Sam Altman on GPT-5 | Lex Fridman Podcast

Lex Clips
21 Mar 202411:30

TLDRThe transcript discusses the impressive capabilities of GPT-4 and its potential as a brainstorming partner and tool for various tasks. It highlights the importance of recognizing both the technological advancements and the need for continuous improvement. The conversation also touches on the challenges of fact-checking information generated by AI and the societal pressures faced by journalists in the age of fast-paced technology.


  • 🌟 GPT-4 is seen as a historically impressive technological advancement, surpassing the capabilities of its predecessors like GPT-3.
  • 🚀 The speaker anticipates that the leap from GPT-3 to GPT-4 will be as significant as the leap from GPT-4 to GPT-5, indicating a rapid pace of improvement.
  • 🤖 GPT-4's capabilities are not just in coding and language translation but also in acting as a creative brainstorming partner, offering new insights and perspectives.
  • 🛠️ The tool's ability to handle long-horizon tasks, breaking them down into multiple steps and executing them, is highlighted as a particularly magical aspect.
  • 🔄 The iterative back and forth between human and AI is valued for its potential in problem-solving, especially when dealing with complex, multi-step issues.
  • 📈 The speaker acknowledges the importance of both the underlying AI model and the post-training steps that make the AI more effective and aligned with human needs.
  • 🖥️ The context window expansion from 8K to 128K tokens in GPT-4 Turbo allows for handling longer and more complex inputs, hinting at future capabilities.
  • 📚 GPT-4 is being used by many, especially younger individuals, as a default starting point for various knowledge work tasks, showcasing its versatility.
  • 🔍 The challenge of ensuring that GPT-4 provides accurate information without fabricating data is recognized, and ongoing efforts to improve this aspect are mentioned.
  • 📝 Journalists' use of GPT-4 without proper fact-checking is criticized, emphasizing the need for responsible use of AI in information dissemination.
  • 🌐 The societal incentives and pressures that encourage quick, potentially inaccurate reporting are discussed, with a call for a greater appreciation of in-depth and balanced journalism.

Q & A

  • What does the speaker consider a historic pivotal moment in the development of GPT models?

    -The speaker considers the progression from GPT-3 to GPT-4 (and possibly GPT-5) as a historic pivotal moment due to the significant advancements and improvements in the models.

  • How does the speaker feel about the capabilities of GPT-4 compared to GPT-3?

    -The speaker is impressed by GPT-4, acknowledging its historical significance. However, they also note that people tend to get used to the amazing capabilities of these models quickly and expect future models to continue improving at a similar pace.

  • What is the speaker's perspective on the future of AI tools?

    -The speaker believes that as we progress into the future, we will look back at current tools like GPT-4 and see them as primitive or 'sucky' compared to what will be available, just as we now view GPT-3.

  • What are some of the best things GPT-4 can do according to the speaker?

    -The speaker mentions that GPT-4 can help with coding more productively, writing faster and better, translating languages, and serving as a creative brainstorming partner.

  • How does the speaker view the role of GPT-4 in long-horizon tasks?

    -The speaker finds it magical when GPT-4 can help with long-horizon tasks, such as breaking down a problem into multiple steps, executing some of those steps, and putting everything together, although they note this doesn't work very often.

  • What are the speaker's thoughts on the importance of the chat interface and post-training of the GPT models?

    -The speaker believes that both the chat interface and the post-training process are super important. The interface and how the model is tuned to be helpful and effective for humans are as crucial as the underlying model itself.

  • How does the speaker compare the context window of GPT-4 to GPT-4 Turbo?

    -The speaker notes that GPT-4 Turbo has an expanded context window from 8K to 128K tokens, which is beneficial for handling longer texts and providing a more comprehensive understanding of the input.

  • What does the speaker envision for the future of context length in AI models?

    -The speaker envisions a future where context length could expand to several billion tokens, allowing AI models to understand and process vast amounts of information, leading to more personalized and comprehensive interactions.

  • What interesting use cases of GPT-4 has the speaker observed?

    -The speaker finds it interesting that younger people use GPT-4 as their default start for any knowledge work task, leveraging its ability to handle a wide range of tasks reasonably well.

  • How does the speaker use GPT-4 for reading books?

    -The speaker uses GPT-4 as a reading partner, helping them to think through ideas, especially when reading classic literature. They find that GPT-4 often provides a more balanced and nuanced understanding than other sources like Wikipedia.

  • What concerns does the speaker have about using GPT-4 for knowledge tasks?

    -The speaker is concerned about the potential for GPT-4 to generate false information that sounds convincing. They emphasize the importance of fact-checking, especially for mission-critical tasks.

  • What is the speaker's view on the use of GPT-4 by journalists?

    -The speaker criticizes the use of GPT-4 by journalists who may not fully understand its limitations and the need for fact-checking. They express a desire for society to incentivize more in-depth and balanced journalistic efforts.



🤖 Reflecting on GPT-4's Impact and Evolution

The speaker discusses the historic significance of GPT-4, comparing it to previous models like GPT-3. They acknowledge the impressive nature of GPT-4's capabilities but also recognize that there is room for improvement. The speaker highlights the importance of living in the future to ensure that tools continue to advance and improve. They mention using GPT-4 as a brainstorming partner and appreciate its potential in creative collaboration. The speaker also touches on the iterative process of working with AI and the potential for handling long-horizon tasks, expressing hope for future advancements.


