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Software Is Changing (Again) - Andrej Karpathy
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Segments Timeline
The Legendary Talk Begins
Andrej Karpathy opens his talk with a humorous introduction, setting the stage for a discussion on the evolving landscape of software. He acknowledges the audience's academic backgrounds and hints at the unique opportunities present in the current software industry, particularly for those entering the field.
"Okay, apparently this is a legendary talk, the future of software again from uh Carpathy. I think he gave the talk of the future of software. So this must be a new iteration on it and everyone is quit..."
Software's Historical Context
Karpathy reflects on the historical changes in software over the past 70 years, contrasting the early days of programming with modern advancements. He emphasizes the significant evolution from punch cards to programming languages, highlighting the fundamental shifts that have occurred in the software development landscape.
"[Music] Wow, a lot of people here. Hello. Um, okay. Yeah, so I'm excited to be here today to talk to you about software and the Sorry, I I loved that intro. Just wow. a lot of people here and then li..."
The Era of Software 2.0
Introducing the concept of Software 2.0, Karpathy discusses how neural networks have transformed the way software is developed. He explains the shift from traditional programming to a model where data and algorithms play a central role, marking a pivotal change in software engineering.
"I mean? Yes, TJ, you are correct. This is going to be literally the Lord of the Rings Blu-ray edition extended scene of this of this talk. We're going deep on and I think fundamentally the reason for ..."
The Rise of Software 3.0
Karpathy introduces Software 3.0, characterized by the use of prompts to program large language models (LLMs). He argues that this new paradigm is akin to traditional programming but utilizes natural language, presenting both opportunities and challenges in software development.
"software changed? There's at least been one change. Can we all agree there was wow that is horrible to hey we have programming languages that run on all systems like there was like the I I would ventu..."
Programming with Natural Language
Exploring the implications of programming in English, Karpathy discusses the ambiguity of natural language and its impact on software development. He highlights the challenges and potential of using English as a programming language, emphasizing the need for clarity in communication with AI systems.
"Look at maybe the realm of software. So if we kind of think of this as like the map of software, this is a really cool tool called map of GitHub. Oh, this is so good. This is so good. I like this. I ..."
Neural Networks as Programmable Entities
Karpathy elaborates on the evolution of neural networks, explaining how they have become programmable and capable of performing complex tasks. He contrasts traditional fixed-function models with the flexibility offered by modern neural networks, showcasing their potential in various applications.
"called this software 2.0 at the time. And the idea here was that software oh we did this in 2017 neural nets 1.0 is the code you write for the computer. Software 2.0 are basically neural networks. A..."
The Future of Software Development
Discussing the future of software, Karpathy emphasizes the need for adaptation in the face of rapid technological advancements. He reflects on the integration of neural networks into existing software frameworks and the implications for developers as they navigate this evolving landscape.
"think like at the time neural nets were kind of seen as like just a different kind of classifier like a decision tree or something like that. And so I think it was kind of like um I I think this fram..."
The Role of GitHub in Software 2.0
Karpathy draws parallels between GitHub and the emerging landscape of Software 2.0, highlighting how platforms are evolving to accommodate new types of code. He discusses the significance of collaborative coding environments in shaping the future of software development.
"what we have is software 1.0 is the computer code that programs a computer. Software 2.0 are the weights which program neurol. By the way this photo this photo is definitely from the 70s or the 80s. ..."
Understanding Software 1.0 vs. Software 2.0
Karpathy contrasts Software 1.0 and Software 2.0, explaining how traditional programming relies on hard-coded logic while modern approaches leverage machine learning. He illustrates the differences in methodology and application, providing insights into the changing nature of software engineering.
"this is that you really have like such a fixed function you're working on. You really like dude you the the amount of things you can solve with it. You have to have such intense data and all that and ..."
The Impact of Large Language Models
Exploring the capabilities of large language models, Karpathy discusses their role in software development and the potential for new programming paradigms. He emphasizes the transformative power of LLMs in automating tasks and enhancing productivity in coding.
"Um so maybe uh to summarize the difference if you're doing sentiment classification for example you can imagine writing some uh amount of Python to to basically do sentiment classification or you can ..."
Programming Autonomy in AI
Karpathy shares insights from his experience at Tesla, discussing the integration of AI in autopilot systems. He reflects on the balance between traditional programming and neural networks, emphasizing the need for robust software solutions in autonomous driving.
"basically we have software so they just train software 2.0 know and I think we're seeing maybe you've seen a lot of GitHub code is not just like code anymore there's a bunch of like English intersper..."
