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Antony Slumbers 07/03/2024 Antony Slumbers 07/03/2024

‘Gradually, then suddenly’ - Part 2

Where’s your moat?

How are you planning on future proofing yourself against a world increasingly mediated through AI?

In Part 1 we asked:

1. Are you thinking how to redesign your business for an AI mediated world?

2. Have you checked to see which of your customers are?

But we should also be asking similar questions as individuals.

Because intrinsically we should, but also because each of us needs to assess how our answers differ, or not, from the same answers when asked about the companies we work for, or with.

After all, our personal incentives are somewhat to very likely to be different, and as the wise Charlie Munger used to say ‘show me the incentives and I’ll show you the outcomes’.

Let’s just take one example.

We often hear people say ‘’AI won’t take your job, but someone working with AI will’, but this is rather simplistic. What if, by working with AI:

We find our jobs commoditised? From various analyses it appears to be that Generative AI benefits below average workers more than it does top tier ones. So whilst that means a levelling up across the board, it also means a greater supply of people are now capable of doing X, augmented with AI, than previously. And when you have a rise in supply with no rise in demand the price for those services goes down. Simply put, employers won’t have to buy in top tier talent to do X anymore, so average renumeration is likely to decrease.
By working with AI, being augmented by it, one is also training the AI. Explaining in granular detail how A impacts B, or X leads to Y. Over time, as we know, AIs improve as they are fed more data to learn from. Whilst not currently a major force, the development of AI ‘Agents’ is rapidly developing and these ‘Agents’ are being designed to be combinatory. I.e they can be plugged together like Lego. Agent 1 does this, then hands over to Agent 2 to do that. So, whilst today we talk about AI only impacting tasks not jobs, this may well change when multiple tasks can be combined into one action, or AI workflow.

So in both these cases being augmented by AI is a net positive for employers, and a net negative for employees. See what I mean by incentives?

How then does one respond?

There are many possible ways, but there are only three core directions of travel:

A certain type of company will push ahead with augmentation hoping for exactly the results noted above, and hoping that their workforce does not click they are training their replacements, or actively reducing their value. I expect to see a lot of this. Sadly, certain types of job, comprising very large numbers of workers, such as commodity call centres, will ‘fit nicely’ into this management strategy. Much senior management of course will be highly incentivised personally to adopt this approach. Getting rid of employees is a great way to boost the bottom line - look at how effectively large tech companies have done this over the last two years.
A different type of company, with a different type of employee/employer relationship, especially around areas of ‘Trust’, will seek to pursue a third way of working with AI. This will be to lean in to commoditisation and substitution of their existing ways of working, but to do so with a view to redesigning these workflows to enable more and/or new value creation. With the underlying mindset being that with these tools we can create better/cheaper/faster. That, as in all previous technological ‘phase changes’, creative destruction has occurred and on balance, over time, society has benefited.
Or alternatively, even disregarding the development of much in the way of new products or services, employers and employees embrace all these trends and try to grab market share. For a time at least, until the market catches up, there is sure to be the opportunity for companies who are super productive to be super competitive. Most likely at the expense of the Type 1 companies mentioned above.

Ultimately, at an individual level, you need to concentrate on two things:

Understanding the incentives of the company or companies you work for, or with. What is the management ethos? How are they likely to embrace AI? Do you trust them with your labour? If not, then plan to move on. You’ll only be disappointed if you don’t.
And even more importantly, think hard about how and where your value resides. What can you do that the machines cannot? Which skills can you develop that will remain premium? Don’t kid yourself with ‘they’re just stochastic parrots’ or ‘they only produce average, generic content’. Often, average, generic content is enough and anyway these tools are improving exponentially. Assume they can do more than most think, and plan accordingly. Pay special attention to where you think value will either remain or move to - for example Uber commoditised knowledgeable taxis drivers, but greatly benefited the designers of Uber’s software and business. Remember Clayton Christensen’s "Law of Conservation of Attractive Profits." which posits that as industries evolve, particularly through processes of modularity and commoditisation, the locus of attractive profits tends to shift along the value chain. There’s always profits somewhere - that’s what you’re looking for.

Looking after number 1 needs to be your starting point. And if you are looking at all of this from the company point of view, look out for people who are looking after number 1. They’ll be best at helping you navigate to a prosperous future.

So much of the future will be determined at the regulatory level (look out especially for Laws levelling out tax treatment of Capital and Labour), and many things are already written in stone in terms of direction of travel, but as individuals we still do have great agency, and we must use it.

Antony Slumbers 07/03/2024 Antony Slumbers 07/03/2024

'Gradually, then suddenly' - Part 1

This was how Ernest Hemingway described going bust. Individually.

I'm increasingly thinking it's going to be more appropriate for companies.

Why?

