From: allin
Current Landscape of LLMs
The field of large language models (LLMs) is experiencing rapid development and intense competition. OpenAI launched GPT 4.5, which was not well-received and largely went unnoticed [00:06:50]. In contrast, Alibaba introduced Gwen, an exceptional model that is considered among the best, if not the best, open-source model available [02:28:41]. Facebook’s open-source LLM, Llama, is reportedly “going sideways” [00:07:12].
Anthropic’s Claude 3.7 model is highly regarded for its performance, especially in automated code generation, where its models are described as “exceptional” and “best in market” [01:25:00]. Anthropic is also undergoing a major funding round [01:27:08].
For consumer applications, Grock 3 is increasingly preferred due to its elegant integration within the X platform (formerly Twitter) [01:25:40]. This integration allows users to click an XAI button on a tweet to get full context and deep research without needing to copy-paste questions [01:25:51]. Google is also integrating AI snippets into its front search page and YouTube summaries [01:29:26]. Meta is planning to launch a standalone AI app, leveraging its ability to reach a billion people quickly [01:29:55].
The rapid progress in LLMs is making AI tools cheaper and more accessible for companies [01:31:42]. For instance, software like Superhuman is now able to compose email replies and summarize content in real-time, which would have been cost-prohibitive just six to twelve months prior [01:30:39].
Challenges in Benchmarking
A significant challenge in the AI industry lies in the current benchmarking methods for LLMs. Models are often overfit to existing evaluation benchmarks (like sbench, IMO, Amy), making their reported performance unreliable [01:27:24]. This means models might excel at tests without necessarily demonstrating true capability or generalization. To address this, there is a recognized need for “extremely difficult and always changing, third party independent verifiable benchmarks” [01:28:18].
Impact and Future Outlook
The rapid advancement and affordability of LLMs are driving a sense of “abundance” in the technology sector [01:29:09]. This is leading to increased productivity and the potential for new job classes, such as “reinforcement learners” and “fact checkers” who train and refine AI models [01:32:20]. These roles could pay between 150,000 annually, as every improvement to an AI benefits everyone globally [01:32:32].
The pace of AI improvement is not slowing down; it’s getting “10 to 15% better every month” and is described as “scary” [01:31:34]. This constant progress, coupled with the increasing number of high-performing models, can be overwhelming, making it difficult to differentiate between them [01:29:02]. The shift towards open source models like Alibaba’s Gwen is also noted [01:28:41].