Navigating the Complexity of Modern Software
In closing, Karpathy addresses the complexities of modern software development, particularly in the context of AI and neural networks. He encourages developers to embrace change and adapt to the evolving landscape, highlighting the importance of continuous learning in the field.
"just seems like kind of crazy. Um, I think it captured the attention of a lot of people and this is my currently pinned tweet uh is that remarkably we're now programming computers in English. Now, wh..."
The Evolution of Autopilot Software
In this segment, Karpathy explains how the capabilities of neural networks have expanded over time, leading to the removal of much of the original C++ code in autopilot systems. He emphasizes the remarkable growth of neural networks and their role in enhancing the functionality of autonomous vehicles, showcasing the transformative impact of AI on software development.
"right it's broken programmer's hand. Yeah, it's a bunch of well I mean neural nets are just a bunch of if statements and they're doing image recognition partial if statements. I kind of observed that ..."
The Paradigm Shift in Programming
Karpathy discusses the need for developers to be fluent in multiple programming paradigms as software evolves. He highlights the importance of understanding when to use different approaches, such as traditional coding versus neural networks, and the implications for future software development. This segment emphasizes the adaptability required in the rapidly changing tech landscape.
"slice with this code versus that code. But thi but this I my brain just wants to understand it. Okay, because he keeps saying these words that make me that make me think like this, but then it looks l..."
AI as the New Electricity
Karpathy references a quote by Andrew Ng, stating that 'AI is the new electricity.' He explores the transformative potential of AI in society, comparing its impact to that of electricity. This segment delves into the implications of AI as a utility and its role in shaping future technologies, raising questions about the reliability and accessibility of AI systems.
"just I know I got I'm letting it go. I'm letting it go right now. I'm letting it go right now. Okay. And functionality that was originally written 1.0 was migrated to 2.0. So, as an example, a lot of..."
Cognitive Outsourcing and Its Risks
In this thought-provoking segment, Karpathy discusses the concept of cognitive outsourcing, where individuals rely heavily on AI for critical thinking tasks. He raises concerns about the implications of this reliance, particularly when AI systems fail, leading to a 'brownout' of intelligence. This segment highlights the need for balance in leveraging AI while maintaining human cognitive skills.
"cheap. They're so cheap and so easy to understand and they work over a fixed range and it's beautiful, right? Like I actually really like I think there's there's some cool stuff that you could do to ..."
The Utility and Fabrication Lab Analogy
Karpathy compares LLMs (Large Language Models) to utilities and fabrication labs, discussing the significant capital investment required to develop these technologies. He reflects on the complexities of LLM ecosystems and the challenges of maintaining quality and reliability, emphasizing the unique characteristics of software compared to traditional utilities.
"about like just code. That's what makes LM so amazing is that you just like is this it and it's like yeah that's probably it. So what I wanted to get into now is first I want to in the first part talk..."
The Operating System Analogy
In this segment, Karpathy draws parallels between LLMs and operating systems, highlighting the complexity of modern software ecosystems. He discusses the competition between closed-source and open-source LLM providers, emphasizing the evolving nature of AI technologies and their implications for software development. This segment underscores the need for a nuanced understanding of LLMs in the tech landscape.
"intelligence over APIs to all of us and this is done through metered access where we pay per million tokens or something like that and we have a lot of demands that are very utility-like demands out ..."
The Future of LLMs and Cognitive Models
Karpathy speculates on the future of LLMs and their potential to evolve into more complex cognitive models. He discusses the implications of this evolution for software development and the importance of understanding the interplay between different AI technologies. This segment highlights the ongoing advancements in AI and their potential impact on various industries.
"about this, though. There's this entire critical thinking path that people have relied on for so long. And now, for the first time, instead of just offloading, say, memorizing phone numbers, memorizin..."
Analogies in Understanding AI
In this concluding segment, Karpathy reflects on the use of analogies to simplify complex topics in AI. He discusses the challenges of accurately representing the multifaceted nature of LLMs and their applications. This segment emphasizes the importance of clear communication in the tech industry and the need for ongoing dialogue about the implications of AI technologies.
"these models which already Dude, that has to be kind of a sta a scary statement. The planet gets dumber with the more reliance we have on these models. Why not a gray out? I agree. A grayout sounds m..."
LLMs as New Operating Systems
Andrej Karpathy discusses the evolving nature of large language models (LLMs) and their comparison to operating systems. He explains how LLMs function similarly to CPUs, orchestrating memory and compute for problem-solving, and suggests that they represent a new kind of computing paradigm.
