Introduction

As generative AI has grown from a fledgling concept to a force disrupting most industries, its broader implications have come under scrutiny. Public perception of generative AI has also evolved significantly due to its association with various Environmental, Social, and Governance (ESG) factors. In this article, we’ll offer an extensive ESG analysis of generative AI, focusing on how different industries react to it, the ESG risks it potentially fuels, and the ESG positive impact events it has given rise to.

Generative AI: Public Perception Since Launch

Generative AI was initially met with widespread enthusiasm as the next evolutionary step in artificial intelligence. OpenAI's ChatGPT garnered significant attention quickly upon its release in 2022, as it amassed 100 million monthly active users in just two months post-launch. However, as its capabilities have become more powerful and universal, many ESG controversies have emerged, impacting the public sentiment towards the technology. A notable drop in sentiment polarity was observed from October to December of ‘22, going from 0.4 to 0.22. The decline in polarity was attributed to some critical topics, notably the environmental toll of its energy consumption and the ethical difficulties posed by its potential to disseminate false information.

* Polarity, a proprietary metric developed by SESAMm,  ranging from -1 to 1, represents the aggregate of positive and negative sentiment.

Generative AI and its Implications on ESG

In What Industries Is Generative AI Mentioned More Often?

As expected, the IT industry was initially the most mentioned, along with Generative AI. However, as the technology became more widespread, other sectors have garnered more attention among web publications and social media. In particular, the communication and finance sectors are capturing a substantial share of the attention. In particular, data privacy in finance and communications are the main concerns, and fraud for finance is also being widely discussed on the web.

ESG Controversies Fueled by Generative AI

When we looked at ESG controversies and risks in detail, we found that most of the attention and mentions are related to social risks, particularly Human Rights (right to privacy), labor rights, and customer relations (customer privacy). Governance has also gotten its fair share of ESG controversies, primarily focused on anticompetitive practices (copyright infringement). On the environmental side, controversies are concentrated on water consumption (by Gen AI tools) and climate change, specifically energy consumption. However, the number of mentions and controversies has decreased considerably.

Data Breaches: The Focal Point

By far, the lion's share of ESG controversies and mentions gravitate towards social risks, specifically data breaches. From Italy banning Chat GPT in April to Samsung’s alleged data leak in August, controversies around data privacy have been among the most concerning topics surrounding Chat GPT ESG risks. In just five months, mentions of data breaches went from virtually 0% to over 10% of total mentions.

Digging deeper into data breaches at companies, we found that the number of breaches did increase significantly after generative AI tools became available. In particular, we see that the number of internal (employees) vs. external (non-company affiliated) data breaches increased by almost 50% when using generative AI tools from 14% to 21%.

The Silver Lining: ESG Initiatives Generated by Generative AI

Despite all the risks and controversies emerging, generative AI is also an enabler of positive ESG initiatives. Interestingly, on the positive impact side, we see a similar volume of mentions of initiatives on the three ESG dimensions.

Generative AI has shown promise in optimizing energy use, reducing waste, and even modeling and mitigating the impacts of climate change. On the environmental side, we see a rapid increase in mentions related to its applications in efficiency and productivity, asset reliability, operational safety, lower energy consumption, and reduced environmental impact.

The technology also has the potential to revolutionize healthcare by enabling more accurate and early diagnosis, thereby contributing to social well-being. Generative AI could also transform web surfing and make it easier for users to navigate the internet and find or generate information.

Conclusion

As our analysis shows, generative AI is bringing unprecedented capabilities and complex ESG risks and controversies. We expect to see it evolving, with public sentiment shifting and industries grappling with its ESG implications. But we are still in the very early stages of this new trend and will continue monitoring its evolution.

SESAMm’s AI Technology Reveals ESG Insights

Discover unparalleled insights into ESG controversies, risks, and opportunities across industries. Learn more about how SESAMm can help you analyze millions of private and public companies using AI-powered text analysis tools.

The name of a fund is often the first and sometimes the only point of contact before investing. Individual investors frequently lack the patience to read through the extensive legal documents that fund managers must produce to ensure transparency. However, fund naming has so far been minimally regulated.

