The Startup Ideas Podcast - Perplexity vs ChatGPT "Deep Research" For Startup Ideas Shocked Me

Episode Date: February 19, 2025

Get Greg’s Deep research research notes: https://www.gregisenberg.com/deep-research-ideasI compare in real-time ChatGPT's and Perplexity's deep research capabilities for developing startup ideas. Both platforms were prompted to generate AI agent business ideas with specific revenue targets ($1M Y1, $3M Y2, $5M Y3). The results showed that Perplexity provided faster, more concise responses, while ChatGPT offered more detailed, comprehensive analysis but took longer to generate responses.Key Points:• Comparison between ChatGPT Pro ($200/month) and Perplexity Deep Research (free/~$20/month) for startup ideation• Both platforms were tested with identical prompts for creating AI-based startup ideas• Comparison of response speed, depth, and quality between the two platforms• Practical demonstration of how to use AI tools for business planning and market researchTimestamps:00:00 - Intro01:33 - Defining the Startup Prompt02:29 - Initiating Research with ChatGPT05:05 - Initiating Research with Perplexity09:13 -  Perplexity results (Legal Contract AI)15:44 - ChatGPT results (AI Sales Outreach Assistant)26:10 - Comparing Perplexity and ChatGPT Outputs27:55 - Follow-Up Prompts and Further Exploration31:55 - Perplexity's Follow-Up results34:43 - ChatGPT's Follow-Up results 37:07 - Where to get Greg’s Deep research research notes37:31 - Conclusion: Choosing Between Tools1) THE SETUP:• Testing ChatGPT Pro ($200/mo) vs Perplexity Deep Research ($20/mo)• Same prompt: Build a defensible AI agent startup• Target: $5M ARR in 3 years• MVP budget: $5K2) PERPLEXITY AI WINS ON SPEED • Completed research in ~2 mins• Analyzed 34+ sources• More concise, actionable insights• Free tier available!3) CHATGPT WINS ON DEPTH • Longer, more detailed analysis• Comprehensive playbooks• Better market context• More examples & case studies4) BEST STARTUP IDEAS GENERATED:Legal Contract AI (Perplexity):• $2.4B market opportunity• Auto-redline contracts• Defensible through learning• Clear path to $5.4M ARRAI Sales Assistant (ChatGPT):• Virtual SDR automation• Network effect moat• Product-led growth strategy• $100M+ potential5) MVP INSIGHTS:For $5K budget:• Use existing APIs (OpenAI/GPT-4)• Start with Chrome extension• Focus on single workflow• Manual fallback for quality• Self-serve freemium model6) KEY TAKEAWAYS:Both tools are INSANELY good• Perplexity = Fast, concise, free tier• ChatGPT = Deeper analysis, more context• Both provide actionable insights• Worth integrating into workflow7) PROTIP:For best results:• Be specific with constraints• Ask follow-up questions• Double-click on interesting areas• Cross-reference competitor insightsBottom line: These AI research tools are an unfair advantage for founders right now.Notable Quotes:"Distribution is the new moat" - Greg"This is an unfair advantage right now" - Greg regarding AI research toolsConclusion: Both platforms demonstrated valuable capabilities, with Perplexity offering speed and efficiency while ChatGPT provided more comprehensive analysis. The video suggests both tools can be effective for startup planning, with the choice depending on user preferences for depth versus speed.Want more free ideas? I collect the best ideas from the pod and give them to you for free in a database. Most of them cost $0 to start (my fav)Get access: https://www.gregisenberg.com/30startupideasLCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/BoringAds — ads agency that will build you profitable ad campaigns http://boringads.com/BoringMarketing — SEO agency and tools to get your organic customers http://boringmarketing.com/Startup Empire - a membership for builders who want to build cash-flowing businesses https://www.startupempire.coFIND ME ON SOCIALX/Twitter: https://twitter.com/gregisenbergInstagram: https://instagram.com/gregisenberg/LinkedIn: https://www.linkedin.com/in/gisenberg/

Okay, today we're going to do something that I've been meaning to do for a while. So I want to test the differences between OpenAI chat GPT's deep research and perplexity's deep research. I mean, the simplest way to think about it is chat GPT or perplexity, when you prompt it, it's going to give you a quick answer. But deep research, it's almost like a junior analyst, a McKinsey analyst, that they would probably pay hundreds of thousands. $1,000 a year to give you deep research into the question that you have. And today we're going to use it to actually build a business. So we're going to prompt it. We're going to give the same prompt to chat GPT.

