Pricing, Cost and Value Delivery of Deep Learning Products as a New Market Paradigm

Non linear vibes

For decades, the pricing of technology services followed a reassuringly predictable logic: more users meant more servers, more storage, more bandwidth—linear costs that could be projected in spreadsheets with an acceptable margin of error. In this orderly world, the business model was a solvable equation: fixed cost divided by the expected number of users, plus a desired margin, resulted in a selling price that ensured profitability—as long as growth projections materialized. Over the last decade, with the commoditization of cloud services from various providers, calculating ROI scenarios for SaaS products became even more predictable.

Today, that mathematical certainty crumbles in the face of a new paradigm: Generative Artificial Intelligence services. Here, the relationship between usage and cost does not follow smooth curves but rather chaotic and unpredictable patterns. A single user can consume in one hour the equivalent of a thousand casual users. The unit cost—the token—fluctuates not only by volume but by complexity, context, and required resolution. We are facing a commodity that rapidly becomes cheaper while growing more powerful, creating a fundamental contradiction: how do you price something that is simultaneously cheaper to produce and more valuable to consume?

Here, I will attempt to explore this critical transition—from the world of linear predictability to the universe of adaptive pricing.

Let’s examine how business models built on stable cost foundations must transform to survive in an ecosystem where the only constant is volatility. It is no longer about calculating margins but about designing pricing systems that learn, adjust, and capture value where only cost was previously measured. I therefore propose that the great disruption lies not only in AI technology but in the economics that sustains it.

While language models become commoditized, true innovation shifts to pricing mechanisms—where survival will depend less on operational efficiency and more on strategic intelligence to monetize value amid uncertainty.

Let’s begin by outlining the differences between the two paradigms.

Aspect	Traditional Cloud Products	GenAI Cloud Products
Cost Basis	Linear and predictable (e.g., storage, bandwidth)	Non-linear and volatile (tokens, complexity)
User–Cost Relationship	Directly proportional (1 user = X resources)	Unpredictable (1 user can vary 1000x in consumption)
Scalability	Incremental planning (accurate forecasting)	Scaling in leaps (need for buffer)
Pricing Structure	Cost-based + fixed margin	Value-based + adaptability
Financial Risk	Mainly in CAC (customer acquisition cost)	In COGS (cost of goods sold) and usage
Key Metric	Cost per active user (CPU)	Cost per unit of value (e.g., complete analysis)
Seasonality	Predictable (known peaks)	Unpredictable (new use cases emerge)

In other words, scalability transforms from an exercise in precise planning into a strategy of buffering and adaptation. Margins, once stable, become dependent on usage patterns that only emerge after deployment. The pricing structure evolves from fixed-cost-plus-margin models to dynamic architectures that must capture value amid uncertainty. Revision cycles shorten dramatically—from annual to quarterly or even monthly—reflecting the speed of changes in both model costs and user behaviors. This new paradigm demands new principles.

Adaptive Pricing Principle;

The new paradigm is not about predicting with precision but about building pricing systems that learn and adjust with real usage data, transforming cost unpredictability into opportunities for differentiation and segmented value capture.

The Three Fundamental Pillars

1. Pricing as a Learning System, Not a Final Calculation

Unlike traditional models that seek to optimize a fixed equation, the Adaptive Principle recognizes that fundamental parameters change monthly—model costs fall, usage patterns emerge, use cases evolve. The pricing system must therefore incorporate real-time feedback mechanisms that transform every customer interaction into data for continuous refinement.

Practical example: Having a pricing model that includes the purchase of new features or usage credits as new use cases emerge. This allows you to measure the cost of a new use case and add it to a "feature basket" that can be purchased separately within a pre-established credit/conversion structure.

2. Structural Alignment Between Cost Risk and Value Capture

While traditional models clearly separate cost management from pricing strategy, the Adaptive Principle intertwines them. Each pricing structure implicitly contains a risk mitigation strategy—whether through consumption bands, automatic revision clauses, or hybrid models that transfer part of the volatility to moments of greater value creation.

Practical example: Implementing "rebalancing triggers" that adjust prices when base model costs fall by more than 15% in a quarter, sharing part of the benefit with loyal customers while maintaining margins.

3. Segmentation by Emergent Patterns, Not Pre-Defined Personas

Traditional pricing segments markets based on known demographic or business characteristics. The Adaptive Principle recognizes that the behaviors most relevant to AI pricing only manifest after use—and builds the capability to identify behavioral clusters in real time, allowing price adjustments based on actual usage patterns, not presumed categories.

Practical example: Selling packages differentiated by usage behavior, i.e., groups of features and the intensity of usage that certain users tend to consume. As an example, Google AI’s plans in India:

From the moment Generative AI products truly begin to replace traditional legacy applications, and a new paradigm starts to influence not only the thinking of product builders but also users, we can expect a shift in the technology market. I dare to speculate what this might be:

Overcoming the Most Central Aspect of the Industrial Revolution: The industrial mindset that still dominates much of economic thinking is a direct legacy of the 19th century: the world as a machine. In this view, businesses are linear input-processing-output systems, where efficiency means standardization, control, and predictability. Traditional product pricing is a legitimate child of this era—a cog in the great production machine, where cost determines price, the margin is calculable, and value is intrinsic to the product.

However, deep learning algorithms are not predictable machines in the sense that one cannot know, predictively and in mathematically explicit terms, what to expect in terms of value delivery from a new language model—let alone the next generation of models. Big tech companies now work in iteration cycles, measuring the impact and use cases that arise from them, reinforcing the adaptive pricing model. What we observe is that prices have tended to fall in recent years, not only due to advances in computing power but also because of evolutions in training techniques and the use of intellectual capital. Two variables that, when combined in opposition, can lead to exponential value generation. And what will be the effects on the market as a whole? How will economists explain or theorize this evolution? I believe that from here on, only a new market—or perhaps even sociological—theory can attempt to explain. Good essays to all!