Past performance does not predict future returns. You may get back less than you originally invested. Reference to specific securities is not intended as a recommendation to purchase or sell any investment.
This article is featured in the Q3 2026 Future Strategist newsletter, you can read the rest of the newsletter here.
Few people doubt AI's capability to change the world at speed. But the big question is, can the infrastructure needed to make this possible keep up?
According to the McKinsey Global Institute's paper, The AI economy: interconnected forces, feedback cycles and speeds of change, the next decade of AI will be shaped less by advances in model quality than by whether power grids, workplaces and regulators can keep pace with the rate of technological change. While AI model capabilities have been doubling every few months, the infrastructure required to deploy them has lagged.
Practical constraints on AI's rollout first emerged in 2023 when chip packaging failed to meet demand, forcing long waits for AI servers. By October 2025, Microsoft's Chief Financial Officer revealed that the company had racks of AI chips sitting idle because it lacked enough electricity to power new data centres. Weeks later, Amazon filed a formal complaint against PacifiCorp, a Berkshire Hathaway-owned utility in Oregon, alleging it had failed to supply sufficient power for four new data centre campuses, in one case providing no power at all. Google, facing the same constraint, moved to secure its own supply, acquiring the energy company Intersect Power in early 2026.
By mid-2026, AI faced myriad structural problems, from shortages of grid connections and copper to shortages of skilled engineers and increasing opposition from communities and governments, all of which were proving serious bottlenecks to expansion.
The power problem
Data centres have come under greater scrutiny because of their immense energy requirements. For context, a single hyperscale facility can consume as much electricity as a small city. This demand strains local grids and contributes significantly to greenhouse gas emissions, often putting states at odds with their own climate targets.
Community concerns, including noise, heavy water use for cooling and limited direct local economic benefit, have fuelled opposition. In early 2026 alone, at least 75 US data centre projects worth $130 billion were blocked or delayed, matching the total for all of 2025.
In a prime example, New York enacted the first statewide moratorium on new hyperscale data centres, effectively pausing permits for a year for facilities using 50 megawatts or more of power. This allowed state agencies to prepare an environmental impact assessment and a regulatory framework covering energy demand, water use and community impact.
In response to this backlash, operators are exploring a range of solutions, from relocating data centres to regions abundant in renewable energy and investing in energy efficiency, to adopting advanced cooling technologies.
Governance fails to keep pace
It is becoming ever clearer that governance is falling behind. AI adoption within organisations is uneven, with most companies simply adding it to existing systems rather than a fundamental reworking; McKinsey's own research finds only 6% of firms have genuinely redesigned how they work around the technology. The concern is that a third of companies have no formal process for assessing AI security risks before deployment, and regulators are now seeing the consequences.
The UK's AI Security Institute recently demonstrated that advanced AI models can autonomously conduct complex cyberattacks that previously required days of human effort, a leap from just two years ago when models could barely handle basic tasks.
Guard rails
With AI adoption proceeding at an unprecedented pace, the expectation is that it will realise its full economic impact in years, not decades. Optimists believe bottlenecks will self-correct as infrastructure catches up, but moving this fast leaves little time to build safeguards. AI should be seen not as a single technology story, but as a system under strain, where each constraint shapes the next and value depends on who can adapt.
Infrastructure, not intelligence, is the real race
Headlines about the latest AI breakthroughs or computing power races can distract from the underlying reality: algorithms may be racing ahead, but progress is now gated by the tangible world of copper, cables and kilowatt hours. Ultimately, it is not just the code that shapes AI, but the ability to overcome physical limits. Those who master the realities of energy, infrastructure and regulation may prove the true winners in the next phase of the technology race.
Leading in AI does not guarantee profit
A further complication remains: leading in AI does not guarantee profit. Today's best model can be matched or overtaken within months, so technical advantage rarely endures. Value is instead migrating to whoever controls the narrower choke points around the technology, including proprietary data, established cloud infrastructure and customer relationships built over years. The real contest is no longer only about who builds the smartest model, but about who ends up owning the pipes it runs through.
The models will keep improving regardless of what happens next; that part is close to guaranteed. What is not guaranteed is that the grid, the regulator, the sceptical town council or the under-governed security team can move fast enough to keep up. Microsoft's idle chips were never really a story about chips; they were a warning that in the race to build the future, the bottleneck would never be intelligence. It would always be everything intelligence depends on.
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