The Uberisation of LLMs and GenAI Tools: A Cautionary Tale

  • January 27, 2025
  • Uncategorised

The Uberisation of LLMs and GenAI Tools: A Cautionary Tale

We know that Generative AI and large language models (LLMs) are transforming industries, promising efficiency, cost savings, and innovation.

However, a trend similar to Uber’s market strategy is likely to emerge, posing potentially significant risks for businesses relying on these technologies.

The Appeal of Affordable GenAI

Businesses are initially drawn to GenAI tools and LLMs for their advanced capabilities, like natural language processing and automation, at relatively low costs. This appeal is especially strong for startups and small enterprises, aiming to streamline operations and enhance customer experiences affordably. This phase mirrors Uber’s early days, where affordable rides quickly built a loyal user base. Companies integrate AI tools and enjoy increased efficiency and a competitive edge, but as their dependence grows, so does the potential for provider strategy shifts.

The Price Hike Trap

Similar to Uber’s fare increases after market dominance, GenAI tool providers may hike prices as businesses become more reliant on their technologies. What once were cost-effective solutions can turn into budget strains, especially for companies deeply integrated with AI.

Transitioning to different providers or developing in-house solutions can become prohibitively expensive, leaving businesses effectively captive to their AI vendors, with significant long-term financial implications.

Build vs. Buy: Strategic Choices

The emerging trend necessitates a careful decision between building or buying GenAI solutions.

Here are some key considerations:

Initial vs. Long-Term Costs: Building in-house in some cases can require significant upfront investment but offers more predictable and controlled costs compared to potentially escalating third-party expenses.

Control and Customisation: Proprietary GenAI tools can be tailored to specific needs, providing better integration and control.

Dependency and Flexibility: External providers introduce dependency risks if prices surge, or strategic directions change. In-house solutions offer more autonomy and flexibility.

Scalability and Innovation: While third-party tools provide advanced capabilities immediately, in-house solutions can be designed to scale and evolve with company growth, offering a competitive advantage.

The “Uberisation” of AI tools and LLMs is a cautionary tale. The initial benefits can lead to financial burdens as pricing strategies change. Businesses must carefully evaluate their GenAI strategies, balancing immediate benefits with potential long-term risks and costs.

By considering goals, budgets, and risk tolerance, companies can effectively leverage GenAI while mitigating dependency and escalating costs.

If you are thinking of implementing GenAI in your business, why not try our free GenAI Readiness Assessment here.

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