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Four AI Tools Reshaping CRE
Four AI Tools Reshaping CRE
While many in CRE focus on AI as a tool for cost-cutting, the real shift is deeper: domain-specific intelligence, creative automation, and human-machine collaboration.
The four tools discussed here are exceptional efficiency boosters, but they are also indicators of a fundamental shift in how AI will impact CRE, transforming how markets are understood, assets are managed, and spaces are conceived. These tools, and others, will ‘slowly, then suddenly’ force a re-evaluation of work itself.
Introducing: NotebookLM, Operator, Deep Research and GPT-4.5
NotebookLM
Google’s NotebookLM is a research and note-taking assistant that uses their latest Gemini LLM, with its enormous 2 million token - 1,750,000 word - context window (think ‘memory’) to synthesise documents, generate summaries, and create podcast-style “Audio Overviews”. You can add hundreds of documents, videos, audio files, web site links or Google Docs/Slides and then interrogate them intensively and specifically. The system is ‘grounded’ in the sources you upload so has minimal hallucinations and provides citations.
Effectively, this gives everyone access to expert-level analysis—at the scale of an entire project.
Which has consequences: Imagine reviewing lengthy lease agreements or environmental reports as audio summaries while commuting or multitasking. Or using it as a collaborative research assistant for geographically dispersed teams, synthesising insights in real time. It could even redefine how companies share knowledge—leasing teams, for instance, might receive AI-generated podcast updates on market trends, technical due diligence, and portfolio changes, making internal communication more dynamic and efficientt.
NotebookLM will streamline the analysis of market reports, technical due diligence documents, even property portfolios.
Operator
OpenAI’s ‘Operator’ is an AI ‘agent’ designed to interact with websites and perform goal-based tasks, such as booking appointments or scraping data. Essentially it can operate your PC just as you can. It is programmable, meaning you can assign it tasks, and it will determine (either through explicit instructions or independently) the best way to complete them. So it could autonomously handle workflows that can proactively manage operations, anticipate issues, and optimise performance. Without constant human intervention.
So it might operate your building’s systems, go on deal sourcing hunts (24/7), or dynamically respond to Helpdesk requests.
Or it might be programmed (via natural language not code) to automate routine tasks such as data extraction from public property records or scheduling property inspections. Eventually it might integrate with CRE platforms and automate repetitive workflows (e.g., updating property listings or extracting market data from multiple online sources).
Think of it as a ‘digital employee’. But unlike traditional RPA, which follows rigid scripts, Operator is a digital employee that learns, adapts, and executes autonomously.
In this world, the human no long does daily tasks, but rather spends their time designing and overseeing these autonomous systems.
System design, high-value strategic thinking and client engagement become the killer skills.
Deep Research
OpenAI’s Deep Research (Google and Perplexity have similar products) is an AI research agent that autonomously browses, synthesises, and produces cited reports. Unlike what has previously been possible, these ‘reasoning’ systems take their time, anything from 5 to 30 minutes, to work their way up and down, back and forward, through a problem. They do pretty much what a human researcher would do, just much faster.
What they also do is open up the world of hyper-local market intelligence and niche market expertise. These systems can be instructed to go exceptionally deep—making them ideal for due diligence, competitor research, and trend analysis.
Are they perfect? No. As of today they can make mistakes and their work needs to be checked. As one would with an intern or junior researcher. But increasingly you will be able to give them secure access to all your internal knowledge and intelligence. And by doing so provide them with known facts and data, greatly reducing the likelihood of inaccurate or misleading information
GPT-4.5
GPT-4.5 is the latest, and last, non reasoning model produced by OpenAI. It is a model 10 times the size of GPT-4, with improved writing capabilities, greater world knowledge, and a more refined conversational personality. Whilst not seeming to be a major breakthrough on the AI Evals Leader Board, it is said to be the first model that really ‘feels’ like one is interacting with another human.
In practice it is going to be used as the ‘Teacher Model’ from which many, domain specific ‘Student Models’ will be ‘distilled’. Distillation creates smaller, faster models that retain most of the accuracy and capabilities of the original but are fine-tuned for specific tasks.
Within CRE we are likely to see it used to enhance communication and contracts. Its refined language abilities could revolutionise the drafting of contracts, marketing materials, and client communications, providing clear, persuasive, and data-backed narratives.
We know GPT-5 is coming and that will add ‘reasoning’ capabilities to what we are seeing in GPT-4.5. And from recent model development history, we know that the power of reasoning models is partly a function of the strength of the ‘pre-trained’ models they are built upon..
So expect to see more of us treating these LLMs as creative partners in CRE, augmenting human intuition and blurring the lines between human and AI creativity. We are going to redesign creative work in CRE, shifting it from individual creativity to collaborative AI-human creativity.
For CRE, GPT4.5 offers practical gains in everyday tasks—from generating property descriptions to automating client correspondence—thereby enhancing productivity while setting the stage for more transformative future models.
What These Tools Tell Us About the Future of AI
Domain Specialisation
First off there is domain specialisation. AI is moving toward tools that are not one-size-fits-all but are increasingly tailored to specific industries, blending general language capabilities with domain-specific data. Each of these tools can be targeted and fine-tuned for our very specific data, information and knowledge needs.
Hybrid Human–Machine Workflows
Secondly hybrid human–machine workflows will become the norm because, despite their impressive automation capabilities, these tools still require human oversight for quality control and ethical decision-making. The future lies in creating seamless collaborations where AI handles data-heavy tasks and humans provide judgment and strategic insight. With the caveat that this human input has to be of the highest quality. And that this does not apply to all workflows (though they will be the ones humans are actually interested in).