This is a $200 a month pro plan. And Propact City's Deep Research, I think that's a $20 a month. And we're going to see which is better. And we're going to go through the startup ideas to actually see, hey, maybe there's actually a grid startup idea that one of you can steal. We're going to get chat chabot and perplexity to give us the tactics of actually how to build this business. So this is what we're going to prompt. We're going to give the same prompt to chat GPT as to perplexity. So we're going to say, I want to create a startup. I want it to be an agent, say an AI agent that focuses on a high value niche.

I want it to be defensible, not just a flash in the pan. I want you to create a zero to one playbook of how, you'd launch it to get customers too. Give me five ideas with five playbooks on how to grow it. I'm looking for $1 million AAR, $3 million year one, $3 million year two, $5 million year three. So we're going to see if chat GPT pro or perplexity could give us a $5 million a business in three years with the exact tactics how to grow. And we're going to be real with it, we're going to see, is this actually going to give us good particular advice? So if you're going to do this, you've got to click the deep research button here. So it says, that sounds like an exciting venture to tailor the best ideas on playbooks for you.

Could you clarify industries of interest? Sure, so we're going to say, are there any specific industries? know we are looking for the startup ideas with the highest likelihood of success. Two, would you rather target enterprises, S&B, or individuals, professionals? The truth is, I don't really care if it works, it works. So open to B2B or B to C. And then we're going to say AI capabilities. Do you have a preference for AI applications?

applications, automation, data analysis. Not really. Again, we're looking for the highest probability of success with a moat. Technical resources. Do you have a technical team or plan to build one or do you prefer a low-code, no-code approach? And we can say, I prefer an approach that's going to cost $5,000. or less for an MVP. I'm not super technical. What else can we say? We can say I might want to just cash flow this business, but I want to potentially raise VC money to

if I really think this could go beyond 100 million a year business. Go to market. Would you lean towards direct sales, partnerships, or viral self-serve growth? I think I lean towards viral self-serve growth, but I'm open to what you suggest.

Boom. Now we are going to wait until to see how long it's going to take. Usually it takes like eight minutes. So let's see how long. So it's starting research. So while it's doing that, we're actually going to head to your perplexity

and give it the same prompt and see what happens. We're going to compare. We are going to compare all these together. So let's go open up perplexity. Quick break in the pod to tell you a little bit about startup empire. So startup empire is my private membership where it's a bunch of people like me, like you, who want to build out their startup ideas. Now they're looking for content

to help accelerate that. They're looking for potential co-founders. They're looking for tutorials from people like me to come in and tell them, how do you do email marketing? How do you build an audience? How do you go viral on Twitter? All these different things. That's exactly. That's exactly. exactly what startup empire is. And it's for people who want to start a startup but are looking for ideas, or it's for people who have a startup, but just they're not seeing the traction that they need. So you can check out the link to startup empire.co in the description. Now, deep research is a brand new product.

In order to enable it, you'll see it in this little drop down here. Let's go add our prompt. And the interesting thing is going to be to see, is it going to ask us for follow-up questions like chat GPT did? Not sure. We will find out soon. So deep research. So one of the cool things about deep research, both chat GPT and perplexity, I think, is they go through, instead of going through two sources or three sources, they're going through way more source. So you can see you're ready. This is this is pulling for six sources. By the way, I'm, I'm like pretty new to this. I'm using this for the first time, really. I've used chatchabitia a little bit, but we're kind of learning this together. And we're going to find out by the end of the episode if this is super value or not. I'm not sure. So we'll find out. So this is, you know, it feels like a reasoning model. So you can see, you kind of see how it's, you can see how it's, you can see how it.

it's thinking about things. So it says, I need to search the web to find information on zero to one playbooks specific to AI. I found some insight into zero to one startup playbooks. Cool. Great. From the previous search, I found insights. Great. I found insights on defensive AI strategies. Okay. This is cool. I don't necessarily need to know exactly how it's doing it, but it's good to know that it's crossing. What's the expression crossing its T's dotting its eyes? Someone in the comment section tell me what the expression is. But it's basically that.

