Perplexity vs ChatGPT "Deep Research" For Startup Ideas Shocked Me

Get Greg’s Deep research research notes: https://www.gregisenberg.com/deep-research-ideas I 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 research Timestamps: 00:00 - Intro 01:33 - Defining the Startup Prompt 02:29 - Initiating Research with ChatGPT 05:05 - Initiating Research with Perplexity 09:13 - Perplexity results (Legal Contract AI) 15:44 - ChatGPT results (AI Sales Outreach Assistant) 26:10 - Comparing Perplexity and ChatGPT Outputs 27:55 - Follow-Up Prompts and Further Exploration 31:55 - Perplexity's Follow-Up results 34:43 - ChatGPT's Follow-Up results 37:07 - Where to get Greg’s Deep research research notes 37:31 - Conclusion: Choosing Between Tools 1) 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: $5K 2) 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 studies 4) BEST STARTUP IDEAS GENERATED: Legal Contract AI (Perplexity): • $2.4B market opportunity • Auto-redline contracts • Defensible through learning • Clear path to $5.4M ARR AI Sales Assistant (ChatGPT): • Virtual SDR automation • Network effect moat • Product-led growth strategy • $100M+ potential 5) 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 model 6) KEY TAKEAWAYS: Both tools are INSANELY good • Perplexity = Fast, concise, free tier • ChatGPT = Deeper analysis, more context • Both provide actionable insights • Worth integrating into workflow 7) PROTIP: For best results: • Be specific with constraints • Ask follow-up questions • Double-click on interesting areas • Cross-reference competitor insights Bottom 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 tools Conclusion: 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/30startupideas LCA 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.co FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Greg Isenberg?

  Okay, today we're gonna do something that I've been meaning to do for a while. So I wanna test the differences between OpenAI ChatGPT's deep research and Perplexity's deep research. I mean, the simplest way to think about it is ChatGPT or Perplexity, when you prompt it, it's gonna 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 thousand dollars a year to give you deep research into the question that you have. And today, we're gonna use it to actually build a business. So we're gonna prompt it, we're gonna give the same prompt to ChatGPT.

• 0:44Greg Isenberg?

  This is a $200 a month pro plan. And Perplexity's deep research, I think that's a $20 a month. And we're gonna see which is better. And we're gonna go through the startup ideas to actually see, hey, maybe there's actually a great startup idea that one of you can steal. We're gonna get ChatGPT and Perplexity to give us the tactics of actually how to build this business. So

• 1:14Greg Isenberg?

  this is what we're gonna prompt it. We're gonna give the same prompt to ChatGPT as to Perplexity. So we're gonna say, I wanna 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.

• 1:37Greg Isenberg?

  I want you to create a zero-to-one playbook of how you'd launch it to get customers to. Give me five ideas with five playbooks on how to grow it. I'm looking for $1 million ARR, $3 million year one, $3 million year two, $5 million year three. So we're gonna, we're gonna see if ChatGPT Pro or Perplexity could give us a $5 million ARR business in three years with the exact tactics how to grow. And we're gonna, we're gonna be real with it. We're gonna see, is this actually, is this actually gonna give us good, particular advice? So if you're gonna do this,

• 2:20Greg Isenberg?

  You gotta click the deep research button here. So it says, "That sounds like an exciting venture to tailor the best ideas and playbook for you. Could you clarify industries of interest?" Sure, so we're gonna say,

• 2:34Greg Isenberg?

  "Are there any specific industries?" No, we are looking for the startup ideas with the highest likelihood

• 2:48Greg Isenberg?

  of success. Two, would you rather target enterprises, SMB, or individuals, professionals? The truth is, I don't really care if it works, it works. So open to B2B or B2C. And then we're gonna say, "AI capabilities. Do you have a preference for AI applications, automation, data analysis?" Not really. Again, we're looking for the highest probability of success

• 3:20Greg Isenberg?

  with a moat. Okay. 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."

• 3:48Greg Isenberg?

  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, too, 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?

• 4:17Greg Isenberg?

  I think I lean towards...

• 4:24Greg Isenberg?

  So 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. And we're going to compare. We are going to compare all these together. So let's go open up perplexity.

• 5:09Greg Isenberg?

  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 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.

• 5:54Greg Isenberg?

