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.