Becoming #FutureProof: 10 Initial Steps

‘One needs to think of AI not as an existential threat (though it might be) but as a transformative enabler.’

Last week we talked about ‘Are YOU #FutureProof. This week we’ll cover 10 initial steps to take to ensure you are.

In every instance I am going to assume you have access to at least one frontier model: ChatGPT, Claude or Gemini. Preferably in paid mode, but you can, mostly, get limited use of these at full power for free. Every time a term is mentioned, or a concept, or hypothesis, or anything you are not sure about, DO ask an AI. They are brilliant at explaining complex matters at whatever level suits you. Personally, I coded software for many years, but I fundamentally think like the History and History of Art graduate that I am. So I often upload complex academic AI papers and ask the AI to ‘summarise and explain this to a Humanities graduate’. You’ll learn so much by doing this routinely and as a matter of course throughout your working day.

Starting with: 

Step 1: Develop AI Fluency (Not Just Literacy)
AI literacy is knowing what AI is. AI Fluency is knowing how to use, critique and apply it.

Literacy can get as complicated as you like, but I think, as business people, there are just a few things that are essential. What is Predictive AI, Generative AI and Causal AI? And what is AI Bias, and what are hallucinations? And the difference between automation and augmentation.

So:

Predictive AI is a branch of artificial intelligence that analyses historical data, identifies patterns, and uses machine learning algorithms to forecast future outcomes, enabling proactive decision-making across industries. Fundamentally this is an analytical tool.

Generative AI is a type of artificial intelligence that creates new content—such as text, images, audio, and code—by learning patterns from existing data and generating novel outputs that mimic human-like creativity and reasoning. Fundamentally this is a creative tool.

Causal AI is a branch of artificial intelligence that goes beyond correlation-based predictions by understanding cause-and-effect relationships, enabling more explainable, reliable, and intervention-driven decision-making. (This is a nascent field but will become important. For obvious reasons)

AI bias refers to systematic errors or unfair outcomes in artificial intelligence systems that arise from biased data, flawed algorithms, or human-driven assumptions, often leading to discriminatory or unbalanced decision-making in areas such as hiring, lending, and law enforcement. We joke about CRE being ‘pale, male and stale’ - which is a prime source of AI bias to look out for!

AI hallucinations refer to instances where an artificial intelligence system generates false, misleading, or nonsensical information that appears plausible but is not grounded in reality or factual data, often due to limitations in training data or model reasoning. There are increasingly good ways to mitigate this, but as with humans ‘Trust, but verify’.

The above alone, well internalised, is a good start but for AI Fluency it is different. Predictive and Causal AI are highly technical, complicated fields that frankly you are not going to be fluent in without a lot of study. Generative AI, however, is just a matter of practice. It does help to have some training in good prompting, but if you just interact with a frontier LLM as you would with a human, you’ll be well on your way. Just keep asking questions. After about ten hours of use you will have a very good feel for what works or doesn’t, and their strengths and weaknesses. Each model has a distinct ‘character’ and particular strengths: Claude excels at coding and natural writing, ChatGPT is strong for business-related writing and strategic thinking, while Gemini’s vast working memory makes it ideal for analysing large documents.

The key though is practice. You need those 10 hours under your belt. Incorporate asking a model about everything you do, and you’ll soon get there.

PS. For imagery, cough up $10 a month (at least once) to play around with MidJourney - it’s the best.

Step 2: Reframe Your Mindset: AI as a Co-Pilot, Not a Replacement
Make no mistake - AI is going to take a lot of jobs. Simply put, AI will dramatically improve productivity in many areas, and so unless the market for these areas grows significantly we will need less people to generate the outputs required. We need a bigger pie. I am confident this will emerge in many sections of real estate but certainly not in all. So we need to ensure we are in the growth areas.

And this is going to be where humans + AI can either do X better, faster, cheaper or where humans + AI enable things to be done that were hitherto impossible. This latter state is known as achieving AI Synergy. When combined the end result is better than either the best human, or the best AI, can achieve on its own.