Incremental Evolution vs. Radical Disruption
Thirdly, we should expect more incremental evolution than radical disruption. Taking GPT4.5 as an example, its benefits will take time to evolve and emerge. But they will come bit by bit, so whilst a big bang should not be expected these small incremental improvements will have a cumulative impact and whilst a tad slower, one must still expect and anticipate significant transformation.
This is akin to the way Dave Brailsford adopted the strategy of the "Aggregation of marginal gains" to improve the Sky cycling team's performance. By improving hundreds of tiny factors by just 1%, his team achieved remarkable cumulative gains—winning the Tour de France two years ahead of schedule.
Operational Efficiency and Risk Management
Operator in particular will be a boon for operational efficiency and risk management but it is going to take some time for it to be ready for mainstream use. And then the design of the systems we apply it to, and where we place the ‘human in the loop’ is going to be critical. That said, once these are finalised we will have an AI that won’t stop doing whatever we want it to, and the productivity gains could be extraordinary.
Redesigning Workflows
How we reimagine work processes to leverage AI's efficiency is going to represent a super-skill amongst humans. It has been shown that adding an AI into a human workflow can actually make it work less well, can make it perform as well as ‘the best human’, or can make 2+2=5. Designing for the latter will take special, and very valuable, talents.
Curating Intelligent Outcomes
When humans don’t need to actually undertake tasks their input is going to be mostly around creating, and curating. Designing prompts, setting the parameters, and fine-tuning the outcomes to align with strategic business goals. Redesigning workflows and curating intelligent outcomes will become a new and large job category.
Reinventing the Office
And finally, while AI’s impact spans all asset types, the office will feel it most acutely. Routine tasks will fade, replaced by ideation, strategy, and creative collaboration. Offices must transform from places of execution into catalysts of human potential.
Conclusion: Embracing the Unseen Revolution
The four tools we’ve discussed are harbingers of the democratisation of expertise, autonomous workflows, niche market mastery, and AI as a creative partner. And this has broad implication for CRE: we’re moving beyond automation, and are shifting towards a more data-driven, proactive, specialised, and creatively augmented future.
Automation will be everywhere but it will no longer be the end point, the goal. Instead we’ll be building products and services of a higher order. We’ll be looking to build a better built environment, not just optimise and manage what we currently have. These and other tools are going to allow us to raise the bar of what is possible, and what we aspire to.
As models improve we’ll increasingly be able to focus on human-centric goals such as well-being, community, sustainability, and economic opportunity. To be sure, we will have to as much of what we humans do in CRE today will be co-opted ‘by the machines’, but that is a feature not a bug. These tools are incredible enablers, and they will open the door to building a built environment we can barely imagine today.
What to do?
One can guess, probably pretty accurately, that certain areas are going to be impacted the most, and soonest: property management, investment, brokerage, development, and corporate real estate. So here are seven things to be doing to prepare:
TOP PRIORITY
1. Experiment with Tools: Use NotebookLM, Deep Research, Operator, and GPT-4.5 to gain direct experience.
Why: Hands-on experience is paramount to understanding the capabilities (and limitations) of these tools. Before you can effectively have internal conversations, identify opportunities, or plan strategically, people need to see what these tools can do. This builds conviction and sparks ideas.
2. Start Internal Conversations: Discuss the implications of AI within teams and across your company.
Why: Before anything else, creating internal awareness and alignment is crucial. Without a shared understanding of the opportunities and challenges, other efforts will be less effective. This helps build a culture of experimentation and innovation. It's also the least resource-intensive way to start.
3. Invest in Upskilling: Prioritise training programs for teams to leverage AI tools effectively, and boost AI literacy.
Why: AI tools are only as good as the people who use them. Upskilling ensures your team can effectively leverage these technologies, maximising their potential. This might involve internal workshops, online courses, or bringing in external experts.
4. Identify Automation Opportunities: Pinpoint workflows ripe for AI-driven automation.
Why: By identifying specific processes that could benefit from automation, you can focus your resources on implementing AI solutions that deliver tangible results. This can also help build a business case for further AI investments.
HIGH PRIORITY
5. Forge Strategic Partnerships: Collaborate with tech experts and AI solution providers - get to know and understand the ecosystem that is developing.
Why: Building relationships with tech experts and AI solution providers provides access to specialised knowledge, resources, and support. These partnerships can help you navigate the complex AI landscape and find the right solutions for your business.
MEDIUM PRIORITY
6. Assess Tech Infrastructure: Evaluate current systems for AI readiness.
Why: Understanding your current tech capabilities is essential for determining which AI solutions can be implemented and what upgrades may be necessary. This is more of a technical step that can be addressed once your automation opportunities are clearer.
7. Plan for Change: Develop a roadmap for integrating AI that maintains human oversight and creative input.
Why: Creating a roadmap ensures that AI integration is well-planned and aligned with your overall business goals. This helps avoid the pitfalls of implementing AI in a piecemeal or uncoordinated fashion.
But philosophically, really engage with the notion of "Building a Better Built Environment": Reflect on what this means in an AI-powered context and how AI can help achieve our goals.
Be as aspirational as you can be. And then some more.
Everything IS possible.
What Can We 'Really' Learn From AI?
What can we ‘Really’ learn from AI
‘AI is giving us a blueprint for how to redesign work, and Cities’
Steam Power, Electricity, the Internal Combustion Engine, Computers and the Internet - five ‘General Purpose Technologies’ that changed the world. Each of them far more than point solutions, like most technologies, that address small, discreet problems. These technologies upended HOW societies worked. They impacted on everything, transformed economies and led to massive social transformation. Despite, over time, disappearing into the background, where we no longer gave them a second thought, they permeated our lives completely. They each represented an ‘Age’.
