Generative AI for Real Estate People - Conclusion/Summary

Summit - One Vanderbilt - New York - Antony Slumbers

Over two long articles (Part 1 here) and (Part 2 here) we’ve looked at how Generative AI could impact on, and be utilised by, Real Estate People. And I specifically say people because in many ways it is going to be easier, and quicker, for individuals to adopt these new tools than it is real estate companies. They will also have very meaningful personal impacts on a lot of ‘People’ so it is vital that individuals push ahead with learning about these new technologies, rather than wait for their companies to do so, as they need to be very cognisant of their own ‘utility’.

So what have we concluded?

First is that we have each been given new ‘superpowers’ - Generative AI provides us all with an infinite army of virtual interns, at our beck and call, day or night, anywhere. Our job is to leverage them.

AI has been around a long time. The term itself was coined in 1955, but the date that matters for us is November the 30th 2022, which was the day OpenAI launched their new product ChatGPT.

Within two months it had a 100 million users and a month or two later increased dramatically in power. 

Critically, GPT-4, which is the ‘Foundation Model’ (known as a Large Language Model, or LLM) underlying the ‘Chat’ front end is now multi model, which means it can deal with text and images. And the version you are using today is the worst one you ever will. It will have a hundred times greater computational power in 10 years time, 8000 in 20 years and a million in 30 years. Today one can assume it has read everything. Certainly everything in English. Now imagine it has also seen everything, and heard everything. What might its capabilities be then?

Today it is excellent at text generation and understanding, plus generating images, music or sounds. As well as having very powerful predictive modelling capabilities. It is also an extraordinary teacher - personalising how you wish to be educated in anything is a joy, that in time hundreds of millions of people will benefit from. ‘Teach the world’ might well be its ultimate purpose.

But in a business capacity it is, out of the box, not very usable by companies, because of security and privacy issues. What you enter information into the public version it becomes just that ….. public. So there is a rapidly developing ecosystem of suppliers building new tools, around two main purposes. Either being able to converse with the LLM around private matters, or integrating one’s own, proprietary data into the system. Both at a company and industry level. There is a lot of customisation going on. A lot of work to create domain specific applications.

Broadly speaking the five areas that can make the most of Generative AI are marketing and sales, customer service, operations, software development and research & development.

In many of these the fundamental strength is making tacit knowledge explicit. Learning from the best in order to teach the rest.

Broken down, utility can be found in areas like the following. Within the legal world it might be around document automation, legal research, dispute resolution and predictions, risk assessment, personalising client interaction, IP creation and protection, case strategy development, legal analytics (such as trends in case law), e-discovery, training & education, and legal coding.

Within commercial real estate it could impact on design, asset performance prediction, location analytics, leasing strategy, property management, tenant screening, space utilisation, market demand forecasting, energy efficiency, valuation and marketing.

In other words, across many of the day to day tasks within any business.

Which is why, like electricity and the internet, it is widely considered a GPT - a General Purpose Technology. 

And those get in to everything.

But, and this is an important caveat, the ultimate benefit of these technologies will only surface when the entire operating model of a business is designed around them, with each part talking to each other and exposing and consuming data from across the enterprise.

Of course none of this will be easy, especially as this is a fast developing area of technology. But whoever does crack it will be mighty competitive.

You need to be prepared though, as an individual and as a company.

At a general level the most important thing is to have a culture of innovation, followed by the other absolute imperative, which is having your data in order. Without both of these you’re not going far.

You also need to be continuously learning, and to have a process by which you can measure the effectiveness of this learning. You need, as in a software company, to ‘Build, Measure, Learn’. And to do this up and down the organisation. Everyone needs to be trained to use these tools, to contemplate use cases and to be thinking about what needs to happen for anything to happen.

If you are in the C Suite you need to enable all off this by making available the resources, the investment and perhaps most importantly of all the ‘air cover’ that will make it possible.

Being more specific you will need, internally or through partners, access to advanced AI literacy, data and programming skills, people with a creative mindset and design thinking skills. And you should look out for, and encourage, those with great domain expertise AND great communication skills. Technical people who can understand and be understood by non technical people. And vice versa.

And above all else you need a workforce with strong abstract and critical thinking skills. In an environment when you never know what is true or real, you must have the ability to discern which is which.

As it is ‘the machines’ that know everything the specialist becomes less imperative. People with generalist skills, and an ability to understand connections and see the big picture will be at a premium.

There are, of course, many risks, concerns and ethical implications with Generative AI.

The most talked about is their ability to hallucinate, to make things up, to sound entirely plausible whilst being absolutely wrong. You need to remember that an LLM is not a database, it's a prediction engine. It is trying to predict what a good answer to the question being asked would look like.

So it is very likely that you need to keep a human in the loop. In fact, within more complicated businesses you should probably develop your own taxonomy as to when the AI can work autonomously, and when not. 

You also need to look out for, and plan around misinformation, deep fakes, IP infringement, bias, unfairness, privacy and data protection, consent & permission, security & malicious use, psychological & social impact, accountability & attribution. None of which is meant to put you off, rather to emphasise that there are serious matters that need to be dealt with seriously.

Over time it is a certainty that legislation and accepted norms are going to change. This is a moveable feast, so be agile.

How quickly is all of this going to come to pass? What is the timetable to adoption?

McKinsey recently issued a new report looking at the impact of Generative AI on business. Compared to their last major review, in 2017, they say

‘the total percentage of hours that could theoretically be automated by integrating technologies that exist today has increased from about 50 percent to 60–70 percent.’

And go on to make three other very pertinent points:

First, that based on historical findings, technologies take eight to 27 years from commercial availability to reach a plateau in adoption. It is often slower than you think, especially in large companies, because…. Well they are large, complicated, unwieldy beasts that have been fined tuned to operate in a particular manner. Changing that is hard.

Second that automation adoption is likely to be faster in developed economies, where higher wages will make it economically feasible sooner. 

And third that technologies could be adopted much more rapidly in an individual organisation. 

Put it all together and the implications are that we are set for rapid change!

So what will the impact be on professionals & jobs?

It might be painful.

Well known computer scientist Pedro Domingos recently tweeted this:

‘AI is the revenge of the working class: now it's the middle class's turn to fear for their jobs.’

And referring back to that McKinsey report he may have a point. Because they write that:

‘Generative AI is likely to have the biggest impact on knowledge work, particularly activities involving decision making and collaboration, which previously had the lowest potential for automation.’ 

Because …

‘its capabilities are fundamentally engineered to do cognitive tasks’.