An example, from Portfolio Optimisation: An AI might propose optimisation strategies (e.g., leasing adjustments or refurbishment needs), but human expertise is essential to evaluate feasibility and, crucially, align these with investor expectations. This AI Synergy enables a more data-informed, holistic outcome.

YOU have to be working where human + AI is required, but don’t kid yourself that is everywhere. Even if that’s true today, you need to be looking a year or two ahead. ‘Is what I am doing something an AI is likely to be able to do ... sometime soon?’

As you get more familiar using the tools this will become clear. You will ‘feel’ what is going to be possible.

Step 3: Focus on the Uniquely Human Skills
AI can handle pattern recognition, automation, and prediction. Anything ‘structured, repeatable, predictable’ will be eaten by AI.

AI can also simulate creativity, judgment, empathy, and complex problem-solving, but it IS simulating these things. And in certain situations one doesn’t need more than ‘simulated’. But you need to discern where first-principles thinking, nuanced decision-making, emotional intelligence, and adaptability is the real value-add, because these are human skills that AI cannot (at least yet) provide.

Being a ‘high quality’ human is going to become a super skill. Sounds odd but some humans are more human than others. And this is going to matter.

As is Critical Thinking - you need to be really good at this in a world where nothing, necessarily, is true. Take ‘The TDH Daily CRE Critical Thinking Challenge’ - https://chatgpt.com/g/g-67d6b0c27ad48191b36763997f2c09f4-the-tdh-daily-cre-critical-thinking-challenge

Step 4: Become a Master of 'Prompt Engineering'
The ability to interact effectively with AI will become as fundamental as typing or using the internet. You need to learn how to craft effective prompts that yield precise, valuable, and creative outputs.

Good prompting makes a real difference. Being able to ask a great question matters.

The joke is that LLMs are ‘the revenge of the Humanities graduate’ because all of a sudden understanding language is important, and the ability to use words clearly, to explain context, and to elucidate constraints, can be the difference between a genius or a trivial answer from an LLM.

You can scout the internet for good guidance on prompting—or simply take my #GenerativeAIForRealEstatePeople course. The latter is easier:)

Step 5: Understand Data and Its Value
Real estate has historically been data-poor, but AI is going to change that. We’ll soon have access to unprecedented amounts of information, and this will be much more democratically spread around. The days of data asymmetry are likely coming to an end (for reasons we’ll cover in another newsletter).

Which is all well and good but not much use if you know nothing about data science and analytics. Knowing how to interpret, validate, and apply data is a skill you must learn.

My top tips relate to one man, Bill Schmarzo, the so-called ‘Dean of Big Data’. He’s a leading authority on data science, analytics, and business value creation through data, and is known for his practical, business-focused approach to leveraging data as an asset.

Top tips are: read his book ‘AI & Data Literacy: Empowering Citizens of Data Science’ and his blog at https://www.datasciencecentral.com/author/billschmarzo/

Honestly, unless you’re a data science specialist, this will cover everything you, as a business person, need to know about data.

Step 6: Adopt an Experimentation Mindset
AI is evolving so rapidly that you shouldn’t get bogged down in perfection. A lot of the best outcomes, today, will come from experimenting. Using new tools, pushing them hard, and seeing what comes out the other end. Once again, you’ll get a feel for what is likely to be possible 6, 12 months hence.

Use AI as a sandbox for innovation—experiment with different use cases. Pro versions of ChatGPT, Claude and Gemini (which includes NotebookLM which we covered recently) are less than $60/£50 a month. Just as an individual this is money well spent. As a company leader, give these to as many of your employees as are interested. Let them experiment and watch what happens.

Step 7: Prepare for New Business Models and Paradigms
AI is or will be reshaping industries, and legacy business models are sure to be disrupted. Understanding quite how this will or might shift value creation is not easy, but I think there are some ‘directions of travel’ for the real estate industry that are clear. For example, AI will likely shift ‘knowledge work’ towards human-centric skills, leading to more hybrid and distributed working, but also more intensive, purposeful collaboration. ‘No one knows what happens next’ is a good mantra to follow, so adaptability and flexibility are going to be at a premium. And at a more fundamental level, the rise of AI is going to necessitate a mass of new energy infrastructure, and a heap of data centres.

