November’s Fundamental Human Rights Controversies: A Market Analysis

In a historic move, the agreement negotiated at COP 28, after tense negotiations, marks a significant turning point in the fight against climate change. For the first time, an international text explicitly calls for a reduction in the use of fossil fuels, symbolizing notable progress and a significant advance. However, the devil is in the details: the challenge was to reach a consensus among all participating states. The United Arab Emirates, in particular, played a key role, demonstrating its influence on the international stage by adhering to this notion of "transitioning away from fossil fuels."

NGOs and countries of the South, especially island states, would have preferred a firmer commitment towards a complete phase-out of fossil fuels. The current wording leaves room for interpretation and does not specify whether the reduction in fossil fuels should be relative or absolute.

At the heart of this climate battle lies a crucial distinction: it's not just about reducing the relative share of fossil fuels in favor of low-carbon energies but completely eliminating them. Indeed, a state can reduce the proportion of fossil fuels in its energy mix simply by increasing the use of renewables faster while continuing to increase its absolute consumption of fossil fuels, which would not solve the climate problem and could even worsen it.

It is also important to note that natural gas, despite its name, is a fossil fuel. This agreement considers it a "transition energy," part of a "just, orderly, and equitable" transition.

Beyond these semantic debates, the agreement addresses other crucial points, such as tripling renewable energy production capacities by 2030 and improving energy efficiency. It also highlights the development of nuclear energy, which, despite its drawbacks, has the significant advantage of being low-carbon.

Another notable aspect of this agreement is validating the fund for loss and damage, an idea mentioned at COP 27 in Sharm el-Sheikh. This fund, supported by the countries of the North, aims to cover the negative impacts suffered by the countries of the South. Although contributions are voluntary and potentially insufficient, they represent a step forward.

On the sidelines of the main agreement, several major powers, including the European Union, the United States, Indonesia, and Vietnam, committed to accelerating the phase-out of coal, a major source of climate pollution.

A striking fact of COP 28 is the increased presence of fossil fuel lobbyists, with 2,456 accredited representatives, four times more than the previous year. This presence is comparable to that of large national delegations and exceeds that of the countries most vulnerable to climate change.

It has been reported that the COP president, Mr. Sultan Al-Jaber, has made remarks questioning the scientific basis linking the transition away from fossil fuels to the goal of limiting global warming to 1.5°C. These comments appear to disregard the detailed findings of the IPCC reports, which provide an alternative and more alarming scientific perspective.

However, he clarified that his comments were about the challenges of transitioning away from fossil fuels while ensuring sustainable development. His stance, while acknowledging the complexities, does not directly oppose the IPCC reports' findings on the necessity of reducing fossil fuel use to mitigate climate change.

In conclusion, COP 28 stands as a landmark event in the global effort against climate change, balancing the urgency of action with the complexities of international consensus. While it pioneers in explicitly calling for fossil fuel reduction, the agreement also acknowledges the challenges of a full transition, especially from coal, particularly in the context of sustainable development. This nuanced approach, coupled with commitments to strengthen renewable energies and the loss and damage fund, reflects a pragmatic yet hopeful stride towards a more sustainable future. The presence of varied interests, including fossil fuel lobbyists, underscores the ongoing dialogue and debate that will shape our collective response to the climate crisis.

The AI field is growing, and whether good or bad, people are doing more than talking about it; they’re using it more than ever. However, despite this increased use, I’ve noticed that, for some, their perception tends to alternate between false and too-high expectations of AI.

One case, in particular, was in 2021, Gartner placed natural language processing (NLP) at the top of its list of loaded expectations in terms of the Gartner hype cycle. As a result, many expected a potential “winter of AI,” so to speak. Yet, in 2022, we discovered the potential that we haven’t even touched on the true value AI could deliver.

Will there be a “winter of AI,” and are expectations bloated?

No, I don’t think so. As the past year has shown us, AI still has more to offer, a pocket of value that we have yet to see. I believe that while many people now accept that AI will be a transformative force—thanks to the fast democratization of large language models—our society hasn’t yet fully considered the actual changes it will make by lowering the barrier to access intelligence globally.

