AI Demos: Sunny’s Back with Luma Labs, Kling, Claude Sonnet & Getting AI Native | E1976

3 Jul 2024 · 53 min

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Podcast Episode Notes: AI Demos with Sunny from Luma Labs, Kling, and Claude Sonnet | E1976

Episode Overview

  • Podcast Title: This Week in Startups
  • Episode Title: AI Demos: Sunny’s Back with Luma Labs, Kling, Claude Sonnet & Getting AI Native
  • Host: Jason Calacanis
  • Guest: Sunny Madra
  • Release Date: [Insert Date Here]

Episode Summary In this episode, Jason Calacanis welcomes Sunny Madra back to discuss the latest advancements in artificial intelligence (AI), including demos of Claude Sonnet, the significance of "AI Native" workers, and Luma Labs' innovative projects. The discussion covers the challenges in today's fundraising environment, comparisons between AI tools and traditional search engines, and predictions for future developments in the AI space.

Key Highlights

  • Tough Fundraising Environment:
  • Discussion on the current state of fundraising, comparing it to the dot-com era.
  • Mention of regulatory impacts on mergers and acquisitions (M&A), specifically citing Lina Khan.
  • AI Demos:
  • Claude Sonnet:
  • Demonstrated the ability to create interactive reports from complex data inputs.
  • Highlighted its capabilities in generating insights and visualizations.
  • Luma Labs:
  • Showcased a recent video project bringing famous memes to life, illustrating the creative potential of AI.
  • Kling and Cartesia AI:
  • Introduced tools for generating realistic AI visuals and voices.
  • AI Native Workers:
  • Emphasis on the growing need for workers who can leverage AI tools effectively.
  • Discussion on how companies must adapt their hiring processes to prioritize AI competency.
  • Comparison of AI Tools to Traditional Search Engines:
  • Critique of Google’s performance compared to advanced AI models, arguing that AI tools like Claude Sonnet and ChatGPT are outperforming traditional search engines in relevance and speed.

Detailed Discussion Points

  1. Fundraising Challenges
  2. Current Climate:
  3. The podcast opens with insights into the difficult fundraising environment, suggesting it mirrors previous economic downturns.
  4. M&A Dynamics:
  5. The impact of regulatory scrutiny on small acquisitions and the mid-market.
  1. AI Demos
  2. Claude Sonnet:
  3. Functionality:
  4. New features allow for more interactive outputs, such as dashboards from financial reports.
  5. Demonstrated using real-world examples, showing ease of generating complex reports.
  • Luma Labs:
  • Innovative Projects:
  • Showcased a creative video that revived popular memes, emphasizing the potential for entertainment and marketing.
  • Kling:
  • Application of AI in Visuals:
  • Demos of AI-generated images that approach realism, indicating advancements in AI graphics.
  1. Workforce Implications
  2. AI Native Workers:
  3. Discussion on the necessity for employees who are adept at using AI tools to enhance productivity.
  4. The shift in job requirements as AI continues to evolve in workplaces.
  1. Comparison of AI Tools vs. Traditional Search
  2. Search Engine Analysis:
  3. The conversation critiques Google’s declining effectiveness in comparison to AI-driven models that provide more accurate and timely information.
  4. Jason and Sunny express concern over the implications for traditional platforms.

Conclusion and Predictions

  • Future Innovations:
  • The hosts speculate that the second half of the year will bring significant surprises in AI technology that could eclipse previous developments.
  • Call to Action:
  • Encouragement for listeners to adapt to AI advancements and consider their roles in a rapidly changing job market.

Additional Resources

  • Links Mentioned in the Episode:
  • [Claude Sonnet](https://claude.ai/new)
  • [Luma Labs](https://lumalabs.ai/dream-machine)
  • [Kling](https://kling.kuaishou.com/)
  • [Qwen](https://huggingface.co/spaces/Qwen/Qwen2-72B-Instruct)
  • [Cartesia AI](https://play.cartesia.ai/)

How to Listen

  • Subscribe to the podcast on [Apple Podcasts](https://rb.gy/v19fcp) and follow the show on social media platforms for updates on future episodes.

--- This markdown file provides a comprehensive overview of the podcast episode, capturing the essence of the discussions and key takeaways related to advancements in AI and its implications for the workforce and business landscape.

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Transcript

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0:00There is a lot of innovation happening now. and it's faster than ever. And it's coming in the way of new types of models, open source models, agentic reasoning, invoice. And so in the second half of this year, J-Cow, we are going to have some surprises that no one was anticipating. Okay, there you go. So that's inside information. That's the inside line or a prediction. Generally, I'm telling you, we're going to be more surprised in the back half of this year than we were when OpenAI first came out. Oh, it's a big claim. It's a big claim. I don't know how we phrase it as a bet, but what you're saying is everything that happened up to now is the epilogue and that the story is coming.

0:35The real story is coming. The story is coming now. This Week in Startups is brought to you by NetSuite, the number one cloud financial system bringing accounting, financial management, inventory, and HR into one platform, giving you one source of truth. By popular demand, NetSuite has extended its one-of-a-kind flexible financing program for a few more weeks. Head to netsuite.com slash twist. OpenPhone. Create business phone numbers for you and your team that work through an app on your smartphone or desktop. Twist listeners can get an extra 20 % off any plan for your first six months at openphone.com slash twist.

1:17And DevSquad. DevSquad helps startups design better products. If you need UI and UX expertise and don't want to hire an entire design team, head to devsquad.com slash startups and book a call. Mention that you are coming from Twist to get 10 % off. All right, everybody. Welcome back to This Week in Startups. Madra Mondays are back. Here is my guy, Sandeep Madra. I don't know if we're releasing this on a Monday. We're taping it on a Monday. Taping it on a Monday. Welcome back to This Week in Startups. Good to be back. Yeah, where you been? Where you been? Everybody's asking. Where's Sunny? Where's Sandeep?

