Cities, AI and the Metaverse? Risks, Opportunities, Actions
The Allegory of Good and Bad Government by Ambrogio Lorenzetti, 1338 - 1339 , Siena, Italy
To answer this I’m going to take a What?, So What?, Now What? approach.
First we’ll define what we mean by artificial intelligence and the metaverse.
Then look at what can you do with AI and the Metaverse. What are they good for? And what’s happening in these sectors.
Next, the So What? What are the implications of these technologies? What are these technologies enabling? What can we do with them that was not possible before?
And finally we’ll look at what practical steps developers, asset owners or policy makers should take today?
What?
To get us going I’d like to add some context.
We are currently experiencing an extraordinary period of technological progress within the field of artificial intelligence.
You’ve probably heard of the phrase, taken from Ernest Hemingway, ‘slowly, then suddenly’. Well that perfectly sums up what has been going on since November the 30th, 2022. That was the day the AI Research company OpenAI released a ‘chat bot’ front end to their large language model GPT-3. Within just two months this new application, ChatGPT, had reached 100 million users.
Within two months! Making it the fastest growing new technology in history.
On March the 14th 2023 they released a much more powerful version, GPT-4, and then on the 23rd they announced the launch of their ‘Plugin’ service which allows integration with selected 3rd party apps and services.
One notable launch partner was the ‘Computational Intelligence’ service Wolfram Alpha. By joining forces here ChatGPT, which is a probabilistic language model, adds the ability to answer factual queries and perform advanced mathematical calculations. This extends its capabilities dramatically.
On March 21st, Bill Gates wrote the following:
‘In my lifetime, I’ve seen two demonstrations of technology that struck me as revolutionary.The first time was in 1980, when I was introduced to a graphical user interface—the forerunner of every modern operating system, including Windows.’
The second was GPT.
‘I knew I had just seen the most important advance in technology since the graphical user interface.’
He went on:
The development of AI is as fundamental as the creation of the microprocessor, the personal computer, the Internet, and the mobile phone. It will change the way people work, learn, travel, get health care, and communicate with each other.
Entire industries will reorient around it.
Businesses will distinguish themselves by how well they use it.’
Now large language models, LLMs, are not all there is within the field of AI, but the critical point is that they enable a new way of interacting with computers. Simply by using natural language, written or spoken, we will be able to instruct computers to do something. We’ll no longer need to be computer scientists to leverage the massive power of modern computational systems.
This is a pivotal moment in history. The start of an age where humans have been augmented by computers in a very profound way.
Using ChatGPT is like having an infinite number of interns at your disposal, at all times. And there will be other such systems, each one most likely finely tuned for particular use cases.
Make no mistake, this is not tech bro hyperbole. What is happening right now is genuinely a really big deal.
And our world, of cities and real estate, will be impacted in very big ways. These technologies will act like a forcing function in how people live, work and play. And of course this comes on top of the forcing function that was the pandemic.
Fortunately, all of this means we will also have better tools to help us plan, analyse and act.
But let’s deal with some descriptions first.
So what is the Metaverse?
People ascribe many meanings to it but essentially the Metaverse can be described as a persistent, shared digital universe: a vast, interconnected digital space where users can interact with one another, create and share content, participate in events, and engage in various activities.
And mostly, in those terms, it does not exist yet. The consumer version of the metaverse today is largely made up of cartoonish standalone virtual worlds, with minimal functionality, and few users. It underwent a massive hype cycle in late 2021 and early 2022 but the bubble burst and anyone who bought virtual land in any of these spaces probably wished they hadn’t.
But there is another framing of the metaverse, and that includes what are known as digital twins. These are:
‘a virtual model designed to accurately reflect a real-world object. A digital twin integrates all the data about a physical object across that object’s entire life cycle. In fact, digital twins can simulate objects in such accurate detail that they mimic every aspect of an object as if it were real.’
And these ‘objects’ can be anything from an individual, small physical item to an entire city. Digital twins can be scaled up to represent entire ‘systems’. And they can incorporate infinite data sets, representing the past, the present and anticipated futures.
And they very much do exist and will be the focus of our attention, because they are massively important in the future of cities and real estate.
Now what is AI and what is it good for?
Simply put Artificial intelligence (AI) is ‘a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence.’