And that the …

‘potential to automate the application of expertise jumped 34 percentage points’

Indeed …

‘many of the work activities that involve communication, supervision, documentation, and interacting with people in general have the potential to be automated by generative AI’

It is too big a topic for here but two thoughts are worth bearing in mind:

First, when AI reduces the cost of writing software down to almost zero we might well see a vast increase in new software that enables us rapidly and widely to create many many new and better, faster, cheaper products and services. When the cost of intelligence trends towards zero we ‘might’ unleash all manner of new opportunities.

And secondly, with lawyers in mind, when AI enables everyone to sue everyone, everywhere, all the time, the sheer scale of new work might keep them all gainfully employed for a long time to come.

Do you remember the ‘Jevons Paradox’ which proposes that ‘increases in energy efficiency may lead to an overall increase in energy consumption, rather than a decrease, as people use the efficiency savings to consume more.’

Historically new technologies have killed off jobs but what they enable has led to many more, new ones. This is not a Law of Physics, and often the gap between the old and new jobs leads to painful dislocation, but the Jevons Paradox ‘feels’ like being appropriate in a Generative AI world.

Even if it is though, who benefits will be down to us, to society, to decide. There is definitely no Law that states technological process is a naturally common good. Daron Acemoglu and Simon Johnson, in their recent book ‘Power & Progress’, make the point that new technology does not necessarily lead to higher living standards, but when it does it is a consequence of societal norms and beliefs that ‘will’ that to be the case.

Either way, general purpose technologies lead to great change. We’re just not sure where, and how.

And then lastly what impact will Generative AI have on real estate and cities?

For this I am going to refer you to my 5 in the series set of articles about ‘Four Great Real Estate Challenges’, and my long article on ‘Cities, AI and the Metaverse? Risks, Opportunities, Actions’.

Generative AI will have an enormous impact on real estate / cities as one, and a very important one, of the forces currently at play. Between the two articles I cover most of what you need to know in some depth.

So that’s it. Generative AI for Real Estate People - hold on to your hats because this is going to be one hell of a ride.

Generative AI for Real Estate People, in 10 Steps - Part 2

Summit - One Vanderbilt - NYC - Antony Slumbers

Part 2 - 6-10

6. Preparedness & Skills

Let’s start at the organisational level.

It’s safe to say that an organisation or company that does not have a culture of innovation is going to struggle getting the most out of Generative AI. It is essential that there is a willingness to adopt new technologies, a preference against maintaining the status quo, and a desire to push for new opportunities with an acceptance of possible failure. You don’t need to go all in on ‘move fast and break things’ but you do need to be comfortable with change.

After that, the most important input will come from having your data in order. The more rich and granular, digital, data you have the better. So much of the power of Generative AI comes from its ability to ingest very large quantitates of data and make sense of it. Clearly if you don’t have much that limits what is possible. That said, Generative AI works very well with unstructured data, such as emails, notes, correspondence, documentation, so is more forgiving than other technologies. And of course, if you are working with a foundational model like ChatGPT you are already working with ‘all the worlds data’, so there is much you can still do. You just won’t be able to tailor outputs to your own precise requirements.

As has been repeated for decades, real estate needs more and better data. These new technologies just re-emphasise the point.

A bias towards continuous learning should already be an organisational imperative but if it isn’t you need to start now. The speed at which these technologies are developing necessitates ongoing curiosity, retraining and re-skilling. You will always be behind the curve, but you must not see it move out of sight. So learning from all stakeholders, internal and external, is vital.

The last essential organisational level trait is to have in place systems to regularly evaluate the effectiveness of any initiatives and to rapidly course correct as necessary. Think ‘Build, Measure, Learn’ at all times.

At the individual level there are many steps to take to be prepared for a Generative AI world. Normally one would think these will differ dependant on whether you are an entry, mid or C Suite level person, but in this case I think one should assume the best ideas, tactics and strategy ‘might’ come from anywhere. So, whilst it is clearly the job of the C Suite to lead they need to be very fluid as to where they take their insights from.

With that in mind, here are some steps each of us should take:

Your organisation or company should be offering you training in these new technologies, but if they are not, do it yourself. There is a great deal available for free online and, offering more structure and help, many paid for courses. Knowledge of new tools is a serious competitive advantage that will repay just about any investment. Whoever you are.

Use the tools. Play around with the free versions of ChatGPT or MidJourney or any number of other offerings. Just getting a feel of what is possible makes a big difference.

Note down ideas about possible use cases. This is especially useful if done by front line employees who often see the actualité of how a company operates, and what it looks like to customers, in a way that more senior managers don’t.

Normally one would task mid level managers with identifying areas where AI can create the most value and developing a roadmap for implementation but frankly they might not be in the best place to do so. Let ideas flourish from top to bottom.

They are though most likely to have to handle the almost inevitable change management programs that these new technologies are going to necessitate. More on this later, but again this might need a more collaborative approach than is often the case.

C-Suite executives will be responsible for signing off on key strategic goals, roadmaps and overall vision, but before they do that their real value will come from enabling the above and making available the investment and resources to do the job well. This is not an area for top down diktats but it sure does need top down air cover, commitment and drive.

That covers general preparedness but what specific skills do companies require in their employees to effectively harness and leverage the capabilities of Generative AI?

First off the more people who understand the fundamentals of Generative AI the better. They don’t need to be practitioners but they do need to understand what it is, broadly speaking how it works, its strengths and weaknesses, and how to effectively utilise its capabilities.

You will need access to people with skills in data analysis and manipulation: proficiency in data analysis and manipulation is essential for working with Generative AI models. So work out where you will find people who have skills in preprocessing and cleaning data, as well as the ability to extract meaningful insights from datasets used to train Generative AI models.

Likewise with programming and software development: strong programming skills are necessary to implement and deploy Generative AI models. So someone needs to be proficient in programming languages commonly used in AI, such as Python, and have experience with relevant libraries and frameworks like TensorFlow or PyTorch.

Employees with a creative mindset and design thinking abilities can leverage Generative AI to produce innovative solutions, designs, or content that adds value to the business. So seek them out!

It goes without saying that domain expertise is super valuable when working with Generative AI. Especially if working on fine tuned systems for specific industries. But what you really want is people with domain expertise who can communicate extremely well with AI technologists. Being able to clearly explain requirements, inputs and desired outputs is a super skill. Look hard for these people.

Everyone needs to have a notion of ethical behaviour. More on this later.

