06-12-2025
With consumer early-adoption rates faster than the PC or Internet, the current wave of Generative AI Large Language Models (LLMs) like Gemini, Claude and those from OpenAI have already won over the public. This jaw-dropping new tech utility, often accessed through free interfaces, creates a compelling illusion that AI is a magical, all-you-can eat resource. However, this user perception is at odds with the complex and expensive reality for traditional businesses working to integrate these capabilities into their own consumer products and services.
Behind the smooth user experience lies unseen fixed-cost investment and marginal computational costs. Training LLMs requires large and specific datasets and hours of expert engineering and robust testing. Iterating these models incurs ongoing operational cost. Consumer use triggers compute cost from making Application Programming Interface
(API) calls on the LLMs. For consumers enjoying "free" experiences with the most powerful technology utility on the planet, the cost is often subsidized by venture capital or as a strategic loss-leader by the tech giants. However, for businesses embedding Generative AI into their customer-facing applications, these costs are very real and demand a sustainable financial strategy.
This presents a challenge. How can businesses deliver the undeniable value of Gen AI without alienating users accustomed to "free lunch" access, while simultaneously covering the non-trivial underlying expenses? Companies must innovate not just in how they use AI, but also rethink their business models to monetize the enhanced value it brings.
Businesses that succeed are those that can deliver an increased consumer value over free AI assistants in order to justify a new cost structure, such as premium subscription tiers, usage-based pricing for advanced features, or brand-new service offerings built around AI capabilities. AI integration cannot be a gimmick; it must solve real problems, offer obvious convenience and efficiency, or unlock experiences that were previously unimagined.
While the front-end magic of Generative AI looks easy and feels free, the back-end reality is expensive. For businesses at the intersection of technology and consumer commerce, the path forward requires more than practical technical integration. It demands innovative business models that acknowledge the cost of computing while delivering such a compelling increase in consumer value that the "price" of this powerful technology becomes a worthwhile investment for the end user.
Stacey Burr (BSIE ’84, MSIA ’91) is a member of the Daniels School Dean’s Advisory Council. A digital health executive at adidas, Google, Fitbit and Textronics, she is currently global head of product management at Panasonic Well.
