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.

In a stunning development that signals a potential turning point for global climate finance, the Net Zero Banking Alliance (NZBA) has announced it is pausing all activities and proposing a fundamental restructuring that would effectively end its existence as a membership-based organization. This move represents the most significant retreat yet in coordinated climate finance initiatives and raises questions about the future of institutional ESG commitments.

The Dramatic Unraveling

The NZBA's announcement comes after a series of high-profle departures that began in late 2024. What started as a coalition of 43 banks at its 2021 launch had grown to over 140 institutions representing $74 trillion in assets by 2024. However, political pressure, particularly from Republican politicians in the US, warning of potential legal violations, triggered a mass exodus.

The departures followed a predictable pattern: Goldman Sachs led the way in December 2024, followed rapidly by all major Wall Street peers within weeks. Canadian banks soon followed, and the bleeding continued through 2025 with HSBC, UBS, and Barclays all exiting. Barclays' departure statement was particularly telling, noting that "with the departure of most of the global banks, the organisation no longer has the membership to support our transition."

Proposed Restructuring

The NZBA has now proposed transitioning from a membership-based alliance to what it calls a "framework initiative." This fundamental change would essentially transform the organization from an active coalition with binding commitments to a more passive guidance provider. The steering group believes this approach would be "the most appropriate model to continue supporting banks across the globe to remain resilient and accelerate the real economy transition in line with the Paris Agreement."

A member vote on this restructuring is currently underway, with results expected at the end of September. However, given the exodus of major institutions, the outcome seems predetermined.

Broader Climate Coalition Collapse

The NZBA's troubles reflect a wider crisis affecting climate-focused financial coalitions:

• The Net-Zero Insurance Alliance (NZIA) was discontinued in 2024
• The Net Zero Asset Managers initiative (NZAM) suspended primary activities after BlackRock's exit
• The Glasgow Financial Alliance for Net Zero (GFANZ) underwent significant restructuring

A member vote on this restructuring is currently underway, with results expected at the end of September. However, given the exodus of major institutions, the outcome seems predetermined.

Political and Legal Pressures

The pause stems largely from sustained political pressure, particularly in the United States. Republican attorneys general and politicians have warned financial institutions of potential antitrust violations and threatened to exclude companies from state business. This "anti-ESG" campaign has proven remarkably effective in fracturing what once seemed like unstoppable momentum toward coordinated climate action.

Recent developments include a 23-state coalition warning the Science Based Targets initiative (SBTi) about potential antitrust risks, demonstrating that the pressure extends beyond banking to other ESG frameworks.

Market Implications

The NZBA's effective dissolution has several implications:

Fragmented Approach

Without coordinated frameworks, banks will likely develop individual approaches to climate commitments, potentially leading to:

• Inconsistent standards and methodologies
• Reduced transparency and comparability
• Weakened collective bargaining power with policymakers
  

Regulatory Response

The vacuum left by voluntary coalitions may accelerate regulatory intervention:

• Mandatory climate disclosure requirements
• Government-imposed transition standards
• Regional divergence in approaches

Investment Impact

For investors, this development signals:

• Increased difficulty in assessing bank climate commitments
• Greater need for individual due diligence
• Potential opportunities in banks with strong standalone commitments

Looking Forward

The NZBA's pause represents more than just one organization's troubles; it symbolizes a broader retreat from coordinated climate finance at precisely the moment when such coordination is most needed. With climate risks accelerating and the urgent need for massive capital deployment, the financial sector's inability to maintain collective action represents a significant setback.

However, this may also create opportunities for more resilient, legally defensible approaches to climate finance. Banks that remain committed to transition goals may find competitive advantages in developing robust standalone frameworks, while regulatory bodies may step in to fill the coordination gap.

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.

In a landmark move for sustainable finance, the UK government has announced plans to regulate ESG (Environmental, Social, and Governance) ratings providers. The Financial Conduct Authority (FCA) will soon be tasked with overseeing these firms, marking a major shift from the current hands-off approach. This development comes amid growing concerns about the inconsistency, opacity, and influence of ESG ratings on investment decisions.

Why Regulate ESG Ratings Providers?

The regulatory gap in ESG ratings is clear when compared to traditional credit ratings. Credit rating agencies (like S&P, Moody’s, and Fitch) operate under strict regulatory oversight and well-defined methodologies, which is one reason their assessments tend to be closely aligned. In fact, one study found the top credit agencies’ ratings are 99% correlated, whereas ESG ratings from different providers showed only about 60% correlation. In practice, that means two ESG raters might disagree as wildly as “AAA” vs “BBB” for the same firm in the same period. By contrast, it’s rare to see such divergence in credit ratings because that industry has long been supervised and standardized.

Absent regulation, ESG ratings have been opaque and inconsistent. Regulators and market watchdogs have likened the ESG ratings arena to a “Wild West” in need of a sheriff. An environment “unregulated and opaque” where even companies with poor environmental track records can sometimes score surprisingly well. The lack of transparency in how ratings are determined makes it hard for investors to trust what an ESG score truly reflects. This opacity not only fuels skepticism but also raises the risk of greenwashing, where unsustainable companies might hide behind inflated ESG scores.

New oversight aims to bring transparency, consistency, and trust to ESG ratings. Authorities around the world are now stepping in. For instance, the UK government has introduced legislation to bring ESG rating providers under the Financial Conduct Authority’s remit. Similarly, European regulators (ESMA in the EU) and others in Japan and India are moving toward tighter standards. The consensus is that ESG ratings need basic guardrails, much like credit ratings, to ensure they are rigorous, reliable, and free of conflicts of interest. As one analysis noted, if a credit rating agency were to suddenly downgrade scores at the scale we’ve seen with ESG re-ratings, regulators would have intervened immediately. Treating ESG ratings “similarly” to credit ratings in terms of oversight is increasingly seen as necessary to prevent nasty surprises (read: unexpected discrepancies) and to maintain market stability.

Regulation can address several issues: it can mandate clearer methodological transparency, require disclosure of rating drivers, and enforce governance standards (for example, to manage conflicts of interest if a rater also offers paid consulting). All of these steps would help investors and companies finally peek behind an ESG rating. In other words, examine the underlying factors, rather than taking scores at face value. Ultimately, effective regulation should turn ESG ratings from a black box into a more consistent, credible tool for decision-making.

What the UK Plans to Do

Under the new legislation, any ESG ratings provider serving UK clients will be required to obtain authorization from the FCA. These firms will need to disclose their methodologies, manage conflicts of interest, and maintain proper governance controls. The regulation is designed to align with international recommendations, such as those from IOSCO, and mirrors similar efforts already underway in the EU.

The goal is to bring greater transparency, comparability, and accountability to a market expected to grow significantly in the years ahead. The FCA plans to consult on specific rules later this year, with implementation expected to phase in over time.

Why This Matters

Bringing ESG ratings under regulatory oversight could be a turning point for sustainable investing. With consistent standards and greater clarity on how scores are determined, investors can better understand the rationale behind ratings and compare them more effectively. It could also reduce the risk of greenwashing by forcing providers to show their work.

Of course, some concerns remain. Smaller ESG ratings firms may struggle with the cost of compliance. Others worry that regulation could stifle innovation or lead to market consolidation. But broadly, the move has been welcomed by investors and industry groups as a necessary step toward improving trust in ESG data.

As global regulators push for greater alignment, the UK's framework could help shape a more transparent and robust ESG ratings ecosystem - one that better serves both capital markets and long-term sustainability goals.

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.