An anonymous reader shares a report: Every year for the past seven, Nathan Benaich, the founder and solo general partner at the early-stage AI investment firm Air Street Capital, has produced a magisterial "State of AI" report. Benaich and his collaborators marshal an impressive array of data to provide a great snapshot of the technology's evolving capabilities, the landscape of companies developing it, a survey of how AI is being deployed, and a critical examination of the challenges still facing the field. One of the big takeaways from this year's report, which was published late last week, is that OpenAI's lead over other AI labs has largely eroded. Anthropic's Claude 3.5 Sonnet, Google's Gemini 1.5, X's Grok 2, and even Meta's open-source Llama 3.1 405 B model have equaled, or narrowly surpassed on some benchmarks, OpenAI's GPT-4o.ââBut, on the other hand, OpenAI still retains an edge for the moment on reasoning tasks with the release of its o1 "Strawberry" model -- which Air Street's report rightly characterized as a weird mix of incredibly strong logical abilities for some tasks, and surprisingly weak ones for others. Another big takeaway, Benaich told me, is the extent to which the cost of using a trained AI model -- an activity known as "inference" -- is falling rapidly. There are several reasons for this. One is linked to that first big takeaway: With models less differentiated from one another on capabilities and performance, companies are forced to compete on price.ââAnother reason is that engineers for companies such as OpenAI and Anthropic -- and their hyperscaler partners Microsoft and AWS, respectively -- are discovering ways to optimize how the largest models run on big GPU clusters. The cost of outputs from OpenAI's GPT-4o today is 100-times less per token (which is about equivalent to 1.5 words) than it was for GPT-4 when that model debuted in March 2023. Google's Gemini 1.5 Pro now costs 76% less per output token than it did when that model was launched in February 2024.â
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