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THE DAILY INDEX

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TLDR AI

XAI'S CFO STEPS DOWN, THE LATEST IN A STRING OF EXECUTIVE DEPARTURES

xAI has seen a number of high-profile departures in recent weeks. Mike Liberatore, xAI's chief financial officer, left the company at the end of July. Robert Keele, xAI's general counsel, announced he was leaving on August 7. Raghu Rao, a senior lawyer who oversaw commercial legal affairs for xAI, left around the same time as Liberatore and Keele. Igor Babuschkin, a veteran of Google's DeepMind AI research lab and OpenAI, announced on August 13 that he was leaving to start his own venture-capital firm focused on AI safety.

THE BITTER LESSON IS MISUNDERSTOOD

While general methods that leverage computation seem more effective, data still plays a very important role. You need 40% more data when doubling GPUs, otherwise you're just lighting cash on fire. AI companies have already consumed the internet. The leaders who win the next era of AI will be scaling data itself. This could be done by architecting better use of limited data or alchemizing entirely new ultimate data universes.

TRUST ME BRO, JUST ONE MORE RL SCALE UP, THIS ONE WILL BE THE REAL SCALE UP WITH THE GOOD ENVIRONMENTS, THE ACTUALLY LEGIT ONE, TRUST ME BRO

AI companies haven't actually done a good job of scaling up RL - they've scaled up to compute, but with low-quality data. While some argue that once these companies actually scale RL up, there will be a big jump in AI capabilities, it is more likely that progress will be the result of numerous hard-won advances. Higher quality RL environments will be a substantial part of what drives progress over the next year or two, and maybe beyond that, but significant improvements will come from several advances, not just scaling RL.

SPEEDING UP PYTORCH INFERENCE BY 87% ON APPLE DEVICES WITH AI-GENERATED METAL KERNELS

Frontier models can write optimized GPU kernels for Apple devices to speed up inference. This post discusses how researchers generated Metal kernels that were 1.87 times faster across 215 PyTorch mod duties, with some workloads running hundreds of times faster than baseline. The results show that AI can take on portions of optimizations, leaving human engineers to focus on the most complex optimizations.

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