1:52business called fundraising and you know how it is when you're doing that the calendar got prioritized for investors and you you know you've had to do it too i don't know jacob actually i'd like some advice from you like you're able to keep the shows going and you have multiple shows and fundraise but for me you know fundraising you know it's kind of like took over my calendar it's like yeah we're meeting with this and you can't really move them around so how did you manage that. I broke myself is how I managed it. I mean, if I'm being honest, I didn't manage it. It literally broke my brain. We are living in the toughest fundraising environment since probably the dot-com era, possibly right ahead of the great recession, I would say.

2:37And so with an AI company, that's a notable exception, obviously. But even still, because there haven't been a lot of returns, If you look at, you know, IPOs, they've been few and far between and they've been muted, right? Instacart coming out at 8 billion, private valuations being 30 or 40 billion for that company, which means LPs and VCs are less apt to put money into products. So we've had a little bit of indigestion. The gears are a little gunked up. We talked about it on this pod a couple of times. M &A is really anemic. We used to have a very vibrant mid-market for M &A since the Biden administration hired Lena Kahn.

3:19That has destroyed the startup space. And I think putting aside any politics, how you feel about different presidential candidates, this is an area that really matters to America's viability. If there's no mid-market for selling Figma to Adobe or Instacart to DoorDash, whatever it is. Or the little teams, the little acquisitions. And the tuck-ins, like you're referring to,$10 million to$100 million tuck-ins, that mutes investment. So now you've got LPs and VCs either not investing in as many companies, not investing as many dollars in companies, companies not being able to raise funding, which then means less job creation, which means a less competitive America.

4:00You have to have two different concepts for M &A. M &A for Facebook, Meta, Google, Amazon. Microsoft should behave one way. In other words, companies over a trillion dollars. They should have one rulebook for M &A. for the mid-market there should be a different rule book so if uber and airbnb coinbase you know call it the 30 billion to 250 billion crowd if they want to do interesting things i say let them you can review them and then i would say any acquisition under i don't know let's pick a number one billion dollars just you can do whatever you want under a billion dollars you can do everyone and if you've watched we've now had two ai acquisitions in the space where they They were aqua hires with leaving the shell company and somehow money's flowing to the investors.

4:49They're getting some of their money back somehow through like backdoor contracts. It's all a giant scam, a hack to route around Lina Kong. So if you tighten your grip, more deals will slip through your finger. So Lina Kong is a complete disaster and binds a complete disaster when it comes to this M &A situation. It also ties into like incentives drive behaviors, right? Like these big companies can also put offers in front of people which look like acquisition, right? Which, you know. Say more. Explain. Well, look, if you're Microsoft and you want to staff up a new AI division, as they did, or you're Amazon and you want to staff up an AI division like they did with the Adept thing that just happened last week, you offer the founders$10 plus million a year or over two years.

5:44Maybe. If you stay there, let's just say you get 10 million a year and you stay there for five years, right? That'd be probably the equivalent of you selling a startup for$500 million and you owning like 10 % of it after a couple of years. Yeah, you and your co-founder are 10%. Exactly. So now what is all this crazy M &A philosophy of future competition done? They just routed around you, Lunacon. Yeah. They routed around you and you accomplished nothing. And then what happened was the big companies get stronger because now they don't have the competitors in the smaller companies. So you're actually having the opposite effect.

6:19And the investors kind of get weary where the investors are like, hey, I'm not sure I want to do this because what if the team gets scooped away or decides to go away or walk away? Yeah. So there's unintended consequence. Show me an incentive. I'll show you an outcome. And here's what happens. So you've got to be very careful if you want to put your thumb on the scale in a con. You could just tilt the scale and everything goes flying off the scale and you don't catch any of it. You don't do any of the things you intended. So it's just a terrible approach. It's been executed very poorly. But let's get to demos.

6:51Everybody wants to see demos. I've been playing with ChatGPT4.0 and the new Claude Sonnet, and I pay for both of those, obviously, for my companies. And it's getting pretty damn impressive. Well, yeah, Claude Sonnet is where I wanted to start today. As we get into it, I just want to give credit here to this really incredible thread that Min Choi put together. And then I'll show a demo from it, but I will give this credit real quick. He really went across sort of a bunch of different ideas and aggregated them. So here's build an iPhone app prototype from scratch. So really the thing that we're going to talk about when we do Claude is they have this mode now where there's like on the right hand side, You have that enabled where it can do sort of an output for you that's different than the text output.

7:37And so here, you know, someone made an app. I'm going to demo this next, which is make an interactive dashboard from an earnings report. You can make games. You can go through this, right? You can get insights on business, one-click SEO tool and not. So we'll put a link to this. But what I did here was I basically dropped in, you know, Tesla's Q1 2024 earnings report. A PDF. Yeah, a PDF. And I said, come up with a detailed analysis and summary with stock recommendation by carefully reading this report, put together a nice and interactive, but highly detailed report. And so here's sort of the summary, which we were all used to getting.

8:15What I got on the right-hand side is an interactive report. Wow. So this is the presentation layer. So instead of just seeing text that you would copy and paste and start building a deck, it knows that you want an interactive report. And that means it's making something beautiful. Now, I don't know what format that is. It looks like HTML, but well done. Yeah. You have to enable this thing called artifacts. If you turn that on, right, in the features preview, you know, I'll just copy this prompt over and I'll add in a, you know, here's NVIDIA's last 10 queue, right? and uh you know you're sort of limited to the upload side which i think is like 30 megabytes or something like that more than enough for any kind of text yeah and i like how you tell it to be like thoughtful or something or to be thorough like the ai needs to know that the just just you know just in case it doesn't know just in case it doesn't it doesn't want to do its best work you've given an encouragement to do its best work we have to be mindful to our future overlords Absolutely.

