AI transcript
0:00:07 Autonomous agents will have huge impact and I think just the question is a matter of when so I started building
0:00:12 Tools that were based on code and it was so fast that I started prototyping just one or two things a week
0:00:16 Sometimes it was something we use at our VC firm and sometimes it was just a peer experiment
0:00:19 People started asking this seems like it’s more than an autonomous startup founder
0:00:24 So I was like I make the world a better place and I just started thinking of ways to make the world a better place
0:00:26 Of course, I went viral I went to got to a million views quickly
0:00:31 And I was like alright, so this is more than an autonomous startup founder and one of my friends had commented, bro
0:00:33 Did you just build a baby AGI?
0:00:39 Hey, you welcome to the next wave podcast I’m Matt Wolf
0:00:44 I’m here with my co-host Nathan Lanz and today we have an amazing guest for you today
0:00:49 We are talking to Yohei Nakajima and we’re gonna be talking all about AI agents
0:00:53 So thank you so much for joining us today. We’re really excited to have you on the show and
0:01:00 Excited to dive in with you. Thank you for having me guys. Nathan. Good to see you again. Yeah. Good to see you again as well
0:01:04 Yeah, I think it’d be kind of interesting to tell people I mean in an odd way, you know
0:01:09 Talking to you about baby AGI about a year ago now, which seems crazy
0:01:15 It’s been a year, but maybe you know a lot a lot of the audience probably doesn’t know what baby AGI is or how did you get that?
0:01:19 Crazy idea and how did a VC who can’t code create this thing that was super viral
0:01:26 Well, yeah, so the background is that I’m a VC and I like to build as partially a hobby and as a way to learn
0:01:31 I was more of a no-coder before I’ve always coded but not that good at it
0:01:35 So I’ve always been more of a no-coder, but when AI came out I could suddenly just have AI write the code
0:01:41 So I started building tools that were based on code and it was so fast that I started prototyping just one or two things a week
0:01:42 sometimes it was
0:01:45 something we use at our VC firm and sometimes it was just a pure experiment and
0:01:49 I think it was project number probably around 70 ish
0:01:55 I was looking at hustle GPT, which was when people were using chat GPT as a co-founder saying I have a thousand bucks
0:02:02 How do I grow it and then just doing whatever chat GPT told them to do and I thought that was a fascinating experiment and wanted to be
0:02:05 Involved but it was too busy and I thought can I automate the human part?
0:02:11 And so that led to a challenge over the weekend to prototype a autonomous startup founder
0:02:15 And so of course I went to chat GPT and said I want to prototype an autonomous founder
0:02:19 Here’s how I think it should work. Can you give me the code then after some iterations?
0:02:22 It started working so I posted a demo online and it went viral
0:02:27 I do got to a million views quickly then people started asking this seems like it’s more than an autonomous startup founder
0:02:32 So I was like I make the world a better place and I just started thinking of ways to make the world a better place
0:02:34 And of course at this point is just reasoning so it’s just text
0:02:38 But I said make as many paperclips as possible and the first thing I said was this is actually unsafe
0:02:40 So let’s first come up with safety protocols
0:02:45 And I was like alright, so this is more than an autonomous startup founder and one of my friends had commented bro
0:02:47 Did you build did you just build a baby AGI?
0:02:53 So that’s that’s where the name came from. I I was like I this is popular like academics are loving it
0:02:57 I need a paper so I went to chat GPT dropped in the code and said give me an academic paper
0:03:02 And I posted on my blog as my paper and then I had the I had a tweet thread about the paper
0:03:05 And then and then I open sourced the code and just each time it just went viral
0:03:09 So just a week of just like non-stop like notifications, you know
0:03:14 I learned about EACC that week and about tumors because they were both in my DMs like some people loved it
0:03:17 Some people hated it, but it was an absolutely wild week
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0:03:53 I love how that the paperclip maximizer scenario was one of your first tests that you would you wanted to see how
0:03:59 How it would go. Well, it’s funny is somebody tagged the odd and then he obviously he actually retweeted or commented on it
0:04:02 Which I think got the doomers noticing me
0:04:07 So what what’s the current state of baby AGI? Where does it stand today?
0:04:11 First after I released it some people that people jumped in started contributing. I’m not a developer
0:04:15 So I don’t know how to manage it. So I have some community members are willing to help support it
0:04:17 So they were managing the PRs for me
0:04:21 But there just wasn’t much support behind it and then I wanted I had some more ideas around the framework
0:04:24 And I realized I wasn’t actually good at reading other people’s code
0:04:27 So I just went back to my original and started modding it
0:04:30 And I went to my like group and I was like, what should I do with it?
0:04:33 They’re like, why don’t just create a classics folder and just stick it in there
0:04:40 So then there was like baby be AGI baby cat AGI where I basically used baby AGI as a way to introduce
0:04:45 Like design patterns that I thought were interesting and useful for building an autonomous asian
0:04:48 So I all the way got all the way up to baby fox AGI. It was it was an alphabetical order
0:04:56 And each time I introduced like self-improvement memory like new UX and then at some point I think around baby fox
0:05:00 I was like it’s the code’s getting a little clunky. I feel like I’m stuck a little bit
0:05:04 I need I need to solve something else and I kind of took a break and then as of a month ago
0:05:11 I just got back into rebuilding baby AGI from scratch. I was a new architecture. That’s fully graph based
0:05:16 So I just started tweeting about that because it’s it’s looking pretty good. But uh, that’s that’s the current state
0:05:22 Are there any specific models that you you really prefer to use because it seems like back when
0:05:29 Baby AGI first came out. I mean we had I don’t remember if gpt4 was out yet or if it was still gpt3.5
0:05:35 But then there’s just been an explosion of llms, right? You’ve got all the clod models. You’ve got google’s models now
0:05:43 You’ve got the llama models. Is there a model that you think would work best as an agent or do they all are they all kind of similar?
0:05:45 What are your thoughts there?
0:05:48 I think picking the right model is extremely helpful
0:05:56 In that and getting your asian to be more reliable faster cheaper. And then of course there’s fine-tuning and there’s a lot of techniques you can do
0:06:04 For me personally, I’m much more interested in ideating around new design patterns
0:06:08 And for that it’s easiest to just use the most powerful model
0:06:12 Um, even if it’s low just to like get it working
0:06:18 And then if it if that new design pattern works, then I’ll go then I in theory could go back and optimize the model to prompt
0:06:22 But oftentimes instead of doing that I just open source it and move on to the next design pattern
0:06:27 So I have so the answer is I have not really thought too much about the model that that being said in my newest
0:06:29 BB agi
0:06:33 I’m actually using a library called light lm that lets me switch between models easily
0:06:36 So I have started using clod
0:06:42 Opus and hyacinth. I mean I remember when we talked before I think one of the use cases we talked about was like research
0:06:47 Was like something would be like a straightforward first use case that like hey these agents
0:06:49 You could go off and talk to each other and kind of figure out like hey
0:06:54 Let’s go do some research whether it’s like you’re starting a new project or researching a new market or whatever
0:06:59 Uh, is that still like a one of the use cases? Yeah, that was one that was working pretty early on
0:07:03 I mean within a few months of BB agi launching there was a plenty of tools that I you know
0:07:09 I was using BB agi UI was one of the tools I was using all the time to pepper meetings a common one right at the right
0:07:13 After BB agi had a whole bunch of big company kind of execs reaching out wanting to pick my brain
0:07:18 I was like they might as well just just network and one of the the regular queries
0:07:21 I had was research this company
0:07:26 Their revenue drivers their cost drivers and their business units as separate web searches
0:07:32 Create a summary of that company based on that research and then give me three strategies
0:07:38 That leveraged large language models for each business unit that would be impactful to their bottom line
0:07:41 And then I would just let it run it would take like five to ten minutes
0:07:44 But then it would give me a report which she had somewhere between, you know
0:07:47 10 to 20 something
0:07:51 Ideas that I could then bring to the meeting and I could just skim through that in 10 seconds
0:07:55 And then cherry put three or four that I thought were good and and sound really smart in every meeting
0:07:59 So it made you sound smart. Was it actually like useful advice?
0:08:05 It was good advice like it was so what I what I what I found was helpful was it skipped that initial ideation
0:08:10 Right oftentimes when you’re presenting an idea, there’s the initial like
0:08:13 A diversion thinking where any every idea is a good idea
0:08:17 Let’s just research was collect all the ideas and then you switch to a conversion thinking mode where you’re like right now
0:08:20 Let’s pair it down to the best idea so that we can present it
0:08:24 Right, I found that these Asians or media in general, but especially if you have these Asians
0:08:28 You can just automate that diversion thinking parts you’re presented with all the ideas that have taken everything into account
0:08:32 And then you and then I think people are still probably better at that conversion thinking part
0:08:37 Which is like let’s really apply it to the current nuance and like pick the handful that are most relevant
0:08:42 Yes, I’m super powerful. I just wonder it feels like it hasn’t really taken off in terms of people using it though
0:08:45 Right, so like I wonder what the roadblock there in terms of uses
0:08:51 So I think there’s a learning curve for sure in using autonomous Asians like the one I just described, right?
0:08:54 Um, I was typing in. Hey, can you give me a report?
0:08:58 Look, can you research company x and then research their cost drivers and revenue drivers?
0:09:02 I think for most people they would just want to say, hey, I want strategy
0:09:07 I want LLM strategies for company x and and they want the LLM to think through what the search should be
0:09:10 To get that done for that, right?
0:09:13 And so, um, in a talk I did last week
0:09:19 I talked about the difference between kind of what I call handcrafted Asians where you’re handwriting each prompt and like api call
0:09:24 And you’re designing the full flow versus what I would call like a specialized general
0:09:28 Specialized autonomous Asian that’s kind of dynamically generating in some task list
0:09:31 I think when we think autonomous Asian we’re thinking of the latter one, right?
