Generative AI Productivity Gains Will Come

But, revolutionary changes take longer, and we need to be patient, argues BCA Research’s Irene Tunkel.


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Excitement about generative AI (GAI) is palpable.

Its life can be measured in dog years — OpenAI’s ChatGPT garnered a hundred million users within the first few weeks of its existence. To put this into perspective, it took Facebook five years to gain that many users, two for TikTok, and a year for Instagram. A year later, ChatGPT is no longer the only game in town, with Google’s Gemini, Perplexity AI, Mistral, and Antropic’s Claude gaining in popularity.

However, the immense promise of GAI goes beyond the ChatGPT “storefront” that most of us are now familiar with, and in corporate use cases, that can change the way companies do business. Indeed, corporate interest in GAI is strong. According to Factset, 179 S&P 500 companies cited the term “AI” during their earnings calls for Q4 2023. This number is well above the five-year average of 73 and the ten-year average of 45.

The majority of companies focus on the cost savings and operational efficiencies that this new technology may deliver, such as streamlined operations, automation of mundane tasks, supply chain optimization, and anomaly detection. GAI may help the corporate bottom line (eventually) but is unlikely to change the way companies make money or manage costs.

The real promise of GAI lies in its potential to revolutionize the economy and disrupt multiple industries, replacing an army of white-collar workers. Consider:

Software development: According to CB Insights, the productivity of software developers who leverage the OpenAI foundational model Copilot has more than doubled.


Drug development: The entire process of developing a new drug, from preclinical research to marketing, can take many years and costs billions of dollars, with 80 percent of costs associated with research and development. GAI can rapidly accelerate the R&D timeline by enhancing the process of identifying compounds for possible new drugs, predicting molecular structures just as it predicts the next word in a sentence, sorting through databases, and matching molecules. Insilico Medicine is a GAI poster child: It developed a new drug candidate in just 46 days with the help of a GAI algorithm, a process that in the past, without GAI, took the company more than four years.

Fintech: Companies are using GAI to transform the way business is done, with chatbots predicting markets and assisting analysts with research. The online consumer lending platform Upstart leverages AI models to assess the creditworthiness of its potential customers. Lemonade, an AI-driven insurance company, continues to disrupt the insurance industry. The company successfully developed a GAI-based claim settlement process, spearheaded by a chatbot, AI Jim. Clever Jim handles 98 percent of claims, with 40 percent of them requiring no human intervention, reducing costs and processing times.

Video game development: GAI will plug into various forms of entertainment and virtual settings, providing new ways to experience gaming. Gaming giant Electronic Arts recently said that 60 percent of its new game development could be impacted by GAI, as the technology can develop storylines, code, and characters and render realistic environments. Essentially, GAI can power game development from start to finish, reducing development times and costs.

We are excited by the opportunities GAI presents, but there is a caveat. The profound and revolutionary changes it can deliver will take time to be fully realized.

It appears that most companies are still in the early stages of investment in GAI. The 2023 MIT Technology Review Insights Survey shows that in the U.S., 60 percent of companies are experimenting with AI, 25 percent are implementing it, and 15 percent are not deploying it at this time.

During Q4 2023 earnings calls, multiple companies noted that capex and R&D expenses will increase as they ramp up AI investment. This is reflected in the supercharged revenue growth of advanced chip designers and manufacturers, such as Nvidia, AMD, and TSMC. However, it may be a little while before those investments bear fruit: In the same MIT survey, 48 percent of companies said they expected projects to come online in the second half of 2024 or in 2025. One complication delaying deployment is the resolution of emerging regulatory and legal requirements.

Productivity gains stemming from AI will be realized, just not as soon as expected. Over the next 12 to 18 months, we will likely observe green shoots in GAI-related productivity. As such, an attribution of the recent rise in productivity to the effects of GAI is most likely premature; we think it is just a rebound after a post-pandemic slump.

GAI will transform the world, but we need to be patient.

Irene Tunkel is the chief U.S. equity strategist at BCA Research.


Opinion pieces represent the views of their authors and do not necessarily reflect the views of Institutional Investor.