9:14And so what you see here, it's obviously doing the standard analysis. And what it's going to start doing once it's done this, it's going to basically open a tab to the right-hand side. Wow. And you see here is where it's starting to create the interactive dashboard. So it's basically writing all the code for it, right? So this is being done in HTML. This looks like JavaScript or something, right? And so that it can be interactive. And so at the end of that, you'll get this. And so now... So it says vehicles delivered, operating margin, the production, and then even a chart. Yes. Their deliverables, revenue, net income.

9:51Exactly. And then a quick summary. And you can download this, you can copy it out. So think about how powerful this is. If you have a bunch of data from, say, an event you just ran, like the Liquidity Summit, if you have all that data about how people interacted and how many people went to different sessions or whatever it was, you throw that in there. ask it to do the work. And basically it comes out with a really, really nice report for you. Whereas before all we were getting were these kinds of text updates and you had to take these and go and copy and paste them into PowerPoint or do something.

10:24Now it's creating the dashboard for us. This is a huge jump forward. The less your business spends on operations, multiple systems, and on delivering your product and service, the more margin you have, the more money you keep, You want to get fit in this new era here in Silicon Valley and in tech broadly. But with higher expenses on materials, employees, distribution, and borrowing, of course, everything is costing more. So to reduce costs and headaches, smart businesses are graduating to NetSuite by Oracle. NetSuite is the number one cloud financial system, bringing accounting, financial management, inventory, and HR into one platform, giving you one source of truth.

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11:39Head to netsuite.com slash twist. That's netsuite.com slash twist. So I don't know if you've tried this with your teams yet, J-Cal, but give me your thought. I haven't. It's absolutely extraordinary. And we have a lot of information that I would like to put through this. So one of the things we've been doing since we're an at-scale venture firm doing 100 investments a year, think like 25 % of what Y Combinator does a year, maybe 20%. They do 500 companies, we do 100. We get a lot of information from those companies back. We put them in a database. We're currently using Notion as our database. And to be able to pull that data from them and then say, hey, make us a report, which of our companies are in group one, two, or three.

12:17And we have a very basic... I like creating simple systems. We put our startups into three buckets. One is high growth, consistent growth. In other words, they're probably series A, series B, they figured something out, they have product market fit. Bucket two, figuring out product market fit. And then number three is they've run out of money. I don't know, the co-founders broke up. In other words, they're still active in some way, but something's in all likelihood going to wind down and they're trying to find a buyer and they're in the middle of M &A. And we have humans figuring that out, analysts and researchers, but eventually AI should be able to figure that out.

12:56And we should be able to then do some analysis over time of companies and say, hey, which companies look like they might be the next to move from group three to group two, from group two to group one. Yeah. And look, this is the one that we just did. So it's slightly different. So you can really tune this and it comes up with incredible. Incredible. I've been using this quite a bit because I think if you ever have any task to do, and even if you just want to brainstorm for yourself just to get this visualization. So So I find, like I said, this type of prompt, which definitely came from one of the threads that Min shared.

13:29So I want to give him kudos for it. But it's a really useful prompt, and it's powerful, and it's a great way to analyze a bunch of things. Yeah, these things are definitely getting smarter. I give this, this is an A plus for me, I'll be honest. I kind of finally feel like these LLMs are not doing random moments of interesting potential. I feel like they're nailing it. And I'll give you one example. Maybe you could just fire up chat GPT 4.0. And since you got the screen there, we're opening up an accelerator and a studio in Austin, Texas. So you heard it here first. And so. All right. And so I'm spending a little more time here.

14:07And imagine you were looking for, I don't know, a personal assistant, right? I don't know how much a personal assistant. I know what that costs in the Bay Area. I don't know what that costs. So here, just write the sentence. Please tell me how much a personal assistant costs on an hourly basis in Austin, Texas. please cite five sources and please put the high low and medium salary in a table and i did this in 4.0 and the result to me was extraordinary okay great oh here it goes okay so when i did this it was unbelievable to me so here you go a personal assistant in austin low salary medium salary high salary and i just said hey can you link to it and it gave me a similar result here And so it's really, and you remember when it used to go out to Bing last year, you know, we do the search and it never worked and it crashed.

14:55And like we'd ask it to put stuff in a table and sometimes it put in the table, right? Sometimes it wouldn't. But here it is. It's now quoted salary.com. And now just give it a follow up and say, give me five different sources of data and average those sources. And so when I did this and I said, give me five different sources, it gave me Glassdoor, Indeed, salary.com. And I was just blown away because when I got, here you go, ZipRecruiter, ZepDia, I don't know that one, Glassdoor, Indeed, Salary. And now you start to see a range of them and you see the sources. This is what a college-educated researcher would have spent in our companies in Silicon Valley.

15:36How many hours? Two, three, four? Half a day. Half a day. This is like, you know, the way I think about it, you'd come in in the morning, you'd tell someone, And maybe in the afternoon, you'd get an email back with this. Right. And so this is wasted work for a human to do now in the age of AI. So when you see that companies aren't adding a lot of positions and you're wondering, why is that happening? How is Uber and Google and Facebook growing, whatever they're growing, 15%, 30 % year over year, adding billions of dollars, but they didn't add any people. This is a perfect example of this. I would normally ask somebody on my team to do this in operation.

16:13The amount of time for me to explain to them and get the result is greater than the amount of time it takes me to ask chat GPT-4. It would take you longer to write the email because you'd have to be a little bit more formal and detailed. Correct. And I'd have to tell them why I'm doing it and all this stuff. So this now is really becoming, as I said, extraordinary. So I just want to say to the team over at OpenAI and chat GPT-4, incredible job on giving citations here, which we're sitting here a year or two ago. we would have been complaining about citations. But this idea that the work is actually good enough to put into actual production is something we saw with developers checking the work.

