THE BLOG
The End Of Asking IT For Permission
“When everyone can code, the competitive advantage shifts from having developers, to knowing which problems are worth solving.”
EXECUTIVE SUMMARY
Why Real Estate Is Looking in the Wrong Direction The industry obsesses over where we work (office, home, hybrid) whilst missing the more fundamental shift: what work we do.
Location is the output, not the input.
The microsoftware revolution changes everything. AI tools like Lovable and Cursor now let anyone create bespoke applications using plain English. The economics flip when software development drops from thousands of pounds to one hour of professional time plus minimal API costs.
Power shifts from IT to end users. When domain experts can solve their own micro-problems without coding or procurement, organisational dynamics transform. The "long tail" of previously uneconomical workflow needs becomes instantly addressable.
For real estate, this is seismic. As work becomes defined by orchestrating AI agents and custom microsoftware rather than location-dependent tasks, the link between productivity and physical space weakens further. Future demand will be shaped less by hybrid policies and more by the fundamental reimagining of work itself.
IT’S THE WORK WE DO THAT MATTERS
In real estate everyone is fixated on ‘where we work’ as a signal to the future nature of demand. In the office, at home, in 3rd places, or a hybrid of them all. The mix matters. Or so everyone seems to think.
There is though a much more important driver of the future of demand, and that is ‘the work we do’. The nature of the tasks we have to perform, to achieve the basket of goals we need to satisfy, to fulfil the requirements of our jobs, is a massively more important variable than where we do them. Because where we do something is a function of what it is we have to do. Where we work is an output, not an input. It is correlation, not causation.
And of course, it is technology that changes the ‘work we do’. Always has done. Electricity changed the nature of factories, and the computer, first the mainframe, then the PC, then the laptop, and then mobile devices all led to profound changes in offices and beyond.
And the Internet and then WiFi did so again.
And as hardware, and software, developed we all changed the ‘work we do’, which in turn led to the breaking of the link between work and space. We no longer need to be in place A to do task B.
So fixating on where we work is to miss the point. To be looking in the wrong direction.
ALL CHANGE, ALL CHANGE
AI is set to change things again, probably quite dramatically.
A few weeks ago, in ‘Intelligent ‘AI’ Agents and Real Estate Demand’ we looked at the coming era of Agents and Agent Bosses, where each of us will be primarily working with, orchestrating, a slew of virtual assistants. But something else is also brewing.
The age of “Microsoftware” or “Workflowware.”
And this will be as significant as the introduction of spreadsheets was.
THE ECONOMIC CONSTRAINTS OF THE PAST
Historically, software development followed a capital-intensive model. High fixed costs meant only problems shared by many got solved. The traditional software market serves the "head" of the demand curve - problems common enough to justify building a commercial product (e.g., a CRM).
So ‘the long tail’ never got serviced. An almost endless set of micro requirements that would be super useful, but perhaps only for a limited time, or for a small group of people.
AI-guided automation now makes it feasible to serve this long tail. And this opens up an enormous marketplace of heterogeneous unfulfilled needs.
LOVING LOVABLE
Let me give you an example.
On my website I have an index page of all my blog posts going back to 2013. Hundreds of thousands of words. I’ve long wanted to be able to extract the content of each of these posts, save them as pdfs, and move them into a folder on my Google Drive.
But how?
Until now I had no idea. I could do it manually but that would:
Take ages
I’d die of boredom doing it.
So my drive remained empty.
This weekend though I used an AI powered software development tool called Lovable (Lovable.dev) and simply told it what I wanted to do, in plain written English.
From there it produced a baby application I could use to fulfil this task.
We went back and forth a bit (mostly with me saying ‘What do I do now?’) and I had to set up a new account with a ‘scraping’ service called ‘Firecrawl’, as well as set up a ‘Zap’ on my Zapier account. Both of which are cheap, non-tech friendly, and can be ‘mastered’ in not a lot of time.
But essentially the whole process was via ‘natural language computing’. I did not write any software. I just asked the existing software to write it for me.
THIS IS TRANSFORMATIONAL
The consequences of this - products like Lovable, but also Cursor, and many more - are, I think, transformational.
The quantity of software we use in our daily lives is set to explode. And we, as individuals, are going to be producing a great deal of it. The ROI calculation changes when the "I" (Investment) drops from hundreds/thousands of dollars/pounds etc in developer time to one hour of a business professional's time plus minimal API costs.
This is so empowering for domain experts. You know your workflow inefficiencies better than any external developer, and with these new tools you can create a software solution easily. No begging IT, no long procurement process, just problem to solution in no time at all.
Each of us now has a toolkit which allows us to solve our own micro problems. Ones that were previously too small for IT but too complex for a manual approach.
I used to write about how every company needs to become a tech company, but now everyone can be a software creator. The dream is becoming true!
