Understanding AI Investor Risk: Analyzing Recent Claims & Their Impact

Decoding the Artificial Intelligence Investing Landscape

Conor Moore of KPMG recently said, “In a year that was full of uncertainty and somewhat lackluster deal-making, AI was far and away the standout superstar.” His words paint a clear picture of the current VC landscape, where AI companies have the biggest and most exciting opportunities ahead of them, but effective governance is essential.

The AI Investment Boom

Although global AI investment experienced a dramatic spike in 2021 amid the VC investment bubble (when it raised $83 billion), the industry’s sustained growth truly began in 2022 after OpenAI’s release of ChatGPT. This human-like chatbot that wowed the world gave others the foundation to start developing their own large language models and AI models with niche applications.

As a result, the market raised $54 and $55 billion respectively in 2022 and 2023 when startups dabbled in the technology and began developing it to new heights. This growth was apparent in 2024, where AI systems dominated VC investments with $100 billion globally.

According to Crunchbase, investors were first attracted to AI startups that sustained the data infrastructure of AI models, but this interest has quickly changed as the technology has soared to new heights. Now that companies have proven that AI has meaningful benefits, decision-makers are putting their dollars in applied AI projects that directly impact specific industries.

The Industry’s Current Investment Opportunities

AI’s rapid evolution has meant that foundational companies in the industry have made successful exits (or many major companies have switched to an AI systems focus) in the stock market, replicating the excitement for these projects in the publicly traded world.

For instance, Microsoft’s stock reached record heights in 2023 after hiring key OpenAI executives like Sam Altman, while Nvidia became a top performer when its chips began leading the AI hardware market. In the past year, AI stocks have gone as low as $20.5 and as high as $42.9, which generally mimicked the highs and lows of the stock market amid recession indicators and regulatory changes.

On the other hand, investment in early-stage AI startups shows no signs of stopping as investors see the value in betting on young innovative companies — early-stage valuations have risen to the point where 79% more startups earned the unicorn status in 2024 compared to 2023.

This rise in early investments has given way to exclusive funds for AI startups, such as Anthology, Menlo Ventures and Anthropic’s $100 million dedicated AI fund that awards recipients access to Anthropic’s models and mentorship. Other major players include Allianz’ AI investment wing and Alumni Ventures’ AI and robotics fund.

Understanding AI Investor Risk

AI’s quick ascent isn’t short of impressive, but if we’ve learned anything from the 2021 salad days is that massive growth should be taken with a grain of salt, as it entails significant risk. The technology is now a mainstay in the VC world with its massive investment opportunities, and has become almost a necessity in many industries. So, what concerns and risks are investors looking out for?

• Financial Risks: Although many AI projects have lived up to their expectations, many have fizzled due to poor execution, business model, or simply selling false promises. The latter has been defined as AI washing, which investors became aware of when companies overstated AI’s involvement in their product merely to attract user attention or investor interest. Such situations have led investors to financial losses after companies fail to live up to expectations.
• Legal and Regulatory Risks: As a fairly new technology, AI is still a hot topic in the regulatory world, suffering from a lack of standardized regulations worldwide, making it difficult to develop and succeed at a wider scale. As regulations evolve, AI startups are abruptly met with lawsuits, fines, and even reputational damage, which directly affects investors involved.
• Reputational Risks: AI systems aren’t a perfect technology. Companies are still discovering its potential and making changes to improve it, but things don’t always go smoothly. Incidents like bias or user misuse (like the infamous case of a woman using ChatGPT as her boyfriend) can tarnish an AI startup’s reputation and take down investors with it. This is why, when investing, firms are now looking at ethical AI development practices to encourage responsible innovation and a successful product down the line.
• Technological Risks: AI’s sheer power often comes at the expense of high-tech hardware, top-of-the-line teams, costly software, and more. Not only are these requirements expensive, but also demanding, making it a complex endeavor to succeed in the AI industry. On top of these hardships, the goalpost continues to move as the technology evolves by the minute, rendering many technologies obsolete on its path.

Analyzing Recent Claims & Their Impact

To fully gauge what investors are looking for and actively avoiding when funding AI companies, it’s best to look at reality. Let’s explore some real-life lawsuits and their impact on the AI industry.

The Trade Desk

This ad-buying platform based on the cloud was recently hit with a securities lawsuit due to its failure to disclose AI risks. After the company announced it would adopt Kokai, a generative AI tool that could help predict better ad spending for users, in lieu of its previous software, Solimar, in 2023, The Trade Desk projected increased revenues.

However, upon failing to meet projections and shares falling beyond 32%, executives admitted to deliberately performing slow rollouts and maintaining both systems running, which made full adoption of Kokai more difficult. In the process, the company also made false revenue attributions to Kokai’s implementation in Q1 2024, which misled investors to believe its integration would have the promised positive results.

As it stands, this case may represent a shift in AI-related securities litigation, moving beyond “AI-washing” claims to focus on the alleged failure to disclose AI-related risks. As such, lawsuits could move beyond the outcome and focus on the risks associated with the adoption process, which, in many cases, is the root of the issue in poor outcomes.

