Andrew Ng: State of AI Agents | LangChain Interrupt

Summary

In a vibrant fireside chat, Andrew Ng delves into the evolution and the dynamic future of AI and agentic systems. Ng discusses his journey with LangChain, highlighting the influence of simplifying the concepts of agentic systems for broader acceptance and implementation. The dialogue explores the skills required for building AI agents, the underrated tools in the AI toolkit, and touches upon emerging technologies like MCP that streamline data integrations. Ng emphasizes the importance of speed and technical knowledge in AI startups, providing insights into voice applications and "vibe coding." The engaging session ends with a nod to AI Fund’s role in fostering tech-driven ventures.

Highlights

• Ng humorously suggests renaming AI conferences to 'agentic conferences' to better capture the scope of discussions. 😂
• The unexpected depth of coding with AI, including 'vibe coding,' despite its misleading name. 🤖
• Ng's insights into voice technology reveal its potential to lower user friction in various applications. 🗣️
• MCP promises to standardize data connections, akin to arranging LEGO bricks for easier assembly. 🧩
• The fireside chat concludes with Ng emphasizing speed as the key predictor of success in AI startups. 🚄

Key Takeaways

• Andrew Ng emphasizes the concept of 'agentic systems' to simplify debates around AI agents, promoting efficiency in implementation. 🤖
• Voice applications are undervalued but hold immense potential, especially in creating less intimidating user interfaces. 🎤
• MCP is streamlining data integration and is a promising step forward for AI ecosystem connections. 🔗
• Fast execution and deep technical knowledge are crucial for AI startup success, according to Ng. 🚀
• 'Vibe coding' is real, but the name might mislead; it's a deeply intellectual exercise, even with AI assistance. 💡

Overview

In an engaging fireside chat, Andrew Ng explores the trajectory of AI agents and how concepts like 'agentic systems' have evolved. His approach is to eliminate debates about whether something is an AI agent by introducing the term 'agentic,' which promotes a more inclusive and efficient way to discuss and create AI systems. He reminisces about his early interactions with LangChain and the mutual journey of exploring such innovations.

Ng shares valuable insights on the skills necessary for AI development. He stresses that while technical knowledge remains rare and crucial, the broad landscape of AI tools—compared to LEGO bricks—allows for creative and efficient problem-solving. Particular emphasis is placed on 'vibe coding' and the importance of learning to code, highlighting challenges and the intellectual rigor required even with AI assistance.

The conversation also touches upon future-facing technologies like MCP, which promises to massively simplify data integration in AI system development. Ng is particularly enthusiastic about voice applications and their potential to significantly enhance user interactions. He encourages startups to focus on speed and deep technical mastery, chief predictors of success in the fast-evolving tech landscape.

