It is like adding saffron to a dish. In the right amount, it is a subtle, expensive enhancement that elevates the flavor profile. But if you lose your mind and dump the whole jar into the pot, the meal doesn’t become “super-flavored”—it becomes metallic, bitter, and completely inedible. We are currently in the “whole jar” phase of corporate strategy, where the desire to be seen as AI-centric has completely overwhelmed the basic requirement for a product to actually work.
The problem isn’t the technology; it’s the cognitive capture. We’ve reached a point where the “AI lens” is no longer a tool for analysis but a mandatory filter for every single decision. When a manager looks at a broken workflow, they no longer ask why the process is broken or how to fix the underlying logic. Instead, they ask how an LLM can be slapped on top of the mess to hide the cracks. It is a mental shortcut that has become a blind spot. When did we decide that a probabilistic guess is a substitute for a business plan? (Probably because it looks great on a slide deck). This is the intellectual equivalent of trying to fix a leaking pipe by painting over the water stains.
This mania creates a surreal gap between the boardroom and the server rack. While executives are dreaming of autonomous agents running their entire supply chain, the engineers are fighting the real-world friction of VRAM limits on A100 clusters and the agonizing latency of a 70B model trying to perform a simple retrieval task. (Anyone who has tried to fine-tune a Llama model on a budget knows the pain). As argued in AI Mania Is Eviscerating Global Decision-Making, this isn’t just a bubble of overvaluation; it is a bubble of degraded judgment. We are seeing a global decline in the ability to think through a problem from first principles because the “AI solution” is the only answer anyone is allowed to give. If you suggest a simple SQL query or a well-documented API when a “neural agent” would suffice, you’re viewed as a Luddite, not a pragmatist.
Here is the take: we are currently enduring a competence crisis. The people with the power to allocate budgets are the ones least likely to understand the stochastic nature of the tools they are buying. It is like a chef who thinks a microwave can replace a slow-roast just because the microwave is faster. They are optimizing for the speed of the “answer” rather than the quality of the outcome. This creates a dangerous incentive structure where the most successful employees aren’t the ones solving problems, but the ones who can most convincingly pretend that an AI integration is solving a problem. Or maybe not—maybe some of these “solutions” actually work—but the ratio of vaporware to value is currently skewed toward the vapor. We are rewarding the theater of innovation over the reality of engineering.
The correction will be brutal and fast because you cannot sustain a global economy on “hallucinations” and hope. We are moving toward a period of extreme disillusionment once the “AI-first” marketing hits the wall of actual ROI. By Q4 2025, at least three of the top ten global consulting firms will be forced to issue a formal pivot on their AI implementation frameworks as the gap between promised efficiency and actual productivity becomes impossible to hide. We will see a sudden, desperate return to deterministic software and boring, reliable heuristics. The industry will realize that while the magic is great for writing poems, it’s a liability for managing a ledger.
The industry has traded its brain for a prompt.
