Investing in GenAI: Balancing Big Returns and Big Risks in the New AI Frontier

Summary: Generative AI (GenAI) is poised to revolutionize industries with its capabilities in automation, decision-making, and creative content generation. While GenAI excels in generating personalized solutions and automating complex tasks, it lacks true understanding, context awareness, and ethical reasoning, which require responsible human oversight. Moreover, its promise is tempered by challenges such as data bias and evolving regulatory landscapes. This article highlights GenAI's potential while identifying critical considerations to ensure these systems remain fair, transparent, and beneficial to society. Drawing on insights from leading AI researchers and ethicists, this article guides investors in making informed decisions in the GenAI space.

Personal Finance Reimagined (PFR) suggests adopting the Investment Barbell Strategy approach, which balances high-risk, high-return GenAI investments with stable, lower-risk assets.

PFR advises capping GenAI exposure at 10 percent of total wealth to effectively manage volatility.

About the author: Jeff Hulett leads Personal Finance Reimagined, a decision-making and financial education platform. He teaches personal finance at James Madison University and provides personal finance seminars. Check out his book -- Making Choices, Making Money: Your Guide to Making Confident Financial Decisions.

Jeff is a career banker, data scientist, behavioral economist, and choice architect. Jeff has held banking and consulting leadership roles at Wells Fargo, Citibank, KPMG, and IBM.

Table of Contents

1. Introduction: Why Invest in GenAI Now?

   • Market Growth and Economic Impact

   • Data Integrity and Regulatory Considerations

2. What GenAI Is—and What It Is Not

   • Defining GenAI’s capabilities: automation, content creation, and predictive insights

   • Understanding GenAI’s limitations: lack of context, ethical reasoning, and true autonomy

3. Key Investment Considerations for GenAI

   • Data Diversity and Representation

   • Safeguards Against Bias and Discrimination

   • Temporal Stability in AI Models

   • Model Documentation and Transparency

   • Balancing Growth with Ethical Use

   • Financial Sustainability and Environmental Impact

   • Stakeholder Engagement and Inclusivity

   • Political and Regulatory Risks

4. Balancing Innovation with Long-Term Safety

   • AI Safety Concerns from Thought Leaders

   • The Investor’s Focus: Key Considerations

5. What Should You Do Now?

   • Limiting Exposure to 10% with PFR’s Investment Barbell Strategy

   • Layering Investments Over Time

   • Using AI-Focused ETFs for Diversification

6. Conclusion: Navigating the GenAI Frontier Responsibly

7. Appendix: GenAI-focused ETFs

8. Suggested Resources and Citations

1. Introduction: Why Invest in GenAI Now?

Generative AI is set to reshape sectors from healthcare to finance and entertainment, offering scalable solutions, increased efficiency, and unprecedented personalization. For investors, this market represents one of the most dynamic and high-growth areas within AI.

Economic forecasts underscore GenAI's growth potential. The global GenAI market, valued at approximately $13 billion in 2023, is projected to grow at a compound annual growth rate (CAGR) of 36.5%, reaching nearly $967.65 billion by 2032. In the United States alone, the GenAI market is expected to hit $33.78 billion by 2030. This surge reflects GenAI's increasing application across industries, with potential economic value between $2.6 trillion and $4.4 trillion. GenAI software revenues are set to expand almost tenfold from $3.7 billion in 2023 to $36 billion by 2028, with a CAGR of 58% [1, 2, 3, 4].

However, alongside these opportunities come high-stakes considerations about data integrity, ethics, and the long-term value of AI systems in critical industries. Generative AI’s reliance on vast, diverse datasets means that the data’s quality—its fairness, accuracy, and inclusivity—directly affects the success and sustainability of AI models. Bias in data isn't merely a technical issue; it’s a material risk that could affect profitability, reputation, and regulatory compliance.

Moreover, the evolving regulatory landscape presents political risks that investors must navigate. In August 2024, the European Union's Artificial Intelligence Act (AI Act) came into force, establishing a comprehensive legal framework for AI development and deployment within the EU. This act addresses potential risks to citizens' health, safety, and fundamental rights, providing developers and deployers with clear requirements and obligations regarding specific uses of AI [5].