Chapters

• 00:00 - 01:00: Introduction and Andrew Ng's Contribution to LangChain The chapter provides an introduction and highlights Andrew Ng's significant contributions to LangChain. It begins with an announcement about a fireside chat with Andrew, who is highly recognized for his courses on Coursera and work in deep learning. The narrator shares a personal anecdote of meeting Andrew over two years ago at a conference, marking the beginning of discussions about LangChain.
• 01:01 - 04:00: Discussion on Agenticness and Autonomy in AI The chapter opens with a discussion on agenticness and autonomy in AI, framed around a course on Lingchain and deep learning where Andrew played a significant role. It highlights his impact on the Lang Chain journey, mentioning a fireside chat with Andrew as the main event, illustrating the excitement and appreciation of his contributions by the community.
• 04:01 - 05:30: Building Agentic Systems and Challenges The chapter discusses the contributions and teaching excellence of Harrison and his team in the field of deep learning at deep learning.AI. Harrison's courses are highly rated based on metrics like the net promoter score. The recent Langrath course, in particular, provided the clearest explanation of various agent concepts, significantly aiding in understanding and building agentic systems. The focus is on the value and impact of these educational resources in comprehending complex concepts in AI.
• 05:31 - 08:30: Skills for Building AI Agents The chapter titled 'Skills for Building AI Agents' delves into the nuances and language used in the AI industry, particularly focusing on the concept of agenticness in applications. It explores the shift in perspective from labeling something strictly as an 'agent' to considering the degree of agentic behavior it exhibits. This discussion arises within the context of an AI conference, suggesting a reevaluation of how AI applications are categorized and conceptualized. The chapter encourages a deeper understanding of the roles and functions AI can perform, promoting a broader and more flexible approach to discussing AI capabilities.
• 08:31 - 14:00: Voice Applications and Their Potential In this chapter titled 'Voice Applications and Their Potential,' the conversation begins with a reflection on a discussion that took place a year and a half or two years ago. The speakers recall a conference they attended over a year ago where they were advocating for the importance of agents, which they believed people should pay attention to. They discuss how the term 'agentic' became popular, particularly among marketers who started using the term widely by last summer. The chapter explores the development and growing interest in voice applications and their potential impact.
• 14:01 - 20:00: MCP and Data Integration in AI The chapter discusses the debate over the definition of 'agent' in AI, emphasizing that instead of arguing whether something qualifies as an agent, it's more productive to consider the degrees of autonomy present in these systems. The suggestion is to focus on building systems with varying levels of autonomy without getting bogged down in terminology.
• 20:01 - 25:00: Agent-to-Agent Interactions and Vibe Coding The chapter discusses the interaction of 'agentic' systems, emphasizing the varying degrees of autonomy they possess. The speaker suggests simplifying discourse by classifying all such systems as 'agentic' to avoid unnecessary debates about terminology and focus on functionality. The team routinely employs advanced techniques, mentioning 'land graph', to address complex challenges efficiently, and the trend reflects a widespread adoption of similar sophisticated methods across the field.
• 25:01 - 27:00: Advice on AI Startups and Conclusion This chapter discusses the existence of linear workflows in many business processes, where tasks such as web searches, database compliance checks, and form filling are prevalent. It highlights the potential for AI startups to capitalize on automating these processes, thereby increasing efficiency. The chapter encourages entrepreneurs to explore simple automation opportunities in both existing and emerging business sectors. It concludes by stressing the ongoing value of AI in transforming typical business operations and the importance of continuing to adapt and innovate.