0:09:32 If it’s if it’s handcrafted
0:09:38 It’s almost just like a zappier workflow and it doesn’t feel autonomous if you’re writing it yourself in building these
0:09:41 More dynamic agents that generate its own task list
0:09:48 One of the things I found was having good task task flows to feed it as examples help this get better
0:09:50 today
0:09:55 The companies that are generating revenue are creating what I think handcrafted Asians where they’ve
0:10:00 Plugged all those in and and you know that the executive can just ask a very
0:10:04 A rough question and and the team has thought through how the AI should break it down
0:10:07 And I think where we’re going with it
0:10:12 Is that eventually these kind of skills will become more dynamic and we’ll get the AI to be better at dynamically
0:10:16 Connect creating these task lists, but we’re just not quite there yet where it’s good at it all the time
0:10:17 interesting
0:10:21 It seems like there’s some kind of opportunity there for like some kind of new kind of sass or something where
0:10:27 The company just subscribes to this agent service and there’s like I want an agent to do this
0:10:32 So like well, we have that like custom tailored for your needs kind of thing and like they just sign up for the service
0:10:34 And yeah, that’s interesting. You know what would be awesome is
0:10:38 Like there’s so many kind of like brainstorming strategies and whatnot, right?
0:10:42 There’s like coaches and there’s like different strategies on like how to brainstorm
0:10:46 It’d be great to have like a database of like something like that
0:10:50 But like tailored to specific tasks then like given like a rough task it like breaks down
0:10:52 Like a really good smart way that an expert would do it
0:10:57 And if there was a massive database that I could just subscribe to and just like pull task lists and you
0:11:01 And and then say take this task list and adopt it to our own that would be pretty valuable
0:11:04 Well after this episode goes out. I mean one might pop up
0:11:13 Or I’ll go build it together. Yeah, exactly. Exactly. So I was I was recently watching this this TED talk recently with
0:11:17 Mustafa Suleyman who’s now the CEO of Microsoft AI
0:11:23 He made a comment at the end during the q&a portion about how when it comes to AI one thing
0:11:26 He doesn’t think we should do is give AI autonomy
0:11:30 And so I just wanted to hear what your thoughts are on like that comment
0:11:35 I I don’t get it like we can’t like why are we not going to yeah
0:11:41 Like clearly someone’s going to so we should figure out like how collectively how we should do it and how to do it
0:11:46 Well and safely yeah, he seemed to be playing this like odd game of like trying to be on the EX side as well as the
0:11:47 Dumer side it was like this odd thing
0:11:52 We’re like he was basically like we should accelerate but we should not have it shouldn’t be like, you know improving itself
0:11:54 It shouldn’t be going off and doing its own thing
0:12:00 Yeah, I feel like most of the people I engaged with saw that and was like that’s that’s pretty much my task list right now
0:12:01 Like trying to get yeah
0:12:05 And well, I mean most of the stuff that’s that’s been out so far it’ll actually ask you
0:12:07 Do you want me to take the next step right?
0:12:12 It’ll it’ll essentially suggest the next step and then say do you want me to take this next step?
0:12:13 Yes or no
0:12:16 Right, so even even most of what people have played with so far
0:12:20 Has had that like extra layer of like are you sure you want to do this step?
0:12:22 Are you sure you want to do this step and
0:12:26 I mean, I don’t know if that’s really the future of agents
0:12:32 I feel like people want to be able to give it a prompt walk away go to dinner come back have their their tasks completed
0:12:36 But so far it seems like a lot of those safety measurements have been built in
0:12:41 Yeah, I think I mean, you know, I think we’re we’re still trying to figure out how to get all the this ai to work
0:12:45 How to work well, what are the correct again design patterns ux patterns?
0:12:48 I think we’re all it’s still trying to discover that some of those questions
0:12:51 We’re also also like to like reduce cost as well
0:12:56 Right, I think when we really when we first talked like because if it just keeps off running and it’s not waiting
0:12:57 It’s not asking you any questions
0:13:01 It could go off and just like run up your api bill for open ai or whatever to like
0:13:07 Thousands and thousands of dollars. Well, there was also a little bit of a tendency from time to time where it would get stuck in a loop
0:13:10 Right, it would sort of do a search not find what it was looking for do a search
0:13:16 And it would just kind of continuously loop and if you just walked away for hours you just rack up those api costs
0:13:20 We’ll be right back
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0:14:03 Listen to science of scaling wherever you get your podcasts
0:14:09 Yeah, I think when we talk about autonomous agents one thing that like
0:14:12 There’s qualitative decisions and quantitative decisions
0:14:16 And there’s types of decisions that we can trust a computer do and some that we can’t and I think it’s
0:14:21 It’s worth pointing out that I think like half the stock trades are autonomously done by like essentially what’s an autonomous agent
0:14:26 That’s only using quantifiable information and as we move to more qualified, you know, like
0:14:29 Qualitative stuff it gets it gets a little bit trickier
0:14:34 But I think it’s ultimately just like what are the tasks that we can we can automate today?
0:14:36 And if we can start relying on it we can slowly
0:14:40 Slowly figure out like, you know more and more things that can be automated
0:14:43 Yeah, so one thing I do want to bring up as well is that
0:14:48 You know, we’ve probably all seen devon right from I think cognition labs, right?
0:14:54 And this is an AI agent that can help with coding you give it a task that you want it to do it writes the code
0:14:59 I think it sort of double checks its own code iterates optimizes and sort of
0:15:04 As this loop until it sort of figures out the right code and completes the task
0:15:09 Well, they were just valued at two billion dollars and the company’s only six months old
0:15:15 And uh to me that’s that’s kind of crazy that a company that is that new
0:15:20 Got that kind of valuation. Do we see this is like the the sort of next
0:15:29 Venture capitalist push is this where a lot of the money is going to start to flow is some of these companies that are building these more autonomous agents
0:15:33 I mean, I think everybody agrees that
0:15:39 Autonomous agents when they work will have huge impact and I think just the question is a matter of when
0:15:46 And so it’s no surprise that I think some vcs right if there’s enough vcs some are going to think that it’s very soon
0:15:48 And if it is very soon, it’s worth making
0:15:54 A big bet that being said I say there’s all types of vcs and the types of vcs who make
0:15:59 Two billion valuation bets into a company six months old is pretty different from a from a vc
0:16:04 Who’s you know from a from an emerging manager micro vc, which is myself
0:16:08 And I’d say that an emerging manager micro vc probably has more in common with an AI builder at least from my perspective
0:16:12 There’s somebody rolling that so I can’t really speak to that specific valuation
0:16:15 I think it’s just a different type of company building totally makes sense
0:16:20 Yeah, I mean Devin seems amazing like the concept but like there’s also been a lot of controversy at least on twitter
0:16:24 People saying like is it even real here? Here’s my guess, right?
0:16:27 And I mentioned handcrafted agents versus like autonomous agents
0:16:33 If you think about developers and and like some of the devin skills they showed was like let’s run a code
0:16:38 If there’s an error, let’s look at the code and decide where to add a print statement
0:16:43 Let’s run the code again read that print statement and then based on the code the new print statement
0:16:46 Guess where we need to fix the code
0:16:48 Fix the code and then run it again
0:16:55 If it’s an error loop this this oh this to me feels like a handcrafted agent like every developer has done that themselves
0:17:00 So it’s it’s it’s a very like it’s not like a you don’t need an AI to make up that workflow
0:17:06 And there’s probably a couple other ones that are common enough that some team like Devin should probably build that tool in a
0:17:14 Modular way where each step is a modular skill so that the AI can in theory dynamically pick and choose and combine them together
0:17:19 But at least based on their demo they’ve gotten it to they’ve probably we can teach at these handcrafted agent skills
0:17:24 Like if if you’re doing this like chain tasks got this way if you teach it that then it’ll do it that way
0:17:26 But as people start using it
0:17:30 There’s going to be edge cases where there’s like a new flow that the team hadn’t thought of or that Devin hadn’t run into
0:17:32 Then it might not be as good and stable
0:17:39 So so getting those stable is is going to come through people using it figuring out where it doesn’t work and then maybe even like
0:17:44 Describe you know describing it to Devin so it does it right and stores it in this memory
0:17:46 So that the next time it’s asked you can do it that way better
0:17:48 So that’s that’s my kind of gut guess on where they’re at
0:17:54 Which would be which would explain why their demos work why people who are using it are getting good results for the for the type of tasks that
0:17:58 Um, they’ve gotten a good ad but they’re probably still working on some of the edge cases would be my guess
0:18:05 It was just the reason I wouldn’t go public yet. Are there any like major technical hurdles that you’re seeing to
0:18:08 To getting to the point where I can just say hey
0:18:10 Go build me this website
0:18:15 And then I walk away go to dinner come back and then the website’s just built for me everything’s done
0:18:21 I mean we we have really powerful large language models most of the large language models now can work with tools
0:18:23 They can interface with apis things like that
0:18:28 What do you think the holdup is from from where we are now to getting to that?
0:18:33 So I think part of it is those kind of people’s expectation when you said go build me a website
0:18:37 right if it’s if I wanted to build a tool that can build you a
0:18:42 General personal website maybe with a blog I could probably build that website and build that tool
0:18:46 Right if I dedicate myself to making it work really well the challenge is if I put it out into the market
0:18:50 Someone’s going to go say build me a sass website or build me a game website
0:18:53 And suddenly this tool doesn’t work because it didn’t create that website
0:18:57 And so how do you present that right? Do you go really really niche in which case?
0:19:00 No one really cares or do you present the big picture that you want to build in which case?