But the definition is not so important. What is is an understanding of the five areas where AI excels, and these are Perception, Communication, Knowledge, Reasoning, Planning.
Perception refers to the ability of an AI system to interpret sensory data from its environment, such as images, sounds, and other signals. This typically involves tasks like object, facial and speech recognition.
Communication refers to AI's ability to understand, generate, and respond to human language. This encompasses natural language processing (NLP), natural language understanding (NLU), and natural language generation (NLG).
Knowledge refers to the ability of an AI system to acquire, store, and use information to perform tasks, answer questions, and make decisions. This encompasses knowledge representation, knowledge graphs, and the integration of structured and unstructured data.
Reasoning refers to the ability of an AI system to process information, draw inferences, and make decisions based on its knowledge. This encompasses various types of reasoning, such as deductive, inductive, and abductive reasoning, as well as common-sense reasoning.
Planning refers to the ability of an AI system to create and execute a sequence of actions to achieve specific goals or objectives. This encompasses various aspects such as goal formulation, action selection, and plan execution, as well as handling uncertainty and adapting to changes in the environment.
Think of these abilities like this: machines can understand what they are looking at, they can read and write extremely well, they can store and synthesise vast amounts of structured and unstructured data, they can apply reasoning to all that data, and they are really good at planning, particularly at scale and at speed.
There are some things us humans can beat them at across these five domains, but mostly they’ve been better than us for some time.
We’re rapidly approaching ‘don’t bring a knife to a gunfight’ territory. Understand what ‘machines’ excel at and let them get on with it.
At the same time remember what Picasso said: ‘computers are useless, they can only give you answers’. Us humans need to excel at asking the right questions.
More on that later.
That’s the What, now let’s address ‘So What’. What do these technologies enable. Can we do with them?
Just stepping back a bit, what’s the big issue we face in our cities?
I think of it like this:
With the rise of hybrid, remote and distributed working (largely as a consequence of what we learnt during the pandemic) city centre offices are much less busy than they used to be, as are retail businesses around them that used to cater to the needs of office workers.
So city centres are quieter, transportation networks are less busy, and people are moving further out in search of more space to live/work.
People want more flexibility, less commuting and more agency over their time.
So the dynamics of cities are changing.
And we need to figure out how to help our cities remain, or return to being, dynamic, thriving, enjoyable, liveable places?
So what therefore are the opportunities and risks for the real estate sector from all of this and what part can AI and the metaverse (more particularly digital twins) play.
Let’s start with risks:
The most obvious risk is around the possible, more realistically likely, decline in demand for for traditional office spaces and commercial properties, leading to increased vacancies, reduced rental income for property owners, and business taxes for the city.
And then secondly the disruption of traditional business models.
We got very used to how things worked: Everyone came into the central business district to work, five days a week, in offices on long leases. And all that footfall supported an entire ecosystem of supporting companies, as well as paid for the infrastructure to get around the city easily.
And now, mostly, this is no longer what people want.
Many employers want the old world to return, and some are having apparent success in forcing the clock back.
But the reality is that those days have past.
The pre pandemic world is not coming back. Employees want different things, and are pretty determined to get them.
The most talented employees have a lot of optionality; they are in demand and so can demand ‘a better way’.
There really is no doubt that companies that are slow to adapt to these changes will face competitive disadvantages and potential obsolescence.
And talking of obsolesce, that will be the fate of a great deal of real estate over the coming years.
The real risk to anyone involved in real estate or managing and running cities is NOT leaning in to these changes, or underestimating them.
We’ve had two years of denial from many leaders; everyone will be back soon, they’ll be back after the summer holidays, come the new year etc etc…
And at every turn they’ve been disappointed.
As Robert Frost wrote ‘the only way round, is through’.
Accept, and embrace, then act.
For opportunities they are aplenty.
Looking at those directly related to AI & Digital Twins I’d like to concentrate on three:
Enhanced property management
AI-driven urban planning and development
Adaptive reuse of existing spaces
We need to dive deep into these, because by doing so we’ll cover all the key ways these technologies can help us achieve our goals. Thought processes, workflows, software and hardware used in these examples will be transferrable to many other situations. The AI & Digital Twin opportunity space is deep, and wide.
Enhanced property management.
