This has been an incredible year so far. The seeds were sown last November when Claude Code and Opus 4.5 ushered us into a transformed AI landscape, where seeing LLMs as the natural language interface to the computing universe came alive. That breakthrough set the scene for this year.
I have been trying to synthesise the implications of all of this for CRE. Both in terms of how we should be organising and running our businesses, but also what this will all mean in terms of demand. What assets will thrive, and why?
Below I highlight the messages of 13 of those newsletters, but the theme has been the same throughout: when AI removes or diminishes the constraints a business or industry was built around, the value does not disappear, it moves. And it moves to whoever redesigns the work to maximise the leverage the technology offers. The argument operates at four levels. At task level, AI makes production cheaper. At professional level, value moves towards judgement and accountability. At firm level, pyramids, pricing models and competitive moats begin to change. At property level, altered headcount, location and operating requirements reshape demand. The mistake is to analyse any one of these levels in isolation.
LEVEL ONE: TASK
1. Build from the bottom up, not top down.
CRE is obsessed about data. Which is remarkable seeing as it has so little, and even less that can talk to each other. But year after year the talk is about sorting our data out, and getting it in a position to apply AI. Everyone is waiting for the perfect predictive CRE systems. But there is no Jim Simons of real estate for a reason. Stop looking.
Meanwhile, from the bottom up, there are incredible things that can be done with decent, but not earth shattering, data, and a solid AI reasoning layer. So much potential is being missed because CRE companies are not grasping what is possible, off the shelf and for all employees, with frontier AI models today.
In September: For every task you do consider… how could AI help with this? If it involves words, images, video or audio the answer will be probably quite a lot. And as AI is now making real contributions to solving decades old mathematics problems, it can probably help you with your spreadsheet as well. Start immediately; no need to wait.
2. AI is like Real Estate - Own what appreciates. Rent what depreciates.
Business tasks can all be mapped to a matrix (our CRE Automation Matrix). Against two axes. Is the work ‘plumbing’ or ‘cognition’, and is it hard or easy to verify. Anything that is verifiable plumbing is going to be commoditised and you should rent the tools to do so. Anything involving cognition that is hard to verify is where the greatest value lies, and you must own that.
And beware of the power of AI lock-in. Where you cannot leave, regardless of cost. Where the workings of your business are in the model you are renting. Without the model, you have no business. And this is about much more than not having suppliers train on your data.
In September: Get smart about procurement. Ask what is being learnt, where that learning lives, and could you take it away with you?
3. Agentic AI is cool, but very hot in terms of cost.
Paying for your monthly licence of ChatGPT or Claude is a ridiculous bargain today. The ROI to anyone using these models proficiently is almost embarrassing. Running swarms of AI agents, in loops, continuously, in contrast, can be ruinously expensive. So far, 2026 has been the year this has become clear. Last year you couldn’t do much agentically - this year there is a wealth of tooling available that enables you to do extraordinary things. But using these tools wisely is something we all need to learn quickly. Mostly that comes down to understanding that the best use of ‘Agents’ is when they can easily verify facts, and perform valuable work at great speed.
In September: When planning how best to use your agents ask two questions: how cheaply can it check each step, and how much do I pay someone to do this now.
LEVEL TWO: PROFESSIONAL
4. Real estate is not the single industry we presume it is.
We cannot define the defensibility of real estate as one industry. That depends on whether you need to actually interact with physical real estate, and whether your signature really matters. Analysts are much more vulnerable than surveyors who walk the building.
In September: Locate where you sit amongst the four quadrants (in the article). Then work on protecting your downside, and maximising your upside. There are choices to be made.
5. We say we have moats - but mostly they are leakier than we make outIn January we looked at six defences the CRE industry thinks it has against technological competition: brand, scale, complexity, data, regulation and relationships. The latter may be a moat for an individual, but friends still ask for ‘the best price’. Regulation requires an activity to take place, but does not determine its price. And complex deals do indeed require human judgement, but in reality, how much of what you do fits in this bucket? As an industry we’re not as immune as many seem to believe.
In September: Consider what you think is your deepest moat - then ask what would need to remain true for it to still be as deep in 2030.
LEVEL THREE: FIRM
6. AI removes business constraints - and changes business models.
Sangeet Choudary has a line in his book Reshuffle which I have deeply internalised: ‘new tech collapses old constraints, and once a constraint disappears the logic of competitive advantage must be reimagined from first principles.’ It is the foundation upon which I built the RIRA framework. Within CRE, as in most businesses, we have always been constrained by a scarcity of time and limited cognitive bandwidth. AI is removing these blockers.