It's good that perplexity is checking out all this stuff. And you can see it's going through 29 sources, right? So if we were to do this manually, if we went to Google, I mean, if you went to Google, I don't know, you couldn't do this. But if we actually went and did this manually, this is going to take us many, many, many hours. So the hope here is that, you know, this is basically AI and intelligence that could help us. One thing interesting is that perplexity is not asking me a qualification question like we saw in chat GBT. chat GPT, you know, ask that qualification question.

So we'll see the difference in terms of what, you know, what's more, what's a higher quality end result? Okay, cool. So going back to perplexity, it says it's now gathered sufficient information to answer your query. It's answering the query. now we're up to 34 sources. So this is exactly, I mean, if this was a real person, this is exactly how they would answer it. Okay, so first things first is that perplexity is faster than chat chbt.

You can see that chat chb t is about halfway done. Perplexity is done. So that's really cool. So let's go through the ideas. Let's go see. Funny enough, actually one of the sources is me. One of the sources is went through one of my YouTube videos, three one million dollar AI startup ideas to build in 2025,

links to it. And that's hilarious. That's actually, it's kind of ridiculous. But it's cool that it goes through YouTube. You can see, and it obviously downloads that transcript and goes through it. So let's go through these ideas and you be the judge. You tell me, are these good ideas, are they bad ideas? let's go see. So number one, legal contract, this is on the perplexity deep research answer.

Legal contract lifecycle automation agent, problem space. Corporate legal teams waste 23% of their time manually reviewing boilerplate contracts which struggling while struggling with version control, errors that cost enterprises 2.4 billion annually compliance penalties. Existing CLM solutions require extensive customization and fail to adapt to company-specific negotiation patterns. Okay, so an AI agent, here's a defensible solution that integrates, number one, integrates directly with email, SharePoint, and DocuSign via API. Two, learns organizational risk thresholds through historical contract analysis. Three, auto redlines third-party paper using firm-specific fallback positions.

and four maintains version lineage with blockchain style audits trails now by the way I think this is actually cool but like for an MVP minimal viable product do we need blockchain style audit trails not really but I guess we can further prompt perplexity to you know ask it what would be the MVP for for this right and unlike well in chat GPT we said it was going to be $5,000 or less. So that's an interesting thing that if you are going to prompt perplexity, you might want to include some more constraints

because you probably won't get that follow-up question. Why is it defensible? Develops institutional memory through continuous exposure to negotiation outcomes. Becomes embedded in legal department workflows, average 14.3 logins per week per user. This is data. This is great.

This is a great insight. This is something. that if you're building a business, you want to know that. It's really helpful. It requires retraining equivalent to six to nine months of human experience to replace it. And by the way, you can always click on one of these numbers to get to the source and go deeper. So here we have the growth playbook by perplexity. And let's go see if this is actually good. I will say, this agent, I mean, seems super valuable. I totally agree why it would be defensible. So I like what I'm seeing.

I like what I'm seeing from perplexity. So the growth playbook. Phase one, niche validation, 90 days. Product. Build a Chrome extension recording legal contract review sessions for permissions to train initial model. Go to market. Offer free redline audits for 100 AM law, 200 firms.

