Editor's take

The assertion is that artificial intelligence, particularly large language models like OpenAI's GPT-4 or Google's Gemini, should be understood as foundational technologies rather than finished consumer products. This distinction is critical because it reframes the current market narrative, which often positions AI advancements as immediate, end-user solutions.

This perspective matters for investors and developers alike, impacting how research dollars are allocated and how companies like Microsoft, integrating AI into Office 365, approach their product roadmaps. It suggests that the true value lies in the underlying capabilities, which will be commoditized and then built upon by myriad applications, similar to how cloud computing evolved.

Future developments will reveal whether this technological distinction leads to a more sustainable AI ecosystem or if the pressure for immediate productization will continue to dominate. The success of companies building atop these foundational models, rather than those solely developing the models themselves, will be a key indicator.