AI investments in plain English...
This Brookfield primer offers one of the clearest overviews we've seen of AI investments.
Here’s a quick cheat sheet on the emerging AI vocabulary shaping the outlook:
Agentic AI:
AI systems that can plan, strategize, and execute tasks independently, adapting to new information without constant human oversight.
AI Factories:
Large-scale digital hubs purpose-built for AI workloads, with dense GPU clusters, advanced cooling, and high-speed networking.
Artificial General Intelligence (AGI):
AI with human-like ability to perform diverse cognitive tasks, including unfamiliar ones.
Artificial Narrow Intelligence (ANI):
AI that is highly capable in a specific domain but lacks general adaptability.
Artificial Superintelligence (ASI):
A hypothetical AI that surpasses human intelligence across all domains and can improve itself without human input.
Compute:
The processing capacity of hardware — especially GPUs — that powers AI training and inference.
Gigawatts (GW):
A measure of power capacity used to quantify the enormous energy demands of AI infrastructure.
GPU:
A graphics processing unit designed for highly parallel workloads; in AI, GPUs excel at training and running large-scale models efficiently.
GPU as a Service:
The rental of high-performance AI chips on-demand, avoiding the need for massive hardware investments.
Hyperscale:
Ultra-large data centers, often run by tech giants, built to serve massive computing needs at global scale.
Jevons Paradox:
The principle that efficiency improvements often increase total consumption rather than reduce it.
Moore’s Law:
The historical observation that computing power doubles roughly every two years with minimal cost increases.
Power Density:
The amount of electrical power a server rack consumes — AI racks often require 10× more than traditional ones.
Quantum Computing:
A next-generation computing approach using quantum mechanics to solve problems exponentially faster than classical computers.
S Curve:
The adoption pattern of new technology: slow start, rapid acceleration, then plateau.
Scaling Laws:
The finding that bigger AI models trained on more data tend to perform better.
Scratchpad Reasoning:
A technique where AI models “show their work” before giving a final answer, improving accuracy on complex tasks.
System 2 Architecture:
An AI approach optimized for deep reasoning and planning, as opposed to quick pattern recognition (“System 1”).
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Think this won’t affect real estate?
It already has. Infrastructure allocations are rising, and they’re coming at the expense of real estate allocations.
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