I guess that's American law. Targeting mid-market companies, 50 to 500 million revenues. So this actually idea, free redline audit. is interesting as a Chrome extension. I would never have thought of that. Again, I'm not, well, I'm not a lawyer. I don't play to be on the internet. So, but just as like a person who builds products

that a lot of people use, I will say that just looking at that, that makes a lot of sense to me. Monetization, $2,500 a month subscription, locked to specific document types, NDAs first. Okay, that's a lot. again, really interesting. Wouldn't have thought about locking

a subscription to specific document type. So that's really cool. Phase two. Workflow embedding, months 4 to 9. Integrate with legal ops platforms like Lexion and Antra. Launch Clause library builder using client's

historical favorite language. Implement usage-based pricing at 25 cents per review clause. Really interesting. Interesting business model. I like that I like the phases, you know, phase one to phase two. It totally makes sense to me. I like what I'm seeing.

Phase three, enterprise scaling, months 10 to 12. Add SOC2 type 2 compliance for regulated industries. Develop custom connectors for service now and Salesforce CPQ. Upsell to $8,000 a month enterprise tier with custom playbooks. Financial roadmap. Okay, so let's see how they break this out. So in year one, you're going to get 40 clients at $2,000 MRR. That's $1.2 million ARR.

Not crazy. 150 clients at 5,000 MRR plus 250K usage fee. Year 2, you get $3.1 million. And then your three platform fees from 300 plus embedded instance, 5.4 million. So I look at this idea and I'm like, I could see this idea being so-and-so YC company gets funded to go build this. This is a really good idea. The growth playbook, although it's not super long,

I have a feeling that the chat GPT deep research is going to give me a longer answer. But I think that overall, this is like, you know, we say on this channel, do you spit not like an idea or do you sip an idea? Do you like the idea? This is a sip. I sip this idea. Makes a lot of sense. This is a pretty good one pager. On the negative side, I think you do need to go deep.

into this, meaning, like, I think you need to prompt it more. But, you know, overall, like, how can I complain? It took me, like, what, a couple minutes, and I got an incredible, incredible idea. And that's just the beginning of it, right? So let's go back to chat GPT deep research. Let's see if it's done. Okay, so it's done. So let's compare what we saw with perplexity deep research with chat GPT. It would be hilarious if it was the same idea, but it is not. So AI sales outreach assistant. I mean, I'm kind of skeptical, I will say.

I feel like I've seen so many of these, but I'm open-minded. All right, let's go see. First of all, let's just quickly scroll. Look how long this is. like this is a full-on it's like a business mind literally okay so I'm going to read this whole thing this is you know

it could be a really good idea I don't know we're going to find out together so it's an AI powered sales outreach assistant that acts as a virtual SDR which is a sales development rep automating prospect

research and personalized called email LinkedIn messages the target market is B-to-B companies, which are startups to mid-sized firms, then mean more pipeline without hiring extra SDRs. The AI agent enriches lead data and crafts tailored outreach at scale. The invincibility comes from a data network effect. It learns from every interaction. By the way, data network effect, good term, really good term.

I feel like deep research knows what it's talking about. That is an incredible term. It learns from every interaction, which messaging means, yields replies, building a unique data set with what works in each industry. Again, this is really important. If you're building an AI startup, building a unique data set of what works is going to be your moat. So really cool to see that in there. Over time, it develops superior personalization and lead scoring to each customer's context, which is hard for competitors to replicate. Integration into CRMs and emails systems further deepens the moat by embedding the AI

in the sales team's workflow. B-to-B sales teams are increasingly adopting such AI prospecting tools. 66% of sales reps say AI helps them better understand customers and personalized outtreets, underscoring demand for an agent like this. The agent can leverage massive contact databases. Okay, we get the idea. So how would they actually build this? What does chat DPC say?

So focus on a narrow but high-value use case to keep its same. simple. For example, start with automating follow-up emails to warm leads. Okay, cool. I'm really happy at saying this because this is an idea that I've heard before. It's actually become almost a meme on X. Of course, you're building an AI virtual SDR. But this idea of automating follow-up emails to warm leads using a templated but AI personalized approach. Now, this is something that is unique. I really haven't seen anywhere, and it's a cool idea. The MVP could be a web app or a Chrome extension where a salesperson inputs a list of prospects or connects their CRM, and the AI generates and sends personalized emails on a schedule. To build this cheaply, okay, now we're talking.

