Throughout most of the 20th century, artificial intelligence (AI) was the stuff of science fiction. However, through the huge leaps in computational power since the late 1990s, AI has jumped from the pages of science fiction to reality.
AI is now a real growth area in the emerging technologies field whose newfound viability is creating new investment opportunities across many industries. In this article we outline a few key questions to consider when looking at an investment in a target company that uses AI to provide products or services.
A 2018 report by McKinsey found the economic impact of AI will most likely follow an s-curve pattern: slow at the beginning, given the investment required to adopt such technology, followed by rapid growth driven by competition and improvements in complementary capabilities.1 AI is being aggressively adopted by forward-looking businesses, particularly in the banking, retail and automotive sectors.2 This rapid adoption is no surprise, as “the mathematical and statistical foundations of current AI are well established”3 and companies (both tech and traditional businesses) will likely face a threat from AI-driven competitors or new entrants in the near term. The companies that power such progress will become attractive targets for acquisition and investment as these trends unfold.
Canada has quickly become one of the world’s leading hubs for AI (alongside the San Francisco Bay area, New York-Boston, London, Bangalore, Berlin, Beijing, Shenzhen and Tel Aviv).4 In particular, Toronto and Montréal have leading research and commercial centres in the AI space. Toronto has support of all three levels of government with the launch of the Vector Institute, a non-profit organization focused on AI research and start-up incubation. The University of Toronto also counts among its ranks Geoffrey Hinton, one of the pioneers of artificial neural networks and an AI advisor to Google. Montréal, on the other hand, is home to the Montreal Institute for Learning Algorithms and to Yoshua Bengio, one of the co-fathers of deep learning.5
As these opportunities progress from venture investments into large-scale corporate transactions, parties should take the time to understand, assess and allocate the AI-associated risks. Key questions to ask are:
Through careful consideration of these questions, an investor or acquiror can pursue diligence activities designed to assess risk, and can include terms in transaction documents that appropriately allocate risks among the transaction parties.
Before acquiring or investing in any AI-driven target, investors will want to conduct a thorough due diligence process. Investors should seek to learn as much as they can about how the AI is actually provided to the target and its customers.
Few organizations selling AI-based products or services have actually built their own free-standing artificially intelligent capabilities. The more likely scenario, however, is that the organization is leveraging the AI capabilities of an AI provider like IBM, Microsoft or Google to create its offering. In that scenario, the relationship between that AI provider and the target is one that should be thoroughly understood.
Even at a high level, an investor will want to understand what is special about the target’s business and service offering. What is it doing that a competitor could not easily replicate by leveraging the underlying AI provider’s service?
AI services are generally subject to license terms and restrictions. These restrictions can be defined geographically, by the number of authorized users, or by specific types of use of the AI provider’s service. If the target’s use is in breach of these restrictions, curing any breach may lead to unexpected costs, or, where the breach cannot be cured, may lead to the target losing access to the AI that powers their product.
AI is only as good as the data that drives its decision-making. Before investing in a target in the AI space, parties should seek to understand what information the underlying AI platform is accessing to build its algorithms.
The key question when it comes to this data is: does the target have the right to use it for their business purposes? In many ways, IP issues related to AI are not much different than those in any technology-heavy transaction.
One (perhaps obvious) question to ask is whether the AI platform accesses, stores or uses personal information. In Canada, the Personal Information Protection and Electronic Documents Act (PIPEDA) has a consent-driven regime which places limits on the scope of use of personal information, and other industries (such as health care and financial services) may have regulations or codes of conduct that may impose restrictions as well. Where the personal information includes data relating to Europeans, the EU General Data Protection Regulation (GDPR) will need to be considered as well.
Breaches of confidentiality agreements may lead to significant reputational harm in the event of a dispute with a customer and may open the target up to a real risk of liability or injunctive orders hindering the operation of the target’s business.
Similarly, AI that makes use of data sets made up of information the target receives from its customers may be breaching the confidentiality terms between that customer and another third party. Breaches of confidentiality agreements may lead to significant reputational harm in the event of a dispute with a customer and may open the target up to a real risk of liability or injunctive orders which could hinder the operation of the target’s business.
Appropriate diligence processes will help put some shape to the risk posed by the AI’s data use, access and storage:
In many cases, the extent to which data sets used by an AI platform access personal information or other proprietary or confidential information may be difficult to discern through reasonable diligence. Inventors and targets should consider including risk allocation mechanisms in the transaction documents, such as specific indemnities and representations and warranties (and in appropriate circumstances backed up by holdbacks or escrowed amounts) to address the AI’s use of data, specifically bearing in mind the fundamental importance of such data to the operation of the AI.
In many ways, IP issues related to AI are not much different than those in any technology-heavy transaction. AI technology, and specifically, AI licensing, raises consideration as to who owns and has the rights to the intellectual property generated by the AI.
Proper diligence activities should help determine what the target has created using the AI, and what sort of IP those creations include. Ownership and license rights will, to a large extent, be determined by the target’s contractual arrangements with its AI provider and customers.
Matters that cannot be determined by diligence can, at least as between the target and the investor (or seller and purchaser), be settled through appropriate indemnities, representations and warranties (and in appropriate circumstances backed up by holdbacks or escrowed amounts).
A special attribute of AI is its ability to effect results with limited human intervention. This makes certain applications of AI inherently riskier than more traditional products and services. For example, if an AI service is meant to serve offers for goods or services directly to consumers, it could inadvertently end up providing preferential pricing to one group of users over another. Even if the AI was not programmed to discriminate, the effects of the AI’s results could be discriminatory. In such cases, the potential consequences are as much reputational as legal in nature.
There are ways to try to understand the scope of these types of risks through proper diligence:
In many cases, however, the AI’s functioning and the impact of its application to the real world will remain opaque. For that reason, a careful allocation of risk through indemnities and representations and warranties is advisable, taking into account the amount of control each party has (or ought to have) over the AI’s outputs at any given time.
AI offers great opportunity for investment in the M&A and venture capital space. Growth potential is only expected to rise as adoption of AI and cost efficiency increase. Particular legal issues emerge when investing in AI due to its data requirements and its autonomous capabilities, but acquisition/investment agreements are flexible instruments. Appropriate diligence and careful allocation of risk through indemnities, representations, warranties, holdbacks, escrow, and price adjustments are the parties’ best defence against the uncertainty inherent in pursuing opportunities in this emerging and exciting space.
The authors would like to thank Vlad Krasner for his contribution to this article.
1 See “Notes from the AI frontier: Modeling the impact of AI on the world economy” McKinsey Global Institute, September, 2018.
2 See “Why Companies That Wait to Adopt AI May Never Catch Up,” Harvard Business Review, December 6, 2018 and “Notes from the AI frontier: Modeling the impact of AI on the world economy.”
3 See “Why Companies That Wait to Adopt AI May Never Catch Up.”
4 See “2017 in Review: 10 Leading AI Hubs” Medium, December 18, 2017 and “Top 6 Artificial Intelligence Hubs in 2018: An Analysis for Their Localization” Analytics Insight, June 6, 2018.
5 See “How Canada became a hotspot for artificial intelligence research” DMZ Ryerson University.