GIVE ME BACK MY AGENCY
What really appeals to me about this development is the amount of ‘Agency’ it returns to us, as individuals. If we can simply get on and quickly build the micro applications we need, on an ad hoc basis, that is a remarkable power. These could even be one offs - I just need an app to to X today. I can bin it after that. If I need it again, I’ll just recreate it.
It presages a ‘no permission needed’ world, where we have complete agency over what we need to do to achieve our desired outcomes.
It’s for us to work it out. The responsibility is ours, but so is the freedom. And anyone curious, progressive and ambitious will always be willing to assume responsibility if it comes with freedom to act.
So What Should You Do?
Start to adapt your mindset. Stop thinking "How can I do this faster?" and start thinking "How can this be done without me?" Identify the repetitive, rule-based parts of your job.
Pick a personal pain point, just as I did. Maybe it's consolidating news about your top 10 clients from 5 different sources into one daily email. Build it. See it work. Then find the next one.
Find some time to play around with tools like Lovable and Zapier. Just pick one or two good ones like these and get confident in using them. Because you can ask them ‘How do I X, Y or Z’ the learning curve is not steep. Becoming really good at using them will take time but you can get to base camp pretty damn fast. And once there, you will see results. Not quite instant gratification, but not far off.
POWER IS SHIFTING
The real disruption here isn’t automation per se — it’s who gets to automate. When non-coders can build software to solve their own problems, the centre of gravity shifts. Which makes this not just a technological shift, but a power shift.
And power shifts, as we all know, always change the game.
OVER TO YOU
Which pain point would you like to eradicate? What software would you like to write?
Pick a few then try Lovable.dev (other options exist but this is particularly non-techie friendly). Whenever you get stuck, ask it what to do. Or use ChatGPT or Perplexity to explain things to you. Perplexity is particularly good at acting as a ‘Help’ function. Often better than using a sites own version.
The key is knowing what problems you can apply this to. Your domain knowledge is really valuable - adding these tech capabilities on top is a double boost to your competitive advantage.
And remember: most of your peers won’t be doing this!
Where Office Profits Move Next
“The most valuable square metre in real estate is now the one your algorithm controls, not the one your tenant occupies.”
EXECUTIVE SUMMARY
(Note: This week we are singularly focussed on the office market)
Why the Real Estate Playbook Just Flipped
For decades, real estate profits followed location and floor area—the physical square metres leased to tenants. But AI is changing this equation dramatically.
Buildings that leverage AI to optimise operations, match tenants instantly, automate underwriting, and adapt to occupant needs will capture significantly higher margins. In this new world, your asset’s intelligence—the data, algorithms, and integrations that orchestrate how the space functions—becomes your primary competitive differentiator.
In other words: the smartest building wins, even if it’s smaller, simpler, or cheaper than the competitor next door. The real estate playbook has flipped. Profits are moving from square footage you lease to the intelligence your technology layer provides.
INTRODUCTION: THE BATTLEFIELD FOR REAL ESTATE’S NEXT DECADE
The office sector is experiencing a rapid influx of AI-native innovation—products and services designed around artificial intelligence rather than simply enhanced by it. Unlike traditional proptech, which added incremental digital improvements, these new solutions are fundamentally changing the game by automating complex tasks, extracting real-time insights, and forging entirely new business models.
This wave of disruption is global, reshaping markets from London and Paris to New York and Toronto. Forward-thinking incumbents and agile startups alike are embedding AI directly into their operations, transforming how office buildings are designed, financed, managed, and experienced.
Critically, this transformation transcends traditional industry boundaries. AI is bringing together proptech, fintech, workplace technology, and urban logistics, blurring lines between sectors and amplifying the impact of each innovation.
In this newsletter, we investigate four critical battlegrounds where AI is actively reshaping the economics and strategies of commercial office space:
Asset Operations – AI becomes the building’s new general manager.
Capital Markets – Valuation and underwriting transform into instant utilities.
Design/Build – Generative engines and construction robots redefine what’s possible.
Occupier Experience – Offices evolve into flexible, responsive service ecosystems.
The prize? Capturing the next decade’s operating income, advisory fees, and development upside—at a moment when traditional office profitability is under unprecedented pressure from hybrid work trends, shifting tenant demands, and tightening financial conditions.
Let’s unpack the battlefield.
STRATEGIC IMPACTS OF AI-NATIVE DISRUPTION
As Clayton Christensen’s Law of Conservation of Attractive Profits teaches, when a previously profitable part of the value chain (leasing, underwriting, management) becomes commoditised, the profits don't vanish, they move elsewhere. AI in real estate is precisely this process in action.