0:19:06 There’s going to be edge cases that don’t work and I think that’s one of the big big blockers is kind of figuring out
0:19:08 There are probably agents that are good at certain things
0:19:10 But maybe they don’t go viral because it’s such a niche group
0:19:14 And then the ones that promise too much are going to hit the market and they’re going to run into edge cases
0:19:17 And people are going to say this doesn’t work and I think that’s one of the holdups
0:19:23 But I think within that what you’re finding is that some people who discover the right tool for themselves are getting a lot out of it
0:19:28 Or the people who can see past the mistakes and are willing to just treat it like a a new employee
0:19:32 That’s just much cheaper like those people I think are getting value out of these
0:19:35 Asian type tools and workflows today
0:19:37 Yeah, that makes sense
0:19:42 It’s it’s it’s the little like nuances of exactly what you’re looking for and everybody that uses the tool
0:19:46 Is going to have slightly different nuances to what they need out of it
0:19:50 I’m wondering like whenever we get to the point where we can not only have it make a website
0:19:55 But like come up with the concepts like I imagine like a future product lab where you know
0:20:01 You’re like I wanted to run experiments in this sector and like the agent actually goes off and like comes up with like the plan and like
0:20:06 Yeah, come, you know, give me five landing pages for different angles on this, you know that we could attack and then
0:20:13 Um, I wonder how many years off we are from that and also just like how much that changes business when you can just like ramp out
0:20:19 New experiments like that again going back to like if it’s a use case that a lot of people want I don’t think we’re that far away from it
0:20:27 Right. I use I use an AI diligence tool that will spit out a 30 50 page report on any given company or industry within 30 minutes
0:20:31 It’s a very specific, you know, what I call handcrafted workflow
0:20:35 Get a ton of value out of it and it makes sense to build that tool because it’s a workflow that
0:20:38 Every investor every consultant goes through it
0:20:41 Which is going and researching the company the news the data and all that kind of stuff
0:20:45 one of the my favorite stuff that came out after baby Asia was auto rpg, which was a
0:20:49 Which was using baby asiai’s framework to auto design
0:20:56 A full game level the storyline for that game level the characters the the their script
0:20:59 And then to a point where they can just upload it and make that
0:21:04 Level playable by the game again. It was just a rough demo and I think they’re still cleaning up the actual back end of it
0:21:06 But I mean that’s pretty cool stuff
0:21:12 Yeah, one of the things I want to ask you about was something you mentioned in your ted talk that I found really fascinating and you made a
0:21:13 comment about how
0:21:18 With AI we essentially have access to the sum of human consciousness
0:21:25 Or the ability to chat with the sum of human consciousness and i’m paraphrasing you probably said it way more eloquently than I did
0:21:29 But I want to kind of talk about that statement and what you meant by that
0:21:35 Yeah, I mean if you think about these massive models, they are trained on
0:21:37 massive corpus of
0:21:39 text
0:21:45 Written by millions of people it’s their words and again, it’s a filtered list of words because it’s only stuff they publish
0:21:49 But when you ask a model a question they’re answering that based on
0:21:56 The writing of millions of people and so I think what I said was that it’s probably the closest thing we have to chatting with our collective conscious
0:22:03 Um, which I think is just a really fun way to like think of the tool and and play with the tool and ask questions against the tool
0:22:09 Actually, one of the first experiments I did early on this was in like the Da Vinci days was I actually had it write a 42 page book
0:22:14 On on on like what it means to be human and I talked to like family and why not?
0:22:17 I just I was just like I want to know what like the average
0:22:22 Like two pages about like what family means to a human is and how did that book come out?
0:22:27 It was great. It’s like a google doc. You know, I just I just like made it for myself as like an experiment
0:22:32 But it was like a it goes through like family death life, you know all those like basic human concepts
0:22:36 It was a there’s a book I loved called Tuesdays with Mori, which was actually the inspiration for it
0:22:38 But uh, it goes through some of the key aspects of life
0:22:42 But I basically just did that with an AI instead of with a wise man named worry
0:22:46 Now, I don’t know if this is a rabbit hole. We want to go down or not, but I’ll bring it up anyway
0:22:52 So, you know, you know continually along those lines when a lot of people are out there talking about like, oh, these
0:22:56 These large language models are biased in one way or the other
0:22:59 does that sort of mean that like
0:23:02 The biases is sort of what the majority of humans
0:23:07 You know believe I would I that I feel like that’s a dangerous jump. I feel like
0:23:13 It’s it’s fair to say that the bias of like human society is probably in there somewhere
0:23:16 But then there’s also the bias layer of who has access to the internet
0:23:21 Which has ties to wealth and bias layer layer of who’s willing to who has access to like
0:23:26 Technical knowledge, right because people who had access to blogging earlier. There’s more content out there
0:23:31 So so there’s there’s a layer of bias beyond like what the biases of the world ingrained into the model
0:23:35 So so I would I would say that as long as you’re making that clear
0:23:39 I think that’s fair to say that there that that bias is reflected in the model to some extent
0:23:42 Yeah, that’s very cool. And I just I love that
0:23:45 I don’t know if you call it an analogy or that way of thinking, right?
0:23:52 I love that idea of we’re chatting with the sort of sum of human consciousness when we’re talking with these AI bots because I mean
0:23:54 they’re trained on, you know
0:24:02 trillions of bits of data from the, you know, all of humans putting the this information on the internet and
0:24:04 Just looking at it from that way
0:24:10 When I was watching your TED talk that one statement just like flipped a switch in my brain of like, oh man, that that is so true
0:24:14 I remember when I was a kid and I don’t think this is even true
0:24:18 But actually somebody told me that like every, you know, anything with mass has gravity
0:24:24 Right, so that if you if you wave your hand that like in a very very minuscule way a star
0:24:29 Is like moving slightly because of your hand movement. Now, I think it’s actually I looked it up
0:24:35 I think it’s actually false because there’s like some diminishing so but I think theoretically speaking that that idea of like
0:24:40 Be having a minuscule aspect on something extremely large and far away has always fascinated me
0:24:45 And I think people I think it’s fascinating to think about like all the people who published on the internet, right?
0:24:50 Some of their ideas and words, right in a very indirect way
0:24:56 Are now we’re consuming it and there’s there’s there’s like an immortality to that that that just fascinates me
0:24:59 Well, yeah, and even hundreds of years old, right?
0:25:04 I mean, it’s it’s trained on data from the the bible and the karan and the all of it, right?
0:25:06 So like hundreds thousands of years old as well
0:25:10 And it’ll be trained on this podcast that we’re doing right now, which is kind of wild, right?
0:25:13 We become part of this infinite collection of all of humanity, right?
0:25:16 Like all of the stories of humanity. Yeah, this is this is a whole rabbit hole
0:25:21 I do think I’ve been talking more about autonomous agents recently just because of the talk and a couple panels
0:25:24 But the the reaction from enterprises are very interesting, right?
0:25:26 You know, there’s the ethics job displacement
0:25:31 Like those are all discussions that are happening around autonomous agents like how and when do you replace
0:25:37 Tasks from humans doing it to autonomously, you know, what is the long long-term impact of it?
0:25:43 And then one of the crazier ideas that I got some reaction of was the idea of like an autonomous CEO at some point, right?
0:25:51 A little 24/7 can talk to all employees in parallel has access to all company data and has at least consistent and transparent bias
0:25:52 Yeah
0:25:56 I’m curious how you respond to people when when the the sort of job displacement question comes up
0:25:59 You know being somebody that creates a lot of content around AI
0:26:04 In my comments and things like that. I you know, I get love but I also get a lot of hate from people
0:26:07 They’re like, oh, you’re part of the problem. You’re part you’re out there sort of
0:26:11 Spreading the message of the thing that’s going to take the jobs from people
0:26:18 What sort of response do you find yourself giving people when they are asking about like well, is what you’re talking about going to take our jobs?
0:26:24 Looking forward I can see the argument as to why autonomous agents will
0:26:26 Displace jobs and I can see why
0:26:31 There’s a reason to fear that potentially I do but looking back on it
0:26:37 I also know that many smart people had that exact same fear about almost every single technology that came
0:26:41 And all those technologies ended up being net job creators
0:26:45 So I’m humble enough to assume that I’m not like necessarily
0:26:48 You know, I’m just as likely to be as long as all the people in the past
0:26:54 And and if I’m going to go with the you know, just what I’ve seen in the past and if I’m looking for my third party
0:26:59 I would assume that the next technology would be a net job creation is engine again
0:27:03 Yeah, I mean when I just look at you know, things like um, you know AI art for instance, right?
0:27:09 Everybody’s worried about the fact that AI can generate art, but it feels like the same arguments that happened when photo shop came out
0:27:13 And most likely the same arguments that came out when cameras were invented
0:27:20 You know and you can sort of go back computers cars printing press radio tv, right every single time like something
0:27:24 You know and then yes, there’s there’s pros and cons of every every single technology
0:27:28 And each one of those created new careers that didn’t exist prior to them
0:27:30 exactly
0:27:32 Yeah, it feels like people will get better jobs as well as my opinion
0:27:36 Like it’ll a lot of the like tedious parts of work the work that people don’t actually like doing
0:27:40 That’s like soul draining kind of work a lot of that will get replaced
0:27:44 And so I I think though with this new technology people have more free time and they can actually explore things
0:27:49 They actually enjoy doing for work versus like oh, yeah, I need to enter this data into this form
0:27:55 Like yeah, I feel like AI is already sort of replaced data entry and I know for me it has like I don’t manually enter stuff
0:28:00 And just spreadsheets anymore because AI can do it for me. Just as good as I can. Yeah, but it didn’t take anybody’s jobs
0:28:04 It just made me more efficient. Yeah, I think jobs will change right?
0:28:08 I mean if you look at a super small scale like our venture fund right as I had mentioned
0:28:11 I have this diligence tool that that does all this, you know research for me
0:28:16 But it doesn’t mean that like I’m never gonna hire people into my fund. It just means that when I hire
0:28:21 I’m not going to be looking for that skill set. I don’t need somebody who who wants to spend time
0:28:24 You know searching the web and copy pasting stuff into a document
0:28:29 I want someone who I’m okay with someone who sucks at that because what I want is different skill sets
0:28:33 And so I think the jobs do change around what AI can automate
0:28:36 Uh, but that’s always been true in any technology, right?
