---
id: "contrarian-ai-as-utility"
type: "contrarian-insight"
source_timestamps: ["§ The AI Investment Diagnostic"]
tags: ["future-predictions", "technology-lifecycle"]
related: ["claim-ai-not-utility", "concept-local-ai-value"]
challenges: "The widespread industry prediction that AI will inevitably become a plug-and-play utility like electricity or cloud computing."
sources: ["spine"]
sourceVaultSlug: "hbr-seg-spine"
originDay: 1
articleStem: "hbr-edu-47-5-types-ai-investment"
sourceUrl: "https://hbr.org/2026/06/the-5-types-of-ai-investment-and-how-to-capture-their-value"
sourceTitle: "The 5 Types of AI Investment–and How to Capture Their Value"
---
# AI will not become a standardized utility

**Conventional wisdom it challenges:** that AI will soon become a utility — as ubiquitous and standardized as electricity or cloud computing.

**Prasad's reframe:** while the *base models* may commoditize, the most valuable applications of AI are inherently local, contextual, and deeply embedded in specific institutional fabrics ([[concept-local-ai-value]]), making them impossible to standardize or easily replicate. The durable value lives in integration, not in the model ([[quote-ai-integration-never-commoditizes]], [[claim-ai-not-utility]]).

**Enrichment / counter-counter.** MIT Sloan's agentic-AI work supports the "local value" thesis (value depends on data standardization, guardrails, governance — all context-specific). But the article frames the point more absolutely than most experts would: base models, tooling, and some workflow components *can* commoditize significantly, and competitors can copy process patterns, vendor stacks, and operating models faster than "never" implies. The defensible claim is the *integration layer* stays local — not that AI can never standardize in any meaningful sense.


## Related across articles
- [[claim-gen-ai-no-new-advantage]]
- [[concept-equal-opportunity-disrupter]]