OpenAI, Google and Anthropic are struggling to build more advanced AI

The next wave won’t be monolithic but network-driven. Orchestration has the potential to integrate diverse AI systems and complementary technologies, such as advanced fact-checking and rule-based output frameworks.

This methodological growth could make LLMs more reliable, consistent, and aligned with specific use cases.

The skepticism surrounding this vision mirrors early doubts about the early internet fairly concisely.

Initially, the internet was seen as fragmented collection of isolated systems without a clear structure or purpose. It really was. You would gopher somewhere and get a file, and eventually we had apps like like pine for email, but as cool as it was it has limited utility.

People doubted it could ever become the seamless, interconnected web we know today.

Yet, through protocols, shared standards, and robust frameworks, the internet evolved into a powerful network capable of handling diverse applications, data flows, and user needs.

In the same way, LLM orchestration will mature by standardizing interfaces, improving interoperability, and fostering cooperation among varied AI models and support systems.

Just as the internet needed HTTP, TCP/IP, and other protocols to unify disparate networks, orchestrated AI systems will require foundational frameworks and “rules of the road” that bring cohesion to diverse technologies.

We are at the veeeeery infancy of this era and have a LONG way to go here. Some of the progress looks clear and a linear progression, but a lot, like the Internet, will just take a while to mature and we shouldn’t forget what we learned the last time we faced a sea change technological revolution.

You are definitely on to something here, but the difference is that the fundamental process was proven. It "just" needed to scale. That's hard and complex, but on a different level.

I don't think anyone doubted the nature of the technology. The bits were being sent. It's not like we were unsure of the fundamental possibility of transmitting information. The potential was shown very, very early on (Mother of all demos was in 1968). What we were and to some extent still are unsure of is the practical impact on society.

AI and LLMs in particular are not even at the mother of all demos level yet notwithstanding the grandiose claims and demos. There is no consensus on what these models are even doing. There is (IMO) justified skepticism surrounding the claims of reasoning and ability to abstract. We are in my opinion not yet at the "bits are being sent" stage.

I see this as entirely surmountable. We’re still making geometric progress in small model accuracy, and breakthroughs like test-time training and synthetic data are poised to deliver immediate gains in self-training performance.

Your point about skepticism being warranted when viewing this linearly is well taken. But this isn’t a linear path. The Internet, at its core, was about connecting computers to unlock the value of those connections—a transformative but relatively straightforward concept.

What we’re dealing with now is the training of cognitive digital intelligence. This is an inherently dynamic and breakthrough-oriented process, one that evolves in ways far less predictable or constrained than simple network effects. While the metaphor of connectivity is useful, it doesn’t fully capture the parallel, multi-dimensional approaches at play here.

Pessimism, in my view, is deeply unwarranted, especially given the history of technological progress. Time and again, advancements have proven to be far more impactful and beneficial than even the most optimistic predictions. Consider the projections for AI in 2017—most futurists undershot its actual progress by an order of magnitude.

This research clearly illuminates a path forward:

https://ekinakyurek.github.io/papers/ttt.pdf

Deeply appreciate your thoughtful comment.