The conventional wisdom about artificial intelligence risk centers on a single moment: when one superintelligent system arrives and either saves or destroys humanity. But after tracking the AI industry daily for over 1,200 consecutive days, technology analyst Michael Parekh argues this framing fundamentally misunderstands how the technology is actually developing.

Parekh's analysis, published as part of his ongoing "AI-RTZ" daily commentary series, challenges several widely held assumptions about AI risk and development timelines. Rather than a sprint to a finish line, he frames the AI wave as a multi-decade buildout similar to previous technology revolutions like the internet and mobile computing. This perspective reshapes how we should think about existential risk, safety concerns, and what actually deserves our attention right now.

Why Are We Obsessing Over a Single Superintelligence?

The popular narrative treats AI development as a race with a clear winner and a defined finish line. Tech leaders and researchers frequently discuss timelines for achieving artificial general intelligence (AGI), a system that could match or exceed human intelligence across all domains. The assumption underlying much of the existential risk debate is that one powerful AI system will arrive on a predictable schedule, and we need to solve safety problems before that moment.

Parekh challenges this assumption directly. He argues that the real AI future will not be one superintelligence but rather billions of specialized AI agents, each built incrementally and deployed gradually across different tasks and industries. This "plurality, not singularity" model fundamentally changes the risk calculus. In a world where no single AI dominates because billions of them exist in parallel, the concentration of power that fuels existential risk scenarios becomes far less plausible.

"Not one AI, but billions of AIs. Built bottom up, step by step. Not top down, on a date," Parekh stated, describing his core thesis about how the technology is actually unfolding.

Michael Parekh, Technology Analyst

This distinction matters because it reframes what we should actually be worried about. If billions of AI agents are deployed gradually, the risks look different from a scenario where a single system suddenly becomes superintelligent. The gradual approach allows for learning, adjustment, and distributed oversight. The race-to-AGI model assumes we need to solve everything before a single moment arrives.

What Does the Evidence Show About How AI Is Actually Being Built?

Parekh points to what has actually happened over the past three years as evidence for his thesis. Rather than a single breakthrough system, the industry has seen a steady stream of incremental improvements, new model releases, and expanding applications. Companies are building AI agents for specific tasks, not attempting to create one universal superintelligence.

The infrastructure buildout also supports this gradual model. Parekh notes that investment is flowing first to the bottom of the technology stack: chips, memory, networking, data centers, and power infrastructure. This is how previous technology waves developed. The applications everyone recognizes come later and take the longest to mature. The current multi-trillion-dollar data center investments are being built to support a long-term ecosystem, not a single moment of arrival.

Parekh also references statements from the researchers actually building these systems. When leaders at AI labs discuss timelines, they increasingly talk about a decade or more of work ahead, not imminent AGI. This suggests the people closest to the technology have moved away from near-term superintelligence predictions and toward longer, more gradual development paths.

How Should We Rethink AI Risk If This Model Is Correct?

If AI development truly follows a gradual, multi-agent model rather than a race to superintelligence, the priorities for managing risk shift significantly. Instead of focusing all attention on preventing a single catastrophic moment, attention should shift to managing problems that emerge continuously as new systems are deployed.

Parekh identifies several categories of challenges that will persist regardless of how fast or slow AI develops. These are what he calls "forever problems," issues that require ongoing management rather than one-time solutions:

• Hallucinations: AI systems generating false or misleading information that sounds plausible, a problem that persists across model generations.
• Prompt Injections: Users manipulating AI systems through carefully crafted inputs to bypass safety guidelines or extract unintended behavior.
• Memory Poisoning: Training data being corrupted or manipulated to change how AI systems behave, a risk that grows as more data is collected.

These problems are not solved by waiting for the next model release or by achieving some theoretical level of AI safety. They require continuous management, monitoring, and adaptation as systems are deployed and users interact with them in unexpected ways.

The implication is significant: getting worked up about existential risk from a superintelligence that may not arrive for a decade or more could distract from managing the real, present-day problems that emerge from AI systems already in use. This is not an argument that existential risk is impossible or unimportant. Rather, it suggests that the timeline and mechanism of that risk may be fundamentally different from the popular narrative.

Parekh's analysis, built on three years of daily tracking and analysis of the AI industry, suggests that the most productive approach to AI risk may be neither panic nor dismissal, but rather sustained, incremental attention to the problems that actually emerge as the technology develops step by step.