THE BLOG
CRE Crosses The Rubicon
CRE Crosses the Rubicon
Industry professionals need to be focusing on the future, not iterating the past.
Change in AI is happening so fast you need to ignore today’s capabilities, and start thinking of what might be possible soon. In just 15 months the real estate landscape is likely to look very different.*
Executive Summary
By December 2026, the commercial real estate industry will have crossed a technological Rubicon. The prevailing paradigm of Artificial Intelligence will have shifted decisively from its current state, a collection of discrete, human-operated tools for task automation, to the deployment of orchestrated systems of autonomous AI agents that manage entire, end-to-end business workflows.
This transformation will render many of today's operational models obsolete and fundamentally redefine the sources of competitive advantage. While today's market leaders leverage AI to enhance the productivity of their human workforce, the leaders of late 2026 will deploy a "digital workforce" of AI agents that function as proactive, collaborative teammates, executing complex processes with minimal human intervention.
Ignore ‘Exponential’ at Your Peril
Julian Schrittwieser is an AI researcher at Anthropic, makers of Claude, and one of the original authors of DeepMind’s AlphaGo and AlphaZero. So he’s something of a superstar. Last week he published an essay ‘Failing to Understand the Exponential, Again’ in which he explained how people make the mistake that when they encounter errors in current AIs they jump to the conclusion that it’ll never be capable of XYZ. Whereas if one follows the data, AI progress is moving at an extraordinary pace, and that we should be expecting that:
Models will be able to autonomously work for full days (8 working hours) by mid-2026.
At least one model will match the performance of human experts across many industries before the end of 2026.
By the end of 2027, models will frequently outperform experts on many tasks.
So whilst we tend to focus on the here and now, we need to appreciate that Commercial Real Estate, alongside other industries, is fast approaching a pivotal moment where what is technologically possible is set to change dramatically.
When the 'Expert' is a Machine - What Then?
And this is going to upend the 'work we do', our business models, and where value and competitive advantage is to be found. When an AI can work autonomously for 8 hours with minimum human supervision whole swathes of industry workflows become possible to execute in an entirely different way to today.
Even more so when the ‘intelligence’ of that autonomous agent matches or surpasses that of a human expert. OpenAI recently released research (GDPval) showing that today, across many tasks, 47.6% of deliverables by Claude Opus 4.1 were graded as better than or as good as the human deliverable. Fast forward 15 months and you get to ‘match the performance of human experts across many industries’. And one more year and ‘frequently outperform experts’ becomes commonplace.
Given the above you need to plan ahead. Whoever redesigns their operations for this world will have an inordinate advantage. And this advantage will compound, as these systems have a flywheel effect where each completed workflow acts as learning material for the next. In contrast those who don’t will find their knowledge degrades in value increasingly fast. Being a great saddle maker when cars arrived was not a great place to be.
Unlocking the Future - Data
To unlock the future, two foundational shifts are necessary:
First, the leap to expert-level, autonomous AI is impossible with the fragmented data infrastructure that plagues the CRE industry today. Professionals spend up to 80% of their time just gathering and cleaning data, a massive bottleneck to high-value work.
The vast majority of crucial information, leases, legal contracts, property photos, is unstructured and remains largely untapped. By thinking of data as a critical asset future ready companies will be creating a data spine that pulls all of this together and makes it possible to be effectively managed.
One often hears real estate people proclaim that proprietary data is their ‘gold mine’, but this will not last. More and more information is becoming open in one way or another, and AI makes the scraping, aggregating and synthesising of disparate data sources increasingly easy. Being able to orchestrate all of these data sources is where competitive advantage will lie. One doesn’t need to own data to extract value from it.
Unlocking the Future - Agents
Secondly, the enormous value of AI that can work autonomously for hours is that one can start to orchestrate whole swarms of customised, bespoke ‘Agents’. By unbundling and rebundling workflows (which we’ve covered multiple times in this newsletter) it becomes possible to chain any number of tasks together to achieve a goal.
Autonomous Agents in Action
Here are four examples of autonomous workflows that will be common by December 2026.
Investment & Acquisitions: Predictive Underwriting
Today: AI-assisted valuation models use historical data. Due diligence is a manual, weeks-long process involving expensive experts.
December 2026: AI agents will perform autonomous due diligence. Fed an offering memorandum, an orchestrated team of agents will extract financials, abstract lease terms, and check planning/zoning laws, producing a comprehensive risk report in hours, not weeks. Valuation will become dynamic, with AI continuously analysing news, social sentiment, and satellite imagery to identify mis-priced assets before the market does.
Development & Construction: Proactive Project Orchestration
Today: AI assists with design optimisation. Project management is manual and reactive.
December 2026: Generative design will produce near-complete schematic designs and BIM models based on a set of constraints (budget, codes, energy targets). On-site, AI agents will act as a central intelligence hub, integrating live data from IoT sensors and supply chain APIs to proactively orchestrate schedules, predicting delays and recommending solutions before they disrupt the project.
Leasing & Tenant Management: Autonomous Leasing
Today: Simple AI chatbots handle basic tenant inquiries. Negotiations are fully human-led.