Likewise everyone needs to develop their abilities with regard to abstract and critical thinking. Employees with strong critical thinking and problem-solving skills can effectively identify opportunities where Generative AI can add value. They can analyse complex challenges, define clear problem statements, and apply Generative AI techniques to develop innovative solutions. These skills can be taught and really are going to become critical skills in companies leveraging increasingly powerful technologies.

So we all need to be prepared and up-skill ourselves appropriately. Hopefully the above give you some ideas to where to start.

7. Risks, Concerns and Ethical Implications

Let’s start with the most common ‘risk’ mentioned around Generative AI, and that is its tendency to ‘hallucinate’.

Hallucinations refer to instances where AI models generate content that may sound plausible but is factually incorrect or completely fabricated. This can lead to misinformation and further exacerbate the challenges of trust, reliability, and authenticity in the digital landscape.

Hallucinations in Generative AI can occur due to various reasons, including limitations in the training data, biases in the learning process, or the inherent complexity of modelling real-world phenomena accurately. When relying on AI-generated content without proper verification, there is a risk of spreading false information or reinforcing misleading narratives.

Whilst the degree of hallucinating has reduced (very much so between GPT-3 and GPT-4) it is a reality that is unlikely to fully go away. In much the same way as humans are not inherently trustworthy, neither are machines.

So we need to be very careful about where we use Generative AI and when we need to maintain a ‘human in the loop’. Roughly speaking one should not use human less systems where the implications of being wrong are great or severe. Such as in medical diagnosis. If giving a definitive answer is vital then these are not the systems to rely on. One could easily be opening up levels of risk in one part of an organisation by using them in another.

It’s worth thinking about creating a taxonomy of when to have a human in the loop and when not to. This will develop over time, as new techniques and tools are developed to mitigate hallucinations but the principal remains valid. Risk management of Generative AI is an important area.

A rather hilarious recent example of the need for taking care happened with a lawyer in the US who used ChatGPT to help in putting together a defence for a client in a court case. The AI duly produced a solid argument and provided multiple, authoritative sounding references to existing case law. The only problem was that whilst they looked perfect they were entirely fictitious. Safe to say the Judge was less than impressed.

The point to understand is that ChatGPT is trying to work out, statistically, what the next word should be and whilst what it concludes might look and sound right that is not a reflection, in absolute terms, that it is right. Looking right isn’t being right. A Large Language Model is not a database, it's a prediction engine. It is trying to predict what a good answer to the question being asked would look like.

This is where the abstract and critical thinking mentioned above comes into play.

Beyond hallucinations here are eight other key risks and ethical considerations to take into account when using Generative AI:

1. Misinformation and Deepfakes: Generative AI can be used to create realistic and deceptive content, including deepfakes, which are manipulated media that appear authentic. This raises concerns about the potential for spreading misinformation, damaging reputations, and undermining trust in media and information sources.

2. Intellectual Property Infringement: Generative AI models can generate content that resembles existing copyrighted material, raising concerns about intellectual property infringement. This includes the unauthorised creation of artwork, music, or written content that closely mimics the style or characteristics of original works.

3. Bias and Fairness: Generative AI models can unintentionally perpetuate biases present in training data. If the training data is biased, the generated content may reflect those biases, leading to unfair or discriminatory outcomes. Addressing bias and ensuring fairness in the training data and model outputs is crucial for responsible use of Generative AI.

4. Privacy and Data Protection: Generative AI models often require access to large datasets, which may contain sensitive or private information. Ensuring robust data protection measures and respecting privacy rights is essential to prevent unauthorised access or misuse of personal data during the training and deployment of Generative AI models.

5. Consent and Permission: Generating content using Generative AI may involve using data or personal information without explicit consent or proper permissions. Respecting legal and ethical requirements for obtaining consent and permissions is crucial to avoid infringing on individuals' rights and privacy.

6. Security and Malicious Use: Generative AI models can also be misused for malicious purposes, such as generating synthetic identities, creating deceptive content for fraud, or producing convincing phishing materials.

7. Psychological and Social Impact: The realistic nature of content generated by Generative AI can have psychological and social implications. For example, generated content that promotes hate speech, violence, or harmful ideologies can contribute to negative societal impacts and pose risks to public safety and well-being.

8. Accountability and Attribution: Generative AI raises challenges in determining accountability and attributing generated content to its creators. This can have legal and ethical implications, especially in cases where generated content is used for illegal activities, defamation, or other harmful purposes.

The overall point here is not to reduce the appeal of working with Generative AI, or to make it seem a legal minefield that might best be avoided, but to emphasise that there is a serious side to all of this that warrants serious consideration. Being aware of the situation alone goes a long way to nullify it. Again, use your abstract and critical thinking skills whenever working with these tools. And keep abreast of legislation and ‘accepted norms’; these are likely to increase and change considerably in the years ahead.

8. Task adoption - timetable to automation

I’m going to keep this section brief and rely on data put out by McKinsey in a report (The Economic Impact of Generative AI) published in June 2023.

To quote the report:

‘Based on developments in generative AI, technology performance is now expected to match median human performance and reach top quartile human performance earlier than previously estimated across a wide range of capabilities (Exhibit 6 - see below). For example, MGI previously identified 2027 as the earliest year when median human performance for natural- language understanding might be achieved in technology, but in this new analysis, the corresponding point is 2023.’

As a result of these reassessments of technology capabilities due to generative AI, the total percentage of hours that could theoretically be automated by integrating technologies that exist today has increased from about 50 percent to 60–70 percent. The technical potential curve is quite steep because of the acceleration in generative AI’s natural-language capabilities (Exhibit 7 see below). 

Interestingly, the range of times between the early and late scenarios has compressed compared with the expert assessments in 2017, reflecting a greater confidence that higher levels of technological capabilities will arrive by certain time periods. 

They make three other very pertinent points:

First that based on historical findings technologies take eight to 27 years from commercial availability to reach a plateau in adoption.

Second that automation adoption is likely to be faster in developed economies, where higher wages will make it economically feasible sooner. 

And third that technologies could be adopted much more rapidly in an individual organisation. 

Put it all together and the implications are that we are set for rapid change!

9. Impact on Professionals & Jobs

‘AI is the revenge of the working class: now it's the middle class's turn to fear for their jobs.’