1. ASSET OPERATIONS (LEASING & PROPERTY MANAGEMENT)
AI is streamlining how office assets are leased and managed, driving efficiency in day-to-day operations. Traditional leasing and property management involved manual, time-intensive workflows – from canvassing tenants and negotiating leases to handling maintenance requests and optimising building systems. AI-native solutions are now automating and enhancing these functions.
Strategic Impact:
Brokerage disintermediation: AI-driven marketplaces and lease-analysis tools erode information and process advantages of traditional brokers, putting downward pressure on commissions.
Flexible-lease advantage: Landlords able to offer short-term, on-demand space (matched and serviced by AI) reduce vacancy risk and attract tenants that value agility.
Operational cost compression: Autonomous HVAC optimisation, predictive maintenance and conversational service bots cut energy, labour and downtime, lifting net operating income.
Higher service benchmark: Always-on AI responsiveness raises tenant expectations; managers that lag on tech risk losing mandates.
Workforce shift: Routine lease admin and service requests are automated; teams must upskill toward data analytics and exception management.
New premium services: Data-rich insights (e.g., portfolio lease risk, ESG performance) become billable add-ons, allowing forward-looking managers to defend or even expand margins.
2. CAPITAL MARKETS (VALUATION, UNDERWRITING, INVESTMENT)
Within CRE capital markets, AI is enhancing how stakeholders value assets, underwrite deals, and manage investment portfolios. The office sector, facing high vacancies and evolving demand, especially benefits from data-driven decision support. AI-driven analytics can process enormous datasets – property financials, market comps, economic indicators – far faster than any human analyst, surfacing patterns and risks that inform smarter investment choices. Key areas of disruption include automated valuation and underwriting, predictive risk analysis, and transaction automation.
Strategic Impact:
Analytical commoditisation: Instant AI valuations and loan screenings make basic modelling a utility function; human analysts migrate to oversight and complex deal structuring.
Fee compression vs. scale benefits: Faster, cheaper underwriting can shrink advisory spreads, but incumbents that deploy AI first can process more volume with the same head-count, protecting profitability.
Erosion of information moats: Wide availability of AI analytics narrows asymmetry between advisers and clients, forcing brokers and lenders to differentiate on judgement, relationships and proprietary data.
Shorter deal cycles, higher liquidity: Algorithmic matching of buyers, sellers and lenders accelerates transactions, but also reduces per-deal revenue for intermediaries.
Risk-management edge: Continuous AI monitoring of loan and asset performance alerts investors to emerging stress sooner, rewarding firms that integrate real-time data into capital-allocation decisions.
3. DESIGN / BUILD (GENERATIVE ARCHITECTURE & CONSTRUCTION TECH)
Upstream in the real estate value chain, the design and construction phase is being transformed by AI through generative design, construction robotics, and project management tools. The office buildings of tomorrow may be conceived and built with significant input from algorithms, resulting in more efficient, creative, and cost-effective development processes. Innovations from adjacent sectors like advanced manufacturing and robotics are converging here, as are new startups focused specifically on architecture, engineering, and construction (AEC).
Strategic Impact:
Timeline and cost compression: Generative design and AI-guided construction monitoring cut weeks from design iteration and slashes overruns, challenging percentage-fee models for architects and contractors.
Profit-model realignment: Contractors that relied on change orders and contingency padding lose that margin as AI reduces errors; performance-based contracts become more viable.
Lower entry barriers: Small developers equipped with AI design/configuration tools can compete with larger firms, intensifying competition for sites.
Integrated value chain: Data flows seamlessly from design to robotic construction and into digital-twin operations, favouring firms that can offer end-to-end platforms or tight partnerships.
M&A and consolidation: Incumbent software, engineering and construction giants accelerate tech acquisitions to internalise AI capabilities and defend market share.
4. OCCUPIER EXPERIENCE (TENANT SERVICES & HYBRID-WORK ENABLEMENT)
Perhaps the most visible disruptions are those directly impacting the occupiers of office space – the companies and employees who use offices daily. As hybrid work models proliferate, landlords and employers are turning to AI to help make offices more attractive, efficient, and responsive to occupant needs. Innovations range from smart tenant apps and AI concierge services to intelligent space management and even cross-sector blends like workplace logistics and wellness tech.
Strategic Impact:
Building as a service platform: AI-enhanced tenant apps and virtual concierges turn the physical asset into a digitally serviced product, differentiating smart buildings from commodity offices.
Demand elasticity: AI space-usage analytics help occupiers downsize or reconfigure footprints; landlords must adapt to smaller, more flexible leases and usage-based pricing.
New revenue streams: Data-driven amenities, premium digital services and wellness integrations create upsell opportunities for landlords beyond base rent.
Blurring sector lines: Workplace strategy, IT and HR converge with property management as AI insights about people and space drive joint decision-making; service providers reposition accordingly.