Now we are entering the ‘AI’ age. Sure, AI has been around since the term ‘Artificial Intelligence’ was coined at a summer workshop at Dartmouth University in 1956. But it has been the rise of Generative AI (as opposed to predictive, Analytical AI) that has marked the dawn of this new age. With ChatGPT’s release in November 2022, the world suddenly saw how each and every one of us was going to have access to unparalleled intelligence via natural language. Andrej Karpathy has written that ‘English is the new programming language’, and it is this fundamental redefinition of what it means to ‘compute’ that is opening the floodgates.
AI is acting as an accelerant, revealing and widening the gap between our industrial-era infrastructure—designed for stability, hierarchy, and predictability—and the fluid, networked nature of the modern economy. Traditional systems were built to support linear, process-driven workflows, but AI thrives in environments that are dynamic, decentralised, and non-linear.
Key areas of mismatch include:
Urban Planning & Real Estate: Most cities are structured for an era when work was location-dependent, but AI enables distributed, asynchronous work, rendering many commercial spaces underutilised.
Education & Workforce Development: Industrial-era education systems focus on static skillsets, but AI demands continuous, adaptive learning.
Regulation & Governance: Many policies were designed for slow-moving technological shifts, whereas AI evolves at an exponential pace, making traditional governance models ineffective.
AI Architecture as a Model for Future Cities, Workplaces, and Institutions
AI models, particularly large-scale neural networks, provide useful metaphors for designing future systems:
We are entering a world where the price of intelligence is trending towards zero. And we have much to learn, and are already learning, from the technology that is delivering this, AI.
For example modern AI systems —especially large language models (LLMs) built on “transformer” architectures— are highly modular and layered. Each layer processes information in a distinct but interconnected way, creating flexible outputs that can adapt to various contexts.
Cities and Infrastructure: Urban planners increasingly talk about “modular urbanism,” where components of the city (transport, energy grids, data centres, housing) are designed to be upgraded or reconfigured without overhauling the entire system. This modular approach parallels how AI layers can be retrained or fine-tuned without redesigning the entire model.
For example:
Smart Grids are modular and able to integrate renewable energy sources, manage distributed energy resources, and adapt to changing demand.
Prefabricated Housing uses modular construction techniques that allow for faster, more flexible, and potentially cheaper building.
Modular Transportation Systems such as bike-sharing, scooter programs, micro-mobility solutions are modular additions to existing transport networks.
Data Centers are often built in modular units, allowing for scalable expansion.
Workplaces: Just as AI systems separate tasks (e.g. natural language understanding, image recognition) into specialised modules, workplaces are moving away from rigid departmental silos to agile, cross-functional teams. In practice, this can mean project-based “squads” that form and dissolve as needed—mirroring the flexible architecture of modern AI. Other manifestations include:
API-fication of Work: Treating teams and departments as "APIs" that can be plugged and played together for different projects.
Skill-Based Teams: Forming teams based on specific skills needed for a project, rather than fixed departmental structures.
Flexible Workspaces: Designing offices that are modular and adaptable to different team sizes and project needs (hot-desking, flexible meeting rooms).
Software Tools as Modules: Increasingly businesses are using modular software suites where they can add or remove functionalities as needed. In a world of millions of ‘AI Agents’, designing these temporary or ongoing networks and ecosystems will be one of the highest skilled, and paid, human jobs.
2. Data-Driven, Learning-Oriented Ecosystems
AI models depend on continuous data input and feedback loops to refine performance.
Cities: Smart cities increasingly gather real-time data on traffic, pollution, and public health to make policy decisions on the fly. This learning cycle allows municipal governments to experiment, measure outcomes, and pivot quickly—akin to how AI continuously refines its internal weights.
Institutions: Traditional organisations (governments, universities, corporations) are recognising the value of continuous feedback. This shift from top-down planning to iterative, data-driven decision-making will transform institutional cultures, much like the shift from rule-based AI to machine learning has transformed computer science.
Contemporary early adopters are good examples of where this is going:
Estonia transformed itself by digitising government services, adopting a secure digital identity framework, and fostering an entrepreneurial tech ecosystem. This nimble governance model shows how legacy bureaucracies can be re-engineered around data-driven processes.
Singapore’s Smart Nation Initiative:
By integrating AI into urban planning (e.g. advanced traffic management, digital services), Singapore demonstrates how a city-state can become a “living lab” for next-generation infrastructure.
Platform Economy in China:
Tech giants (e.g. Alibaba, Tencent) have used AI to drive innovations in fintech, e-commerce, and urban services. The speed and scale of adoption offer lessons in how platforms can reconfigure entire economic sectors and consumer behaviour.
3. Network Effects and Distributed Intelligence
AI architectures often rely on distributed processing (cloud computing, edge devices) to handle large-scale tasks efficiently.
Future Cities: We see an emerging trend toward “polycentric” or multi-nodal cities, where multiple urban centres interconnect rather than relying on one central business district. This networked structure allows for distributed resources (e.g. satellite innovation hubs) that share data and resources across the region.
Future Workplaces: Remote and hybrid work models enable distributed teams operating across different time zones and geographies. This mirrors AI’s capacity to run distributed computations, pooling resources from multiple nodes (cloud servers, edge devices) to achieve a collective outcome.
Modular architecture and workplaces, data driven decision making, feedback loops, distributed networks of ‘offices’, and edge computing (intelligence in our buildings and our devices). Our industry, without realising it, is mimicking how AI works. And, slowly, developing into a constantly self-learning system. We’re becoming less reliant on centralised, rigid structures and more fluid, adaptive and ‘anti-fragile’.
Now, some of the required changes to accommodate AI will, as above, sort of happen by osmosis but a lot more, structurally, needs to be done. And the first two Industrial Revolutions (1760 - 1840 and then 1870-1914) offer us many lessons. Such as:
Infrastructure Investment: The need to invest in new infrastructure (railways, factories, electricity grids) to support the new economy. For AI, this means digital infrastructure, data centers, and potentially new forms of energy infrastructure to power AI.
