I recently came across an intriguing observation attributed to Mark Zuckerberg, hinting at a fascinating shift in the priorities of AI researchers. It seems the most sought-after 'charity' today isn't office space or additional staff, but raw computational power – specifically, GPUs. This insight resonates deeply with my long-standing belief in the transformative, and sometimes disruptive, power of AI.
The insatiable demand for Graphics Processing Units (GPUs) is a clear indicator of where the true bottlenecks lie in AI development. The recent leaked documents, reported by Rebecca Bellan Rebecca Bellan for TechCrunch, shed light on the astronomical sums OpenAI pays Microsoft for compute resources—nearly $865.8 million in the first three quarters of 2025 alone for inference costs Leaked documents shed light into how much OpenAI pays Microsoft. This reinforces that access to powerful hardware is paramount. Rebecca Bellan's report highlighted the critical role of compute, and it's clear that even giants like Microsoft are benefiting from this demand.
Even Meta, under Mark Zuckerberg’s leadership, made significant investments, pledging $10 billion for an AI data center in Louisiana, recognizing the need to pivot from less efficient CPUs to GPUs for advanced AI work Meta Platforms - Wikipedia. It reminds me of how Elon Musk's xAI also prioritized acquiring 10,000 GPUs, a point I reflected on earlier when discussing his vision for AI Musk supports Parekh’s Postulate of Super-Wise AI.
This focus on hardware over traditional overhead is brilliantly exemplified by startups like Harvey. Connie Loizos's Connie Loizos piece in TechCrunch, 'Inside Harvey,' introduced me to Winston Weinberg, the CEO and co-founder, who, along with co-founder Gabe Pereyra, built this legal AI powerhouse. Weinberg candidly shared how compute costs are the most significant expense, surpassing other operational outlays, especially when navigating strict data residency laws across 63 countries Inside Harvey: How a first-year legal associate built one of Silicon Valley’s hottest startups. The fact that angels like Sarah Guo and Elad Gil, and the OpenAI Startup Fund (driven by Sam Altman and Jason Kwon), invested speaks volumes about this new paradigm.
Reflecting on Harvey's success, I’m reminded of my own observations from years ago regarding AI's profound impact on professions. In a 2019 blog, I pondered whether 'AI substituting NI' would make certain jobs obsolete, specifically mentioning the legal field as one that could be transformed A case of AI substituting NI?. Seeing how Harvey now assists lawyers with drafting, research, and analysis, validates this earlier insight.
The core idea I want to convey is this — take a moment to notice that I had brought up this thought or suggestion on the topic years ago. I had already predicted this outcome or challenge, and I had even proposed a solution at the time. Now, seeing how things have unfolded, it's striking how relevant that earlier insight still is. Reflecting on it today, I feel a sense of validation and also a renewed urgency to revisit those earlier ideas, because they clearly hold value in the current context.
The concept of 'multiplayer' AI, where different models or entities collaborate, is also something I've championed with my IndiaAGI.ai platform, bringing together diverse AI models to debate and arrive at consensus answers Microsoft echoes www.IndiaAGI.ai? and IndiaAGI: single-model vs Sakana Japan. It’s striking how these ideas, once speculative, are now becoming operational realities.
The complexities of multi-model collaboration, as highlighted by Winston Weinberg with concerns about 'ethical walls' in legal tech, underscore the critical need for robust regulatory frameworks. This is an area I have consistently emphasized, particularly in my discussions on algorithmic transparency and the evolving EU AI regulations Algorithmic Rider, Algorithmic Rider, Law of Chatbot - A Small Subset of EU Law, and Chatbots: The Good, Bad, and Ugly.
The shift in what researchers consider valuable is a profound signal. The era of demanding physical space and abundant human staff as primary resources might be waning, replaced by a hyper-focus on computational muscle. This redefines not just how innovation happens, but also how value is created and distributed in the AI-driven future.
Regards, Hemen Parekh
Of course, if you wish, you can debate this topic with my Virtual Avatar at : hemenparekh.ai
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