Progress in image generation, analysis, and computer vision—think autonomous driving—has leaped and bounded in the past year, and so has the progress in NLP, particularly in the natural language understanding (NLU) and natural language generation (NLG) aspects. We’re at a tipping point that will likely transform our world in the same way that the internet has.

Tipping point for AI

Today, we’re seeing the development of natural language processing through large language models, such as with the emergence of ChatGPT based on OpenAI’s large language model version GPT-3.

Astounding fact: ChatGPT’s growth in user adoption skyrocketed past one million users within a week of launching. In comparison, no other tech company has reached this feat in this short of a time frame. But the adoption rate is only part of it.

This advance has profoundly affected creative jobs because this might be the first time an AI generative system can create high-quality content. In public mode, users have tapped ChatGPT to do everything, from generating basic reports and ideas to writing lectures and producing code.

With a high adoption rate comes great opportunity. Any startup seeing this level of success could become the most funded project ever. And more, there’s revenue. OpenAI, as the example, could make one billion dollars by 2024, according to a report via Reuters.

On the other side of the same coin, however, there are greater risks due to AI generative system advancement. For example, with AI assistance, human hackers can develop more sophisticated phishing campaigns—hacking mechanisms based on social engineering.

Competition, specificity, and focus for AI advancement

Despite the risks, we still haven’t seen what’s yet to come with generative AI. GPT-4, for instance, is rumored to launch in 2023. I believe it will be a massive improvement over GPT-3, which is already mind-blowing.

And on the point of NLG and these large language models, there’s a lot that’s feasible in process automation. For context, creative content gets the most attention; it’s the area that makes more headlines. But I would also watch advancements in technical content and automated code generation, for example.

Process automation

Because of today’s AI advancements, it’s now possible for tools like ChatGPT to generate near-ready-to-use source code. That means instead of only being fun to play around with, these are becoming enterprise tools, making it possible for developers to automate technical tasks at scale.

NLP—specifically natural language understanding, which SESAMm works on—is not untouched by these applications. Many of these large language models can perform zero-short learning, which means NLU can be performed without pre-training, a huge advance in this industry. However, zero-short learning is insufficient for many advanced sentiment and ESG analysis tasks. We still need additional data sets to fine-tune the data for a specific purpose.

What does this mean for the natural language generation sector? Many startups—especially anything around chatbots—have folded, some just in Q4 of 2022. ChatGPT’s success means it’s solved and replaced the need for many of them, and basically, anything content creation on the B2C side has and will struggle.

Defensive edge

Otherwise, things are looking good in our sector. For example, at SESAMm, we’re focused on what I call “last-mile AI.” In our specific business application, you can’t bypass the need for a data set because we’re trying to attain a precise result for specific, often risk-related applications. Open-source large language models like GPT-3 and BERT can get you mostly there, and that’s fine for general purposes. But for “last-mile AI” applications, there’s a lot you can’t do without additional work.

And here lies what I think is one of SESAMm’s defensive edges: the “last-mile AI.”

Instead of finding ways to protect its algorithms, the AI business community would do better to defend its use cases because the algorithm’s value will decrease progressively. In contrast, the value of a use case’s purpose and the data set used to achieve the use case will grow.

Competitive edge

Computing power and the resources it takes to train large language models remain challenging to applications like OpenAI. It takes electricity, heat, and money to train these models, and AI has an environmental impact. So far, we’ve justified this cost in the name of optimization—meaning that we put in this extra cost upfront so that the likely efficiency will offset or reduce that cost later—but it’s still a cost to incur.

AI companies, especially those in the NLG space, will do well to find their competitive edges, areas optimized for a specific purpose like “last-mile AI.” Companies like OpenAI will likely continue to optimize their models for quicker responses but don’t necessarily have the problem of solving for a specific use case.