December 2026: End-to-end autonomous leasing agents will manage the entire workflow 24/7 - from engaging prospective tenants with hyper-personalised conversations to conducting virtual tours, running automated screening, and generating customised lease documents. For complex negotiations, a generative AI "copilot" will assist humans in real-time by redlining contracts, flagging risks, and suggesting legally compliant alternative clauses based on the firm's playbook.
Asset & Portfolio Management: Real-Time Risk Orchestration
Today: Predictive maintenance alerts are common. Portfolio analysis is a periodic, backward-looking activity.
December 2026: AI will enable continuous, real-time portfolio risk orchestration. Agents will work 24/7, monitoring tenant credit risk, tracking loan covenant compliance, and identifying ESG compliance gaps. Crucially, they will move to prescriptive intelligence, not just flagging a risk but autonomously modelling "what-if" scenarios and recommending quantified, data-driven solutions for human asset managers to approve.
Humans Must Maintain Agency
In each of these cases designing the autonomous workflow is a super-skill. It is up to you what to automate, and where to insert a ‘human in the loop’. You’ll probably design different processes for different circumstances. But the point is to balance autonomy with agency.
Much of the process for which you used to charge will become commoditised, so you need to recreate value elsewhere.
The focus will move from being a "doer" of tasks to a "strategic overseer" who can:
Provide Strategic Judgment: AI lacks common sense and an understanding of local nuance; human experts will provide the critical context and make the final judgment call.
Master Negotiation and Relationships: AI cannot replicate the empathy, rapport, and emotional intelligence required for high-stakes deal-making.
Build Trust: In a world of automation, trust becomes the ultimate currency. Authenticity and integrity remain fundamentally human differentiators.
Recommendations for 2026 Readiness
The transition to an autonomous, AI-driven operational model is not optional; it will trigger a period of "digital Darwinism" where technologically advanced firms gain insurmountable advantages.
1. Make Data Your Core Strategy: Immediately begin the work of breaking down data silos and building a unified "data spine." This is a C-suite level business initiative, not an IT project.
2. Domain-Specific Reasoning: Autonomous Agents need exceptional ‘context, clarity and constraints’, so assembling detailed instructions as to what inputs are required, what processes need to be executed, and what outputs are desired is essential. You can feed all the domain-specific information you have access to into the system, but you have to be very clear as to what the Agent can access, where it is, and how to get to it.
3. Invest in Your People: Proactively manage the cultural shift by framing AI as a tool for augmentation, not replacement. Invest heavily in upskilling programs to equip your team for higher-value strategic roles.
4. Establish Robust AI Governance: The regulatory landscape is evolving rapidly. Build frameworks now to ensure data privacy, prevent algorithmic bias, and maintain transparency. This will become a source of competitive advantage.
Conclusion
The above may seem like science fiction and a world away from what you think AI is capable of. But it is not. This is short term prediction based on what we know for sure today. By the end of next year we will all have access to models that can perform autonomously (but based on our supervision) for a full working day, and they will be ‘expert’ in many of the tasks you have to do.
To make use of these capabilities you will have to have to have developed a unified data spine, and thought hard about what value you can generate now that many of your workflows will be commoditised by automation. I’ve no doubt this exists in spades: we all spend so much time processing data rather than thinking hard about it. In that thinking there is surely much value?
And, of course, much of the industry won’t have done any of this, or even thought much about it, so short term at least, they’ll be huge value to be had from being an early adopter.
OVER TO YOU
How ready are you? What’s the state of your data? Do you have a decent grasp of the principles of data science? Have you built any Agents yet? Are you working for a future ready firm? If not, is it time to move on?
PS *
Some will argue this timeline is too aggressive, that implementation will lag capability, that I'm underestimating organisational inertia. They might be right. But here's the crucial asymmetry: if you prepare for rapid transformation and it takes 36 months instead of 15, you've merely invested in data infrastructure and AI literacy earlier than necessary. If you wait for certainty and transformation happens on the aggressive schedule, you've lost your business. In an environment of exponential change, the rational strategy is to prepare for the aggressive scenario.
When Bricks Meet Compute
How AI’s Economic Shift Will Reshape Real Estate
Economic growth is moving from human labour to computational resources. For the real estate industry, this means new tenants, new asset classes, and new rules for value creation.
Executive Summary
Artificial intelligence is not just another technology cycle. It represents a fundamental change in how economies grow, how work is organised, and how value is distributed. For the first time in modern history, economic progress is being driven less by the productivity of human workers and more by the expansion of computational resources, “compute”, and the energy that powers them.
For commercial real estate, for you, this shift is profound. The centre of gravity is moving away from routine office work and toward infrastructure that supports AI at scale: data centres, energy hubs, specialised R&D environments, and logistics platforms. Demand across asset classes will diverge sharply.
Three recent reports illuminate this future:
Epoch AI’s “AI_2030” projects the scaling of compute, data, and energy through 2030, with tangible capability milestones.
Agrawal, Gans & Goldfarb’s “Genius on Demand” models how knowledge work will be reallocated as AI “geniuses” enter the workforce, pushing humans to the creative frontier.