The urgency of climate issues and the growing interest in sustainable and responsible investment have increased the supply of ESG investment funds. Many funds now use terms such as "ESG," "Sustainable," "Transition," and "Net Zero" in their names. Yet, not all of these funds have adopted a sustainable finance label (e.g., French SRI Label, Towards Sustainability, FNG Siegel), which somewhat guarantees alignment with their marketed features. A common and ambitious label is still missing from the sustainable finance directives adopted in the “Green Deal” package.

How can we ensure the integrity of fund names for investors? A recent example is the German manager DWS, fined $19 million for exaggerating the ESG characteristics of its investment funds. To prevent such issues, the European Securities and Markets Authority (ESMA) published its final report on the names of ESG funds in May 2024, incorporating feedback from stakeholders. Key points include:

Fund Naming Categories

1. Transition Funds: "Transition," "Improve," "Progress," "Evolution," "Net Zero"
   
2. Environmental Funds: "Green," "Environmental," "Climate," "ESG," "SRI"
3. Social Funds: "Social," "Equality"
4. Governance Funds: "Governance," "Controversies"
5. Impact Funds: "Impact Investing," "Impactful," "Impacting"
6. Broad ESG Funds: "Sustainability," "Sustainable"

Each category must comply with minimum standards to use these keywords based on European legislative guidelines. The main criteria are:

• 80% of investments must meet social or environmental characteristics (based on the European taxonomy and indicators in Annex II and III of the SFDR directive).
• Exclusions from the Paris Aligned Benchmark (PAB) directive:
  • Controversial weapons
  • Tobacco production
  • Violation of UNCG or OECD guidelines
  • Coal extraction (1%)
  • Oil extraction (10%)
  • Gas extraction (50%)
  • Carbon-intensive electricity production (+100gCO2/kWh)
• Exclusions from the Climate Transition Benchmark (CTB), equivalent to points a, b, and c above, also known as "minimum safeguards."

Generalist funds, “S” funds, and “G” funds must apply only the minimum safeguards. "Transition" and "Impact" funds, in addition to the minimum safeguards, must meet the 80% investment threshold with social or environmental characteristics. "Transition" funds must demonstrate a clear and measurable social/environmental trajectory, while "Impact" funds must show that their investments generate a measurable positive impact alongside financial returns.

Funds with an environmental emphasis, including terms like ESG and SRI, must meet all these criteria simultaneously and exclude fossil fuels.

In conclusion, these guidelines will provide investors, especially individual ones, with certain guarantees to select more sustainable investments. Managing controversies will be a crucial challenge for any fund manager offering a range of ESG funds.

Reach out to SESAMm

TextReveal’s web data analysis of over five million public and private companies is essential for keeping tabs on ESG investment risks. To learn more about how you can analyze web data or to request a demo, reach out to one of our representatives.

By Magnus Billing, SESAMm advisor, with insights from Sylvain Forté, CEO of SESAMm

Investors have faced so-called “black swan” events throughout history: unexpected crises with severe consequences, often rationalized only in hindsight. Yet in an era defined by generative AI and vast, real-time data lakes, the question arises: could such events be understood and acted upon before they unfold?

The 2023 U.S. regional banking crisis offers a striking case study. The rapid collapses of Silicon Valley Bank and Signature Bank revealed how quickly stress can spread and how difficult it remains to connect early warning signs across sources.

While traditional financial analysis focuses on fundamentals such as capital ratios, liquidity positions, governance, and earnings, a new class of tools is expanding the lens. AI-driven controversy data aggregates and analyzes millions of public sources, from regulatory statements to media and industry discussions, to detect emerging issues as they surface. It does not replace quantitative and fundamental analysis; it complements it by tracking the visibility of risk as it enters public conversation.

This combination of approaches may offer investors a fuller picture: the structural risks visible in balance sheets, and the narrative risks revealed through public dialogue. To test this idea, we revisited the 2023 crisis through both perspectives, starting with what traditional analysis could have shown and what it missed.