How can companies create office spaces that are not only more efficient and sustainable, but also more appealing and enjoyable for employees. That encourage in-person collaboration, enhance employee satisfaction, and ultimately, make offices a desirable destination for people to work and interact.
How can AI be leveraged to help achieve this:
Workspace personalisation:
AI can analyse individual preferences, work habits, and schedules to tailor office spaces according to each employee's needs. This can include adjusting lighting, temperature, and desk arrangements, providing a more comfortable and enjoyable work experience.
Smart meeting scheduling:
AI can optimise meeting schedules based on participants' availability, preferred meeting times, and locations, reducing conflicts and improving the overall meeting experience. Meetings are a constant bugbear - AI can make them better.
Enhanced collaboration tools:
AI can be integrated with collaboration tools to streamline communication, facilitate idea sharing, and support decision-making. Features such as real-time transcription, language translation, and intelligent content summarisation can help create a more inclusive and engaging work environment, fostering collaboration and innovation.
Health and well-being:
AI can be used to monitor and analyse environmental factors, such as air quality, noise levels, and natural light, and make adjustments to improve the overall well-being of employees. Additionally, AI-powered wellness programs can provide personalised recommendations for breaks, exercises, and mental health support, contributing to a healthier and more balanced work-life experience.
Efficient resource management:
AI can optimise the utilisation of office resources, such as meeting rooms, workstations, and shared amenities, ensuring that these spaces are available when needed and used efficiently. This can reduce overcrowding, improve overall employee satisfaction, and create a more organised work environment.
Dynamic workplace layouts:
AI can analyse data on how employees interact with their physical environment, identifying patterns and trends that can inform the design of adaptive, flexible workplace layouts. This can help create office spaces that foster collaboration, creativity, and productivity while still providing areas for privacy and focused work when needed.
Enhanced security and access control:
AI-powered security systems can provide more effective and non-intrusive access control, using technologies like facial recognition, behaviour analysis, or biometrics. This can help create a secure environment while minimising disruptions and inconvenience for employees.
Real-time feedback and analytics:
AI can collect and analyse real-time feedback from employees regarding their experiences in the office. This information can be used to identify areas for improvement, monitor the impact of implemented changes, and continuously refine the work environment to cater to the evolving needs and preferences of employees.
Together, these AI-driven use cases converge to create a truly exceptional user experience for those in the office, designed to help people be as happy, healthy, and productive as possible.
They embody the essence of "space-as-a-service" initiatives, where the goal is to understand people's "jobs to be done" and provide them with spaces and services that optimise their time in the office.
In this high-tech, high-touch world, quantitative data is harnessed to craft a qualitatively superior experience.
Next, AI-driven urban planning and development
AI-driven urban planning and development can greatly contribute to the creation of more liveable and sustainable urban spaces by leveraging data analysis and predictive modelling. Here are some key ways:
Site selection and land use optimisation:
AI can analyse various factors, such as land use, Zoning regulations, environmental conditions, and infrastructure availability, to identify optimal locations for new developments. This can help developers maximise the value of their investments while minimising the impact on the environment and existing communities.
Demand forecasting:
AI can process vast amounts of historical data and current trends to predict future demand for different types of properties, such as residential, commercial, and mixed-use developments. This allows developers and city planners to make data-driven decisions about the scale and type of projects needed to meet future demand and avoid oversupply.
Transportation and infrastructure planning:
AI can analyse traffic patterns, public transportation usage, and pedestrian behaviour to optimise transportation networks and infrastructure. This can lead to reduced congestion, improved accessibility, and more efficient use of resources.
Environmental sustainability:
AI can help identify opportunities for integrating green spaces, renewable energy sources, and energy-efficient technologies into urban developments. This can contribute to more sustainable, eco-friendly cities that prioritise the well-being of both residents and the environment.
Community engagement and public feedback:
AI can be used to collect and analyse public feedback on proposed developments and urban planning initiatives. This enables city planners and developers to better understand community needs, preferences, and concerns, and incorporate them into the planning process. By incorporating public input, urban spaces can be designed to be more inclusive and responsive to the needs of the community.
Socio-economic impact analysis:
AI can analyse the potential socio-economic impacts of proposed developments, including factors such as job creation, housing affordability, and access to essential services. This can help ensure that new developments contribute positively to the overall quality of life in urban areas and promote equitable growth. Very much the S in ESG.