Pascual Restrepo’s “We Won’t be Missed” explores the long-run economy after Artificial General Intelligence (AGI), where compute drives growth, labour’s share declines, but absolute human prosperity can still rise.
Together, these reports suggest three horizons:
To 2030: Scaling continues unless constrained; AI transforms digital R&D.
2030–2040: Knowledge work reorganises; humans specialise in frontier creativity, AI dominates routine.
Post-2040: Growth decouples from labour, but humans still work and may prosper in absolute terms; outcomes hinge on how compute is owned and taxed.
The message for CRE professionals: fast change is underway, but multiple paths are possible. Up to 2030, we can map a baseline. Beyond that, the direction of travel is clear, though the speed and distribution are uncertain.
What the Three Papers Tell Us
"By 2030, a single AI training run could rival the annual consumption of a mid-sized city."
Epoch AI’s “AI_2030” is the near-term anchor. Compute used to train the largest AI models has grown 4–5x annually since 2010. Extrapolating this trend suggests training runs in 2030 could be 1,000 times larger than today, with costs in the hundreds of billions of dollars. Frontier training already consumes tens of gigawatt-hours of electricity per run; by 2030, a single training run could rival the annual consumption of a mid-sized city.
Capabilities are expected to follow:
Software engineering benchmarks (SWE-bench) solved by 2026.
Mathematics reasoning (FrontierMath) potentially cracked by 2027.
Molecular biology protein-ligand modelling (critical for designing new drugs) benchmarks solved within this decade.
Weather prediction is already outperforming numerical methods on hours-to-weeks horizons.
Epoch stresses that scaling is contingent on energy, chip supply, and investment and not guaranteed. But it provides the most concrete baseline for 2030.
Agrawal, Gans & Goldfarb’s “Genius on Demand” takes a microeconomic lens. It models routine workers (who apply known knowledge) and geniuses (who create new knowledge at rising cost the further from what is known). Before AI, scarce human geniuses were allocated at the boundary of routine work. With AI geniuses entering, humans are pushed outward to more novel questions. Routine roles erode; the economy bifurcates into AI geniuses handling the mainstream and human geniuses at the frontier. The paper assumes managers allocate questions optimally, though in reality orchestration will be messy.
Pascual Restrepo’s “We Won’t be Missed” looks at the long-run equilibrium. Distinguishing bottleneck work (essential for growth, e.g. energy, logistics, science) from accessory work (non-essential, e.g. arts, hospitality), he argues:
Bottlenecks are automated as compute becomes abundant.
Economic growth becomes constrained by, and proportional to, the expansion of compute.
Human wages converge to the compute-equivalent cost of replicating their work.
Crucially, this cap is above today’s wages. Humans may prosper in absolute terms, but their share of growth declines as compute compounds faster.
Work does not disappear - humans still perform accessory tasks and some bottleneck complements.
Restrepo also stresses political economy: societies may tax compute, redistributing its returns. The long-run outcome is not jobless dystopia, but a shift in power between labour and compute.
Three Horizons — With Scenarios
Horizon 1 (to 2030): Scaling Baseline
If current scaling holds, AI delivers predictable capability gains in digital science. Desk-based research - software, maths, biology - flourishes. Compute and energy become the new bottlenecks.
Baseline Scenario: Scaling persists. Data centre demand grows rapidly, AI R&D tenants proliferate.
Alternative Scenario: Scaling slows. Algorithmic efficiency replaces brute force; AI progress continues but with narrower use cases and less energy demand.
Horizon 2 (2030–2040): Labour Market Reallocation
AI geniuses reshape knowledge work. Routine roles erode; humans concentrate on creativity and judgement. Realistically, this is not binary: tasks within jobs get unbundled, automated in parts, recombined into hybrid roles.
Baseline Scenario: Office demand will bifurcate: routine-heavy employers will shrink their footprints, while frontier-intensive occupiers will invest in specialised, collaboration-rich environments.
Alternative Scenario: Cultural and regulatory drag slows adoption; hybrid human-AI roles persist longer.
Horizon 3 (Post-2040): Compute-Driven Growth
Growth is pinned to compute. Labour’s share declines, but wages rise above today’s levels before flattening. Humans still work, particularly where compute is uneconomical or socially valued.
Baseline Scenario: Wealth concentrates among compute owners; housing affordability pressure grows.
Alternative Scenario: Societies tax compute, redistribute gains, and sustain broad-based prosperity.
What Could Break the Forecast?
Energy constraints: Grid capacity, renewable intermittency, and 3–7 year approval cycles for new projects.
Semiconductor limits: Approaching physical boundaries at atomic scale.
Regulation: EU AI Act, China’s state-led model, US antitrust and export controls.
Capital cycles: AI clusters costing $10–50bn may hit financing headwinds.
Public trust: Safety failures or backlash could slow deployment.
Regional Divergence
United States: Advantage in hyperscalers, shale energy, and capital depth. Strong growth in data infrastructure and frontier AI hubs (Bay Area, Austin).
Europe: Regulation-first (EU AI Act, sustainability mandates). Growth in data infrastructure capped by energy and planning constraints.
