This is the Electi Review. Today, how much AI your business should actually be using, and why the answer is almost certainly not what you think. The core argument is blunt. The right amount of AI is not as much as possible. It is as much as adds value without undermining judgment, quality, and differentiation. That line alone should reframe how most managers are currently thinking. Here is the context. In Italy, AI adoption in businesses doubled in a single year from 8.2% in 2024 to 16.4% in 2025, according to ISDOT data. That acceleration means the question has shifted. It is no longer whether to use AI, it is where to stop. The article introduces what it calls the AI laugher curve. The logic is borrowed from economics. At first, each additional unit of AI generates strong returns, less repetitive work, faster execution, more standardized output. Then a threshold is reached. Beyond that threshold, marginal benefit declines and hidden costs begin to accumulate. Errors that look plausible but are wrong, blurred accountability, and content that is indistinguishable from everyone else's. That last point is the B plus trap. When every company uses the same models and the same prompts, outputs converge. The result is work that is readable and technically correct but strategically generic. No edge. No differentiation. No competitive advantage. The counter framework is called middle-to-middle. AI handles the core of a process. A human defines the problem at the start and validates the output at the end. In Electi's own editorial pipeline, applying this principle reduced human work from three hours to roughly 30 minutes per cycle, landing at approximately 80% AI, 20% human. The article also provides a decision matrix. Mechanical, low error cost tasks can run at up to 90% AI. Strategic high-error cost decisions should stay at around 30% AI. The dividing line is always the cost of an undetected mistake. Three structural limits are worth holding on to. It comes from knowing exactly where to stop. That's the review.