16:53Now we're seeing it in operations. We're seeing it in data analysis. This is heating up, right? We're trying the same thing just in Claude Sonnet, right? Yeah, and Claude Sonnet did a very good job at this. Yeah. So here you go. I mean, boom. And you could actually create a visualization. Yeah. Now, what you have to worry about here, I think, is this goes back to our discussion about is this stealing or not? And so why would I ever click through to ZipRecruiterSalary or Indeed.com? There is no reason for me to ever go to those websites again. 100 % of my attention and money is going to Claude and to OpenAI.

17:34I'm paying 20 bucks each for these services, maybe 30, I can't remember. yeah and so not only is the human being taken out of this that would have worked for me previously when i say previously last year last year i had humans on my team working on this i do no i no longer have those humans working on our team this is definitely a real life problem for you where you were like hey i need to get an assistant please help me so i know yeah now the next phase of this would be write a job description okay it's going to do that very easy so now just say write me a job description for this job for somebody with five years of experience and give me 10 bullet points to choose from for the skill set.

18:12That would be something, again, I would ask a human being working in Silicon Valley or working remote, let's just call it$50 ,000 to$100 ,000 a year salary, depending on if they were right out of school or if they had five or 10 years. And so here we go, boom, about the role, key responsibilities. to write up a job description would have been a ton of work as well. So now the job description is done. So if you're an HR professional who's been in the field for 10 years and you work with management to tell them in an interactive discussion, how much it costs to hire people in a city and to write a job description, it's done.

18:51Now, the next phase of this would be post this to five services, use my corporate card. And then the next phase of this is sort through the candidates, ask them five questions on email, and then rank them for me and put them in a table and schedule an appointment with them. You want an agent that's designed specifically to work on this for you, both in real time and offline, right? Where it starts and then once it's going, it's like, hey, look, post it, come back, and even set up interviews, maybe even do a screen of these folks. Well, yeah, that's kind of the next part of it. So if you're an HR startup, these kind of things are easy.

19:27The next hard part is, of course, the agent doing stuff. And now when we talked last year about the Maestro concept I had, where I'm in Maestro running a company, if you're a startup, you're not hiring an HR department, obviously. And you might have hired an HR consultant, or you might have hired the proverbial jack of all trades, right? The utility player on your startup. There was a place for the utility player on the startup. I don't think there is anymore, because you can just do this. If you're the sales executive, you can just do that. So I think there's a large group of administrators, operators who are on the cusp of just being 10 to 1.

20:05So if you had 10 of these people who are operators in a 100-person company, which is what I would say, like 10 % of people are in operations, I think they could just go down to 1. Maybe 2 if you want in redundancy and people to answer requests over the weekend on call. Okay, juggling multiple devices and apps to run your business is a mess. We all know that. open phone is here to make that simple. I have an open phone number. I use it to communicate with founders. And it really works for me. I have a desktop app. Boom, I can go in there, I can do voiceover IP in this beautiful, elegant app, right on my phone or on my desktop.

20:35I use it a lot on my desktop. I'm being totally honest, because I have my headset on. And sometimes founders want to do a call, they don't want to pop open a video conference, my sales team loves it. Why? Because they get to keep their private phone number for their private phone number. And then they have all of their business stuff track in one location. So if they need to make a phone call to somebody they talked to last week, they can see their call history, click on it, send a text message and start a phone call with that person. And the ops team uses it. When we have people calling, they have questions about their investments, LPs, etc.

21:03We have a round robin phone number. So it will forward the call to two or three people on our team because we like to have really good customer support. And all this is easy to do with open phone. It's super affordable at just$13 a month. But Twist listeners get an extra 20 % off of any plan for the first six months at open phone.com slash twist. What if you have an existing phone number with another service? No problem. Easy peasy, lemon squeezy. Open Phone is going to port them over at no extra cost. So head over to openphone.com slash twist. Start your free trial. Get 20 % off. You're going to love this product.

21:31It is so affordable and it's so elegant. Just to the product team at Open Phone, great job. I look at products all day long and yours is elegant and simple and powerful. Well done. One really good AI native. You know how we used to say like internet native and then cloud native. Mobile native. Cloud native. Exactly. the mobile natives ai native yeah ai native that just thinks the first thing they do is go here and try to you know get it done i just want to give clothes on it in a plus yeah and i want to give and i want to give 4-0 an a plus officially because we've been we've been off for a month you've been busy fundraising congrats on getting that done hopefully yeah and so i just want to get both these a pluses these are ready for prime time and if you are listening to this and you're wondering why you're not getting a job you were previously considered a very valuable contributor and now you're wondering why you got laid off and why you're not finding a job, it's because the stakes have gone higher.

22:24The stakes have just gotten higher. You have to provide more value. And what is the more value? Well, what can't this do? I described what it can't do. It can't post it to Indeed and it can't sort through the resumes, etc. And so every company right now is just figuring out, you know, I have 10 people in ops, I need four. I have four people in ops, I need two. I have two, I need one, whatever it is. Or twice as much productivity and I may have to switch some people up. Yeah. And so this is just what's happening at startups and big companies. And big companies, I think, are not AI natives, but they will be soon.

22:56And so if you work at a big company and you see me do something like this, and your HR department's the same size, you probably need, and listen, I don't want anybody to lose their jobs, but those people need to, half the people need to be cut loose and reassigned to something more productive in your org. Maybe it's sales, maybe it's marketing. There might be some other place. But I suppose this level of efficiency is coming to everything. And in sales, unlike operations, being more effective just means you increase your sales per person. And can I add one thing, Jacob? Yeah, please. Yeah, I think we both give them A-pluses.

23:25I'm an A-plus on both as well. I think really well done. More and more powerful by the day. You know, the other thing that's happening is the model rollouts are just happening on a continuous basis now, right? And so you just have to start using the tools and maybe use more than one of them. and you'll see the advantages that come from it. The other thing that is related to, so you have to change your behavior, you have to become an AI native. If you just did sort of what JCal was asking for and dropped it in Google, look, and I have generative search enabled, look at how weak this result set is now.