"of anything cuz yeah, I mean, I guess you could say that, right? But you could pretty much say that about everything, right? Right. Like note-taking apps, you have Google Keep, you have notion, and th..."
The Centralization of AI Compute
Karpathy draws parallels between the current state of LLMs and the computing landscape of the 1960s, emphasizing the centralization of AI in the cloud. He argues that while AI may become more personalized, it remains under the control of large corporations, limiting individual access and ownership.
"definitely if you look at it looks very much like operating system from that perspective. I think my statement stands true that given a complex enough topic you can make it look like anything. I hone..."
The Future of Personal Computing
In this segment, Karpathy reflects on the potential for personal computing with LLMs, likening the current situation to the early days of personal computers. He discusses the economic challenges of running LLMs independently and the need for advancements to make them more accessible.
"Okay. You know, hell yes, brother. uh more analogies that I think strike me is that we're kind of like in this 1960sish era where LLM compute is still very expensive for this new kind of a computer an..."
AI's Role in Everyday Tasks
Karpathy critiques the current applications of LLMs, noting that they often assist with trivial tasks rather than transformative technologies. He highlights the irony of using advanced AI for mundane queries and expresses concern over the implications of relying on AI for cognitive functions.
"think that's where everything kind of breaks down here. That would actually be a really cool argument. Also, I just want to throw this out here. This is uh a bookmark image. So we just did something t..."
The Evolution of Technology Diffusion
Karpathy discusses how LLMs represent a reversal in the typical diffusion of technology, where consumers are often the first adopters. He contrasts this with historical technologies that were initially utilized by governments and corporations, suggesting a shift in how we interact with AI.
"will own intelligence and that we will have a psychological dependency now on all these corporations. We're no longer just relying on corporations for say our calendar for say our email and communicat..."
The Challenge of Efficiency in AI
In this segment, Karpathy emphasizes the need for LLMs to become significantly more efficient to be economically viable. He discusses the current inefficiencies and the potential for future advancements that could lead to more practical applications of AI technology.
"by the way that doesn't involve burning down rainforest just to be able to run stupid queries like how to boil an egg or some like just like oh how to make breakfast, how do I how do I do like just li..."
Understanding LLMs: Beyond Personification
Karpathy explores the nature of LLMs, arguing against personifying them as having psychology. He explains that LLMs are stochastic simulations that reflect human language patterns, emphasizing their limitations and the misconceptions surrounding their capabilities.
"my graph just one more time. Can can we just just just one more time? Can we just look at it for a quick second? Okay. Just that's all I have to say. Okay, now that I got that out of the way, we can g..."
The Memory and Limitations of LLMs
Karpathy compares LLMs to individuals with exceptional memory, like the character in 'Rain Man.' He discusses their ability to recall vast amounts of information while also highlighting their cognitive deficits and the challenges they face in understanding complex tasks.
"missile ballistics was like just computers themselves for a long time like that's how we did all the calculations for World War II was just all this nonstop you know nonsense. Hey thank you very much ..."
The Future of AI and Its Ethical Implications
In this concluding segment, Karpathy reflects on the ethical considerations of using LLMs, particularly in sensitive areas like military applications. He warns against the dangers of relying on AI for critical tasks and emphasizes the need for responsible development and deployment of AI technologies.
"late, bucko. Please. Please. I begged lagging behind the adoption of all of us of all of these technologies. So it's just backwards and I think it informs maybe some of the uses of how we want to use ..."
LLMs and Memory: The Rainman Analogy
Andrej Karpathy draws a parallel between large language models (LLMs) and the character from the movie Rainman, highlighting how LLMs possess an encyclopedic memory that surpasses human capabilities. He discusses the implications of this memory in relation to copyright issues, suggesting that LLMs may inadvertently infringe on copyrights by memorizing and recalling vast amounts of text.
"makes us feel as if it has some sort of psychology going to it. Uh LLM have encyclopedic knowledge and memory. Autism spotted and they can remember lots of things a lot more than any single individu..."
Cognitive Deficits of LLMs
Karpathy elaborates on the cognitive limitations of LLMs, comparing their performance to that of an autistic savant. He explains that while LLMs can recall information, they often struggle with tasks like playing chess, revealing their reliance on statistical patterns rather than true understanding. This segment emphasizes the hallucination problem in LLMs, where they generate incorrect or nonsensical outputs.
"easily. So, it's so interesting that he says those things like they remember Shaw hashes and all these things really really easily. I mean, they also can't play chess just given like a situation. If ..."