Pedro Domingos, Professor of Computer Science, Washington University

This is a pithy way of putting it but it is undoubtedly true that Generative AI has really upended the consensus as to whom AI is going to have the most impact on. Because of its ability to be creative and to handle high cognitive tasks ‘Generative AI is likely to have the biggest impact on knowledge work, particularly activities involving decision making and collaboration, which previously had the lowest potential for automation.’ As stated by McKinsey but pretty much reflecting the new consensus. Because ‘its capabilities are fundamentally engineered to do cognitive tasks’.

‘Our estimate of the technical potential to automate the application of expertise jumped 34 percentage points, while the potential to automate management and develop talent increased from 16 percent in 2017 to 49 percent in 2023. 

As a result, many of the work activities that involve communication, supervision, documentation, and interacting with people in general have the potential to be automated by generative AI, accelerating the transformation of work in occupations such as education and technology, for which automation potential was previously expected to emerge later’

McKinsey, in their report, are actually quite bullish for jobs and productivity. But based on the time involved with tasks being lost to Generative AI being repurposed to work on new tasks that are at least as productive as those they are substituting for.

Which strikes me as a big ask!

At least in the short term. As we know historically, whenever a new technology has displaced jobs they end up being more than replaced by new jobs that only exist because of that new technology. But we also know that there can be a very long gap between the two. In the Industrial Revolution this was known as the ‘Engels Pause’ during which living standards for the working class stagnated or declined, despite increases in productivity, and which lasted for decades.

How are knowledge workers going to create new high paying, cognitive work when ‘the machines’ can now do high end cognitive work?

The Economists Daron Acemoglu and Simon Johnson, in their recent book ‘Power & Progress’, make the point that new technology does not necessarily lead to higher living standards, but when it does it is a consequence of societal norms and beliefs that ‘will’ that to be the case. Whether AI does so or not is going to be down to whether ‘we’ are happy to go along with the structural situation where returns in technology mostly go to Capital rather than Labour.

As Mr Domingos suggests, the middle classes were not so bothered when major changes impacted the working classes, but maybe now they are in the firing line things might be different.

Either way this is too big a topic for here but two thoughts are worth bearing in mind:

First, when AI reduces the cost of writing software down to almost zero we might well see a vast increase in new software that enables us rapidly and widely to create many many new and better, faster, cheaper products and services. When the cost of intelligence trends towards zero we ‘might’ unleash all manner of new opportunities.

And secondly, with lawyers in mind, when AI enables everyone to sue everyone, everywhere, all the time, the sheer scale of new work might keep them all gainfully employed for a long time to come.

Truth is we don’t know what might come to pass, and much of the consequences will be down to actions ‘we’ take, but nevertheless it is certain that a lot of change is coming our way.

10. Impact on real estate / cities.

For this I am going to refer you to my 5 in the series set of articles about ‘Four Great Real Estate Challenges’, and my long article on ‘Cities, AI and the Metaverse? Risks, Opportunities, Actions’.

Generative AI will have an enormous impact on real estate / cities as one, and a very important one, of the forces currently at play. Between the two articles I cover most of what you need to know in some depth.

Conclusion

This is long enough for now so the 'Conclusion' is to follow:)

Generative AI for Real Estate People, in 10 Steps

Summit - One Vanderbilt, New York City - Antony Slumbers

Part 1 - 1-5

The fundamental point to understand about Generative AI is that it provides each of us with an infinite army of virtual interns, at our beck and call, day or night, anywhere. It affords us the luxury of having not just ‘access to all the worlds information’ (a la the original aim of Google) but endless help in searching, parsing, analysing, synthesising and learning from it.

We have been graced with ‘superpowers’.

Our only task is developing our ability to leverage this. When confronted with knowledge about ‘everything’ we need to up our skills in handling the cognitive load. In the future the valuable human (in commercial terms) will be the one who can ‘conduct’ technologies to produce extraordinary outputs. Anyone will be able to produce ordinary; it will be the extraordinary that matters.

To get to there we need to build some solid foundations. Starting with understanding where all these capabilities have come from, what they are, what they foretell and the ups and downs of using them. We need to know what’s what.

1. History

AI has of course been around for a very long time. The term “artificial intelligence” itself harks back to August the 31st, 1955, when it was coined in a proposal for a “2 month, 10 man study of artificial intelligence” to be held the following year at Dartmouth University. A summer study group was all that would be needed to crack it.

Of course that did not come to pass but some half century later things really did start to kick off. A combination of more data, greater compute power and ever more refined algorithms did lead to some remarkable progress. In 2006 Geoffrey Hinton of the University of Toronto released new research updating how his own invention, the technique of ‘Back Propagation’ could be made vastly more powerful, and useful, in the fields of speech and object recognition. 

From here on these areas improved rapidly and the ability of computers to understand text and images at human levels was reached, roughly speaking, by 2015.

In 2017 a team of researchers at Google released a paper entitled ‘Attention Is All You Need’ which introduced the notion of ‘self attention’. This is utilised in a deep learning model called a ‘Transformer’ that processes data (like text or images) in parallel rather than sequentially, and allows for faster and more contextual understanding, making them particularly effective in tasks like language translation and text summarisation. And notably it means a system can ingest exponentially more data than was previously possible. Which is why these ‘Foundational Models’ are called Large Language Models.

And this is the basis for GPTs, Generative Pre-trained Transformers, and the technology behind Open AI’s ChatGPT.

GPT-1 was released in 2018, and just a year later came GPT-2 which had increased in scale by a factor of 10.

GPT-3 followed in 2020 with a yet greater jump in scale. Whilst GPT-2 has 1.5 billion ‘Parameters’ (think of them as the building blocks of the system) GPT-3 had an incredible 175 billion.

Until November 2022 interacting with the GPT-3 model was something only advanced technologists could do. This was very much a tool for geeks only, and even then only invited geeks.

That all changed on November 30th, when OpenAI launched ChatGPT, which put an easy to use front end onto the system that meant it was usable by anyone who could type a question into a text box. And that kicked off an explosion of interest. Within just two months ChatGPT hit 100 million users, far and away the fastest adoption of a technology ever.

Just four months after this ChatGPT was upgraded to run off the hugely more powerful GPT-4, and by the end of March 2023 the ability to add ‘Plugins’ was also rolled out. The new version was also multi-modal, which meant it could process images as well as text, though for now this is not available via ChatGPT.

Getting us to where we are now. Where, for $20 a month, anyone can corral their army of interns to help them in all manner of ways.

Key Difference between AI and Generative AI

As discussed above, within the realm of AI we had already got to human levels of understanding of text or images by 2015 or so. Since then these computational skills have just got better. The accuracy and speed at which a machine can ‘read’ and understand text or identify the contents of an image or video has increased exponentially.