23:57What is going on? In fact, I feel like Google is going backwards here. I mean, it's giving you the lengths that Sonnet and Foro ingested very quickly. I mean, the speed is also the really, you know, a very important Google asset. You know, if you make things faster, consumption goes up and usage goes up. Here we go. Like these things are going really fast. I do think ZipRecruiter and Glassdoor in these places have to block the crawl. Have to block chat. Unless there's a relationship. Unless they pay per citation, per query, per whatever. Because these things are going out to the web. And there is no reason to click through to ZipRecruiter or Indeed or Glassdoor and go to their website and get bombarded with ads, pop-ups, whatever it happens to be, calls to action, etc.

24:50Just since we're here, J.Cow, this wasn't even part of the plan, but I'm going to drop it in Gemini Advanced as well. Okay. Gemini Advanced. Yeah. So Google's Gemini Advanced. Okay. It didn't do the table. Oh, no, it did do the table. It did down here. Yeah. Oh, and let you export to Sheets, which is, I remember that feature. Such a great feature. Okay. I mean, it looks comparable. I mean, they're all comparable. I started also doing this with product searches. So I was looking for a new portable speaker. I was just curious, like, what the highest rated ones. And I asked it specifically, tell me what's on Wirecutter.

25:22Oh, really? Yeah. Oh, okay. So go ahead and do this. What are the best portable speakers? Please cite Wirecutter. Put it in a table with a link to Amazon. give me up to 10 now what's interesting about this is the way wire cut and other people who do the review sites make money is they get an affiliate link and so here it didn't put the links in but if you do if you cut and paste this and you put the same one into chat gpt 4.0 or omni it gave me an amazon link obviously without the wire cutters thing now what you're going to see is like the wire cutters behind a paywall so how does it not always not always sometimes it's not yeah um because i do this all the time oh here you go all right and there's the amazon link okay um and i think you could put in here give me uh when you do the follow-up just say give me the same table with a quote from wire cutter about each one yeah modify the table with a quote from wire cutter for each product i hope somebody from the new york times is watching this because here's the wire cutter quote and by the way this is a much better illusion i don't know but it's really good i mean i don't know i've not done this but i'm gonna start doing it because i'm i always do i whatever i might my go to is this search but like on you know uh on google and then i basically end up on wire cutter right like that's it so i use google as navigation in those cases so you know if you have other rating sites like i also like pc magazine they have a lab or something like that so you know you can start to say put the a quote from pc magazine put a quote from here put a quote from here that's what a personal assistant does so i did this and i'm like well why am i getting a personal assistant again and that it's for real world stuff right so if it does this then we know it's hallucinating though you got to be careful but let's see and they had a quote from pc magic for each product yeah there you go this is i can't do it you go it says restrictions oh so wow whoa that's the first i'm seeing it it seems that i am unable to retrieve the PC mag reviews directly due to restrictions on their website.

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27:26However, I can't provide general speakers based on available reviews and where I put it. That's interesting. Oh, wait, general PC mag insights. It did it anyway. No, I know. But I think that's like, it's hallucinating a little bit. Like, yeah. Interesting. Well, we'd have to cross-reference this. But again, you know, you start thinking about where you're going to start your journey. What do we use Google for? I use Google to find restaurants. I use it to buy products. I use it to hire people, you know, and this is a better experience. The results here are better. And they give me a shout out. But I do think if you're a PC magazine or Wirecutter, you need to say, if you use any of our data, you need to pay us.

28:01And if you are, in fact, in the lawsuit from the New York Times and OpenAI, Wirecutter is why I pay for New York Times. It's like Wirecutter, then New York Times in order. I am no longer visiting the Wirecutter website. So if you want a piece of evidence in your lawsuit, clip this and submit it in your briefs. That is super wild. Hey, startups. Does your product need a facelift? Well, a lot of companies get up and running quickly, and that's part of the idea here in Silicon Valley and in startup land. But hey, the design suffers. Maybe you got flawed UI, the UX decisions were made in a rush, and that can be the difference between product market fit and not getting product market fit, or weak product market fit, medium product market fit, or strong product market fit.

28:49Well, here's the good news. Dev Squad's asynchronous design squad is here to help you. Our long-term partner, Dev Squad, built this service specifically for startups like ours. That means you don't have to worry about winging it yourself and you don't have to buy a subscription to an unlimited design service and have them under-delivered. No, thanks to Dev Squad, you can buy ours from expert designers who will handle everything for you, from UX audits to very sophisticated UI design. And the result is going to be an improved user experience and ultimately happier customers and a more successful business.

29:25So if you need to map your user flows, maybe you need to craft some interactive prototypes, or maybe you need to conduct a rigorous usability test, DevSquad has you covered. So if you are ready to transform your startup's UI without the painful overhead of having to hire an in-house design team, just head to devsquad.com slash startups and book a call. That's right. DevSquad.com slash startups. Mention you heard about the service here on This Week in Startups. You get 10 % off. I mean, I was not expecting that. We use these things all the time. So I really like this. I got to say that is a way to...

30:04And this is what being AI native is about. I think AI native is the name of this episode. Yeah. You've become AI native. When I'm in the car with my daughters now, they ask me questions. And I told you I'd put my quick key on my iPhone to be the chat GPT for dialogue where you talk to it, the talk interface, and that's actually getting better and better. Yeah, getting better and better. So you can do these similar type things. Tell me the best three sushi restaurants near me and what their top dishes are, and that will work. Tell me the top three sushi restaurants in San Francisco and what their top two signature dishes are.

30:40Here are the top three sushi restaurants in San Francisco in their top two signature dishes. One, Waco, squid with shiso and lemon. This dish features a delicate combination of squid, shiso leaf, lemon, offering a refreshing and balanced flavor. You know, if you're driving in a car and you can't look at your phone, this is pretty amazing. And then when this has a hookup to make the reservation amazing, it really is game over. Sometimes with these changes in technology, we see there's like a tipping point as we've talked about in the world. And when a tipping point gets hit, I think people are going to collectively realize, wait a second, I'm not an AI native.