📈 GPT-4's Contextual Expansion and Product Development

The speaker explores the increase in context window from GPT-4 to GPT-4 Turbo, noting that while most users may not require the full 128k tokens, the potential for future advancements is vast. They draw a parallel to the early days of computing, predicting that context lengths will eventually reach levels that feel infinite. The speaker also discusses the challenges of scaling up AI technology and the dual focus on developing the underlying technology and creating a product that is both useful and accessible to a wide audience.


📚 GPT-4's Role in Knowledge Work and Fact-Checking

The speaker shares insights into how GPT-4 is being used as a default tool for knowledge work, particularly by younger individuals. They highlight the versatility of GPT-4 in various tasks, such as coding, searching, and editing. The speaker personally uses GPT-4 as a reading companion, finding it more nuanced than Wikipedia. However, they also express concern about the need for fact-checking to ensure the accuracy of information provided by GPT-4. The speaker acknowledges ongoing efforts to improve the reliability of AI-generated content.




GPT-4 refers to the fourth generation of the Generative Pre-trained Transformer, an advanced language prediction model developed by OpenAI. It is noted for its significant improvements over GPT-3, including better understanding and generation of human-like text. In the context of the video, GPT-4 is seen as a historic and impressive technological advancement, yet it is also acknowledged that future iterations will likely render it less remarkable, just as GPT-3 is now viewed.

💡historic pivotal moment

The term 'historic pivotal moment' refers to a significant point in time that marks a major turning point or change in a particular field or society. In the video, the speaker considers the development and release of GPT-3 and GPT-4 as such moments in the history of artificial intelligence and technology, indicating a shift in what is possible and the impact on the future.


In the context of the video, 'capabilities' refers to the range of functions and abilities that GPT-4 possesses, such as language understanding, text generation, coding assistance, translation, and more. These capabilities are seen as impressive and transformative, yet the speaker also notes that there is room for improvement and that future AI models will likely surpass the current ones.


The term 'brainless' in the video is used informally to express the speaker's opinion that GPT-4, while impressive, still has limitations and is not yet at the level of fully replicating human intelligence or creativity. It suggests that the AI, despite its advanced capabilities, lacks the depth and nuance of human thought processes.

💡brainstorming partner

A 'brainstorming partner' refers to a tool or entity that aids in the creative process of generating ideas or solutions. In the context of the video, the speaker finds value in using GPT-4 as a brainstorming partner due to its ability to provide novel insights and contribute to problem-solving in a creative way.

💡longer horizon tasks

Longer horizon tasks refer to complex, multi-step problems that require planning, execution, and potentially searching for information or writing code. In the video, the speaker mentions the ability of GPT-4 to assist with such tasks, although it notes that this functionality is not always reliable and represents a glimpse of the potential for future AI development.

💡iterative back and forth

The phrase 'iterative back and forth' describes a process of repeated exchange or interaction between two parties to improve a product, idea, or solution. In the context of the video, it refers to the collaborative interaction between a human and GPT-4, where the AI can contribute to problem-solving by providing suggestions and receiving feedback.

💡context window

The 'context window' refers to the amount of text or information that an AI model like GPT-4 can consider at one time. An expanded context window allows the model to process and understand larger pieces of text, which can improve its comprehension and coherence. In the video, the speaker discusses the transition from an 8K to a 128K token context window, indicating a significant increase in the model's ability to handle long-form text.


Post-training refers to the process of fine-tuning and adjusting a pre-trained AI model after its initial training phase. This can involve techniques like Reinforcement Learning from Human Feedback (RLHF) to make the model more aligned with human values and preferences. In the video, the speaker emphasizes the importance of the post-training step in making GPT-4 not only technologically advanced but also more helpful and effective for users.

💡knowledge work

Knowledge work refers to tasks or activities that involve the creation, manipulation, and dissemination of information or knowledge. In the video, the speaker notes that people, particularly younger individuals, are increasingly using GPT-4 as their default starting point for various knowledge work tasks, leveraging its versatility and ability to assist across a wide range of activities.

💡fact checking

Fact checking is the process of verifying the accuracy of information. In the context of the video, the speaker expresses concern about the need for fact checking when using GPT-4 for knowledge tasks, as the AI can sometimes generate convincing but false information. This underscores the importance of critical evaluation of AI-generated content.


The historic significance of GPT-3 and GPT-4 as pivotal moments in AI technology.

The expectation of a similar leap in capabilities from GPT-4 to GPT-5 as there was from GPT-3 to GPT-4.

The importance of viewing current AI tools as primitive from a future perspective to ensure continuous improvement.

GPT-4's ability to act as a creative brainstorming partner, offering a glimpse of future potential.

The potential for GPT-4 to assist with long-horizon tasks by breaking them down into multiple steps and executing some of those steps.

The iterative back and forth between GPT-4 and humans can be very effective, especially when working on complex, multi-step problems.

The transition from disbelief to belief in AI capabilities, particularly with the advent of chat interfaces like GPT.

The dual challenge of inventing underlying AI technology and figuring out how to productize it for widespread adoption.

The significance of post-training steps like RLHF in tuning AI models to be more effective and productive for humans.

The expansion of context windows from 8K to 128K tokens and the憧憬 for even longer contexts in the future.

The use of GPT-4 as a default starting point for various knowledge work tasks, especially among younger users.

GPT-4's ability to serve as a reading partner, helping users think through ideas presented in books.

The need for fact-checking when using GPT for knowledge tasks due to the risk of the model generating convincing false information.

The concern that as AI models improve, users may become less vigilant about fact-checking the information they generate.

The critique of journalistic use of AI, where the pressures and incentives of the profession may lead to misuse of AI-generated content.

The desire for societal incentives that reward in-depth, balanced journalism and discourage the clickbait culture.