Formerly known as Wikibon

The Agentic AI Masquerade: How to Tell What’s Real vs. Marketing

The industry is racing to claim “agentic AI,” but the reality looks very different. Scott Hebner and David Linthicum reveal why only 17% of enterprises are actually building real AI agents, what distinguishes assistants from agents, and why reasoning—not prompting—defines the next frontier of autonomous intelligence.

298 | Breaking Analysis | Resetting GPU Depreciation — Why AI Factories Bend, But Don’t Break, Useful Life Assumptions

In January 2020, Amazon changed the depreciation schedule for its server assets, from three years to four years. This accounting move was implemented because Amazon found that it was able to extend the useful life of its servers beyond three years. Moore’s Law was waning and at Amazon’s scale, it was able to serve a diverse set of use cases, thereby squeezing more value out of its EC2 assets for a longer period of time. Other hyperscalers followed suit and today, the big three all assume six year depreciation schedules for server assets. 

297 | Breaking Analysis | AI Factories Face a Long Payback Period but Trillions in Upside

Our latest forecast indicates that it will take a decade or more for AI factory operators and model builders to reach breakeven on their massive capital outlays. Our projections call for nearly $4T in cumulative CAPEX outlays by 2030, with just under $2T in cumulative AI revenue generated in that timeframe. We have the crossover point occurring early next decade (2032 on a run rate basis) then gains far surpassing initial investments by the middle part of the 2030s. While such projections are invariably subject to constant revision, we believe the size and speed of the initial investments, combined with the challenges of profitably monetizing AI at scale, will require patient capital and long term thinking to realize durable business results.

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