In this scripted solo episode of the Seed to Harvest podcast, Paige Finn Doherty of Behind Genius Ventures outlines the firm’s framework for investing in early‑stage artificial‑intelligence startups. She begins by citing a quote about investing genius and positions AI as a sector where many are “going crazy.” She then explains the two core value drivers she sees in AI: (1) the ability of models to self‑improve with more data, and (2) the capacity to disrupt data‑heavy industries such as healthcare, finance, transportation, and manufacturing. A macro driver—strong AI funding appetite despite a broader venture‑capital slowdown—is presented with Crunchbase data (AI deals 18% of Q2 2023 VC funding). Doherty enumerates the primary risks she evaluates: technical feasibility (model constraints, compute costs, talent scarcity), consumer trust and explainability, incumbency risk (large tech giants entering the space, illustrated by a Duolingo case study), a rapidly shifting regulatory landscape, and data‑privacy concerns. She then maps how these drivers and risks reshape the traditional diligence checklist: founder/team AI expertise, founder story, technology and data moat, market competition, round dynamics (valuation, ownership targets, cap‑table health), macro‑economic and trend analysis, and business‑model sustainability (revenue assumptions, growth‑equity perspective). Throughout, she emphasizes disciplined, hypothesis‑driven diligence, skepticism of hype, and portfolio diversification. The episode concludes with a call‑to‑action for AI founders to reach out, acknowledgments to collaborators, and mentions of the fund’s website, blog, and new merchandise.
Chapters
Hi everyone, and welcome back to Seed to Harvest. I was reading this book today called The Psychology of Money by Morgan Housel, and it's really wonderful. And one of the quotes that I really liked was, a good definition of an investing genius is the man or woman who can do the average thing when all those around them are going crazy. Tales drive everything. And I'm really excited for today's topic because I do feel like it's an area where everyone has kind of been going crazy recently. And I wanted to take a moment and talk about our approach to investing in early stage AI companies, the value that I see in investing in the space, as well as the significant risks And then how I've shaped our overall approach in this specific vertical at Behind Genius Ventures. So I'm really excited today to share some of the framework of my decision-making process. I would say a lot of the past year has been spent really honing in this decision-making process, reviewing thousands of deals and seeing how the timelines of companies play out. And so this is not going to be the most data-driven, lots of numbers podcast. It is specifically derived from my experiences evaluating many, many companies in this space. I also did my senior thesis on applied machine learning and speech processing at San Diego State, so it's an area I have a deep interest in as well. So I'll start with our introduction today. 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 for 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 The excitement about the potential of AI, although warranted, needs to be supplemented with a healthy dose of discipline, caution, and diligence. Today, we'll dig deeper into the value of investing early in AI, the risks, and our unique approach to the space. Thanks to Mike Arpia from Moonfire and Arkady Kulik from RPV for reviewing this piece. So, The value in AI investing, the 2 core value drivers we see. 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. So here are 2 key elements and 1 macro driver driving the value of AI that we see today. Value driver number 1, AI can passively improve through additional data. Because AI can learn from data and optimize performance over time, it becomes a self-improving system compared to other codebases that decay and accrue expensive and time-consuming technical debt, AI learns and improves. While the algorithm may degrade over time if the training data is poor, AI's growth still provides a potential competitive edge to startups by allowing them to continuously refine their products and services with minimal incremental cost. Value driver number 2 is that AI can disrupt data-heavy industries. So AI can take vast amounts of data and derive meaningful and targeted insights, highlighting patterns that would be close to impossible for a human to identify. Whether it's autonomous vehicles revolutionizing transportation, AI algorithms transforming financial trading, or machine learning enhancing predictive maintenance and manufacturing, the scope for innovation is vast. For example, in a field like healthcare, AI can analyze large datasets to improve diagnostics and treatment plans. Or in finance where it can help predict market trends. The macro driver, as you have all seen today, is heavy funding appetite. So in a rapidly declining funding environment, AI seems to be the one bright spot. According to Crunchbase, global venture funding in Q2 of 2023 fell to $65 billion, down 49% compared to Q2 of 2022. However, AI deals represent 18% of the overall venture investments. And anecdotally, at the early stage, most of our calls with multi-stage firms include the question, what are your thoughts on AI? What are you looking at in the space? And I will say I was hesitant to add this as a core value driver, as early-stage funding appetite doesn't necessarily indicate future success. But I found it important to mention because it is occurring and it is very common in today's venture funding ecosystem. So 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 and not just standalone products. Companies that effectively incorporate AI into their offerings can enhance the user experience, improve efficiencies, and ultimately build more sustainable competitive advantage against other players in the space. Now the fun part. Risks associated with AI investing. So the potential for high returns in a hot field is alluring. It's crucial to balance enthusiasm with a keen awareness of the inherent risks. Risks in investing in AI early 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 