How AI Innovators Are Reshaping Office Real Estate Byte by Byte

The rise of artificial intelligence (AI) companies is not just redefining algorithms, it's also reshaping the office market.

The United States is home to 65 of the top 100 globally ranked AI company headquarters, according to Pitchbook. As the number of AI companies rises and the scale at which they operate increases, the data center industry is under pressure to meet the new demand.

In California, the cities that have the most AI company headquarters are San Francisco with six; Palo Alto with three; Santa Clara with three; San Jose with two and Campbell with two.

Seven of the nine AI companies headquartered in Texas are in the Austin area.

Demand is highest in the San Francisco Bay Area, where tenants are looking for about 2.5 million square feet of office space mostly in Silicon Valley, according to Cushman & Wakefield's recent report "From AI to Absorption: Office Demand, AI Talent Concentrations and What it means for Data Centers." Austin has the second-highest tenant demand, but it's concentrated with a single large tenant requirement.

With the need for office space also comes demand for the talent to fill it. An average of 5,550 jobs seeking AI talent in the U.S. are posted each month. Demand for AI talent started increasing in 2021 after it saw slight downward pressure from the pandemic.

California is a force for AI-related talent with three of the state's cities in the top 10 metropolitan statistical areas (MSAs) for the tech workers.  Companies in the state hire more AI-related employees than any other state. California also has the highest concentration of AI firms in the U.S., most of which are in the San Francisco Bay area.

In Texas, hiring is concentrated in the Texas Triangle: Dallas, Austin, Houston and San Antonio.

The semiconductor market is expected to see significant growth through 2030, which will prompt an increase in spending on data center systems, infrastructure and public cloud services.

Semiconductors address two phases of the AI lifecycle: training and inference. In training, AI chips process vast amounts of existing data and perform complex calculations. In the inference phase, AI chips are used to make inferences on the data on which they've been trained.

By 2030, the leading application in the global semiconductor industry is expected to be servers, data centers and storage. In 2020, the market was valued at $76 billion and is expected to increase by 227% by 2030.

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