In the United States, the regulatory environment is also evolving. As of September 2024, more than 120 bills related to regulating artificial intelligence are currently under consideration in Congress. These bills vary widely, addressing issues from improving AI knowledge in public schools to requiring model developers to disclose the copyrighted material used in their training [6].

For instance, the Algorithmic Accountability Act aims to require companies to assess the impact of automated decision systems and take corrective measures if necessary. Additionally, the Facial Recognition and Biometric Technology Moratorium Act proposes a pause on the use of facial recognition technology by federal entities until appropriate safeguards are established. Other proposals, like the AI Training Data Disclosure Act, call for transparency regarding the data used to train AI models, which could increase costs and expose companies to intellectual property risks.

Investors in GenAI must navigate these complexities carefully to capture the industry’s potential without falling into its pitfalls. This article presents key questions for investors to consider, helping them understand where the GenAI industry is today and what to watch for as it evolves.

2. What GenAI Is—and What It Is Not

Generative AI (GenAI) refers to advanced machine learning models, like neural networks, that create new content by analyzing and synthesizing vast amounts of existing data. GenAI systems are trained to generate coherent text, images, audio, and even predictive insights across domains, offering transformative capabilities in creative industries, customer service, diagnostics, and more. By identifying patterns and structures in large datasets, GenAI can produce outputs that mimic human-created content, providing unprecedented automation and personalization possibilities. This technology holds the potential to revolutionize sectors by reducing labor-intensive tasks, accelerating innovation, and enhancing customer engagement with personalized solutions, creating substantial economic impact [1, 2].

However, GenAI is not a perfect, autonomous problem-solver, nor is it inherently neutral. While powerful, these models lack true understanding, relying solely on statistical patterns without awareness of meaning or context. They can unintentionally amplify biases present in training data, creating ethical and accuracy concerns, particularly when applied to high-stakes areas like healthcare, hiring, or criminal justice. Additionally, GenAI cannot independently make ethical decisions or foresee the consequences of its outputs—responsible human oversight is crucial to avoid unintended harm. Unlike human intelligence, GenAI does not innovate or create from “nothing”; it depends entirely on past data, which can limit its applicability in situations requiring genuine creativity, judgment, or values-based decisions. Consequently, while GenAI offers robust tools for automation and augmentation, it is not a replacement for human insight, ethical reasoning, or context-aware decision-making [7, 8, 11].

3. Key Investment Considerations: Foundational Questions for GenAI

Anyone considering an investment in a GenAI-focused company would be well served to ask these questions, which draw on insights from leading AI researchers and ethicists and provide a solid foundation for any GenAI investment thesis: [7]

1. How Does the Model Handle Data Diversity and Representation?

   • Large language models often struggle with inclusivity, as massive datasets don’t necessarily translate into balanced or representative views. Investors should ask:

     • How does this GenAI model ensure diversity in its training data?

     • Is there a system to regularly audit and update data to reflect current demographics and social norms?
       

2. What Safeguards Are in Place Against Bias and Discrimination?

   • GenAI’s reliance on historical data can inadvertently reinforce past biases. As AI permeates high-stakes areas like finance, healthcare, and criminal justice, the cost of biased outcomes could be both human and financial. Key considerations include:

     • What processes does the company use to detect and correct bias in its models?

     • Is there transparency around the data sources used, and are there controls to flag high-risk biases?
       

3. How Do Models Account for Temporal Stability?

   • GenAI models often rely on outdated data, which can introduce noise and reduce predictive accuracy. The time between when data is collected and when it is used can lead to significant instabilities, especially in sectors where long-term decisions are critical.

     • How does the company manage the risk of time-based noise in predictions?

     • Are there mechanisms to adjust or retrain models to align with evolving social, economic, or environmental conditions?
       

4. What is the Approach to Model Documentation and Transparency?

   • Investors should look for companies that thoroughly document the origins, purposes, and known limitations of their models. These disclosures build trust and can safeguard against regulatory issues.

