inclusive), i.e., 未t 鈭� (0, 1].

4. Regret in the context of multi-armed bandit (MAB) experiments, is the expected difference in outcome between always choosing the best-performing treatment (arm) and the outcome achieved by the algorithm used in the experiment. In simpler terms, regret quantifies the opportunity cost of not consistently choosing the best option. The goal of many MAB algorithms is to minimize regret over time by learning which arm yields the best results.

References

[1] Biyonka Liang and Iavor Bojinov, 鈥�An Experimental Design for Anytime-Valid Causal Inference on Multi-Armed Bandits,鈥� (June 14, 2024): 1-48, 1.

[2] Liang and Bojinov, 鈥�An Experimental Design for Anytime-Valid Causal Inference on Multi-Armed Bandits,鈥� 2.

[3] Liang and Bojinov, 鈥�An Experimental Design for Anytime-Valid Causal Inference on Multi-Armed Bandits,鈥� 15.

Meet the Authors

is a PhD candidate in Statistics at 性视界 University. Her research, which is supported by the NSF Graduate Research Fellowship, focuses on developing statistical methods and models for challenging practical problems in adaptive experimentation, reinforcement learning, and causal inference.

is an Assistant Professor of Business Administration and the Richard Hodgson Fellow at HBS as well as a faculty PI at the HBS AI Institute Data Science and AI Operations Lab and a faculty affiliate in the Department of Statistics at 性视界 University and the 性视界 Data Science Initiative. His research focuses on developing novel statistical methodologies to make business experimentation more rigorous, safer, and efficient, specifically homing in on the application of experimentation to the operationalization of artificial intelligence (AI), the process by which AI products are developed and integrated into real-world applications.

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