Because every business is going to be attacked by startups (or cutting edge incumbents) who are going to look at how, in an AI mediated world, they can automate what can be automated, augment talent where it adds value, and redesign each and every workflow to maximise leverage from a tsunami of exponential technologies.

Every business is going to be competing with others who are built from scratch to be not 10% but 10X more productive.

Yet most companies are still debating how to deal with new ways of working four years after the pandemic started. Most companies invest next to nothing in training, least of all in training management. And most companies have no idea what is coming down the track. Their thinking is gradual, when suddenly is about to hit them.

And most companies are incentivised to operate like this. It's 'how we do it here'.

Which never used to matter that much. For all the constant 'sound and fury' nothing much did change year to year. You could afford to, indeed were encouraged to, iterate rather than disrupt. It worked. And still does work for many.

These were the 'decades where nothing happens' whereas we're now being repeatedly hit by the 'weeks where decades happen' to bastardise Lenin's famous quotation.

This week Klarna issued a press release explaining how their new AI Customer Service bot was now doing the work of 700 people. At a higher level of performance. And not as a co-pilot but as a replacement for those people.

And in this video LTX Studio released a teaser for their AI powered video production software, Version 1. Prepare to be blown away. This was supposed to be decades away.

For those in real estate two thoughts:

1. Are you thinking how to redesign your business for an AI mediated world?

2. Have you checked to see which of your customers are?

And one hypothesis: Failing to do 1 and 2 leaves you terribly exposed. Either to competitors who already are, or to seeing your portfolio turned upside down as your customers find their industries turned upside down.

As ever, change can be a bug or a feature. It's just that today the stakes are rather higher than normal.

Antony Slumbers 29/02/2024 Antony Slumbers 29/02/2024

Human-Centric Real Estate & Generative AI - Part 1

Here are three questions relating to the role and impact generative ai will have in the development and operation of human-centric real estate.

How is generative AI poised to transform human-centric real estate and shape the future of workspaces?
How will the relationship between generative AI and personalised workplace experiences evolve in the coming years?
What implications does the widespread adoption of generative AI have for traditional notions of office space and design?

In part 1 of this series of posts we’ll answer question 1.

How is generative AI poised to transform human-centric real estate and shape the future of workspaces?

First, we need to define what is meant by the term ‘human-centric real estate’. My definition would be that:

‘Human-centric real estate is a design and operational philosophy for buildings and spaces that prioritises the needs, health, and well-being of the people who use them. In this approach, the design, construction, and management of a building are all centred around creating an environment that is conducive to the comfort, productivity, and overall satisfaction of its occupants.’

In turn this description can be unbundled into representing six key pillars that build on the general description. These are:

1. Wellness and Health: Buildings that are designed to promote the physical and mental health of occupants. This can involve air quality management, natural light, green spaces, and facilities that encourage physical activity.

2. Ergonomics and Comfort: Ensuring that the physical environment (like temperature, lighting, and acoustics) is optimised for comfort and reduces strain or discomfort.

3. Technology Integration: Using Smart technologies to enhance the user experience. This can include automated climate control, adaptive lighting systems, and other innovations that respond to the occupants' needs and preferences.

4. Community and Connectivity: Designing Spaces to foster a sense of community and connectivity among occupants. This can involve communal areas, shared resources, and design elements that encourage interaction.

5. Flexibility and Adaptability: Human-centric spaces need to be flexible and adaptable to meet the changing needs of their occupants over time. Sam Altman has a sign above his desk that reads ‘no one knows what happens next’. If he doesn’t know, what hope have we. Flexibility and adaptability have never been as important as they are now.

6. Sustainability: Ensuring that the building is energy-efficient and environmentally friendly, is a non negotiable. Sustainable buildings are great enablers of the other five pillars, and they cannot exist without it.

Now, before we go on, it’s worth thinking about why we need to care about ‘human-centric real estate’. After all, for many decades, offices (and this is the core asset class under consideration here) were very much ‘investor centric real estate’ - we weren’t developing buildings for people but assets for owners. Occupiers needed to work somewhere so had to use offices, but the key purpose of these buildings was to produce secure and stable long term cash flows for investors. Every effort was made to secure ‘Tenants’ but once leases were signed real estate companies were famously uninterested in interacting with the humans who actually worked in these buildings.

This has now changed. The trend was building pre covid but the experience of the pandemic turbo charged the understanding that getting work done was no longer dependant on attending the office five days a week, for 40 or more hours. Technology has ‘slowly then suddenly’ ripped away the NEED for offices. Nowadays the game is all about making occupiers actually WANT to occupy their offices. And judging by the two or so years since society opened up following the pandemic, there isn’t that much WANT going on. In the US office attendance has flatlined for over two years at roughly 2.5 days a week. In Europe it is slightly more, and even in Asia it’s not back to 5 days a week.