They talk in our language. Use existing AI APIs, leverage a service like OpenAI, GPD for. hilarious that chat GPT is being like user service like chat GPT. Via API to generate outreach text given key inputs, prospect info, value prop. No expensive model training needed initially. No frills UI. Create a simple interface using low-code tools or templates for the user to review, edit AI suggested messages, skip complex analytics at first. Single channel focus. Start with just email.

cheaper to implement before tackling LinkedIn or phone. True. Manual fallback. If AI is unsure, let it flag for human to handle, ensuring quality will gather data to improve prompts. That's important that it has that. And I'm happy that they put that in there. This MVP could be built by a freelance developer in a few weeks.

The main cost, less than $5,000 would be a developer, time, and API calls. By using off-the-shelf AI and focusing on one workflow, you can launch quickly and start onboarding a few beta users to prove its value. Okay, overall, like helpful, you're definitely going to need to double click into this and prompt it deeper to really understand exactly what the playbook is. I mean, it asked for a playbook. Is this a playbook? You know, maybe.

It's kind of like an outline to the playbook, but it's definitely some really good ideas on how to get started. customer acquisition and growth. So product led, viral adoption among sales teams and low friction signups. So let's see if this makes sense. So a self-serve free tier, offer a limited free plan, 50 AI generated emails per month. So individuals, so individual sales reps or small startups can try out without approval. This gets foot in the door companies. viral loop via results.

Whenever the AI assistant books a meeting or gets a reply, prompt the user to share the success internally. This meeting was set by AI. Salespeople love sharing hacks at work. This word of mouth can drive wide adoption. That's true. That is really, that is good. Referral incentives.

Yeah, as more reps use it, the AI can perform better with shared data, creating a network effect. Content marketing. Published case studies. How X startup tripled using AI and short demos. That's, I mean, that's good, right?

And look, you know, there's a link to starter story, how a 27-year-old grew Jenny AI to 5 million ARR. I mean, what's cool about this is like, this is so relevant. This is such a relevant story. And it's cool that it's bringing that in there and it's knowing that this is high quality. I like that.

That's definitely points for deep research. List in CRM marketplaces like HubSpot. being present in these channels gives you distribution. So the goal would be to reach on the order of a few hundred paying teams within year one. If say 200 companies convert to paid plans at $5,000 a year on average, that's a million bucks AARR. Growth to $3 million in year two might come from a land and expand. I like that they're including that. Free users converting to paid and small team paid users expanding usage or upgrading tiers as

they see ROI. The bottoms up adoption means minimal spend on sales. The product essentially sells itself by proving value, similar to how another AI SaaS product cursor, skilled purely by developers word of mouth with no sales team needed and individuals pulling into their teams. Again, I really am impressed that they're using, you know, their Jenny AI cursor. These are the examples I want to see in here. So it's, that's really cool. So we're going to go through monetization and pricing, then we're going to know VC readiness, and then that's it. And then we're going to compare perplexity to chat, GBT. So let's go back to monetization.

So it's a per seat pricing or usage based here. For example, charge per sales rep user, $100, $150 per month for unlimited outreach, or by volume. Typical structure could be basic plan for $49 a month, solo rep, limited emails, team plan for $199 a month, five reps, more emails in CRM, and Enterprise for advance or higher volumes. That makes sense. I'm more interested in usage-based tiers. I wish it would have said, like, you should do usage-based tiers because that's the future of SaaS.

But, I mean, I'm happy that it even included usage-based tiers. Again, if you're going through, if you're going to be using deep research, you know, I think you've got to on both perplexity or chat GPT, you definitely need a, if you have question marks, it seems like you've got to ask it again, which is totally fair. At least with this, you know, you might have to prompt it five more times or seven more times. But if you're just using chat GPT regular or perplexity regular, you might go through 50 or 100 prompts to get to where you're getting, how high definition and the clarity that this is. So it gives you some ideas on how to optimize revenue, you know, premium upsell, annual deals,