China: Pursues domestic chip scaling, centralised AI strategy, state-backed data infrastructure build-out. Implications for different demand patterns in industrial and logistics.
Middle East: Energy-rich states (Saudi, UAE, Qatar) investing in sovereign AI clusters; likely to become global destinations for hyperscale campuses.
Singapore: Illustrates capacity limits, restricting new data centres despite demand.
For CRE, this means opportunities and risks are uneven: location, regulation, and energy matter as much as demand.
Mapping to Real Estate Asset Classes
1. Data Centres & Energy Infrastructure
Compute is the new growth driver; data centres are its factories. If scaling persists, demand is exponential. If scaling slows, demand is still strong, but more efficiency-driven. Either way, land near power, cooling, and fibre is strategic. Expect competition from hyperscalers, sovereigns, and utilities.
2. Industrial & Logistics
AI-enabled supply chains, robotics, and predictive systems reshape demand. Expect bifurcation: generic warehouses vs high-tech hubs with energy and compute integration. Adaptive reuse into AI-ready facilities is a major opportunity.
3. Offices & R&D / Life Sciences
Office demand does not split neatly into routine vs frontier. More likely: gradual unbundling of tasks, hybrid AI-human roles, and new formats for orchestration. Frontier R&D and life sciences demand grows; routine-heavy tenants shrink. Offices become compute-rich collaboration hubs, not desk farms.
4. Retail
AI reshapes supply chains and consumer engagement. But inequality is the deeper driver: luxury and subsidised segments expand, mid-market weakens. CRE must prepare for divergence.
5. Residential
Housing demand persists. Absolute wages rise, but stagnation relative to compute-driven growth stresses affordability. Luxury remains buoyant, subsidised/social expands, mid-market shrinks. Policy (e.g. compute taxation) will heavily influence outcomes.
How to Handle the Change
For Individuals
Build AI fluency (workflows, orchestration, oversight).
Develop frontier skills (framing, synthesis, judgement).
Prepare for hybrid roles where tasks are constantly reallocated.
For CEOs / Firms
Treat compute as strategic: not a back-office cost, but a core input.
Focus on workflow orchestration: integrating AI into valuation, leasing, asset management.
Pursue ecosystem partnerships: with energy, data, and tech players.
For the CRE Industry
Adapt valuation and leasing standards to AI-driven occupier models.
Adjust sector weighting: overweight data infrastructure, grid-adjacent industrial, life sciences. Underweight routine office.
Engage regulators: compute taxation, energy allocation, and AI policy will shape demand as much as economics.
Conclusion: The Decade to Position
Fast change is underway - but multiple futures are possible. To 2030, the baseline suggests explosive compute demand and tangible AI capabilities. Beyond that, labour reallocates and growth decouples from wages, but humans still work and likely prosper in absolute terms.
For CRE, the imperatives are clear:
Data infrastructure, AI-linked industrial, and frontier offices are growth categories.
Routine-heavy offices and mid-market retail face structural headwinds.
Housing remains resilient, but affordability pressures grow.
Doing nothing is not an option. Those who lean in, building AI fluency, repositioning assets, rethinking strategies, will not only survive but thrive in an age where compute, not labour, drives growth.
OVER TO YOU
Does this resonate? How are you underwriting the risk of 'routine office' tenants shrinking their footprint?
That '1000X more compute' figure isn't abstract. It means a land rush for property near power substations. Is your team mapping these locations?
I specialise in helping firms build a strategic response to these horizons. If you're ready to move from thinking to acting, let's talk.
Agents, Agents, Agents
The low hanging fruit of generative AI. Waiting to be picked.
AI 'Agents' come in many forms, and OpenAI’s ChatGPT contains three that you can learn and leverage in no time at all - Custom GPTs, Projects and ‘Agent' mode.
So What are AI ‘Agents’
Let’s start with what AI ‘Agents’ are. They are simply intelligent systems that can be designed at different levels of complexity — from lightweight assistants to fully autonomous problem-solvers. They come in many forms, each suited to different purposes, whether guiding a single workflow, managing a project, or operating as a flexible digital teammate.
To be clear this is a sliding scale, all the way up to what purists mean when they talk about ‘Agentic AI’:
‘A true AI agent is an autonomous system that persistently pursues goals through iterative environmental sensing, decision-making, and action-taking, whilst adapting its strategies based on feedback and changing conditions.’
These exist today, in limited numbers, but mostly the AI Agents we work with today are not fully autonomous and are very much designed with a ‘human in the loop’.
Think of these three as rungs on a ladder: Custom GPTs for repeatable text, Projects for structured workflows, and Agents for proactive automation.
ChatGPT Custom GPTs
What are they?
They’re bespoke versions of ChatGPT tailored for a specific role, task, or style of work. They are configured with your own instructions, tone of voice, and reference material so outputs are consistent and repeatable. And you can embed templates, checklists, or frameworks relevant to your domain (e.g., investment memos, ESG plans, board packs) into them.
When to use them?
