The rapid evolution of AI technologies is reshaping the investment landscape, with data transitioning from a prized asset to a commoditized resource. In the early days, when I worked with AI startups, the question of "Who owns the data?" was one of the most critical considerations for VCs. The ownership of unique datasets could make or break a deal, significantly impacting valuations and even determining whether a startup was fundable. I recall working with an AI cybersecurity startup in 2018 where simply mentioning "machine learning and deep learning" was enough to draw significant attention from potential investors. We projected the startup’s potential to achieve autonomous decision-making, a promise that was highly valued at the time.
However, as we move into the era of Generative AI (GenAI), those once-glorified capabilities have become almost obsolete. The emphasis has shifted from owning data to deriving actionable insights and integrating them with other data paradigms. The ability to generate proactive, results-oriented insights now takes precedence (see Exhibit 1).
The AI Revolution: Data as a Commodity
The transformation from "data is king" to data as a commodity marks a significant shift in how AI startups should approach their business models and investor pitches. As illustrated in McKinsey's analysis (Exhibits 2 and 3), the use of GenAI has doubled between 2023 and 2024. In 2023, 38% of people regularly used AI for tasks outside of work, a figure that jumped to 55% in 2024—all within a single year. This rapid adoption underscores the importance of agility and innovation in today’s AI market.
My Advice to AI Startups in this “Confusing Era”
Given the high fluctuation, rapid innovation, and commoditization of technologies, AI startups must navigate these challenges with strategic foresight. Here’s my advice (See Exhibit 4):
- Embrace Multimodal Models: Develop AI models that can process and integrate multiple data types—text, images, and voice. Leveraging foundational models from leaders like OpenAI and Cohere can significantly enhance your AI capabilities. Use endorsements from industry Key Opinion Leaders (KOLs) to build credibility and craft a unique, scalable go-to-market strategy.
- Focus on Vertical AI: Target specific industries with AI solutions that automate high-cost, repetitive tasks. Highlight the potential to expand into other verticals while delivering "quick wins". Emphasize robust data governance to address concerns around privacy and IP infringement. The ability to seamlessly integrate with other AI systems or data sources will be a critical selling point. Additionally, approaching Corporate Venture Capital (CVC) firms may yield better results than traditional VCs, given the ongoing battles between tech giants in the AI space. In the fierce competition among tech giants like Alphabet, Apple, NVIDIA, Amazon, and Microsoft, attempting to create a new AI paradigm might feel like trying to "boil the ocean." However, the AI landscape is vast, and even a focused niche that meets the above criteria can lead to a successful product and company.
- Demonstrate Clear ROI: Investors are looking for tangible returns. Showcase real-world applications and provide case studies that highlight the effectiveness of your AI solutions. Utilizing value calculators to quantify the benefits can significantly enhance investor confidence. For more on how to effectively use value calculators, check out my detailed article on the topics.
Funding Success in a Turbulent AI Market
The AI revolution is shifting the focus from mere data ownership to the effective utilization and integration of AI technologies. Startups that can innovate, apply, and efficiently use AI models will find themselves well-positioned to thrive in this ever-changing market. For more detailed insights, I encourage you to explore the full Bessemer article here.
