What AI promises to our future
AI is having its steam-engine moment. The buildout is visible in cranes over new data centers, invisible in the tools creeping into every spreadsheet, inbox, and factory line. The promise is enormous, but so is the bill.
The Generative AI productivity upside is real, but it’s uneven
Let’s start with the big claim: AI can move the productivity needle. Multiple independent sources point in the same direction. McKinsey estimates generative AI could add $2.6 to $4.4 trillion in value each year across 60+ use cases, with the fattest gains in customer operations, sales and marketing, software development, and R&D.
Zoom out and the pattern holds. The 2025 Stanford AI Index synthesizes dozens of controlled studies and finds consistent productivity improvements, with AI often narrowing skill gaps by giving less-experienced workers smart scaffolding.
If you’re an executive, what does this mean in practice?
Budget for enablement. The difference between a demo and durable productivity is training, prompts that reflect your process, and guardrails that reduce rework. OECD surveys show employers and workers are mostly positive on AI’s impact when implementation is thoughtful, but they flag job transition risks that leaders must actively manage.
The cost curve of AI - steel, silicon, and watts
Every promise rides on a physical foundation. Right now that foundation is scaling at record speed. U.S. data-center construction hit a fresh high in mid-2025, driven largely by AI workloads from hyperscalers.
Energy is the headline constraint. The IEA projects data-center electricity use could roughly double by 2030 to about 945 TWh, with AI the main driver. That’s close to Japan’s current annual consumption. Independent analyses and explainers echo the direction and the urgency.
Greener infra isn’t a nice-to-have. Power purchase agreements, demand response, and siting near clean baseload will become competitive advantages as AI loads climb. The macro trend is up and to the right; waiting for a silver bullet is not a strategy.
Leaders should read those facts as a design brief.
What all this means for the next 24 months?
Expect visible wins in operations, customer service, and software delivery. They’ll look like cycle-time reductions and quality lift, not sci-fi.
Capex and energy questions will move from back office to board agenda. The organizations that measure and optimize early will scale cheaper and face fewer surprises.
The macro numbers will underreport AI’s contribution for a while. Don’t wait for perfect national statistics to validate local ROI. Track your own attributable outcomes.
Thank you for reading - Arjus