at Behind Genius Ventures when evaluating early-stage AI startups. Number one is technical feasibility. While many of these ideas can appear groundbreaking on paper, the reality of developing and commercializing these solutions can be littered with technical obstacles, Normally in the form of model constraints, API call costs, overreliance on non-proprietary language models, or the volume of computing power necessary to execute, all in the face of ensuring that the business can still reach a level of profitability. AI startups also need to attract talented developers, which are in high demand, but usually short supply, increasing the labor costs associated with attracting top talent to a company. Number 2 is consumer trust. There are also concerns around product development and consumer trust. Diving deeper on a technical aspect, there's the issue of explainability, or the lack thereof, in some use cases. Deep learning models are very frequently critiqued for being black boxes. Their decision-making processes are obscure, complex, and not easily understandable, which can be a barrier to trust and widespread adoption. Is the AI giving me the right answer? Is it accurate? What data was this trained on? Is the output getting better or worse over time? These are often questions we ask ourselves when we're demoing a product or evaluating a company. Number 3 is incumbency risk, and by far this is the one we tend to see the most of. The highly competitive nature of the AI field means that startups will be pitted not just against other emerging startups. But also well-established tech giants and research institutions with deep pockets and extensive resources. Microsoft, OpenAI, Apple, Meta, and more. One example of this incumbency risk is months ago we analyzed a mobile-based AI-powered language learning application. Although enticing initially, our due diligence 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 a household name, an immense distribution advantage, and abundant engineering and financial resources essentially single-handedly overtook a startup. Number 4 is a rapidly shifting legislative landscape. The fluid and often unsettled state of AI-related regulations provides an additional layer of risk. AI technology is advancing at a pace that far exceeds the development of corresponding legal and regulatory frameworks and policies, causing a degree of uncertainty for companies attempting to navigate this space. The 5th risk that we think about is data privacy considerations. So legal and privacy implications are also to be considered. AI at its core is about leveraging computational models to simulate and augment human intelligence. As a result, the lifeblood of many AI models is data, unimaginably vast and hopefully quality-assured datasets. Acquiring this data, however, isn't always straightforward, and issues related to data collection, usage rights, and quality control can significantly hamper an AI startup's long-term successes. So how do these value drivers and risks apply to our investment approach? Overall, our Behind Genius Ventures 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 to see beyond the hype. I'll go into a deeper dive into just some of the factors that we think about analyzing and how these value drivers and risks apply or change our investment parameters. So the founder and team, we always assess the founders and the team's competency. However, with AI, we evaluate their expertise in AI specifically, understanding of the problem they're trying to solve, and the founder's ability to attract and retain top talent. The story. So we're a founder-first fund at Behind Genius Ventures, driven by the underlying story that drove the entrepreneur to pursue their idea. In almost any investment we make, we look for a really strong why. Normally this is manifested in the founder's personal experience with a 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 manages, sources, 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. In 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. We invest in pre-seed and seed stage startups, and whether we can capitalize on our desired ownership target, currently 2 to 3%, and ensuring there's 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 capital growth downstream. But in the landscape of AI, we are very cautious of lofty valuations and cognizant around the hype in the space. As far as 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. A lot of this comes from my background in growth equity, evaluating sustainable business models. While I invest at a venture scale, I still take many of these learnings into my investing today. In conclusion, 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 2 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, at Behind Genius Ventures, 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 part of building a diversified portfolio to drive risk-adjusted returns to our investors. If you're an early-stage founder building an AI and this podcast resonated with you, feel free to reach out to me. My email is paige@behindgeniusventures.com. And if you're curious about learning more about Behind Genius Ventures, our website is behindgeniusventures.com. And I hope you enjoyed this podcast on how we think about AI investing at Behind Genius Ventures. And I will see you all next week. Just wanted to say a special thank you to our investment analyst, Andy Schraun, for his incredible work co-writing this piece with me. And you can check out more of our investment themes on our blog. That's behindgeniusventures.com/blog. Thanks. And let me know what you think. This was a little new format for me, a little more scripted, um, but I really enjoyed it. I've always found it easier to communicate through writing and reading written pieces is a very interesting medium for me. So yeah, let me know if you like it. Also, I designed new merch with the wonderful Melissa Sia, um, and I love it. It's like disco balls that are rainbow. It has the coordinates of our office, says San Diego, and then Behind Genius Ventures on the back. Um, I think I'm gonna put these on the website soon. Anyways, hope you all have a wonderful day. Bye!