The Hallucination Problem
In this segment, Karpathy discusses the hallucination phenomenon in LLMs, where they produce inaccurate information. He shares his experiences with an LLM tool, strudel, highlighting its tendency to fabricate answers and the challenges of understanding its documentation. This segment underscores the need for improved clarity in LLM interactions and the importance of user guidance.
"stuff constantly like it it can't even produce s it can't it just it is so broken and so filled with hallucinizations. It is just redonkulous. Just read the docs. I am reading the docs. But sometimes ..."
Context Windows and Memory Limitations
Karpathy explains the concept of context windows in LLMs, likening them to working memory. He points out that LLMs do not naturally consolidate knowledge like humans do, leading to limitations in their understanding over time. This segment highlights the challenges developers face when programming LLMs to retain context and knowledge effectively.
"this has gotten better, but not perfect. They display jagged intelligence. So they're going to be superhuman in some problem solving domains and then they're going to make mistakes that basically no h..."
Cultural References and AI Relationships
In a light-hearted discussion, Karpathy references cultural phenomena related to AI, including a story about a man who became emotionally attached to an AI girlfriend. He connects this to the limitations of LLMs, emphasizing the absurdity of such relationships when the AI's context window expires. This segment explores the intersection of technology and human emotion.
"think a lot of people get tripped up by the analogies u in this way in popular wasn't there just like a story about some guy who had a girlfriend I saw I saw some brief snippet about it on some show ..."
Security Risks of LLMs
Karpathy addresses the security vulnerabilities associated with LLMs, including their susceptibility to prompt injection and potential data leaks. He raises questions about how LLMs could access private data, prompting a discussion on the ethical implications of AI technology. This segment emphasizes the importance of understanding the risks involved in using LLMs.
"Culture. I recommend people watch these two movies, uh, Momento and 51st Dates. In both of these movies, I did not like Momento. The protagonist. And by the way, the the Momento, dude, he has conte..."
The Future of LLM Applications
In this segment, Karpathy shifts focus to the opportunities presented by LLMs, particularly in developing partial autonomy applications. He critiques the initial user experience of LLMs and highlights the potential for more effective tools, like cursor, that enhance coding efficiency. This discussion emphasizes the need for better integration of LLMs into practical applications.
"like, well, how did it get the private data to begin with, right? Because I assume my context window is only some sort of like ephemeral session. So, how did that work? I'm a little bit confused by th..."
The Limitations of Vibe Coding
Karpathy shares his thoughts on vibe coding, expressing skepticism about its effectiveness for serious software development. He reflects on his experiences with AI-assisted coding and the confusion it can create, emphasizing the importance of traditional coding practices. This segment highlights the tension between innovation and established coding methodologies.
"comprehensive list, just some of the things that I thought were interesting for this talk. The first thing I'm kind of excited about is what I would call partial autonomy apps. So, for example, let's ..."
Navigating the Confusion of AI Tools
In this segment, Karpathy discusses the challenges of using AI tools in software development, particularly the confusion that arises from relying too heavily on vibe coding. He shares personal anecdotes about feeling lost in the coding process and the need for clarity in understanding data transformations. This segment underscores the importance of maintaining a solid grasp of coding fundamentals.
"operating system it makes a lot more sense to have an app dedicated for this and so I think many of you uh use uh cursor I do as well and uh cursor is kind of like the thing you want instead you don't..."
The Role of Traditional Interfaces
Karpathy emphasizes the value of traditional interfaces in software development, arguing that they allow for better manual control and understanding of the coding process. He discusses the balance between using LLMs and maintaining a hands-on approach to coding, highlighting the need for human oversight in AI-assisted development.
"confused and I just feel like I have all the ideas of an app that works but I've only ever like lightly code reviewed it and that's it. And so therefore I don't actually know what's happening and and ..."
The Impact of Vibe Coding on Productivity
In this reflective segment, Karpathy shares his experiences with vibe coding and its impact on his productivity. He discusses how reliance on AI tools can lead to distractions and a passive approach to coding, ultimately affecting his workflow. This segment explores the psychological effects of using AI in software development.
"admin panel. The admin panel is purely vibecoded and it's just at this point where where some problems exist and my only solution is like dude the data transfer and the state and how state is set up i..."
The Autonomy Slider in LLM Applications
Karpathy introduces the concept of the autonomy slider in LLM applications, discussing how tools like cursor allow users to adjust their level of control over coding tasks. He highlights the importance of balancing autonomy and manual input in software development, emphasizing the need for user agency in AI-assisted environments.
"some of the properties of LLM apps that I think are shared also the single worst C++ uh thing of all time. This is right. Isn't this a syntax error? Am I correct on this? That you need a space betwee..."
