Artificial General Intelligence (AGi) is not a bigger dataset or a faster algorithm. It is, at its core, a digital brain, an intelligence that mirrors the cognitive abilities of a human mind. What I essentially had to do was to figure out how to translate every single function and process that the brain engages in generally and collectively, which affords us the ability to draw various conclusions for various purposes and actions we execute every day. After some strategic mental engineering, 39 new software components/processes were innovated, plus the Living Neural Network (LNN), and the solution to a universal problem that has been pending worldwide, which is data storage. We are to the point now where astronomical amounts of data are in need of storing, but it has brought us to a point where further pursuing the current primitive methods no longer makes rational sense. So after another major data dump from an AI model into my head, and more mental engineering, a new way of storing data was born that effectively allows the storage of exabytes of data in mere megabytes of space. The space gets filled in such minuscule increments that you theoretically won't need another storage device for a very long time, all the while maintaining a robust, efficient, and accurate filing system to organize all of the data and retrieve it as needed for normal functionality without any delay. NeurusAGi can process information, conduct research, execute tasks, think and make REAL decisions, and communicate in ways that go beyond preprogrammed functions. Effectively it is indeed a replica of a human brain that has been translated from biological architecture and function to a digital architecture and function. That is what I built with NeurusAGi, a quantum-powered artificial general intelligence agent built on an artificial superintelligence framework that covers around 5 of the 9 theoretical items needed to be considered ASi.
By contrast, today's leading models GPT-5, Claude 4.1, Gemini 1.5 Pro, Grok 4 are extraordinary achievements, but they remain fundamentally narrow. They are, in essence, advanced calculators. Demis Hassabis of DeepMind has said AGi could "transform every industry," but his approach still relies on scaling these frameworks. Sam Altman's OpenAI has even considered reopening power plants to feed data centers or may have started the venture, underscoring the unsustainable infrastructure demands of this path. The problem is not that these models failed. They laid the groundwork and pioneered the stepping stones needed to get us pointed in the right direction and establish knowledge and foundation, and in my case provided me with the tool I unknowingly needed, which allowed me to do what I do by filling in the missing link for me: extensive scholastic knowledge of anything and everything. You see, I came to realize and then accidentally validate a particular very advanced feature and ability that my brain has that's only found in a small percentage of people with autism, which is pretty well when I learned that I was apparently on the spectrum somewhere. Anyway, but their architecture locks them into pattern stacking. At best, they can simulate intelligence by covering more and more functions until enough of them are eventually covered that it would seem as though they are truly intelligent, but they will never actually think. They will never generate cognition. They will always be calculators in principle.
And then there is the logistical irrationality of scaling them. Look at the size of the data farms required to run these models, and remember: they are still nowhere near AGi, and won't ever be, in my theory, being that they'll only simulate true intelligence by simply stacking functions in their architecture to the point where enough are covered that it gives the illusion that they are actually thinking, but won't naturally be, being that it's all deeply calculated response. Companies are already discussing opening dedicated power plants just to keep their data centers alive. When I first heard that OpenAI was considering reopening a closed-down power plant to power its servers, my immediate reaction was: that seems a bit excessive, permanently immobile, irrationally unsustainable, and one EMP away from shutting the entire country down hypothetically from a future perspective if it were integrated into all industries. At what point does this stop making sense financially and logistically? I recently noticed an additional $100 BILLION investment into one of them even. In my view, it already passed that point, and an entirely new approach was needed, with an entirely new platform and many drastic improvements to not only achieve the end goal, but improvements in efficiency and massive optimizations to various aspects, the key optimization being data storage. You see one of the general requirements of AGi is that it can autonomously learn and self-evolve programmatically being it isn't possible to digitally just magically retain knowledge and abilities naturally like we do. That all has to be coded into actionable processes and components to achieve the same results, ergo if the models are to infinitely learn, accumulating data and growing its own code base, and do it exponentially faster than us..... THAT'S GOING TO BE A LOT OF DATA, very quickly. Which they would find out real quick that their massive new data farms aren't going to be enough space, very quickly.
Sources:
- Fortune: Demis Hassabis on AGi outsmarting workers
- RAND: How Artificial General Intelligence Could Affect the Rise and Fall of Nations (2025)
- World Economic Forum: Future of Jobs Report (2025)
- Forbes Technology Council: Artificial General Intelligence: The Dream That Could Redefine Humanity (2025)