  So you can check out the link to startupempire.co in the description. Now, deep research is a brand new product. In order to enable it, you'll see it in this little dropdown here. Let's go add our prompt. And the interesting thing is going to be to see if, is it going to ask us for follow-up questions? Like ChatGPT did? Not sure. We will find out soon. So deep research. So one of the cool things about deep research, both ChatGPT and perplexity, I think, is they go through, instead of going through two sources or three sources,

• 6:35Greg Isenberg?

  they're going through way more sources. So you can see already, this is pulling for six sources. By the way, I'm like, pretty new to this. I'm using this for the first time, really. I've used ChatGPT a little bit, but we're kind of learning this together and we're gonna find out by the end of the episode if this is super value or not. I'm not sure. So we'll find out.

• 6:59Greg Isenberg?

  So this is, you know, it feels like a reasoning model. So you can see, you kind of see how 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 defensible AI strategies. Okay. This is cool. I don't necessarily need to know exactly how it's doing it, but it's, you know, it's good to know that it's crossing, it's, what's the expression, crossing its Ts, dotting its Is? Someone in the comment section, tell me what the expression is. But, you know,

• 7:43Greg Isenberg?

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

• 8:23Greg Isenberg?

  ChatGPT, you know, asked that qualification question. So we'll see the difference in terms of what, you know, what's more, what's, what's, 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. Thirty, now we're up to thirty four 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 ChatGPT.

• 9:05Greg Isenberg?

  You can see that ChatGPT 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, 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, 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

• 9:48Greg Isenberg?

  bad ideas? Let's go see. So number one, legal contract, this is on the perplexity, 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 C L M 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.

• 10:32Greg Isenberg?

  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 audit trails? Not really, but I guess we can further prompt perplexity to, you know, ask it, what would be the MVP for this, right? And unlike, well, in ChatGPT, we said it was going to be $5,000 or less.

• 11:17Greg Isenberg?

  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 the, you want to know that. It's really helpful. Requires retraining equivalent to six to nine months of human experience replacement. By the way, you can always click on one of these numbers to get to the source and go deeper.

• 12:01Greg Isenberg?

  So here we have the growth playbook by perplexity. And let's go see if this is actually good. This, I will say, this agent, I mean, seems super valuable. And I totally agree why it would be defensible.

• 12:15Greg Isenberg?

  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 contact, 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. This actually idea, free redline audits is interesting as a Chrome extension. Like 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.

• 12:56Greg Isenberg?

  So, um, but just, just as like a person who builds products

• 13:04Greg Isenberg?

  that, you know, 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 and days first. Okay, that's it. Again, really interesting. Wouldn't have thought about locking a subscription to specific document types. So that's really cool. Phase two, workflow embedding months four to nine. Integrate with legal ops platforms like Lexion and Ontra. 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.

• 13:49Greg Isenberg?

  It totally makes sense to me. Um, I like what I'm seeing. Phase three, enterprise scaling, months 10 to 12. Add SOC to type to compliance for regulated industries. Develop custom connectors for ServiceNow and Salesforce CPQ. Upsell to $8,000 a month enterprise tier with custom playbooks. Financial roadmap. Okay. So let's see how they, how they break this out. So in year one, you're going to get 40 clients at $2,500 MRR. That's 1.2 million ARR. Not crazy. 150 clients at 5,000 MRR plus 250 K usage fee.

• 14:28Greg Isenberg?

  Year two, you get 3.1 million and then your three platform fees from 300 plus embedded incidents, 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, I think that, uh, 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.

• 15:11Greg Isenberg?

  On the negative side, I think you do need to go deeper 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 of minutes and I got an incredible, incredible idea. And that's just the beginning of it, right? So let's go back to ChatGPT deep research. Let's see if it's done.

• 15:38Greg Isenberg?

  Okay, so it's done. So let's compare what we saw with perplexity deep research with ChatGPT. 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.

• 16:12Greg Isenberg?

  Like, this is a full on, it's like a business plan, 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 cold email LinkedIn messages. The target market is B2B companies, which are startups to midsize firms that need more pipeline without hiring extra SDRs. The AI agent enriches lead data and crafts tailored outreach at scale. The devens ability comes from a data network effect and learn from every interaction. By the way, data network effect.

• 16:59Greg Isenberg?

  Good term, really good term. I feel like deep research knows what it's talking about. Like, that is an incredible term. It learns from every interaction, which messaging 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 tuned 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.

• 17:42Greg Isenberg?

  B2B sales teams are increasingly adopting such AI prospecting tools. Sixty-six percent of sales reps say AI helps them better understand customers and personalized outreach, 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 ChatGPT say? So focus on a narrow but high-value use case to keep it simple. For example, start with automating follow-up emails to warm leads. Okay, cool. I'm really happy it's saying this because this is an idea that I've heard before. It's actually become almost a meme on X, like,

• 18:26Greg Isenberg?