Resilience and adaptation planning:
AI can be used to evaluate the potential risks and vulnerabilities of urban areas to climate change, natural disasters, and other hazards. By identifying these risks, city planners and developers can incorporate resilience measures and adaptive strategies into urban designs, ensuring the long-term sustainability and safety of urban environments.
Simulation and scenario analysis:
AI can be used to create realistic simulations and evaluate various development scenarios, helping planners and developers to visualise the potential outcomes of different planning decisions. This can lead to more informed decision-making and better alignment with long-term goals and objectives.
In essence, the fusion of AI-driven urban planning and development heralds a new era of informed, data-driven, and responsive approaches to creating liveable and sustainable urban spaces.
By embracing AI, city planners, developers, and other stakeholders can work in concert to construct cities that are better equipped to meet the diverse and ever-evolving needs of their inhabitants.
And now, Adaptive reuse of existing spaces
AI and Digital Twins can be used together in the adaptive reuse of existing spaces by combining data analysis, simulation, and optimisation techniques. This synergy can help real estate developers identify the most suitable strategies for repurposing under-utilised properties and maximising their value.
Let’s have a detailed look at how AI and Digital Twins can work together in this context:
Data collection and analysis:
AI can be used to gather and analyse vast amounts of data on building conditions, local market trends, community needs, and regulatory constraints. This information can help identify under-utilised properties with the greatest potential for adaptive reuse and provide insights into the most suitable new uses for those spaces.
Building performance assessment:
Digital Twins, which are virtual representations of physical assets, can be used to simulate and evaluate the existing performance of a building. This includes factors such as energy efficiency, structural integrity, and indoor environmental quality. By analysing this data, developers can identify the necessary improvements and modifications required for adaptive reuse projects.
Design optimisation:
AI algorithms can be used to generate and evaluate multiple design options for the adaptive reuse of a building, taking into account factors such as cost, functionality, aesthetics, and sustainability. Digital Twins can be used to simulate the impact of these design options on the building's performance, helping developers choose the most optimal solution.
Stakeholder engagement:
AI and Digital Twins can facilitate more effective communication with stakeholders, such as local authorities, investors, and community members, by providing data-driven insights and realistic visualisations of proposed adaptive reuse projects. This can help build consensus and support for the projects, ensuring that they meet the needs and expectations of all relevant parties.
Construction planning and management:
AI can be used to optimise construction schedules, resource allocation, and logistics, ensuring that adaptive reuse projects are completed efficiently and cost-effectively. Digital Twins can be used to monitor the progress of construction and detect potential issues early, allowing for timely adjustments and minimising the risk of delays or cost overruns.
Post-occupancy evaluation and optimisation:
After the completion of an adaptive reuse project, AI and Digital Twins can be used to monitor and assess the performance of the repurposed space. This can include factors such as energy consumption, indoor environmental quality, and user satisfaction. By continuously collecting and analysing this data, developers can identify opportunities for further optimisation and improvement, ensuring that the space remains functional, efficient, and appealing to users.
In summary, the combination of AI and Digital Twins in the adaptive reuse of existing spaces can help real estate developers make more informed decisions, optimise designs, and improve project outcomes. By repurposing under-utilised properties into new, innovative spaces, developers can contribute to the vibrancy and diversity of urban environments while making the most of existing real estate assets.
From these three use case examples you can see that they are leveraging the core capabilities of AI, the Perception, Communication, Knowledge, Reasoning and Planning we discussed earlier.
And via a digital twin creating a virtual representation of the actualité of a physical space.
Combining these is enormously powerful.
Not only does it enable you to really understand how places and spaces are being used, but it also enables you to test out various optimisation strategies in a virtual environment prior to rolling them out ‘in real life’. Real life is very expensive to tinker with - virtual life is not. Far better to test run scenarios first.
So that’s the What and the So What around AI, the Metaverse and its critical component in our case, Digital Twins.
Now let’s look at Now What? What is it that you, as developers, asset owners or policy makers, need to do from today onwards to bring all of this together?
In a nutshell you need to consider a strategic approach that focuses on data, skills, technology, and product/service selections.
So here are six essential actions to take:
Establish clear goals and priorities:
Identify your key objectives and desired outcomes. This may include improving efficiency, enhancing sustainability, or promoting equitable growth. It is essential you are clear about what you are trying to achieve. You have to have a North Star to aim at, a definite purpose.