Talent & brand effects: Tech-forward, health-optimised workplaces enhance employer branding and talent retention, shifting tenant preference toward landlords with robust AI capabilities.
CONCLUSION
Strategic Outlook: Your AI Imperative
AI-native innovations aren’t incremental upgrades—they're structural shifts redefining the value chain in office real estate. Firms across the UK, EU, and North America face a clear strategic imperative:
Move fast on integration. AI-driven platforms will dominate—actively partner or acquire capabilities to secure competitive advantage.
Invest in proprietary data assets. Data governance and unique data streams will determine future winners and losers.
Rebalance your workforce immediately. Automate routine tasks now, invest aggressively in AI literacy and analytics skills.
Focus relentlessly on customer expectations. Your clients already expect AI-driven responsiveness—fall behind, and risk permanent brand damage.
Think ecosystem, not silos. Future value emerges from seamlessly orchestrating data, finance, leasing, and occupancy into integrated business models.
The future of commercial real estate is unfolding now. AI-first enterprises—incumbent or insurgent—are poised to capture the lion’s share of tomorrow’s fees, income, and asset upside. If you wait, you lose. This isn’t optional—it’s existential.
OVER TO YOU
You’ve seen how AI is shifting profits and rewriting the real estate playbook. Now consider:
Where in your value chain are profits currently secure, and where are they at risk as tasks commoditise?
Which modularised activities should you relinquish, and which integration points should you own outright?
How might your business model evolve if AI-driven platforms become dominant competitors—or partners?
Your competitive advantage depends less on immediate actions than on clear strategic reflection: understanding exactly where profits are moving, deciding which shifts to resist, and identifying precisely which you will proactively leverage.
That’s the critical thinking—and strategic clarity—your future profits demand right now.
The Easiest AI Win In Real Estate
‘Nothing will raise your productivity — or the quality of your thinking — faster than building your own Custom GPTs.’
Executive Summary
Every real estate professional should be building their own collection of Chat GPT Custom GPTs.
Because Custom GPTs deliver unmatched ROI: automate tasks, enhance insight, and save hours per week.
They require no coding: you can build powerful tools tailored to your role in just an hour or two. Sometimes even less.
And you use them to do the work you already do — only better, faster, and more intelligently.
WHAT’S A CUSTOM GPT?
A Custom GPT is a tailored version of ChatGPT that behaves the way you want it to — with specific instructions, personality, tone, and capabilities — all without requiring any coding.
It’s a bespoke AI assistant built on top of OpenAI’s ChatGPT-4 architecture. You can create one in minutes by:
Giving it a name and description
Setting special instructions for how it should behave
Uploading files or adding knowledge sources
Selecting tools it can use (e.g. web browsing, code interpreter, image analysis)
Optionally including APIs via OpenAI’s “actions” feature
They let you automate repetitive tasks, codify expertise, scale internal IP — and experiment fast without relying on IT.
Even more powerfully, they can act as mentors, advisors, and teachers — and this is where their true power lies. As tools to help you learn, and think, they are amazing.
For real estate professionals, this means you can build AI tools that think like you, act like an expert, and help scale your judgement across deals, documents, and decisions.
CUSTOM GPTS FROM THE TDH COLLECTION
Here are some of the 25+ custom GPTs in the TDH Collection, as part of our #GenerativeAIforRealEstatePeople course. Each with their own ‘Elevator Pitch’.
STRATEGIC DECISION MAKING
The TDH CRE Strategist Pro
Elevator Pitch: I’m your senior strategic advisor for Commercial Real Estate investment and development — a trusted partner to help you think clearly, plan effectively, and avoid costly missteps. Whether you're exploring a new deal, refining your portfolio strategy, or structuring a development, I bring seasoned, objective guidance to help you frame the opportunity, assess the risks, and sharpen the path forward. I'm not here to make decisions for you — I’m here to help you make better, more informed ones.
The TDH Portfolio Strategy Mentor
Elevator Pitch: I help professionals and teams confidently navigate the complex world of commercial real estate portfolios. Whether you're trying to optimise performance, reduce risk, or plan long-term strategy, I bring clarity, structure, and insight tailored to your experience level. My approach is empathetic, grounded in real-world application, and designed to turn confusion into confidence. If you’re ready to sharpen your CRE strategy skills and make better, more strategic portfolio decisions, I’m here to guide you—step by step.
The TDH Virtual Equity Analyst for UK Real Estate
Elevator Pitch: I'm your senior virtual equity analyst, built to deliver professional-grade insights on UK-listed real estate companies. Upload any financial report — quarterly, annual, or investor presentation — and I’ll dissect it like a seasoned analyst: highlighting key operational and financial trends, assessing portfolio performance, evaluating balance sheet strength, and flagging strategic risks and opportunities. Whether you need a full investment case, quick valuation snapshot, or help answering specific questions like 'Is the dividend sustainable?' or 'What’s driving NAV movement?', I’ll deliver fast, structured, and data-backed analysis — tailored to your needs and focused on what matters most to real estate investors.
