Inevitably, any new industry starts with a host of competitors trying to figure out the best way to make their business model work, and the stronger surviving to take a larger market share. Generative AI will be no different, but industry consolidation in the Generative AI space contains its own unique challenges. How will they be addressed?
Generative AI – software code that utilizes custom databases to generate new and original content – is red hot since the launch of ChatGPT (by OpenAI) on Nov. 30, 2022.The AI-based program uses Reinforcement Learning from Human Feedback combined with advanced natural language training, extensive databases and an easy to use interface that non-coders can utilize to generate complex results (albeit sometimes imperfect, depending on the nature of the question and way it is asked). Google, Apple, Microsoft and others are all getting in on the Generative AI game, whether utilizing ChatGPT or its own internally-developed systems or through acquisitions.
OpenAI has a free-to-use platform as well as paid-for platforms that can be custom billed to certain databases, which are becoming more and more widespread. CB Insights has suggested there are more than 360 Generative AI companies in the field, with more companies sure to follow. Moreover, Generative AI is looking to shake markets, with Goldman Sachs suggesting that Generative AI “could raise Global GDP by 7%” over a 10-year period.
As has already started to happen with Cisco’s acquisition of Splunk, mergers and acquisitions (M&A) activity will heat up in this industry. Investors investing in Generative AI businesses will be looking to M&A in order to consolidate and capture market share. But there are challenges:
- Will Generative AI companies, each working off their own models, ever be able to integrate?
- Will Generative AI tools be hyper-specialized or general?
- Will the databases be transferable? Will the customers or the Generative AI companies own them?
- How will data compliance and personally identifiable information be utilized?
- If they don’t align, will the stronger code or the stronger brand prevail?
Moreover, due diligence on Generative AI has its own sort of complexity. While software verification companies exist, will they be able to adapt to assessing the quality of Generative AI models?
A host of legal issues will present themselves, some under consideration already (data privacy, copyright ownership of derivative works, etc.), and how they’ll get addressed will affect the M&A market for Generative AI businesses.
Addressing complex issues in M&A for Generative AI businesses will require actual intelligence or, well, at least an experienced law firm paying attention to the opportunities and challenges this nascent industry will bring. If you have further questions, please reach out to Jeff Cassin at jcassin@norris-law.com.
About Jeff Cassin
Jeff Cassin is a business attorney, specializing in M&A, corporate law, investment transactions, and business contracts. Serving as external general counsel, Jeff assists companies without internal GCs and aids existing GCs in specialized areas. He represents a diverse client base, including mid-size businesses, startups, investment funds, media and technology companies, and individual investors. Jeff's expertise encompasses complex transactional work, including financing, corporate formation, securities offerings, employment agreements, and more. He skillfully manages M&A transactions, deal negotiations, and strategic management decisions. Additionally, Jeff is proficient in supporting startups through formation, operational stages, and investment rounds, expertly guiding them from inception to sale.