Publication date: Oct 10, 2024

They showed that the parallel computation power of GPUs was perfect for training ML models because like computer graphics, ML model training relied on highly parallel matrix computations. To train the latest models, companies must either construct their own data centers or make significant purchases from cloud service providers to leverage their data centers. The value chain encompasses end application builders, AI model builders, cloud service providers, chip designers, chip fabricators, and raw material suppliers, among many other key contributors. Model size has shown to correlate with improved performance, and the best funded players could differentiate by investing more in model training to further scale up their models. End Application Builders From scaled startups like Palantir to tech giants like Apple and non-technology companies like Goldman Sachs, everyone is developing AI solutions. Few companies can afford to allocate billions toward training an AI model (only tech giants or exceedingly well-funded startups like Anthropic and Safe Superintelligence). The integrated model provides greater control over the entire production process but requires significant investment in both design and manufacturing capabilities.

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