I read a paper yesterday suggesting we might see ‘a century of change in a decade.’ That feels hyperbolic, but it’s certainly wise to anticipate significant transformation in the coming years. 

Step 8: Think in Terms of ‘Space as a Service’
Which brings me back nicely to my hobby horse ‘#SpaceasaService’.

In the world of AI-optimised work and cities, physical spaces will need to become smarter, more adaptive, and experience-driven. AI will drive dynamic demand forecasting, flexible workspaces, and real-time decision-making in urban environments, and being on top of the intersection of AI, automation, and physical infrastructure will be a key competitive advantage.

But above all else AI will enable us to provide real #SpaceasaService - Spaces that provide the services that enable every individual to be as happy, healthy and productive as they can be.

AI is going to enable us to ‘Build a Better Built Environment’. This is surely the point of being in the real estate industry?

Step 9: Cultivate an Anti-Fragile Career and Business Approach
As we’ve discussed, AI will automate many traditional jobs, but new roles will emerge. So we need to design and build careers that thrive in volatility—where adaptability, interdisciplinary knowledge, and AI augmentation are core. This will require a portfolio of skills, projects, and networks that position us as ‘AI-powered professionals’. Following the other 9 steps will, I think, deal with this one!

Step 10: Think About AI Ethics, Governance, and Human Implications
I am full of positivity about the next decade but it could go very badly wrong. WE need to ensure the future of AI is not just about what it can do but also what it should do. Sometimes it feels very dull and dreary but we must engage with discussions around AI bias, transparency, and responsible usage.

AI is a ‘General-purpose technology’ and these ‘affect an entire economy. They have the potential to drastically alter societies through their impact on pre-existing economic and social structures.’

Dull or not, this is serious stuff!

Final Thought: Be Curious, Not Fearful
One needs to think of AI not as an existential threat (though it might be) but as a transformative enabler. A technology that ‘enables’ us to do the currently impossible. With it we should be able to deal with bigger challenges. And looking around, the world is full of them. There is no shortage of work to be done. Should we choose to do it.

The winners in an AI-mediated world will be those who embrace learning, adaptability, and have a deep curiosity, and dare I say it, love for the world around them.

Hopefully that’s you. 

OVER TO YOU

Which of these 10 steps are you implementing first? Let me know. Need any help?

Are YOU Future-Proof?

‘Commercial real estate is facing its biggest transformation since steel-frame construction. Are YOU ready?’

This is the first in a series of articles looking at how to ‘Future-Proof’ yourself for a world changing at remarkable speed.

I am a super optimist about the promise of AI. In my mind I see it as enabling us humans to address, and fix, the most wicked problems on the planet. I look at the UN’s 17 ‘Sustainable Development Goals’ and think, with AI, we can actually achieve these. Today, we tinker around the edges of these grand challenges - but with super powerful AI we could conquer them.

Certainly this is something to aspire to. Ensuring we ‘make cities and human settlements inclusive, safe, resilient and sustainable’ is rather more aspirational than optimising ads on Facebook. AI will dramatically raise the bar of what is achievable.

My worry is timing.

I’ve been following AI since the early 2000’s. The term itself was coined in 1956, and has gone through a few ‘Springs’ and ‘Winters’ since then. For a long time progress was glacial. But the pace started to pick up with the rise of Deep Learning from 2012, and has grown in momentum since then. But it was the launch of ChatGPT at the end of November 2022, that saw the flood gates open. Since then progress has been extraordinary. Jenson Huang, CEO of Nvidia (upon whose GPUs the current AI industry is built) has talked about technology developing at ‘Moore’s Law Squared’. Seeing as the power of their chips has increased 1000X in the last 8 years, he’s maybe only exaggerating a bit.

Now this is amazing, but worrying. At least potentially. It means the future is coming at us much faster than ever before. If we are going to have access to 1000X more computing power in ten years time than today, what does that mean? For how we live, work, and play?

