The High Cost of AI Compute and Evergreen Notes: Navigating the Challenges of AI Infrastructure and Note-Taking

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Aug 07, 2023

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The High Cost of AI Compute and Evergreen Notes: Navigating the Challenges of AI Infrastructure and Note-Taking

Introduction:

Artificial intelligence (AI) has become a driving force in many industries, revolutionizing the way we work and live. However, the high cost of AI compute has presented significant challenges for businesses and researchers. At the same time, the concept of evergreen notes has emerged as a powerful tool for effective thinking and knowledge management. In this article, we will explore the connection between these two seemingly unrelated topics and provide actionable advice for navigating the challenges of AI infrastructure and note-taking.

The High Cost of AI Compute:

The cost of AI compute has become a predominant factor in the industry today. Reputable sources have revealed that the demand for compute resources outstrips the supply by a factor of 10. Many companies are spending a significant portion of their capital on compute resources, with some even using up to 80% of their total raised funds. The cost of training and inference depends on the size and type of the AI model. Transformers, for example, require approximately 2*n*p floating point operations (FLOPs) for inference and 6*p FLOPs per token for training. Models like GPT-3, with 175 billion parameters, have computational costs in the trillions of FLOPs.

The high cost of AI infrastructure is primarily due to the algorithmic complexity of AI tasks. Generating a single word with a model like GPT-3 is significantly more computationally challenging than sorting a database table with a million entries. To mitigate these costs, it is essential to choose the smallest model that solves the desired use case. Fortunately, the compute and memory requirements of transformer models can be easily estimated based on their size. Specialized chips, such as GPUs (graphics processing units), are crucial for accelerating AI tasks. However, challenges like data transfer, memory limitations, and optimizing computation techniques need to be addressed.

Navigating AI Infrastructure:

For startups and app companies, building their own AI infrastructure from scratch may not be necessary on Day 1. Hosted model services offer a viable alternative, allowing founders to search for product-market fit without managing the underlying infrastructure. Services like OpenAI, Hugging Face, and Replicate provide hosted models and APIs, enabling developers to achieve control over model performance through prompt engineering and higher-order fine-tuning abstractions. However, some companies, particularly those focused on training new foundation models or vertically integrated AI applications, need fine-grained control over their AI infrastructure to achieve specific capabilities or reduce costs at scale. In such cases, managing the infrastructure can become a source of competitive advantage.

When it comes to choosing the right AI infrastructure, the cloud is often the most suitable option for most businesses. However, at a large scale, running your own data center may become more cost-effective. Factors like hardware selection, availability, compute delivery models, network interconnects, customer support, memory requirements, and latency considerations all play a crucial role in determining the optimal AI infrastructure solution. It is important to assess specific needs, such as GPU performance, spikiness in demand, and software optimizations, to make informed decisions.

Evergreen Notes and Better Thinking:

While the high cost of AI compute focuses on infrastructure, the concept of evergreen notes revolves around better thinking and knowledge management. Evergreen notes are designed to evolve, contribute, and accumulate over time across projects. The key is to prioritize effective thinking rather than just better note-taking. Writing notes for oneself by default, disregarding the audience, is an important principle. The emphasis should be on associative ontologies rather than hierarchical taxonomies, dense linking, and concept-oriented note-taking. This concept draws inspiration from Niklas Luhmann's Zettelkasten, which he considered an independent intellectual partner in writing his 70 books.

Connecting the Dots:

At first glance, the topics of AI infrastructure and evergreen notes may seem unrelated. However, there are common points that connect them naturally. Both require a strategic approach and a focus on optimization. In the case of AI infrastructure, optimizing the size and type of models, selecting the right hardware, and leveraging software optimizations are critical to managing costs. Similarly, evergreen notes emphasize the importance of optimization through associative thinking, dense linking, and concept-oriented note-taking. By connecting these dots, we can identify actionable advice that applies to both domains.

Actionable Advice:

  • 1. Prioritize Efficiency: In AI infrastructure, focus on optimizing the size and type of models to solve the desired use case. Choose the smallest model that meets your needs to reduce computational costs. Similarly, in note-taking, prioritize efficiency by adopting associative ontologies, dense linking, and concept-oriented notes. This will enhance your thinking process and make knowledge management more effective.
  • 2. Leverage Specialized Services: For startups and app companies, hosted model services can provide a cost-effective alternative to building AI infrastructure from scratch. Take advantage of platforms like OpenAI, Hugging Face, and Replicate to search for product-market fit without the burden of managing infrastructure. Similarly, in note-taking, leverage specialized tools and platforms that facilitate the organization and evolution of your evergreen notes.
  • 3. Embrace Continuous Learning: Both AI infrastructure and evergreen notes require a mindset of continuous learning and improvement. Stay updated with the latest advancements and techniques in AI compute, explore new software optimizations, and seek collaborations with third parties specializing in model optimizations. Similarly, in note-taking, continuously evolve your thinking process and refine your note-taking practices to enhance the accumulation and contribution of knowledge over time.

Conclusion:

The high cost of AI compute presents significant challenges for businesses and researchers. However, by adopting strategic approaches to AI infrastructure, such as optimizing model size, selecting the right hardware, and leveraging software optimizations, it is possible to navigate these challenges effectively. Similarly, the concept of evergreen notes provides a powerful framework for better thinking and knowledge management. By prioritizing effective thinking, associative ontologies, dense linking, and concept-oriented note-taking, individuals can enhance their intellectual capabilities and accumulate knowledge over time. By connecting the dots between AI infrastructure and evergreen notes, we can uncover valuable insights and actionable advice that apply to both domains.

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