The Importance of GUI in AI Interactions
In this concluding segment, Karpathy discusses the significance of graphical user interfaces (GUIs) in enhancing user interactions with LLMs. He argues that GUIs facilitate better understanding and control over AI outputs, making it easier for users to audit and manage the work of AI systems. This segment reinforces the need for intuitive design in AI applications.
"this point a little bit uh later as well. I'm still actually not sure if it makes you go faster. I know like people do move faster and all that. I just don't you know again this is old man yelling at..."
LLM Applications and User Interaction
In this segment, Karpathy explores the features of LLM applications like Perplexity, which allow users to audit AI-generated content. He raises questions about the reliance on AI for summarization and the potential decline in source verification among users.
"I just don't you know again this is old man yelling at clouds. I I don't know. I don't know. I've been proven wrong, though. I know someone that there's what I call the autonomy slider. So, for examp..."
The Future of Autonomous Software
Karpathy speculates on the future of software becoming partially autonomous. He discusses the challenges of ensuring LLMs can operate effectively while maintaining human oversight, particularly in creative and complex tasks.
"I'm curious about that search or you can research or you can do deep research come back 10 minutes later. So this is all just very I know there's a couple people saying I do but you guys are also I ..."
The Nature of Mathematics: Observation vs. Creation
In a thought-provoking discussion, Karpathy debates whether mathematics is a human creation or an observation of natural laws. He emphasizes the distinction between the notation we create and the mathematical truths that exist independently of us.
"So one thing I want to stress with a lot of these LLM apps that I'm not sure gets someone's saying math for example for example first off math is not something we created okay math is just something w..."
Base Systems and Counting: A Philosophical Take
Karpathy humorously reflects on the history of counting systems, discussing the arbitrary nature of base systems like base 10 and base 2. He engages with the audience on the implications of these systems in programming and mathematics.
"That's what I'm saying. The Yeah. The arbitrary ch the the arbitrary choice of bass because we got little 10 little fingies is pro is that's at least how I assume we got base 10 counting is because we..."
The Importance of Security in Software Development
Karpathy stresses the complexities of software security, sharing anecdotes about vulnerabilities that can arise from seemingly simple coding practices. He warns against the dangers of automated systems in security and the need for thorough verification.
"what you mean by recently. Like I don't know what the Romans were up to, but their numerals were all crazy. I don't know what I don't even I don't even think they had a base system to begin with. I'm ..."
Balancing Speed and Control in AI Development
In this segment, Karpathy discusses the trade-offs between speed and control in AI-assisted coding. He emphasizes the need for developers to maintain oversight while leveraging AI tools to enhance productivity.
"Okay, there are two major ways that I think uh this can be done. Number one, you can speed up verification a lot. Um, and I think guies, for example, are extremely important to this because a guey uti..."
Understanding the Complexity of Security Issues
Karpathy elaborates on the intricate nature of security vulnerabilities in software, explaining that they often arise from complex interactions rather than simple oversights. He highlights the importance of understanding these complexities to prevent security breaches.
"the the whole there's no security issues or any of that. I I've told this story many times about me like one line of code could take down Netflix. Um there is no like security isn't just simply as rev..."
The Role of AI in Coding Workflows
Karpathy shares insights on integrating AI into coding workflows, advocating for small, incremental changes to maintain quality and control. He encourages developers to adopt AI tools thoughtfully to enhance their coding practices.
"that. Yeah, it's a cascade effect. It's it's a very Yeah, timing like timing attacks are a great example. H how would you ever know that someone would be able to measure whether the computer is doing ..."
Getting Up to Speed with AI
In a light-hearted conclusion, Karpathy summarizes key takeaways for developers looking to leverage AI in their work. He emphasizes the importance of accurately describing problems and taking small steps to effectively utilize AI tools.
"people don't think like that. You're not going to get someone that has never worked with computers and be like, "Don't forget to use JWT. uh shot, you know, MD5 is insecure, right? Like you don't they..."
The Challenge of Coding Speed
Andrej Karpathy discusses the challenges of coding speed in large organizations, emphasizing the need for effective collaboration among teams. He highlights the benefits of AI tools like Claude and Cursor for navigating complex codebases and improving search capabilities, advocating for AI-assisted coding workflows that prioritize small, incremental changes.
"help me on this is is really our biggest problem speed of writing code whenever I worked in big organizations the big problem was like I had to get 15 teams together to do a whole bunch of crap like t..."