And what if it is even faster?

This was rammed home to me this week as I read a quotation from Jack Clark, one of the co-founders of Anthropic, the AI Research Lab behind Claude, the ChatGPT competitor.

He wrote:

‘People underrate how significant and fast-moving AI progress is. We have this notion that in late 2026, or early 2027, powerful AI systems will be built that will have intellectual capabilities that match or exceed Nobel Prize winners.

They’ll have the ability to autonomously reason over all kinds of complex tasks for extended periods.

They’ll also have the ability to interface with the physical world by operating drones or robots.

Massive, powerful things are beginning to come into view, and we’re all underrating how significant that will be.’

What if he is right?

That is the worry. Just a few years to adapt to ‘alien intelligences’ of extraordinary power.

Are you future proofed against that?
That is the career threatening question. Because you need to be.

Too many people talk of these AI’s taking over the menial work we all have to do, leaving us free for higher things.

But what if it's the higher things they can do?

Frankly speaking there is a general delusion around this. That grows out of a lack of understanding of how powerful these tools are today, and where they are likely to be shortly.

Technology is speeding up, but most of the world is oblivious how fast.

How then to become ‘Future Proof’?
The key here is to extrapolate forward, and build scenarios of the years to come that we can work back from. Only by anticipating what is likely to happen can we formulate ways to not just stay relevant, but to actively thrive in this coming world.

So let’s look forward and see what might/would be the consequences for the CRE industry IF Jack Clark’s prognosis comes to be.

Here are a few:

Strategic Transformation of Core Business Models
The advanced AI capabilities described in Jack Clark's statement might fundamentally redefine how properties are valued, designed, constructed, managed, and transacted.

Property Valuation and Investment
Analysis might evolve from art to science, with AI systems capable of processing billions of data points simultaneously to identify opportunities and risks invisible to human analysts. The resulting precision could compress cap rates for optimally positioned assets while dramatically widening spreads for properties deemed suboptimal by these systems.

Property Management
Traditional property management might transform into "intelligent asset optimisation”, with autonomous systems handling everything from tenant relations to predictive maintenance. Building management costs could decline through AI automation, whilst simultaneously improving tenant satisfaction through responsive, anticipatory building systems.

Physical Asset Evolution
The built environment itself would transform to accommodate and leverage advanced AI capabilities. New commercial developments would be designed from inception as physical-digital hybrids, with embedded intelligence throughout all building systems. The distinction between building management systems, IoT networks, and AI infrastructure would blur into unified intelligent environments.

Construction Methodologies
These might fundamentally change as AI-directed robotics handle increasingly complex building tasks. There might be significant cost reductions alongside a compression in build schedules for AI-optimised construction processes.

Energy Consumption
Energy consumption patterns might transform through microsecond-level optimisation of all building systems, potentially reducing energy usage considerably compared to current high-efficiency buildings. This would redefine sustainability benchmarks whilst creating new retrofit imperatives for existing stock.

Market Structure Disruption

Information Asymmetries
The competitive landscape might undergo tectonic shifts as traditional information asymmetries—long the basis of competitive advantage in CRE—are eliminated by ubiquitous market intelligence. Value will increasingly derive from implementation excellence rather than proprietary market knowledge.

Brokerage
Traditional brokerage faces existential challenges, with transaction processes potentially compressed from months to days through AI due diligence and documentation. Surviving brokerages will evolve from transactional facilitators to strategic advisors, helping clients navigate an increasingly complex technological landscape.

New Investment Imperatives
Capital allocation strategies would require fundamental recalibration. Properties designed without AI integration would face accelerated functional obsolescence, potentially creating a wave of stranded assets in certain submarkets. The premium for "AI-ready" buildings could mirror or exceed current sustainability premiums.

Investment horizons may compress as AI enables faster market clearing and reduces information friction. Simultaneously, modernisation capital expenditure budgets will likely need significant expansion to accommodate AI infrastructure implementation.

New asset classes optimised specifically for AI operations—such as buildings designed to house large language model computing infrastructure alongside human workers—could emerge as specialised investment categories with distinct risk-return profiles.

