When Bricks Meet Compute
How AI’s Economic Shift Will Reshape Real Estate
Economic growth is moving from human labour to computational resources. For the real estate industry, this means new tenants, new asset classes, and new rules for value creation.
Executive Summary
Artificial intelligence is not just another technology cycle. It represents a fundamental change in how economies grow, how work is organised, and how value is distributed. For the first time in modern history, economic progress is being driven less by the productivity of human workers and more by the expansion of computational resources, “compute”, and the energy that powers them.
For commercial real estate, for you, this shift is profound. The centre of gravity is moving away from routine office work and toward infrastructure that supports AI at scale: data centres, energy hubs, specialised R&D environments, and logistics platforms. Demand across asset classes will diverge sharply.
Three recent reports illuminate this future:
- Epoch AI’s “AI_2030” projects the scaling of compute, data, and energy through 2030, with tangible capability milestones.
- Agrawal, Gans & Goldfarb’s “Genius on Demand” models how knowledge work will be reallocated as AI “geniuses” enter the workforce, pushing humans to the creative frontier.
- Pascual Restrepo’s “We Won’t be Missed” explores the long-run economy after Artificial General Intelligence (AGI), where compute drives growth, labour’s share declines, but absolute human prosperity can still rise.
Together, these reports suggest three horizons:
- To 2030: Scaling continues unless constrained; AI transforms digital R&D.
- 2030–2040: Knowledge work reorganises; humans specialise in frontier creativity, AI dominates routine.
- Post-2040: Growth decouples from labour, but humans still work and may prosper in absolute terms; outcomes hinge on how compute is owned and taxed.
The message for CRE professionals: fast change is underway, but multiple paths are possible. Up to 2030, we can map a baseline. Beyond that, the direction of travel is clear, though the speed and distribution are uncertain.
What the Three Papers Tell Us
"By 2030, a single AI training run could rival the annual consumption of a mid-sized city."
Epoch AI’s “AI_2030” is the near-term anchor. Compute used to train the largest AI models has grown 4–5x annually since 2010. Extrapolating this trend suggests training runs in 2030 could be 1,000 times larger than today, with costs in the hundreds of billions of dollars. Frontier training already consumes tens of gigawatt-hours of electricity per run; by 2030, a single training run could rival the annual consumption of a mid-sized city.
Capabilities are expected to follow:
- Software engineering benchmarks (SWE-bench) solved by 2026.
- Mathematics reasoning (FrontierMath) potentially cracked by 2027.
- Molecular biology protein-ligand modelling (critical for designing new drugs) benchmarks solved within this decade.
- Weather prediction is already outperforming numerical methods on hours-to-weeks horizons.
Epoch stresses that scaling is contingent on energy, chip supply, and investment and not guaranteed. But it provides the most concrete baseline for 2030.
Agrawal, Gans & Goldfarb’s “Genius on Demand” takes a microeconomic lens. It models routine workers (who apply known knowledge) and geniuses (who create new knowledge at rising cost the further from what is known). Before AI, scarce human geniuses were allocated at the boundary of routine work. With AI geniuses entering, humans are pushed outward to more novel questions. Routine roles erode; the economy bifurcates into AI geniuses handling the mainstream and human geniuses at the frontier. The paper assumes managers allocate questions optimally, though in reality orchestration will be messy.
Pascual Restrepo’s “We Won’t be Missed” looks at the long-run equilibrium. Distinguishing bottleneck work (essential for growth, e.g. energy, logistics, science) from accessory work (non-essential, e.g. arts, hospitality), he argues:
- Bottlenecks are automated as compute becomes abundant.
- Economic growth becomes constrained by, and proportional to, the expansion of compute.
- Human wages converge to the compute-equivalent cost of replicating their work.
- Crucially, this cap is above today’s wages. Humans may prosper in absolute terms, but their share of growth declines as compute compounds faster.
- Work does not disappear - humans still perform accessory tasks and some bottleneck complements.
Restrepo also stresses political economy: societies may tax compute, redistributing its returns. The long-run outcome is not jobless dystopia, but a shift in power between labour and compute.
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Three Horizons — With Scenarios
Horizon 1 (to 2030): Scaling Baseline
If current scaling holds, AI delivers predictable capability gains in digital science. Desk-based research - software, maths, biology - flourishes. Compute and energy become the new bottlenecks.
- Baseline Scenario: Scaling persists. Data centre demand grows rapidly, AI R&D tenants proliferate.
- Alternative Scenario: Scaling slows. Algorithmic efficiency replaces brute force; AI progress continues but with narrower use cases and less energy demand.
Horizon 2 (2030–2040): Labour Market Reallocation
AI geniuses reshape knowledge work. Routine roles erode; humans concentrate on creativity and judgement. Realistically, this is not binary: tasks within jobs get unbundled, automated in parts, recombined into hybrid roles.
- Baseline Scenario: Office demand will bifurcate: routine-heavy employers will shrink their footprints, while frontier-intensive occupiers will invest in specialised, collaboration-rich environments.
