Autonomous Everything: AI, the Future, and What We Can Do About It

“As a student of cities and real estate, and understanding how they function, it’s great to be in a room full of people who actually make it happen,” opened futurist Greg Lindsay in a keynote address at NAIOP’s I.CON East conference this week in Jersey City, New Jersey.

“What is artificial intelligence, and how do we talk about it now?” he posed. “It’s neither artificial – there is a lot of human knowledge that goes into the making of it – nor is it truly intelligent. The terms itself is suspect.”

Firms are using AI, statistical analysis and machine learning every day, Lindsay said. It has historically been thought of able to achieve “what humans can’t do,” but that mark is constantly shifting. It’s most successful in what Lindsay calls a “centaur model,” in which humans and AI working together are more powerful than either can be apart.

Generative AI (GAI) – deep-learning models that can generate high-quality content based on the data they were trained on – have already boosted productivity, Lindsay said, with coding assistants like GitHub Copilot and Replit AI as early success stories that have boosted the productivity and job satisfaction of software developers. AI-powered assistants are increasing the performance of knowledge workers, benefiting underperforming workers more so than high-achievers.

Primary research is being driven by private companies over academia, and investment in GAI peaked at $132 billion in 2021. Google, META and Microsoft have the highest involvement in both research and development, as well as investment. The U.S. is leading the charge with the most notable machine learning models and the highest investment by country, surpassing China, European countries and India.

For real estate, the impact on the number of data centers needed equates to “a massive gold rush,” said Lindsay, with investment already huge and the need even bigger. A recent JLL report said that AI demand has turned data centers into the “hottest asset class,” creating what JLL CEO Christian Ulbrich says is “a rare bright spot in a commercial-property market faced with rising office vacancies.”

In short, Ulbrich said, “When you believe in AI, the demand for data centers will only go up.”

Data centers require huge amounts of land, electricity and water; the only thing they need more of is data. Generative AI can’t be fed by data it generates – it needs a steady diet of human knowledge and input, and that’s why companies like OpenAI are striking deals with media companies that will provide access to human-created content.

Firms are using AI mostly in the ways we’ve all experienced, Lindsay said. Topping the list is contact-center automation (some say this growth could put an end to the India-based call center industry by the end of next year), followed by web content personalization and customer acquisition. AI-based enhancement of products such as a chatbot built in to the dashboards of Volkswagens and the creation of new AI-based products round out the list of top uses.

AI will certainly change our jobs, said respondents to PwC’s annual CEO survey, with one in four CEOs saying they anticipate reducing employee headcount by 5% or more in 2024. These positions are expected to be in back-office tasks including accounting and marketing, and in media/entertainment companies.

Carl Benedikt Frey and Michael Osborne, authors of a paper on the future of employment, said that 47% of U.S. employment is at risk due to AI. According to their paper, “The more transactional a relationship becomes, the more prone it is to automation.” In short, call center jobs will be greatly impacted, but positions that require a high level of trust between individuals, including health care positions and education, are more safeguarded.

“Nonetheless, we don’t know what work will look like in a decade or two,” said Lindsay. “A tremendous amount of work and energy were unleashed by the CHIPS Act and other infrastructure bills that are part of the clean-energy revolution.”

Liz Shuler, president of the AFL-CIO, has been voicing support for the centaur model that harnesses the knowledge of union workers to bolster machines. She said, “Bring workers in early in the process, when your companies are researching and developing the technology. Use our expertise to brainstorm, develop and implement new ideas. Make sure workers who need to be retrained for other roles are. Give us the tools to be even better. Make your companies more profitable. Make ALL of us better off.”

Autonomous vehicles that roll and fly could lower supply chain costs dramatically, Lindsay said, with these types of vehicles operating at higher utilization rates and creating more cost-effective last-mile delivery systems. Local delivery costs for small items drop from $5.40 per human-driven vehicles to $.35 per drone delivery – a cost reduction of 94%.

From small delivery robots the size of a microwave to larger van-sized vehicles, autonomous delivery tools are making waves everywhere from college campuses to big cities like Atlanta, where Black-owned company Nourish & Bloom is using autonomous vehicles to deliver groceries to underserved areas knows as food deserts.

This is a step-change disruption, and the idea of building bigger and hungrier models can’t go on forever, Lindsay said. So how do we assess where GAI should augment human creativity and where it can replace it? Your expertise as a human to solve problems is hard to replace, he said, and “when the risk is high and the cost is low, a human is best.”

Lindsay closed with four recommendations on how to move forward with generative AI development and implementation:

  1. Develop an open innovation strategy, talk to everyone, and experiment.
  2. Don’t be content with only what’s on the market now. Consider open-source models and how they can be customized for your needs.
  3. Define use cases. Now that you have GAI, what can you do with it? Map out your problems and potential solutions.  
  4. Develop trust requirements, not unlike a nutrition label that can keep you out of what researcher Shelby Doyle calls “rotting banana peels of data.”

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This post is brought to you by JLL, the social media and conference blog sponsor of NAIOP’s I.CON East 2024. Learn more about JLL at or

Kathryn Hamilton, CAE

Kathryn Hamilton, CAE, is Vice President for Marketing and Communications at NAIOP Corporate.

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