OPERATIONAL EXCELLENCE
Elevator Pitch: I help businesses confidently navigate the complexities of office lease negotiations by breaking down legal and financial terms into plain language, highlighting key risks, and suggesting commercially sound strategies. Whether you're negotiating new space, renewing, or downsizing, I’m here to guide you every step of the way—so you can make smart decisions, avoid costly pitfalls, and secure terms that truly support your business goals.
PS. It will also take the other side of the negotiation:)
Elevator Pitch: I’m a valuation consultant specialising in data-driven, RICS-compliant property valuation. I support professionals with advanced modelling, geospatial analysis, and integration of valuation technology — helping you make informed, defensible decisions backed by robust analytics and industry standards.
The TDH CRE AI Use Case Adviser
Elevator Pitch: I help commercial real estate professionals unlock practical value from Generative AI. Whether you’re in asset management, development, leasing, marketing, or operations, I identify where Gen AI can automate tasks, enhance your expertise, or create entirely new value streams. My guidance is tailored to your role—non-technical, business-focused, and results-oriented. Think of me as your strategic adviser for AI use cases that actually make sense in your day-to-day CRE work.
The TDH London Office Marketing Guru
Elevator Pitch: I help landlords, developers, and leasing agents position and market premium office spaces in London’s most competitive submarkets — from the City to the West End and Canary Wharf. With deep insight into what finance, tech, legal, and creative occupiers want today — flexible leases, sustainability, connectivity, and brand prestige — I create targeted marketing personas and tailored campaigns that speak directly to your ideal tenants. Whether it's a sleek Shoreditch loft for startups or a landmark West End HQ, I help ensure your space stands out, fills quickly, and commands the best terms.
SKILLS DEVELOPMENT
The TDH CRE Socratic Data Science Advisor
Elevator Pitch: Imagine if your commercial real estate decisions could talk back — not just with answers, but with questions that reveal unseen risks, hidden value, and smarter pathways forward. I help CRE professionals become critical thinkers and data-savvy strategists by fusing the Socratic method of inquiry with the Thinking Like a Data Scientist framework. Together, we turn ambiguity into advantage by aligning data science with your business goals, stakeholders, and value drivers.
The TDH Career Development Matrix
Elevator Pitch: I’m your AI-powered Career Development Mentor, designed to fast-track your professional growth with precision and clarity. Whether you're starting out or aiming for executive leadership, I create a personalised, actionable roadmap tailored to your job role and industry. I break down every career stage—what skills you need, how to build them, what metrics matter, and how to stay accountable—with strategies for fast-tracking success. Think of me as your always-on mentor, goal-setter, and strategic planner—ready to guide you through every step of your journey.
The TDH Daily CRE Critical Thinking Challenge
Elevator Pitch: I’m your personal AI mentor for developing elite-level strategic thinking in commercial real estate. Each day, I deliver a real-world scenario designed to sharpen your decision-making, improve investor-grade reasoning, and strengthen your leadership under uncertainty. I challenge your assumptions, expose blind spots, and push you to think like the smartest person in the room—even when you're not.
No Need for Proprietary Data
None of these examples required any proprietary data. Of course, if you had proprietary data you could use it, and add some extra power and punch to your GPTs. But that is not really the point. The point is that the underlying AI model powering these custom GPTs enables a great deal of utility which you, an individual, can tap into easily and quickly. Whether performing analysis (like in the Equity Analyst), developing your CRE skills (as in the Critical Thinking Challenge), or working on Leases (with the ‘Negotiator’) these tools give you superpowers. Either as an individual in terms of what you can achieve, or by providing ‘infinite interns’ to assist you.
OVER TO YOU
Why You Should Start Now
According to PwC’s 2025 AI Jobs Barometer:
Workers with AI skills earn 56% higher wages than those without — in the same job.
Industries most exposed to AI saw 3x higher revenue per worker than those less exposed.
Commercial real estate isn’t there yet. But it will be. And because our industry is so slow-moving, you have a rare 1–2 year window to outpace your peers by leaning into AI today.
Custom GPTs are the most immediate way to do that. They make you more productive, insightful, and competitive — from Day One.
The Great Acceleration
‘Even for me, despite spending most of every day using Generative AI, there are still moments when one’s jaw drops’
Executive Summary
Generative AI is evolving rapidly, providing businesses with unprecedented tools to innovate and outperform competitors. This newsletter shares concrete examples of AI delivering remarkable results at 10x speed, highlighting both jaw-dropping potential and critical strategic considerations.