31:19If I am an AI native, I'm just going to get a lot more done a lot faster. And then you'd be super competitive. And I just look at what my startups are doing when they are planning an event, a retreat, a offsite, a new product, a press release. It's just going faster and faster and faster. So velocity is going up. Yeah, definitely at liquidity, I could tell this year, Jason. And obviously, your teams are always getting better because you're pushing them. But it felt like there was more AI behind the scenes for organizing. Oh, a thousand percent AI is starting to work a lot better for planning events, 100%.

31:51Any key insights from that? I think if you were to just ask, like when I was doing the All-In Summit and we moved it to LA last year, you can pull up, chat to me 4.0. I started asking you questions like, tell me arenas and theater spaces that could have over you know could have between 2 000 and 4 000 people uh and seats and it was kludgy slow and it would find me some it had you know half the information was wrong but it did find some places that i may not have thought of so i would just ask it what theaters have between 2 000 and 5 000 seats in the los angeles area for events and put it in a table and put the address Yes.

32:37And you do some sort of interesting search like that. It didn't work 18 months ago when we were planning that one. It didn't work comprehensively. So I worked with a producer and then I told him, hey, you didn't have these three places. And they're like, oh, yeah, I didn't think of that. And one of them was the YouTube theater. It turns out there's a YouTube theater and there's what used to be called the Nokia theater, then the Microsoft theater. and here you know you're starting to see links to the theaters for more information their address and now if you ask give me 20 theaters and put it in a table it's going to give you 20 and it's going to find the youtube theater and the other ones and that was the comprehensiveness is moving up and the speed is moving up uh so here you see it's searching the web it's re and it found 10 different websites dude 10 different websites at once and started doing this you know it's got the same one so far but there's the adobe theater i mentioned there's uh the hollywood palladium there's a shrine and uh there's the greek theater walt disney concert hall uh el capitan the region theater man i mean this is incredible this is more than a morning this would you'd give someone this task and they'd come to you a week later yeah yeah this is a week project now if you said add a column with the price to rent the theater now that's where you know sometimes they have the information on the website sometimes third-party websites that have it this is where you start getting into the job of you know an event producer and so you know we're going to add a price here to rent this that would be the next thing your client would ask if you were and you know it couldn't come up with anything and here we'll see if these are you know if these are actually you have a good sense right i think the dolby theater might be more expensive than five thousand an hour but anyways yeah i mean these prices are probably wrong so yeah you know and that's because a lot of times these prices aren't available.

34:25So it's probably estimating here. So what you'd want to say is cite the source. If you can cite a source of the cost per day to rent this, let me know. Or you could say, get me the email of the contact person or find me a person on LinkedIn, right? Remember, we're trying to do LinkedIn searches. Like, these are all the next steps. And so I don't know if there are somebody at OpenAI looking at the search stream and saying, hey, solve this next. or the AI is just watching people do it and trying to figure it out or it's ingesting more information. But brave new world here, right? Well, I think to answer your question, I think what the focus really is, and we're going to see more of this in the back half of this year, is we're going to see a greater focus on agentic type use cases where I think the first aha when this came out was like, oh my God, I can just type something comes out.

35:22But now it's like, hey, go do a bunch of stuff for me and come back. I think that's where we're going to see the biggest improvements. And when we think of the next generation of these coming out, and it's like we did here, all of this didn't, you know, we're doing what's called multi-shot, right? We're working through a problem, but we're going to see the agents just kind of work through it on their own. And, you know, you saw that in the very first iteration, people were trying that. Remember with auto GPTs? We'd be like, come up with like a work plan and then try to go fill it out. I think what we're going to see now is it's going to be able to do that just for a question you ask, and it will be able to sort of figure out, hey, I should probably put this in a table and figure out prices and do that.

35:59Because you can literally farm the task off to a bunch of different agents and come back and aggregate the results. Yeah. So the ability to do the same search against five different things, five different language models, and have it then build one table from five different models, some master one do it, or say, you know, what other questions should I know about renting a space? And it's like, well, you need an AV company and, you know, you're going to need sound and you're going to need food and, you know, it's just powerful. Okay, let's go through it. You have more. I do. I do. And I know there were a lot of image stuff that came out.

36:34So I'm very interested in the image stuff. I don't know if you saw those trending and if you have it on your docket, but somebody was making, they were taking famous memes and then bringing them to life on Twitter. oh yeah yeah i i i mean i have that one in twitter i didn't i didn't demo it but like i can show you who did it let's pull it up that was a fun one i have a demo slated with these guys i didn't do the meme real thing yeah i have them pulled up for my next demo which is awesome it's a good call-up you always do that you have this superpower this is like phil helmuth like he knows how to read your cards and he'd be like oh you had this but you want a range of hands yeah so here it is this video was created using luma ai yeah so we have them queued up for the next demo.

37:14We'll just start here. So this is the start of the video. Okay, there's the guy with the beard, the bear guy. There's Keanu eating a bagel or something. Oh, this is good. The girlfriend and the whistle at the... Oh, the... There's the girl with the fire behind her. There's the crying guy from some TV show. Here's Charlie and the Chocolate Factory. I know that one. Oh, here's the guy. Thinking guy. Yeah, I love that one. Taps his temple. Okay, here's Spaceballs. Spaceballs.

37:57Wow. Luke Skywalker meme. I didn't know that was a meme. Rick, are you just looking at things in the office and saying that you love them? Describe what Marcellus Wallace looks like. There you go. Pulp Fiction. Incredible.

