Transformative technology

Artificial intelligence (AI), specifically generative AI (GenAI), is poised to affect virtually every sector of the global economy and transform the workforce. 

GenAI alone could add between $2.6 and $4.4 trillion to annual global GDP and is expected to automate 60-70% of tasks currently performed by employees.¹ Compared to previous waves of automation, these changes in the labor market will disproportionately impact workers with higher wages and educational attainment. However, advances in AI are expected to accelerate labor productivity growth between 0.1-0.6% annually through 2040¹ depending on the rate of technology adoption and redeployment of worker time.

To understand AI as a megatrend, we must understand how AI has evolved and the opportunities and risks it will bring across real assets.

What’s changed

AI and machine learning algorithms have been in use for many decades. But GenAI such as ChatGPT brings renewed attention to how these transformative technologies will change our daily lives and physical world. 

GenAI’s ability to create new information from existing information will enable a fundamental shift in the economics of business and white-collar industries. 

GenAI presents opportunities and risks for both corporations and individual workers. Large language models do not necessarily compete with existing software but are now competing with parts of companies and people. Workers who do not learn to embrace AI to enhance their work are at risk of obsolescence. 

However, these tools also present significant opportunities for individuals. For the first time, those without data science or technical expertise can harness the full power of AI, democratizing innovation across an organization. Everyone throughout an organization now has tools to reconsider what should be versus what has been, and an ability to reimagine how we can better utilize existing technologies. We are still in the earliest innings of these use cases, but advances are being made at a remarkable pace. 

Implications and opportunities for real assets

Real estate

New technologies powered by AI and GenAI are rapidly emerging across the real estate technology (PropTech) world. These new use cases are being tested along the real estate value chain, from acquisition, to development and redevelopment, in operations and through disposition. In general, AI is enabling the industry to evolve in ways that will create more dynamic, efficient, sustainable and customer-focused real estate experiences.

How could AI change real estate?

Data centers: Data centers are experiencing unprecedented demand and is the first sector to directly see the positive impacts of the AI boom. GenAI and cloud computing platform growth requires robust data infrastructure, leading to an increased need for advanced data centers with specialized cooling and power requirements. The global data center rental market is forecast to grow at a five-year compound annual growth rate of 23%, up to 36% is contributed by the demand for AI.²  AI and cloud-driven demand are exceeding constrained supply, creating record low market vacancy and all-time high pre-let market absorption.

Office: There is a near-perfect correlation between educational qualifications and GenAI impact.³ Legal services are projected to be the most affected by large language models like ChatGPT. The sector is closely followed by financial activities like banking and insurance, professional services including accounting and consulting, technology sector specialties like computer data processing and media sectors. The degree AI will influence these sectors remains unclear, meaning it will take time to unravel the implications for real estate investors in the office space. 

Retail: The push and pull between the ethical use of customer data and privacy will continue to be top of mind for retailers and technology companies, but will likely produce outsized benefits that unlock enhanced customer experience, new product discovery, higher sales volumes and larger customer lifetime values. On the retailer side, GenAI has the potential to create operating efficiencies across the value chain e.g. inventory management, sales forecasting and analysis. While potentially disruptive in the short-term, AI has the potential to create a more seamless omni-channel experience. For physical retail, AI could also enhance analytics leading to profitability, a deeper relationship with customers and better real estate outcomes.

Multifamily and mixed use: AI-powered tools can offer residents increasingly personalized experiences. For example, leasing chatbots and smart home technologies can customize settings for lighting, temperature and security. Furthermore, GenAI and the permanence of hybrid work will continue to increase the share of the workforce that is self-employed or working for small companies. 

Infrastructure

The transportation and intermodal logistics industries have been early and significant adopters of AI, bringing advancements in operational efficiency, safety, customer service and environmental sustainability. We expect the next wave of AI to enhance the functionality of many of the AI use cases being deployed in intermodal transportation and logistics today. 

How could AI change infrastructure?

Power: As the renewables industry matures and storage assets like batteries come online, AI can better assess demand pressure points and enable use cases like battery optimization (to dispatch at the right time) and battery life optimization (ensuring a battery doesn’t go below recovery). Integration of renewable energy can be smoother with AI-powered forecasts for renewable outputs, helping to integrate them smoothly into the grid. AI may also improve site selection for battery deployments, enabling better insight to reduce grid congestion and transmission costs to end customers. 

