GPT 5 and what it means for the future of AI

OpenAI recently unveiled GPT 5, and for everyday use, it appears to be somewhat unremarkable compared to the 4 series models. That said, initial reviews are touting GPT 5 as an absolute game-changer for coding. This comes as a bit of a surprise, as OpenAI has frequently discussed a strategy of leveraging AI to write the code that will further expand AI capabilities, yet a coding-focused model wasn't what I had anticipated from this particular release based on the chatter leading up to it. This shift alters my perspective on the future trajectory of AI.

We've been involved in developing "AI" automations for the past 12 years, long before we collectively began referring to machine learning systems as "AI." The statistics-based methods inherent in machine learning have consistently struggled with what I call the "quality gap"—the persistent 10-20% of the time that models tend to get things wrong. This gap is particularly stubborn due to edge cases—rare circumstances that are crucial to handle correctly but are, by their very nature, on the edges of the statistical bell curve.

These are inherently difficult to address using traditional machine learning approaches. Attempting to balance data to account for these outliers often poisons the "golden middle" of the bell curve, thereby degrading the model's performance on typical operations. We've typically mitigated this by implementing rules-based backstops around machine learning systems and/or through human-in-the-loop methods. Both approaches are costly and not particularly scalable. I always viewed this as a significant, potentially insurmountable hurdle on the path toward Artificial General Intelligence (AGI). It's a foundational problem for machine learning that seemed unlikely to be resolved simply by adding "more data."

With the release of GPT 5, I'm beginning to envision a world where we could establish a fairly scalable ecosystem. In this system, machine learning models could handle the bulk of our tasks, while specialized expert systems could be GENERATED to address the specific edge cases associated with various use cases. While this isn't the fully scalable solution we often imagine when contemplating AGI, it could dramatically accelerate the deployment of highly reliable AI implementations. This aligns perfectly with the Nearly Human and Manada Technology Enterprise AI model, which is incredibly exciting to me.

What remains to be seen, is how this development will enable OpenAI and other hyperscalers to advance agent software. This is an area where it feels like there is still a considerable distance between our current capabilities and the envisioned potential of AI agents. It's truly an exciting time to be innovating in the technology space!