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The march toward the Singularity—an era of superintelligent AI surpassing human capability—faces bottlenecks, but they’re not what you might expect. Hardware, specifically transistor density, once loomed as the primary constraint, holding back neural networks despite their conceptual existence since the 1980s. Moore’s Law shattered this barrier, doubling computational power every two years, unleashing scalable AI and proving silicon’s predictable rise was the key all along. Meanwhile, fears of a “data wall” or energy crises fizzled out—self-play algorithms conjured infinite training data, and exponential efficiency gains, alongside AI-driven energy breakthroughs, turned obstacles into springboards.
Yet, as technology races ahead, money emerges as a stubborn bottleneck. Training frontier AI models demands exponentially ballooning budgets, defying market competition and hinting at a future where global resource pooling might be the only path forward—imagine humanity uniting, Armageddon-style, to birth superintelligence. Human expertise, another temporary snag, is rapidly fading as a limiter, thanks to open-source collaboration and AI itself outpacing top researchers. By next year, AI could snowball into millions of virtual geniuses, leaving scarcity of human brilliance in the dust.
The real threat? Us. Not physics, not math—our own folly. We’re nowhere near the limits of computation, yet fear-driven regulations, doomsday hype, and plain old conflict could derail us. Picture this: AI’s potential soars, but politicians grandstand, wars drain resources, and shortsightedness stalls progress. The Theory of Constraints says there’s always one bottleneck—right now, it’s silicon’s pace; soon, it might be cash. But the grand irony? Humanity’s quirks—our brilliance and our blunders—remain the wildcard between here and the Singularity. We’re the bottleneck that matters most.
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