← Startup Stories - Mixergy
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- [00:00]
A
Hey there, freedom fighters. I've got a story about a guy who's creating a content business. That's exciting. Check it out. I read about a guy who uses AI and people to create over three quarters of a million pieces of content. So I asked him to do an interview about how he does it. Let's watch. Anirudh Singla is the founder of Pepper, which uses AI and humans to create content for enterprise clients.
- [00:25]
B
The next new thing.
- [00:27]
A
Anirudh Singhla, what's your revenue right now at Pepper?
- [00:33]
B
So we just crossed 10 million in ARR and we're now in the 10 to 25 to 50 journey. So, yeah, been it took us some time to get us here, but yeah, very excited to finally cross this.
- [00:47]
A
When you were working on upwork as a writer, how much money were you making a year then?
- [00:53]
B
You know, it's funny. So just for reference, I was trying to figure out a way to finance my education, and the goal was to earn $2,500 in two months. And I've never earned a single penny in my life. And I slogged about 17, 18 hours on Upwork, fiverr all these chegg and all these other tutoring platforms and made that happen. And, you know, that actually gave me this one very unique insight that, you know, hey, I put in the hustle and the slog to get this done. But there's a real need out there where there's like. Like I discovered these Facebook groups which had like hundreds and thousands of writers at that time. And I figured someone needs to aggregate this market because huge value. And if you just recently saw Mercer, which went crazily to, I don't know, 250, there is 2,300 million revenue. Like 250. 300 million where they're getting freelancers to train on AI data sets. So just see how the journey moves from, you know, people going from content production to not training AI data sets.
- [02:01]
A
And the story, as I understand it, is you said, this is way too difficult to find another writing gig. Every time I have to look, I end up spending more time looking and dealing with the business of getting writing work than actually doing the writing, which is what people are paying me for. There's got to be a better way, a better model. And you created it. You called it Pepper Content. And. And it was a marketplace where you personally at first were matching customers with writers. The writers were in India, so the price was lower than it might be where you are today, which is San Francisco. Right. That's the model that you Had.
- [02:32]
B
Yeah. And you know, the model evolved quite heavily where we now have a global talent network. So we have about close to 150,000 freelancers who've applied to write from Pepper. And this is not just now lighting as well. So just a minor correction. We also do design, video production, and so it's a multimodal in terms of what we do. And the talent pool is Now Global. So 50% of our user base on the talent marketplace side sits out of the US and we work only with the top 3%. So our value proposition to an enterprise today is we have subject matter experts in retail, cybersecurity, crypto who are US native exports, and we're also working with global organizations who need regional exports. So Pepper also produces content in 45 languages. So we right now are working on with customers where someone needs Arabic strategy for search or someone needs a native Chinese transcription. So we have that level of, you know, global imprint. So far from, you know, where we.
- [03:46]
A
Started on and also far from just humans, AI is the next thing. How did, how, how does AI and humans, how do AI and humans work together with you?
- [03:57]
B
Yes. So I'll give you a quick evolution of how Pepper also evolved. So we started off with this managed services marketplace for content production where we were matching customers and these writers. And then we stumbled upon this thing called chat, called GPT3. And this is two years before ChatGPT. This is 2019 we're talking about. And we were the first 50 users to get access to this. I had cold emailed Sam Altman and Greg Brockman to get access. They were happy to give it to us and realize very soon that, you know, if you put content on a number line of 1 to 10, there is probably, and you know, this is my prediction that time, and obviously the strengths changed one to four, which is high volume, low value content will get automated and everything above that 5 to 10 will need experts enabled by AI. I can say, safe to say that with AI now it's 1 to 7 or 1 to 8, and we keep getting better and better. And that's when we realize that AI needs to be natively embedded in workflows to produce content at scale and do it effectively. So we built a lot of tools. We built this tool called Pepper Type AI that was an AI writing tool that scaled from zero to half a million users while we were running the marketplace. And we realized that, hey, there needs to be this marriage which embeds both. So we started building in this agentic technology called Nimbus, which combines How AI can enhance humans to produce content at unparalleled scale, but while still having a human review layer or editorial layer in picture. So it's still a human engine, but it's heavily propelled by AI. And just for reference, we have now about 500 custom apps which are powering, you know, this human plus AI content engine for the world's largest enterprise brands while ensuring there is human level quality checks.