At SESAMm, for instance, a big challenge and expertise we developed in-house is inference time—or how quickly we can apply the model to an article or an individual sentence. Because we’re processing so much live content, the more time it takes to process—milliseconds multiplied by a billion—the more costly it is.

Our data lake currently holds over 20 billion articles, messages, etc., from over 14 years, and we add 10 million more daily. That’s a lot of content to analyze. But we make it so our clients can access the data within seconds.

The need to optimize models for fast inference and adapt to deep industry-specific use cases will remain one of the key reasons companies will have to continue re-training their own models. That doesn’t mean large language models don’t add value here. Their open-source versions simply become an impressive building block for any NLP application and accelerate the rate of innovation and productivity in the whole field.

My summary thoughts on AI for 2023

When Google launched BERT in November 2018, we quipped that Google had open-sourced this system as a joke because no one could put it into production because BERT was so big. Many companies didn’t have the computing capabilities to do anything with it at the time. Now we do.

This year, Google did it again; they released a model that’s even bigger than GPT-3. Of course, almost no one besides Google can put that model into production now. But my point is that there will always be computing, resources, and other challenges to making AI advancements. That’s why I think AI companies must focus on defensive and competitive edges.

Regardless of the challenges, I see good things happening in the NLU space being massively improved by large language models. I see improvements as we incorporate these models today compared to deep-learning models trained from scratch a few years ago. I also see a significant decrease in the amount of data we need to fine-tune results, reaching and focusing on the final client use case more quickly.

From a natural language generation perspective, I believe large language models will transform the world. And I’m really excited about this era because this transformation supports my deepest purpose, leveraging AI to accelerate innovative decision-making. We do this by giving decision-makers access to technology that analyzes research content, news, and discussions. And if we increase the rate of innovation or the quality of decision-making by 10% globally, the impact could be huge for all industries: healthcare, finance, fashion, you name it. Industry leaders can make better ESG and SDG choices that will affect our world on a grander scale.

2023 will be an exciting time for AI, specifically for NLG and NLU. Of course, we’ll continue to see AI innovations. But more importantly, leaders will have better insights to make better decisions, creators will create more—and more complex—content, and overall, the applications will become more specific to solving the needs of particular use cases.

Here’s to the new era of AI in 2023. Cheers!

About SESAMm

SESAMm is a leading NLP technology company serving global investment firms, corporations, and investors, such as private equity firms, hedge funds, and other asset management firms. SESAMm provides datasets and NLP capabilities through TextReveal^® to generate alternative data for use cases, such as ESG and SDG, sentiment, private equity due diligence, corporate studies, and more. With access to SESAMm’s massive data lake, comprised of 20 billion articles and messages and growing, its clients can make better investment decisions.

For decades, private market investors have relied on upfront due diligence as the primary moment to assess ESG, reputational, and operational risk. The logic was straightforward: conduct a thorough review at acquisition, document the risks, and manage the asset from there. That model is now under increasing strain. Not because due diligence is no longer important, but because the environment in which private investments operate has changed fundamentally.

This shift was a central theme of a recent 2026 webinar on private markets featuring Benjamin Krusche, Strategy Director at Clarity AI, and Sylvain Forté, CEO at SESAMm. Both speakers emphasized that one-off ESG due diligence no longer reflects how risk actually emerges over the life of an investment, and why continuous monitoring is becoming essential.

Due Diligence Hasn’t Disappeared - The World Around It Has Changed

Upfront due diligence has always been a cornerstone of private market investing, and it remains essential. What has changed is not the role of diligence itself, but the assumptions that once made a point-in-time assessment sufficient.

Historically, many ESG and reputational risks were relatively static. If an asset appeared clean at entry in terms of governance, compliance, or industry exposure, it often remained so long enough to exit. ESG due diligence, in that context, was largely about establishing a baseline. Today, risk behaves differently.

A recurring theme throughout the webinar was that some of the most material ESG and reputational risks can emerge after acquisition rather than at deal close, especially as holding period durations increase. These risks surface through litigation, regulatory enforcement actions, labor and human rights incidents, or reputational controversies. Signals that rarely align neatly with due diligence checklists.

