I’ve just given a keynote entitled ‘AI or Die? The Silent Revolution Coming for Residential Living’, and I discovered whilst working on it that the future of AI in the PBSA sector provides a blueprint for most real estate asset classes.

The future is not as uncertain as we think.

We already know most of what we need to know about AI in real estate:

• What works now
• What’s likely possible within five years
• The barriers and strategic risks
• The implications for skills, technology, and human capability
• The need for a staged roadmap
• And, above all, that Human is the New Luxury.

What we don’t yet know:

• Whether organisations can execute on the available capabilities
• How long real transformation will take - timing is a fools errand!
• Whether “fewer but better” employees will generate additional revenue
• And whether the market will ultimately value “Human as Luxury” the way we think it will.

The Technological Flywheel

So let’s map this out. What do we know about the future of AI?

First, even if all AI progress was to stop today much of what is likely to occur by 2030 will happen anyway. In fact, there is probably ten years of optimising, fine tuning and ‘tweaking’ to be done on the frontier models as they stand today. OpenAI’s ChatGPT, Google’s Gemini and Anthropic’s Claude are already extraordinarily ‘intelligent’.

And before anyone jumps up and screams ‘But they’re not intelligent’, we all know they are not ‘intelligent’ in a human sense, but if they can reach the same destinations as much of human intelligence, frankly who cares. They’re intelligent.

Jensen Huang, CEO of Nvidia (whose chips power 80%-95% of AI output) a year ago stated that their power had increased by 1000X in just 8 years. And that currently they are improving at ‘Moore’s Law Squared’. And the highly regarded research company Epoch AI recently wrote that leading AI models are likely to grow another 1,000x between now and 2030, assuming current scaling trends continue.

A total of 5 current and former employees of Google and DeepMind have won AI-related Nobel Prizes in the last two years (2024 and 2025).

Both Google (specifically their AI research lab DeepMind) and OpenAI have achieved gold medal-level performance in the 2025 International Mathematical Olympiad (IMO), which is widely recognised as one of the most prestigious mathematics competitions in the world.

Mostly people do not realise just how far AI has already progressed or how fast it is continuing to develop.

So we already have enormously powerful AI to work with, and we know it is going to only get better, fast. Which underpins why change in real estate will accelerate. Guaranteed.

The Kernel Shift

Andrej Karpathy was one of the founders of OpenAI and head of AI for Tesla for many years. He is in the pantheon of AI researchers, and when he talks, everyone listens.

One of his signature ideas from late 2023 was that LLMs, Large Language Models, would develop to be the kernels of a new computer operating system.

Increasingly we would interact with computers using natural language (he quipped that ‘the hottest new programming language is English’), and the LLM would understand our requirements and pull in any extra tools needed to fulfil them.

Maybe a web browser, or a calculator, or Python coding terminal. The point being that the Language Model acted as our interface to any digital service we needed.

This is coming to pass at incredible speed. Far faster than was presumed two years ago.

Many of these ‘Agentic’ systems, as they are known, are already available to us within the frontier models (have you tried ChatGPT in ‘Agent’ mode?), and a whole industry of specialist engineers is developing, building domain specific ‘Agents’ and the ‘Orchestration’ layer needed to operate them.

Orchestration is best described as a way to synchronise the activities of multiple agents according to a desired plan. Like those drone displays becoming commonplace in replacement for fireworks.

And in this environment the individual tools become less important than the creation and curation of the workflows we want them to perform. This orchestration becomes the strategic battleground. Who has the best ‘orchestra’?

The PBSA Playbook

There is a ‘Low Regret’ AI playbook within the PBSA sector. Not pervasive today, but those using AI to date are mostly following it. It involves doing what we know works! Adopting applications mature enough to be tried and tested (many from other sectors such as multi-family).

These include utilising dynamic pricing (used in hospitality for a long time), predictive maintenance, and energy optimisation. With the latter two being enabled by ubiquitous connectivity and cheap yet powerful IoT sensors.

Simply put, you can’t really go wrong with these: they have been around a while, been heavily stress tested, and are available from multiple credible suppliers. Dynamic pricing ‘should’ provide 3-5% revenue uplift, preventive maintenance ends expensive ‘panic fixes’, and energy optimisation is perhaps the lowest hanging fruit with 10-20% savings being pretty easy pickings in most assets, and more in others.

As in PBSA, many other CRE asset classes should be utilising these as table stakes. It’s really quite delinquent if one is not.

The Future Frontier

Rapidly we should be moving to the next phase where the tech we use moves from being discrete apps to integrated systems, with AI becoming the “digital nervous system” of assets.

Within PBSA there are five obvious use cases, each of which, once again, are applicable across multiple CRE asset types.

1. Agentic Operations & Digital Twins

Integrated AI "agents" will orchestrate complex decisions simultaneously across entire portfolios, optimising pricing, maintenance, and energy usage based on real-time data and simulations run on digital twins. Leading to holistic, automated asset management.

Keep your eyes on the Finance sector for early instances of this, or the likes of Walmart.

2. AI-Assisted ESG & Grid Flexibility

PBSA buildings will evolve into smart energy nodes. AI energy managers will decide when to store, consume, or sell excess power back to the grid, enabling participation in virtual power plants (VPPs) and creating new ancillary revenue streams.

Look at what Octopus Energy are already doing with their vehicle-to-grid (V2G) technology.

3. Automated Inspections & Computer Vision

Drones or computer vision systems (analysing CCTV feeds) will perform regular inspections of roofs, façades, and communal areas to detect defects, monitor cleanliness, and automatically generate work orders.

Drones improve in line with AI, so they are getting to be very powerful tools, very quickly.

4. Hyper-Personalised Engagement

AI systems will act as proactive digital concierges, learning student preferences (e.g., study habits, event attendance) to provide tailored suggestions for study groups, events, or services.

Watch for the growth in AI model ‘memory’. Frontier models are growing in their ability to remember - so they can ‘get to know you’ in quite some detail.

5. Advanced Data-Driven Student Welfare

AI may analyse non-intrusive data (such as access card usage or facility logs) to identify students at risk of distress, dropping out, or loneliness, allowing staff to conduct proactive welfare checks. This application, however, faces significant privacy barriers.

Watch though for AI therapists with robust guardrails. Many bad actors want to sell personal AI, but so do many good actors. Regulation is likely to favour the latter over time, albeit probably not arriving until someone ‘misbehaves’ badly.

All of the above are nascent technologies but developing fast. They are complicated to get right but not rocket science. And every time the power of ‘natural language computing’ increases the easier it will be to implement them. Science fiction they are not.