- [05:58]
A
But the first layer is AI starts the work, creates the first draft always. Then a human being takes over from there. Do you then take what you've learned from how the human being adjusted the AI content and feed it back in for you? Do talk to me about that.
- [06:13]
B
Yes. So we have what we call as a reinforcement loop where it's not just Andrew about AI generating the first draft and the human reviewing it later. As part of the platform technology we've built called Nimbus. And we're publishing a lot of blogs on our website now which talk about how Nimbus works. We are able to create unique insights and fetch data from a lot of sources and throw that into the LLM. And it's not like I'm going to chat GPT and putting in a topic saying, hey, help me create content on this. And it gives me something and I use that. We chain multiple prompts and these are very complex prompts that we write for enterprise customers and we chain those responses and each response's output goes as an input to the next response. So for example, I want to create an article on something, I'll pick up that topic. My platform technology called Nimbus, what it'll do is we create workflows where we'll put the topic on SERP. We will see the top 10 URLs that come in. Our platform will scrape all those top 10 URLs and see what all have they covered. What are the H1s, H2s, all of those things. We combine all of that intelligence into one flow and then identify what's the most optimized SEO focused article. And obviously now it's geo. So we are also inculcating workflows you need to optimize for AI search as well. Okay, into the brief when the article is getting produced. So it's not just about producing an article, it's about producing an article which can optimize and rank. And we are taking insights from what's already ranking from competitors. And what we also end up getting is a human who used to end up doing comparisons across 10 competitors, seeing, hey, they've Added this, they've added this. All of that is already being done as jobs to be done by the AI just in part of the research. And then we get the human, we get a draft generated, we have a human review goes live. Then we do another two, three things. We also look at data from Google Search console and also have figured out workflows which can re optimize that content six months down the line if it's not performing. And that's something that customers are loving peppers technology for where people have websites which are like thousands and ten thousands of pages of content which they produce sometime or the other but it's probably not doing anything to their business today. How can we re optimize that content automatically to start driving more hates and traffic? And that also helped in Pepper's evolution as a company to now a content led growth company.
- [09:01]
A
Okay, you and I were looking at a Humana article earlier today where you said this is an indication of how content can be written. Let me bring it up right now. Tell me why this is good. What am I looking at here that says that is leading it to perform especially well in results for AI searches?
- [09:25]
B
Yeah, so I'll, I'll give you a quick, you know, analysis on this. So if you start at the top you'll see this one thing called publish date and updated date right now this is very powerful. You'd be surprised. So just for reference, all LLMs give, there's a 25% probability that your content will get surfaced up better if you map out freshness of content. So this is actually a signal to the LLM that this content was recently updated. So LLMs love freshness. So that's one way small insight. By the way, if you go down.
- [10:04]
A
To small note, the published date was April 21st. The update date is April 16th. So this seems like a bug or some kind, right?
- [10:14]
B
But okay, yeah that's probably a bug but, but if you get, yeah, that's a bug right now on this. But just to get you a sense that notifying the LLM when this was last updated is like the, you know, one of the school hacks that is driving a lot of search.
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A
So that's one.
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B
If you go down further.
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A
Keep going further.
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B
That's one. If you go down further you'll see this key points, you know, summarization. It's like a TLDR summary of the article. Now if you normally have seen, we've all been, you know, brought up with this logic of throw everything you have on that topic in that article and make this like a 3500 word long form article. Guess what? The user doesn't care. They want to get their answers in a very simple MANNER. And the LLMs want to summarize this in a microsecond split decision. So if you can do the job for the LLM, if you can already summarize your existing content in a TLDR summary, that is huge advantage.
- [11:11]
A
I see I'm taking one more step away from the LLM. It doesn't have to summarize it to analyze whether it should provide it. Okay, so that's the next thing. I like that. That's the key point section.
