Despite its promises, the clean energy sector, featuring companies like NextEra Energy, First Solar, and Siemens Energy, faces significant challenges across ESG fronts. Governance issues include lawsuits over misleading financial practices and greenwashing accusations. Environmentally, the sector struggles with defective technology and product performance failures, eroding trust in renewable energy solutions. Social and labor challenges are also prevalent, including discrimination lawsuits, unsafe working conditions, and customer dissatisfaction stemming from product failures and poor service. Furthermore, the growing threat of cybersecurity vulnerabilities within clean energy infrastructure poses risks to both operational systems and consumer data.

What are the most pressing ESG challenges currently facing the clean energy sector? Read on to find out.

NextEra Energy: ESG Challenges and Legal Disputes

NextEra Energy faces several ESG controversies despite being less exposed than its peers. Governance issues include a $1.2 billion impairment related to the Mountain Valley Pipeline and a $350 million antitrust lawsuit for allegedly obstructing a competitor’s clean energy project. It has also faced legal challenges, notably investigations into its political donations connected to its bid for Jacksonville’s public utility. Environmental concerns involve growing opposition to its solar and battery projects and lobbying against rooftop solar policies, along with protests over its wind and drilling operations in Florida, which are subject to a class-action lawsuit for environmental risks. Additionally, the company is investigating wind turbine collapses and is engaged in legal disputes regarding employee rights, including a retaliation lawsuit and a $500,000 settlement over debt collection practices.

Key Controversies:

• NextEra takes $1.2bn charge as pipeline project comes under pressure
• NextEra Energy Faces Antitrust Lawsuit Over Alleged Efforts to Block Competitor’s Project
• NextEra Energy Investors Urge Revival of Political Meddling Suit
• 'Dangerous Drilling': Coral Gables Lawyer Files Motion for Class Certification Against Florida Power & Light
  

First Solar: Legal and Regulatory Risks

First Solar has been dealing with a mix of ESG challenges. The solar manufacturer is locked in patent litigation with JinkoSolar while struggling with broader industry headwinds like polysilicon oversupply, hurting bookings, and creating manufacturing problems. CEO Mark Widmar has pointed to policy uncertainty as a major roadblock, saying failed climate legislation is hampering domestic solar production. The company is also cleaning house with its Malaysian contractors over unethical labor practices. Despite proposed subsidy cuts dragging down the stock, RBC analysts still see nearly 40% upside potential as First Solar works through these operational and regulatory pressures.

Key Controversies:

• First Solar forecasts higher 2025 sales, initiates legal action against JinkoSolar
• Why First Solar Stock Remains a Frenzied Buy Despite Trump's Spending
• FSLR - First Solar: No Floor In Sight - Pending Further Policy Clarity
• Climate bill defeat a blow to U.S. solar production, First Solar CEO says
• Human Resources Ministry to investigate four contractors who used forced labour at First Solar Malaysia

Siemens Energy: ESG Controversies and Operational Challenges

Siemens Energy is facing multiple ESG controversies, including criticism for its reliance on fossil fuels and accusations of greenwashing. Its Siemens Gamesa unit struggles with turbine quality issues, resulting in financial losses, employee layoffs, and legal disputes, including a blocked asset sale in India and corruption allegations. The company is also under legal scrutiny for delays in the Akkuyu nuclear project and past sanctions violations. Additionally, Siemens Energy has been criticized for a data breach involving MOVEit software and vulnerabilities in its products. Socially, it has faced backlash for abolishing its women’s quota in the U.S. and labor unrest at Siemens Gamesa facilities, including strikes over working conditions.

Key Controversies:

• Siemens Energy Faces Criticism Over Fossil Fuel Dependence and Greenwashing Accusations
• Siemens Gamesa Faces €1.6 Billion in Repair Costs Due to Wind Turbine Quality Issues
• Russia’s Rosatom to sue Siemens over delay of equipment for Akkuyu nuclear power plant construction
• An agreement has been signed at Siemens Gamesa Arazuri after two months of strike demanding equality with other centres

Conclusion

In conclusion, the clean energy sector faces critical ESG challenges that threaten its potential for sustainable growth. Companies like Tesla, Siemens Energy, and NextEra Energy must prioritize transparency, improve governance, and address environmental and social issues to regain stakeholder trust. By proactively tackling these concerns, the sector can strengthen its credibility and better contribute to global sustainability efforts. The path forward is challenging, but addressing these challenges is essential for the clean energy industry's success in combating climate change.

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.

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.

Below is an approximation of this video’s audio content. Watch the video for a better view of graphs, charts, graphics, images, and quotes the presenter might be referring to in context.

