In the high-speed world of AI, compute power isn't just a luxury. It's a necessity. But here's the kicker, since 2012, the compute needed to train a neural network on ImageNet has plummeted. We're talking a decrease by a factor of 2 every 16 months. That's not just progress. it's a landslide.
Breaking Down the Numbers
Back in 2012, training a neural net to match AlexNet’s performance was a herculean task. Today, it's a walk in the park. AI now demands 44 times less compute. Moore's Law? It would've only given us an 11x improvement over the same period. AI doesn't play by old rules.
Algorithmic advancements are the real heroes here. They've raced past hardware efficiencies. If you're betting on silicon alone, you're backing the wrong horse. Algorithms have redefined what's possible. The speed difference isn't theoretical. You feel it.
Why This Matters
So why should you care? Because this isn't just about technology. It's about accessibility. When algorithms make things more efficient, AI becomes cheaper, faster, and more available. It’s democratizing technology. Solana doesn't wait for permission, and neither does AI innovation.
Consider this: High investment in AI tasks pushes algorithms to evolve. What's next? Maybe training models on your smartphone? We might be closer than you think. If you haven't bridged over to this new reality yet, you're late.
Looking Forward
Here's a question: How long until algorithmic progress hits a wall? Or does it ever? AI's pace is blistering. The industry's learned not to underestimate the power of algorithms over hardware. That’s a lesson the tech giants are banking on. The real bet is on brains, not brawn.
Another week, another leap in AI doing what traditional tech couldn’t even imagine. And the best part? This revolution is just getting started.
