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For the first time in history, intelligence itself is becoming a resource we can scale.
Artificial intelligence has taken markets by storm, going from niche experiment to mainstream productivity engine. Adoption is accelerating, with a large share of workers now using AI in their daily workflow. AI is touching almost every function, from code-writing and customer support to inventory management and creative work. The result is a step change in productivity and a rebuilding of how organizations are structured from the ground up.
Key takeaways from the year in AI:
We’re starting to see leaner teams and flatter org charts as AI handles repetitive, lower-value tasks. Large technology firms are already reporting substantial savings from cutting back-office headcount and embedding AI tools. It’s as if every knowledge worker suddenly gained a tireless digital deputy.
More importantly, AI is not just about efficiency; it’s enabling capabilities that weren’t feasible before. Modern models can draft legal documents, debug complex codebases, design campaigns, and reason through multi-step problems. Breakthroughs like GPT-4 opened the gates, and within two years, ChatGPT has scaled to hundreds of millions of weekly users. Enterprise co-pilots are becoming standard across productivity suites, but on this front, we’re only getting started.
Entire industries are being rewired. In retail, AI-driven systems are cutting operating costs and lifting sales through better personalization and inventory accuracy. Call centres, software development shops, banks, logistics operators, and healthcare providers are deploying AI to compress cycle times and raise throughput.
This is not a simple robots-versus-jobs story. History shows that general-purpose technologies change the type of work we do rather than eliminating work entirely. The agricultural revolution pushed labour from farms to factories. The computer pushed work from paper to screens. AI will push work from routine execution toward higher value creativity, judgment, and relationship building.
I believe those who embrace AI as a tool could see their productivity and compensation rise. Those who ignore it risk being left behind.
In that sense, AI is both the steam engine of the mind and the great reorganizer of the workplace. The advantage could belong to those who skate to where the puck is going by embracing AI-enabled productivity.
This enthusiasm around AI has triggered the largest technology CapEx cycle in decades. We’re still early in the game, yet CapEx numbers already look like something out of the 1960s space race.
The global hyperscalers, including Microsoft, Amazon, Google, and Meta, were collectively projected to spend more than $450 billion in AI-related CapEx in 2025 alone, according to Evercore ISI Research. That investment has primarily gone into data centres, cutting-edge chips, networking, and supporting systems. Evercore forecasts suggest this could rise to $600 billion by 2026, as nobody wants to be the cloud platform that runs out of AI capacity.
The spend is building the digital factories of the AI age. Tens of billions of dollars are going into new campuses stuffed with advanced semiconductors, dense optical networking, sophisticated cooling, and high-voltage power infrastructure. Semiconductors are at the tip of the spear. Demand for AI accelerators is far ahead of supply.
NVIDIA has been the clear early winner. Its GPUs became the default “picks and shovels” name for AI, driving year-long order backlogs and extraordinary earnings growth. It’s rare to see a $1 trillion company grow at triple-digit rates, supported by gross margins in the 70s and robust free cash flow. That reflects not only silicon dominance but also a software ecosystem that locks developers in.
The CapEx wave is broader than one company. Equipment makers, networking vendors, specialty memory, data centre builders, and integrators are all seeing powerful tailwinds. Cloud providers are also designing their own silicon, such as TPUs at Google and Trainium at Amazon, to diversify supply and improve economics.
The less glamorous bottlenecks may ultimately matter most. AI data centres consume enormous electricity and create intense heat. They already account for a meaningful share of U.S. power demand, with credible scenarios where that share climbs materially by 2030. In several key regions, the local grid, not the chip supply, is becoming the gating factor on new builds. Transformers, switchgear, cooling towers, and backup generation are all needed, which is slowing projects and reshaping where new capacity can be sited.
In aggregate, AI-related infrastructure spend is likely to hit $3 trillion-$4 trillion globally. Unlike prior manias, this is not being funded primarily with leverage. The megacaps are spending out of enormous cash flows and cash balances. Microsoft alone is pacing toward roughly $80 billion in annual CapEx spending, while still generating strong free cash flow. That self-funding provides a margin of safety versus debt-fueled booms.
With that said, the sheer size and speed of the investment cycle naturally raises the question: Is this the foundation of a new computing era, or are we laying track too fast?
Next time: Addressing bubble concerns and the winners in the AI ecosystem.
Nicholas Mersch is Associate Portfolio Manager at Purpose Investments Inc.
Visit Purpose Investments Inc. for information on Purpose Global Innovators Fund.
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All data sourced from Bloomberg unless otherwise noted.
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