“AI will not destroy the world, and in fact may save it.” - Marc Andreessen
As early stage investors, we're constantly on the hunt for innovative companies that hold high optionality value.
AI’s power to automate, optimize, and revolutionize makes it a goldmine of opportunities, so understanding this ever-evolving domain is essential for those of us looking to invest in the companies of tomorrow.
However, because of the rapid accelerating pace of AI innovation, what seems like a cutting-edge investment today could be outdated in a few short years or even months or weeks. The excitement about the potential of AI, although warranted, needs to be supplemented with a healthy dose of discipline, caution and diligence.
Below, we’ll dig deeper into the value of investing early in AI, the risks, and our unique approach to the space.
The value of investing in AI-based startups holds true for almost all shiny, new technological innovation: the transformative power that the technology holds. AI startups are often characterized by their ability to drive efficiency, automation, and insightful decision-making on a scale and speed previously unattainable. Most AI products promise efficiency, cost-cutting, time-savings, and more. Here are two key elements, and one macro driver, driving the value of AI we see today:
Beyond vertical-specific AI, AI can serve as a key differentiator and value driver in a wide range of existing applications by serving as an enhancer, not just standalone products. Companies that effectively incorporate AI into their offerings can enhance the user experience, improve efficiencies, and ultimately build a more sustainable competitive advantage against other players in the space.
The potential for high returns in a “hot field” is alluring. However, we would argue risks are most prevalent in the “hot fields,” AI. It’s crucial to balance enthusiasm with a keen awareness of the inherent risks. Risks in investing early in AI range from technical feasibility and regulatory uncertainties to ethical concerns and market adoption challenges.
As a result, this necessitates a disciplined approach to investing - rigorous due diligence, skepticism of hype, portfolio diversification, patience, and a commitment to continual learning to stay up-to-date with the latest developments in the space.
Here are some of the risks we think about:
One example of incumbency risk: Months ago, we analyzed a mobile-based, AI-powered language learning application. Although enticing initially, in our due-diligence we uncovered that DuoLingo, which holds over 64% market-share in the language learning application space, recently rolled out their own AI-based learning tool. An incumbent with house-hold name, an immense distribution advantage, and abundant engineering and financial resources essentially single-handedly overtook a startup.
Overall, our approach to investing in a company in many ways remains unchanged. We still assess the founder, the team, the story, the technology, the market, the underlying business model, the competitive landscape, downstream funding implications, macroeconomic factors, and much more. As noted previously, a rigorous diligence process and overall investment process allows us to see beyond the hype. Below is a deeper dive into just some of the factors that we analyze.
The Founder + Team: We always assess the founders and the team's competency; with AI however, we evaluate their expertise in AI, understanding of the problem they're trying to solve, and the founder’s ability to attract and retain top talent.
The Story: we’re a founder-first fund, driven by the underlying story that drove the entrepreneur to pursue their idea. In almost any investment that we make, we look for a strong “why.” Normally, this is manifested in the founder’s personal experience with the problem they are setting out to solve. This remains unchanged.
The Technology and Data: We look to see whether the AI solution being developed is truly innovative and capable of achieving what it purports to do, and whether the business model is sustainable. AI is a data-dependent vertical, so we do consider how the startup sources, manages, and uses data, ensuring they adhere to data privacy regulations, and whether their data is proprietary enough to constitute a moat relative to market competitors.
The Market: We take a strong look at the competitive landscape and see if there’s a way in which a well-funded incumbent can enter the space and disrupt the potential investment.
The Round Dynamics: With any investment, we look to see whether the round has favorable terms, whether we can capitalize on our desired ownership target, ensuring there is a favorable cap table with minimal dilution, and more. As early-stage investors, we make sure that the company is raising money with the potential to still raise further growth capital downstream; in the landscape of AI, we are cautious of lofty valuations and cognizant of the hype around the space.
The Larger Economy and Trends: We take a step back from the underlying technology to see where the larger economy is headed, whether the company is subject to any geopolitical risk, and if there are any large trends either working for or against the company. This can mean demographic trends, trends in behavior and much more.
The Business Model: We analyze how the company expects to generate revenue, taking into account whether this is realistic and sustainable for the long-term. When we analyze the financials of a startup and review their projections, we look to see whether the underlying assumptions driving the projections are rooted in reality.
AI provides immense opportunities for early-stage investors due to its transformative power and potential to drive efficiency, automation, and insightful decision-making. However, investing in AI requires discipline and caution due to the rapid pace of innovation and associated risks.
We believe two core value drivers of AI investment are its self-improvement capabilities and its potential to disrupt data-heavy industries. Risks include technical feasibility, consumer trust, incumbency risk, and the rapidly shifting legislative landscape.
To navigate this space effectively, we must conduct rigorous due diligence and analyze factors such as the founder's expertise in AI, the technology's innovativeness, market competition, round dynamics, and the business model's sustainability.
Overall, successful AI investments necessitate understanding the underlying technology, market trends, and larger economic factors. We will diligently pursue exciting AI opportunities as a part of building a diversified portfolio to drive risk-adjusted returns.