- [11:22]
B
Now if you go down further, if you see this, Compare Medicare supplement plan costs. Now see how it's giving an example. If you see how LLMs respond, they're great at giving these example structures because people are going in and putting in comparison queries. They want to know differences between different plans. And you'll see LLMs are very great at whipping up tables. So if you go down further below, you'll see this table summary, which is almost like a comparison analysis, which gives you a sharp understanding of what's working, what's not. Companies starting to do that is super interesting. So you'll start seeing that these are things that drive a lot of LLMs to suddenly start pushing out this data because they want to give a user a holistic sense and try to appear unbiased.
- [12:17]
A
Okay, I get it. I've heard that too, that this kind of data is what the user wants. And so you might as well put it in a table and make it easy for the LLM to give it to them. Anything else I should see here?
- [12:28]
B
I have a few factors that they could use to even further optimize this better. If you see the H1s and the H2s, the headings, right. The. Or the subheadings. If you convert the subheadings into interrogative questions. So instead of sentences, if you would have written how, what are the factors that affect your Medicare supplement plan?
- [12:51]
A
Instead of what's written here, which is factors that may affect your Medicare plan costs. That's the title you're saying. Turn it into a question would have.
- [12:58]
B
Been even better because interrogative question, because FAQ schemas are. And FAQs are driving a lot of this growth in terms of user discovery. So people are seeing like we've worked with customers where they've seen massive traffic uptick just on converting the H1s and H2s into interrogative questions. Okay. And that's something that can be done across the website because suddenly now your website's already FAQ optimized and it's, you know, being built and showing trends based on that.
- [13:33]
A
Okay, I'm with you now. I see this. And this is what you do at scale, how many articles would you say that Pepper is responsible for creating a day?
- [13:42]
B
So just for reference, We've done about 750,000 articles till date. There have been months where we've done 30,000 articles a month. So we've seen scale of all kinds of. Right now what we're focused more on is not just huge volume content production, but content which will actually drive meaningful outcomes. So Pepper has now evolved into a content LED growth engine where we're helping companies show up on AI search through our own platform. And the interesting part is we have workflows built in. These are autonomous workflows which can accelerate content velocity, which can accelerate things like content refresh, which can give you a sense of what are low hanging suits that can help pick up traffic at scale. Because if you see 80% of the websites in the world, traffic's been going down, people don't know how to respond. So we're working with CMOs, so we do a lot of these CMO dinners. So we've done like 40 CMO dinners over the last one year. And you'd be surprised, everyone's still not able to get that. You know, it's not about SEO being dead, it's now SEO plus geo. We call it Geo Generative Engine Optimization, which is, it's creating new real estate in the Internet. And suddenly it's not just about optimizing your own website. You need to think about platforms like Reddit, Quora, having the Wikipedia page. You'd be surprised. We've literally created 15 Wikipedia pages or helped create that over the last few weeks. So there is this huge value in now starting to analyze what factors do LLMs spend a lot of time giving weights to. And that's how you have to now think innovatively about organic content and search.
- [15:42]
A
And what you're doing is automating a lot of it. And so for example, if a page has been published and it needs to be refreshed, your software will automatically refresh it and add the new updated time to the top of the post. If you're seeing that something's not ranking, you're looking at Google Search Console understanding what's going on and editing the page for your client in real time, updating their pages.
