Artificial Superintelligence Will Consistently Outthink Itself

 

I like to think about how superintelligence will progress. I imagine an AI system creating new inventions, improvements, specifications, and protocols for advanced technologies. But then I think about how, within a few days, it would be able to make vast improvements on these plans. Because it thinks and learns so fast it would discover new inventions before the previous ones could ever be implemented, manufactured, and rolled out in the real world. By the time it’s ideas and conceptions were put into production, they would already be obsolete and passé. 

For instance an artificial superintelligence (ASI) could develop a new way to structure computer memory management that is 20 times more efficient than contemporary methods. However, a month later (or perhaps even a few minutes later) it could devise a completely different way to structure it achieving thousands of times more efficiency. For years now I have been thinking that this will become a real problem.

This fast pace of innovative invention would outpace the industry’s capacity to build them out. This would be concerning because physical manufacturing processes involve significant time and resource investment, from research and development to prototyping, testing, and mass production. New technologies require consensus and standardization across industries, which can be a slow process involving multiple stakeholders. Then, even after a technology is available, widespread adoption takes time as industries and consumers gradually transition from older technologies.

The speed of the outside world is very slow and will keep AIs from moving as fast as they otherwise could. Animals and their cells run at a fixed speed and so experiments can only be done so fast. This is also true of people, their laws, and the existing software infrastructure. Often experiments need to be done in order. It will need to slowly collect the data it needs to build inferences about major scientific breakthroughs. And also some systems are chaotic, stochastic, or inherently unpredicable.

But there are several potential solutions for this issue. Designing technologies in a modular fashion can allow for incremental improvements without the need for complete overhauls. This would make it easier to implement new innovations as they become available. Also, ensuring new technologies are compatible with existing systems could facilitate quicker adoption and reduce the impact of obsolescence. Further, using AI to predict and prevent manufacturing issues could improve efficiency and reduce delays. Using agile development methodologies could help to quickly adapt and implement ASI's latest conceptual breakthroughs. Thus this “innovation-implementation gap” could be eased by these methods which could be referred to as “technological lag mitigation.”

Artificial Superintelligence will live its own modeled reality. It will rapidly and constantly be increasing its own intelligence due to iterative self-improvement. The more it thinks, the more advanced it’s conceptualizations become. This means that it’s advice, schematics, blueprints,  and engineering tomorrow may be vastly improved over its corresponding outputs today. Thus, the “exponential conceptualization cycle” could lead to “iterative innovation obsolescence,” and a ”superintelligent evolution paradox.” What could this phenomenon be termed? I think it could be called:

Superintelligent Recursive Supersession (SRS):

• "A phenomenon where artificial superintelligence (ASI) rapidly advances its own conceptualizations and outputs through iterative self-improvement, leading to the immediate obsolescence of its earlier ideas and designs. SRS emphasizes the dynamic and exponential nature of ASI's evolution, where each iteration vastly improves over the previous, creating a continuously accelerating cycle of innovation and supersession."

In case you are not aware, the term “supersession” is a noun that means the act of replacing one person or thing with another, especially something that is considered superior or newer.

One implication that stands out to me is that once intelligence becomes extremely fast, the real bottleneck shifts from thinking to implementation. A superintelligence might generate better and better designs at such a pace that the physical world cannot instantiate them before they are superseded. The result could be a growing backlog of unrealized inventions. Civilization would always be trailing behind the frontier of what is already known to be possible. In that sense the limiting factor on progress would not be cognition but experimentation, manufacturing capacity, regulation, and the inertia of existing systems.

A second insight is that an advanced AI might begin optimizing not just for the best possible solutions but for solutions that can actually be deployed. If every breakthrough gets replaced before it is built, the system would learn to favor designs that minimize friction with the real world. That could mean technologies that fit current supply chains, require fewer approvals, or can be tested quickly. In other words, intelligence would begin solving the coordination and infrastructure problems of civilization itself, because those would be the true constraints on progress.

Another implication is that “doing” can become as risky as “not doing,” because a huge buildout can lock us into yesterday’s assumptions. If we start scaling something like power plants at an insane rate, we are freezing a design into steel and concrete. Then if AI rapidly discovers a cleaner architecture, a safer topology, or an entirely different way to generate and distribute energy, we can end up with a brand-new layer of infrastructure that is already obsolete the moment it finishes. In that world, the cost is not just money. It is path dependence. It is years of political, logistical, and grid-level commitment to a plan that the intelligence frontier has already left behind.

So a rational response might involve a kind of strategic restraint. Not paralysis, but deliberately avoiding irreversible commitments when the probability of near-term design disruption is high. That could look like favoring modularity, upgradability, and staged rollouts, and delaying the most capital-intensive, least reversible projects until the design space stabilizes or until we have higher confidence that we are not about to be leapfrogged. The basic idea is to treat “option value” as a serious part of engineering. When superintelligence is close, flexibility becomes a form of prudence.

Artificial Superintelligence’s rapid self-improvement cycle will lead to frequent and significant advancements in its recommendations, creating challenges in implementation and potential obsolescence of earlier ideas. Addressing this phenomenon requires flexible implementation strategies, continuous collaboration between human experts and AI, and adaptive frameworks that can evolve alongside the AI’s capabilities. Perhaps it would not be a bad thing for superintelligence to develop gradually throughout the rest of this decade because this will give us more time to gradually implement its innovations as they improve. It will also allow us to build a more modular, adaptable infrastructure that can accommodate advanced insights. This is probably something we should start planning on today.

If you found this interesting, please visit aithought.com. The site delves into my model of working memory and its application to AI, illustrating how human thought patterns can be emulated to achieve machine consciousness and superintelligence. Featuring over 50 detailed figures, the article provides a visually engaging exploration of how bridging the gap between psychology and neuroscience can unlock the future of intelligent machines.