  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're talking our language. Use existing AI APIs, leverage a service like OpenAI, GPT-4. Hilarious that ChatGPT is being like, use a service like ChatGPT.

• 19:10Greg Isenberg?

  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 while gather data to improve prompts. That's important. It'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.

• 19:53Greg Isenberg?

  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 asks 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.

• 20:35Greg Isenberg?

  So product-led viral adoption among sales teams and low friction signups. So let's, let's see how, you know, 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 it without approval. This gets foot in the door companies. Viral Lube 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.

• 21:21Greg Isenberg?

  Yeah, as more reps use it, the AI can perform better with shared data, creating a network effect. Content marketing, publish 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 the 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, it's definitely points for, for deep research.

• 21:58Greg Isenberg?

  List in CRM marketplaces like HubSpot. Being present on these channels gives you distribution. Yeah.

• 22:08Greg Isenberg?

  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 ARR. Growth to 3 million in year two might come from a land and expand. I like that, 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 sale, uh, sales. The product essentially sells itself by proving value, somewhat how another AI SaaS product, Cursor, scaled purely by developers word of mouth with no sales team needed. And individuals you, uh, pulling into their teams. Again, I really am impressed that they're using,

• 22:52Greg Isenberg?

  you know, their Jenny AI Cursor, like, these are the examples I want to see in here. So it's, it's, that's really cool.

• 23:01Greg Isenberg?

  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 ChatGPT.

• 23:13Greg Isenberg?

  So let's go back to monetization.

• 23:19Greg Isenberg?

  So it's a per-seat pricing or usage-based tier. For example, charge per sales rep user, hundred, hundred fifty dollars per month for unlimited outreach, or by volume.

• 23:34Greg Isenberg?

  Typical structure could be basic plan for forty-nine dollars a month, solo rep, limited emails, team plan for a hundred ninety-nine dollars a month, five reps, more emails and CRM, and enterprise for advanced or higher volumes. That makes sense.

• 23:51Greg Isenberg?

  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 doing, if you're going through, if you're going to be using deep research, you know, I think it, you gotta, on both perplexity or chat GPT, you definitely need a, if you have question marks, it seems like you gotta ask it again, which is totally fair. At least, at least with this, and 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,

• 24:33Greg Isenberg?

  you might go through fifty or a hundred prompts to get to where you're getting, what, how, how high definition and the clarity that this is.

• 24:46Greg Isenberg?

  So it's, you know, it gives you some ideas on how to optimize revenue, you know, freemium upsell, annual deals, premium features, performance-based options. These are all good ideas.

• 25:01Greg Isenberg?

  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.

• 25:14Greg Isenberg?

  The VC readiness and scaling a hundred million dollars ARR. So I think I included that in the, in the second prompt where I was like, this needs to be able to get to a hundred million dollar ARR. It's cool, it's cool that it's, it says that it's possible.

• 25:30Greg Isenberg?

  Like, it's cool that this is like a hundred billion dollar a year idea that it's like, yeah, it's possible.

• 25:38Greg Isenberg?

  And it's cool that it's like to get from to $100 million ARR, you're probably gonna need to raise VC like it's it's pretty awesome. It's pretty honest with it.

• 25:50Greg Isenberg?

  So that's the chat GPT deep research now perplexity.

• 25:59Greg Isenberg?

  And now if we can compare them, you can see perplexity is a lot smaller, right? This is a lot more in depth, but

• 26:10Greg Isenberg?

  this is really like, there's no fluff with this, I gotta say. This is, you know, the question is, is chat GPT pro worth 10 times the price of

• 26:28Greg Isenberg?

  perplexity?

• 26:31Greg Isenberg?

  I'll let you decide, you know? It it depends on how, you know, do you like in this, you're gonna have to prompt a little bit more. This, you're gonna one shot it more often. Chat GPT, you're gonna 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

• 26:59Greg Isenberg?

  you can even use this for free. So and 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 that's incredible. It's incredible that. Let's see here, let's just click.

• 27:16Greg Isenberg?

  Yeah.

• 27:19Greg Isenberg?

  Okay. So I'm not sure exactly how many free deep research you get. Probably not that many.

• 27:29Greg Isenberg?

  But the good news is both of these are really, really good. These are like insanely good, scary good.

• 27:39Greg Isenberg?

  And let's just do for fun.