Don't Get Left Behind in AI
Karpathy addresses the urgency of adapting to AI technologies, warning that those who fail to engage with AI may find themselves left behind. He shares practical advice for leveraging AI effectively, such as accurately describing problems and taking small steps in development, while reflecting on the rapid evolution of AI tools.
"way, you know that you know that phrase where people always tell you like, you know, those who aren't getting great at AI are going to get left behind. Hey, you want me to give you the last three year..."
AI's Role in Education
Exploring the intersection of AI and education, Karpathy expresses concern about the potential pitfalls of AI in learning environments. He emphasizes the importance of structured educational frameworks to keep AI 'on a leash' and ensure that students receive accurate and meaningful information, rather than relying on vague prompts.
"in. Um, I also saw a number of blog posts that try to develop these best practices for working with LLMs. And here's one. Dude, this is literally what I'm saying. Notice the top one is just like, "Hey..."
Mastering Knowledge vs. Quick Answers
Karpathy contrasts the depth of true understanding with the temptation for quick answers in today's fast-paced world. He uses music theory as an example, arguing that mastery requires time and practice, and warns against the dangers of superficial learning facilitated by AI.
"make the greatest failure we've ever done, ever of all time. What does education look like? And I think a a large amount of thought for me goes into how we keep AI on the leash. I don't think it just ..."
The Distance from Knowledge to Understanding
In this segment, Karpathy discusses the significant gap between knowing facts and truly understanding concepts. He emphasizes the need for experiential learning and internalization of knowledge, using analogies from music theory and martial arts to illustrate his point.
"take so many years to master, but hey, it's really great that you're taking the step now. This is fantastic. And you know, you just read it and you're just like, man, how often does somebody go into s..."
AI's Impact on Work Dynamics
Karpathy reflects on the changing dynamics in workplaces due to AI advancements. He warns that reliance on AI could lead to superficial productivity metrics that overlook the importance of thoughtful, quality work, urging a balance between speed and substance in coding practices.
"right? Yeah. Just like be Yeah. Be Brazilian jiu-jitsu is another great another great example. Any of these things are great examples. You can understand things, but it doesn't mean you have it here, ..."
The Future of Autonomy in AI
Discussing his experience at Tesla, Karpathy shares insights on the challenges of achieving true autonomy in AI systems. He critiques the current state of self-driving technology and emphasizes the need for careful consideration of safety and regulatory factors in the development of autonomous vehicles.
"This may not be good at all. It's just a thing that we have these weird pressures on that we've allowed people to do. And so I'm I'm kind of like generally I try to push back on that. This is part of ..."
The Reality of Self-Driving Cars
Karpathy reflects on the long journey of self-driving technology since his first experience in 2013. He discusses the ongoing challenges and the gap between expectations and reality in the development of autonomous vehicles, cautioning against overhyped timelines for AI advancements.
"Wow, that's normal. Instead of like hold on uh sound menu sl Oh [ __ ] I just I just ran over somebody, dude. Like what kind of crazy? Like who thought that was okay? Boomer take. Glad to have this as..."
Regulations and AI Development
In this concluding segment, Karpathy addresses the role of regulations in AI and self-driving technology. He acknowledges the necessity of regulations while also critiquing their impact on innovation, emphasizing the importance of balancing safety with technological progress.
"autonomy. um you are still working on driving agents and even now we haven't actually like really solved the problem like you may see Whimos going around and they look driverless but you know there's ..."
The Glass Hole Phenomenon
Karpathy critiques the early adopters of Google Glass, referring to them as 'glass holes' and discussing the overestimation of technology integration in everyday life. He contrasts the initial excitement around augmented reality with the current perception of such technologies, noting that many people, including his own mother, are indifferent to these advancements. This segment explores the gap between tech enthusiasts and the general public's acceptance of new technologies.
"which by the way, you know me and the government. You already know that I'm not a huge fan of the government. So, I'm kind of already on your team, okay? You're you don't even have to sell me. I'm lik..."
The Iron Man Analogy
Using the Iron Man franchise as a metaphor, Karpathy discusses the balance between augmentation and autonomy in technology. He argues that while the idea of fully autonomous agents is appealing, the current reality is more about creating tools that assist humans rather than replacing them. This segment emphasizes the importance of maintaining human oversight in the development of AI and software solutions.
"I mean, I could give, you know, I'd give I'd give some Ray-B bands a try with a little with a little bit of hearing uh on it. I'd give it a shot. I'd give it a shot on those ones. But it's kind of cra..."
Programming for Everyone
Karpathy highlights the democratization of programming through natural language interfaces, suggesting that advancements in AI are making it easier for non-programmers to engage with technology. He reflects on the historical barriers to entry in software development and how recent innovations are changing the landscape, allowing more people to participate in programming and tech creation.