But traditional AI only ‘understands’. The key to Generative AI is in the name; this AI can generate new text and images (and music). Both AI and Generative AI are trained by looking for patterns in large corpuses of data, but only Generative AI is able to ‘generate’ new material.

And that is why this is such a big deal. For however impressed we may be with the capabilities of these current systems they are of course the worst version we will ever use. Now there is not a 100% causality between time and computing power (perhaps progress will slow down) but it looks like exponential growth is likely to persist for quite some time. So one should consider the consequences of a hundred times greater computing power in 10 years time, 8000 in 20 years and a million in 30 years.

Most likely we’ll see the content these systems are trained on grow from being solely text and image based, to including video and sound. And that adds vastly to the corpus of knowledge.

Effectively one can assume that when using ChatGPT it has read everything. Certainly everything in English. Now imagine it has also seen everything, and heard everything.

2. Capabilities

We need to be clear about the different capabilities of non generative and generative AI. They can be used in tandem but generally they will be performing different functions.

So non generative AI, which is what we have seen most use of in business to date has these key capabilities:

Machine Learning: algorithms that use statistics to find patterns in massive amounts of data, anything from numbers to words to images or other digital information. Once trained this tech underpins what we could call the ‘Prediction Machines’.

Natural Language Processing: enabling machines to read and understand human language.

Computer Vision: the ability to understand the contents of an image or a video.

Robotic Process Automation: automating repetitive tasks, such as data entry, transaction processing, or even more complex activities like auditing and compliance reporting.

Whilst Generative AI has these key capabilities:

Text Generation: Writing essays, reports, presentations, creating poetry, generating scripts for movies, or even simulating a chat conversation. It uses patterns and structures it learns from the input data to create entirely new sentences and paragraphs.

Text Understanding: Beyond generating text, Generative AI is able to classify, edit, summarise and answer questions about text based content.

Image Generation: Creating entirely new images or altering existing ones. For example it can generate human-like faces, design graphic elements, or create scenes for a video game.

Music and Sound Generation: Composing new music or sounds based on the patterns and structures it learns from its training data.

Predictive Modelling: Generating simulations or predicting outcomes based on large amounts of data. Critically it can be used to generate a full set of plausible data or outcomes, instead of making a single-point prediction like traditional predictive models. In essence, it involves creating new data samples that resemble your training data.

These are different capabilities but often they will be used in combination. For example, in the field of autonomous vehicles, computer vision (non generative AI) is used to interpret the surrounding environment, while Generative AI could be used to simulate potential scenarios for training the self-driving system.

But for our purposes, we are going to concentrate now on use cases for Generative AI.

3. Ecosystems and Customisation

Before that though it is important to understand how ecosystems of suppliers are developing rapidly to allow intense customisation to be built on top of Foundational Models like GPT-4 (and others such as Google’s Bert and Meta’s LLaMa), and much of this is developing within the Open Source community.

There are two key reasons for this Cambrian explosion: 

First, businesses need to be able to interact with these large language models using their own data but they cannot do so via public systems as that then exposes their data to the outside world. So they need mechanisms to be able to leverage the power of these models without impacting their privacy. 

And secondly, whilst the likes of GPT-4 have ingested everything on the public internet they do not, of course, have access to proprietary data. So businesses need a way to bring the full power of these models to bear on their own, private, data.

In response AI companies, and open source software, are being built to answer these needs. Every major company is likely to train these systems on their own data but also, at an industry level, it is highly likely that domain specific models will emerge that are fine tuned and built atop industry specific data.

It is probably inevitable that we will see Legal and Real Estate specific models becoming available in the near future. One trained on every legal case imaginable and the other on every piece of documentation attached to real estate assets.

These will be immensely powerful.

4. Applications - Generic & Legal

So let’s look at some applications of Generative AI now.

There are five areas where Generative AI will be most applicable. These are Marketing and Sales, Customer service, Operations, Software Development and Research & Development.

So:

Marketing and Sales: creating personalised content for campaigns, generating product descriptions, predicting customer behaviour, SEO optimisation, designing new product concepts based on consumer trends and preferences, lead generation and nurturing.

Customer Service: self service systems, dispute resolution, call centre operator prompting and support, issue summarising. Helping the support team better support customers.

Operations: dramatically improving internal knowledge management. Making available what is needed, whenever and in a frictionless manner. Making tacit knowledge explicit.

Software Development: software developers are already using Generative AI to assist with coding. Particularly through Github CoPilot which ‘turns natural language prompts into coding suggestions across dozens of languages.’

Research & Development: with access to ‘all the worlds information’ Generative AI is an extraordinarily powerful tool for R&D. Can be used to uncover information, debate tactics, and strategy, develop RFPs, research competitors, develop business models, virtually design products, or create simulations. Teach others or teach yourself.

All of these capabilities are generic and cross industry, but it’s not hard to see how they could apply at specific domains.

Thinking of the Legal industry, Generative AI could be used for:

Document Automation: Automating the creation of legal documents such as contracts, deeds, or wills, saving time and reducing errors.

Legal Research Assistance: Generating summaries of long, complex legal texts, aiding lawyers in understanding and digesting these documents.

Dispute Resolution and Predictions: Simulating a variety of scenarios based on case facts and past judgments to predict possible outcomes, assisting lawyers in devising effective strategies.

Risk Assessment: Predicting potential legal risks of different business strategies, providing valuable guidance for companies.

Personalised Client Interaction: Using chatbots to interact with clients, answering routine legal questions and gathering necessary information.

IP Creation and Protection: Assisting in creating and protecting intellectual property by generating patent applications or identifying potential IP infringements.

Case Strategy Development: Helping lawyers build more effective strategies for upcoming cases by learning from past court cases and their outcomes.

Legal Analytics: Generating insights from large amounts of data, such as trends in case law, judge's decisions, or the success rate of different types of legal arguments, helping lawyers make data-driven decisions.

E-Discovery: Sorting through and organising large volumes of electronic documents quickly and efficiently, identifying the most relevant materials for a case.

Training and Education: Creating realistic case scenarios for students to practice on, helping them gain practical experience.

Legal Coding: Assisting in legal coding or tagging, where AI can help automate the process of assigning legal taxonomy tags to documents.

And on and on. A Generative AI system fine tuned for the entire corpus of legal knowledge will be awesomely powerful. And surely coming soon.

