Large language models (LLMs) like ChatGPT, Claude, Bard, and Llama which can generate human-like text and power conversational AI applications, are some of the most advanced and buzzed-about AI innovations today. However, experts predict these models are likely to become commoditized technologies in the not-so-distant future.
Just as cloud infrastructure and services became more affordable and accessible over time, the core capabilities of large language models will be packaged into inexpensive and easy-to-integrate services. Companies will be able to choose from a variety of LLM providers to power their applications. The models themselves will become table stakes — a basic infrastructure instead of a true source of competitive advantage.
For developers and companies building products enabled by AI, this shift will bring both opportunities and challenges. On the positive side, commoditization of LLMs will significantly lower costs and barriers to leveraging these powerful models. Small startups will have access to technology previously only within reach of large tech firms.
However, simply integrating an LLM will not be enough to create value. The real differentiation will come from domain expertise, insight into customer needs, and ability to craft superior end user experiences. Companies that focus merely on the technology elements risk building solutions lacking utility in the real world.
Developers of the future will need creative vision to identify useful applications of AI, combined with empathy and business sense to solve real problems for their target users. The companies that thrive in an era of commodity LLMs will be those that understand how to build engaging and helpful applications on top of the basic AI building blocks.
For now, LLMs remain complex, expensive technologies with capabilities untapped by most organizations. But over time, they are likely to become as ubiquitous and affordable as the cloud services powering much of our world today. The winners will be those looking beyond the core technology to ask: how can we best use AI and human-centered design to help our customers?
For developers asking the right questions, exciting possibilities lie ahead.