There would also be -

Changes in User Demand

Evolving Tenant Expectations
Commercial tenants would rapidly develop sophisticated expectations for building intelligence that go far beyond current "smart building" capabilities. Properties will be evaluated not just on location and physical attributes, but on their AI integration sophistication and adaptability.

User interfaces will transform from explicit controls to ambient intelligence that anticipates needs before they're articulated. Occupants will expect buildings to recognise them, remember their preferences, and proactively adapt to their needs—creating new standards for personalisation at scale.

The pandemic-accelerated hybrid work paradigm will evolve further as AI enables new collaboration models between in-office and remote workers. Physical space will increasingly be valued for its ability to facilitate human connections that AI cannot replicate, whilst routine tasks migrate to digital environments.

Spatial Requirement Transformations
Space utilisation patterns will undergo profound changes as tenant organisations implement their own AI strategies. Knowledge worker density requirements may decline by 30-50% as AI automation absorbs routine cognitive tasks, whilst collaborative spaces for human-to-human interaction may expand.

Facilities designed to house AI infrastructure alongside human workers will create demand for new building specifications addressing power density, cooling requirements, and physical security considerations that don't exist in current commercial building standards.

The rise of AI-powered autonomous delivery and service robots will necessitate new building interface designs, from loading docks capable of handling autonomous vehicles to internal navigation pathways optimised for robotic movement alongside humans.

Value Perception Shifts
Traditional valuation metrics like price per square foot might be supplanted by new measures such as "intelligence quotient," "adaptation capacity," or "computational density" that better reflect a building's ability to support AI-enhanced business operations.

Lease structures would likely evolve to include specific provisions for data rights, as building usage information becomes increasingly valuable. Tension between landlord and tenant interests regarding who owns occupancy data will create new negotiation dynamics and potentially new regulatory frameworks.

The perceived value of location may undergo significant recalibration, as AI enables new distributed work models whilst simultaneously creating premium districts where AI talent and infrastructure concentrate—potentially redefining prime markets in unexpected ways.

Implications
All of this would have huge implications for property owners and investors, industry professionals, policymakers and regulators.

Overall the commercial real estate industry would stand at an inflection point comparable to the introduction of electric lighting or elevator systems in terms of transformative potential. The timeline described by Jack Clark—late 2026 to early 2027—suggests an urgency that the industry has not fully internalised.

Now Clark might be right about what AI will be capable of by 26/27 but in all likelihood the CRE world is not going to look like the above in just a couple of years. However, I think the direction of travel is clear, and the CRE world WILL look like this in the not so distant future. Give it 10 years and I think much of the above will be a reality. Perhaps not across the board, but in terms of new buildings and the prime end of the market. With enormous consequences. Fundamental, structural ones. The future is not looking likely to be a mere extrapolation of the present.

Future-Proofing a Career in Commercial Real Estate Amid AI Disruption
All of this emphasises the imperative to become ‘future proof’, because it is not just going to happen. We are all going to have to make very conscious decisions about how we are preparing ourselves for a very different world.

So, over the next few weeks I am going to concentrate on just this. How to take agency over our fortunes.

I’ll be covering:

• An Immediate Action Plan

• Understanding the New Value Equation

• Developing Technical Literacy

• Positioning at Strategic Intersection Points

• Developing an Adjacent Skill Stack

• Strategic Career Positioning

I see these times as a dialogue between ‘bug and feature’. A lot of what we need to do will be hard and can be considered a bug. But in its very hardness it is actually a feature, as most people will give up, or not even begin. As I’ve said many times before these are great times for you, as individuals, to thrive. You can both protect your downside, and maximise your upside.

Ultimately, we are all going to need to adapt, and society will change considerably. But one step at a time. Start with you. I’ll show you how.

Over to You
What parts of this vision feel inevitable to you? Which parts seem further off? Are you already seeing signs of this shift in your own work? What scares you? What excites you? Let me know.

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:

1. Most companies will not be providing adequate training in AI

2. Most people won’t bother to train themselves

3. 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.