Enjoyed the article and thought many of the points were good. Here's a counterargument. > In other words, the billions that AWS spent on building data centers

Enjoyed the article and thought many of the points were good. Here's a counterargument. > In other words, the billions that AWS spent on building data centers is a lasting defense. The billions that OpenAI spent on building prior versions of GPT is not, because better versions of it are already available for free on Github. The money that OpenAI spends on renting GPUs to build the next model is not what builds the moat. The moat comes from the money/energy/expertise that OpenAI spends on the research and software development. Their main asset is not the current best model GPT-4; it is the evolving codebase that will be able to churn out GPT-5 and GPT-6. This is easy to miss because the platform can only churn out each model when combined with billions of dollars of GPU spend, but focusing on the GPU spend misses the point. We're no longer talking about a thousand line PyTorch file with a global variable NUM_GPUs that makes everything better. OpenAI and competitors are constantly discovering and integrating improvements across the stack. The right comparison is not OpenAI vs. AWS, it's OpenAI vs. Google. Google's search moat is not its compute cluster where it stores its index of the web. Its moat is the software system that incorporates tens of thousands of small improvements over the last 20 years. And similar to search, if an LLM is 15% better than the competitors, it has a good shot at capturing 80%+ of the market. (I don't have any interest in messing around with a less capable model if a clearly better one exists.) Google was in some sense "lucky" that when they were beginning to pioneer search algorithms, the hardware (compute cluster) itself was not a solved problem the way it is today with AWS. So they had a multidimensional moat from the get-go, which probably slowed early competition until they had built up years' worth of process complexity to deter new entrants. Whereas LLM competition is currently extremely fierce for a few reasons: NLP was a ripe academic field with a history of publishing and open source, VC funding environment is very favorable, and cloud compute is a mature product offering. Which explains why there is currently a proliferation of relatively similar LLM systems: > Every LLM vendor is eighteen months from dead. But the ramp-up time for competitors is only short right now because the whole business model (pretrain massive transformers -> RLHF -> chatbot interface) was only discovered 18 months ago (ChatGPT launched at the end of 2022) - and at that point all of the research ideas were published. By definition, the length of a process complexity moat can't exceed how long the incumbent has been in business! In five years, it won't be possible to raise a billion dollars and create a state of the art LLM system, because OpenAI and Anthropic will have been iterating on their systems continuously. Defections of senior researchers will hurt, and can speed up competitor ramp-time slightly, but over time a higher proportion of accumulated insights is stored in the software system rather than the minds of individual researchers. Let me emphasize: the billions of dollars of GPU spend is a distraction; we focus on it because it is tangible and quantifiable, and it can feel good to be dismissive and say "they're only winning because they have tons of money to simply scale up models." That is a very partial view. There is a tremendous amount of incremental research going on - no longer published in academic journals - that has the potential to form a process complexity moat in a large and relatively winner-take-all market.