Recently, an MIT professor and economist named Erik Brynjolfsson expressed his hopes that artificial intelligence (AI) could significantly boost productivity growth rates. He believes that AI has the potential to increase the current anemic 1.2% rate to 3% or even 4%, which would be beneficial for businesses and governments. It could help address labor shortages, drive earnings growth, and increase tax revenues, thereby tackling current debt levels.
While there has been a lot of hype surrounding generative AI and its impact on certain market sectors like AI startups, the macro effects have not been widely felt yet, as AI adoption remains largely experimental. In a recent Breaking Analysis segment, ETR’s Erik Bradley and Daren Brabham joined the program to discuss the latest trends in AI adoption, the use of generative AI, deployment models, and the AI leaderboard based on spending momentum and market presence.
One interesting observation is the impact of AI spending on other sectors of the enterprise information technology market. The graphic provided above shows the various sectors tracked in the Enterprise Technology Research Technology Spending Intentions Survey. The red dotted line represents highly elevated spend velocity in a sector. It is evident that AI momentum slowed down as we transitioned out of the isolation economy, but it quickly picked up again after the announcement of ChatGPT. Currently, ML/AI has become the top sector in terms of spending momentum. However, it’s important to note that overall enterprise tech spending expectations have decelerated from 7.5% growth to 2.9%. Instead of allocating discretionary spending for AI, chief financial officers and chief executive officers are shifting budget from other sectors to fund AI initiatives.
Erik Bradley adds some additional context to this data. He mentions that although a 3% growth rate may not sound impressive, it is a significant improvement compared to the previous year’s rate of 0.8%. He also highlights the fact that the worst spend numbers in the past came from the largest organizations in the world, but now they are increasing their spend, indicating a positive trend. Furthermore, ML/AI currently has a sector net score of 52%, which is significantly higher than the average score of 20% in the survey. This demonstrates the immense potential of AI.
Another interesting finding is that AI adoption is tracking the hype. The latest spending data on AI adoption reveals that more organizations are evaluating generative AI and large language models for business use cases. The chart above shows a decline in the percentage of organizations not evaluating gen AI and an uptick in the use cases being explored. It’s worth noting that the percentage of customers in the Global 2000 evaluating gen AI is higher than the industry average, with only 14% of them not evaluating gen AI.
However, despite the increasing interest and evaluation, most of the AI activity remains in the evaluation phase rather than full production. The use cases primarily focus on productivity and straightforward tasks similar to ChatGPT. Daren Brabham suggests that organizations are being cautious due to tight budgets and uncertain business environments. They are starting with familiar and safe use cases such as AI chatbots for customer service, text and data summarization, and code generation. While these use cases are not revolutionary, they still contribute to progress in AI adoption.
Finally, let’s discuss the power law of generative AI. The CUBE Research team has developed a framework that applies the power law concept to gen AI. The graphic above illustrates the relationship between the size of large language models (LLMs) and model specificity. It is comparable to the dominance of four major music labels in the music industry. Currently, large cloud companies, Nvidia Corp., and OpenAI LP are at the forefront of the gen AI narrative due to consumer adoption and industry economics. However, there are third-party and open-source initiatives that also play a significant role in the gen AI landscape.
Overall, the potential of AI to drive productivity growth and its increasing adoption are promising. While there are challenges and budgetary considerations, it is clear that AI is gaining momentum in various sectors. The future of AI looks bright, and it will be interesting to see how it continues to evolve and shape industries.