5. Applications - Commercial Real Estate 

Similarly there are multiple domain specific uses for Generative AI in Commercial Real Estate. Here are a few:

Property Design and Development: Producing multiple building designs based on predefined parameters, helping to streamline the design process.

Performance Prediction: Predicting how a particular building design might perform in terms of environmental efficiency or user experience.

Predictive Modelling and Risk Assessment: Generating a range of potential market scenarios by analysing market trends, economic indicators, and demographic data, assisting in making informed property investment and development decisions.

Location Analysis: Identifying locations that may become 'hotspots' in the future or warn about areas that might see a decline in property values.

Leasing Strategy Optimisation: Simulating the effects of different commercial leasing strategies, helping to optimise revenue.

Property Management: Improving efficiency in managing properties by predicting maintenance needs or optimising resource utilisation.

Tenant Screening: Predicting tenant reliability based on historical rental data and tenant information, helping property managers make informed decisions.

Space Utilisation: Generating optimal layouts for office or retail spaces, maximising utilisation and enhancing user experience.

Market Demand Forecasting: Simulating various scenarios to predict future demand for different types of commercial spaces in various locations.

Energy Efficiency: Modelling and predicting a building's energy usage, helping to design more sustainable and efficient properties.

Property Valuation: By analysing historical sales data and market trends, generating accurate property valuations, aiding in investment decisions.

Real Estate Marketing: Generating personalised marketing content for different segments of clients, improving engagement and conversion rates.

You might be wondering how Generative AI can achieve some or many of the above. So let’s take one example and break it down as to how it could actually work.

‘Performance Prediction: Predicting how a particular building design might perform in terms of environmental efficiency or user experience.’

What would be the process for this?

Well, Generative AI can be trained on datasets that include a variety of building designs, each paired with its historical performance data in terms of energy use, internal climate control effectiveness, occupant satisfaction, and other metrics that indicate environmental efficiency and user experience. These metrics could be derived from actual utility data, building management systems, or occupant surveys from existing buildings.

Once the model is trained on this data, it essentially understands the relationship between building designs and their performance. For example, it might learn that buildings with certain window placements or orientations perform better in terms of natural lighting and energy efficiency. Or it could learn that open-plan designs are rated better by occupants for collaboration in a workspace.

When a new building design is proposed, the Generative AI model can then analyse it and predict its performance. It would generate a range of possible outcomes based on what it has learned from the training data. It could predict how energy-efficient the building will be, or how well it will meet the needs of its users.

Of course none of this is possible without the detailed, granular data being available, and it’s not giving you THE answer, but rather helping you make better decisions and more informed judgements.

It’s also critical to think about how all of this technology is used within your business. The temptation will be to do a bit here and a bit there, but really, even if you start small, you need to try and think through how one use case might interact, or impact on, another. You need to be thinking in systems not silos. The ultimate benefit of these technologies will surface when the entire operating model of a business is designed around them, with each part talking to each other and exposing and consuming data from across the enterprise.

Of course none of this will be easy, especially as this is a fast developing area of technology. But whoever does crack it will be mighty competitive.

Real Estate's Four Great Challenges - Part 5

Wanderer above the Sea of Fog - Caspar David Friedrich - 1818- Hamburger Kunsthalle, Germany

Conclusion

To recap, the ‘Four Great Challenges’ we have discussed in this series are:

1. Decarbonising real estate

2. Adapting to the impact of the move to hybrid, distributed and remote working

3. What to do with a flood of obsolete buildings

4. Saving our cities from the consequences of the above

Each, on their own, would trouble the capabilities of the real estate industry, but together they represent the start of an industry defining era. To have these ‘in hand’ by 2030 will necessitate a level of technological capability, imagination, commitment, financial prowess and skill that is not yet on display across the industry. There is a big step up required to match these challenges. By 2030, if it succeeds, the industry will be a very different place to today. As we’ve repeatedly said, this is the time for the creative, the innovative, the visionary, to step forward.

To add to the load there is, to plagiarise the best Apple keynotes, ‘one more thing’. And that is the cambrian explosion currently underway around AI, in particular Generative AI. 

Generative AI has been a technology bubbling away since 2017, when a team of researchers at Google published a paper entitled ‘Attention Is All You Need’ which introduced a new network architecture, the ‘Transformer’, that reinvented and dramatically improved natural language translation AI. From there on the technique has been built on and expanded in scope until with the launch of Open AI’s ChatGPT, on the 30th November 2022, it exploded into the public consciousness by providing a layman friendly interface to extraordinary computational power. AI had moved from ‘Non coders need not apply’ to the simplicity of typing text. As if by magic, anyone who wants them has been given ‘superpowers’.

And in just two months, 100 million people had tried it out. The fastest growing technology of all time.

Fast forward to March and a much improved version, ChatGPT-4, was released, and in May ‘Plugins’ were added than make the core technology extensible, enabling the easy addition of new capabilities and functionality.

Whilst not a ‘challenge’ like the other four it is a certainty that Generative AI will have a profound impact on the supply and demand side of real estate. How we design, build and operate real estate, and how our customers occupy and utilise real estate, and to what purpose, has a new set of inputs that will ‘transform’ many existing norms.

I would like to emphasise two things.

First, that this is not tech bro hyperbole. In March 2023 Bill Gates wrote that:

‘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.’

Genuinely, what is going on IS of real consequence.

The second point is that we can learn from history a good lesson in how to leverage technology to raise productivity.

In 1881, Thomas Edison built the first electricity generating stations at Pearl Street in Manhattan and Holborn in London. Within a year he was selling electricity as a commodity, and a year after that electric motors started to turn up in manufacturing plants. But little progress was made for many years. By the turn of the century just 5% of factories were using electric motors, and it wasn’t until the 1920's that electric motors completely took over from steam power, and productivity saw a huge boost.

Why do it take so long? Simply put because the steam powered factory represented a ‘system’ where replacing the power source made little difference to how the system worked. The factory still consisted principally of one giant drive shaft powering a myriad of pulleys, chains and belts. Changing from steam to electric changed almost nothing.

It wasn’t until factories themselves were redesigned, and tasks divided up into different areas, each powered independently by smaller electric motors, that the ‘system’ itself could be rethought and the advantages of the new technology leveraged. Everything needed to change to make changing anything worthwhile.

Now, Generative AI is easier to integrate into the ‘white collar factories’ of today but it will be those who redesign their ‘systems’ who will benefit the most from its use. And of course, an office is nothing if not a ‘system’. So we need to be thinking hard about what this new technology will mean for the work we do, how we do it, and therefore what products and services, and what environments we’ll need as it gets rolled out and improved.