"is software. Let's be serious here. One more kind of analogy that I always think through is sounds like coming. I know people get I think this is I always love Iron Man. I think it's like so um cor..."
The Reality of Coding
In this segment, Karpathy discusses the difference between casual coding and professional programming. He shares insights from a conversation with a lawyer about the impact of AI on legal work, emphasizing that while AI can assist in generating boilerplate documents, it cannot replace the nuanced understanding and experience that comes with being a skilled professional. This highlights the ongoing need for expertise in various fields despite technological advancements.
"Iron Man suits that you want to build. It's less like building flashy demos of autonomous agents and more building partial autonomy products. And these products have custom gueies and UIUX and we're t..."
Vibe Coding and Its Impact
Karpathy introduces the concept of 'vibe coding' as a fun and engaging way to learn programming. He shares his personal experience of building an iOS app without extensive knowledge of Swift, illustrating how accessible tools can spark interest in coding. This segment emphasizes the importance of curiosity and experimentation in fostering a new generation of programmers.
"nonetheless but it seems like this is really only geared towards programming and I I'm just trying to apply this to other fields like what other fields could we do this with I'm not really like I don'..."
The Challenges of Real-World Development
Karpathy reflects on the difficulties of turning a coding project into a viable product. He discusses the challenges he faced while developing his app, Menu Genen, particularly in areas like authentication and deployment. This segment highlights the gap between coding for fun and the complexities of real-world software development, emphasizing the importance of understanding the full development lifecycle.
"also completely unprecedented. Everyone's not a programmer, right? I mean, we could you could make this like argument like a hundred times over. Grace Hopper did this with with like Cobalt. Anyone bus..."
Navigating the Landscape of LLMs
In this segment, Karpathy addresses the implications of using large language models (LLMs) in software development. He raises concerns about the reliance on specific libraries and services, questioning how this might shape the future of programming. This discussion highlights the potential pitfalls of integrating AI tools into development workflows and the need for critical thinking in technology adoption.
"and which tweet like fizzles and no one cares. And I feel like I have a pretty good I have a pretty good one. Like I think that tweet that I just got done tweeting will probably not exceed 600 likes...."
The Challenge of Real-World Coding
Andrej Karpathy discusses the difficulties of transitioning from coding a demo to implementing real-world applications. He highlights the complexities of integrating authentication and payment systems, emphasizing that while coding can be straightforward, the DevOps processes often require extensive manual effort and navigation through numerous instructions.
"vip coding part the code was actually the easy part of of V coding menu. Oh, I think this is going to be a good section. Look at this. Look at all the things he's saying here. Okay, I want this is wh..."
The Dangers of LLM Dependency
Karpathy warns about the potential dangers of relying on large language models (LLMs) for coding tasks. He raises concerns about the implications of using paid services for authentication and how this could lead to a dependency on specific libraries, questioning the balance between convenience and the risk of being locked into certain technologies.
"And the reason for this is this was just really annoying. Um, so for example, if you try to add Google login to your web page, I know this is very small, but just a huge amount of instructions of this..."
Emergence of Digital Agents
In this segment, Karpathy introduces the concept of digital agents as a new category of entities that manipulate digital information. He contrasts traditional human and computer interactions with these agents, suggesting that they represent a new form of digital presence that requires a different approach to documentation and interaction.
"certain libraries, right? Like when he said he wanted authentication, he used clerk, right? That's a paid for service. So how much of that is going to start There there's a whole there's a whole like ..."
Optimizing Documentation for LLMs
Karpathy discusses the importance of making documentation accessible for LLMs. He suggests that transitioning documentation to formats like markdown can enhance LLM usability, allowing for better interaction and understanding. He cites examples of companies that are already adapting their documentation to be more LLM-friendly.
"and they need to I do not like the term people spirits I'll accept people reflections I'm more of in the term reflection than anything else but spirit is way too much to interact with our software i..."
The Balance of Learning and Automation
Karpathy reflects on the balance between leveraging LLMs for quick solutions and the necessity of developing expertise in coding. He questions when it is appropriate to rely on LLMs versus when one should invest time in learning the underlying technologies, emphasizing the importance of maintaining coding skills.
"documentation is currently written for people. So you will see things like lists and bold and pictures and this is not directly accessible by an LLM. So I see some of the services now are transitionin..."
