Can you please rewrite these points in a well-formed blog article that highlights the cyclical nature of software and hardware chasing each other? 1. Generative AI has changed the world profoundly since ChatGPT in November 2022. The significance of ChatGPT is that it did the hard 10% of what was missing from prior generations of generative auto-regressive models, which was to scale up significantly from previous generations. 2. In addition, the use of human feedback (RLHF) to improve the data and the model was unique. 3. Large Language Models have profound implications for software and hardware scaling. Is Hardware Chasing Software or Vice-Versa? 4. Since the days of the early microprocessor, the industry has been on two sides of the equation, asking the following questions: -- Q1: Will software ever catch up and use the power of the hardware? -- Q2: Will hardware ever catch up to software requirements? 5. Depending on our hardware cycle, Q1 or Q2 was being asked more. For instance, in the early days of x-86 processors, the operating system folks wanted more memory. When x86 processors hit 1Ghz clock frequency, most software was trying to use the faster hardware to do more, essentially catching up to the hardware. 6. Minor technical segments always require out-sized computing resources, such as chip design, simulation, weather prediction, etc. 7. LLMs compute requirements are similar to the 32-bit to 64-bit transition in x-86. Before 64-bit, the maximum addressable memory was 4GB. 8. The 4GB memory limit was not a limitation for most software applications. The applications that felt the 32-bit address limit were video apps, simulation software, EDA and CAD software, etc. 9. These constraints led to tremendous innovation in software architecture, such as distributed multiprocessing and lightweight multi-threading in the 2003 Linux kernel. It was a breakthrough for software developers but was still limited to 32 bits and 4GB of addressable memory. 10. Intel released the first commercially available x86-64 CPU in late 2003. 11. Today, we are at a similar crossroads, where DeepSeek has created a SOTA model that is at par with OpenAI Gpt4 with an 80% reduction in GPU usage. 12. Does this feel like the 32-bit to 64-bit transition? 13. This means there will be much cheaper and more efficient hardware, and software optimizations won't matter, just like all the efforts that went into protecting the 32-bit 4GB memory limitations were made redundant by the 64-bit memory address space architecture. - VerifAI | MultiLLM

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