Not least of all, are our customers going to be using AI to remove labour, or augment it? Amongst the more financialised, and those whose incentives push them, we are certain to see much displacement of labour. But might we also see much augmenting of ‘human resources’ by smart and forward thinking companies looking to build a bigger pie? This is not a topic for here, but the answers will further add to real estate’s challenges, one way or the other.

Now back to the conclusions relating to our ‘Four Great Challenges’.

First off, sustainability is a ‘Killer App’. It will create & destroy value at an unprecedented rate. Effectively the need to make assets sustainable is an imperative of the highest order. They will very simply be hard to finance, or sell, if they are not so. ‘Green Premiums’ will disappear as being sustainable becomes the norm amongst assets that anyone wants to buy or occupy, but ‘Brown Discounts’ will grow rapidly amongst assets that no-one either wants or is willing or able to occupy. And as valuations eventually catch up to how to determine what is sustainable or not we’ll see a lot of the mispricing in the market disappear. Many assets are overvalued because their sustainability is not fully understood or priced in. As markets become more sophisticated these errors will disappear. Quite possibly, very quickly. Beware. And be aware.

And 2030 is a ‘Killer Date’ for much the same reason. The regulations implicit in that date will act like a ‘forcing function’ for the industry. As we said, it’s not to be thought of like any other date, but rather as a brick wall we are hurtling towards. Unless you are convinced governments and other authorities will blink as the date gets closer and let people off of complying with what is currently planned you need to be moving fast to position your portfolio.

There is no question of hybrid, remote & distributed work being anything other than here to stay. For the majority of occupiers, a very large percentage, there will be no return to five days a week in the office. It didn’t exist even before Covid, and the inefficiency and ineffectiveness of full office centricity is so obvious to all but the wilfully blind that at least one part of the future is clear. As with the year 2030 this will act as another ‘forcing function’ within the market. And it will benefit those who understand what the market wants, and gives it to them, and will punish hard those that don’t. There is no single ‘office’ market anymore. Plenty will rise as others fall. We are not all in the same boat.

It is not just workplaces that must change though. Companies need to change. They need to update, edit and rewrite their own operating procedures to benefit as they can, and should, from new ways of working. You cannot run a hybrid work policy if your company is designed to be office centric. It does not, and will never work. Many are finding this out now, and mistakingly issuing ever stronger mandates for employees to ‘RTO’ but they are fooling themselves. That will only lose them their best employees. They need to change their ‘system’. They need to redesign the factory.

And in very many cases adopting the principles of #SpaceasaService will be the answer. It will be the defining characteristic of the modern ‘place of work’. Some will outsource their requirements to 3rd party operators but many will also run their own real estate in this fashion. Enabling people to be as ‘happy, healthy and productive’ as they are capable of being is the required output. Whatever it takes.

All of this will mean that obsolescence will be everywhere. Either through the inability to achieve sustainability or through simply not being fit for purpose. Not being able to provide customers with what they want. And the scale of this will be huge. Very little office real estate ticks both boxes. Very little.

Which of course is either a massive bug or a massive feature. It will either wipe you out or is the opportunity of a lifetime. There is unlikely to be all that much in the middle. Great space is good. Cheap space is good. Average, middle of the road space is …… nigh on dead.

Most likely we’ll be seeing a lot of refurbishing and repurposing. Refurbishing when the asset is intrinsically attractive, or characterful or a place where people simply like to be, and repurposing when the office days are over. But whatever happens assets will need to be designed for people (or very specific purposes like urban farming). The 7 trends we discussed in Part 3, of what people actually DO want, need to be adhered to. It’s a myth that we do not know what people want. We do. This isn't rocket science. But they do require effort, imagination and a sense of purpose to achieve. What someone wants does not happen by chance. 

Everything above just emphasises that our Cities ARE vulnerable. Entering into a vicious circle, a doom loop, won’t actually be that hard. Unless Cities double down on what makes them ‘special’, and just about every City has something that makes it special, they’ll find themselves in a very bad place. If a doom loop sets in it could be decades before things recover. Some Cities never recover.

So it is essential that those in power work exceedingly hard to understand what their ‘customers’ want and then work back to what is required to give it to them. Factors like sustainability, community, innovation, technology, culture and recreation will be front and centre of all this. All the while though the paramount thought needs to be 'what does it feel like to be in a great, human-centric City'. Because if you cannot create this the only way is down.

And finally, for those in PropTech, all these great challenges represent a boom time. PropTech, alongside other technologies, is needed ‘everywhere’. Technology is necessary but not sufficient. Alone, without a great amount of qualitative, human input, it’ll achieve nothing much. But together….. there is no upper boundary.

The only thing left to say is that these challenges make one realise that the real estate industry is bigger than real estate. We sit at the centre of everything, and impact on everything. If you want to change the world, then real estate is the industry for you.

But. These challenges are real, and 2030 really is a brick wall we’re hurtling towards. This is serious, and as the great American poet Robert Frost wrote…..

‘The only way is through’ 

Good luck!

Real Estate's Four Great Challenges - Part 4

View of Delft - Johannes Vermeer - 1660–1661 - Mauritshuis Museum, Netherlands

This is part 4 in a series of 5 posts looking at the four great challenges facing the real estate industry on the road to 2030.

Part 1 is here

Part 2 is here

Part 3 is here

———————————-

Challenge No 4 is:

How to Reinvigorate our Cities?

In combination, the first three challenges add up to challenge number 4. How are we to steer our cities through all of this change?

The challenges come in three buckets: Economic, Real Estate, and Social.

1. Economic challenges: With the rise of remote work, there are fewer people commuting to the CBDs each day, leading to a decrease in spending in the area and a potential decrease in business taxes related to buildings. This can lead to a reduction in revenue for businesses and the city itself, which could result in budget cuts for essential services and infrastructure. Potentially starting a vicious circle, a doom loop.

2. Real estate challenges: The potential obsolescence of office buildings is a major concern for cities, as they are a significant source of revenue and employment. If these buildings become less relevant due to the rise of hybrid, distributed and remote work, it could lead to a decline in property values and a reduction in the city's tax base. Additionally, there is a growing need to decarbonise real estate, which will require significant investment in retrofitting existing buildings and constructing new buildings that are energy-efficient.

3. Social challenges: The rise of remote work could lead to a reduction in the sense of community and social connection that is often associated with cities. This could lead to a decline in the quality of life for city residents and a potential decrease in civic engagement and participation.