- [16:06]
B
And Andrew, it's Also giving them visibility on where they get shown up on LLMs. So we've built this platform called Atlas, which is our own proprietary tool which where we define a universe of prompts that could potentially get you that probably users are using for and we have these volumes attributed to those prompts and we throw those prompts on these five different LLMs like Cloud, Perplexity, Charge, GBD, Gemini and so and all of that. And we see where all is your brand getting mentioned? And we start seeing is your brand getting mentioned? Do you have citations? Are these LLMs picking up content from your website or not? And once we get that analysis and most companies are doing don't get a great analysis because of them not being LLM ready, we then give them a comprehensive checklist of Here are the 10 things you need to fix. These are the low hanging fruits. And then we have technology which can automate a lot of those jobs to be done that they would have probably needed like 10 people to do in a far effective manner. But like I said, our differentiation is we're a platform plus service, so it's a solution versus it just being like a tool that a company can use and be happy about. Because fundamentally the lift needed to pivot up things like this is huge. Like we work today with Atlassian, Sprinklr, ClickUp, Instacart. These are big enterprise companies which care a lot about organic search driving a big part of their business. And for them impact is directly linked to revenue. So we now think a lot about speed in terms of how can we get organic transformation in these enterprises. And one big learning and what I want to share also is video search is going to be huge. We're helping companies show up and start producing a lot of content on YouTube because soon LLMs will actually push up videos a lot more. How are you doing traditional articles?
- [18:14]
A
It's not. You and I talked before we got started about how it's not just human beings creating videos. You're using Sora and other tools to create videos using AI. When you're using Sora, you're not using the Sora like social app, are you? No. What are you using?
- [18:28]
B
No. So what we have is obviously all of these models and their APIs integrated. And like I told you, we have built an agentic technology which allows us to create custom apps and custom workflows for customers. So imagine we're working with a travel tech company where we're helping them create hundreds of videos which are performance marketing ads on Instagram where we're putting in the text almost converting a top 10 places to visit, say in west coast into like almost a reel which can be then run as a performance ad with colorful creatives.
- [19:08]
A
Now give me this whole workflow. So you're using AI to create videos that you know will perform and there's doing. When they do well, you buy ads for them. So where do you find the content? How do you know what content to turn into videos on Instagram and YouTube?
- [19:21]
B
Yes. Yeah. So if you see most of Instagram and TikTok, they've just become indexable on search Instagram specifically. So which means LLMs can now actually go through your video and see all the text you have on your video and then plop that video up specifically.
- [19:39]
A
Okay.
- [19:40]
B
So it's become machine readable metadata. So that's why what we do is we pick up all these listicle kind of topics and queries like top 10 places to visit in West coast or here, the five things you're not doing. All those article content which no one now reads but wants to love seeing good videos and visuals about. We, you know, create summaries through, you know, a normal article builder. We then convert it into a video script and that video script is made punchier with respect to audience cohorts. So we have like, we work with customers to define. These are the 10 audience cohorts you might want to build on. We create 10 scripts, we basis each script, we then add in the video elements and the brand elements that they care about, get a video popped out, play those videos organically, see which ones perform, do ab testing and then run ads on the ones which perform the best. So this is like agentic workflows and telling you these videos very soon are going to start popping up on search instead of that article you were reading from, you know, maybe booking dot com.
- [20:46]
A
Okay, and who's doing the voice? What tool are you using for voice? What tool are you using to get the writing to not feel like AI slop? Or is it AI slap? And that's okay right now?
- [20:57]
B
Yeah. At least in short form. Video it is AI slop and it is getting better as you know, these models become more.
- [21:05]
A
You're saying it's a, that you're producing, but it's still doing well.
- [21:10]
B
It's still doing well, but I'll tell you, it's not sustainable long term. So one has to eventually create that workflow with scriptwriters and you know, basically smarter people have to use these tools. It's not like, you know, a normal person can just go in and Write in this and get this out. You need person with creative input, creative output, understanding to get this workflow in place. We use tools like 11 labs, which help us with voice. And we're also building a lot of synthetic AI models where we can have a human be generated completely from AI. So imagine and you are speaking and we have a platform which understands how we're speaking on a live basis and then starts mimicking us and then starts the next time. Ayan, you don't need to get on a podcast. You can just speak to Aniruddh's AI avatar who's going to make the same, you know, hand movements, hand gestures, facial expressions, and probably speak to you in a much better condensed manner than I am doing.