Andrew Ng: State of AI Agents | LangChain Interrupt Transcription

• 00:00 - 00:30 I'm really excited for this next section. Uh, so we'll be doing a fireside chat with Andrew Wing. And Andrew probably doesn't need any introduction to most folks here. I'm guessing a lot of people have taken some of his classes on Corsera or deep learning. Um, but Andrew's also been a big part of the lane chain story. So, I met I met Andrew a little over two years ago at at at a conference um when we started talking about Lingchain and he
• 00:30 - 01:00 he he graciously invited us to do a course on Lingchain with deep learning. I think it must have been the second or third one that they they they ever did and I know a lot of people here probably watched that course or got started on Lingchain because of that course. So, Andrew has been a huge part of the Lang Chain journey and I'm super excited to welcome him on stage for a fireside chat. So, let's welcome Andrew in. [Music] [Applause] Thanks for being
• 01:00 - 01:30 [Music] here. But, by the way, Harrison was really kind. Um, I think Harrison and his team has taught six short causes so far on deep learning.AI and our metrics by net promoter score and so on are that Harrison's causes are among our most highly rated. So, go take all of Harrison's courses. I think the recent Langrath one had the clearest explanation I have seen myself of a bunch of agent concepts. So, they they've definitely helped make our our
• 01:30 - 02:00 courses and explanations better. So, thank you guys for that as well. Um, you've obviously touched and thought about so many things in this industry, but one of one of your takes that I cite a lot and and and probably people have have heard me talk about is your take on kind of like talking about the agenticness of an application as opposed to whether something is an agent. And so, you know, as we're here now at an agent conference, maybe we should rename it to an agentic conference, but would you mind kind of like clarifying that?
• 02:00 - 02:30 and and I think it was like almost a year and a half two years ago that you said that and so I'm curious if things have changed in your mind since then. So I I remember actually Harris and I spoke both spoke at a conference like a year over a year ago and at that time I think both of us were trying to convince other people that agents are a thing and we should pay attention to it. And uh that was before maybe I think it was midsummer last year that a bunch of marketers got a hold of the agentic term and started sticking that sticker everywhere until last meeting. But to to
• 02:30 - 03:00 Herren's question, I think about a year and a half ago, I saw that a lot of people are arguing is this an agent, is this not there different, you know, arguments. Is it truly autonomous not to an agent? And I felt that um it was fine to have that argument, but that we would succeed better as a community if we just say that there degrees to which something is agentic. So um and then if we just say if you want to build an agentic system with a little bit of autonomy or a lot of autonomy is all fine. No need to spend time arguing is this truly an agent. Let's just call all
• 03:00 - 03:30 of these things agentic systems with different degrees of autonomy. Um, and I think that actually hopefully reduce the amount of time people wasted spend arguing of something as an agent and let's just call them all agentic and then and then get on with it. So I I think it's actually worked out. Where where on that spectrum of kind of like a little autonomy to a lot of autonomy do you see people building for these days? Yeah. So um my team routinely uses land graph for our hardest problems right with complex flows and so on. Um I'm also seeing tons
• 03:30 - 04:00 of business opportunities that frankly are fairly linear workflows or linear with just occasional side branches. So a lot of businesses there are opportunities where you know right now we have people um looking at a form on a website doing web search checking some of the database to see if there's a compliance issue or if there you know someone we shouldn't sell certain stuff to. And it's kind of a or take something copy paste it maybe do another web search paste in a different form. So in business processes there actually a lot of fairly linear workflows or linear
• 04:00 - 04:30 with very small loops and occasional branches usually conoting a failure because they reject this workflow. So um I see a lot of opportunity but but one one challenge I see businesses have is it's still pretty difficult to look at you know some stuff that's being done in your business and figure out how to turn into an agentic workflow. So what is the granularity with which you should break down this thing into micro tasks? uh and then you know after you build your initial prototype if it doesn't work
• 04:30 - 05:00 well enough which of these steps do you work on to improve the performance so I think that whole bag of skills on how to look at a bunch of stuff that people are doing break it into sequential steps u where the small number of branches how do you put in place evals you know all that that skill set is still far too rare I think and and then of course the much more complex agentic workflows I think you heard a bunch about uh with very complex loops um uh that that's very valuable as well. But I see much more in terms of number of opportunities
• 05:00 - 05:30 still about value. There's a lot of simpler workflows that I think are still being built out. Let's let's talk about some of those skills like so you've you've been doing deep learning. I think a lot of courses are in in in pursuit of helping people kind of like build agents. And so what are what are some of the skills that you think agent builders all across the spectrum should kind of like master and get started with? Boy, it's a good question. And I wish I knew the good answer to that. I've been thinking a lot about this actually recently. I think a lot of the challenge is um uh if you have a business process
