The AImptying of Commercial Real Estate

 In Artificial Intelligence

The Salesforce Tower, a 1,070 ft skyscraper in San Francisco, has just been topped out. This LEED Platinum building is filled with innovative equipment and careful design to reduce its energy consumption. No doubt the facility team will also be top notch, working with the latest control systems. But the HVAC, plug, and lighting loads assume a big workforce filling each of the 61 floors. How likely is that?

According to the McKinsey Global Institute, “about half of all the activities people are paid to do in the world’s workforce could potentially be automated by adapting currently demonstrated technologies.” Think that doesn’t include your work? McKinsey estimates that 23 percent of a lawyer’s job can be automated.

For a thought experiment, imagine the Salesforce Tower were being built in 1960. We’d assume that there would be a large mailroom and a small army of typists and secretaries. The plugload at each desk would be minimal but all the lights would be incandescent. Many of the windows could be opened manually. We’d plan our HVAC system accordingly. We could never imagine the computerized office of 2000, easily within the lifetime of the building.

So let’s think about the office building of 2037, a mere twenty years from now. Those law firms with lots of associates and partners (a staple of class A office space) are down to two partners and their cloud-based AI assistants. Those marketing teams running lead-gen campaigns are now staffed with one creative and one business lead – the AI runs the campaigns, analyzes the results, and tweaks the message. The CFO has one analyst. The investment bank has one research head, with all the heavy lifting being done by AI and an offshore team in Uganda (East Asia has become way too expensive). The minimal amount of cleaning needed can be done in the dark by robots.

You might be thinking, “This is not my problem. Our building will have been sold 3-4 times by 2037.” You might be lucky with the next buyer, but savvy professionals are soon going to start asking tough questions as the true implications of AI become clear.

If you’re creating a strategic energy plan for commercial real estate, this is not a trivial issue. Who is going to fill these spaces? How much energy are they really going to need? How do you avoid over-procuring renewables? How can you modularize your systems to be responsive to changing uses?

The smart people at Boston Properties and Hines have likely thought through these issues for the impressive Salesforce Tower. Otherwise, we facing a situation we’ve seen before …

Image Courtesy of Penn State University

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