Strategic advice to leverage new technologies

This Advisor examines results from a recent Cutter Consortium survey on enterprise adoption of generative artificial intelligence. Based on our survey findings, approximately one-third of organizations currently indicate they plan to integrate large language models into their own applications, while nearly half are taking a wait-and-see approach.

Arthur D. Little’s Michael Papadopoulos, Nicholas Johnson, Michael Eiden, Philippe Monnot, Foivos Christoulakis, and Greg Smith debunk the idea that security concerns about LLMs are entirely new. They examine each concern to show that these issues are merely new manifestations of existing security threats — and thus manageable. “LLMs highlight and stress test existing vulnerabilities in how organizations govern data, manage access, and configure systems,” they assert. The article concludes with a list of 10 specific ways to improve LLM-adoption security.

Cutter Expert Curt Hall takes a fascinating dive into data on key GAI trends and examines findings from a Cutter survey of more than 100 organizations worldwide. Hall calls the rate of adoption of GAI tools “amazing.” Hall‘s article looks at enterprise adoption of LLMs, strategy and oversight for GAI adoption and usage, and enterprise experience with GAI to date.

The potential applications for GAI are almost limitless, saving companies enormous amounts of time and money compared to current processes, whether they relate to internal knowledge sharing and exploitation or external market analysis and customer service. We are still at the very beginning of this revolutionary curve, and if we are to fully enjoy its advantages, there are important issues to be resolved in areas such as IP protection, regulation, security, and environmental impact. This Amplify takes a look at these issues — and more.

Cutter Fellow Stephen J. Andriole presents a no-holds-barred discussion of the predictions and fearmongering swirling around GAI. Clearly, Andriole says, we should stop panicking and start thinking about how to optimize GAI. We should also acknowledge that some form of regulation is necessary. Andriole turns to ChatGPT and Bard (who else?) for advice on potential regulation, looks closely at what other countries and regions are doing in this area, and highlights the importance of addressing IP infringement issues. He concludes by saying that regulatory decisions should not be anchored in technology capabilities, pointing out that social, political, and economic concerns about the impact of regulation will exert as much, if not more, influence on the regulatory scenarios that emerge.

Cutter Expert Paul Clermont takes a down-to-earth look at what we can expect from AI in the near term. For one thing, he says, we’re still in the garbage-in, garbage-out phase with LLMs; for another, it’s nowhere close to artificial general intelligence. There are, of course, ethical and social implications, including the fact the AI puts what we don’t like about today’s Internet (disinformation, loss of privacy, and more) on steroids. A host of new legal issues also needs attention, Clermont notes, which may lead to governments playing a role in the evolution of AI usage that they did not assume in the advent of the computer or the Internet.

Ryan Abbott and Elizabeth Rothman believe we must address the legal, ethical, and economic implications of AI-generated output if we want to foster innovation, promote the responsible use of AI, and ensure an equitable distribution of the benefits arising from AI-generated works. The authors look at the complicated relationship between AI and IP, then discuss the Artificial Inventor Project, which filed two patent applications for AI-generated inventions back in 2018 in the UK and Europe. The project aims to promote dialogue about the social, economic, and legal impact of frontier technologies like AI and generate stakeholder guidance on the protectability of AI-generated output. Clearly, say Abbott and Rothman, AI systems challenge our existing IP frameworks and necessitate a thorough rethinking of what rules will result in the greatest social value.

Arthur D. Little's Greg Smith, Michael Bateman, Remy Gillet, and Eystein Thanisch scrutinize the environmental impact of LLMs. Specifically, they compare carbon dioxide equivalent emissions from LLMs with using appliances such as electric ovens and kettles, streaming videos, flying from New York City to San Francisco, and mining Bitcoin. Next, the authors look at how fit-for-purpose LLMs and increased renewable energy usage could help LLM operators reduce their carbon footprint. Finally, this ADL team points out the relationship between smaller LLMs and responsible, democratized AI.