• 05:30 - 06:00 workflow you often have people in compliance legal HR whatever doing these steps how do you um put in place the plumbing um either through a land graph type integration or we'll see if MCP helps with some of that too uh to ingest the data and then how do you prompt or process and do the multiple steps uh in order to build this end to end system and one thing I see a lot is um putting in place the right eval framework um uh
• 06:00 - 06:30 to not only understand the performance of the overall system but to trace the individual steps you can hone in on what's the one step that is broken what's the one prompt is broken to work on I find that a lot of teams probably wait longer than they should just using human evals where every time you change something you then sit there and look at a bunch of output receivers right I see most teams probably slower to put in place eval systematic evals is ideal. But I find that um having the right
• 06:30 - 07:00 instincts for what to do next in a project is is still really difficult, right? The skilled teams um the the the teams that are still learning these skills will often, you know, go down blind alleys, right? Where you spend like a few months trying to improve one component. The the more experienced team will say, you know what, I don't think this can ever be made to work. Uh so just don't just find a different way around this problem. I I I wish I knew I wish I I I knew more efficient ways to get this kind of almost tactile knowledge. often you're there you you
• 07:00 - 07:30 know look at the the output look at trace look at the lang output uh and you just got to make a decision right in minutes or hours on what to do next and that's still very difficult and and is this kind of like tactile knowledge mostly around LLMs and their limitations or more around like just the product framing of things and and and that skill of of of taking a job and breaking it down that's something that we're still getting accustomed to I think it's all of the above actually so I I feel like over the last couple years uh AI tool
• 07:30 - 08:00 companies have created an amazing set of AI tools and this includes tools like you know Lang graph uh but also uh how do you ideas like how do you think about rack how do you think about building chat bots uh many many different ways of approaching memory um I don't know what else uh how do you build evals how do you build guardrails but I feel like there's this you know wide sprawling array of really exciting tools one One picture I often have in my head is um if
• 08:00 - 08:30 all you have are, you know, purple Lego bricks, right? You can't build that much interesting stuff. But and and I think of these tools as being akin to Lego bricks, right? And the more tools you have is as if you don't just a purple Lego bricks, but a red one and a black one and a yellow one and a green one. And as you get more different colored and shaped Lego bricks, you can very quickly assemble them into really cool things. And so I think a lot of these tools like the ones I was rattling off as different types of Lego bricks and when you're trying to build something,
• 08:30 - 09:00 you know, sometimes you need that right squiggly weird shaped Lego brick and some people know it and can plug it in and just get the job done. But if you've never built evals of a certain type, then you know, then you could actually end up spending whatever three extra months doing something that someone else that's done that before could say, "Oh, well, we should just build evals this way. use the OM as a judge and well and just go through that process to get it done much faster. So one of the for unfortunate things about AI is not just one tool and in when I'm coding I just
• 09:00 - 09:30 use a whole bunch of different stuff right and I'm not a master of enough stuff myself but I've learned enough tools to assemble them quickly so um yeah and I think having that practice with different tools also helps with much faster decision- making on for and and oh one of the thing is it it also changes so for example because OM have been having longer and longer context memory um a lot of the best practices for rag from you know a year and a half ago or whatever are much less relevant today
• 09:30 - 10:00 right I remember Harrison was really early to a lot of these things like play the early lang chain rag frameworks recursive summarization and all that as OM context memories got longer now we just dump a lot more stuff into context it's not that rack has gone away but the hyperparameter tuning has gotten way easier there's a huge range of hyperparameters that work you know like just fine so so as OM keep progressing um the instincts we hold you know two years ago may or may not be relevant
• 10:00 - 10:30 anymore today. You you mentioned a lot of things that I wanna I want I want to talk about. So okay what are what are some of the Lego bricks that are maybe underrated right now that you would recommend that that people aren't talking about like eval you know we we had we had three people talk about evals and I think that's top of people's mind but what are what are some things that most people maybe haven't thought of or or haven't heard of yet that you would recommend them looking into? Good question. I don't know. Yeah. Uh maybe maybe I'm sure so even though people talk about evals, for some reason people don't do it. Uh near why why don't why