- [22:15]
A
You know What? I've used 11 labs for audio and it works so well that when I do it for people using their voice, they can't tell that they didn't talk. They think it's them, and that I captured a video of them somewhere. I understand also how to use AI to write in a certain voice. Like you can kind of write using my voice. There's enough content out there. What about video? Are you saying now that you can even have talking head, or do you need to have images only? Can you do a talking head like me and move and gesture and everything you can? What are you using for that? That works?
- [22:46]
B
Yes. So there are a lot of proprietary tools right now. So these are not like, you know, mass tools at this point. We're experimenting with a lot of them. Nothing where I've seen huge success where I can tell you that, you know, hey, this definitely works. But I can tell you this entire concept of Synthesia. I don't know if you've heard of them. They've now obviously expanded into a much larger company. I used to, you know, I had a friend who used to run this company called Rephrase AI, which used to do this, which was basically synthetic voice cloning. And they got acquired by Adobe eventually because Adobe wanted to introduce them in their Genai platform features. But there's hundreds and thousands of experiments being built and run on these things.
- [23:37]
A
Are these good enough right now that Pepper is using any in their videos? Are you using Synthesia? Are you? You are. You're using Synthesia. So here's what I understand that you're doing. You are finding articles that work already in a format that people just aren't going back to, which is text. You're turning them into scripts. You now have a script that works. You're asking for based on the user that this script needs to be customized for. You're customizing the script. You're then having a talking head using Synthesia or some other tools. You're still playing around with what works best. You have a human being plus some images that go on top, plus an audio, an AI generated voice, all creating a video that's going on Instagram and YouTube. We're not talking about stuff that's competing with Mr. Beast or Mark Rober, but we're talking about stuff that competes with text, which people aren't reading anyway. Got it. And you're saying we're going to keep iterating and improving it. I see what you're doing and the tools that right now that. I'm sorry, go ahead.
- [24:35]
B
And you know, the vision with it is not just the perfecting workflow, it is to engineer growth. So we're thinking about content LED growth, where we're saying, can I engineer growth where I create 100 videos which can get you potentially this engagement rate or half a million views. And it's imbued with data. So we know the search volumes are the keywords. There are a lot of tools like Vidiq which tell you search volumes on YouTube you can also have. There are a lot of tools. Mr. Beast also has one of his own tools around that. And once YouTube analytics is getting very good. And similarly all of these other platforms, analytics are getting better. If you can thrust back those analytics into the workflow process, you can re engineer how you can do content better. And that's what we're about driving.
- [25:25]
A
I have no idea you're doing all that. Okay, images too. You and I were talking before we got started about cling and other tools that you're using. Because it's not just for text, it's not just for video. What are you doing with images and what AI tools are enabling you to do that?
- [25:40]
B
Yes. So we use a bunch of AI tools, all the standard ones, including Runway for video, Mid Journey for Creative. What we're seeing very interestingly with this is we're doing a lot of always on creative content. So imagine you go on DoorDash and Instacarts app. You will see these banners floating around which are offer banners or which are talking about deals and all of that. Now imagine, and you'll be surprised, most large enterprises still do it humanly in a manual manner, including the push notifications, including the credits and all of that. We've created these custom engines which can produce banner images, ad creatives at scales like 30,000 creatives over last six months. And we're a B testing this with data to see which creatives work and then automatically that's queuing an engine to the LLM engine to refresh that creative to see how we can drive more CTRs up and with scale at say instacart or doordash level, you'd be surprised that this translates massively into revenue. So we're seeing CRM teams or customer lifecycle marketing teams in all these big consumer apps, heavily iterating on AI usage in push notifications, in anything customer comms, which could drive more revenue and acceleration up.
- [27:06]
A
If you are an individual owner of a content business and you couldn't hire Pepper because Pepper is aimed for a higher level of customer. Right. Enterprise and middle market, I guess is the way that you've said it.
- [27:19]
B
Yeah, sure.
- [27:19]
A
But if you are smaller and trying to create content using AI today, what would you do? What tools would you use? How would you think about it?