You use them for repeatable, text-driven tasks such as memos, reports, checklists or templates. They work best when you want a GPT that consistently “thinks like your team” without re-explaining context each time, and when you want to share a standardised tool with colleagues, so everyone produces outputs in the same style and structure.
In a nutshell, you use a Custom GPT when you want repeatable outputs, in a consistent style, that you can share with others.
Use Cases
Here are four use cases:
Investment Committee Support: draft polished IC memos in your firm’s preferred format.
Fund Reporting: produce NAV updates and ESG reports in a consistent structure.
Recruitment: creates job specs, interview packs, and scoring templates with the right tone.
Heads of Terms Negotiation: draft clauses, flags risks, and ensure standardised outputs.
In the #GenerativeAIforRealEstatePeople course we have 20+ ‘TDH GPTs’ that do everything from provide career advice, act as sustainability consultants, act as IC Committee Advisors, and help you negotiate Leases.
Outside real estate progressive companies use Custom GPTs throughout their business. Vaccine developer Moderna has over 3,000 among a workforce of 5,600. Nearly every workflow could benefit from a Custom GPT.
One of the TDH Custom GPTs will even help you work out where best to use them in your own business.
ChatGPT Projects
What are they?
They are workspaces inside ChatGPT designed for multi-step, data-driven, or ongoing workflows. They let you store files, instructions, and conversation history so you can return and build on work over time. And they support advanced data analysis (spreadsheets, models, scenario runs) alongside natural language prompting.
When to use them?
They are best used for complex workflows that need structured inputs and iterative runs (e.g., portfolio stress testing, budgeting, capex prioritisation), and when you need to upload and reuse data or documents (e.g., lease schedules, ESG data, financial models). In addition when you want to track progress across sessions, and not just deal with one-off answers.
A major difference though is that Projects are not shareable - they are personal workspaces, unlike Custom GPTs which can be distributed across a team.
Use Cases
Here are four use cases:
Portfolio Stress Testing: run vacancy and interest rate scenarios with uploaded data, saving results for comparison over time.
Capex Prioritisation: rank ESG retrofits and fit-outs using criteria and data files, updating iteratively as assumptions change.
Budgeting & Forecasting: manage Opex/Capex scenarios, store models, and track forecasts across sessions.
Planned Preventive Maintenance (PPM): schedule, prioritise, and update maintenance tasks across sessions.
Within the course we have a Project that lets you see a building through the eyes of its occupiers. It surfaces the pain points that drive churn, disputes, or reputational risk, and generates practical AI/tech interventions to fix those problems.
You can push Projects pretty hard; for workflows that you repeat, are quite complex but follow certain patterns, and require updated datasets they can be incredibly useful.
ChatGPT Agent
What are they?
They are AI assistants that can work continuously in the background, not just when prompted. They can monitor systems, fetch data, update trackers, and send alerts across different tools, and are designed for proactive workflows that go beyond “ask and answer.”
When to use them?
They are best used when a workflow needs ongoing monitoring (e.g., lease events, arrears, compliance deadlines). Also when tasks require cross-tool coordination (e.g., pulling from CRM, data room, spreadsheets, and messaging platforms). Overall, when you want the AI to act without being asked each time, and are best for time-sensitive or repetitive processes where a missed step carries risk.
Note: Agents are still evolving - they often need careful setup and integration with your existing systems. They’re powerful, but not always “plug and play.”
Use Cases
Here are four use cases:
Lease Event Management: track renewals, break clauses, and re-gears, fetching market data and prompting timely action.
Arrears Management: monitor payments, flag arrears, and draft notices automatically.
Compliance (Safety, etc.): track fire, asbestos, and statutory deadlines, sending reminders and updating logs across systems.
Deal Pipeline Tracking: continuously monitor NDAs, bids, and due diligence statuses, reducing manual oversight.
Most of the above would take some setting up, and are perhaps more aspirational than practical today. Nevertheless, one should keep track of the capabilities of ‘Agent’ mode because it is developing fast. In a year it’ll be unrecognisable. Within a year, expect agents that handle entire leasing workflows — scheduling viewings, updating deal trackers, and flagging risks automatically — with minimal human input.
That said, in Agent mode today you can send your ‘Agent’ off to visit websites, read news, do calculations and report back. You just have to try ideas out and see how they get on. It’ll be good practice for when you have dozens of these virtual helpers working for you 24/7.
Conclusion
These three ‘Agents’ are not widely used, but they should be. And no doubt will be over time. But for now, as I repeatedly stress, if you lean into these advanced uses of Generative AI you’ll be doing yourself an enormous favour. These are low hanging superpowers - I’d be amazed if you weren’t amazed at what they are capable of if you give them a serious go.
OVER TO YOU
What workflows resonate with you? Where do you think you could use a Custom GPT, a Project or an Agent? There are probably dozens of use cases - just pick a few, and dive in!
This week, try building one Custom GPT that mirrors your team’s tone. Use it three times. If it doesn’t save you time, email me and tell me why.
Changing Assumptions
"When the facts change, I change my mind” - John Maynard Keynes
Real estate is heading towards an operating model where job functions run on prompts—and agents do the rest. The shift is coming faster than expected.