• 10:30 - 11:00 don't you think they do it? And I think I think it's because people often have um I saw a post on this on on eval writers blog. People think of writing evals as this huge thing that you have to do, right? Um, I think of evals as something I'm going to throw together really quickly, you know, in 20 minutes and it's not that good, but it starts to complement my human eyeball evals and and so what often happens, I'll build a system and there's one problem where I keep on getting regression. I thought I made it work, then it breaks. I thought
• 11:00 - 11:30 I made it work, then it breaks. Well, darn it, this is getting annoying. Then I code up a very simple eval maybe with, you know, five input examples and some very simple LMS judge to just check for this one regression, right? Did this one thing break? And then I'm not swapping out human evals for automated evals. I'm still looking at the output myself. But when I change something or run this evals to just, you know, take this burden something so I don't have to think about it. And then what happens is um just like the way we write English maybe once you have some slightly
• 11:30 - 12:00 helpful but clearly very broken uh imperfect eval then you'll start to go you know what I can improve my eval to make it better and I can improve it to make it better. So just as when we built a lot of applications we built some you know very quick and dirty thing that doesn't work and we incrementally make it better. For a lot of the way I built evals, I built really awful evals that barely helps. And then when you look at what it does, you go, you know what, this eval is broken. I could fix it. And you incrementally make it better. Uh, so
• 12:00 - 12:30 that's one thing. I'll mention one thing that people have talked a lot about, but I think is so underrated um is the voice stack. Um, it's one of the things that I'm actually very excited about voice applications. A lot of my friends are very excited about voice applications. I see a bunch of large enterprises really excited about voice applications. very large enterprise, very large use cases. For some reason, while there are some developers in this community doing voice, the amount of developer attention on voice stack applications there, there is some, right? It's not the people
• 12:30 - 13:00 ignored it, but that's one thing that feels much smaller than the um large enterprise uh uh importance I see as well as applications coming down the pipe. Um and not not all of this is the real-time voice API. It's not all uh speechtoech native uh audio in audio models. I find those models are very hard to control. Uh but when we use more of an agentic voice stack workflow which is we which find much more controllable um uh boy a fan working with a ton of teams on on voice stack stuff that some
• 13:00 - 13:30 of which hopefully will be announced in the near future. I'm seeing a lot of very exciting things. Um and then other things I think underrated one other one that maybe is not underrated but more business should do it. I think many of you have seen that um developers that use AI assistance in our coding is so much faster than developers that don't. Uh I've been uh it's been interesting to see how many companies CIOS and CTO's still have you know policies that don't let engineers use AI assisted coding. Um
• 13:30 - 14:00 I think maybe sometimes for good reasons but I think we have to get past that because frankly I don't know my teams and I I just hate to ever have to code again without AI assistance. So, but I think some businesses still need to get through that. Um, I think underrated is the idea that I I think everyone should learn to code. Uh, uh, one one fun fact about AI fund, um, everyone in AI fund, including, you know, the person that runs our front desk receptionist, uh, and my CFO and my, uh, at and the
• 14:00 - 14:30 general counsel, everyone in AI fund actually knows how to code. And um it's not that I want them to be software engineers, they're not. But in their respective job functions, many of them by learning a little bit about how to code are better able to tell a computer what they wanted to do. Um and so it's actually driving meaningful productivity improvements across all of these job functions that are not software engineering. So that that's been exciting as well. Talking about kind of like AI coding, how how what what tools
• 14:30 - 15:00 are you using for that personally? So we're working on some things that we've not yet announced. Um Oh, exciting. Yeah. So maybe I I I do use cursor winds surf uh um uh and some other things. All right, we'll come back to that later. Um talking about voice, if if people here want to get into voice and they're familiar with building kind of like agents with LLMs, how how similar is it?
• 15:00 - 15:30 Are there a lot of ideas that are transferable or or what's new? what will they have to learn? Yeah. So, it turns out um there are a lot of applications where I think voice is important. It creates certain interactions uh that um that are much more it turns out that uh it turns out from an application perspective um a input text prompt is kind of intimidating right for a lot of applications. Well, we can go to user and say tell me what you think. Here's a block of text prompt. Write a bunch of text for me. That's actually very
• 15:30 - 16:00 intimidating for users. And one of the problems with that is um people can use backspace and so you know people are just slower to respond via text whereas for voice you know time rolls forward you just have to keep talking you could change your mind you could actually say oh I changed my mind forget that earlier thing and our model is actually pretty good at dealing with it but I find that um there a lot of applications where the user friction to just getting them to use it is lower and we just say you know tell me what you think and then they they respond in voice Um so in terms of