0:28:42 If there’s certain tasks that technology will replace then just figure out what other tasks will support will be needed to support that
0:28:47 I want to talk a little bit about like the the venture capital world for a minute too
0:28:52 Specifically as it relates to AI right because there’s this narrative. We had Greg Eisenberg on the show
0:28:55 We talked to him about this exact concept
0:29:02 but there’s this narrative that sass companies don’t really have a moat because almost all of these platforms are either built on
0:29:06 existing apis or open source platforms and
0:29:14 Anybody could go and use AI to build a sass that connects to these apis or these open source platforms
0:29:20 Even if you don’t know code, I mean, you know, what what you’ve managed to do is is kind of proof of that concept
0:29:27 But so like I look at some of these companies that like when AI was really bubbling up at the end of 2022 beginning of 2023
0:29:31 That seems to be when the sort of big like boom happened where the
0:29:35 The world consciousness started to realize that AI is is getting really popular, right?
0:29:39 and we saw platforms pop up like tools that were like, hey, we can um
0:29:45 Summarize PDFs for you or chat with your PDF or tools like that
0:29:48 Some of those companies raised capital early on when they first popped up
0:29:54 But then you know fast forward a year and now you can just do that directly inside of chat gpt or clod
0:29:57 And who’s going to go buy a chat with pdf software?
0:30:04 So when you’re looking at like potential companies to invest in are there things that you’re looking at
0:30:10 That might future-proof the companies like what what sort of criteria are you looking at?
0:30:15 I think there’s two parts. I mean being an early stage investor, right investing at precede
0:30:20 I’m comfortable with the idea that my companies might pivot. So at precede, I mean team comes first, right?
0:30:24 If I’m talking to a team that’s closely following what’s happening and is able to like adapt quickly
0:30:26 Like that’s that that can be enough
0:30:30 But kind of thematically, which I think is more the question around for us at least
0:30:32 We do try to get deep into
0:30:40 Like both the building and testing of tools and talking to customers to to build conviction around the direction that we think
0:30:45 AI is going to go right and looking back on how the models have advanced in the past to get a sense of where we think
0:30:47 The models are going to go
0:30:48 And it’s through that
0:30:53 We have conviction in certain types of plays. We feel like this is going to be a good play versus
0:30:59 Conviction that certain plays won’t work and then and then some a lot of plays are just like we’re not sure what the right play is here
0:31:04 And then as an investor we tend to invest in the areas where like I have strong conviction in this play
0:31:07 And there’s plenty of other really interesting ideas where I’m just like not
0:31:13 Don’t really have conviction and and that just market direction that I might pause and like watch a little bit make sense
0:31:16 I mean, I’ve been you know
0:31:18 It’s slightly connected. I’ve been I’ve been wondering like with the vc
0:31:21 Like how will it look in five or ten years because it’ll probably be really different
0:31:27 I mean, I’m interested to see what happens in terms of like lowering the cost of company starting and what those teams look like
0:31:32 I mean, that’s been a consistent trend right over over decades where the cost of starting companies continue to get lower
0:31:36 So so I hope to see that of what we’ve seen is the vc landscape change as well
0:31:40 Right, we saw vcs becoming smaller and smaller and more micro and micro
0:31:44 At the same time we did we have seen some shifts around
0:31:49 crowdfunding around, you know syndicates like, you know rolling funds
0:31:53 So we are starting to see some innovation over the last I mean less than a half decade
0:31:56 I’d say is where we’ve started to see a lot more innovation around vc
0:31:58 So it’ll be interesting to see where it goes
0:32:05 Are there are there any like tools or research or anything that you know has come on to your radar that’s really exciting you’re right?
0:32:10 Yeah, that’s a good question. I feel like that’s uh, that’s this constantly. Yes. Um
0:32:16 I’m curious to learn more about like the what Sakana is doing by combining models that kind of modular approach of
0:32:22 Of mixing and matching models and being able to use them as as different models. I think that’s a fascinating area
0:32:29 Um right now that the overarching trend seems to be like let’s throw more money and more power to get better performance
0:32:34 But I think if we can start playing around with more modular models and start combining them
0:32:38 We can start seeing different economics and plays there. So I’m really fascinated by that
0:32:40 Yeah, something like that seems great for agents, right?
0:32:44 It’s like depending on what use case you can change out the model like okay right now
0:32:47 I don’t need the smartest one and I don’t need to spend a fortune on it
0:32:53 Another area is web 3 and ai. I feel like I’ve seen a lot more that’s been really bubbling up with web 3 coming back everything from
0:32:56 You know, uh data
0:33:03 You know data marketplaces on web 3 ip plays leveraging kind of ai first ip plays on web 3
0:33:08 As well as agentic web 3 right help agents pay help agents have an identity
0:33:10 and this might be
0:33:14 Uh more of a rabbit hole that we want to go down today, but what what’s your definition of web 3?
0:33:20 I mean, I think the simplest is is one is technologies that leverage the blockchain. I might I might I may be on
0:33:26 A smaller group that kind of sees web 3 as almost a philosophy and ethos
0:33:28 But but I actually think most people would disagree with me there
0:33:34 Yeah, I mean, I’d probably agree with you like when it when it comes to the whole like blockchain sort of crypto discussion
0:33:40 I think the technology the sort of distributed ledger concept. I think is really really powerful
0:33:47 I think a lot of drifters and bad actors and people trying to really push the make money narrative has
0:33:52 Really screwed up the the the sort of web 3 blockchain concept
0:33:55 But I do think the underlying tech is really powerful and I do think it’s the future
0:33:59 Yeah, and I always thought it was kind of wild to like, you know, it’s it’s a great marketing
0:34:05 I’m sure web 3 but it’s like when web 2 happened which was like all the big social companies that term kind of came out
0:34:08 You know, I think sarah lacey and a few others started spreading that term
0:34:11 Uh, and and that was like after the big social companies were already working
0:34:16 They never like really dramatically changed the internet like people were using dig
0:34:19 They were using facebook they were using myspace and all this stuff, right?
0:34:23 And then that term kind of came out versus web 3 was like, here’s this cool concept
0:34:27 It’s got it’s got branding issues for sure. Yeah. Yeah
0:34:34 So, you know as we kind of wrap up here a large portion of the audience that listens to this show is
0:34:38 You know small medium businesses is a hub spot podcast
0:34:42 What advice would you give to business owners that are seeing AI?
0:34:46 They’re seeing all of this stuff bubble up. Maybe they haven’t experimented with AI yet
0:34:52 What would you tell them to do is sort of a first step to get their toes wet with AI or their business?
0:34:58 I mean, I think for anybody who’s played with it will tell you that the first couple of times they played with something like chat
0:35:01 Upt, they were pretty blown away by his capabilities
0:35:06 I think it’s pretty low hanging fruit to start there if you haven’t like pull up chat Upt
0:35:09 I think there’s a free version some people tell you oh the free version is not good enough
0:35:11 But realistically just start playing with it, right?
0:35:15 And I think for most people when you when you ask it a couple of questions when you’re blown away by it
0:35:19 You’ll start thinking of hey, how can I maybe use this at my work?
0:35:22 And I’ll ask you a couple questions that’s relevant to your work
0:35:26 And again, my guess is that like a majority of people we’ve blown away by his capabilities there
0:35:32 And hopefully just by doing that a little bit you’ll start thinking of ideas on like how you can start using it
0:35:39 And initially I just nudge people to like, you know use chat Upt or any pick an AI tool and just start playing around with it
0:35:45 So that you kind of get into the habit for people who are more creative pick a pick an image generation tool like mid-journey
0:35:48 Stable diffusion or if you’re a music person then pick sueno or audio
0:35:52 Then just pick an AI tool and just start playing with it and just get into that habit
0:35:57 And then once you do I think the ideals will start coming and just like and just know that like, you know
0:35:58 Some of the stuff you’re going to try won’t work
0:36:02 But like if you keep trying stuff, they’ll keep discovering new cool ways to like use it
0:36:05 Well, very cool. Thank you so much for for joining us on this show today
0:36:09 Where should people go learn more about you go check you out follow you on x
0:36:12 You know, what’s what’s the best place to learn more about you and what you’re up to?
0:36:16 Probably the uh, the the place I post most is on x slash twitter
0:36:20 I’m constantly posting about AI discoveries rvc fund
0:36:25 Um for a lot of the stuff I build I do have a build in public log at yohey.me where you can see
0:36:30 Most of my experiments some of them are have tutorials some of them are just pure experiments in place
0:36:36 But uh, those two are the probably two most interesting places. Amazing. Awesome. Well, thank you so much for joining us today
0:36:38 This has been such a fun and fascinating conversation
0:36:42 I’m so glad that you were able to make the time to do this with us today
0:36:45 And I couldn’t appreciate you enough for doing it. So thanks again. Thank you for having me
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0:37:09 [MUSIC]
0:00:12 Tools that were based on code and it was so fast that I started prototyping just one or two things a week
0:00:16 Sometimes it was something we use at our VC firm and sometimes it was just a peer experiment
0:00:19 People started asking this seems like it’s more than an autonomous startup founder
0:00:24 So I was like I make the world a better place and I just started thinking of ways to make the world a better place
0:00:26 Of course, I went viral I went to got to a million views quickly
0:00:31 And I was like alright, so this is more than an autonomous startup founder and one of my friends had commented, bro
0:00:33 Did you just build a baby AGI?
0:00:39 Hey, you welcome to the next wave podcast I’m Matt Wolf
0:00:44 I’m here with my co-host Nathan Lanz and today we have an amazing guest for you today
0:00:49 We are talking to Yohei Nakajima and we’re gonna be talking all about AI agents
0:00:53 So thank you so much for joining us today. We’re really excited to have you on the show and
0:01:00 Excited to dive in with you. Thank you for having me guys. Nathan. Good to see you again. Yeah. Good to see you again as well
0:01:04 Yeah, I think it’d be kind of interesting to tell people I mean in an odd way, you know
0:01:09 Talking to you about baby AGI about a year ago now, which seems crazy
0:01:15 It’s been a year, but maybe you know a lot a lot of the audience probably doesn’t know what baby AGI is or how did you get that?