The Hypothesis
It is my belief that a (very) large percentage of workflows in real estate can be broken down into a series of tasks, and that these tasks can be completely, or nearly, automated by the application of ‘Prompt Packs’.
Prompts are Enough
Each ‘Prompt’ in the pack - they’d work sequentially through 3-7 steps - would contain the essence of that task. By which we mean they’d contain:
Inputs – the required data (rent rolls, EPCs, abstracts, comps).
Processing steps – filtering, benchmarking, compliance checks.
Outputs – tables, reports, approvals.
Examples – to make the flow transparent.
We looked at workflows across nine categories, such as Leasing & Occupier Management, Valuation & Investment and Asset & Portfolio Strategy. And realised that almost all the workflows could be fitted into the I/P/O/E framework above.
It became clear why Morgan Stanley (after analysing tasks performed by 162 real estate investment trust and commercial real estate firms, with a combined $92 billion of labor costs and 525,000 employees) recently wrote that ‘AI can automate 37% of tasks in real estate, representing $34 billion in operating efficiencies.’ **
Two Breakthroughs This Year
This still holds true from when we devised this at the beginning of the year, but since then, two breakthroughs have emerged to push the whole idea forward.
Automation of automation (Master Prompts)
First, it may be possible to ‘automate the automation’. Since GPT-5 came out in early August we’ve seen how much better it is at multi-step problem solving, instruction-following for complex and evolving tasks, and invoking additional ‘tools’ as and when required. And with these new capabilities we’ve found it is possible to develop ‘Master Prompts’ that allow you to enter a workflow and the models work out the entire input/process/output/example framework and build the series of prompts required.
In effect a prompt can create ‘Prompt Packs’.
Now this is at an early stage but the technology has developed to such an extent that a lot of shortcuts to the future are now available.
Practical Agents (Agentic AI)
The second breakthrough to emerge is that the long-forecasted world of ‘Agentic AI’ is arriving fast. Whilst still somewhat brittle, the idea of creating discrete software services that can be given instructions and that can then be left to autonomously work out how to complete them, is coming to pass. Anyone who has used ChatGPT Agent will have experienced the rather odd sensation of watching a virtual entity thinking and acting its way through a problem whilst boxed in a computer within a computer.
This is opening up a whole new world of opportunity and whether more robust forms of Agents take 3, 6, 9 or 18 months to arrive, they will definitely be arriving. And so one can plan for them.
From Prompt Packs to Agents
Which means that in the near future we are very likely to see large swathes of the real estate industry become industrialised. Prompt Packs will naturally morph into collections of Agents.
Just as lean manufacturing, via ‘The Toyota Production System’, codified shop-floor know-how into standard work instructions, Agentic AI will be doing the same for CRE.
Now, ’The Toyota Production System’ is one of the most consequential management innovations of the 20th century — arguably as influential to industrial organisation as double-entry bookkeeping was to finance.
The fundamental point is that the concept of codifying tacit knowledge is hardly new in management thinking. So real estate should not be surprised that it appears to be finally reaching our industry.
There are already domain specific AI companies offering a range of services dealing with high value, document heavy and repeatable real estate workflows. Where this ‘Prompt Pack/Agentic’ framework differs is that it applies to all the other workflows one has to deal with day-to-day that do not merit VC backed startups addressing. The implementation of ‘Prompt Packs/Agents’ will be led by all of us. Each creating our own swarm of virtual helpers to suit our particular needs.
As we’ve discussed before , the days of us being ‘Agent Bosses’ are near. We’ll create, monitor and curate these tools. We’ll industrialise ourselves!
So, between the high-end outsourced agent creators and our ‘build your own’ efforts, a huge amount of what we’ve been paid for as an industry in the past is about to be automated.
Critical Questions
Which leads to some critical questions:
1. Do we buy or build?
It has long been unfashionable within real estate to entertain industry players building their own technology. Always better to buy in technology from specialists has been the mantra. On the basis that ‘you don’t know what you’re doing, don’t have the talent, and can’t afford to do it properly’.
Today the tables are turned. Given the ease with which a lot of new tools can be developed, or utilised, every real estate company needs to build a level of technical competency, or at least literacy, in-house. You need to know what you’re doing, you need technical talent, and now YOU CAN afford it.
2. If we buy, what does that mean?
As we’ve seen ‘Prompt Packs’ and Agents are going to become easy enough to create and curate in-house, but for the heavy lifts you are going to need help from the AI services companies working on things such as Lease Extraction, Asset Performance Reporting and ESG Analysis.
These are high value, repeatable tasks that are complicated and nuanced - but they can be largely automated. Just not, most likely, by you.
Which means that, unless you are a large player who SHOULD be building this capability in-house, over time the value in this work will accrue to the AI service providers. If you are using someone else’s tools you are vulnerable to being commoditised. And most likely will be.
AI unbundles knowledge from jobs, and reduces the cost of intelligence. Value will move to those that enable this.
The strategic trade-off is clear:
Build = defensibility, talent, control point.
Buy = speed, commoditisation risk, margin erosion.