The Evolution of Coding Practices
In this segment, Karpathy discusses how coding practices are evolving with the advent of LLMs. He shares personal anecdotes about his experiences with tools like Tailwind and reflects on how reliance on automation can impact one's coding abilities over time, advocating for a balance between using tools and maintaining coding proficiency.
"huge amount of um kind of use and um I think this is wonderful and should should uh happen for you know how we were talking about this uh on the stand up how like everything has kind of got inchified..."
The Future of LLMs and Software Development
Karpathy speculates on the future of LLMs in software development, suggesting that they will become integral tools for coders. He emphasizes the need for a new approach to software that accommodates LLMs, while also acknowledging the limitations and challenges that still exist in their current capabilities.
"up any site I wanted, my ability to do that has greatly shrunk because I'm just not using it anymore ever because I'm just like, dude, I don't want to touch any of this. Make it happen. But maybe I sh..."
Understanding LLMs as Tools
Karpathy challenges the notion of LLMs as traditional operating systems or frameworks. He argues that they should be viewed as statistical generators that produce outputs based on inputs, rather than as comprehensive systems. This perspective encourages a more nuanced understanding of how LLMs function and their role in software development.
"So, I'm very bullish on these ideas. The other thing just brushed right over MCPs. Yeah. uh the end I really like is a number of little tools here and there that are helping flexbox is the best part..."
The Historical Context of Technology
In this reflective segment, Karpathy draws parallels between the evolution of technology and historical practices in computing. He shares anecdotes about the early days of RAM production and how technological limitations shaped the development of operating systems, emphasizing the importance of understanding the historical context of current technologies.
"is deep wiki where it's not just the raw content of these files. Uh this is from Devon but also like they have Devon basically do analysis of the GitHub repo and Devon basically builds up a whole docs..."
The Future of Quantum AI
Karpathy speculates on the potential future of quantum computing and its implications for AI. He discusses the current limitations of technology and the exciting possibilities that quantum advancements could bring to the field of artificial intelligence, suggesting that we are on the brink of a significant technological shift.
"it's not that great. URL and it makes something accessible to an LLM great and u I think there should be a lot more of it. One more note I wanted to make is that it is absolutely possible that in the ..."
The Hand-Rolled RAM Era
Andrej Karpathy discusses the historical challenges of building RAM, highlighting a time when expert weavers were employed to thread wires for RAM construction. He reflects on how this manual process shaped the development of operating systems, emphasizing the limitations of technology in the past compared to today's advancements.
"because we just didn't know how to build physical computers that could go fast. We didn't have the technology to then build the next thing that was better. At one point, apparently there was like a ve..."
Hitting the Limits of Computing
Karpathy explores the current state of computing, suggesting that we are reaching the physical limits of hardware capabilities. He draws parallels between the evolution of operating systems in the 1960s and today's AI technologies, questioning whether we will see significant advancements in large language models (LLMs) akin to those in the past.
"that you know the whole operating system thing is that they are completely bound to these things. I don't think we have the same bounding in the sense that Our GPUs aren't We are like hitting literal ..."
The Bullish Outlook on AI
In this segment, Karpathy expresses a bullish perspective on the future of AI, despite his skepticism about the current state of programming code. He acknowledges the rapid improvements in video and sound quality while questioning whether similar advancements are occurring in coding practices.
"definitely is not the same. Anyways, so it's hard for me to like make these analogies because then if you think of it like this, you might like put on this guise or this you might put this understandi..."
The Iron Man Suit Analogy
Karpathy uses the Iron Man suit as a metaphor for the future of AI development, predicting a shift in capabilities over the next decade. He shares his concerns about potential frustrations with AI services while recognizing the exciting possibilities that lie ahead.
"a lot of the analogies cross over. Um and these other ones are kind of like these fallible uh you know people spirits that we have to learn to work with. refus to do that properly, we need to adjust o..."
Navigating AI's Complexities
In this reflective segment, Karpathy discusses the complexities of AI as a utility and the challenges of understanding its analogies. He shares his thoughts on the simplifications made in AI discussions and emphasizes the importance of recognizing both the potential and limitations of AI technologies.
"obviously. I mean, I played that banger, that banger of a tune for us. [Music] be written for the agents more directly. But in any case, going back to the Iron Man suit analogy, I think what we'll see..."
Final Thoughts and Encouragement
Karpathy wraps up the discussion by encouraging viewers to engage with the content fully. He expresses gratitude for the audience's support and emphasizes the importance of watching the video to the end to grasp the full context of his insights.
"Really was happy about it. Great talk. Good job, Andre, for that one. Uh, I know some people might think I'm a bit negative on things. I'm not trying to be negative. Oh, by the way, friends, let the v..."