The conversation revolves around the rapid progress of Large Language Models (LLMs) and their applications, particularly in the context of startup ideas. Here are some key points discussed:

  1. Progress of LLMs: The speakers mention how LLM-powered apps have evolved significantly since early 2023, with notable advancements in voice AI, text generation, and model capabilities.
  2. First wave of LLM apps: They reminisce about the initial wave of LLM apps, which were relatively basic and focused on incremental improvements over human-written content. These apps were quickly outdone by more advanced models, demonstrating a “boiling frog” effect where progress seems gradual until it suddenly becomes significant.
  3. Current state of LLMs: The conversation highlights how we’ve reached a point where full-on vertical AI agents can replace entire teams and functions within enterprises, with impressive progress in just two years.
  4. Competition among foundation models: The speakers discuss the emergence of new players like Claude, which is competing directly with OpenAI’s flagship model. This competition is seen as healthy for the market, providing consumers with choices and founders with opportunities.
  5. Identifying a profitable vertical for LLMs: The conversation turns to how startups can identify promising areas for LLM applications. A common thread among successful companies is finding “boring, repetitive admin work” that an LLM can automate. This approach seems to be a winning strategy, with examples provided in industries like government contracting and medical billing.
  6. Robotics analogy: The speakers draw an analogy between robotics and vertical SaaS (Software as a Service) ideas for LLMs. They suggest looking for “dirty and dangerous jobs” that are perfect candidates for automation by AI agents.

Overall, the conversation celebrates the rapid progress of LLMs and highlights their potential to transform various industries and businesses. The discussion provides insights into how startups can identify promising areas for LLM applications and tap into this emerging market.

Translation

1. **LLM 进展**: 讨论者提到了自 2023 年初以来，LLM 权力应用程序的进步显著。其中特别突出的是语音 AI、文本生成和模型能力的提高。 2. **LLM 应用程序第一波**：他们回顾了初期LLM应用程序的情况，这些应用程序相对简单，主要关注的是在人类写作内容上实现增量改进。这些应用程序很快就被更先进的模型超越，展现出一种“热水煮青蛙”的效果，即进步似乎逐渐增加，但突然变得显著。 3. **当前LLM状况**：对话突出了我们已经到达一个阶段，在此阶段全面的垂直 AI 代理能够替代整个企业和功能，仅仅是在两年内就取得了令人印象深刻的进展。 4. **基金模型竞争**: 讨论者讨论了新玩家Claude出现，这与OpenAI旗舰模型直接竞争。这被视为健康市场，对消费者提供选择，对创始人提供机会。 5. **寻找LLM盈利垂直**：对话转向了如何让创业公司找到有吸引力的领域应用 LLTM。成功企业的一个共同点是找到了“枯燥、重复的管理工作”，LLM可以自动化。这似乎是一个赢得策略，在政府采购和医疗账单等行业提供了例子。 6. **机器人类比**：讨论者用机器人类比来与垂直 SaaS（软件即服务）想法相关联。他们建议寻找“脏腻危险的工作”，这些都是使用 AI 代理自动化的理想候选者。 总体而言，讨论庆祝了 LLTM 的快速进步，并突出了它们在多个行业和企业中实现转型潜力的巨大潜力。讨论提供了有关创业公司如何找寻有吸引力的领域应用 LLTM 以及进入这个新兴市场的机会的见解。

Reference:

https://www.youtube.com/watch?v=ASABxNenD_U

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