Generative AI hardware - the other arms race

New network models: a way forward

In response to these constraints and challenges in the manufacture of Generative AI chips, we are witnessing the emergence of novel responses by chip manufacturers and customers to the shortages in and constraints on Generative AI chips. These are examples of strategies being used by companies looking to circumvent the growing cost of supply chain and regulatory constraints.

On one level, we are seeing the rise of AI “Infrastructure-as-a-Service” models, where Generative AI hardware is virtualized and accessed in a manner similar to ordinary cloud-based computing offerings. With the constraints in AI hardware, access to and availability of AI chips are critical to developing these capabilities. This has led chip manufacturers to partner directly with cloud service providers to allow for commercializing capacity rather than providing chips to OEMs only. They can therefore access a different strategy to deliver to more end-users without incurring much risk, while end-users can access GPU computing power without suffering delays to hardware manufacture and supply.

We also see the complementary rise of “edge AI”, where AI chips are deployed directly on local edge devices, like sensors or internet-of-things devices – rather than on centralized servers in data centers. This reduces latency and bandwidth consumption, permitting edge systems to run Generative AI applications efficiently, although this approach is limited to running “smaller” or narrower focused language models. 

These evolving models can, however, give rise to challenging legal issues for companies engaging in Generative AI cloud computing or edge AI.

We are seeing chip manufacturers applying significantly more commercial leverage in AI chip supply arrangements – mandating significant pre-commitments, revenue sharing or equity models. We are also seeing regulators engage with the scope of export restrictions for Generative AI hardware – including whether to extend it to cloud models. This will be front of mind for service providers and their customers further down the service chain. For edge AI, industries must contend with additional regulations – for example, car companies developing smart cars or autonomous vehicles must ensure compliance with privacy, data processing, and telecommunications regulations, and this may extend to AI regulations depending on the adoption of Generative AI models and hardware. 

Protecting trade secrets

In response to increased demand, technology hardware companies are investing in the accelerated development of chip design and technologies to drive increasingly energy-efficient and powerful AI hardware.

While the classic means of protecting this valuable intellectual property is patents, patent protection requires the disclosure of the innovation to the world – including to competitors. Trade secret protections, by contrast, have the benefit of preserving the secrecy of valuable intellectual property. 

Typically, for information to qualify as a trade secret, it must: (a) not be generally known, (b) have had reasonable steps taken to ensure the information remains secret, and (c) have actual or potential value arising from the fact that it is not generally known. So long as the test is met, trade secrets can cover all kinds of information, and that information does not need to be novel or unique, as is the case with patents. For example, the recipe for Coca-Cola and the make-up of WD40 are both trade secrets. The protection provided by trade secrets can - potentially - last indefinitely without the need to file an application or seek approval. Unlike patents, however, owners of trade secrets have no defensive rights – they do not have the right to exclude others from the trade secret and can only prevent others from misappropriating the trade secret.

The major advantage of relying on trade secrets as the manner of protection for intellectual property is that it keeps one’s competitors in the dark for as long as that information remains “secret”. And, in an area such as AI chip technology - where what is known and being discovered is evolving so rapidly - keeping any new discovery a secret can be immensely valuable.

Conclusion

Although the demand for Generative AI development and applications are exploding, there are constraints in the manufacture and supply of the underlying hardware. These, and the imposition of regulatory restrictions, pose challenges for the industry. Companies that use Generative AI in their businesses – whether internally or as part of their market offerings - must remain alert to the supply chain and geopolitical considerations affecting the Generative AI chip supply. In response, companies may look to new computing and networking models, such as AI “Infrastructure-as-a-Service” models and “edge AI”, that are beginning to gain ground. In doing so, however, they must address novel legal issues that can arise. Companies looking to develop new intellectual property in Generative AI chip design to meet increasing demand must also seek the appropriate protection. As Generative AI becomes integrated into our businesses and lives, the stakes for securing the necessary AI hardware infrastructure will only increase. It is essential for companies to stay informed, proactive, and adaptable in this dynamic environment.