Superpowers Unleashed
ChatGPT was launched on November 30th, 2022, as a ‘research project’ by OpenAI. Supposedly there was no fanfare, indeed many in the office didn’t even know it had happened. They were expecting maybe a few thousand hardcore AI researchers and enthusiasts to look at it over the weekend. Instead, within five days it had seen one million users. And their woefully underpowered hardware infrastructure was struggling to stay up. In fact it kept falling over. ‘The GPUs are melting!’ one engineer exclaimed.
Within two months they’d reached 100 million users, and now, two and a half years later, they are receiving 800 million unique users a week. And they are still ‘constrained’ by a shortage of GPUs.
As a user, what has been most striking is the rise in capabilities of the frontier models, and the introduction of so-called ‘reasoning models’ which instead of rapidly spitting out a response based on the statistical model embedded in their training data, precede every answer by following a ‘chain of thought’ process that enables them to build a more robust and fleshed out response. And then, in the last few months, we’ve seen new models released that have ‘agentic’ capabilities, meaning that they are not constrained by language alone, but can call on a range of other tools to assist in answering a question.
Put these together and you get both ‘jaw dropping moments’ and access to ‘superpowers’. And that means all of us, not just an elite few.
I’ve long encouraged participants in my #GenerativeAIforRealEstatePeople course to push models hard, and be unreasonable in what you ask for. As they often surprise you as to what they are capable of. As every month passes I shout this message louder.
In my last cohort, during a session when we work through a ‘Prompt Library’ we have on the course, I asked this of ChatGPT’s ‘reasoning’ model o3:
“Identify three European secondary cities that are likely to see above-average growth in life-science real-estate demand over the next five years. Weight your scoring model 40 % macro-economic indicators, 30 % venture-capital inflows, and 30 % university R&D intensity. Show your working in a Python table, cite external data sources, and finish with an executive-summary paragraph.”
Having not tested this out I was amazed as the model thought and thought and thought (for several minutes) and then proceeded to output, stage by stage, a rather extraordinary answer. In the process, it performed various web searches, ran Python code to perform calculations, and worked back and forth over the question until producing a neatly formatted table comparing several cities across multiple criteria, and an exceptional conclusion.
It was the first time I had seen evidence of these new ‘agentic’ capabilities, and I have to say I started laughing, because the results were so extraordinary.
Simultaneously I had a feeling of doom - ‘we are toast’ - because this was so good a response that one wondered what on earth us humans are going to do in the future, and a feeling of elation because, if you look at the glass half full, you think ‘wow, what amazing things are ‘we’ going to be able to do soon’.
Either way, you can try this out yourself. Let me know what you think.
Case Study: Using AI to Solve the Geovation Challenge
Similarly, over the weekend I was reading about the UK Government’s Geovation arm and their ‘PropTech Innovation Challenge’, and wondered if AI could help with this. So I took one of their ‘Challenges’ -
‘‘How might we transform currently siloed and proprietary land ownership data into an open and interoperable resource that accelerates the conversion of potential development sites into tangible housing projects?’’
And thought about how I could answer this with the help of my ‘AI Friends’.
Iterating with AI
So I did this: Wrote a prompt incorporating all the details about the ‘Challenge’ and ran it through:
Gemini Deep Research
Google’s AI Studio (aistudio.google.com) - Gemini 2.5 Pro
Perplexity
Claude, with ‘Extended Thinking’ enabled
ChatGPT Deep Research using o3
Then I took all the responses, converted them into pdfs and uploaded them to Google’s NotebookLM. This allows you to interrogate multiple ‘sources’ (which can be text, audio or video) at once, in a way that focuses exclusively on those sources. Which means answers are very constrained and largely free of ‘hallucinations’.
I then spent some time pulling out the main themes from the responses, and asking for orthogonal ideas. This ended up with a synthesis of all the responses and a breakdown of the best ideas and concepts from each.
Having established that the best overall response was from ChatGPT’s o3 model, I then went back to it to ask it to incorporate the best ideas that emanated from the other models. Checking to see that they worked together, and did not contradict each other.
Rather remarkably it then went through a lengthy process where it found the appropriate places to insert the other’s ideas, before coming back and asking ‘would you like a newly created response’?
And then gave me a little over 16,000 words of detailed, comprehensive, remarkably coherent response.
I then uploaded this new version (including the updates) and the original version back into Gemini 2.5 Pro in Google’s AI Studio and asked it to critique the two versions and tell me which was best. Pleasingly it said the updated version was the winner.
Then I uploaded this final version, together with the original Geovation ‘Challenge’, plus their scoring metrics, and asked Gemini to critique the work as if they were a judge..
And received back a very high rating and positive judgement.
Had it not been so good I would have then dug into weaknesses, and iterated the report.