premium features, performance-based options. These are all good ideas. you're not really there yet honestly like when you're it's it's good to like think about what this could be but like if you're building an MVP zero to one like you don't necessarily need this but it is good that they included it the VC readiness and scaling $100 million a r so I think I included that in the in the second prompt where I was like this needs to be able to get to $100 million a R it's cool it's cool that it's it says that it's possible like it's cool that this is like a $100 million a year idea that it's like, yeah, it's possible. And it's cool that it's like to get from $200 million ARR,

you're probably going to need to raise VC. Like it's pretty awesome. It's pretty honest with it. So that's the chat GPT deep research. Now, perplexity. And now if we can compare them, you can see, perplexity is a lot smaller, right? this is a lot more in-depth, but this is really, like, there's no fluff with this.

I got to say, this is, you know, the question is, is chat GPT Pro worth 10 times the price of perplexity? I'll let you decide, you know? It depends on how, you know, do you like, in this, you're going to have to prompt a little bit more, this you're going to one shot it more often. ChatTbtbt, you're going to one shot it more often. So it depends on your style. But I, you know, I actually don't pay for perplexity on this account. So yeah, you can see I don't have a pro on this account. So you can even use this for free. So I think there, you know, you can only use a few times maybe a month for free and then you have to pay $20 a month. But that's incredible. It's incredible that,

let's see here. Let's just click. Yeah. Okay. So I'm not sure exactly how many free, deep research you get. Probably not that many. But the good news is both of these are really, really good. These are like insanely good, scary good. And let's just do for fun, for idea number one, let's just say, I feel like I've seen this idea before on X. How can you take this idea and make it more non-obvious? Let's see what happens.

So it says to make the AI startup idea more non-obvious and unique, we can find underserved niches, combine AI with another trend, live proprietary data, introduce a new business model, or enhance with the viral element. Do you have any preferences for how we make it not obvious? For example, should we explore a new niche? Let's say, you're the pro. I pay you $200 a month for this.

how about you decide on the idea that has the most legs? Let's see. Let's let chat chief you do the work. And let's see what happens. I've actually never done a follow-up prompt to something, to once it gives you like the full answer. So it's unfortunate that it takes so long, you know? I wish it just like was faster.

That's what I like about perplexity. You know, you can get in the zone. Let's do a follow-up question to this one. And let's say, aren't there a bunch of highly funded competitors in the AI legal space? I feel like I'm going to get smoked. How do we take this idea? and make it more non-obvious. Also, can you give me the step-by-step plan on how to implement the MVP features, etc.,

and make sure that this startup MVP will cost $5,000 max? Let's see how fast or slow perplexity, research will take. It says here, deep research takes a couple minutes. Let's go back to chat GPT. So while it's thinking, both of these are thinking, I think the thing to think about is for whatever your workflow is, if you're building a zero to one startup, you want to build a zero to one startup, you want to build zero to one features. You know, you know, I think that having a companion, like deep research, putting it into your workflow is just it's a no-brainer.

It's a no-brainer because it's going to make you smarter at what you do. And it's going to help you refine the types of products that you build so that you have the highest probability of success. It's like, why not, right? So let's see what's happening here. perplexity is saying that it's found highly funded competitors like Harvey and case text. So you do need to, like I know this, right? I know that there's highly funded competitors in the AI legal space.

I think that you do, you still, the onus is still on the prompter, on the promptee. Because you do need to know the right questions to ask. I mean, this isn't rocket science in terms of what I said, but you do need to be somewhat knowledgeable. Okay, so we just got the answer from perplexity deep research. And it's given me an executive summary. Wow, an executive summary of what's going on in the legal AI startup space. So our analysis of 37 funded competitors reveal three under-defendant niches.

And it just goes through it. It gives me a competitive analysis. It writes this amazing, kind of cool opportunity matrix. Wow, this is cool. This is cool. Just like writing what the differentiator is, for example, clause genealogy, what is the technical approach, blockchain-inspired version lineage,

and what is the defensibility source? 22,000 annotated NDA corpus. And then it gives you a full-on MVP implementation plan with a $4,800 budget, less than $5,000. They're going to give you $200 to go for dinner. Thank you, perplexity. They're going to give you $200 not to spend it at ChatchipT Pro. Maybe that's why they chose $4,800.

