Recently four companies (@IneffableLabs , @amilabs, @thinkymachines, @unconvAI) raised over $4.5B to make AI chips several orders of magnitude better than NVIDIA … and they all raised the money with no clear roadmap of how they are going to accomplish this. For all their computation power, NVIDIA chips, TPUs, Trainium, Groq, all suffer from being massively power inefficient. They use way more power than they could if we had a different AI paradigm. The human brain is often cited as a counter example, running on a mere 20 watts of power. So these companies are all attacking this power problem from many different directions: New training algorithms using non-linear dynamics. Analog electronics instead of digital. Spiking neural nets. Compute in memory. Yann LeCun’s JEPA architecture. Multi-modal intelligence. Using Alpha Zero’s approach of not using any human training data to create a super learning algorithm. There’s no guarantee any of these companies will succeed. But if one of them does, it’ll massively disrupt the AI industry. It’s no surprise that NVIDIA and Google invested in some of these companies, they are hedging their entire revenue stream on the possibility of such disruption. Elon’s investment in Terafab, but more specifically on his R&D fab, is also a hedge against some breakthrough (the R&D fab is specifically going to investigate “new physics” for chipmaking). It won’t happen quickly even if a breakthrough happens tomorrow. It takes time to make new semiconductor devices, prove them out in a manufacturing lab, and ramp up production. We will get years of warning. The AI industry’s course is set for the next five years at least: massive data centers and a race to secure new massive energy infrastructure. But it’s nice to know there’s a lot of venture money working on something that's potentially massively more efficient.