3. If the ‘machines’ are doing all this work, what are we humans supposed to do?
This is not nearly as hard, or as worrying, as often stated. You just have to be clear about relative competencies. Think of it like this:
AI provides:
Rapid processing of complex, multi-dimensional datasets
Identification of patterns humans might miss
Consistent analytical frameworks across large portfolios
Probabilistic insights to inform human decision-making
Humans provide:
Market context and nuanced interpretation
e.g Local knowledge, regulatory nuanceStrategic judgment and risk assessment
e.g Portfolio capital allocation, risk appetite.Stakeholder relationship management
e.g Tenant trust, investor alignment.Creative problem-solving for novel situations
e.g Complex mixed-use repositioning, adaptive reuse.
Changing Assumptions
So you need to be paying huge attention to what we discussed in ‘AI Fluency is Not Enough’ - as AI removes existing constraints (eg intelligence) it creates others (eg data, coordination and trust). You need to be over-indexing on how the above will reshape the industry.
Where can we add value?
Where can we act as a ‘control point’?
How can we absorb customer risk and guarantee an outcome?
How can we change the story - what was expensive is now cheap, but you now need XY or Z.
And above all else, changing your assumptions about the future:
What constraints within our industry will go away, but what will endure?
What do we need to know that we did not know before, and how long will it take for us to acquire this new knowledge?
What coordination problems will be commoditised (for example which reports)?
What will be defensible - of those four ‘Human’ skills listed above which ones am I/We strongest at?
Should I be updating my five-year plans to two. Or one?
OVER TO YOU
Ask yourself:
Which of my workflows are most at risk of commoditisation?
Where can I act as a control point?
Which human skill is my differentiator?
What’s my planning horizon — five years, or one?
** Obstacles remain, of course, such as fragmented data, tacit knowledge, and organisational inertia. But the trajectory is clear: agents will chip away at each, and this rebundling will force CRE leaders to redefine where they add value.
Two Years In: The Workforce Changes Reshaping Office Demand
Extraordinary AI capability gains are reshaping labour markets faster than most realise—with profound implications for which tenants will need more space, and which will need far less.
Executive Summary
Two years in, AI has leapfrogged in capability. Labour markets are shifting, with junior roles evaporating fastest. For CRE, this means some tenants will shrink, but others—those leveraging AI to expand—will grow, reshaping demand patterns.
Capability Development Phases
Two years ago I ran the first cohort of my #GenerativeAIforRealEstatePeople course. This week we’re starting the 12th.
I thought this would be a good time to review how the AI scene has changed over this period.
We’ve seen three phases of development:
Phase 1: Multimodality (late 2023 – mid 2024)
Focus: AI systems expand from text-only to unified models with images, voice, data, and search.
Nov 2023 – ChatGPT unified model (text, images, data, web search)
Nov 2023 – Arrival of Custom GPTs
Feb 2024 – Gemini expands to 1m token context window
May 2024 – ChatGPT 4o (advanced voice + vision)
June 2024 – Anthropic launches Claude Artifacts & Projects
Phase 2: Reasoning (mid 2024 – late 2024)
Focus: Models get better at structured thinking, coding, and sustained tasks.
Jun 2024 – Claude Code launched (dramatic leap in machine coding)
Sep 2024 – ChatGPT Projects launched
Oct 2024 – ChatGPT o1 (first “reasoning” model; advanced voice & search)
Nov 2024 – Google NotebookLM with audio overview
Nov 2024 – Lovable launched (“vibe coding” movement)
Dec 2024 – ChatGPT o3 (inference training + multi-pass consensus)
Dec 2024 – Gemini 2 & 2.5 Pro (“Thinking mode”, fully multimodal)
Dec 2024 – Deep Research arrives across models
Phase 3: Autonomy (2025 onwards)
Focus: Agents, orchestration, and early autonomous computing.
Jul 2025 – ChatGPT Agent launched (autonomous computing)
Aug 2025 – GPT-5 launched
Exponential Improvement Trajectory
Which means, in short, an enormous increase in the breadth and depth of capabilities.
When ChatGPT-5 launched recently the response was somewhat underwhelming. It was better, but not dramatically so, to the previous models.
However, if one compares the capabilities of GPT-5 with those of GPT-4, launched in March 2023, the difference is enormous.
It’s like the frog that’s been slowly brought to the boil without noticing.
Some have said AI progress is slowing down but, as David Shapiro has said ”AI is slowing down, insofar as most people are not smart enough to benefit from the gains from here on out!”
We have come a long way and have incredible power at our fingertips. All the time whilst, as Google announced last week, their energy cost of AI queries had dropped by 33X in just one year, and for any given unit of intelligence the ongoing trend of becoming 10X cheaper each year is holding steady.
Labour Market Impact Evidence
All of which is starting to have big consequences, even whilst it is still only circa 10% of ‘knowledge’ workers who use these tools every day as part of their workflows.
This week, three Stanford professors released a report, ‘Canaries in the Coalmine’, which examines early signs of how AI is reshaping the US labor market, especially for young and entry-level workers.
Key findings were:
Young workers (22-25) in AI-exposed jobs have seen a 13% drop in employment since late 2022, even as older colleagues in the same roles maintain or increase employment.