Traditional Analysis and Its Blind Spots

In hindsight, the vulnerabilities of regional banks such as Silicon Valley Bank and Signature Bank were visible before the start of 2023. Unrealized losses on long-term securities, heavy reliance on uninsured deposits, and exposure to interest-rate risk pointed to potential liquidity stress. Yet these indicators were neither fully recognized nor connected in the market.

Traditional analysis has a tendency to evaluate banks based on their specific niches: Silicon Valley Bank focused on technology and venture financing, while Signature Bank served commercial real estate and digital asset clients. However, this approach risks overlooking the common and shared structural factors: concentrated depositor bases, high sensitivity to interest rate changes, rapid growth, and weaknesses in governance. Few, if any, observers recognized how rapidly these vulnerabilities could interact and escalate in a modern, digitalized banking environment.

While financial reports contained the data, there was little discussion connecting these risks in the public domain. But what about controversy data? Would it have caught the impending crisis? To find out, I asked Sylvain Forté, CEO of SESAMm, to provide an AI perspective.

What the Data Showed: Signature Bank

Signature Bank displayed a gradual pattern of emerging risk visible through public discussion. From mid-2022 onward, controversy data showed a rise in coverage related to governance practices, management oversight, and deposit concentration risks, often in the context of its ties to the digital-asset industry.

Importantly, it was not the crypto exposure itself that led to the bank’s collapse. The bank even announced in December 2022 that it would reduce its crypto-related business. Instead, the FDIC’s Supervision of Signature Bank report concluded that, “the root cause of SBNY’s failure was poor management. SBNY’s board of directors and management pursued rapid, unrestrained growth without developing and maintaining adequate risk management practices and controls.”

From a controversy perspective, those signals were publicly visible but fragmented. As shown in the chart above, AI-powered monitoring could have aggregated them into a clear view of a sustained drift in governance-related discussions, offering an early indication that oversight and internal controls were under pressure and risk was increasing.

What the Data Missed: Silicon Valley Bank

In contrast, Silicon Valley Bank presented a markedly different pattern. While controversy data registered some activity in late 2022,  including investor reactions to financial forecasts and coverage of routine business operations, these signals were fundamentally different in character from Signature Bank's governance-related warnings.

The September 2022 increase reflected market disappointment with financial guidance rather than operational or governance concerns. The subsequent activity captured normal business news, such as arranging syndicated loans. Critically, there was minimal public discussion of the bank's balance-sheet structure, unrealized losses, or depositor concentration risk until the crisis was already unfolding in March 2023.

This example underscores a key distinction: AI controversy monitoring excels at capturing reputational, governance, and operational risks as they enter public dialogue, but may not surface structural financial risks that remain confined to regulatory filings and analyst reports.

Lessons from Both Cases

The contrast between these two banks illustrates the complementary roles of quantitative and fundamental financial analysis vs AI-driven controversy monitoring.

In Signature Bank’s case, controversy data captured a steady accumulation of governance-related warnings, a slow build-up of risk visible through public discussion.

In Silicon Valley Bank’s case, the risks were structural but not yet discussed, leaving little for AI-powered controversy data to detect.

As Sylvain explains, “AI controversy monitoring helps investors understand how and when risks start to emerge in public dialogue. It does not replace fundamental analysis. It complements it by showing when the conversation begins to shift.”

Conclusion

Black swan events are often rationalized only in hindsight, but the 2023 regional banking crisis suggests a more nuanced reality. Some signals existed. What remained difficult was connecting them across sources before stress became contagion.

AI-driven controversy monitoring proved effective at surfacing governance and operational risks as they entered public dialogue, as Signature Bank demonstrated. Yet structural financial vulnerabilities like those at Silicon Valley Bank may not generate discussion until crisis forces the conversation, underscoring that no single lens captures all risk.

The advantage lies not in prediction, but in preparation: combining the structural risks visible in balance sheets with the narrative risks revealed through public discourse. In an era of real-time data and generative AI, the question is no longer whether information exists, but whether investors can connect it before it becomes consensus.

Reach out to SESAMm

TextReveal’s web data analysis of over five million public and private companies is essential for keeping tabs on ESG investment risks. To learn more about how you can analyze web data or to request a demo, reach out to one of our representatives.