Authors: Louis Lehot, Natasha Allen, and David W. Kantaros

Amid a period of recalibration, the artificial intelligence industry is experiencing a transformational phase.

According to a recent report from Stanford’s Institute for Human-Centered Artificial Intelligence[1] that closely monitors AI trends, there’s been a notable adjustment in global investment patterns within the sector.

Despite a decline in overall AI private investment last year, funding for generative AI surged from 2022 to reach $25.2 billion, and notable companies in the generative AI space, including OpenAI, Anthropic and Mistral, reported substantial fundraising rounds.

AI ventures continue to attract significant investment, like Anthropic’s recent multibillion-dollar investment[2] from Amazon, and Apple has already started the year off with the acquisition of DarwinAI,[3] a company working to make AI systems smaller and more efficient.

The potential of AI technologies is immense, with the global market projected to reach $407 billion by 2027.[4] This translates to an implied compound annual growth rate of 36.2% during the forecast period of 2022 to 2027. For buyers that are listening for the sound of opportunity knocking, here it is.

With AI acquisitions becoming an increasing area of focus for investors and technology buyers, this article will hone in on specific areas to focus on when structuring and executing a transaction with a company that has an AI-centric business model.

From target identification to due diligence to navigating regulatory frameworks, the journey of acquiring an AI company is better spent after careful preparation and strategic foresight.

Target Identification

As the cost of programming AI-powered algorithms and large language models comes down, buyers will look beyond pure engineering talent of the target team and seek to ensure that the target is acquiring unique and proprietary datasets.

It’s no longer just about the algorithms and large language models, it’s about having access to proprietary data that no one else can get, and the ability to use the data in the AI system in the manner desired. Sellers looking to position themselves for acquisition should highlight, expand and protect their access to proprietary data as much as engineering talent.

Due Diligence Concerns

Conventional due diligence practices in the tech industry often focus on tangible assets like proprietary software and hardware.

However, for AI companies, the real value lies in intangible assets.

Layered, multifunction algorithms where there are not likely to be patents, large language models that are unprotected by copyrights, and increasingly, the exclusive access to proprietary datasets that together produce valuable answers, will drive monetization.

And so it goes to reason that buyers need to conduct technical and legal due diligence to capture a wider range of the target’s technology functionality. From the legal and compliance side, buyers will need to understand where the target operates and from where can users access the product in order to determine the breadth and scope of applicable laws.

Similarly, buyers will want to ensure that data privacy rules are not violated, that cybersecurity is intact, and that copyrights are licensed or excluded. Buyers will also need to confirm the genealogy of the data, from its origin to the assignment to the buyer, and will want to establish that seller is transferring to buyer contractual rights to use the data for the buyer’s intended purpose.

From a technology side, this means examining whether the AI solution is authentic, robust, scalable and aligned with business objectives. With thorough technical due diligence, investors can avoid exposure to “fake AI,”[5] or misrepresented solutions lacking genuine capabilities, potentially leading to substantial financial losses.

Key questions to address during the process include algorithms and models, purchase agreements, data privacy and security measures, and ensuring compliance with relevant regulations such as the General Data Protection Regulation or the California Consumer Privacy Act.

Additionally, look for industry-specific or geographical regulatory concerns, and consider how regulatory changes could affect the company’s operations and future growth.

Special Considerations for Purchase Agreements

In crafting a purchase agreement for a company with an AI-centric business model, it’s important to recognize the distinctive nature of AI systems. While conventional agreements cover typical risks associated with intellectual property or software, the unique attributes of AI demand special attention.

Buyers want assurances and guarantees from sellers to mitigate specific risks linked to the target company’s business. However, standard provisions may not adequately address the complexities of AI, and lawyers must assess the AI company’s risk profile thoroughly, considering its potential for high-risk outcomes.

Addressing these concerns requires tailored provisions in the purchase agreement. Considerations include representations and warranties regarding AI assets, encompassing ownership and noninfringement, not to mention compliance with data privacy rules and cybersecurity integrity and maintenance. Sellers must disclose any known risks or limitations associated with the AI technologies being transferred.

To manage risks effectively, buyers often seek indemnities for breaches of these representations and warranties. Additionally, holdbacks and escrow arrangements can be utilized to ensure sellers meet their obligations and address potential post-closing liabilities. Increasingly, third-party insurance carriers are underwriting these risks in the course of transactions.

Any combination of these measures can streamline the transaction process and promote clarity for the parties involved.

Navigating Regulatory Frameworks

As the regulatory landscape surrounding AI is nascent, governments and regulatory bodies worldwide are dealing with issues such as data privacy, algorithmic bias and ethical considerations. This becomes important for counsel involved in AI acquisitions, as they must stay abreast of potential changes and ensure compliance with relevant laws and regulations, especially those that are not yet in force or were never contemplated when the business was created.

According to the Stanford University AI Index Report, the number of AI-related regulations in the U.S. has risen significantly over the last five years.

In 2023, there were 25 AI-related regulations, up from just one in 2016. Last year alone, the number of AI-related regulations grew by 56.3%. In 2023, the count of U.S. regulatory bodies crafting AI regulations climbed to 21, up from 17 in 2022, signaling an expanding interest in AI governance across a broader spectrum of American regulatory entities.

Among the newcomers to implement AI-related regulations for the first time in 2023 are the U.S. Department of Transportation, the U.S. Department of Energy, and the Occupational Safety and Health Administration.

Adherence to data privacy regulations such as the General Data Protection Regulation, the California Consumer Privacy Act, and the Health Insurance Portability and Accountability Act is especially important for businesses that collect, process and create data outputs containing or based on datasets containing sensitive personal information.

Acquisitions of AI companies are facing special scrutiny from antitrust and competition regulators around the world, who are concerned with not only monopolistic practices and anti-competitive behavior, but also about the effect of AI on jobs.

Effect on AI Startups From Acquisition

Major Big Tech firms significantly shape the trajectory of AI startups.

The outcomes of AI acquisitions hinge on various factors, such as the preservation or erosion of autonomy, cultural integration and the fostering of innovation within the acquired entity. For instance, Google’s acquisition of DeepMind stands as a model of success, as DeepMind has retained its autonomy under Google’s umbrella, fostering continued innovation.

Conversely, Apple’s assimilation of Siri, one of Steve Jobs’ final major creations, saw it lose autonomy[6] as it became Apple’s voice assistant.

Cultural clashes during integration can lead to the departure of key personnel, yet some acquisitions manage to maintain cultural harmony. The spectrum of outcomes and effects from acquiring AI startups encompasses triumphs and hurdles for the involved parties.

While acquiring an AI company offers substantial prospects for growth and innovation, the risk profile for AI-centric business models is so different from acquisitions of other technology businesses that a new approach is required.

With a retargeted due diligence investigation, tailored purchase agreement and adherence to regulatory mandates, buyers can optimize the benefits of acquiring an AI company and avoid the many hidden pitfalls.







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