38:12Oh, you were the chosen one? It wasn't one or her. Yeah. Oh, yeah. That girl with the little girl with the crazy look and actually putting on glasses. I mean, it's nuts. And this is like. All right, we get it. Oh, that's Putin and Kim Jong Un driving together. Oh, wow. I didn't watch it all the way through. I didn't watch it all the way through. I didn't know they did modern ones. Oh, and they're having them do the drop off. I wonder if they told it make a story about memes. they rickrolled us at the end so there's the like a guy at the celtics game who's just up there's doge dog yep shima inu whatever and then finishes with the kid wow wow i mean pretty impressive yeah i'm gonna have to go through our bet list i think i'm gonna owe you a lot of money you always took the short end of these well because you know i'm i'm a technologist right so i always say i you know slow than fast but um so so that was luma labs that did those i did this one earlier because it was taking some time uh to do them and i said a panda you know riding a bicycle through new york city and uh not bad these things are just incredible now right in terms of their capabilities and what they're able to do.

39:37It's just mind-blowing. I mean, I... It hasn't crossed the uncanny valley for me. It still looks like it's AI generated. I would not have been fooled by either of these. I would have said, oh, that's cool. AI is getting better. Yeah. I think they would have to put these into post-production to fool me. And I think so too. And I think the framework we should use is just enhancement, right? What I would say, J.C.L., is let's say you're trying to produce something and you need some screens or some storyboards is what they're called. Yeah. We're in the storyboard era. And I think it's not that far away.

40:08I think when we see the really, really highly produced stuff, well, basically Luma was one and we'll just rate two back to back. I had another one queued up here as well. This is a Chinese company and they really blew some people's minds away over the last couple of weeks. And it's called Kling. Did you see this one? No. Show me. Kling. K-L-I-N-G. Yeah, the video that these guys had. Kling. You know, you could just see some of these. Yeah. Look at those flowers. Here's a panda playing a guitar. Yeah. I mean, so close to the Uncanny Valley. This one of the parrot, it did cross the Uncanny Valley for me.

40:46I wouldn't have known that was fake. Yeah. Obviously, a rabbit drinking coffee. Even this one. I think if you were making a product. Yeah, that's a flat white. In a Cortado glass, you know, like the glass. glass. That's a Cortado. And that's one of my favorite beverages, coffee beverages. And that looks like it's from a stock image. Now, what company is this? Kling is the name of it? Kling. Now, you can't use it because you have to download an app and you have to be in the Chinese app store to do it. So I can only demo the screen here. But I think the point on these, maybe the next episode, we'll review the bets is I think some of these have really crossed.

41:22Now, I do think these require a lot of tuning. So it's like being in mid-journey and creating things. And so in order to get there, you really have to do, you know, you have to kind of do many, many iterations of it. But I do think it's definitely crossed sort of the point of convincing us that, you know, these are real. I think we've crossed the uncanny valley, but. Fantastic. What do you give those two? I give those B pluses. Okay. Yeah. And what do they need to be better? I'm grading them on crossing the uncanny valley. So I don't know what's AI created. So if you were saying, judge it on it being a storyboard tool, like a creative tool to give you ideas, I give it an A.

42:04I give it a solid A. Now, if you said, you know, to actually make production videos, not storyboards, I give it a B, B plus. I think they're six months away from crossing the uncanny valley. Like even the coffee one, if you said, look at and tell me AI or not AI, I probably would have got AI from just some of the artifacts. The parrot, maybe not. So I felt like out of the maybe five or six we saw, it's not going to get me the majority of the time. Okay. Yeah. I'm going to give them an A minus because they're going to help you win bets against you so they can get to an A plus. Awesome. So you're just, yeah, the fix is in.

42:42Got it. If you're trying to motivate them. Yeah, exactly. Guys, get an A+. Let's win some money here. Whatever bet you're saying, I'm taking the under. Whatever you're proposing going forward, I'm going to get shellacked here. No, no, no. What I was going to suggest is we do a little bake-off where we do a real image and AI image. We'll do that. How about next episode we do that? Yes, we'll just do it for$100 a pop. We'll be just like flips. They're just flips. Just flips. Just flips. AI flips. Deal. Next episode, we'll do in two weeks, AI flips. get two full weeks to do it oh that is so fun i think producers get on it make us one cortado from a stock image library get one of these ones you know take the watermarks off and let's see let's see if we can do flips now the last one i want to do here is just actually there's two things i want to do really quickly just one i want to lightning around talk about open source so we'll do a quick lightning round and a few things that have also happened uh since sunny's been out um so a couple of models have come and the this quen 2 it's this uh team out of alibaba people that are not tracking this is probably right now the best open source model and so it's come out it's really powerful they you know uh it's fully open source they have a lot of different sizes ranging from 500 million all the way to 72 billion trained on a bunch of different languages and a really big context, like 128K tokens.

44:07You can put a lot in. So this is available. You can try it out at Hugging Face if you want to. They have a version here that's running. Okay. So this is open source. Open source from the Alibaba team. And this is really interesting because what we're seeing is these teams in China are pushing for these open source models and they're coming to the top of the leaderboard. Interesting. Yeah. There's a great tradition of not respecting IP and just wholesale copying stuff. I'm curious in these code bases for new projects from China, are these built from scratch or do you think these are, you know, inspired by common LLMs?

44:49Do you think these are, you know, backdoor hacks into proprietary stuff? I'm just curious. Well, I'm going to actually share something slightly different. I don't want to comment a lot about where they're getting the data from because it's just not known. But I know one thing that's happening because I definitely heard some of the real professionals in the space talk about it is they're using the current large language models that are available to generate synthetic data so that they can make their model better. Got it. So think about what makes a model really good, not so much as just open wild data on the internet, but it's matched paired information.

45:28So it's like, if we just took the stuff that we were doing, J. Cal, which is like a question and an answer and then mapped it all, and you gave that as training data, it's really, really high quality data. Interesting. So that search we did on salaries or speakers, then you say, get me some more data similar to this, and then you feed it in. So it's kind of meta. yeah i mean there's also the chance in china that you know they would just rip the entire new york times archive the entire magazine archive from somewhere where other people in the united states now would respect that ip and you know we just saw the lawsuits against two of the music companies that you and i played with last year yeah like those companies are gone by the way i'll just tell you right now i don't think you're calling it i'm calling it right now both of those companies are going to you're calling the napster i'm calling i'm just saying it's straight up Napster.