0:01:19 Crazy idea and how did a VC who can’t code create this thing that was super viral
0:01:26 Well, yeah, so the background is that I’m a VC and I like to build as partially a hobby and as a way to learn
0:01:31 I was more of a no-coder before I’ve always coded but not that good at it
0:01:35 So I’ve always been more of a no-coder, but when AI came out I could suddenly just have AI write the code
0:01:41 So I started building tools that were based on code and it was so fast that I started prototyping just one or two things a week
0:01:42 sometimes it was
0:01:45 something we use at our VC firm and sometimes it was just a pure experiment and
0:01:49 I think it was project number probably around 70 ish
0:01:55 I was looking at hustle GPT, which was when people were using chat GPT as a co-founder saying I have a thousand bucks
0:02:02 How do I grow it and then just doing whatever chat GPT told them to do and I thought that was a fascinating experiment and wanted to be
0:02:05 Involved but it was too busy and I thought can I automate the human part?
0:02:11 And so that led to a challenge over the weekend to prototype a autonomous startup founder
0:02:15 And so of course I went to chat GPT and said I want to prototype an autonomous founder
0:02:19 Here’s how I think it should work. Can you give me the code then after some iterations?
0:02:22 It started working so I posted a demo online and it went viral
0:02:27 I do got to a million views quickly then people started asking this seems like it’s more than an autonomous startup founder
0:02:32 So I was like I make the world a better place and I just started thinking of ways to make the world a better place
0:02:34 And of course at this point is just reasoning so it’s just text
0:02:38 But I said make as many paperclips as possible and the first thing I said was this is actually unsafe
0:02:40 So let’s first come up with safety protocols
0:02:45 And I was like alright, so this is more than an autonomous startup founder and one of my friends had commented bro
0:02:47 Did you build did you just build a baby AGI?
0:02:53 So that’s that’s where the name came from. I I was like I this is popular like academics are loving it
0:02:57 I need a paper so I went to chat GPT dropped in the code and said give me an academic paper
0:03:02 And I posted on my blog as my paper and then I had the I had a tweet thread about the paper
0:03:05 And then and then I open sourced the code and just each time it just went viral
0:03:09 So just a week of just like non-stop like notifications, you know
0:03:14 I learned about EACC that week and about tumors because they were both in my DMs like some people loved it
0:03:17 Some people hated it, but it was an absolutely wild week
0:03:24 When all your marketing team does is put out fires they burn out but with HubSpot
0:03:30 They can achieve their best results without the stress tap into HubSpot’s collection of AI tools
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0:03:53 I love how that the paperclip maximizer scenario was one of your first tests that you would you wanted to see how
0:03:59 How it would go. Well, it’s funny is somebody tagged the odd and then he obviously he actually retweeted or commented on it
0:04:02 Which I think got the doomers noticing me
0:04:07 So what what’s the current state of baby AGI? Where does it stand today?
0:04:11 First after I released it some people that people jumped in started contributing. I’m not a developer
0:04:15 So I don’t know how to manage it. So I have some community members are willing to help support it
0:04:17 So they were managing the PRs for me
0:04:21 But there just wasn’t much support behind it and then I wanted I had some more ideas around the framework
0:04:24 And I realized I wasn’t actually good at reading other people’s code
0:04:27 So I just went back to my original and started modding it
0:04:30 And I went to my like group and I was like, what should I do with it?
0:04:33 They’re like, why don’t just create a classics folder and just stick it in there
0:04:40 So then there was like baby be AGI baby cat AGI where I basically used baby AGI as a way to introduce
0:04:45 Like design patterns that I thought were interesting and useful for building an autonomous asian
0:04:48 So I all the way got all the way up to baby fox AGI. It was it was an alphabetical order
0:04:56 And each time I introduced like self-improvement memory like new UX and then at some point I think around baby fox
0:05:00 I was like it’s the code’s getting a little clunky. I feel like I’m stuck a little bit
0:05:04 I need I need to solve something else and I kind of took a break and then as of a month ago
0:05:11 I just got back into rebuilding baby AGI from scratch. I was a new architecture. That’s fully graph based
0:05:16 So I just started tweeting about that because it’s it’s looking pretty good. But uh, that’s that’s the current state
0:05:22 Are there any specific models that you you really prefer to use because it seems like back when
0:05:29 Baby AGI first came out. I mean we had I don’t remember if gpt4 was out yet or if it was still gpt3.5
0:05:35 But then there’s just been an explosion of llms, right? You’ve got all the clod models. You’ve got google’s models now
0:05:43 You’ve got the llama models. Is there a model that you think would work best as an agent or do they all are they all kind of similar?
0:05:45 What are your thoughts there?
0:05:48 I think picking the right model is extremely helpful
0:05:56 In that and getting your asian to be more reliable faster cheaper. And then of course there’s fine-tuning and there’s a lot of techniques you can do
0:06:04 For me personally, I’m much more interested in ideating around new design patterns
0:06:08 And for that it’s easiest to just use the most powerful model
0:06:12 Um, even if it’s low just to like get it working
0:06:18 And then if it if that new design pattern works, then I’ll go then I in theory could go back and optimize the model to prompt
0:06:22 But oftentimes instead of doing that I just open source it and move on to the next design pattern
0:06:27 So I have so the answer is I have not really thought too much about the model that that being said in my newest
0:06:29 BB agi
0:06:33 I’m actually using a library called light lm that lets me switch between models easily
0:06:36 So I have started using clod
0:06:42 Opus and hyacinth. I mean I remember when we talked before I think one of the use cases we talked about was like research
0:06:47 Was like something would be like a straightforward first use case that like hey these agents
0:06:49 You could go off and talk to each other and kind of figure out like hey
0:06:54 Let’s go do some research whether it’s like you’re starting a new project or researching a new market or whatever
0:06:59 Uh, is that still like a one of the use cases? Yeah, that was one that was working pretty early on
0:07:03 I mean within a few months of BB agi launching there was a plenty of tools that I you know
0:07:09 I was using BB agi UI was one of the tools I was using all the time to pepper meetings a common one right at the right
0:07:13 After BB agi had a whole bunch of big company kind of execs reaching out wanting to pick my brain
0:07:18 I was like they might as well just just network and one of the the regular queries
0:07:21 I had was research this company
0:07:26 Their revenue drivers their cost drivers and their business units as separate web searches
0:07:32 Create a summary of that company based on that research and then give me three strategies
0:07:38 That leveraged large language models for each business unit that would be impactful to their bottom line
0:07:41 And then I would just let it run it would take like five to ten minutes
0:07:44 But then it would give me a report which she had somewhere between, you know
0:07:47 10 to 20 something
0:07:51 Ideas that I could then bring to the meeting and I could just skim through that in 10 seconds
0:07:55 And then cherry put three or four that I thought were good and and sound really smart in every meeting
0:07:59 So it made you sound smart. Was it actually like useful advice?
0:08:05 It was good advice like it was so what I what I what I found was helpful was it skipped that initial ideation
0:08:10 Right oftentimes when you’re presenting an idea, there’s the initial like
0:08:13 A diversion thinking where any every idea is a good idea
0:08:17 Let’s just research was collect all the ideas and then you switch to a conversion thinking mode where you’re like right now
0:08:20 Let’s pair it down to the best idea so that we can present it
0:08:24 Right, I found that these Asians or media in general, but especially if you have these Asians
0:08:28 You can just automate that diversion thinking parts you’re presented with all the ideas that have taken everything into account
0:08:32 And then you and then I think people are still probably better at that conversion thinking part
0:08:37 Which is like let’s really apply it to the current nuance and like pick the handful that are most relevant
0:08:42 Yes, I’m super powerful. I just wonder it feels like it hasn’t really taken off in terms of people using it though
0:08:45 Right, so like I wonder what the roadblock there in terms of uses
0:08:51 So I think there’s a learning curve for sure in using autonomous Asians like the one I just described, right?
0:08:54 Um, I was typing in. Hey, can you give me a report?
0:08:58 Look, can you research company x and then research their cost drivers and revenue drivers?
0:09:02 I think for most people they would just want to say, hey, I want strategy
0:09:07 I want LLM strategies for company x and and they want the LLM to think through what the search should be
0:09:10 To get that done for that, right?
0:09:13 And so, um, in a talk I did last week
0:09:19 I talked about the difference between kind of what I call handcrafted Asians where you’re handwriting each prompt and like api call
0:09:24 And you’re designing the full flow versus what I would call like a specialized general
0:09:28 Specialized autonomous Asian that’s kind of dynamically generating in some task list
0:09:31 I think when we think autonomous Asian we’re thinking of the latter one, right?
0:09:32 If it’s if it’s handcrafted
0:09:38 It’s almost just like a zappier workflow and it doesn’t feel autonomous if you’re writing it yourself in building these
0:09:41 More dynamic agents that generate its own task list
0:09:48 One of the things I found was having good task task flows to feed it as examples help this get better
0:09:50 today
0:09:55 The companies that are generating revenue are creating what I think handcrafted Asians where they’ve
0:10:00 Plugged all those in and and you know that the executive can just ask a very
0:10:04 A rough question and and the team has thought through how the AI should break it down
0:10:07 And I think where we’re going with it
0:10:12 Is that eventually these kind of skills will become more dynamic and we’ll get the AI to be better at dynamically
0:10:16 Connect creating these task lists, but we’re just not quite there yet where it’s good at it all the time
0:10:17 interesting
0:10:21 It seems like there’s some kind of opportunity there for like some kind of new kind of sass or something where
0:10:27 The company just subscribes to this agent service and there’s like I want an agent to do this
0:10:32 So like well, we have that like custom tailored for your needs kind of thing and like they just sign up for the service
0:10:34 And yeah, that’s interesting. You know what would be awesome is
0:10:38 Like there’s so many kind of like brainstorming strategies and whatnot, right?
