There's a huge difference between spending years studying to become a doctor, and actually seeing 500 patients a day once you're done.
The first is training. The second is inference. And that gap is exactly where AI's next battle is happening.
Since 2020, almost all the effort in AI went into training bigger, smarter models.
GPT-3 could only answer 43.9 percent of questions correctly on a popular knowledge and reasoning benchmark. Just four years later, GPT-4o hit 88.7 percent, essentially matching human experts.
But here's the catch: no matter how brilliant a model gets, someone has to actually run it, every second, for millions of users worldwide.
That's where the real bottleneck showed up. Companies spent years building smarter 'brains' but underinvested in the muscle needed to run them fast and cheap.
Enter companies like Tensordyne, whose new Napier chip isn't designed to help AI learn, it's built purely to make AI respond instantly.
Think of it like building the world's smartest pilot, then realizing you still need a faster plane to actually get anywhere on time and on budget.
This shift has a name: the Inference Hardware Revolution. And it's what will decide who can actually deliver AI to every phone and household affordably, not just who owns the smartest model.
The next battle in AI isn't about intelligence. It's about speed and cost.
🔗 Original Source & Reference: https://spectrum.ieee.org/inference-hardware-revolution
Published automatically via FeedMind AI Content Pipeline.
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