Education and Skill Development: The importance of adapting education systems to prepare workers for new jobs and industries. We need to focus on AI literacy, data skills, and adaptable skill sets.
Social Safety Nets: The need for social safety nets to cushion the impact of job displacement and inequality during periods of rapid change. See dickens for evidence of how brutal this can be!
Regulation and Governance: The necessity of developing new regulations and governance structures to manage the ethical and societal implications of new technologies.
The Rise of Electricity: Looking at the history of electricity has three distinct lessons, all of its own -
Gradual Adoption and Integration: Technologies are not adopted overnight but are gradually integrated into existing systems. With AI, we’ll likely see small, agile, ultra-productive superteams leaning in heavily, but across the board the cadence, is likely to be more ‘slowly, then suddenly’, though I expect this process to be faster than historically (8-28 years according to McKinsey)
Unexpected Applications: The full impact of a technology is often realised through unforeseen applications and innovations. No-one thought of Uber, Airbnb or Netflix before the technology that enabled them arrived. And even then was years before they seemed ‘obvious’. We’re very bad at guessing future jobs.
The Need for Standardisation: Standardisation is crucial for widespread adoption and interoperability. AI is turning out to be more ‘open-source’ than many expected but this needs to be encouraged to underpin the universality that is needed for real impact.
There are also more contemporary developments we can learn from:
The Internet and Mobile Revolution: This demonstrates the speed and scale of digital disruption. Lessons include:
The Power of Network Effects: The value of technologies increases exponentially as more people adopt them.
The Rise of Platform Economies: The emergence of platform-based business models that leverage networks and data.
The Importance of Cybersecurity and Data Privacy: The growing importance of protecting data and ensuring cybersecurity in a networked world.
Companies Adapting to Remote Work Post-Pandemic: This shows organisational agility and the rapid adoption of digital tools in response to a crisis. Lessons include:
Flexibility and Adaptability: The ability to quickly adapt organisational structures and processes.
The Importance of Digital Infrastructure and Tools: The necessity of having robust digital infrastructure and tools to support remote work and distributed operations.
Focus on Employee Well-being and Connection: The need to address the social and emotional challenges of remote work and maintain employee connection.
Smart City Initiatives (both successes and failures): These provide real-world examples of attempts to integrate technology into urban environments. Lessons include:
Focus on Citizen Needs: Successful initiatives prioritise citizen needs and solve real problems.
Data Privacy and Security Considerations: The importance of addressing data privacy and security concerns in smart city deployments.
Interoperability and Open Standards: The need for interoperable systems and open standards to avoid vendor lock-in and promote innovation.How can we thrive amid extreme uncertainty and rapid change?
Thriving in this era requires a multi-faceted approach at individual, organisational, and societal levels:
Individual Level:
Cultivate Lifelong Learning and Adaptability: Embrace continuous learning and be willing to adapt to new skills and roles throughout your career.
Develop "Future-Proof" Skills: Focus on skills that are less likely to be automated, such as critical thinking, creativity, emotional intelligence, complex problem-solving, and communication.
Embrace Agility and Resilience: Develop the ability to navigate uncertainty, bounce back from setbacks, and embrace change as an opportunity.
Build Strong Networks: Cultivate diverse networks of connections for support, learning, and opportunity.
Focus on Purpose and Meaning: Find work and activities that provide a sense of purpose and meaning in a rapidly changing world.
Organisational Level:
Foster a Culture of Innovation and Experimentation: Encourage experimentation, learning from failures, and continuous improvement.
Embrace Agile Methodologies and Flexible Structures: Adopt agile methodologies and organizational structures that allow for rapid adaptation and response to change.
Invest in Employee Development and Reskilling: Provide opportunities for employees to learn new skills and adapt to evolving roles.
Prioritize Data-Driven Decision Making: Leverage data and analytics to understand changing trends and make informed decisions.
Build Resilient and Diverse Supply Chains and Operations: Develop robust and adaptable supply chains and operational models that can withstand disruptions.
Societal Level:
Invest in Education and Reskilling Infrastructure: Create accessible and affordable education and reskilling programs to prepare the workforce for the future.
Strengthen Social Safety Nets: Provide robust social safety nets to support those displaced by technological change and ensure a more equitable distribution of benefits.
Develop Ethical and Regulatory Frameworks for AI: Establish clear ethical guidelines and regulatory frameworks to guide the development and deployment of AI in a responsible and beneficial way.
Promote Digital Literacy and Inclusion: Ensure that everyone has access to digital technologies and the skills needed to participate in the digital economy.
Foster a Culture of Collaboration and Dialogue: Encourage dialogue and collaboration between government, industry, academia, and civil society to navigate the challenges and opportunities of AI-driven change.
In summary, thriving in an era of AI-driven uncertainty and rapid change requires:
Adaptability and Learning: At all levels, from individuals to societies, we need to prioritize learning, adaptation, and agility.
Investment in Infrastructure: We must invest in both physical and digital infrastructure, as well as "human infrastructure" (education, skills, social safety nets).
Ethical Considerations and Governance: We need to proactively address the ethical and societal implications of AI and develop appropriate governance frameworks.
Collaboration and Inclusivity: Navigating this complex landscape requires collaboration across sectors and ensuring that the benefits of AI are shared broadly.
2.3 Key Takeaways
• Regulatory Foresight: Countries or cities that proactively shape regulation (rather than reacting to disruption) create more stable environments for AI-driven transformation.
• Public-Private Collaboration: Successful transformations often hinge on close collaboration between governments, private industry, and academia—mirroring how AI breakthroughs typically result from collaborative, interdisciplinary research.