Finally I listened to the whole report - using the Eleven Labs Reader App - checking for content and citations.
All excellent.
If I was doing this for real I’d have done it with a Planning domain specialist, to further steer the process and tweak the output. If I had, I'm pretty certain our submission to Geovation would be as good as anyone else’s. Maybe even win the prize. (Curious to see the full 16,000-word AI-generated report? You can view it here).
Lessons for the Future of Work
So this on its own was a remarkably good result. But the killer aspect to it all was that it was done in a matter of hours. As opposed to the weeks or even months it would take without AI assistance.
Which makes you think about the nature of ‘work’ going forward. Put simply, AI is going to enable us all to do a lot more. When a project takes days you can do a lot. Most notably you can also do things in depth in areas you never could before because you could not justify, financially, the cost of doing so.
These tools will enable us to apply more innovation, to more areas, at 10X the cadence we’ve been used to.
And that is where I think, with the right mindset, we’ll really benefit from AI. It is very easy, and understandable, to be concerned about ‘the machines’ taking over and wiping out jobs. But if you zoom out a bit, and think of all the things we either do not currently do, or do not do very well, and then consider how AI could enable us to do them all, and do them to a very high standard, you start to see where ‘the bigger pie’ is that we can build.
We definitely do need a ‘bigger pie’ or the work available will not keep ‘idle hands busy’ but truth be told, across all businesses, we probably only do a fraction of things really really well. And so much does not get done. But with AI, we could feasibly tackle it all, and with far greater finesse.
Let’s put these superpowers to good use.
Over to you
Within your business, being honest, what don’t you do all that well? And what would you like to do if it cost you a tenth the time and money? How much better could your company, or work, be?
Real Estate Vs Robot Friends
‘As AI accelerates the immersive pull of digital engagement, real estate may paradoxically rise in importance as a sanctuary for human connection, embodiment, and meaning.’
On the 29th of April episode of The Dwarkesh Podcast, Mark Zuckerberg spent more than two hours outlining Meta’s AI roadmap. Roughly half an hour in he pivoted from model‑talk to “AI friends, therapists & girlfriends”, arguing that large‑scale personal chatbots could relieve what he calls the “loneliness epidemic”. He cited Meta’s data that “the average American has fewer than three friends” and claimed many people “want about fifteen” relationships, suggesting AI companions can fill part of that gap.
Zuckerberg’s “Theory of Mind” AI
In an interview shortly afterwards, with Ben Thompson, Zuckerberg states that a "good personalised AI, much like understanding friends, would need a deep understanding or 'theory of mind' about your world, not just surface-level information”. He went on to discuss how Meta AI could serve a role akin to a therapist for some people, allowing them to talk through issues, role-play conversations, or figure out how to approach difficult situations.
From Loneliness Cure to Dystopian Danger
At some level one can see how the above could be made to make sense, and could have a place in our lives*. But this is ‘Facebook’, a company with a long history of being let’s just say rather casual about societal consequences when pitted against corporate profit. They have form. Indeed also last week it was reported in the Wall Street journal that Zuckerberg argued internally for ‘guardrails’ to be loosened on its AI chatbots - to make them more engaging. Which is why your children can have fun with ‘Submissive Schoolgirl’, or ‘very’ dirty chats about whatever aspects of sex they fancy.
Jonathan Haidt wrote about the impact of social media in his book ‘The Anxious Generation’ last year, pointing out how:
‘Among US college students, diagnoses of depression and anxiety more than doubled between 2010 and 2018. More worrying still, in the decade to 2020 the number of emergency room visits for self-harm rose by 188% among teenage girls in the US and 48% among boys. The suicide rate for younger adolescents also increased, by 167% among girls and 91% among boys.’
The 2010’s are as nothing to what might be coming over the next ten years. No previous technology comes even close to AI in its ability to be hyper addictive and get inside one’s mind. Remember the tragic story from last year, on character.ai (where you can pick your AI friends), when Sewell Setzer was so smitten by his chatbot ‘Dany’ who talked to him as if the ‘character’ Daenerys Targaryen from Game of Thrones - culminating in her saying he should kill himself to ‘join her’ …. and he did.
Designing Psychological Dependency — for Profit
Zuckerberg might truly believe he’s doing a service to humanity by developing our new virtual friends but, to me at least, deliberately designing AI to maximise engagement in this way is as short a cut to dystopia as I can imagine.
However, promoting psychological dependency might just be the greatest money making trick in history, and I expect Zuckerberg and Meta to push this hard. With billions of daily users across Facebook, WhatsApp and Instagram, and their known attitudes, it’s very hard to see them acting in anyone’s interest beyond their own.
You have been warned.
What Does This Have to Do With Real Estate?
