Investing in AI: Navigating Skepticism and Seizing Opportunities

Wait! Is There Skepticism??

100%. And it’s becoming increasingly paramount.?

As the AI revolution continues to gain momentum, concerns about irrational exuberance, high capital expenditures, and uncertain returns are valid topics of debate among industry experts and investors alike.?

In a recent interview, Kash Rangan and Eric Sheridan, both Senior Equity Research Analysts at Goldman Sachs highlight these concerns, bringing them to the forefront of the AI investment conversation in a recent Goldman Sachs Global Investment Research (Top of Mind Issue 129).

Understanding the Skepticism

Here’s some context to make sure we’re on the same page.

Skepticism about AI investments often centers around the substantial amounts of capital being funneled into AI initiatives. There’s a ton. $1 trillion to be more precise. And critics argue that these expenditures might mirror past tech bubbles, where significant spending did not always translate into proportional returns.?

Eric Sheridan points out that while current investments in AI are massive, comparing these to previous capex cycles without context can be misleading. Instead of focusing solely on the absolute dollars spent, it's crucial to consider these investments relative to company revenues and their strategic importance.

This skepticism is understandable, especially with the the nascent state of many AI applications.

There are some high-profile issues at the application layer, such as AI chatbots providing unreliable answers. Moreover, the hype surrounding GenAI has led some to question whether the technology can deliver on its promises or whether it will follow a similar trajectory to past overhyped technologies that fell short of expectations. Concerns about the scalability and practical applications of AI further fuel this skepticism, as many businesses and investors await the emergence of a definitive "killer application" that validates the current wave of AI investments.

Why Skepticism is Understandable but Misleading

While it's prudent to question the immediate returns on AI investments, dismissing the long-term potential of AI would be shortsighted. As Eric Sheridan emphasize, the current phase of AI development is heavily focused on infrastructure buildout—a necessary step before applications can be developed and widely adopted. This mirrors historical tech cycles where infrastructure investment preceded widespread application and profitability.

Moreover, significant advances in AI are already being realized in various domains. For example, AI has automated low-level code writing, dramatically improving developer productivity. It has also revolutionized customer support, as Kash Rangan points out regarding ServiceNow's 80% reduction in average problem resolution time thanks to AI technology. These practical applications demonstrate AI's potential to enhance productivity and efficiency, which will only grow as the technology matures.

Another critical point is that the current investment climate is markedly different from past tech bubbles. Today, leading technology companies with deep capital reserves and established market positions are driving AI investments. These incumbents have the financial stability, distribution networks, and customer bases necessary to experiment with AI applications and integrate successful innovations into their operations. This strategic approach reduces the risk of underutilized capacity and increases the likelihood of achieving meaningful returns on AI investments.

Guidance for Investors

For investors looking to navigate the AI investment landscape, several key considerations can help mitigate risks and capitalize on opportunities. Here’s what to look for and why optimism is justified:

1. Focus on Data Quality and Integration:

The foundation of successful AI applications is high-quality data. Companies that prioritize data quality and have robust data integration solutions are better positioned to develop effective AI models. Look for investments in platforms that ensure comprehensive, clean, and integrated data.

2. Evaluate Infrastructure Investments:

Pay attention to how companies are building their AI infrastructure. Investments in scalable, flexible, and high-performance computing environments are critical. Companies that invest in the right infrastructure today will be well-prepared to develop and deploy innovative AI applications in the future.

3. Monitor AI Applications and Use Cases:

Keep an eye on emerging AI applications and their real-world impact. Companies demonstrating tangible productivity gains and efficiency improvements through AI are more likely to achieve long-term success. Successful use cases in creative design, code development, and customer support are indicators of AI’s transformative potential.

4. Consider the Strategic Vision and Leadership:

Companies with a clear strategic vision for AI and strong leadership are better equipped to navigate the challenges of AI development. Leadership that understands the long-term nature of AI investments and is committed to continuous innovation and improvement will drive sustainable growth.

5. Stay Informed About Regulatory and Ethical Considerations:

AI development comes with regulatory and ethical implications. Companies that proactively address these issues and integrate ethical considerations into their AI strategies are likely to gain trust and maintain a competitive edge.

A Final Word

Investing in AI involves balancing skepticism with optimism.?

Always.?

And while concerns about high expenditures and uncertain returns are valid, they should not overshadow the significant long-term potential of AI technology. I echo Rangan and Sheridan’s point that the current phase of infrastructure buildout is a necessary precursor to the development of groundbreaking applications. By focusing on data quality, infrastructure, practical applications, strategic leadership, and ethical considerations, investors can make informed decisions and position themselves to benefit from the AI revolution.?

The landscape is evolving. And those who understand and navigate these dynamics will be best positioned to capitalize on AI's transformative potential.

Where are you in this dynamic?