     • Does the company provide clear documentation of the model’s data sources, assumptions, and risks?

     • How accessible is this information to stakeholders, regulators, and end-users?
       

5. How Does the Company Balance Growth with Ethical Use?

   • As GenAI expands, there’s a tension between maximizing growth and ensuring ethical AI use. Many models are dual-use, capable of both beneficial and harmful applications. This tension poses a reputational risk for investors.

     • Dual-use technology refers to innovations that can be applied for both beneficial and potentially harmful purposes. How does the company mitigate risks associated with these dual-use technologies?

     • Is there a focus on high-stakes applications, or are there controls in place to prevent misuse?
       

6. What is the Long-term Financial Sustainability and Environmental Impact?

   • Training large GenAI models is resource-intensive, often requiring significant financial and environmental resources. Investors should ensure these costs are balanced by sustainable gains.

     • Does the company have a roadmap for managing the financial and environmental costs of model training?

     • How does the company assess and mitigate the environmental impact of its models?
       

7. What Role Do Stakeholders Play in the Development Process?

   • Responsible AI development involves ongoing engagement with diverse stakeholders, particularly those most likely to be impacted by AI-driven decisions. Companies that value stakeholder feedback reduce the risk of unintended harm and improve model inclusivity.

     • Does the company incorporate feedback from diverse stakeholder groups in its model design and deployment?

     • Are there value-sensitive design practices to ensure the system aligns with broader societal goals?
       

8. How Do Political and Regulatory Risks Impact the Company’s Strategy?

   • With GenAI under increased regulatory scrutiny worldwide, companies operating in this space must be prepared to navigate complex legal landscapes. Recent legislative actions in both the U.S. and Europe highlight the potential for a stricter regulatory environment, which could affect GenAI’s development, deployment, and profitability.

     • European Context: The EU’s Artificial Intelligence Act, which took effect in 2024, sets stringent standards for AI systems to ensure transparency, accountability, and minimal risk to fundamental rights. This legislation requires GenAI companies to implement risk assessments and provide detailed documentation of their AI processes, adding operational costs and compliance obligations [5].

     • U.S. Legislation: In the U.S., a range of proposed bills could significantly impact GenAI firms. For example, the Algorithmic Accountability Act mandates assessments of AI-driven decision systems, requiring firms to evaluate and address any potential harm from their AI models. The Facial Recognition and Biometric Technology Moratorium Act proposes limitations on certain AI applications, such as facial recognition, until regulatory guidelines are established. Other proposals, like the AI Training Data Disclosure Act, call for transparency regarding the data used to train AI models, which could increase costs and expose companies to intellectual property risks [6].

     • Investors should ask:

       • How is the company preparing for compliance with both existing and potential regulations in key markets like the U.S. and EU?

       • Are there resources dedicated to managing regulatory risks, and how does the company plan to stay agile as policies evolve?

   
   

4. Balancing Innovation with Long-Term Safety

AI safety has emerged as a pressing concern among thought leaders like Elon Musk and Eliezer Yudkowsky, who caution that unchecked development of highly advanced AI could lead to unintended, even existential risks. They highlight the need for strict alignment of AI goals with human values to prevent scenarios where AI systems prioritize their objectives over human welfare. While such concerns underscore the importance of long-term safety measures, for investors today, the immediate focus should be on the foundational aspects covered in the key considerations—data diversity, bias mitigation, transparency, regulatory compliance, and ethical safeguards—which are essential for capturing GenAI’s growth potential responsibly [11, 12].

5. What Should You Do Now?

For investors considering exposure to the GenAI sector, Personal Finance Reimagined (PFR) offers a strategic approach within the Investment Barbell Strategy framework. [13] This approach suggests balancing high-risk, high-return investments with stable, lower-risk assets for long-term financial resilience.

1. Limit Exposure to 10%: Given the high volatility and low diversification inherent in GenAI, PFR advises limiting exposure to this sector to no more than 10% of your total wealth. This recommendation recognizes the speculative nature of the GenAI market and aims to protect the portfolio’s overall stability.

