We are in the early days of an AI-driven productivity boom.
Here in the US, worker productivity rose 3.2% in Q4 2023, as you can see in the chart below. Notably, the combined productivity growth rates for Q3 and Q4 2023 were more than twice as high as the rates from 2005 to 2019.
This is only the beginning. Artificial intelligence is going to boost business productivity 40% or more in some industries. This will draw a clear line dividing the winners and losers.
We all know everyone’s talking about AI. Companies mentioned it on earnings calls over 30,000 times last year. And PwC reports that 73% of businesses are already using AI. But “using AI” could mean something as benign as managers favoring ChatGPT over Wikipedia when they need marginally reliable background information. That isn’t enough to drive up productivity.
Instead, the productivity winners will be the companies that deeply integrate customized AI into their enterprise software and business systems, pairing it with automation whenever possible. Despite all the chatter, Nitin Mittal and Thomas H. Davenport note in their book All in on AI that only a handful of major companies have already done this.
Over the next few years, the companies that quickly and aggressively take a comprehensive and customized approach to AI will radically outperform the ones that don’
Look at the banking industry, which has a prime setup for AI. Banking is already highly systemized with robust data pools, and most banking customers use digital platforms. Banks can use AI to improve risk and fraud analysis and to better target high-quality customers.
Banks like CapitalOne and JPMorgan Chase are going all in. CapitalOne has submitted the most AI patents of any US bank. It brought on AI talent from Bell Labs and Facebook, and it acquired AI consultancy Notch in 2018. JPMorgan Chase has a large in-house AI research department, and last year it trademarked IndexGPT, a “cloud computing software using artificial intelligence” for analyzing and selecting securities.
It's next to impossible for small banks to compete here. They can use open-source or off-the-shelf AI products. But they don’t have the same ability to recruit talent, fund AI research, hire AI consultants, and design bespoke systems. They’re going to suffer as the large banks leading the AI charge become much more productive.
The construction industry is another example. Labor typically accounts for 40%–50% of construction costs. And labor is hard to come by. Associated Builders and Contractors estimates that the construction industry needs over half a million new workers this year to meet labor demands.
This is where companies like Monumental will come in. Monumental is an Amsterdam-based startup that makes AI-driven bricklaying robots. They can be used on nearly any building site. (One of its cofounders previously sold a company to Palantir.) When construction firms use these kinds of tools, they can boost productivity while sidestepping the lack of available labor.
Again, this puts large construction firms at an advantage. It’s easier for them to pay the substantial upfront costs of bespoke AI-driven tools and robots. But the ones who do can work faster, cheaper, and attract more business.
We run a relatively small team here at Mauldin Economics, and even we have an internal AI think tank aimed at implementing AI into our business where we think it will ultimately benefit our readers. Our software development team, for example, uses it to tackle lower-level tasks, which frees up time and brain space for the more creative parts of the job. In other words, it’s making us more productive.
For the Macro Team, most of our focus is on pinpointing the publicly traded companies using AI to lead the productivity boom, as well as the consultancies helping them do it. In fact, we have a premier AI consultancy in the Macro Advantage portfolio, and we’ve seen some nice gains since we added it in December.
Best regards,
Ed D’Agostino
Publisher & COO
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