Companies Outpace End-Users in Scaling AI's "Deep Thinking" with Proprietary Optimizations

Former OpenAI policy lead Miles Brundage highlighted a significant disparity in the ability to scale Test Time Compute (TTC) for artificial intelligence models, stating that while end-users face considerable hurdles, companies possess "proprietary tricks" to achieve this at scale. In a recent social media post, Brundage observed, > "Why can’t end users just apply test time compute at scale on their own? They can, but not to the same extent due to rate limits, marked up token costs, safeguards, etc…. But perhaps most importantly, companies are likely to have proprietary tricks for scaling TTC well."

Test Time Compute refers to the computational power an AI model utilizes during its inference phase to generate a response or perform a task. Unlike the initial training, TTC enables models to engage in "System-2 thinking," a process of slow, deliberate, and logical reasoning crucial for complex problem-solving. This dynamic allocation of resources allows models to "think harder" when faced with intricate queries, significantly improving accuracy and depth of response.

For individual end-users, scaling this advanced reasoning is often hampered by practical limitations imposed by API providers. These include stringent rate limits, which cap the number of requests or tokens processed within a given timeframe, and marked-up token costs, where users pay per input and output token, making extensive or complex queries prohibitively expensive. Safeguards and default API configurations also restrict the dynamic resource allocation that TTC demands.

In contrast, leading AI companies leverage sophisticated optimization techniques to efficiently scale TTC. These "proprietary tricks" encompass methods like quantization, which reduces the precision of model weights to decrease memory footprint and speed up computation, and advanced attention mechanisms such as FlashAttention, which optimize how models process information. Efficient Key-Value (KV) cache management, often through techniques like PagedAttention, further reduces memory overhead, allowing for longer context windows and larger batch sizes.

Beyond software optimizations, companies benefit from dedicated hardware and infrastructure. They employ model parallelization strategies like pipeline and tensor parallelism to distribute large models across multiple GPUs, enabling the processing of massive workloads. Techniques such as continuous or in-flight batching maximize GPU utilization by processing multiple requests simultaneously, dynamically adjusting to varying output lengths and ensuring consistent throughput. These enterprise-level solutions often involve custom agreements and direct access to underlying infrastructure, providing a significant advantage over standard API access.

Miles Brundage, a respected AI policy researcher and former Senior Advisor for AGI Readiness at OpenAI, emphasizes that these internal capabilities allow companies to push the boundaries of AI reasoning far beyond what is accessible to the general public. This disparity highlights a growing gap in AI accessibility and the potential for advanced AI capabilities to remain concentrated within organizations possessing the resources and expertise to optimize and deploy them at scale.