- [27:28]
B
Yeah. So I would recommend that don't go in for the staple, put it on ChatGPT, get whatever it's throwing out and put it out. You need to make content more intuitive. See, what's happening is with this onslaught of AI sloth being put out on the Internet, people need to be smarter about how they create content. They have to. And especially it's becoming harder with showing up on LLMs as well. So you need to build an authority into whatever you're doing. So here's what I would do. I would actually go and create videos and I'll start putting myself out on LinkedIn. I'll use those videos and repurpose those videos into content and then create articles about me and then start referencing that. He say Anirudh Singh spoke about this on ABC and start building more trust signals around it. I would create a lot of content on LinkedIn Pulse. Just as a experiment, you'll see LinkedIn gets cited almost on 12% of ChatGPT citations. So if I start I want to build out a personal brand as a founder, you know, to drive leads for my business and eventually grow. I start putting out a lot of content on LinkedIn Pulse, which starts to drive this narrative of a UGC growth lever, which an LLM would want to promote and embed my video at the top of the article and then have a summary of that down there. So you got to become intelligent about the way you put out there yourself. Like at pepper, we have 320,000 followers on LinkedIn. I have 80,000 followers on LinkedIn. And we've seen that you need to engineer these growth loops into content. So just be smarter about it. Don't just, you know, copy paste from.
- [29:18]
A
ChatGPT, but post on my own LinkedIn account about myself.
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B
Yeah, like you can talk about, you know, hey, these are the things that you're basically what you need to start building out on is authority and signaling. Like, for example, look at you. You focus on bootstrap giants. That's the theme. Right. And today when I search about anything around bootstrapping, either you, Jesse, pick up, come up. So it's a lot about owning a niche and building that authority very strongly.
- [29:51]
A
And I would do video, you're saying, and post it on there. And you'd also do ChatGPT for scripts and for text. But don't just take what comes out of there, add the human element afterwards. Is that what you said?
- [30:04]
B
Yeah.
- [30:05]
A
Okay, I like that. By the way, I do see a lot of LinkedIn used in ChatGPT and others, which frankly, helps me. I looked you up on ChatGPT and I like that it brought up LinkedIn because I would have to go and do it myself if not. And there was a period there when they weren't, and that was a pain in the neck. You also mentioned earlier Reddit, Reddit is used all the time. I don't think I'm going to quote Sean Pory properly, but he said something like, ChatGPT is a search engine that gives you answers based on what someone said on Reddit five years ago. And in many ways that is true. How do you, as a company that works en masse, get Reddit posts created?
- [30:43]
B
Yeah. So our Reddit playbook to customers is about, don't go out there and, you know, try to get a random. Create a random post about you getting created because Reddit will ban them. Reddit is a highly strongly moderating platform. What we would say is treat Reddit as a social media channel. So what Reddit is now undergoing through, obviously, they've obviously got a huge lot of publicity. They're the 11th most visited website in the world. They've started creating and inviting brands to create brand pages on Reddit and create communities on Reddit. So Reddit's become like your social community platform. Everyone's been thinking about, hey, I want to create my own community. Which platform should I list it on? I would say go do it on Reddit where engage with users, add value, do AMAs. And it's not just having marketing speak on Reddit because like I said, Reddit typically tends to ban promotional posts. You've got to show value. Like some of the best companies I know are actually getting people from their product teams and engineering teams on Reddit to start posting out on queries which are helping them win deals. You'd be surprised. And they're doing it authentically from their handle. It's not advocating, hey, use us. It's advocating, this is how you solve a problem. So don't, you know, go the route of influencer marketing and all of that on Reddit. It's great. Won't work. Build value, build it as a social media channel and do it credibly.
- [32:14]
A
I see. And so with Pepper, you don't have any automated, AI driven way of doing it. You're just encouraging companies to post on Reddit.
- [32:21]
B
Or are you? So we have an automated way to pull up threads which you should be answering. So what we've done is we have a Reddit module where we can scan through your website and the themes you want you care about and pull out all subreddits and all threads that need your attention and answering so that at least gives you an intelligent way of how you can frame your content strategy on this channel. I see. All right.
- [32:50]
A
I keep coming back. By the way, I don't want to forget to ask you, who at OpenAI did you cold email? What did you write that got a response from them?
