W2 4 Cost intuition

This episode explores the cost-effectiveness of using large language models (LLMs) in software applications. The speaker begins by providing pricing examples from various providers like OpenAI, Google, and Amazon, highlighting the cost per 1,000 tokens (roughly equivalent to 750 words). More significantly, the analysis delves into the calculation of costs based on typical adult reading speed, estimating the expense of generating enough text to keep an employee occupied for an hour. For instance, using OpenAI's pricing, generating 30,000 words (including prompts and output) costs approximately 8 cents. Against the backdrop of minimum wage rates, this cost is presented as relatively inexpensive, especially considering potential productivity gains. However, the speaker cautions that this low cost can escalate significantly with a large user base and no associated revenue. In conclusion, the episode offers a practical cost analysis of LLMs, suggesting that for many applications, the technology is more affordable than commonly perceived.