46:20There's two of them, I think, that got sued. I think they're both going to be Napster roadkill, or they will have a$50 million fine against them, and they will have to work it off. And then I don't know who's paying for that$50 million fine, because they're going to need a perpetual license to the music. So they'll be fined$50 million,$25 million for what they've done already. Then they're going to get charged on top of that, right? For future use. And they don't have enough revenue to make that work, straight up. So I think anybody who invested in those companies and i don't know if you saw they said in their training data i forgot the name of the company uh yeah there's suno there was two there's suno was the one and i think suno is saying they can't tell them how they train their music because it's proprietary in discovery yeah which means they're screwed yeah but in china they'll just take every song ever written and they'll make a better open source llm and then what is the music industry going to do they're going to have to do an injunction this is what i predict will happen this is as crazy as it's They're going to do an injunction, the music industry, because they are the most powerful.

47:23And organized, correct. More than anyone, because they have the RIAA, right? Correct. Reporting Industry Association of America. And then you have the songwriters, and then you have the live stuff, you have the labels. I mean, you're going to get it from all angles. And you're just trying to understand how many different ways you're going to get sued requires a major legal team. Because they're going to come at you from all ends. So what will happen is a Chinese or whatever firm that is operating in an area that doesn't have restrictions, or even Israel or India or Pakistan with LinkedIn data, right?

47:56With spray data. You're going to have an LLM come out of LinkedIn and other Facebook data, Instagram data. And you're going to be able to use that LLM to search and say, hey, I'm looking for CTOs at this thing, and it's going to just spit it out. Like things you can't do now. And that's going to then wind up on Hugging Face as an open source project. Yeah. and the weights and everything and the data. And then they'll do it for the music industry. They'll do it with every movie ever made. And then those language models are going to get sued and GitHub, Hugging Face are going to get sued if they publish them.

48:28So just think about that. If you host them, they're going to say you're contributing. You know that this is built off stolen data. They're going to get sued. Now, I don't know if that lawsuit works or not or Hugging Face hosting an open source project, but I could see that happening. That's how much is at stake. You paint a very doomed picture for a wonderful place. We got to find a better compromise, OJ Cal. We can't just shut innovation down either. Yeah, it's a very simple one. Pay the man. The U.S. companies paid the man his money. Pay the man his money. Yes, exactly. It's like we did to OG Ananobi at the Knicks.

49:01We paid the man his money. Yeah. All right. Last one. And I'm really excited by this. So, you know, there's been a lot of energy and notion around like large language models, But there's a lot of innovation happening in the space of voice. And Cartesia AI, what they're doing is, so they do something called state space models. And specifically... State space models. Okay. Yes. What does it mean in English? It's basically a model that uses a different area for the search space, which is not necessarily language, right? Okay. And I'll do a more detailed explanation of it because I have to get up to speed.

49:36I don't think I'm like fully there 100%. Okay, fair enough. And so, yeah. It shows the product. Yeah. Okay. Of course. And so what they've done here is they've created these voices using this model. And these voices are incredible. So let me just make sure the audio comes. So this is a 1920s radio man. And I know you love to do this voice. Ladies and gentlemen. Ladies and gentlemen. Gather around the wireless as we delve into a world of speakeasy. The tragedy of the Hindenburg. Roaring excitement. Yeah. I mean, it's literally the classic. And I know you'd love this, right? Hey there. Have a seat and relax.

50:08you're in for the best haircut and some great conversation oh asmr barbershop guy yeah yeah i feel like he's about to slip my throat yeah like you know here's the indian man every journey starts with a single step i'm not doing my indian accent i'm not getting canceled you try sonny i will not do my indian accent yeah um but they've done an incredible you're indian you're allowed no no i i'm not gonna do it i'm not gonna do it i don't do a good one but uh The wizard. How about this is the last one? With a flick of my wand and a whisper of incantations, mysteries unravel and magic fills the air.

50:44All we have to do is decide to do with the time that's been given to us, Sadiq. Yes. It's Ian McAllen. Yes, it is. Exactly. Exactly. So do they have one for her and for Scarlett Johansson in here? No, they don't. I don't see one here. But no, but what's really happening here is like what, you know, there's so much innovation happening now, J. Cal, and maybe in one of the next episodes, we'll just do a, that's what, you know, we talked about it a little bit in liquidity, but, or at liquidity, there is a lot of innovation happening now. And it's faster than ever. And it's coming in the way of new types of models, open source models, agentic reasoning, and voice.

51:27And so one thing that I want to go into is in the second half of this year, J-Cow, we are going to have some surprises that no one was anticipating. Okay, there you go. That's just a, that's something that you know, running developer.brock.com, or that's something, so that's inside information. That's the inside line. No, I don't know. It's not inside. I'm just saying like being in the thick of it, you know, seeing how everyone is innovating. I have a very, very strong, and I don't know exactly what it'll be. I mean, I have insights in a few places that I can't share, but. Okay. But beyond like just sort of generally, I'm telling you, we're going to be more surprised in the back half of this year than we were when opening I first came out.

52:07Oh, it's a big claim. It's a big claim. I don't know how we phrase it as a bet, but what you're saying is everything that happened up to now is the epilogue and that we're going to really see the stories coming. The stories coming. Yeah. Okay. There you have it, folks. Stick with us. X.com slash Sundeep. X.com slash Jason. This is This Week in Startups. Rate, subscribe, write a review, and then make sure you tell Sonny how much to love him and that you missed him for a month so that he is no longer MIA on the pod. We need him here doing these demos. See you all next time. Bye-bye. Bye.

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(36:40) Sunny demos Luna Labs, including the recent creation of bringing famous memes to life in one killer video.

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