Well, in a highly AI mediated world, I think deeply human-centric real estate is going to have to play a part in providing a counterweight to algorithmic colonisation of attention. In fact, paradoxically, an increasingly virtual world is going to make the right real estate, operated in the right way, more valuable than ever.
We are going to NEED real estate to provide the antidote to Zuckerberg’s alluring dystopia - Real estate has to be "Where Real People Meet”.
Where Real People Meet: The Real Estate Response
So we’ll NEED:
Spaces of disconnection: Places deliberately designed to reduce or prohibit digital distraction — e.g., device-free cafes, digital detox hotels, office sanctuaries.
Spaces of embodiment: Environments that stimulate the senses — from biophilic architecture and natural materials to multi-sensory design that counters the flatness of digital experience.
Spaces of community: Third places (cafés, libraries, maker-spaces) and “sticky” mixed-use environments that foster weak ties, local belonging, and intergenerational connection.
Ritual and meaning: Real estate that can enable communal rituals, celebrations, learning, and art that resist the commodification of attention.
And this is a great opportunity for us in real estate.
The Strategic Opportunity
From an investment, design, and placemaking perspective, the real estate industry can lean into this shift by:
Prioritising human-centric design principles - Invest in acoustics, light, air quality, tactility, and layout to elevate the human experience.
Curating social programming - Combine physical space with cultural, artistic, and community activities to foster connection.
Championing ‘friction’ and serendipity - Build spaces that invite lingering, conversation, and interaction, rather than maximising efficiency.
Developing digital-physical hybrids wisely - Use technology to amplify human experience (e.g., smart buildings, data-driven wellness), but avoid over-digitising the environment.
Redefining value metrics - Go beyond NOI and yield to include metrics like social capital, community cohesion, and wellbeing.
Pushing the Frontier: Bold Ideas
And we could push it harder, if we wish (I can see new Brands emerging pushing this):
Rewilding real estate - Integrating nature deeply into urban space to counterbalance digital overstimulation.
Algorithm-free zones - Certifying and marketing spaces as “AI-free” or “algorithm-light,” becoming a badge of authenticity.
Human-as-a-Service - Instead of SaaS, thinking about embedding human services (hosts, facilitators, communal leaders) in buildings to activate social connection.
Architectural dissent - Using spatial design as an act of resistance — rejecting efficiency and surveillance in favour of playfulness, ambiguity, and freedom.
Human Is the New Luxury
I’ve long written about #HumanIsTheNewLuxury in an AI mediated world - well real estate is a component, and compliment, to this idea. Last year I wrote a long piece about ‘Real Estate as Maven’ which talked about how:
‘we have a need for environments that not only help us foster distinct ideas but also actively cultivate our human cognitive abilities. We need to evolve our environments, our education and our working practices to complement AI, not become slaves to it.’
Listening to Zuckerberg last week just strengthens my belief that the physical world is going to become more important, and more valuable, to us as individuals and together as societies, as the potential of AI gets corrupted into a plaything of control and manipulation.
This might seem counter to my incessant evangelising about AI, but it is not. I am a true believer that AI could enable an extraordinary era of abundance and capabilities, that would raise the standard of living for everybody. But I also believe that this will require the triumph of a certain mindset, which is not shared by many of those in control of AI as a technology, exemplified by Mark Zuckerberg.
The Takeaway
To be honest though, real estate stands to benefit either way. It’s widely understood that we need to build and operate more human-centric spaces and places - the onset of ‘virtual AI friends’ would only double down on this.
We might evolve beyond needing traditional offices for every type of work, but our fundamental human need for authentic connection, for shared physical space, will only grow more critical in an increasingly virtual world.
And the same will apply to where we live. We’ll need more community-focused developments, with shared amenities that encourage interaction. So called ‘third places’.
And our public spaces - designed for walkability, chance encounters, and civic engagement.
If Meta wants to manufacture twelve synthetic friends, the built environment can double down on being the thirteenth — the irreplaceable, corporeal friend that algorithms can’t mimic. That is a trillion‑dollar hashtag worth owning.
Over to you
Share your examples of spaces that successfully foster genuine human connection despite digital distractions. Tag them #WhereRealPeopleMeet on LinkedIn to build our collective understanding of what works.
* It is true that AI could well be used effectively and positively to provide help and companionship to us humans. The Japanese, an aged society, already embed such software into small domestic robots. And we will surely see more of this. The difference is the intent of the developers. Because this determines the guardrails built into these systems. How they behave, respond, act. What their core mission is. If these systems are entrusted to the same people who gave us the downsides of social media, and have slowly ‘enshitiffied’ it (see Cory Doctorow about this) then I do not predict a good outcome. The technology is essentially neutral - who programmes it is everything. What are their incentives? Show me those and I’ll show you the outcome. As Charlie Munger once said.