• 16:00 - 16:30 voice the one biggest difference is uh in in terms of um engine requirements is latency because if you can if someone says something you kind of really want to respond in you know I don't know sub one second right less than 500 millconds is great but really ideally sub one second and with a lot of um agentic workflows that will run for many seconds. So when DBWI worked with real avatar to build an avatar of me, uh this is on a web page. You can talk to an avatar of me if you want. Um uh our
• 16:30 - 17:00 initial version had kind of five to nine seconds of latency and was and it's just a bad user experience. You say something, you know, 9 seconds of silence, then my avatar responds. But so we w up building things like um uh we call a pre-response. So just as you know, if you ask me a question, I might go, "Huh, that's interesting." Or, "Let me think about that." So, we prompted an ARM to basically do that to hide the latency. Um, and it actually seems to work great. And there all these other little tricks as well.
• 17:00 - 17:30 Turns out if you're building a voice um customer service chatbot, uh, it turns out that if you play background noise of a customer contact center instead of dead silence, people are much more accepting of that of that, you know, latency. So I find that there are a lot of these things that um uh that are different than a pure textbased LM but in applications where a voice-based modality lets a user be comfortable and just start talking. Uh I think it sometimes really reduces the user friction to you know getting some information out of them in a safe but I
• 17:30 - 18:00 think when we talk we don't feel like we need to deliver perfection as much as when we write. Um, so it's somehow easier for people to just start blurting out their ideas and change their mind and go back and forth and that lets us get the information from them that we need to help the user to move forward. So, huh, that's interesting. Yeah. Um, one of the one one of the new things that's out there and you mentioned briefly is MCP. How are you seeing that transform how people are
• 18:00 - 18:30 building apps, what types of apps they're building or what's generally happening in the ecosystem? Yeah, I think it's really exciting. Uh just this morning we released with anthropic uh short halls on MCP. Um uh I actually saw a lot of uh stuff you know on the interweb on MCP that I thought was quite confusing. So when we got together anthropy we said you know let's let's create a really good short course on MCP that explains it clearly. I think MCP is fantastic. I think it was a very clear market gap and you know that that OpenAI
• 18:30 - 19:00 adopted it also I think speaks to the importance of this. Um I think the MCP standard will continue to evolve right so for example so I I think many many of you know what MCP is right makes it much easier for agents primarily but frankly I think other types of software to plug into different types of data when I'm using OM myself or when I'm building applications frankly for a lot of us we spend so much time on the plumbing right so I I think for those of you from large
• 19:00 - 19:30 enterprises as well the AI especially you know reasoning models are like pretty darn intelligent They could do a lot of stuff when given the right context. But so I find that I spend my team spend a lot of time working on the plumbing on the data integrations to get the context of the OM to make it you know do something that often is pretty sensible when it has the right input context. So MCP I think is a fantastic way to try to standardize the interface to a lot of tools or API calls as well as data sources. Um it feels like uh it
• 19:30 - 20:00 feels a little bit like wild west. You know a lot of MCP servers you find in the internet do not work right and then the authentication systems are kind of you know even for the very large companies you know with MCP servers a little bit clunky. It's not clear if the authentication token totally works and expires. There's a lot of that going on. Um I think the MCP protocol itself is also early right now. MCP gives a long list of the resources available. you know, eventually I think we'll need some
• 20:00 - 20:30 more hierarchal discovery. Imagine you want to build something um I don't know even I don't know if there ever be an MCP uh interface to to a land graph but Lang graph has so many API calls you just can't have like a long list of everything under the sun for agent to sort out. So I think some sort of hierarchal discovery mechanism. So I think MCP is a really fantastic first step. Definitely encourage you to learn about it. it will make your life easier probably um if you find a good MCP server implementations to help some of the data integrations and I think I
• 20:30 - 21:00 think it'll be important this this idea of um when you have you know n models or n agents and m data sources it should not be n* m e effort to do all the integration should be n plus m and I think mcp is a is a fantastic first step it will need to evolve but it's a fantastic first step toward that type of data integration Another type of protocol that's seen less buzz than MCP is some of the agent to aagent stuff. And I remember when we
• 21:00 - 21:30 when we were at a conference a year or so ago, I think you were talking about multi-agent systems which this would kind of enable. So how how do you see some of the multi-agent or agentto agent stuff evolving? Yeah. So I think you know agent AI is still so early most of us right including me we struggle to even make our code work. And so making my co my agent work with someone else's agent, it feels like a two miracle, you know, requirement. Um, so I see that