• Infrastructure Investment: Building the “rails” for AI—cloud computing, data security frameworks, broadband networks—remains a critical enabler.
Defining Your Own Fate: A Call to Action
Does this make you more or less nervous about the future? Can you, will you ‘Look after Number One’. Does it feel harsh, or pragmatic? Now is the time to act—before the AI transition defines your fate for you. Explore the resources in the 'Thrive in Tumult' framework and start implementing one strategy this week.
Looking After Number One
“We are living in tumultuous times and you need to actively take control of your own destiny”
I’m finding it hard to think of a time during my life when everything feels so very much …. up in the air.
Yet I don’t think, en masse, we are taking it nearly as seriously as we should. Indeed we are in a strange world where those who are working on advanced technology are telling us one thing whereas politicians and the commentariat something else altogether.
The AI Revolution: Divergent Narratives and High Stakes
For example, at the recent AI Action Summit, in Paris, US Vice President JD Vance, in a hyper ‘go, go, go AI’ speech said:
“Finally, this administration wants to be very clear about one last point. We will always center American workers in our AI policy. We refuse to view AI as a purely disruptive technology that will inevitably automate away our labor force. We believe and we will fight for policies that ensure that AI is going to make our workers more productive, and we expect that they will reap the rewards with higher wages, better benefits, and safer and more prosperous communities.”
I.e workers have nothing at all to fear from AI.
Meanwhile Marc Andreessen, CEO of Tech VC a16z, and one of the administrations key advisors on AI, is actively investing tens of billions of dollars in AI companies building ‘Agentic’ systems whose primary purpose is substituting AI for human labour. He has also recently predicted that by 2034, the traditional 9-to-5 job will become obsolete, and that AI will inevitably lead to a collapse in human wages (a thousand-fold reduction in the cost of high-value professional services such as legal advice, medical diagnostics, and management consulting).
At the same Paris Summit, Dario Amodei, CEO of AI research lab Anthropic (who produce Claude) said, in a written statement:
“Time is short, and we must accelerate our actions to match accelerating Al progress. Possibly by 2026 or 2027 (and almost certainly no later than 2030), the capabilities of Al systems will be best thought of as akin to an entirely new state populated by highly intelligent people appearing on the global stage—a "country of geniuses in a datacenter" —with the profound economic, societal, and security implications that would bring. There are potentially greater economic, scientific, and humanitarian opportunities than for any previous technology in human history-but also serious risks to be managed.”
So Vice President JD Vance is publicly arguing the opposite of what those ‘in the know’ know. Now Marc Andreessen also argues that jobs obsolescence and collapsing wages is actually a good thing and will lead to huge economic growth because we’ll be living in a world of abundance where everything will cost next to nothing. So Vice President JD Vance might be riffing off that techno-optimism. But Amodei is more in line with how the AI industry generally speak about what is occurring - yes there are huge upsides but there absolutely are ‘serious risks to be managed’. Sam Altman of OpenAI, and Demis Hassabis of Google DeepMind have said the same.
One can criticise tech industry leaders for many things but one cannot accuse them of not being upfront about how fast they believe AI is progressing, or that they expect it to be deeply societally disruptive.
My belief is that one should be heavily discounting populist Politicians with bases to pander to, or CEOs following the ‘our people are our greatest asset’ scripts.
When those at the coalface of developing e ‘General Purpose Technology’ are telling you that they’ll soon have tools that can act autonomously, in multi step problem solving, it makes sense to believe them, rather than those massively incentivised to say ‘nothing to see here’.
Hence, ‘Looking after Number One’. (See *** below for exactly what I mean by this)
Historical Lessons
Two economic phenomena are worth bearing in mind.
The Jevons Paradox: Efficiency Gains and Resource Consumption
First, the Jevons Paradox posits that technological advancements which increase the efficiency of resource use can lead to increased consumption of that resource, rather than decreased use as might be expected. Essentially as things get cheaper we don’t spend less, we use more. This supports, in many ways, Andreessen’s techno-optimist approach; as the cost of intelligence (and energy) tends towards zero it will likely impact unit costs dramatically but will also enable us to have more… of everything.
The Engels Pause: Technological Upheaval and Wage Stagnation
But secondly, there is the ‘Engels Pause’. This refers to the period from 1790 to 1840 when British working-class wages stagnated while per-capita GDP expanded rapidly during a technological upheaval. This period was characterised by stagnant wages, rapid economic growth, increasing income inequality and major technological advancements. Think of the works of Charles Dickens, especially ‘Hard Times’ - they effectively captured the social and economic conditions of the period that coincided with this phenomenon.
So, whilst historically it IS true that we adjust to new technologies and they DO improve the lot of mankind, the period of transition can last a long time and be very brutal.
Shaping AI's Impact: The Role of Policy and Incentives
And as famed US Economist Daron Acemoglu wrote about, at length last year in his book ‘Power and Progress: Our Thousand-Year Struggle Over Technology and Prosperity’ it DOES NOT JUST HAPPEN. Society, and governments, have to be redesigned to make the most of new technologies. We have to set the incentives that drive the outcomes we desire. He uses, as an example, the fact that, from a cost point of view, the tax system in western economies highly favours investing in technology over labour. With very obvious consequences. Similarly the tax treatment of debt over equity.
The Imperative for Self-Investment
So ….. contrary to what many are telling you, “We are living in tumultuous times and you need to actively take control of your own destiny”
Over time policies will change, economies will adjust, and (with my biased hat on) eventually people will be suitably trained in how to leverage and make the most of AI. But in the meantime three things are going to be occurring:
Most companies will not be providing adequate training in AI
Most people won’t bother to train themselves
Many companies WILL be swept along by the stock markets love of layoffs (see all tech co’s performance for emphatic evidence of this)
All of which are very bad and/or short term wins at the expense of longer term paybacks.
