The Real AI Race Is in Silicon
The rise of AI creates a new paradigm in software as well as in hardware engineering. All AI needs huge computation to produce better results, and for this there was only one company that had a clear control over the market - NVIDIA. But now we are seeing a lot of competition emerging. Companies have started building chips, and there are two main reasons behind this: 1. Control and 2. Companies are seeing more profit at the infrastructure level of AI.
In the last few weeks we have seen that Google has open-sourced their model Gemma, and this was a perfect timing to release an open-source model. A few days later, Google released a new version of their TPU. When you see major companies jumping into open-source models, it is obvious that their major revenue generation is not focused towards AI - they are playing a larger game to create a monopoly in the chip market. This clearly shows the market shift that is happening.
This is happening all across the industry. No doubt NVIDIA is ahead in this race, but Apple has spent a significant amount of time refining its custom silicon and integrating it with hardware and software to have better control over performance and efficiency. Google has invested in its TPU - we have already discussed that. Amazon is also building custom chips to support its AI cloud offerings, as it is already a major player in cloud services.
The bigger picture, as I see it, is that AI will not be a major revenue stream for big companies - but chips will be. Google launching their open-source model a few weeks before the launch of their TPU is a clear sign.
My Take:
AI is not going anywhere - it has become an integrated part of our workflow. There will be companies like Anthropic and OpenAI that make money through AI models, but there are still limitations.
If you look at the bigger picture, companies that are concerned about their data will train their own AI models. However, those models still need to run somewhere, typically in the cloud. That's where tech giants are focusing.
Whether you use a public AI model like GPT or a private, company-specific model, both require computation - and that's where the real money lies. It is also true that the race for AI chips has just begun.