Inflection Point

Navigating the real costs and commercial challenges in the generative AI innovation race

Earlier this month, AI safety and research company Anthropic announced that it was launching three new models for Claude, its generative AI assistant and family of large language models (LLMs). The new models would offer increasingly powerful performance to its users. Costs for each model are correlated with model intelligence and the number of tokens required to support processing power.

The most sophisticated Claude 3 model, Opus, exhibited “near-human levels of comprehension and fluency” on more complex tasks, and would come out ahead of Anthropic’s peers (including OpenAI’s GPT-4 or Google’s Gemini Ultra) on a number of benchmarks, including grade school math, code, or reasoning over text. One of the more unique elements of the launch was the reduction in incorrect refusals on behalf of Claude models; from Threads:

This is a significant (and exciting!) development, but questions remain as to whether the velocity in innovation for AI startups can offset the time it takes them to scale. As generative AI models like Claude 3 improve across the board…