- Alternative Scenario: Cultural and regulatory drag slows adoption; hybrid human-AI roles persist longer.
Horizon 3 (Post-2040): Compute-Driven Growth
Growth is pinned to compute. Labour’s share declines, but wages rise above today’s levels before flattening. Humans still work, particularly where compute is uneconomical or socially valued.
- Baseline Scenario: Wealth concentrates among compute owners; housing affordability pressure grows.
- Alternative Scenario: Societies tax compute, redistribute gains, and sustain broad-based prosperity.
What Could Break the Forecast?
- Energy constraints: Grid capacity, renewable intermittency, and 3–7 year approval cycles for new projects.
- Semiconductor limits: Approaching physical boundaries at atomic scale.
- Regulation: EU AI Act, China’s state-led model, US antitrust and export controls.
- Capital cycles: AI clusters costing $10–50bn may hit financing headwinds.
- Public trust: Safety failures or backlash could slow deployment.
Regional Divergence
- United States: Advantage in hyperscalers, shale energy, and capital depth. Strong growth in data infrastructure and frontier AI hubs (Bay Area, Austin).
- Europe: Regulation-first (EU AI Act, sustainability mandates). Growth in data infrastructure capped by energy and planning constraints.
- China: Pursues domestic chip scaling, centralised AI strategy, state-backed data infrastructure build-out. Implications for different demand patterns in industrial and logistics.
- Middle East: Energy-rich states (Saudi, UAE, Qatar) investing in sovereign AI clusters; likely to become global destinations for hyperscale campuses.
- Singapore: Illustrates capacity limits, restricting new data centres despite demand.
For CRE, this means opportunities and risks are uneven: location, regulation, and energy matter as much as demand.
Mapping to Real Estate Asset Classes
1. Data Centres & Energy Infrastructure
Compute is the new growth driver; data centres are its factories. If scaling persists, demand is exponential. If scaling slows, demand is still strong, but more efficiency-driven. Either way, land near power, cooling, and fibre is strategic. Expect competition from hyperscalers, sovereigns, and utilities.
2. Industrial & Logistics
AI-enabled supply chains, robotics, and predictive systems reshape demand. Expect bifurcation: generic warehouses vs high-tech hubs with energy and compute integration. Adaptive reuse into AI-ready facilities is a major opportunity.
3. Offices & R&D / Life Sciences
Office demand does not split neatly into routine vs frontier. More likely: gradual unbundling of tasks, hybrid AI-human roles, and new formats for orchestration. Frontier R&D and life sciences demand grows; routine-heavy tenants shrink. Offices become compute-rich collaboration hubs, not desk farms.
4. Retail
AI reshapes supply chains and consumer engagement. But inequality is the deeper driver: luxury and subsidised segments expand, mid-market weakens. CRE must prepare for divergence.
5. Residential
Housing demand persists. Absolute wages rise, but stagnation relative to compute-driven growth stresses affordability. Luxury remains buoyant, subsidised/social expands, mid-market shrinks. Policy (e.g. compute taxation) will heavily influence outcomes.
How to Handle the Change
For Individuals
- Build AI fluency (workflows, orchestration, oversight).
- Develop frontier skills (framing, synthesis, judgement).
- Prepare for hybrid roles where tasks are constantly reallocated.
For CEOs / Firms
- Treat compute as strategic: not a back-office cost, but a core input.
- Focus on workflow orchestration: integrating AI into valuation, leasing, asset management.
- Pursue ecosystem partnerships: with energy, data, and tech players.
For the CRE Industry
- Adapt valuation and leasing standards to AI-driven occupier models.
- Adjust sector weighting: overweight data infrastructure, grid-adjacent industrial, life sciences. Underweight routine office.
- Engage regulators: compute taxation, energy allocation, and AI policy will shape demand as much as economics.
Conclusion: The Decade to Position
Fast change is underway - but multiple futures are possible. To 2030, the baseline suggests explosive compute demand and tangible AI capabilities. Beyond that, labour reallocates and growth decouples from wages, but humans still work and likely prosper in absolute terms.
For CRE, the imperatives are clear:
- Data infrastructure, AI-linked industrial, and frontier offices are growth categories.
- Routine-heavy offices and mid-market retail face structural headwinds.
- Housing remains resilient, but affordability pressures grow.
Doing nothing is not an option. Those who lean in, building AI fluency, repositioning assets, rethinking strategies, will not only survive but thrive in an age where compute, not labour, drives growth.
OVER TO YOU
Does this resonate? How are you underwriting the risk of 'routine office' tenants shrinking their footprint?
That '1000X more compute' figure isn't abstract. It means a land rush for property near power substations. Is your team mapping these locations?
I specialise in helping firms build a strategic response to these horizons. If you're ready to move from thinking to acting, let's talk.
Thank you for reading Trillion Dollar Hashtag. Here’s to rethinking the future of real estate together.
Best regards,
All things
#SpaceasaService
Exploring how AI and technology are reshaping real estate and cities to serve the future of work, rest, and play.