In September: What do you charge for that AI is commoditising? How will that compress fees? What AI do you need to leverage to maintain margins, or at least limit their destruction?
7. Professional services was a pyramid - those days are gone.
AI, badly implemented, is likely to reduce the number of juniors professional services firms hire, and the Stanford Digital Economy Lab’s work on entry-level employment in AI-exposed occupations suggests it already has.
Pierson Ferdinand launched in January 2024 with over 130 partners and now has more than 270, with no associates by design. Its founders call it “AI-native” and say the model handles the first drafts juniors used to produce, though nearly half the firm’s revenue comes from partners sharing work sideways with each other, which is the more interesting half of the story. The pyramid did not vanish into the machine; it flattened into a mesh, with AI absorbing the drafting layer beneath. One may not like it, but the logic is impeccable. Professional services solved this problem once, by making training compulsory rather than commercial. AI has not broken that bargain, it has made breaking it affordable. Train no one, hire whoever your competitors were foolish enough to train. Everyone runs the same numbers, so nobody trains, and the middle ranks hollow out on someone else’s watch.
In September: Spend time thinking about organisational structures. Within five years getting them wrong might bite you back, hard.
LEVEL FOUR: PROPERTY
Everything above happens inside businesses. This is where it shows up in the asset.
Pierson Ferdinand again. At Level 3 the AI story got the headlines; the property story is that a firm doing nine figures of revenue has no offices at all, and channels the saved overhead straight into partner compensation. That is what these four levels look like when you follow one firm all the way down. The drafting layer automates, the pyramid flattens into a mesh, the real estate line goes to zero, and the money reappears as pay.
8. A tenant with a strong balance sheet is no cause to sleep easy
Of course it is comforting to know your tenant can pay the rent. But in an AI world a thriving tenant might not want your space anyway. It might enable them to thrive, to pay more, but to fewer employees. We need to look deeper at how AI will impact different types of customers. There is no direct correlation between growth and headcount anymore.
In September: Ask your occupiers - how is AI changing how you work? Are you doing more with more, or more with less. Then connect that to the space they’ll need.
9. A new genre of research is emerging. Call it AI-impact modelling.
There is an old-school task-based way of modelling occupations for AI-exposure. Still being heavily peddled. But it’s really rather useless.
What matters is how much more productive AI makes a company, and whether that company exists in a world of growing demand. Jevons Paradox doesn’t apply in markets where demand is inelastic.
Where a firm’s output demand is elastic, productivity gains convert into more output at flat or rising headcount; where it’s inelastic, they convert directly into fewer people, and therefore less space. A regional law firm’s caseload doesn’t triple because drafting got cheap. A growing software firm’s roadmap does. Though the elasticity that matters most sits in the market you are not currently serving, which I come back to at the end.
Currently space shortages are masking underlying demand. Don’t mistake a tailwind for a strategy.
In September: AI is going to make demand very jagged. Underwriting against aggregate demand is a dangerous game. The criticality of having the right product in the right location has never been greater. Look ruthlessly at your own portfolio, and get weeding.
10. Jagged demand shows up in pricing before it shows up in occupancy.
There is no newsletter behind this one. It is not a separate argument, it is what items 8 and 9 imply once you follow them out to where the money actually gets committed.
The demand story becomes a pricing story. Valuation runs on expectation, not event. If the market comes to believe that a building’s occupiers will renew for less space, that belief prices in years ahead of the lease event that would prove it right.
The effect is dispersion rather than decline. Today the yield difference between prime and secondary is a gradient: wide, but continuous, and you can price your way along it. Jagged demand turns a gradient into a step. There is stock on one side and stock on the other, and progressively less in between.
Meanwhile the underwriting inputs quietly stop meaning what they used to mean. Covenant strength tells you whether a tenant can pay. It tells you nothing about whether they will want the same floorplate in 2031, which is the actual risk, and it does not appear in a credit rating. WAULT flatters you right up to the break. And LTV covenants get tested against valuations that assume reletting at market rent within a conventional void period, precisely the assumption AI puts under pressure for the wrong half of the stock.
The risk was never default. It was renewal.
In September: take your three weakest assets and stop asking whether the tenants can pay. Ask whether they will want the same quantum of space at expiry, then read what your valuation assumes about the void if they do not. If those two answers disagree, you have found your problem.