- [32:58]
B
Yeah, so I primarily cold emailed Greg Brockman, who's a co founder and president, and I cc'd Sam Altman and I got a reply from Greg in less than an hour from sending an email when they probably had less than 50 users, GPT3.
- [33:15]
A
And what did you write that got a response? Was it just I want to use the software that I've heard about?
- [33:20]
B
No, I think I, I think we, we kind of detailed out our use case in terms of what we could do and so on. And I think, you know, the good thing is the use case stick sticks because we said, hey, we have this marketplace of hundreds and thousands of writers which can be AI turbocharged and aligned and we want to build tools on these LLMs which are more personalized and customized for different platforms. So I think that was probably the most sensible use case that made sense. And yeah, I think they gave us access. It took us 20 days to build out a platform called Pepper Type, which we launched on Product Hunt. We were number one product of the month and we got to a half a million users in one year just for that tool. But we did realize that just standalone tool Won't help. You have to integrate this deeply into the workflows. So like I've been giving examples this entire podcast about make it workflow oriented which can give value to the customer so that it can compound growth. So don't solve for short term.
- [34:24]
A
All right, I want to close it out with this. I want to know how you use AI personally.
- [34:29]
B
So what we're seeing so obviously run a very busy, tight schedule managing multiple geographies customers. I want to figure out how can I save more time for me. So I plugged in my calendar into ChatGPT and started audit and I told it to start auditing my calendar and starting to tell me which meetings are potentially time sinks or how can I re architect my calendar to make more sense. Now obviously it's trying to give me some good, interesting insights which I'm trying to use and cut down meetings and optimize them. What's going to be gold for me is if I can marry my note taker with the calendar and ChatGPT to start actually seeing which meetings make sense or actually are driving high value outcome decision making for the business. And if I can figure out that loop, I think that this is going to be pretty awesome.
- [35:22]
A
I have an idea for how to do it and I know we're running out of town, but you tell me what you think of this, go into your note taker and say which meeting have I talked less than 5% of the time.
- [35:32]
B
What?
- [35:32]
A
And then say what is it about these meetings that is unique? And frankly, you could even go back to ChatGPT which has access to your calendar and say here are the meetings that I didn't really talk. What is it about these meetings that they have in common? Now you have a new prompt that you can feed in and say, look at my upcoming meetings. Which ones fit these criteria? Does that make sense?
- [35:50]
B
That makes a lot of sense. And I think we can. And as you say, as you're saying, there's a lot more workflows we can keep building on this. But I think what all of this has to lead to is insane productivity. We've been always talking about can I clone five of me if that's possible and hopefully we can make that happen. I think we're going to skyrocket.
- [36:11]
A
By the way, having interviewed the founder of Zapier about this, if you do this in ChatGPT, I think you'd have to do it manually. If you use Zapier, it will every day be able to analyze based on what we just said.
- [36:21]
B
All right.
- [36:22]
A
I would love to see. Can I see the email that you sent out to OpenAI? Can I share it with the audience?
- [36:27]
B
Can you see restree?
- [36:28]
A
It's coming up right now. Yes, I can.
- [36:32]
B
Here it is. So this is to Greg, Greg Brockman. This is the 1st of December 2020. This is two years before ChatGPT, so we will manage marketplace of experts. So we gave them insights on what we can be doing, what could the benefits be and we could get access. This is me.
- [36:51]
A
And, yeah, responded so quickly.
- [36:55]
B
Yeah, this was in just a few. Yeah, one and a half hours.
- [37:02]
A
Would you be able to forward that to me so that I can post it online? I won't. I mean, their email addresses are super easy to find, but if I can post an image of it, I think it'll help with the story. All right, killer.
- [37:11]
B
Thank you for the extra time.
- [37:13]
A
Dude, you're so much better than I even hoped for. I knew that you were good, but the way that you were riffing on all these different AI techniques was just like, so both, like, possible and big, but at the same time relatable. And today, anyway, I know you gotta run. Thank you for doing this.