• 21:30 - 22:00 when one team is building a multi- aent system, that often works because we build a bunch of agents, they go with themselves, we understand the protocols, blah, blah, that works. But to right now, at least at this moment in time, and maybe I'm off, the number of examples I'm seeing of when, you know, one team's agent or collection of agents successfully engages a totally different team's agent or collection of agents. I think we're a little bit early to that. I'm sure we'll get there, but I'm not personally seeing, you know, real success, huge success stories of that yet. I I'm not sure if yall seeing No, I
• 22:00 - 22:30 agree. It's it's I I think it's super early. I think if MCP is early, I think agent agent stuff is even earlier. Um, another thing that's kind of like top of people's mind right now is is kind of vibe coding. Um, and all of that, and you touched on it a little bit earlier with how people are using the these AI coding assistants, but how how do you think about vibe coding? Is that a different skill than before? What what what kind of purpose does that serve in in the world? Yeah. So, but I I think you know, many of us code with barely
• 22:30 - 23:00 looking at the code, right? I think it's a fantastic thing to be doing. Um, I think it's unfortunate that that call Vive coding because it's misleading a lot of people into thinking just go with the vibes, you know, accept this, reject that. And frankly, when I'm coding for a day, uh, uh, you know, with Vive coding or whatever with air coding assistance, I'm frankly exhausted by the end of the day. This is a deeply intellectual exercise. Um, and so I think the name is unfortunate, but the phenomenon is real and it's been taking off and is great. Um so I I I I I over the last year a few
• 23:00 - 23:30 people have been advising others to not learn to code on the basis that AI will automate coding. I think we look back at some of the worst career advice ever given um because over the last many decades as coding became easier uh more people started to code. So it it turns out, you know, when we went from punch cards to keyboards and terminals, right? Or when it it turns out, I actually found some very old articles when programming went from assembly language
• 23:30 - 24:00 to, you know, literally cobalt. There were people arguing back then, yep, we have cobalt, it's so easy, we don't need programmers anymore. And and obviously when it became easier, more people learn to code. And so with AI coding assistance, more a lot more people should code. Um uh but I think and it turns out one of the most important skills of the future for developers and non-developers is the ability to tell a computer exactly what you want so they will do it for you. And um I think understanding at some level which all of
• 24:00 - 24:30 you do I know but understanding at some level how a computer works lets you prompt or instruct a computer much more precisely which is why I still try to advise everyone to you know learn one programming language learn Python or something. Um, and then I I think so maybe some of you know this. I I personally am a you're much stronger Python developer than say JavaScript, right? Uh, but with um AI assisted coding, I now write a lot more JavaScript and TypeScript code than I
• 24:30 - 25:00 ever used to. But even when debugging, you know, JavaScript code that something else wrote for me that I didn't write with my own fingers, really understanding, you know, what are the error cases? What does this mean? that that's been really important for me to write debug my JavaScript code. So, if you if you don't like the name vibe coding, do you have a better name in mind? Oh, it's a good question. I should think about that. We'll we'll get back to you on that. That's a good question. Um, one one of the things that you announced recently is a new fund for AI
• 25:00 - 25:30 funds. So, congrats on that. Thank you. For people in the audience who are maybe thinking of starting a startup or looking into that, what advice would you have for them? So um AI funds a venture studio. So we built companies and we exclusively invest in companies that we co-ounded. So um I think in terms of looking back on AI funds, you know, lessons learned, the the number one I would say the number one predictor of a startup success is speed. Um I know we're in Silicon Valley, but I see a lot of
• 25:30 - 26:00 people that have never seen yet the speed with which a skilled team can execute. And if you've never seen it before, I know many of you have seen it, it's just so much faster than you know anything that um slower businesses know how to do. Uh and I think the number two predictor also very important is uh technical knowledge. It turns out if we look at the skills needed to build a startup, there's some things like how do you market, how do you sell, how do you price, you know, all that is important, but that knowledge has been around. So
• 26:00 - 26:30 it's a little bit more widespread. But the knowledge that's really rare is how does technology actually work because technology been evolving so quickly. So I I have deep respect for the go to market people. Pricing is hard, you know, marketing is hard, um positioning is hard, but that knowledge is more diffused and the most rare resource is someone that really understands how the technology works. So AI fund, we really like working with deeply technical people um that have good instincts or understands do this, don't do that. This
• 26:30 - 27:00 lets you go twice as fast. Um, and then I think uh uh a lot of the business stuff, you know, that knowledge is very important, but it's usually easier to figure out. All right, that's great advice for starting something. Um, we are going to wrap this up. We're going to go to a break now, but before we do, please join me in giving Andrew a big hand and thank you.