Ai No Data – Zero-Training AI™ Interactive Demos

Budget Allocation for TV Infomercials

Simulation Inputs

Initial cash available to test and then to re-buy TV stations

Demo is a 12-month run; you can reduce this, but not exceed 12

Number of new TV stations bought monthly by auction for between $20 and $200 for a half-hour

Select performance profile that controls Pull Ratio (PR) range used for each station

Retail price for a one-month supply of the diet (e.g., 59.95)

One-Year Supply Upsell Price. Upsell price for a full one-year supply of the diet (e.g., 499.00)

Percentage of buys that are cancelled before they air (e.g., 6%)

12-Month TV Income vs Media

How Zero-Training AI™ Drives Media Buying

This demo is powered by a proprietary, Patent Pending, math-based decision engine. It is NOT an algorithm and does NOT use rules, training data, heuristics, or machine-learning models. Instead, it operates as a dynamical optimization system.

Zero-Training AI™ defines a global decision potential:

F(q, p) = (1/2) Σ pᵢ²  −  α Σ (ROIᵢ · qᵢ)  +  λ ( Σ qᵢ  −  1 )²

• qᵢ = allocation weight for station i
• pᵢ = momentum of allocation change (decision inertia)
• ROIᵢ = front-end pull ratio combined with upsell amplification
• α = reward strength for profitability (user adjustable)
• λ = constraint strength enforcing total budget conservation

Each month, Zero-Training AI™ evolves its internal state (q, p) across multiple internal time steps. Media dollars flow naturally toward stations that generate higher total economic value — without rules, presets, or explicit instructions.

Although Zero-Training AI™ is built entirely from mathematics rather than data, it qualifies as real artificial intelligence because it performs autonomous decision-making. The system continuously evaluates thousands of possible allocations, responds to changing outcomes, and self-organizes its strategy in real time.

There is no training phase, no stored examples, and no static model. Intelligence emerges directly from the dynamics of the system itself. In short, Zero-Training AI™ does not execute decisions — it evolves them.