2. Layer Investments Over Time: PFR suggests a phased approach to entering the GenAI space, or dollar-cost averaging. By gradually layering investments over time, investors can reduce the risk of market timing and smooth out the effects of price volatility.

3. Consider AI-Focused ETFs: To address the diversification challenges in this emerging market, PFR recommends using AI-based exchange-traded funds (ETFs). These ETFs offer exposure to a broader portfolio of GenAI and AI-related companies, reducing the need to pick individual stocks and mitigating the risk of potential failures among some companies. As the AI market matures and winners and losers emerge, ETFs provide a structured way to benefit from overall sector growth without having to predict which companies will thrive. As always, consider the ETF’s fee loads as a key selection criterion. When possible, seek ETFs with low fund fee loads to maximize long term returns.

By following PFR’s Investment Barbell Strategy and layering investments through AI-focused ETFs, investors can gain exposure to the transformative potential of GenAI while managing risk. This balanced approach supports wealth-building in a high-growth sector without overcommitting to a rapidly evolving and unpredictable market.

6. Conclusion: Navigating the GenAI Frontier

In summary, investing in Generative AI (GenAI) offers substantial growth potential as the technology reshapes industries through automation, personalization, and innovation. However, with its rapid expansion come significant challenges—ranging from data bias to regulatory uncertainties—that investors must navigate wisely. By asking key questions about data integrity, ethical safeguards, stakeholder involvement, and regulatory strategies, investors can position themselves to capture the benefits of GenAI while managing the risks. Through a measured approach like PFR’s Investment Barbell Strategy, which balances high-growth GenAI investments with stable assets and emphasizes diversification through AI-focused ETFs, investors can responsibly engage with this transformative market, fostering both financial growth and societal advancement.

7. Appendix: GenAI focused ETFs

To help the investor research ETFs, next are iterative ChatGPT prompts. In the tables that follow, the suggested ETFs provide a starting point to help the investor confirm their ETF choice. The wise investor will consider the investment thesis from this article to help them guide the ChatGPT suggestions. The Investment Barbell Strategy is an investment decision approach to help the self-directed investor.

Preparatory Prompt: "Summarize Jeff Hulett's article to prepare for researching ETFs" Before submitting the prompt, copy and paste Jeff Hulett's article Investing in GenAI: Balancing Big Returns and Big Risks in the New AI Frontier located at https://www.financerevamp.com/post/investing-in-genai-balancing-big-returns-and-big-risks-in-the-new-ai-frontier.

Prompt 1: "Suggest ETF's focused on Generative AI, sort by expense ratio. Show at least 20 ETFs with over $500 million in assets, with a 12-month return of over 15%"

Prompt 2: "Divide the table by ETFs investing in more large-cap technology companies vs. ETFs specializing in smaller-cap AI-focused companies"

Below are 2 ETF tables showing the results from the iterative ChatGPT prompts run on November 30, 2024. Please verify the results.

The difference between the 2 tables:

Large-cap technology companies are more likely to purchase smaller Generative AI companies to help the larger company grow. Their challenge is one of integrating the GenAI technology.

Pros: Relatively lower risk, proven market for their core business, ETFs have lower expense ratios.

Cons: Bigger challenge to grow.

Smaller-cap GenAI companies specialize and attempt to scale their technology. Successful companies will be more likely to be purchased.

Pros: Singular focus, growing GenAI market, success leads to buy-out optionality.

Cons: Relatively higher risk, ETFs have higher expense ratios.

ETFs Investing in Large-Cap Technology Companies

ETF Name	Ticker	Expense Ratio	AUM (USD)	12-Month Return	Description
Vanguard Information Technology ETF	VGT	0.10%	50.3B	20.03%	Provides exposure to U.S. information technology sector, including companies involved in AI.
Vanguard S&P 500 Growth ETF	VOOG	0.10%	7.1B	16.59%	Focuses on growth stocks within the S&P 500, encompassing AI leaders.
Vanguard Mega Cap Growth ETF	MGK	0.07%	13.5B	20.03%	Invests in mega-cap growth stocks, including those in the AI sector.
iShares U.S. Technology ETF	IYW	0.39%	11.2B	23.5%	Offers exposure to U.S. technology companies, including leaders in AI.
iShares Exponential Technologies ETF	XT	0.46%	3.1B	17.50%	Focuses on companies involved in disruptive technologies, including AI.
SPDR S&P Kensho New Economies ETF	KOMP	0.20%	1.2B	18.0%	Targets companies driving innovation in AI and other emerging technologies.