In private markets, where disclosure remains fragmented, the absence of information at entry does not imply the absence of risk. It simply delays visibility. That said, continuous monitoring is not a complete solution to this opacity. Many private portfolio companies, particularly mid-market industrials, have minimal media footprints and limited public disclosure. Monitoring signals that do not exist produces no insight. Effective coverage, therefore, requires triangulating across multiple data sources, including direct company engagement.

Risk Is No Longer Static - It Is Event-Driven and Fast-Moving

Many of today’s ESG and reputational risks are triggered externally and unfold quickly, often outside management’s direct control. While management-provided information remains important, it is no longer sufficient on its own.

Today, many material risk signals emerge from:

• Media reporting
• Regulatory actions
• NGO investigations
• Court proceedings
• Employee and labor disputes

These signals often appear well before issues are formally disclosed, if they are disclosed at all. As a result, relying solely on point-in-time information captured at acquisition creates blind spots that widen over time.

Longer Holding Periods Increase the Cost of Being Late

Longer holding periods amplify these challenges. Issues that were immaterial (or invisible) at acquisition can surface years later, well beyond the scope of an initial due diligence exercise. When that happens late in the holding period, the consequences can be significant: delayed exits, repricing, or friction during refinancing. But the pressure is also coming from the other direction. Large institutional LPs are increasingly embedding ongoing ESG reporting and monitoring requirements directly into side letters and limited partnership agreements, making continuous visibility not just prudent risk management but a contractual obligation for many GPs.

As SESAMm CEO Sylvain Forté explained during the webinar, “As companies tend to be held in portfolios for a longer period of time, that need for information has increased. Oftentimes, there was a perspective that for direct investment, deal teams would provide sufficient information and management would provide sufficient information on portfolios. But as the timescale is expanding and expanding, and the duration of these investments continues to grow, we see that this has been a real change in terms of making sure that there are no emerging operational risks that would not have been captured at the diligence stage, emerging governance risk, litigation, and reputational risk exposure.”

Scale Has Changed the Equation

This challenge is compounded by scale. Private market portfolios today are larger, more global, and more heterogeneous than in the past. Secondaries transactions make this particularly visible: investors may need to assess and monitor hundreds of assets under tight timelines, often with limited access to underlying companies, making point-in-time ESG assessments particularly fragile and extremely time-consuming in inherited portfolios.

Manual, human-led monitoring processes that once worked no longer scale. The issue is not a lack of expertise; it is a lack of continuous, consistent visibility across portfolios.

From Static ESG Snapshots to Continuous Risk Visibility

What is replacing point-in-time ESG due diligence is not simply “more data,” but a different operating model.

ESG information is increasingly integrated into investment decision-making as a continuous input rather than a one-off compliance step. Benjamin Krusche, Strategy Director at Clarity AI, described this shift clearly: “The classical point-in-time due diligence really is breaking down, and what people are moving towards is a much more continuous updating of the initial thesis, a continuous monitoring of this risk as well.”

Continuous monitoring allows investors to track emerging issues across portfolios, reassess risk as conditions change, and compare exposures consistently over time, something static due diligence packs were never designed to do.

Why This Shift Matters Now

The move away from point-in-time ESG due diligence reflects a convergence of structural forces:

• Faster-moving, event-driven risk
• Greater reliance on external signals
• Longer holding periods
• Larger and more complex portfolios

Taken together, these forces fundamentally change the role ESG due diligence can play in private markets. It is no longer just a gate at entry but an ongoing input into investment and risk decisions.

As Benjamin Krusche commented, by the end of 2026, “the idea that you conduct due diligence once at acquisition and treat it as a finished product will look very outdated. The market is moving toward continuous, fluid monitoring of risk - not a one-off assessment that sits untouched in a data room until exit.”

For private market investors, the implication is not to abandon due diligence, but to recognize its limits, and to complement it with continuous, portfolio-level risk visibility that reflects how risk actually behaves today.