What Every CEO Should Know About Generative AI and the Impact of the Real Estate Bust on San Francisco

What Every CEO Should Know About Generative AI and the Impact of the Real Estate Bust on San Francisco

In recent months, the public-facing version of ChatGPT, a generative AI model, has gained immense popularity, reaching 100 million users in just two months. This unprecedented success has democratized AI in a way that was previously unseen, making it accessible to a wider audience. What sets generative AI apart from its predecessors is its out-of-the-box accessibility. However, the development and deployment of generative AI require a comprehensive value chain to support its training and utilization.

To begin with, specialized hardware plays a crucial role in providing the extensive compute power needed to train generative AI models. These hardware components are essential for the efficient functioning of the models and contribute to their overall performance. Additionally, cloud platforms offer organizations the ability to leverage this hardware and tap into its capabilities, further enhancing their generative AI capabilities.

Furthermore, the emergence of MLOps and model hub providers has significantly facilitated the adaptation and deployment of foundation models within organizations. These providers offer a wide range of tools, technologies, and practices that enable companies to seamlessly integrate generative AI into their end-user applications. As a result, businesses can leverage generative AI to perform specific tasks, such as assisting customers with service issues, thereby improving overall customer satisfaction and experience.

The transformative potential of generative AI is already evident in various industries. Companies across sectors, including pharmaceuticals, banking, and retail, are actively exploring and implementing use cases that harness the power of generative AI. These use cases offer practical benefits for jobs and the workplace, driving value creation and innovation. Organizations have the flexibility to start small or go big, depending on their aspirations and resources.

It is important for companies to consider the costs associated with pursuing generative AI. The expenses can vary significantly depending on the specific use case and the data required for software, cloud infrastructure, technical expertise, and risk mitigation. While it is tempting to get started quickly, it is advisable for companies to build a basic business case first. This will enable them to navigate their generative AI journeys more effectively and make informed decisions about resource allocation and risk management.

Shifting gears, the impact of the real estate bust in San Francisco is a topic of great interest, particularly for financial institutions like JPMorgan and others. San Francisco's downtown properties were once among the most valuable in the country, attracting buyers who loaded up on debt to invest in the booming city. However, the current situation tells a different story. Vacancies in San Francisco are now approaching twice the national rate, according to CBRE Econometric Advisors. This downturn has left property owners on the hook for nearly $12 billion of office bonds, as reported by S&P Global.

One prime example of the real estate bust is the 650 California Street office building, which has defaulted on its mortgage. This 34-story building is now facing financial difficulties as higher interest rates have impacted its value. Similarly, 101 California, another prominent property, has experienced setbacks with its second-largest tenant leaving last year, and its biggest tenant reducing staff and office space. Additionally, Embarcadero Square, which was purchased for $245 million in 2018, is now up for sale at $90 million. These instances highlight the challenges faced by property owners and investors in the wake of the real estate bust in San Francisco.

In conclusion, generative AI presents a unique opportunity for CEOs and organizations to leverage AI technology in a more accessible and practical manner. The value chain supporting the development and deployment of generative AI is expanding rapidly, with specialized hardware, cloud platforms, MLOps, and model hub providers playing crucial roles. While the costs associated with pursuing generative AI can vary, it is essential for companies to consider risk issues and build a solid business case before embarking on their generative AI journeys.

Additionally, the impact of the real estate bust in San Francisco serves as a cautionary tale for investors and property owners. The once valuable downtown properties are now facing significant challenges, with vacancies on the rise and property values declining. It is essential for financial institutions and investors to closely monitor the situation and adapt their strategies accordingly.

Three actionable pieces of advice for CEOs and organizations considering generative AI and navigating the real estate market are:

• 1. Conduct a thorough cost-benefit analysis: Before diving into generative AI, carefully assess the costs involved, including software, cloud infrastructure, technical expertise, and risk mitigation. Similarly, in the real estate market, evaluate the potential risks and returns before making investment decisions.

• 2. Start small and scale up: While generative AI offers immense potential, it is advisable to start with smaller pilot projects to test the waters and gain valuable insights. Similarly, in the real estate market, consider diversifying investments across different properties and locations to mitigate risks.

• 3. Stay informed and adaptable: Keep a close eye on industry trends, technological advancements, and market conditions. Stay informed about the latest developments in generative AI and real estate to make informed decisions and adapt strategies accordingly.

By following these actionable pieces of advice, CEOs and organizations can navigate the complexities of generative AI and the real estate market more effectively, unlocking opportunities for growth and success.