The bottom line is we have vast quantities of office space largely unloved and unliked by customers. And on top of that, much of it is unsustainable environmentally with little or no clear financial pathways to becoming so. In short, we are drowning in space either currently obsolete or on the way there.

Which of course is where human-centric real estate comes in. We have a desperate need to create buildings and spaces that are ‘conducive to the comfort, productivity, and overall satisfaction of occupants.’

Many are still debating this point but it really is a fools errand. Not only is the evidence all around us, both anecdotally but also in the form of academic research, but with every day that passes the technologies that are the root cause of this dislocation between work and place are getting better, and as they do the imperative to focus on human-centricity grows and grows.

But, as we will see, the newest technologies are also here to help us turn a bug into a feature. We know people respond positively to human-centric spaces, and generative ai in particular can help us develop them.

Predictive AI also has a strong part to play but for now let us concentrate on how generative ai can help us with each of the six pillars mentioned above.

This is how:

Pillar 1. Wellness and Health

With Custom Generative AI models could design wellness programs or environment layouts tailored to individual health needs or preferences.
And Off-the-Shelf Generative AI, such as ChatGPT or individual GPTs could provide health and wellness tips, suggest ergonomic practices, or offer mental health support through conversational interfaces.

Pillar 2. Ergonomics and Comfort

With Custom Generative AI models might develop ergonomic furniture or workspace designs customised to individual user’s physical needs.
And with Off-the-Shelf Generative AI tools like ChatGPT could offer advice on ergonomic setups and comfort improvement based on user queries.

Pillar 3. Technology Integration

With Custom Generative AI they could be used to create personalised user interfaces for building management systems, adapting to individual preferences and usage patterns.
And Off-the-Shelf Generative AI can assist in troubleshooting technology issues, offering user support, and providing recommendations for tech upgrades.

Pillar 4. Community and Connectivity

With Custom Generative AI models might design communal spaces or community-building activities tailored to the occupants’ profiles.
And Off-the-Shelf Generative AI can offer advice on community engagement strategies and facilitate connectivity through digital platforms.

Pillar 5. Flexibility and Adaptability

With Custom Generative AI models could generate design modifications for spaces, that adapt to evolving use cases or occupant needs.
And Off-the-Shelf Generative AI can provide suggestions on how to make spaces more adaptable or multifunctional based on current trends and user input.

And Pillar 6. Sustainability

With Custom Generative AI we could help develop sustainable building materials or innovative green solutions tailored to specific environmental conditions. Think green roofs, living walls, solar panel layouts and geothermal systems.
And Off-the-Shelf Generative AI can educate occupants on sustainable practices and suggest eco-friendly changes as well as organise community sustainability initiatives, like recycling or shared renewable energy projects.

As you will have noticed the key focus in the use of generative ai is to ‘prioritise the needs, health, and well-being of the people’ who use our offices. It’s all about creating spaces that catalyse human skills, on an individual by individual basis.

Strategically it is not about the adoption of a silver bullet technology (the classic approach of too many ‘Smart Building’ advocates) but rather an ‘operational manifesto’ that is much more personal and intensive. Human-centric real estate is not a fixed and final product. It is much better thought of as a combination of physical, digital and human inputs carefully curated to maximise the health, happiness and productivity of its users.

That is the future of workspaces.

——————————-

In Part 2 we will answer the question ‘How will the relationship between generative AI and personalised workplace experiences evolve in the coming years?’

Antony Slumbers 26/02/2024 Antony Slumbers 26/02/2024

The future of commercial real estate is highly predictable …..

Combine the need for:

Flexibility
Adaptability
Affordability
Sustainability
Health & Well Being
Productivity

With:

Pervasive connectivity
Exponential data growth
Advanced technologies
Generative (and non) AI

And you get:

Distributed working
More flexible, project-based, and collaborative work structures
Automated administration
Higher value human to human connections
Human + Machine workflows
Ecosystems over companies
Networks over defined spaces
Hyper productivity
Looser definitions of live/work/play

Leading to:

CBDs turning to CSDs (central social districts)
More mixed use development/neighbourhoods
Growth of great and/or liveable/walkable/affordable cities
Death of dull - cities, neighbourhoods, regions
Rise of suburban, near home, third party ‘places of work’
Preference for ‘latest/greatest’ buildings - flight to quality
As strong ‘flight to character’ - aesthetics matters
Focus on ‘catalysing human skills’
Human-centricity

Culminating in:

#SpaceAsAService as the defining characteristic of the modern ‘place of work’.

Not a niche within the real estate market, but THE market.

Form follows Function follows Technology.

Agree? Disagree? It’s complicated?

#GenerativeAIforRealEstatePeople

Antony Slumbers 22/02/2024 Antony Slumbers 22/02/2024

Mega Myth - To use AI you need a lot of Data

It is generally assumed that to leverage AI you need to have a lot of data at your disposal. No data = no play! This is such a prevalent belief it’s almost assumed to be a Law of Nature. I’m sure you have heard and read it a million times.