• 27:43Greg Isenberg?

  For idea number one, let's just say I feel like I've seen this idea before on X.

• 27:54Greg Isenberg?

  How can you take this idea and make it more non-obvious?

• 28:01Greg Isenberg?

  See what happens?

• 28:08Greg Isenberg?

  So it says, to make the AI startup idea more not obvious and unique, we can find underserved niches, combine AI with another trend, leverage proprietary data, introduce a new business model, or enhance with viral element. Do you have any preferences for how we make it not obvious? For example, should we explore a new niche?

• 28:28Greg Isenberg?

  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 ChatGPT do the work.

• 28:49Greg Isenberg?

  And let's see what happens. I've actually never done a follow-up prompt to

• 28:57Greg Isenberg?

  to something, to once it gives you like the full answer. So it's unfortunate that it takes so long, you know?

• 29:07Greg Isenberg?

  I wish it just like was faster. That's what I like about perplexity, you know, you can get in the zone. Let's see, 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

• 29:49Greg Isenberg?

  features, etc.? And make sure that this startup MVP will cost $5,000 max. Let's see how fast or slow perplexity deep research will take. It says here, deep research takes a couple of minutes. Let's go back to ChatGPT.

• 30:16Greg Isenberg?

  So while it's, while it's thinking, both of these are thinking. I think the, 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.

• 30:37Greg Isenberg?

  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 reflect, 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?

• 31:02Greg Isenberg?

  So let's see what's happening here. Perplexity is saying that it's found highly funded competitors like Harvey and Case Techs.

• 31:14Greg Isenberg?

  So you do need to, like, I know this, right? I know that there's highly funded competitors in the AI legal space.

• 31:23Greg Isenberg?

  I think that you do, you still, the onus is still on the prompter, on the promptee.

• 31:32Greg Isenberg?

  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.

• 31:43Greg Isenberg?

  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 undefended niches.

• 32:04Greg Isenberg?

  And it just goes through it. It gives me a competitive analysis. It writes this amazing, kind of cool opportunity matrix. Wow, this is, 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.

• 32:33Greg Isenberg?

  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.

• 32:46Greg Isenberg?

  They're going to give you $200 not to spend it at ChatGPT Pro. Maybe that's why they chose $4,800. And they say, here's a Chrome extension prototype, records anonymized contract review sessions, tracks cursor movements, edit frequencies, external research sources, and the tools are Manifest V3 and Tensor Flow JS, which is zero dev cost, legal team interviews.

• 33:16Greg Isenberg?

  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, this is pretty darn good. This is like, this is really good. And then the go-to-market sequencing, which to me like 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 go-to-marketing is really important. Offer fee-free clause vulnerability audit, generating historical enforcement

• 33:59Greg Isenberg?

  risk scores, alternative language access rates, and then 15 cents a clause auto redline. I mean, this is, these are great ideas. It's giving you the monthly searches, what the CPC is. So it says, go to organic search. It's telling you how to do webinars. I mean, it's not as deep as the ChatGPT Pro, but I love these tables. I think this is awesome. And this is like, this is what you need to know. And you just, you have to just re-prompt it to, to go deeper into these sections. So let's go see if ChatGPT is finally done. It is done. So it gave me new ideas. And it says, AI platform

• 34:41Greg Isenberg?

  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.

• 35:02Greg Isenberg?

  Or if you're watching, 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, basically what the competitive note mode is, the proprietary data advantage. So for example, as the platform onboards podcasts and runs sponsorship campaigns, it accumulates unique data on what content, 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 matches, messages yield results.

• 35:46Greg Isenberg?

  This creates a continuous learning loop that improves targeting. So it's got some good ideas. In the MVP development for 5000, 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 just, I do feel, feel a bit weird that it like recommends its own product, but I get it. You know, it's kind of biased, but here we are. So, and then it tells you, you know, make a basic web interface, manual Wizard of Oz sponsor message matching. It just goes a little deeper.

• 36:31Greg Isenberg?

  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.

• 36:53Greg Isenberg?

  So I didn't have time to go through all five of the ChatGPT deep research startup ideas and the five from perplexity deep research, but I've included 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.

• 37:38Greg Isenberg?

  It's like driving manual instead of driving automatic on your car. ChatGPT 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, you know, either product. They're both awesome and it's really, you know, what floats your boat. I think you just need to, 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, is, you know, use it, play with it, come up with ideas,

• 38:19Greg Isenberg?

  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 into my day to day.

• 38:30Greg Isenberg?

  If you, 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.