ACROSS ALL FOUR LEVELS — HOW TO FIND IT
11. Imagine harder. There are three levels of ambition. Don’t stop at the first.Within the RIRA framework the ‘I’ stands for Imagine - imagine what AI enables. There are three horizons: H1 is the same but better, H2 is something new bolted on to what we do now, H3 is doing something in an entirely new way. Faster taxi, more comfortable taxi, Uber!
Most of what you see with AI is H1. People applying AI to some existing workflow, and proclaiming transformation. It’s not. It’s mostly just doing what everyone else could/should/will do. With a very limited time competitive advantage.
Some things have limits, and making them better/faster/cheaper is the right thing to do, but to make a difference you need to be looking to H2 or H3. This is where the new moats will be built - in places your peers simply did not look.
‘Better, Faster, Cheaper, Fewer’ and ‘The Workflow You Should Have Burned’
In September: pick a workflow that is meaningful to you, then decompose it according to the RIRA framework and work through what AI now enables that was not possible before. And map out how you could make it 10X not 10% better.
12. The Five Provocations.
In February I published ‘Five Provocations’: a framework for finding where AI creates genuinely new ventures, not just better processes. They follow a narrative arc — what becomes free → what falls apart → what gets built → who wins → who pays.
The horizons tell you how ambitious to be. The provocations tell you where to point that ambition. Start with what becomes free, because everything else follows: once a cost goes to near-zero, the business built on charging for it falls apart, and whatever gets built in its place is where the next decade’s money sits.
In September: name one thing in your business that AI has just made free. Then walk it through all five. If you cannot get as far as “who pays”, you have found a capability rather than a business.
WHAT IS ALREADY BAKED IN
This year has seen barely believable capability development. The frontier labs are now using AI to build AI, and the exponential is being pushed harder as a result. Headline economic statistics show little of it yet. The statistics will catch up within two to three years, and in some sectors abruptly.
You do not have to predict the future. You do need to recognise what is already baked in: models will have more memory, more domain-specific intelligence, stronger reasoning, more tools, the ability to operate your computer as you do but faster — and each year they get roughly an order of magnitude cheaper to run at a given level of capability. Last year’s state of the art is a fraction of its original cost today.
At some point, perhaps one to three years off, last year’s model will be both capable enough to do everything you need doing in your business and dirt cheap. Most of what we do at work is not that complicated. Fiddly, subject to nuance, requiring varying degrees of judgement — but too hard for AI? Don’t bank on it.
That last point is the killer, and it needs a caveat. Price per token collapses; total spend does not. This is Jevons working exactly as designed. OpenAI’s CFO has talked about “useful intelligence per dollar” - what is uneconomic today becomes economic next year, so you use more of it.
But note the asymmetry. Demand for compute is elastic: cheaper tokens mean more spend, automatically, with nobody deciding anything. Demand for professional output is elastic too, but only in the market that currently cannot afford it. Among the clients already being served, cheaper production means fewer people. The bigger pie is real; I have written a whole report on where it is. But it does not bake itself. It requires someone to build the offer, price it for a customer they have never served, and accept eating their own margin to do it. Absent that, the efficiency gain lands in the labs’ revenue and in your vacancy rate.
And note where it lands even in the good case. The firms that go and serve that new market will look like Pierson Ferdinand, not like Big Law. Distributed, low-overhead, built for a price point that cannot carry a City lease. Growth in output has stopped implying growth in space. Both branches of this argument reduce conventional office demand. They just do it for opposite reasons.
WHERE I MIGHT BE WRONGCapability is not adoption. The dynamo took thirty years to show up in productivity statistics, and the constraint was never the dynamo, it was that factories had to be redesigned around it, and the people who could not do that had to retire first. Liability and professional indemnity will keep humans signing things long after machines can produce them. If I am wrong, it will be about timing rather than direction. But timing is what you underwrite against, so that matters.
Which is why the strategy here is asymmetric rather than predictive. If this moves slowly, the cost of having built fluency early is some wasted licence fees and a few workflows you redesigned sooner than strictly necessary: workflows you were going to have to redesign anyway. If it moves fast, the cost of not having built it is that you cannot buy the capability at the moment you need it, because nobody can. Fluency is not procurable on demand.
There will be great winners and great losers, and you have more agency over which you are than the discourse suggests. Start with one workflow, in September.
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