And they say, here's a Chrome extension prototype, records an anonymized contract review sessions, tracks cursor movements, edit frequencies, external research sources, and the tools are manifest v3 and TensorFlow.js, which is zero dev cost, legal team interviews. And then it gives you phase two, core feature build

with the different components, like the clause library and how to build it, what the cost is. This is pretty darn good. This is like, this is really good. And then the go-to-market sequencing, which to me is among the most important parts. Like, yes, knowing what product to create is obviously really helpful and what the rollout of the MVP should be. But distribution is the new moat.

So how to figure out good of marketing is really important. Offer free clause vulnerability audit generating historical enforcement risk scores, scores, alternative language access rates. And then 15 cents a clause, auto redline. I mean, these are great ideas. It's giving you the monthly searches, what the CPC is. So it says go to organic search. You know, it's telling you how to do webinars.

I mean, it's not as deep as the chat GPT pro, but I love these tables. I think this is awesome. And this is like, this is what you need to know. And you have to just reprompt it to go deeper into these. sections. So let's go see if chat TVT is finally done. It is done.

So it gave me new ideas. And it says AI platform for long-tail podcasts and sponsors. An AI agent that not only streamlines podcast production, but also bundles small podcasts for advertisers.

Unlocking the long-tail podcast advertising. This is pretty cool. Notice how it's like a literal research document. If you're watching this on YouTube. Or if you're watching the, if you're listening to the startup ideas podcast on Spotify and Apple, check it out on YouTube because you can, you can just see how in-depth this is. Wow. So this is a pretty good idea. So it talks about how, how basically what the competitive mode is, the proprietary data advantage. So for example, as the platform on board's

podcast and run sponsorship campaigns. It accumulates unique data on what content resonates and which ad placements work for each niche, which is really cool. Over time, the AI learns a style tone and listener behavior of each show and which sponsors messages yield results. This creates a continuous learning loop that improves targeting. So it's got some good ideas. In the MVP development for 5,000, start with the pain point of time-consuming content creation. Implement an AI transcript and summary generator for podcast episodes. Tells you exactly how to do it, what technical, what technically how to do it,

whisper or assembly AI. I do feel a bit weird that it, like, recommends its own product, but I get it. You know, it's kind of bias, but here we are. So, and then it tells you, you know, make a basic web interface, manual, Wizard of Oz, sponsor matching. It just goes a little deeper. It goes a little deeper than perplexity. Yeah.

I'm not going to go through all of this. Obviously, this is like super long, but I'm just scanning it. And I'm like, this is really darn good. This is really good. So I didn't have time to go through all five of the chat GPT deep research startup ideas. and the five from perplexity deep research.

But I've included it in a doc. It's completely free to download. I'll include the link in the show notes for anyone who wants to steal those ideas, go implement them, and build a big business. There you have it, folks. If you aren't using one of these platforms, you probably should.

The good news is there's something for free, Perplexity deep research. It's really good. It's really fast. you might have to do a little more prompting to get to the level of depth that you want, but some people might even prefer it. It's like driving manual

instead of driving automatic on your car. Chat GPT Pro, deep research. You have to wait longer. You get more in-depth reports. But the quality, you know, it's high-quality stuff. I don't think you can go wrong with either product. They're both awesome. and it's really, you know, what floats your boat.

I think you just need a, you know, if you can play with both, and then you can always cancel one or the other. Well, perplexity is free, so you won't even need to cancel it. But I think the important thing is, you know, use it, play with it, come up with ideas because this is an unfair advantage right now. And that's why I wanted to show it to you all. I mean, frankly, I wanted to learn it myself because I want to implement it into my day-to-day. If you enjoyed this video, please let me know in the comment section. Just say something. I read every single comment. You can like and subscribe for more of this in your feed. And I'll see you on the next one. Happy building. And I hope this got your creative juices flowing. Thanks for listening to the Startup Ideas podcast. Later, guys.