0:10:42 There’s like coaches and there’s like different strategies on like how to brainstorm
0:10:46 It’d be great to have like a database of like something like that
0:10:50 But like tailored to specific tasks then like given like a rough task it like breaks down
0:10:52 Like a really good smart way that an expert would do it
0:10:57 And if there was a massive database that I could just subscribe to and just like pull task lists and you
0:11:01 And and then say take this task list and adopt it to our own that would be pretty valuable
0:11:04 Well after this episode goes out. I mean one might pop up
0:11:13 Or I’ll go build it together. Yeah, exactly. Exactly. So I was I was recently watching this this TED talk recently with
0:11:17 Mustafa Suleyman who’s now the CEO of Microsoft AI
0:11:23 He made a comment at the end during the q&a portion about how when it comes to AI one thing
0:11:26 He doesn’t think we should do is give AI autonomy
0:11:30 And so I just wanted to hear what your thoughts are on like that comment
0:11:35 I I don’t get it like we can’t like why are we not going to yeah
0:11:41 Like clearly someone’s going to so we should figure out like how collectively how we should do it and how to do it
0:11:46 Well and safely yeah, he seemed to be playing this like odd game of like trying to be on the EX side as well as the
0:11:47 Dumer side it was like this odd thing
0:11:52 We’re like he was basically like we should accelerate but we should not have it shouldn’t be like, you know improving itself
0:11:54 It shouldn’t be going off and doing its own thing
0:12:00 Yeah, I feel like most of the people I engaged with saw that and was like that’s that’s pretty much my task list right now
0:12:01 Like trying to get yeah
0:12:05 And well, I mean most of the stuff that’s that’s been out so far it’ll actually ask you
0:12:07 Do you want me to take the next step right?
0:12:12 It’ll it’ll essentially suggest the next step and then say do you want me to take this next step?
0:12:13 Yes or no
0:12:16 Right, so even even most of what people have played with so far
0:12:20 Has had that like extra layer of like are you sure you want to do this step?
0:12:22 Are you sure you want to do this step and
0:12:26 I mean, I don’t know if that’s really the future of agents
0:12:32 I feel like people want to be able to give it a prompt walk away go to dinner come back have their their tasks completed
0:12:36 But so far it seems like a lot of those safety measurements have been built in
0:12:41 Yeah, I think I mean, you know, I think we’re we’re still trying to figure out how to get all the this ai to work
0:12:45 How to work well, what are the correct again design patterns ux patterns?
0:12:48 I think we’re all it’s still trying to discover that some of those questions
0:12:51 We’re also also like to like reduce cost as well
0:12:56 Right, I think when we really when we first talked like because if it just keeps off running and it’s not waiting
0:12:57 It’s not asking you any questions
0:13:01 It could go off and just like run up your api bill for open ai or whatever to like
0:13:07 Thousands and thousands of dollars. Well, there was also a little bit of a tendency from time to time where it would get stuck in a loop
0:13:10 Right, it would sort of do a search not find what it was looking for do a search
0:13:16 And it would just kind of continuously loop and if you just walked away for hours you just rack up those api costs
0:13:20 We’ll be right back
0:13:25 But first I want to tell you about another great podcast you’re going to want to listen to it’s called science of scaling
0:13:33 Hosted by mark roberos and it’s brought to you by the hub spot podcast network the audio destination for business professionals
0:13:38 Each week host mark roberos founding chief revenue officer at hub spot
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0:13:59 And I personally learned a lot from it. You’re going to want to check out the podcast
0:14:03 Listen to science of scaling wherever you get your podcasts
0:14:09 Yeah, I think when we talk about autonomous agents one thing that like
0:14:12 There’s qualitative decisions and quantitative decisions
0:14:16 And there’s types of decisions that we can trust a computer do and some that we can’t and I think it’s
0:14:21 It’s worth pointing out that I think like half the stock trades are autonomously done by like essentially what’s an autonomous agent
0:14:26 That’s only using quantifiable information and as we move to more qualified, you know, like
0:14:29 Qualitative stuff it gets it gets a little bit trickier
0:14:34 But I think it’s ultimately just like what are the tasks that we can we can automate today?
0:14:36 And if we can start relying on it we can slowly
0:14:40 Slowly figure out like, you know more and more things that can be automated
0:14:43 Yeah, so one thing I do want to bring up as well is that
0:14:48 You know, we’ve probably all seen devon right from I think cognition labs, right?
0:14:54 And this is an AI agent that can help with coding you give it a task that you want it to do it writes the code
0:14:59 I think it sort of double checks its own code iterates optimizes and sort of
0:15:04 As this loop until it sort of figures out the right code and completes the task
0:15:09 Well, they were just valued at two billion dollars and the company’s only six months old
0:15:15 And uh to me that’s that’s kind of crazy that a company that is that new
0:15:20 Got that kind of valuation. Do we see this is like the the sort of next
0:15:29 Venture capitalist push is this where a lot of the money is going to start to flow is some of these companies that are building these more autonomous agents
0:15:33 I mean, I think everybody agrees that
0:15:39 Autonomous agents when they work will have huge impact and I think just the question is a matter of when
0:15:46 And so it’s no surprise that I think some vcs right if there’s enough vcs some are going to think that it’s very soon
0:15:48 And if it is very soon, it’s worth making
0:15:54 A big bet that being said I say there’s all types of vcs and the types of vcs who make
0:15:59 Two billion valuation bets into a company six months old is pretty different from a from a vc
0:16:04 Who’s you know from a from an emerging manager micro vc, which is myself
0:16:08 And I’d say that an emerging manager micro vc probably has more in common with an AI builder at least from my perspective
0:16:12 There’s somebody rolling that so I can’t really speak to that specific valuation
0:16:15 I think it’s just a different type of company building totally makes sense
0:16:20 Yeah, I mean Devin seems amazing like the concept but like there’s also been a lot of controversy at least on twitter
0:16:24 People saying like is it even real here? Here’s my guess, right?
0:16:27 And I mentioned handcrafted agents versus like autonomous agents
0:16:33 If you think about developers and and like some of the devin skills they showed was like let’s run a code
0:16:38 If there’s an error, let’s look at the code and decide where to add a print statement
0:16:43 Let’s run the code again read that print statement and then based on the code the new print statement
0:16:46 Guess where we need to fix the code
0:16:48 Fix the code and then run it again
0:16:55 If it’s an error loop this this oh this to me feels like a handcrafted agent like every developer has done that themselves
0:17:00 So it’s it’s it’s a very like it’s not like a you don’t need an AI to make up that workflow
0:17:06 And there’s probably a couple other ones that are common enough that some team like Devin should probably build that tool in a
0:17:14 Modular way where each step is a modular skill so that the AI can in theory dynamically pick and choose and combine them together
0:17:19 But at least based on their demo they’ve gotten it to they’ve probably we can teach at these handcrafted agent skills
0:17:24 Like if if you’re doing this like chain tasks got this way if you teach it that then it’ll do it that way
0:17:26 But as people start using it
0:17:30 There’s going to be edge cases where there’s like a new flow that the team hadn’t thought of or that Devin hadn’t run into
0:17:32 Then it might not be as good and stable
0:17:39 So so getting those stable is is going to come through people using it figuring out where it doesn’t work and then maybe even like
0:17:44 Describe you know describing it to Devin so it does it right and stores it in this memory
0:17:46 So that the next time it’s asked you can do it that way better
0:17:48 So that’s that’s my kind of gut guess on where they’re at
0:17:54 Which would be which would explain why their demos work why people who are using it are getting good results for the for the type of tasks that
0:17:58 Um, they’ve gotten a good ad but they’re probably still working on some of the edge cases would be my guess
0:18:05 It was just the reason I wouldn’t go public yet. Are there any like major technical hurdles that you’re seeing to
0:18:08 To getting to the point where I can just say hey
0:18:10 Go build me this website
0:18:15 And then I walk away go to dinner come back and then the website’s just built for me everything’s done
0:18:21 I mean we we have really powerful large language models most of the large language models now can work with tools
0:18:23 They can interface with apis things like that
0:18:28 What do you think the holdup is from from where we are now to getting to that?
0:18:33 So I think part of it is those kind of people’s expectation when you said go build me a website
0:18:37 right if it’s if I wanted to build a tool that can build you a
0:18:42 General personal website maybe with a blog I could probably build that website and build that tool
0:18:46 Right if I dedicate myself to making it work really well the challenge is if I put it out into the market
0:18:50 Someone’s going to go say build me a sass website or build me a game website
0:18:53 And suddenly this tool doesn’t work because it didn’t create that website
0:18:57 And so how do you present that right? Do you go really really niche in which case?
0:19:00 No one really cares or do you present the big picture that you want to build in which case?