As with obsolescence, challenge number 3, you can view this situation as Good News or Bad News?

This is the Bad News Perspective:

‘These challenges threaten to undermine the economic vitality and social cohesion of cities, and may lead to a decline in public services and quality of life for residents. Without bold action and strategic planning, cities may struggle to adapt to these changes and remain competitive in a rapidly changing global economy’

Sounds plausible doesn’t it? Maybe cities have had their heyday. And it’s all down from here. After all, such cycles have repeatedly happened throughout history.

But there is a Good News Perspective

What about if we looked through this lens:

‘there is an opportunity to create more liveable, sustainable, and human-centric cities by embracing technology, investing in smart infrastructure, and fostering a culture of innovation and entrepreneurship. By prioritising initiatives that support public health and well-being, promote sustainability and resilience, and enhance quality of life, cities can attract and retain talent, stimulate economic growth, and create a more equitable and inclusive urban environment.’

Genuinely, we could go both ways.

So I thought the way to approach this might be to think about what would success feel like?

What should it feel like to live and/or work in a great, human-centric city?

If we try and envisage the output we desire perhaps we can work back to what inputs are needed to make it happen?

Once again I’m going to recall the great line from Steve Jobs from 1997 when he said:

“You've got to start with the customer experience, and work back to the technology.”

And just think about what customer experience do we want for everyone in our cities and then work back to the technology, processes, policies, infrastructure and systems we’ll need.

So here are some ideas about how it would/should/could feel to live and/or work in a great City?

A great, human-centric city should ….

‘make you feel safe and secure, with well-lit streets, reliable public safety services, and a sense of community that fosters trust and belonging.’

‘make you feel connected and engaged, with opportunities for socialising, learning, and pursuing your interests.’

‘make you feel healthy and happy, with access to affordable and nutritious food, clean air and water, and green spaces that promote physical activity and mental well-being.’

‘make you feel inspired and creative, with a dynamic and supportive environment that fosters innovation, entrepreneurship, and artistic expression.’ 

‘make you feel empowered and included, with opportunities for all residents to participate in the decision-making processes that shape their communities’

With these as our outputs what do we need as inputs? And who provides the inputs.

In terms of provision, investors, developers, city governors, and technologists all have a major role to play. Public and private sectors have to work together, as do technologists and the wider business community. No-one can go AWOL with this project.

And in terms of actions to take there are not surprisingly many, but they fit into one of four categories.

First, Sustainable Development.

1. Prioritise sustainability: Make it a key priority in all development projects, focusing on reducing carbon emissions, promoting renewable energy, and increasing energy efficiency in buildings.

2. Promote active transportation: Encourage the use of active transportation, such as walking and cycling, by investing in infrastructure and amenities that make it easier and safer for residents to get around the city without relying on cars.

Secondly, Community and Inclusion.

1. Encourage remote work: Embrace the trend towards remote work by investing in technology and infrastructure that makes it easier for workers to work from home, while also providing incentives for workers to visit the city centre for meetings and social events.

2. Foster inclusion: Ensure that all residents have access to the benefits of urban life, including transportation, and public services, and prioritise the needs of marginalised communities in all development projects.

3. Create policies that support affordable housing: Develop policies and incentives that support the creation of affordable housing, such as subsidies, tax credits, and zoning regulations that encourage the development of affordable housing units.

4. Foster community engagement: Promote community engagement by encouraging residents to participate in cultural events and supporting local initiatives that promote social interaction and civic engagement.

5. Prioritise accessibility: Ensure that cultural and hospitality offerings are accessible to all residents, including those with disabilities and those from marginalised communities, by investing in infrastructure and providing financial support to local organisations that promote inclusivity.

Thirdly, Innovation and Technology.

1. Embrace technology: Leverage technology to enhance urban resilience, improve efficiency, and promote innovation in all aspects of urban life.

2. Enhance public safety: Develop programs and initiatives that enhance public safety, such as community policing and neighbourhood watch groups.

3. Emphasise accountability: Establish clear metrics and evaluation criteria to ensure that development projects are delivering value for the community and meeting the needs of all stakeholders, and hold developers and investors accountable for achieving these goals.

4. Foster entrepreneurship and innovation: Promote entrepreneurship and innovation by supporting startups, incubators, and accelerators that can contribute to the growth of the local economy and the creation of new jobs.

And fourthly, Culture and Recreation.

1. Create mixed-use developments: Encourage the development of mixed-use projects that promote walkability, social interaction, and economic vitality.

2. Create green spaces: Prioritise the creation of green spaces and public amenities that promote health and wellbeing, and help to create a more human-centric urban environment.

3. Foster collaboration: Create partnerships and collaborations among stakeholders, including government agencies, businesses, and residents, to promote innovation, enhance sustainability, and address community needs.

4. Support local artists and cultural institutions: Provide financial and logistical support to local artists and cultural institutions, such as museums, theatres, and concert venues, to promote a vibrant and diverse cultural scene.

5. Create public spaces for cultural events: Invest in the creation of public spaces that can be used for cultural events, such as outdoor concerts and festivals, and encourage partnerships between artists and local businesses to promote economic vitality.

6. Promote local cuisine and hospitality: Encourage the development of local cuisine and hospitality by investing in food and beverage infrastructure and promoting partnerships between local businesses and cultural institutions.

Once one starts thinking about all of this as a massive system problem to be solved you start to see not only the complexity of the human task but the enormous range of technologies that would be needed and also how they might need to interlink.

Maybe the biggest challenge for the PropTech (and wider real estate) sector will be learning how to build for a system rather than a silo. The ‘job to be done’ here necessitates design and system thinking. None of this will happen without a large amount of great tech that works together; the industry needs to demonstrate it is up for this task. 

Can we get all of this working together?:

• Building Management Systems

• Digital Twins

• AI (including LLMs providing a natural language interface)

• Augmented Reality

• Virtual Reality

• IoT

• Collaboration tools (Zoom, Teams etc plus Built World specific)

• Crowdsourcing platforms (talking to and with the community)

• Intelligent Transportation systems

• Geographical Information Systems (GIS)

• Integrations with mobile apps and platforms - Citymapper etc

• Smart Infrastructure (Cloud/Data/Traffic/Energy)

• 3D Printing

• Drones

• Autonomous vehicles

• Smart City Platforms (collaboration, data sharing, engagement)

And then integrate them into all of the inputs required to create a great, human-centric city?

Quite a challenge isn’t it?