Transcript: There is some level of core tension that I feel and I'm going to try to articulate it. It is basically the tension between, well, I guess, ownership and renting and maybe this is just something that we have come to accept. And maybe my perspective here is on the wrong side of history in some way. Something to explore. But I am very curious when I look at OpenAI and, say, Microsoft Azure, we are effectively renting compute time. And that's totally okay. But, and I guess same with the cloud, generally speaking, we are effectively renting space somewhere. And it is interesting to me that I guess we have come to take the cloud for granted in some way. And I think that even for myself. In the sense that I may own a computer, but most of the data on that computer, in fact, is not local to it. That's not to say that the computer doesn't compute things locally. It certainly does. But things are in the cloud for some level of convenience. So this is a fascinating little piece. And one of the things that I recognize in thinking about what I talked about earlier is that OpenAI has done a fair degree of work in this direction already. That is, they have their assistance API, which effectively, as far as I can tell, implements much of, you know, the brain piece in some way that I guess we all could be effectively chatting into one thread. I don't know how it works with audio files and the like, but they supposedly embed them all. So it could reach out to them. It's possible that this is worth playing around with. Yeah, I think something to think about. I was taking quite a strong position when I was speaking on local earlier. And I'm just basically questioning self in a way. Trying to sharpen positions. So I'm going to stop this and probably do another round of thinking on that in some way and just try to fight myself.
The author is considering the dilemma between renting and buying AI hardware, particularly GPUs, for a company that requires significant compute resources to take off. Renting encourages minimal use of funds, which conflicts with the need for extensive GPU utilization to create something noteworthy. The author suggests that constantly running GPUs at full capacity for inference is a unique strategy that could provide a competitive edge by allowing real-time, high-performance applications. This approach implies a constant inference process on data, making it more accessible and valuable for sorting and classifying, a concept the author is pondering on.
The writer expresses enthusiasm for the potential of recent technological advancements, specifically with regard to enhancing individual engagement and benefit rather than corporate application. They believe in the potential of mobile devices to run large language models, ultimately changing how individuals interact with computers and information. They draw parallels between early computing and the current focus on corporate-oriented technology, expressing a preference for the democratization of such capabilities. The writer feels optimistic about the direction of technology and its potential for widespread value, despite current perceptions.
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The speaker emphasizes their unique strategy regarding AI, recognizing the existing interest in such pervasive technology and its demonstrated potential. They argue that success in this field isn't solely about attractive designs but also about hiring the best engineering talent to make technological advancements possible. Acknowledging their own limitations, the speaker notes the importance of fundamental technology developed by their friends and the need for substantial technological work, implying that simple technology orchestration is not enough for sustained success. Despite the rambling nature of their thoughts, the speaker seems to aim for a blend of business and consumer offerings, driven by core technological innovation and top engineering expertise.
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The author emphasizes the need for personal AI to be holistic and know a fair bit about the user to answer complex questions. They express skepticism about current devices like Tab and Rewind catching on but foresee their eventual adoption. They ponder the societal implications of pervasive surveillance and advocate for thoughtful consideration. The author envisions using an AI system to capture and analyze their conversations at home to elucidate thinking patterns and make them accessible. Additionally, they discuss the limitations of vector algorithms in representing complex questions and suggest the need for a new approach. The speaker suggests that while their idea is a starting point, further exploration is necessary to determine its relevance and significance. They reflect on the process of developing a deeper understanding and consider the practical aspects of implementing their thoughts about how the brain is constructed.
The writer discusses the contrast between ownership and rental, noting that ownership usually represents a capital asset while renting is just an expense. The consideration of owning versus renting becomes relevant when ownership is cost-effective compared to renting. The writer questions the economic implications of owning technology, such as gaming PCs, and how certain technologies may not qualify as capital assets due to depreciation. The discussion then shifts to the potential development of a dedicated chip, such as a GPT-4 ASIC, and the feasibility of widespread adoption and cost-efficiency compared to cloud services. Ultimately, the writer expresses a preference for ownership over renting, as it provides leverage in the world, particularly in relation to valuable tech companies that own the IP and computational resources for large language models. The text reflects on the potential commoditization of computing resources and its impact on the industry. The speaker believes that compute resources will inevitably become commoditized, presenting challenges and opportunities for those involved. The discussion also touches on the shift from cloud-based subscriptions to hardware ownership as a response to commoditization. Additionally, there are considerations about the storage and computing landscape, particularly regarding the efficiency of capital allocation. The passage raises significant questions about the impact of commoditization on both personal and large-scale computing, emphasizing the need for further analysis and collaboration to address these complex issues.