Elastic

Elastic is an AI company with sales teams spread out across many regions — America being its strongest performer. On May 30, 2024, Elastic released its Q4 and fiscal year 2024 results along with fiscal year 2025 guidance, projecting revenue growth of 16% year-over-year and claiming operations were stable and thriving despite making significant changes to its American sales wing.

However, three months later, the company reduced its full-year revenue guidance to 14%, attributed to segment changes that took longer than expected to settle. This news led the company’s shares to fall by over 26%, which led to their current securities lawsuit.

The company’s lack of transparency when first releasing its fiscal year 2025 guidance shows the volatility risks associated with AI company valuations and the potential for securities litigation even when financial performance is relatively strong. As a growing industry, stealth changes can take many directions that must be disclosed or discussed with key stakeholders beforehand, proactively facilitating transitions rather than adopting reactive measures.

Telus International

The Canadian publicly-traded company Telus announced in 2020 that it would make acquisitions to enhance its AI data solutions, hinting at a possible transition towards AI-based services in the future.

However, in 2024, the company reported a $29 million year-over-year revenue decrease due to “below average margins” in its AI offering, causing an 18% stock price drop. A few months later, Telus announced further margin reductions and the retirement of its CEO, which prompted an additional 38% share price decline followed by a 20% drop the next trading day. As a result, the company is facing a securities lawsuit.

Rather than misrepresenting AI capabilities, this case shines a light on more AI-related risks beyond AI washing due to poor adoption and execution, leading companies to face legal challenges for not adequately disclosing the potential downsides of AI adoption.

As AI becomes more ingrained in business operations, could these types of lawsuits become more prevalent as AI investor risk increases, especially with regulatory considerations.

AI Investor Risk Management: Best Practices

Recent claims demonstrate that, even when exerting their best efforts, companies can easily go wrong with AI implementation. Here are some best practices to reduce risks when integrating this powerful technology and potentially increase investment opportunities.

Transparency and Communication

As seen in Elastic’s case, a lack of clear communication and transparency about AI-related risks and challenges can lead to abrupt consequences in the long run. As such, maintaining constant and truthful contact with stakeholders, investors, and other key players is paramount to keeping an AI company afloat.

Whether it’s operational difficulties, staffing cuts, financial downturns, or other situations, being proactive about potential issues can lead to better resolutions rather than long-term effects for the company. Every startup faces hardships — investors are well aware of this — and coming clean with difficulties reflects better than building false expectations.

Due Diligence and Risk Assessment

Before adopting AI systems, companies must perform their best due diligence to ensure they undergo a smooth transition process. This includes understanding how the technology will affect operations, setting realistic timelines and expectations, preparing the workforce, investigating vendors’ security and ethical AI practices and ensuring compliance with privacy laws , among other crucial aspects.

The Trade Desks’ case, for example, shows the company might not have been ready to undertake the hefty endeavor of shifting from one AI system to another, facing complexities and timeline overruns that affected their operations and, consequently, their stock price and investor confidence.

So, these steps simply prepare companies to mitigate AI-related risks such as technological bias, security vulnerabilities, and legal challenges, ensuring the reliability of their systems.

Ethical AI Development

Ethical AI is increasingly taking center stage as the technology sheds faults, often rooted in subpar development practices. Its ethics involve using diverse datasets for inclusion and heightened accuracy, accountability, explainability, and more factors coupled with constant monitoring.

Thus, ethical AI will encompass every aspect of a startup, from hiring diverse teams to avoid bias to relying on cyber security and regulatory compliance experts to offer a comprehensive and safe product.

By implementing these necessary measures, startups can rest assured that their models will run appropriately and without future risks of reputational damage and poor quality service, which can result in loss of confidence from investors, shareholders, users, and the general public.

Insurance and Risk Transfer

AI is as powerful as it is risky these days — you can never be too careful. By successfully adopting it, companies can enjoy massive benefits, from happier customers to even happier investors. Part of this success hinges on asset protection and risk transfer to insurance companies, which can be a much-needed cushion when diving into such an innovative industry.

• For example, from a data security standpoint, Cyber Liability supports AI startups in protecting their finances and operations when claims related to data breaches arise.
• Errors and Omissions (E&O) is also relevant when integrating an ever-evolving technology like AI, which could result in faulty products or services.
• Moreover, Directors and Officers (D&O) insurance can cover the legal expenses on behalf of executive teams in case of claims, whether for defrauding investors, misleading, or other complex reasons.

No one AI company is the same. As the technology entrenches itself in more niche applications, companies will have vastly different needs. This is why closely working with insurance brokers is important to properly protecting the hardware, intangible assets, leaders, and workers of a company.

Meeting the risks of AI with equally adequate management strategies is what currently differentiates successful companies from those struggling to break the glass ceiling. Cultivating healthy investor relationships is just as important, underlying a leader’s commitment to reducing AI investor risk and upholding steady growth. It’s all in the attention to detail, due diligence, transparency, and delivering quality products and services.

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