Intro to SESAMm

Thank you very much, Greg. Thank you, everyone, for listening to this presentation. I’m Sylvain. I’m CEO and co-founder of SESAMm. SESAMm is an AI company. We extract billions of articles and messages from the web in order to identify critical insights related to financial institutions and corporates. We’re a team of close to a hundred people. And what we aim to show you today is our new product that helps financial institutions and corporates identify ESG controversies in the form of alerts on all of their investments, on all of their clients, and all of their suppliers.

So there are more than 23 million companies in the world right now. These companies are your investments, your suppliers, your clients, and no one is actually tracking them. Most of these companies are never tracked day to day. SESAMm’s solution aims at automatically identifying controversies on these companies and finding the critical information that you’re missing.

See a dashboard example

So let’s take a quick example first. Here we have dashboards where we analyze a company called Wirecard. Wirecard is a fintech company—German—that went bankrupt a few years ago due to a two billion fraud scandal. That company was heavily embedded into the financial sector, working with a lot of banks, a lot of corporates worldwide.

On our dashboards, we can immediately identify all of the key controversies and all of the key risks on the companies. And we have a score called a virality score that helps assess the severity of each ESG event so as to understand whether that company should be excluded from your list of suppliers, for example, or even discussed as a client.

SESAMm solution benefits

There are key benefits to providing this information and to the way that this product is brought to the market. First, SESAMm covers more companies than anyone else. We cover close to five million firms, whereas most ESG providers have coverage limited to 50,000 firms in total. In addition to that, we’re able to detect controversies in real time and generate daily alerts where normally a bank, for example, would have to go through that process manually and update it just a few times a year instead of receiving that live information.

In addition to that, as you can see on the demo here, we have information for more than 14 years of data. So anytime you onboard a new supplier, anytime you check for information—ESG information, on a new client, or on an investment—you’ll automatically be able to go back in history and understand whether that company was exposed to issues in the past.

Trusted by major financial institutions

SESAMm solutions are already adopted by major banks such as Raiffeisen or Nomura, for example, in this industry, major private equity firms such as Carlyle. And what’s interesting in this solution is that we’re seeing specific interests from commercial banks that are missing the solution in order to track ESG risk on their suppliers and their clients. And it makes sense. Most of these suppliers and clients are small firms, local firms that no one else is going to track. And AI is enabling us to automate the process of monitoring these firms and making sense of that data in real time.

SESAMm's solution in action

So now, let’s go to the second part of the demo. We want to take an actual life example. So let’s take a company like Twilio, for example. So you may know Twilio communications, API, messaging services, phone services, and the like. This company is a typical provider of banks or of financial institutions or any other corporates in the world.

So you see on the left, we immediately identify all of the information related to Twilio. And we can rank this based on negative sentiment so as to understand what are the key critical topics that I should care about and that I should evaluate before actually working with Twilio or in the context of already working with Twilio. We go through that process by handling more than 20 billion articles and messages from more than four million sources worldwide. So that’s an insanely large amount of information.

And on Twilio—say Twilio is one of your suppliers or one of your clients—we immediately identify a large controversy related to a data breach and cybersecurity issue, and we identified both in news but also in some of the specialized cybersecurity websites. In addition to that, we can go in even more granularity and look transparently at the content themselves, read the contents from the platform, and not just rely on a numeric rate saying that “Hey! This company is problematic.” We can actually read the underlying content and understand how the controversy emerged.

SESAMm solution benefits

So the key benefits and the real advantages of that solution is getting information immediately. You don’t have to wait for a due diligence for someone to check for someone to send a questionnaire to the company. You just type in the name, get the information in a few seconds wherever the company is, and however local that company is. It could be the most obscure company. And as you can see our system also covers many different languages, including Asian languages that are monitored automatically.

The second part is that we have access to millions of sources, including very industry-specific sources. I was mentioning cyberthreats. We also have access to NGO websites that identify these types of ESG issues in real time.

So this is really the information that is aimed at helping you monitor controversies and ESG events in just one place on any number of companies, public and private, whether they are your suppliers, your clients, or your investments. You can make sense of that data in real time using AI.

Presentation summary

I’ll finish this presentation a bit early, and we’ll actually bring the point to three calls to action. The first one is, first, please come to our booth. We’re actually on the left of the exhibit hall right when you come in. The second one is, please visit our website. It’s spelled SESAMm, sesamm.com, and you can get a free trial from the website. And finally, come talk to our amazing team with Dave and the rest of our team at our booth. And please ask us for a free POC—whether you’re a bank, an asset manager, or a fintech company—and help us help you track all of the ESG controversies on millions of companies.

Thank you very much.

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. For example, ESG Alerts provides a better way to research and monitor your investment portfolio. To learn more about how you can analyze web data or to request a demo, reach out to one of our representatives.