Invest in data infrastructure:
Robust data infrastructure is essential for harnessing the full potential of AI and Digital Twins. Develop a strategy for collecting, storing, and managing data from various sources, ensuring data privacy and security.
This may involve collaborating with partners, such as technology providers, data analytics firms, and research institutions.
Either way, until you have your data sorted out you cannot move forward. This is non negotiable. Without a solid data infrastructure you will fail. Not might, will.
Develop a skilled workforce:
Invest in training and talent development to ensure your team has the necessary skills to understand, deploy, and manage AI and Digital Twin technologies. This may involve upskilling existing employees or recruiting new talent with expertise in data science, machine learning, and digital simulation.
However, skills in this area are very much a moveable feast; a constantly evolving menu to pick and choose from. So whilst it is important that your core teams understand the fundamentals around these technologies, in particular what they are good at, and not good at, it is most likely you will need to build a network and ecosystem of partners whom you can call on for specialist, and ongoing assistance.
Have someone on your team who is constantly looking at, and researching, new tools and techniques. And have them regularly appraise the C Suite as to the changing landscape. Frankly it is almost impossible to keep up fully, but you mustn’t slip far behind. It’s a relative game; always try to be one step ahead of your peers.
Identify use cases and pilot projects:
Start by identifying specific use cases and projects where AI and Digital Twins can deliver tangible benefits. Focus on pilot projects that can demonstrate early successes, generate insights, and build momentum for broader adoption. Iterate from the use cases we discussed earlier, as those definitely work. Again, concentrate on what the technology is good at.
Evaluate technology and vendor options:
Research and assess various technology platforms, tools, and vendors that can support your AI and Digital Twin initiatives. Engage in conversations with vendors and industry experts to gain a comprehensive understanding of the available solutions and their potential impact on your organisation. This fits neatly with the need to develop a skilled workforce. Everybody should be a scout for new technologies and new vendors.
And the last essential action is to embrace a culture of innovation:
Encourage a culture of experimentation and continuous learning, allowing your organisation to adapt and thrive in an evolving technological landscape.
Be open to new ideas and approaches, and maintain a forward-looking perspective to stay ahead of industry trends and emerging opportunities.
Most companies say they do this, but in reality most either do not or do not do it well.
Given the speed of technological change, around AI in particular, and the broad and deep impact it is going to have on all of us in the next few years, it really is imperative to double down on becoming an instinctively innovative company.
We have all got a little tired of people crying wolf, or screaming ‘it’s different this time’ - but that does not mean there never is a wolf or actually, it is different this time.
Remember what Bill Gates said - what is happening in AI today is only the second utterly transformational technology he has known.
And that technology is being integrated into everything we have talked about today. Innovation is something we want to impose on ourselves, rather than have it imposed on us. Act accordingly.
So What, So What, Now What? Let’s Recap.
We are living at a time of massive technological change. After decades of promise but mostly underachievement, we have hit a confluence of data availability, compute power and algorithmic sophistication that has catalysed the creation of Large Language Models, AI programs that by learning the patterns and connections between words and phrases enable us to use natural language to interrogate ‘all the worlds information’.
Machines can now understand and create images and text, they can analyse and synthesise massive corpuses of data and information, and they can simulate the availability of infinite interns working for each of us personally, on-demand and at warp speed. In essence we have all been given access to hitherto unavailable computational superpowers.
And maybe these have come just in time, because the after effects of a global pandemic have reset how we all think about work, where we do it and what we want from it. In turn our cities are being repurposed; how we use them, and what we want from them has been fundamentally upended. With great consequences. They need redesigning.
So we need to embrace these new technologies to help us in this redesigning process. Amongst other things we can use them to enhance property management, drive new forms of urban planning and development and learn how to adaptively reuse existing spaces that are no longer fit for purpose.
Our mission, and we need to think of it like this, is how can we leverage technology to create, and then curate, a better built environment for all. That is sustainable, that is human-centric and that embraces all our humanity. It is doable, but the risk of failure is high. The problems are wicked.
We’ll need all the technology we can lay our hands on, but none of it will work unless we up our games as humans.
We need to think, feel and do like we’ve never done before.
We need to unearth our own ‘superpowers’!
Good luck.