4 Morrison Foerster 1 Following technological breakthroughs and an explosion of interest, companies are rushing to acquire or develop AI resources.2 AI tools have proliferated throughout businesses in all sectors, raising AI issues even in non-AI deals. Key Drivers for AI M&A AI infrastructure companies augmenting core product markets. Big tech companies, particularly cloud/data center providers, have partnered with AI startups through commercial deals with minority equity investments to help solidify their positions as suppliers of AI infrastructure components. For example, Microsoft committed $13 billion for OpenAI (a MoFo client), and Google and Amazon committed up to $2 billion and $4 billion, respectively, for Anthropic, with significant commitments for these startups to utilize the investors’ cloud services. Companies designing semiconductor chips for AI applications have snapped up companies to facilitate the adoption of their chips. For example, NVIDIA bought OmniML, whose software shrinks machinelearning models, including large language models, so that such models can run on NVIDIA chip-powered devices. Enterprise software companies capitalizing on market demands. Enterprise software companies have moved to add AI capabilities in their product offerings, so their customers can build their own AI tools. For example, Databricks, a data management solutions provider, acquired MosaicML for $1.3 billion, adding capabilities for its business customers to build AI models using their own proprietary data. Vertical players seeking to accelerate AI adoption. Players in industries with more mature AI use cases, including biotech, legal, fintech, and edtech, have acquired AI startups with industry-specific AI expertise. For example, Thomson Reuters paid $650 million to acquire Casetext, whose key product, CoCounsel, is intended to act as an AI legal assistant. These acquisitions are frequently intended to accelerate the acquirors’ own AI strategies as well as to acquire the startups’ existing products. Startups accelerating exit timetable. Amidst rapid evolution, AI startups risk quick obsolescence and an uncertain road to commercialization, all in the face of fierce competition from more mature companies with established distribution channels and other resources. Such reality checks may prompt startup founders and their venture capital backers to move up their exit timetables, resulting in AI startups being sold sooner than anticipated. Emerging AI-Related Deal Features Retention structures top of mind. Talent acquisition is often a key component for tech M&A, and the talent wars have intensified in AI. Acquirors must consider retention and related tax, fiduciary, and other structuring concerns in the face of the rapidly growing demand for such talent. Generative AI Takes Flight and Raises Questions for Many Deals For regular discussions of AI developments, visit MoFo’s AI Resource Center. For an overview of generative AIrelated issues, see our April 25, 2023 client alert, “Key Issues in Generative AI Transactions.”
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