But it is not true.

At least it is not when you are dealing with Generative AI.

With Predictive AI it most certainly is, as that is all about predicting, clustering and classifying. You are applying these actions to specific datasets. So obviously without data you are facing a brick wall.

But with Generative AI the data comes built in. Take ChatGPT - the GPT stands for Generative Pre-trained Transformer, where the training has already taken place on the near entirety of data that exists on the open Internet. And from that vast corpus of data has been developed a statistical model that allows for the creation (the generative bit) of new text, code, images, video, speech or actions.

So when you are using generative AI you have at your disposal if not ‘all the worlds information’ then pretty close to it. You have vast amounts of data at your beck and call. You can, if you have it available, augment this with proprietary data, but to a large degree that is not necessary, or at least does not bring as much as you think to the party. Your data is pretty small compared to what the ‘Large Language Model’ already has intrinsically.

The bottom line is that there is a huge amount you can do with Generative AI without the need for any other data. This is what is not generally appreciated and is why this technology is also known by a different interpretation of the acronym GPT - a General Purpose Technology. Which signifies, like electricity, the internal combustion engine, the Internet itself, a technology that is not a point solution but one that is or will become pervasive throughout society. Generative AI will seep in, often invisibly, to everything. There is little you will be doing within a few years that is not, in one way or another, mediated through AI.

Indeed, there is no need to wait. Below are examples of use cases, by business department, you can implement today. No data required.

McKinsey reckon 75% of potential productivity value and gains will come from the first four categories, but the others are included to show just how much is possible ‘out of the box’. Much of this can be achieved by an individual using public tools like ChatGPT, Claude, Google Gemini and Midjourney. Whilst other areas might require customised products or coding. But either way, almost all of this is available to anyone in your company. And again, with no data required.

Sales & Marketing

Content Creation: Generate engaging marketing copy, blog posts, and social media content.
Email Campaigns: Craft personalised email messages for different customer segments.
Market Analysis: Summarise market trends and news from publicly available sources.
Customer Segmentation: Predict customer preferences using open-source demographic data.
Product Recommendations: Suggest products based on general market trends.
Interactive Content: Create dynamic web content to enhance user engagement.
Predictive Analytics: Analyse customer behaviour for better targeting and segmentation.

Product and R&D

Idea Generation: Brainstorm product ideas based on market analysis.
Prototype Testing: Simulate user feedback on prototypes with AI-generated personas.
Research Summarisation: Compile relevant research to support R&D.
Competitive Analysis: Analyse competitors' product strategies.
Design Optimisation: Propose product design improvements using generative models.
Material Research: Summarise findings on new materials from public databases.

Customer Operations

Chatbots and Virtual Assistants: Implement AI-driven chatbots for customer support.
Feedback Analysis: Analyse customer feedback from public reviews.
FAQ Generation: Automatically generate FAQ content.
Operational Efficiency: Optimise workflows to manage high-volume periods.
Personalisation: Personalise interactions based on behaviour trends.

Software Engineering (Product Development and Corporate IT)

Code Generation: Generate boilerplate code and documentation.
Bug Fixing: Identify potential bugs using publicly available datasets.
Automated Testing: Adjust tests automatically to application changes.
Architecture Design: Suggest improvements based on public best practices.
Security Vulnerability Identification: Identify vulnerabilities from public databases.

Strategy

Trend Analysis: Identify emerging industry trends.
Scenario Planning: Generate business scenarios for planning.
Benchmarking: Benchmark against industry standards.
Innovation Tracking: Track industry innovation trends.
Strategic Diversification: Analyse potential diversification areas.

Legal

Contract Generation: Generate standard legal documents.
Legal Research: Summarise legal precedents from public databases.
Compliance Monitoring: Track changes in laws and regulations.
Dispute Resolution: Suggest resolutions based on similar public cases.
Policy Development: Analyse public compliance standards for internal policies.

Risk and Compliance

Regulatory Compliance Tracking: Monitor regulatory changes.
Risk Assessment: Conduct assessments based on public threat data.
Fraud Detection: Detect fraudulent activity patterns.
Ethical Compliance Monitoring: Monitor public sentiment for ethical issues.
Cyber Risk Analysis: Analyse public data on cyber threats.

Talent and HR

Resume Screening: Automate initial resume screening.
Employee Engagement: Analyse engagement trends to inform strategies.
Training Programs: Develop AI-driven training programs.
Workforce Planning: Use labour market trends for planning.
Diversity and Inclusion: Inform policies based on diversity data analysis.

So, as you can see, it’s time to bury the ‘you need data’ myth. You absolutely do to get the most out of a lot of AI, but with Generative AI the biggest constraint is not data, but your own curiosity, vision and willingness to just get stuck in.