0:19:06 There’s going to be edge cases that don’t work and I think that’s one of the big big blockers is kind of figuring out
0:19:08 There are probably agents that are good at certain things
0:19:10 But maybe they don’t go viral because it’s such a niche group
0:19:14 And then the ones that promise too much are going to hit the market and they’re going to run into edge cases
0:19:17 And people are going to say this doesn’t work and I think that’s one of the holdups
0:19:23 But I think within that what you’re finding is that some people who discover the right tool for themselves are getting a lot out of it
0:19:28 Or the people who can see past the mistakes and are willing to just treat it like a a new employee
0:19:32 That’s just much cheaper like those people I think are getting value out of these
0:19:35 Asian type tools and workflows today
0:19:37 Yeah, that makes sense
0:19:42 It’s it’s it’s the little like nuances of exactly what you’re looking for and everybody that uses the tool
0:19:46 Is going to have slightly different nuances to what they need out of it
0:19:50 I’m wondering like whenever we get to the point where we can not only have it make a website
0:19:55 But like come up with the concepts like I imagine like a future product lab where you know
0:20:01 You’re like I wanted to run experiments in this sector and like the agent actually goes off and like comes up with like the plan and like
0:20:06 Yeah, come, you know, give me five landing pages for different angles on this, you know that we could attack and then
0:20:13 Um, I wonder how many years off we are from that and also just like how much that changes business when you can just like ramp out
0:20:19 New experiments like that again going back to like if it’s a use case that a lot of people want I don’t think we’re that far away from it
0:20:27 Right. I use I use an AI diligence tool that will spit out a 30 50 page report on any given company or industry within 30 minutes
0:20:31 It’s a very specific, you know, what I call handcrafted workflow
0:20:35 Get a ton of value out of it and it makes sense to build that tool because it’s a workflow that
0:20:38 Every investor every consultant goes through it
0:20:41 Which is going and researching the company the news the data and all that kind of stuff
0:20:45 one of the my favorite stuff that came out after baby Asia was auto rpg, which was a
0:20:49 Which was using baby asiai’s framework to auto design
0:20:56 A full game level the storyline for that game level the characters the the their script
0:20:59 And then to a point where they can just upload it and make that
0:21:04 Level playable by the game again. It was just a rough demo and I think they’re still cleaning up the actual back end of it
0:21:06 But I mean that’s pretty cool stuff
0:21:12 Yeah, one of the things I want to ask you about was something you mentioned in your ted talk that I found really fascinating and you made a
0:21:13 comment about how
0:21:18 With AI we essentially have access to the sum of human consciousness
0:21:25 Or the ability to chat with the sum of human consciousness and i’m paraphrasing you probably said it way more eloquently than I did
0:21:29 But I want to kind of talk about that statement and what you meant by that
0:21:35 Yeah, I mean if you think about these massive models, they are trained on
0:21:37 massive corpus of
0:21:39 text
0:21:45 Written by millions of people it’s their words and again, it’s a filtered list of words because it’s only stuff they publish
0:21:49 But when you ask a model a question they’re answering that based on
0:21:56 The writing of millions of people and so I think what I said was that it’s probably the closest thing we have to chatting with our collective conscious
0:22:03 Um, which I think is just a really fun way to like think of the tool and and play with the tool and ask questions against the tool
0:22:09 Actually, one of the first experiments I did early on this was in like the Da Vinci days was I actually had it write a 42 page book
0:22:14 On on on like what it means to be human and I talked to like family and why not?
0:22:17 I just I was just like I want to know what like the average
0:22:22 Like two pages about like what family means to a human is and how did that book come out?
0:22:27 It was great. It’s like a google doc. You know, I just I just like made it for myself as like an experiment
0:22:32 But it was like a it goes through like family death life, you know all those like basic human concepts
0:22:36 It was a there’s a book I loved called Tuesdays with Mori, which was actually the inspiration for it
0:22:38 But uh, it goes through some of the key aspects of life
0:22:42 But I basically just did that with an AI instead of with a wise man named worry
0:22:46 Now, I don’t know if this is a rabbit hole. We want to go down or not, but I’ll bring it up anyway
0:22:52 So, you know, you know continually along those lines when a lot of people are out there talking about like, oh, these
0:22:56 These large language models are biased in one way or the other
0:22:59 does that sort of mean that like
0:23:02 The biases is sort of what the majority of humans
0:23:07 You know believe I would I that I feel like that’s a dangerous jump. I feel like
0:23:13 It’s it’s fair to say that the bias of like human society is probably in there somewhere
0:23:16 But then there’s also the bias layer of who has access to the internet
0:23:21 Which has ties to wealth and bias layer layer of who’s willing to who has access to like
0:23:26 Technical knowledge, right because people who had access to blogging earlier. There’s more content out there
0:23:31 So so there’s there’s a layer of bias beyond like what the biases of the world ingrained into the model
0:23:35 So so I would I would say that as long as you’re making that clear
0:23:39 I think that’s fair to say that there that that bias is reflected in the model to some extent
0:23:42 Yeah, that’s very cool. And I just I love that
0:23:45 I don’t know if you call it an analogy or that way of thinking, right?
0:23:52 I love that idea of we’re chatting with the sort of sum of human consciousness when we’re talking with these AI bots because I mean
0:23:54 they’re trained on, you know
0:24:02 trillions of bits of data from the, you know, all of humans putting the this information on the internet and
0:24:04 Just looking at it from that way
0:24:10 When I was watching your TED talk that one statement just like flipped a switch in my brain of like, oh man, that that is so true
0:24:14 I remember when I was a kid and I don’t think this is even true
0:24:18 But actually somebody told me that like every, you know, anything with mass has gravity
0:24:24 Right, so that if you if you wave your hand that like in a very very minuscule way a star
0:24:29 Is like moving slightly because of your hand movement. Now, I think it’s actually I looked it up
0:24:35 I think it’s actually false because there’s like some diminishing so but I think theoretically speaking that that idea of like
0:24:40 Be having a minuscule aspect on something extremely large and far away has always fascinated me
0:24:45 And I think people I think it’s fascinating to think about like all the people who published on the internet, right?
0:24:50 Some of their ideas and words, right in a very indirect way
0:24:56 Are now we’re consuming it and there’s there’s there’s like an immortality to that that that just fascinates me
0:24:59 Well, yeah, and even hundreds of years old, right?
0:25:04 I mean, it’s it’s trained on data from the the bible and the karan and the all of it, right?
0:25:06 So like hundreds thousands of years old as well
0:25:10 And it’ll be trained on this podcast that we’re doing right now, which is kind of wild, right?
0:25:13 We become part of this infinite collection of all of humanity, right?
0:25:16 Like all of the stories of humanity. Yeah, this is this is a whole rabbit hole
0:25:21 I do think I’ve been talking more about autonomous agents recently just because of the talk and a couple panels
0:25:24 But the the reaction from enterprises are very interesting, right?
0:25:26 You know, there’s the ethics job displacement
0:25:31 Like those are all discussions that are happening around autonomous agents like how and when do you replace
0:25:37 Tasks from humans doing it to autonomously, you know, what is the long long-term impact of it?
0:25:43 And then one of the crazier ideas that I got some reaction of was the idea of like an autonomous CEO at some point, right?
0:25:51 A little 24/7 can talk to all employees in parallel has access to all company data and has at least consistent and transparent bias
0:25:52 Yeah
0:25:56 I’m curious how you respond to people when when the the sort of job displacement question comes up
0:25:59 You know being somebody that creates a lot of content around AI
0:26:04 In my comments and things like that. I you know, I get love but I also get a lot of hate from people
0:26:07 They’re like, oh, you’re part of the problem. You’re part you’re out there sort of
0:26:11 Spreading the message of the thing that’s going to take the jobs from people
0:26:18 What sort of response do you find yourself giving people when they are asking about like well, is what you’re talking about going to take our jobs?
0:26:24 Looking forward I can see the argument as to why autonomous agents will
0:26:26 Displace jobs and I can see why
0:26:31 There’s a reason to fear that potentially I do but looking back on it
0:26:37 I also know that many smart people had that exact same fear about almost every single technology that came
0:26:41 And all those technologies ended up being net job creators
0:26:45 So I’m humble enough to assume that I’m not like necessarily
0:26:48 You know, I’m just as likely to be as long as all the people in the past
0:26:54 And and if I’m going to go with the you know, just what I’ve seen in the past and if I’m looking for my third party
0:26:59 I would assume that the next technology would be a net job creation is engine again
0:27:03 Yeah, I mean when I just look at you know, things like um, you know AI art for instance, right?
0:27:09 Everybody’s worried about the fact that AI can generate art, but it feels like the same arguments that happened when photo shop came out
0:27:13 And most likely the same arguments that came out when cameras were invented
0:27:20 You know and you can sort of go back computers cars printing press radio tv, right every single time like something
0:27:24 You know and then yes, there’s there’s pros and cons of every every single technology
0:27:28 And each one of those created new careers that didn’t exist prior to them
0:27:30 exactly
0:27:32 Yeah, it feels like people will get better jobs as well as my opinion
0:27:36 Like it’ll a lot of the like tedious parts of work the work that people don’t actually like doing
0:27:40 That’s like soul draining kind of work a lot of that will get replaced
0:27:44 And so I I think though with this new technology people have more free time and they can actually explore things
0:27:49 They actually enjoy doing for work versus like oh, yeah, I need to enter this data into this form
0:27:55 Like yeah, I feel like AI is already sort of replaced data entry and I know for me it has like I don’t manually enter stuff
0:28:00 And just spreadsheets anymore because AI can do it for me. Just as good as I can. Yeah, but it didn’t take anybody’s jobs
0:28:04 It just made me more efficient. Yeah, I think jobs will change right?
0:28:08 I mean if you look at a super small scale like our venture fund right as I had mentioned
0:28:11 I have this diligence tool that that does all this, you know research for me
0:28:16 But it doesn’t mean that like I’m never gonna hire people into my fund. It just means that when I hire
0:28:21 I’m not going to be looking for that skill set. I don’t need somebody who who wants to spend time
0:28:24 You know searching the web and copy pasting stuff into a document
0:28:29 I want someone who I’m okay with someone who sucks at that because what I want is different skill sets
0:28:33 And so I think the jobs do change around what AI can automate
0:28:36 Uh, but that’s always been true in any technology, right?
0:28:42 If there’s certain tasks that technology will replace then just figure out what other tasks will support will be needed to support that
0:28:47 I want to talk a little bit about like the the venture capital world for a minute too
0:28:52 Specifically as it relates to AI right because there’s this narrative. We had Greg Eisenberg on the show
0:28:55 We talked to him about this exact concept
0:29:02 but there’s this narrative that sass companies don’t really have a moat because almost all of these platforms are either built on
0:29:06 existing apis or open source platforms and
0:29:14 Anybody could go and use AI to build a sass that connects to these apis or these open source platforms
0:29:20 Even if you don’t know code, I mean, you know, what what you’ve managed to do is is kind of proof of that concept
0:29:27 But so like I look at some of these companies that like when AI was really bubbling up at the end of 2022 beginning of 2023
0:29:31 That seems to be when the sort of big like boom happened where the
0:29:35 The world consciousness started to realize that AI is is getting really popular, right?
