AI Business Defensibility
Its More than Technology
Despite the hype, algorithms, models and AI technology by themselves are not enough to fund a startup, let alone create an industry leader. When evaluating an AI company (as an investor or potential employee) there are several characteristics and qualifiers that should be considered as it relates to business defensibility (and viability).
Bob Mason, Managing Partner at Argon Ventures, and I will be speaking at the GenerAIte Conference on June 15th, 2023 on this very subject.
As part of our conversation, we hope to cut through the AI hype to help interested parties make sense of the ever-expanding AI landscape.
To that point, below are 10 factors that contribute to the business defensibility of AI companies:
Technology: AI companies that develop unique and proprietary algorithms, models, or methodologies can get product market fit, defend their market share and establish strong patents, copyrights, and trade secrets. This is a significant – and is perhaps the most important factor related to defensibility, but it’s no silver bullet; there are many more factors which contribute to business defensibility.
Network Effects: Companies that have established networks or ecosystems that benefit from increasing scale can enjoy defensibility and should be given points for this attribute. For example, platforms that leverage user-generated data to improve their AI models can become more valuable and difficult to compete against as their user base grows. The "Winner-Takes-All" (“WTA”) network effect is often considered the most impactful and powerful form of network effect – especially among B2B solutions. The reason why its effective is because the value of the model or software increases as more users join the platform, leading to a positive feedback loop. WTA is something that is hard to replicate as a new market entrant and is a big boost for the company that is able to leverage it.
Platform: AI platforms provide infrastructure --both hardware and software - that supports the development and deployment of AI applications. Established platforms provide many benefits in terms of learning frameworks and libraries and they have the potential to simplify the development and deployment process through the use of APIs that facilitate integration with other vendors and systems, and enable efficient data management and integration.
Data: Companies that have access to large – ideally HUGE, high-quality datasets can develop AI models with superior performance. These datasets may be difficult for competitors to acquire, giving the company a competitive edge. (Keep in mind that having the data is one thing, knowing how to extract value using AI and/or data science is another thing entirely).
Intellectual Property (IP): Companies can protect their AI-related innovations through patents, copyrights, or trade secrets. Patents provide legal protection for novel and non-obvious AI algorithms or technologies, while copyrights can protect specific software implementations. A debate rages among legal scholars, VCs and software experts on the subject of the effectiveness of IP as defensive measure, but companies with relevant IP in an emerging market that is garnering significant levels of investment certainly have value
Capital: Adequate capital is lifeblood to any emerging company, let alone an AI focused organization where access to top talent is critical. The presence of capital is also a telling sign of either the previous success of the founder, or that the company has gained the stamp of approval by an outside entity (VC) who has had a “behind the curtain” look into the business plan, market opportunity and pipeline. While this is not a guarantee, it should at least provide some comfort that the company has been vetted.
Community: A vibrant and engaged user community can provide product and market-fit feedback, network effects, collaboration and innovation. It can also result in a form of organic (read: economic) growth, much like open source software communities.
Talent and Expertise: Building a team of top AI researchers and experts can create a significant advantage. The knowledge, skills, and experience of the team members can be difficult for competitors to replicate, especially in highly specialized areas of AI.
Brand: Building a strong brand associated with delivering high-quality AI solutions can create customer loyalty and trust. Being known and trusted in a new market can make it difficult for competitors to attract and retain customers, thereby enhancing defensibility. While there are always diamonds in the rough, having an established brand is always a good thing.
Partnerships and Strategic Alliances: Collaborations with other companies, research institutions, or government entities can enhance an AI company’s defensibility and market opportunity. Such partnerships can lead to shared resources, access to specialized expertise, and collaborative innovation, making it challenging for competitors to match the company’s capabilities. OpenAI’s partnership with Microsoft Corporation (involving technology, a major investment, adoption and distribution) is a key example of how a partnership can be transformative to both the business and industry. Partnerships and alliances take time to cultivate and develop, having them in place is a boon to a company’s go to market efforts.
Overall, a combination of these factors, along with continuous innovation, a deep understanding of product-market needs, and the ability to adapt to evolving technology trends, contributes to the defensibility of AI companies.