The decline is not due to industry-wide layoffs, but to firms quietly not backfilling entry-level positions as they become vacant, erasing the bottom rungs of traditional career ladders.
AI-driven job reductions are concentrated in roles where AI automates codified or rule-based tasks ("book learning"); jobs where AI augments human skills (especially those reliant on experience or tacit knowledge) are not impacted similarly.
Older and more experienced workers in the same occupations are not affected and may even see job growth, underscoring that this is age-specific displacement, not a general workforce reduction.
Wages across age groups remain flat, indicating that firms are reducing headcount rather than cutting salaries—for now.
Researchers controlled for industry and firm factors, showing that the phenomenon is linked specifically to AI-exposure rather than broader economic trends or unique company shocks.
Office Demand Implications
Meaning what for office demand?
Probably less people needing space for doing entry level work, and more space for more senior types capitalising on their ‘tacit’ knowledge.
Anecdotally one is hearing similar dynamics occurring across the professional services arena. The great intake of yearly graduates, populating the base of the pyramid, may be coming to an end?
Similarly, stories such as that recounted by Salesforce boss Mark Beniof last week, that he has sacked 4,000 out of 9,000 customer support staff, are moving from rarity to commonplace.
Now I am sceptical that this trend will last, and indeed my projection is that junior people will come to do well out of this new tech - see Issue #33 - but for now at least things aren’t looking good.
Either way, if younger people DO end up doing better than the research is suggesting right now, that might well be at the cost of more senior people. Same capability, but cheaper, has consequences.
For the office market, we’re going to need a lot of growth to support the market. The dynamic is 100% less people visiting, maybe more space per person for ‘collaboration’, and no mass return to 5 days a week. Without growth, this is a solid ceiling. If you don’t have the ‘right’ space, you’re in trouble.
In Pursuit of Tacit Knowledge
A key question for younger people is how to gain tacit knowledge? I think this might happen in two ways:
‘Someone’ will aggregate corporate tacit knowledge and publish it. This will happen across industries, and will be incentivised by the high value of this knowledge. Maybe ‘old timers’ codifying all they’ve learnt and passing it on. Or maybe through some sort of AI driven automated research tool, that gamifies in some way the passing on of knowledge.
Within corporates, Slack Channels, email, diaries, notetakers and the like will be able to codify all the tacit knowledge in this unstructured data. Hybrid working just makes this easier - as we increasingly work asynchronously, our ‘office’ becomes virtual and all our knowledge and information is contained inside this virtual ‘beast’. Getting to understand the actual workings of a company has never been easier. Tacit knowledge will become explicit just through the process of working.
And so either of these levels the playing field for younger people. And solves the problem of it being all very well getting rid of the cost of juniors but they’ll be no-one to enter the ranks of the seniors. Companies cutting down their intake lose the optionality that has historically given them.
‘Forecasting is hard - especially about the future’, said Nobel prize-winning Quantum physicist Niels Bohr. But…. I’d wager we’re going to see a different dynamic emerge anyway.
And this goes back to something we covered in #Issue4 and #Issue11 - https://www.flexos.work/trillion-dollar-hashtag
Strategic Imperatives
The future belongs to fast, agile, ultra-productive superteams and those designing their business ‘for a bigger pie’.
Incumbents planning their futures on doing what they’ve always done, just faster and cheaper, will be the death of the workforce. A mentality going nowhere and destroying all notions of a ‘social contract’.
Without new ways of doing, and aiming for growth, we’ll all be in terrible trouble.
So let’s not go there.
The lesson of the last two years of Generative AI is that it IS going to be increasingly easy to substitute machines for labour, and that many companies will be incentivised to do just that, ahead of any restructuring or redesigning of their businesses.
So, within CRE, we need to become very good at identifying these types of company, as they are definitely going to be needing less space. And frankly, are customers to avoid.
Focus on Two Things
Where we need to focus is on two things:
First, identifying those companies leaning in to the new technologies, coming up with new business models, and with aggressive growth targets.
And secondly, applying the same thinking to ourselves; as individuals, teams, divisions and companies. We need to be the ones ‘smart enough to benefit from the gains from here on out!’
In Preparation For Next Week
Next week I am going to build on this hypothesis: that we are moving towards an operating model where we’ll execute complete job functions from detailed prompts—define inputs, methodology, outputs, and let the model deliver results. And once optimised, these prompts will be evolved into autonomous agents.
The human role will transform into three functions: workflow design, data curation, and strategic intervention at critical decision points.
On the course we dig deep into where Humans + Machine works best. Where can we achieve AI Human Synergy - doing together what neither could do on their own?
This is going to require new types of working. And a very different mindset.
But, from the work I’ve been doing over the last few weeks, and based on the speed of progress since our first cohort, I’m increasingly convinced that this is not the pipe dream I thought it was a year ago, but actually the framework for what is definitely going to happen.
We shall see. Soon!
OVER TO YOU
Are you feeling the change? Are you thinking of moving job, redesigning your business, looking for different markets?
Do you think we can go straight for ‘a bigger pie’, or will we have to go through the painful transition first?