0:29:39 and we saw platforms pop up like tools that were like, hey, we can um
0:29:45 Summarize PDFs for you or chat with your PDF or tools like that
0:29:48 Some of those companies raised capital early on when they first popped up
0:29:54 But then you know fast forward a year and now you can just do that directly inside of chat gpt or clod
0:29:57 And who’s going to go buy a chat with pdf software?
0:30:04 So when you’re looking at like potential companies to invest in are there things that you’re looking at
0:30:10 That might future-proof the companies like what what sort of criteria are you looking at?
0:30:15 I think there’s two parts. I mean being an early stage investor, right investing at precede
0:30:20 I’m comfortable with the idea that my companies might pivot. So at precede, I mean team comes first, right?
0:30:24 If I’m talking to a team that’s closely following what’s happening and is able to like adapt quickly
0:30:26 Like that’s that that can be enough
0:30:30 But kind of thematically, which I think is more the question around for us at least
0:30:32 We do try to get deep into
0:30:40 Like both the building and testing of tools and talking to customers to to build conviction around the direction that we think
0:30:45 AI is going to go right and looking back on how the models have advanced in the past to get a sense of where we think
0:30:47 The models are going to go
0:30:48 And it’s through that
0:30:53 We have conviction in certain types of plays. We feel like this is going to be a good play versus
0:30:59 Conviction that certain plays won’t work and then and then some a lot of plays are just like we’re not sure what the right play is here
0:31:04 And then as an investor we tend to invest in the areas where like I have strong conviction in this play
0:31:07 And there’s plenty of other really interesting ideas where I’m just like not
0:31:13 Don’t really have conviction and and that just market direction that I might pause and like watch a little bit make sense
0:31:16 I mean, I’ve been you know
0:31:18 It’s slightly connected. I’ve been I’ve been wondering like with the vc
0:31:21 Like how will it look in five or ten years because it’ll probably be really different
0:31:27 I mean, I’m interested to see what happens in terms of like lowering the cost of company starting and what those teams look like
0:31:32 I mean, that’s been a consistent trend right over over decades where the cost of starting companies continue to get lower
0:31:36 So so I hope to see that of what we’ve seen is the vc landscape change as well
0:31:40 Right, we saw vcs becoming smaller and smaller and more micro and micro
0:31:44 At the same time we did we have seen some shifts around
0:31:49 crowdfunding around, you know syndicates like, you know rolling funds
0:31:53 So we are starting to see some innovation over the last I mean less than a half decade
0:31:56 I’d say is where we’ve started to see a lot more innovation around vc
0:31:58 So it’ll be interesting to see where it goes
0:32:05 Are there are there any like tools or research or anything that you know has come on to your radar that’s really exciting you’re right?
0:32:10 Yeah, that’s a good question. I feel like that’s uh, that’s this constantly. Yes. Um
0:32:16 I’m curious to learn more about like the what Sakana is doing by combining models that kind of modular approach of
0:32:22 Of mixing and matching models and being able to use them as as different models. I think that’s a fascinating area
0:32:29 Um right now that the overarching trend seems to be like let’s throw more money and more power to get better performance
0:32:34 But I think if we can start playing around with more modular models and start combining them
0:32:38 We can start seeing different economics and plays there. So I’m really fascinated by that
0:32:40 Yeah, something like that seems great for agents, right?
0:32:44 It’s like depending on what use case you can change out the model like okay right now
0:32:47 I don’t need the smartest one and I don’t need to spend a fortune on it
0:32:53 Another area is web 3 and ai. I feel like I’ve seen a lot more that’s been really bubbling up with web 3 coming back everything from
0:32:56 You know, uh data
0:33:03 You know data marketplaces on web 3 ip plays leveraging kind of ai first ip plays on web 3
0:33:08 As well as agentic web 3 right help agents pay help agents have an identity
0:33:10 and this might be
0:33:14 Uh more of a rabbit hole that we want to go down today, but what what’s your definition of web 3?
0:33:20 I mean, I think the simplest is is one is technologies that leverage the blockchain. I might I might I may be on
0:33:26 A smaller group that kind of sees web 3 as almost a philosophy and ethos
0:33:28 But but I actually think most people would disagree with me there
0:33:34 Yeah, I mean, I’d probably agree with you like when it when it comes to the whole like blockchain sort of crypto discussion
0:33:40 I think the technology the sort of distributed ledger concept. I think is really really powerful
0:33:47 I think a lot of drifters and bad actors and people trying to really push the make money narrative has
0:33:52 Really screwed up the the the sort of web 3 blockchain concept
0:33:55 But I do think the underlying tech is really powerful and I do think it’s the future
0:33:59 Yeah, and I always thought it was kind of wild to like, you know, it’s it’s a great marketing
0:34:05 I’m sure web 3 but it’s like when web 2 happened which was like all the big social companies that term kind of came out
0:34:08 You know, I think sarah lacey and a few others started spreading that term
0:34:11 Uh, and and that was like after the big social companies were already working
0:34:16 They never like really dramatically changed the internet like people were using dig
0:34:19 They were using facebook they were using myspace and all this stuff, right?
0:34:23 And then that term kind of came out versus web 3 was like, here’s this cool concept
0:34:27 It’s got it’s got branding issues for sure. Yeah. Yeah
0:34:34 So, you know as we kind of wrap up here a large portion of the audience that listens to this show is
0:34:38 You know small medium businesses is a hub spot podcast
0:34:42 What advice would you give to business owners that are seeing AI?
0:34:46 They’re seeing all of this stuff bubble up. Maybe they haven’t experimented with AI yet
0:34:52 What would you tell them to do is sort of a first step to get their toes wet with AI or their business?
0:34:58 I mean, I think for anybody who’s played with it will tell you that the first couple of times they played with something like chat
0:35:01 Upt, they were pretty blown away by his capabilities
0:35:06 I think it’s pretty low hanging fruit to start there if you haven’t like pull up chat Upt
0:35:09 I think there’s a free version some people tell you oh the free version is not good enough
0:35:11 But realistically just start playing with it, right?
0:35:15 And I think for most people when you when you ask it a couple of questions when you’re blown away by it
0:35:19 You’ll start thinking of hey, how can I maybe use this at my work?
0:35:22 And I’ll ask you a couple questions that’s relevant to your work
0:35:26 And again, my guess is that like a majority of people we’ve blown away by his capabilities there
0:35:32 And hopefully just by doing that a little bit you’ll start thinking of ideas on like how you can start using it
0:35:39 And initially I just nudge people to like, you know use chat Upt or any pick an AI tool and just start playing around with it
0:35:45 So that you kind of get into the habit for people who are more creative pick a pick an image generation tool like mid-journey
0:35:48 Stable diffusion or if you’re a music person then pick sueno or audio
0:35:52 Then just pick an AI tool and just start playing with it and just get into that habit
0:35:57 And then once you do I think the ideals will start coming and just like and just know that like, you know
0:35:58 Some of the stuff you’re going to try won’t work
0:36:02 But like if you keep trying stuff, they’ll keep discovering new cool ways to like use it
0:36:05 Well, very cool. Thank you so much for for joining us on this show today
0:36:09 Where should people go learn more about you go check you out follow you on x
0:36:12 You know, what’s what’s the best place to learn more about you and what you’re up to?
0:36:16 Probably the uh, the the place I post most is on x slash twitter
0:36:20 I’m constantly posting about AI discoveries rvc fund
0:36:25 Um for a lot of the stuff I build I do have a build in public log at yohey.me where you can see
0:36:30 Most of my experiments some of them are have tutorials some of them are just pure experiments in place
0:36:36 But uh, those two are the probably two most interesting places. Amazing. Awesome. Well, thank you so much for joining us today
0:36:38 This has been such a fun and fascinating conversation
0:36:42 I’m so glad that you were able to make the time to do this with us today
0:36:45 And I couldn’t appreciate you enough for doing it. So thanks again. Thank you for having me
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0:37:09 [MUSIC]
Episode 7: Why and how, Yohei Nakajim built a baby AGI. Matt Wolfe (https://x.com/mreflow) and Nathan Lands (https://x.com/NathanLands) sit down with Yohei Nakajima (https://x.com/yoheinakajima), a venture capitalist and serial AI tool builder.
In this episode, Yohei dives deep into the transformative potential of AI in automating tedious tasks, revolutionizing venture capital, and redefining job markets. He shares insights on the practical applications of AI for small and medium businesses, discusses the branding of “web three,” and explores the development and impact of autonomous agents like his Baby Agi project. Whether you’re interested in tech innovation, business adaptability, or AI ethics, this conversation covers it all.
Check out The Next Wave YouTube Channel if you want to see Matt and Nathan on screen: https://lnk.to/thenextwavepd
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Show Notes:
- (00:00) VC turned coder using AI for prototyping.
- (03:38) Community support led to modding and improvements to Baby AGI.
- (08:16) Learning curve in using autonomous agents.
- (11:31) AI agents offer guided next step suggestions.
- (15:04) Developers emphasize handcrafted agents over autonomous agents.
- (17:10) Balancing niche and broad market expectations in tech.
- (20:12) Massive models trained on millions of people.
- (22:52) Childhood fascination leads to pondering internet immortality.
- (27:24) Venture capital, AI, SaaS, future-proofing investment criteria.
- (30:03) Interest in lowering company startup costs and VC innovation.
- (33:34) Try using free version of chats first.
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Mentions:
- Yohei Nakajima: https://yohei.me/
- Baby AGI: https://discord.com/invite/TMUw26XUcg
- Claude: https://claude.ai/
- Get HubSpot’s Free AI-Powered Sales Hub: enhance support, retention, and revenue all in one place https://clickhubspot.com/gvx
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Check Out Matt’s Stuff:
• Future Tools – https://futuretools.beehiiv.com/
• Blog – https://www.mattwolfe.com/
• YouTube- https://www.youtube.com/@mreflow
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Check Out Nathan’s Stuff:
- Newsletter: https://news.lore.com/
- Blog – https://lore.com/
The Next Wave is a HubSpot Original Podcast // Brought to you by The HubSpot Podcast Network // Production by Darren Clarke // Editing by Ezra Bakker Trupiano