Opportunities Ahead
They do however mean that a HUGE opportunity is arising. For those that do ‘look after number one’ first (I know it is not a robust long term strategy) there is enormous opportunity to put clear blue water between oneself and one’s peers/competitors. Those people that understand how to marry human+machine, and how doing so will enable entirely new workflows, business models, and value propositions, have, I think, at least 12-24 months to maximise personal competitive advantage.
First by ensuring that, as an AI literate person, you’re not going to be on the chopping board, and secondly because such skills are and will remain, for a while, rare. Currently only circa 10% of people use AI on a daily basis. Even fewer have woven it into how they work and operate.
The AI Productivity Dividend: Achieving 3X Gains and Beyond
And the evidence is mounting about how much more productive they are. In a paper released last week from Stanford University (“The Labor Market Effects of Generative Artificial Intelligence”) they write:
“On average, workers that use generative AI to complete their task, they spend about 30 minutes working with a generative AI tool… Without the use of generative AI, workers estimate that it would take them about 90 minutes on average to complete the same task”
3X productivity is not to be sniffed at.
Reimagining Work: How AI Will Transform Business Models and Workflows
Personally I think 3X is just the start of things. I see people building agent type AI automations today that eliminate 80-90% of a workflow, but that are still in some ways automating the past. As one thinks through workflows it becomes increasingly obvious that the future trick is going to be doing things in entirely different ways. Just take meetings: we now have really good AI meeting assistants helping us transcribe, summarise and set up ‘to-dos’, but I often look at these and wonder why the meeting is happening at all. Most of what goes on could be automated, optimised and enhanced by AI systems working in the background. Once people really start focussing on ‘killing the irritant’ (and for most people endless meetings are irritants), we’ll see true change.
Conclusion
In my last newsletter I talked about the need to ‘Build a Bigger Pie’ - the notion that AI will certainly lead to fewer people being needed to output a given quantum of work, and therefore without ‘a Bigger Pie’ a lot of people are going to have nothing to do. With this newsletter I hope the message is clear - we need early adopters to ‘Think of Number One’ and start building the Bigger Pie. For their own short term security, but also because WE really do need to shorten, as much as possible, the adjustment period between now and the ‘end of work’.
*** Looking After Number One: A "Thrive in Tumult" Framework
What do I mean by Looking after Number One?
Here’s a "Thrive in Tumult" Framework (We cover much of this in my #GenerativeAIforRealEstatePeople course)
Phase 1: Assess and Understand the Landscape ("Know Thyself & the World")
Self-Assessment:
Identify your strengths and weaknesses: What are you good at? Where are you vulnerable? What are your career and personal goals?
Analyse your current skills: Which skills are becoming less valuable? Which are in demand or will be in the future?
Understand your values and priorities: What truly matters to you? How can tech help you align your work with your values?
Environmental Scan:
Research industry trends: How is Gen AI impacting your industry or field? What are the emerging opportunities and threats?
Explore Gen AI tools relevant to you: Identify specific tools that can enhance your productivity, creativity, or skills in your area. (e.g., for writing, design, coding, research, etc.) Start with the foundational Models: ChatGPT, Claude, Gemini, then image generators like Midjourney, research and study applications like NotebookLM, and combination search/LLMs like Perplexity.
Understand the ethical and societal implications of AI: Be informed about the broader context.
Phase 2: Actively Integrate and Leverage Gen AI ("Embrace the Tools")
Skill Up on Gen AI Literacy:
Learn the basics: Understand what Gen AI is, its capabilities, and limitations.
Experiment with different tools: Try out free or low-cost Gen AI tools to get hands-on experience.
Focus on practical application: Learn how to use these tools to solve real problems and enhance your workflow.
Identify Productivity Boosting Opportunities:
Automate repetitive tasks: Use Gen AI to streamline mundane or time-consuming activities.
Enhance creative processes: Use AI for brainstorming, idea generation, content creation, and overcoming creative blocks.
Improve information processing and decision-making: Leverage AI for research, data analysis, and gaining insights faster.
Develop "AI-Augmented" Skills:
Focus on skills that complement AI: Critical thinking, complex problem-solving, emotional intelligence, strategic thinking, creativity, communication, leadership.
Learn how to work with AI: Develop the ability to effectively prompt, guide, and evaluate AI outputs.
Become a "human-in-the-loop": Understand that AI is a tool to augment human capabilities, not replace them entirely.
Phase 3: Adapt and Thrive Long-Term ("Stay Agile & Resilient")
Continuous Learning and Adaptation:
Stay updated on AI advancements: Technology is constantly evolving. Make continuous learning a habit.
Be flexible and willing to adapt: Embrace change and be ready to adjust your skills and approach as needed.
Seek out new opportunities: Be proactive in exploring how AI can open up new career paths or personal projects.
Build Resilience and Well-being:
Maintain a healthy work-life balance: Avoid burnout by setting boundaries and prioritising well-being.
Cultivate strong networks: Connect with others, share knowledge, and build support systems.
Focus on your human strengths: Nurture your emotional intelligence, empathy, and social skills – these will become even more valuable in an AI-driven world.
Ethical and Responsible Use:
Use AI ethically and responsibly: Be mindful of bias, privacy, and the potential impact on others.
Contribute to a positive AI future: Advocate for responsible AI development and deployment.
Defining Your Own Fate: A Call to Action
Does this make you more or less nervous about the future? Can you, will you ‘Look after Number One’. Does it feel harsh, or pragmatic? Now is the time to act—before the AI transition defines your fate for you. Explore the resources in the 'Thrive in Tumult' framework and start implementing one strategy this week.
