Empire of AI

Over the next four years, OpenAI became everything that it said it would not be.

Over the next four years, OpenAI became everything that it said it would not be. Initial altruism. Founded as a nonprofit by figures like Elon Musk and Sam Altman, OpenAI initially pledged $1 billion to develop artificial general intelligence (AGI) for humanity's benefit, emphasizing openness, collaboration, and even self-sacrifice if another project surpassed them. The goal was to prevent AGI from being controlled by a single corporation like Google. Shift to commercialization. Financial pressures and internal power struggles, particularly after Musk's departure, led Altman to restructure OpenAI into a "capped-profit" entity. This allowed it to raise significant capital, notably a $1 billion investment from Microsoft, but fundamentally altered its trajectory towards aggressive commercialization and secrecy, prioritizing being first to AGI over its founding ideals. Erosion of principles. The transition marked a clear departure from the original mission. Transparency was replaced by secrecy. Collaboration gave way to fierce competition. The focus shifted from open research to building lucrative products like ChatGPT, seeking massive valuations. This transformation highlighted that the project, despite its noble framing, was also driven by ego and the pursuit of dominance.

OpenAI’s Law, or what the company would later replace with an even more fevered pursuit of so-called scaling laws, is exactly the same. It is not a natural phenomenon. It’s a self-fulfilling prophecy. The scaling hypothesis. Inspired by the observation that AI performance improved with increased computational resources ("compute"), particularly after the 2012 ImageNet breakthrough, OpenAI leaders, especially Ilya Sutskever and Greg Brockman, theorized that scaling simple neural networks to unprecedented sizes was the fastest path to AGI. They noted that compute use in AI was growing faster than Moore's Law. The need for massive compute. This hypothesis dictated an insatiable demand for GPUs and supercomputers, far exceeding the resources available to a nonprofit. Training GPT-3 required a supercomputer with 10,000 GPUs. Future models like GPT-4 and beyond would need tens or hundreds of thousands. The estimated cost for a future "Phase 5" supercomputer could reach $100 billion. This escalating need for capital and infrastructure solidified the shift to a for-profit model and reliance on partners like Microsoft. A strategic imperative. Scaling became not just a technical approach but a business strategy. Being first or best required staying ahead on the scaling curve. Falling behind meant losing influence over AGI development. This belief in "scale above all" set the rules for the new era of AI, pushing the entire industry into a resource-intensive race, regardless of alternative approaches or potential downsides.