Editor’s word: I’m within the behavior of bookmarking on LinkedIn and X (and in precise books) issues I feel are insightful and attention-grabbing. What I’m not within the behavior of doing is ever revisiting these insightful, attention-grabbing bits of commentary and doing something with them that will profit anybody apart from myself. This weekly AI infrastructure column is an effort to appropriate that.
Do we have now the facility to fulfill AI infrastructure demand? It’ll take focus
NVIDIA CEO Jensen Huang usually describes the present AI infrastructure buildout supercycle as a trillion-dollar-plus transformation of information facilities into “AI factories” the place intelligence is manufactured. Throughout his GTC keynote earlier this yr, Huang described a single rack in an AI manufacturing facility as containing 600,000 components and weighing 3,000 kilos. “AI factories are so difficult,” he stated.
If AI factories are advanced methods designed to fabricate intelligence, then it is smart to borrow administration frameworks from conventional manufacturing to know how we scale them. One of the enduring is the Concept of Constraints, launched by Eliyahu M. Goldratt in his 1984 dialogue-driven novel The Purpose. It’s a easy premise: each system has a limiting issue. Focus your efforts on that bottleneck, and also you’ll get the quickest good points in throughput. As soon as resolved, a brand new constraint emerges and the method repeats.
Infrastructure Masons identifies energy as main problem dealing with the AI infrastructure supercycle
Within the AI infrastructure world, the first constraint is energy. In its “State of the Digital Infrastructure Annual Report 2025,” Infrastructure Masons counts 55 gigawatts of lively knowledge middle energy capability worldwide, with one other 15 GW beneath development and 135 GW within the improvement pipeline. That pipeline has jumped by 80 GW in only a yr. The report authors are blunt: “Entry to energy is the highest business problem. AI amplifies this problem.”
The AI arms race, marked by coaching more and more large fashions and fast development in each day customers, consumes an immense quantity of electrical energy, and cutting-edge AI knowledge facilities are nearer to heavy business than conventional knowledge facilities. Actually, Infrastructure Masons CEO Santiago Suinaga put it within the report, “In the present day, the facility capability of our business is on observe to triple and even quadruple inside the subsequent 5 to seven years, which can put it in the identical energy consumption league because the cement, metal and petrochemical industries.” The mismatch between present energy availability and projected demand with utility improve timelines means energy is the gating issue, the constraint, in AI infrastructure deployment.
So how does the decades-old factory-floor logic of the Concept of Constraints assist us assume extra clearly about AI infrastructure immediately? It outlines 5 “focusing steps” to handle bottlenecks. Right here’s what that may appear to be when utilized to the AI energy downside:
- Establish the constraint: Energy availability is now the dominant issue shaping when and the place knowledge facilities get constructed.
- Exploit the constraint: Optimize round what’s obtainable. Design inside fastened energy envelopes. Prioritize high-revenue workloads. Schedule compute-intensive workloads when grid pricing is lowest.
- Subordinate all the things else to the constraint: Align rack deployments, GPU deliveries, and buyer onboarding with powered web site timelines. Don’t overbuild on websites the place energy continues to be speculative.
- Elevate the constraint: Interact earlier with utilities. Pursue PPAs and behind-the-meter options like gasoline cells and microgrids. Spend money on energy procurement as a core competency, not an afterthought.
- Stop inertia from turning into the constraint: As soon as energy is not the bottleneck, be able to determine and handle the subsequent one whether or not that’s cooling, community fiber, allowing, or expert labor.
As Huang builds AI factories and Suinaga compares digital infrastructure to metal mills, we’d do effectively to ask: what’s the bottleneck? Goldratt reminds us, “Specializing in all the things is synonymous with not specializing in something.” In the present day, the bottleneck is energy. Focus there. Then transfer on to what comes subsequent.
For a big-picture evaluation of AI infrastructure, together with 2025 hyperscaler capex steerage, the rise of edge AI, the push to synthetic normal intelligence (AGI), and extra, try this lengthy learn.