10 Themes for the Next Ten Years - No 10
Number 10: We Need a Bigger Pie - Expanding Opportunities in an AI-Driven World
‘by AGI we mean a highly autonomous system that outperforms humans at most economically valuable work’ - OpenAI’ definition.
We are, probably (but by no means certainly), many years away from AGI - Artificial General Intelligence - which, as above, involves systems that outperform humans at most economically valuable work. But truth be told, the road to AGI is a sliding scale, and we do not need to have achieved it absolutely before we start seeing significant changes in our businesses, and across society. Already we are seeing AI dramatically increase the productivity of certain tasks, such as software development. Google have talked about some 25% of their ongoing code creation being produced by AI, and superstar Andrej Karpathy has written about ‘vibe coding’ where "It's not really coding — I just see stuff, say stuff, run stuff, and copy-paste stuff, and it mostly works”. Substantial boosts in efficiency are here today.
The bottom line, an absolute certainty, is that:
‘AI means we will need fewer people to achieve our current level of output.’
So we need to ‘build a bigger pie’.
And by a ‘bigger pie,’ I mean expanding the scope of economic activity, creating new industries, and redefining the very nature of work and value.
But how?
Job Creation vs. Job Displacement
While AI will undoubtedly automate many existing roles, it also creates new opportunities. The challenge lies in ensuring that job creation outpaces displacement. AI opens up entirely new job categories, from AI ethics specialists to prompt engineers and digital experience designers. Governments, businesses, and educational institutions must collaborate to facilitate workforce transitions, ensuring that displaced workers can pivot into new, AI-enabled careers. The goal is not just to replace lost jobs but to create an abundance of higher-value opportunities that did not previously exist.
New Market Opportunities
AI is unlocking markets that were previously inaccessible or economically unfeasible. For example, AI-driven language translation enables businesses to enter global markets with unprecedented ease, while AI-powered analytics help companies identify untapped customer segments. In healthcare, AI facilitates remote diagnostics and personalised medicine, expanding access to high-quality care worldwide. By leveraging AI’s capabilities, businesses can scale into new domains and reach customers who were previously beyond their grasp.
Value Redefinition
Traditional economic metrics focus on tangible production and financial transactions, but AI is forcing a rethinking of value itself. In an AI-augmented economy, intangible assets such as data, knowledge, and creativity take on heightened significance. Companies must evolve beyond traditional revenue models, exploring AI-driven value creation through hyper-personalisation, experience curation, and digital ecosystems. This shift challenges industries to move beyond efficiency gains and instead reimagine what constitutes value in a world where information is abundant and intelligence is increasingly commoditised.
Entrepreneurship and Innovation
AI significantly lowers the barriers to entrepreneurship by democratising access to knowledge, tools, and markets. Startups can now leverage AI-powered automation, reducing operational costs and allowing smaller players to compete with established enterprises. The ability to prototype, test, and scale innovations rapidly cultivates a culture of creative problem-solving and continuous disruption. Moreover, AI-driven platforms facilitate innovation ecosystems where entrepreneurs can collaborate across geographies and disciplines, further expanding the economic pie.
Skill Evolution
Rather than simply replacing human labour, AI necessitates a shift in skills. The workforce must transition from routine, repetitive tasks to roles that complement AI capabilities—such as critical thinking, creativity, and interpersonal skills. Continuous learning and adaptability become central to career longevity. Nations and companies that invest in upskilling their workforce will be better positioned to capture the full economic benefits of AI, ensuring that people can participate meaningfully in an evolving labour market.
Economic Expansion
Historically, technological advancements have driven economic expansion by creating entirely new industries. AI’s potential follows a similar trajectory, enabling sectors such as synthetic biology, quantum computing, and immersive virtual environments. Crucially, AI does not merely optimise existing processes—it enables the emergence of economic activity that previously did not exist. By focusing on overall economic expansion rather than efficiency-driven cost-cutting, businesses and policymakers can ensure that AI contributes to a broader, more inclusive prosperity.
Inclusive Growth
For AI-driven economic expansion to be sustainable, its benefits must be widely distributed. Without intentional efforts, AI risks exacerbating inequality by concentrating wealth and opportunity among a small subset of individuals and companies. Policies that promote fair access to AI tools, invest in digital infrastructure, and encourage ethical AI deployment are essential. A more inclusive approach ensures that AI-driven growth is not just about building a bigger pie but ensuring that more people get a fair slice.
Conclusion: Embracing AI as a Growth Catalyst
AI is not just a tool for efficiency—it is a catalyst for market expansion, value creation, and economic reinvention. To fully realise its potential, businesses, governments, and individuals must shift their mindset from one of job protection to one of opportunity creation. By focusing on new market opportunities, skill evolution, and inclusive growth, AI can help build a future where economic prosperity is not a zero-sum game but an ever-expanding horizon of possibilities.
The challenge ahead is not merely about managing AI’s risks—it is about ensuring that AI is harnessed to grow economies, enrich societies, and create a world where innovation leads to shared abundance rather than scarcity.
Everything we’ve discussed over this series will come to naught if we do not ‘build a bigger pie’, but combined it does provide us with the raw materials to do so. Let’s just recap the essence of what we’ve been hypothesising:
The synergy between human intelligence and AI is becoming increasingly seamless, leading to unprecedented levels of productivity and innovation.
Traditional business models and processes are being deconstructed and reconstructed, with AI playing a central role in this transformation.
The concept of value is being redefined, with AI democratising access to high-quality services and experiences previously reserved for the elite.
Human skills like creativity, empathy, and complex problem-solving are becoming more valuable, even as AI capabilities expand.
The physical aspects of real estate are evolving to become more flexible, adaptive, and responsive to human needs.
There's a shift towards outcome-based services, where providers take more responsibility for delivering specific results.