ETFs Specializing in Smaller-Cap AI-Focused Companies

ETF Name	Ticker	Expense Ratio	AUM (USD)	12-Month Return	Description
Global X Robotics & Artificial Intelligence ETF	BOTZ	0.68%	2.62B	36.68%	Invests in companies involved in robotics and AI technologies.
iShares Robotics and Artificial Intelligence ETF	IRBO	0.47%	475M	28.37%	Targets companies expected to benefit from AI and robotics advancements.
First Trust Nasdaq Artificial Intelligence ETF	ROBT	0.65%	410M	20.51%	Offers exposure to AI and robotics companies, focusing on innovation leaders.
Global X Artificial Intelligence & Technology ETF	AIQ	0.68%	1.33B	36.68%	Invests in companies poised to benefit from AI technology development and utilization.
ROBO Global Robotics & Automation Index ETF	ROBO	0.95%	1.5B	25.0%	Invests in companies across the robotics and automation sectors, including AI.
ARK Autonomous Technology & Robotics ETF	ARKQ	0.75%	1.0B	22.0%	Includes companies in autonomous technology and robotics, with AI exposure.

8. Suggested Resources and Citations

1. Grand View Research. "Generative AI Market Size, Share & Trends Analysis Report by Component, by Technology, by Application, by End-use, by Region, and Segment Forecasts, 2023 - 2032." Grand View Research, 2023. https://www.grandviewresearch.com

2. McKinsey & Company. "The Economic Potential of Generative AI: The Next Productivity Frontier." McKinsey Digital, June 2023. https://www.mckinsey.com

3. Fortune Business Insights. "Generative AI Market Size, Share & COVID-19 Impact Analysis, by Component (Software, Services), by Technology (GPT-3, GANs, Transformer), by Application (Customer Service, Content Creation), and Regional Forecast, 2023-2030." Fortune Business Insights, 2023. https://www.fortunebusinessinsights.com

4. S&P Global Market Intelligence. "Generative AI Software Market Forecast to Expand Near 10 Times by 2028 to $36 Billion." S&P Global Market Intelligence, 2023. https://www.spglobal.com

5. European Commission. "Europe’s AI Act Enters Into Force." European Commission, August 2024. https://commission.europa.eu

6. Technology Review. "Here Are All the AI Bills in Congress Right Now." MIT Technology Review, September 2024. https://www.technologyreview.com

7. Bender, E. M., Gebru, T., McMillan-Major, A., & Mitchell, M. "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?" FAccT '21, March 2021. https://dl.acm.org

8. IBM. "What is Backpropagation?" IBM Knowledge Center, 2023. https://www.ibm.com

9. Federal Reserve Board. "A Guide to HMDA Reporting: Getting It Right!" Federal Reserve Board, updated 2024. https://www.federalreserve.gov

10. Built In. "Backpropagation in a Neural Network: Explained." Built In, 2023. https://builtin.com

11. Musk, Elon. "Elon Musk on the Dangers of AI and Why He Fears It More than Nukes." Lex Fridman Podcast, Episode #252, 2021. Available at youtube.com.

12. Yudkowsky, Eliezer. "AGI Ruin: A List of Lethalities." LessWrong, 2022. https://www.lesswrong.com.

13. Hulett, Jeff. Making Choices, Making Money: Your Guide to Making Confident Financial Decisions. Personal Finance Reimagined Publishing, 2023.

14. Hulett, Jeff. The Essential Guide to Partnering with GenAI: Achieve Both Accuracy and Precision. Personal Finance Reimagined Publishing. 2024.