Port logistics: AI-powered tools are expected to improve container management, traffic flow optimization and customs clearance. For example, technology can be used to optimize container storage and movements within ports. This includes predicting the best storage locations for containers to minimize movement and accelerate loading and unloading processes. AI can also help manage the flow of trucks and ships, schedule dock assignments and improve loading processes to reduce congestion and improve efficiency. 

Fleet management: Advanced technologies are being used for fleet optimization, particularly around routes, fuel consumption and load optimization. AI algorithms analyze traffic data, weather and other factors to determine the most efficient routes for transportation fleets. Machine learning models can help analyze driving patterns and conditions to suggest improvements, leading to reduced fuel consumption and emissions. AI is also used to optimally allocate and schedule cargo loads to maximize capacity usage and minimize empty return trips.

Natural capital

While the impacts of AI on natural capital will not be as significant as in real estate and infrastructure, AI will enable improvements in productivity, sustainability and biodiversity. 

How could AI change natural capital?

Farmland: AI and machine learning algorithms are enabling newer practices such as precision farming, which involves the precise application of water, fertilizers and pesticides, based only on the needs of specific areas of a field which optimizes resource use and reduces costs, potentially increasing crop yields. AI tools can also detect plant diseases and pest infestations early through image recognition technologies, minimizing potential damage. 

Timberland: AI can aid in forest management, improve monitoring of timber assets and enhance sustainability practices. Similar to farmland use cases, AI-enabled image recognition technologies can identify tree species and count populations over large areas. Finally, AI systems an also detect signs of wildfires from satellite and aerial imagery more quickly. 

Intersection with other megatrends

Transition to green economy: AI will play a significant role in how the grid evolves and how the built world interacts with it. The need for expanded electricity capacity for both data centers and EV charging will be increasingly important in the evolution of cities as the competition for power between households, businesses and data centers intensifies. 

Growth of the South and East: APAC is embracing AI more quickly, but certain sectors that have experienced significant growth in those regions (e.g., call centers) are at significant risk. However, AI tools can make communication more seamless.

Aging population: AI has the potential to significantly increase average life expectancies by enhancing healthcare services and research capabilities. Algorithms can analyze complex medical data more quickly and often more accurately than humans, leading to better disease diagnosis and prediction. AI can also  speed up the process of new drug discovery and bring greater personalization to medicine, potentially increasing the effectiveness of treatments. 

Decoupling/Protectionism: Advance chip manufacturing and supply chains are global and complex. There are possibly significant future implications for the data center industry as complicated AI supply chains are intertwined with geopolitically important regions like Taiwan.

Risks and limitations

Understanding and addressing the limitations of AI is essential for the responsible and effective use of these powerful technologies. 

General limitations: GenAI models can sometimes produce outputs that are nonsensical, irrelevant or factually incorrect. Further, the quality of the output from GenAI models is heavily dependent on the data they are trained on. If the training data is biased or limited, the AI can perpetuate or amplify the biases in its outputs. Importantly, GenAI also raises questions around authorship, copyright and ethical use. Data privacy and compliance departments are actively working to develop and roll out updated policies and guidelines that take into account these new considerations, especially as they relate to confidential information.

Real asset idiosyncrasies: Real estate is a hyper-local industry and data sources for real assets are imperfect tools for completely automated decision making. The availability and robustness of integrations with legacy software and systems is a significant and persistent challenge in the real estate industry. The PropTech and startup ecosystem is pushing to change these entrenched dynamics, but it will continue to be an uphill battle as accounting and property management system incumbents have varying incentives to open up their technology stacks more fully. 

How transformative will AI be?

PropTech boomed in the late 2010s and 2020s, introducing many new, innovative technologies to the real estate ecosystem, though not all have survived. iBuying, a technology that sought to streamline residential housing transactions by using algorithms to set house prices for cash buyers, is a clear example. Despite the single-family sector having the most comprehensive data sources, and users typically staying within residential markets with the most homogenous housing stock; some of the world’s leading technologists, investors and venture capitalists could not scale sustainably competitive businesses with the technology. iBuying serves as a cautionary tale and underscores how challenging it is for new technologies to replace, or even replicate, real estate investment and asset management professionals.

For now, AI is full of potential, exactly how far reaching this technology can go, how it will change the way we work across vastly different sectors and how far it will change the course of real asset investments remains to be seen. Change is the only constant we can predict with certainty.