These are powerful drivers of change. That will redefine much about how we understand the world around us and how and what we prioritise. To me they represent an extraordinary opportunity to both build a better built environment and to create and then curate a more inclusive, fulfilling, purpose driven society. One where, by embracing the new tools at our disposal, we raise the bar of what denotes the norm and ushers in a much more pervasive world of excellence. Why can’t we all have more - of everything? It is within our grasp. We can make it happen. For sure, this will involve wholesale disruption and require reinvention on a scale we’ve not dealt with before. But think back to the great gardener and landscape architect Lancelot ‘Capability' Brown who got his moniker from his fondness for describing country estates as having great 'capabilities' for improvement. Well, our current societies are just the same.
The next step is for businesses to embrace AI not just for efficiency but for expansion. Policymakers must create frameworks that encourage responsible innovation. And individuals should actively invest in skills that align with this new reality. The opportunity is vast—let’s seize it.
What do you think?
I’d love to hear your feedback about this series of ten themes for the next ten years. What excites you? What do you hope to achieve? How far do you think we can go?
10 Themes for the Next Ten Years - No 9
Number 9: #SpaceasaService loves Generative AI: The Progressive Company Nexus
‘Companies at the forefront of adopting Space as a Service concepts are often the same ones embracing Generative AI in their workflows.
This creates a synergy between physical and digital transformation efforts, marking these companies as ideal clients for innovative real estate solutions and advanced AI implementations.’
An underlying characteristic of all the themes we have looked at in this series is a progressive mindset. One that is doing everything to stifle those looking to tax the future to protect the fast. As the great American poet, Robert Frost, wrote ‘The only way out is through’ and in difficult times likes now, where we are morphing from the computer age to the AI age, this is apposite. You have to embrace the future in order to shape it.
And this mindset is shared by those in real estate embracing #SpaceasaService, and those in technology embracing Generative AI. Both of these are fundamentally creative movements that are looking to enable humans and machines to work together to produce things neither could achieve on their own.
This similarity of mindset means, I think, that progressive companies in real estate and companies embracing Generative AI are natural bedfellows and should be each others best customers.
Let’s walk through where these two exemplars of the current zeitgeist overlap:
Predictive Space Management, where AI enables us to forecast occupancy and usage patterns.
Personalised Environments where we create spaces that adapt to individual preferences automatically.
AI-Driven Design where Generative tools allow us to create optimal layouts and aesthetics.
Automated Facility Optimisation where AI manages and continually optimises energy, maintenance, and resources throughout a building.
Cognitive Buildings that are structures that learn and evolve based on inhabitant behaviours.
AI Concierge Services are advanced AI assistants that manage all aspects of space use.
Wellness-Centric Design uses AI to optimise spaces for human health and wellbeing.
Biophilic Integration utilises AI to seamlessly incorporate nature and natural elements into built environments.
And so on. #SpaceasaService aims to create the physical, social and emotional environments that enable people to be as successful as they are capable of being, and to make people, happy, healthy and productive. Generative AI (as well as non generative) provides many tools that contribute to making this possible. These are very much high tech and high touch places. Achieving the desired human outcome actually requires the successful synthesis of data, technology and advanced human skills. Hard and soft components are required. This is a quantitative and qualitative game.
There are other similarities in culture between #SaceasaService and Generative AI companies.
Dual Innovation meaning that companies at the forefront of AI adoption are also at the forefront of business model and operating model innovation, so the standard office is unlikely to be human-centric enough for them.
Automated + Augmented - both types of company favour balancing task automation with AI-augmented human roles.
Flexible Spaces, Flexible Tech - there appears to be a correlation between adaptable workspaces and adaptable tech stacks. If you’re in favour of one you’ll likely be in favour of the other.
Human-Centric Approach - prioritising employee experience in both the physical and digital realms. Talent density is essential in AI companies, so looking after your talent really matters.
Data-Driven Decisions - using AI insights to optimise both space utilisation and business processes is a default setting in these types of companies.
Continuous Adaptation - another default setting are cultures of ongoing learning and evolution in both space and tech use.
Holistic Productivity - viewing workspace and AI tools as integrated productivity enhancers. One provides a flywheel for the other.
Future-Ready Mindset - and finally management teams have to be prepared for rapid changes in both real estate and technology landscapes, and thus have to have an extremely well attuned future-ready mindset.
Taking all of this together I hope the point is clear that progressive real estate thinkers need to seek out progressive technology companies (or companies aggressively using technology) as they will be the most likely to buy into new ways of delivering value.
Everything about these ten themes will NOT be across the board. Companies pursuing them will be pre selecting who their target market needs to be. Looking at the innovation adoption bellcurve we’re after the early adopters and the early majority. That’s where the differentiation will be, as well as the premium market. After all there is a strong argument to be made that 30-40% of existing office real estate is already obsolete, or heading there fast. I don’t think we are far off the following scenario;
The top 15-20% of the market will be designed for large multi-national companies, that have the in-house scale and substance to create exceptional workplaces, in exceptional buildings, for their employees.
The next 40-45% of the market will operate #SpaceasaService - either by taking space in one of the growing breed of high quality flex operators, or by having one of these operators run their own buildings for them. CBRE buying Industrious suggests this is now a key plank of their thinking.
The remainder of the market either disappears (through shrinking down space requirements) or operates fully distributed. There is no future for low to medium quality commodity office space. It simply will not be needed.
This theme, number 9, is all about aligning yourself for the vanguard of this inevitability, which will likely play out over the next 5-10 years.
What do you think?
The future appears as much about human creativity as it is about algorithmic precision. Are we ready to embrace a world where our workspaces learn, adapt, and even anticipate our needs? What do you see as the most exciting opportunities—and the most daunting challenges—in melding advanced AI with the very fabric of our built environment? I’d love to hear your insights.