How I AI - How to digest 36 weekly podcasts without spending 36 hours listening | Tomasz Tunguz (Theory Ventures)

Episode Date: August 25, 2025

Tomasz Tunguz is the founder of Theory Ventures, which invests in early-stage enterprise AI, data, and blockchain companies. In this episode, Tomasz reveals his custom-built “Parakeet Podcast Processor,” which helps him extract value from 36 podcasts weekly without spending 36 hours listening. He walks through his terminal-based workflow that downloads, transcribes, and summarizes podcast content, extracting key insights, investment theses, and even generating blog post drafts. We explore how AI enables hyper-personalized software experiences that weren’t feasible before recent advances in language models.What you’ll learn:1. How to build a terminal-based podcast processing system that downloads, transcribes, and extracts key insights from multiple podcasts daily2. A workflow for using Nvidia’s Parakeet and other AI tools to clean transcripts and generate structured summaries of podcast content3. How to extract actionable investment theses and company mentions from podcast transcripts using AI prompting techniques4. A systematic approach to generating blog post drafts with AI that maintains your personal writing style through iterative feedback5. Why using an “AP English teacher” grading system can help improve AI-generated content through multiple revision cycles6. How to leverage Claude Code for maintaining and updating personal productivity tools with minimal friction—Brought to you by:Notion—The best AI tools for workMiro—A collaborative visual platform where your best work comes to life—25k giveaway: To celebrate 25,000 YouTube followers, we’re doing a giveaway. Win a free year of my favorite AI products, including v0, Replit, Lovable, Bolt, Cursor, and, of course, ChatPRD, by leaving a rating and review on your favorite podcast app and subscribing to the podcast on YouTube. To enter: https://www.howiaipod.com/giveaway—Where to find Tomasz Tunguz:Blog: https://tomtunguz.com/Theory Ventures: https://theory.ventures/LinkedIn: https://www.linkedin.com/in/tomasztunguz/X: https://x.com/ttunguz—In this episode, we cover:(00:00) Introduction to Tomasz Tunguz(03:32) Overview of the podcast ripper system and its components(05:06) Demonstration of the transcript cleaning process(06:59) Extracting quotes, investment theses, and company mentions(10:20) Why Tomasz prefers terminal-based tools(12:38) The benefits of personalized software versus off-the-shelf solutions(15:31) A workflow for generating blog posts from podcast insights(17:34) Using the “AP English teacher” grading system for blog posts(18:25) Challenges with matching personal writing style using AI(22:00) Tomasz’s three-iteration process for improving blog posts(26:13) The grading prompt and evaluation criteria(28:16) AI’s role in writing education(30:28) Final thoughts—Tools referenced:• Whisper (OpenAI): https://openai.com/research/whisper• Parakeet: https://build.nvidia.com/nvidia/parakeet-ctc-0_6b-asr• Ollama: https://ollama.com/• Gemma 3: https://deepmind.google/models/gemma/gemma-3/• Claude: https://claude.ai/• Claude Code: https://claude.ai/code• Gemini: https://gemini.google.com/• FFmpeg: https://ffmpeg.org/• DuckDB: https://duckdb.org/• LanceDB: https://lancedb.com/—Other references:• 35 years of product design wisdom from Apple, Disney, Pinterest, and beyond | Bob Baxley: https://www.lennysnewsletter.com/p/35-years-of-product-design-wisdom-bob-baxley• Dan Luu’s blog post on latency: https://danluu.com/input-lag/• GitHub CEO: The AI Coding Gold Rush, Vibe Coding & Cursor: https://www.readtobuild.com/p/github-ceo-the-ai-coding-gold-rush• Stanford Named Entity Recognition library: https://nlp.stanford.edu/software/CRF-NER.html—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [e-mail].

I have a list of 36 podcasts, but I don't have 36 hours every week to listen to 36 podcasts. So what I did is I created a system that goes through each of those podcasts every day and download the podcast to files and then transcribes them. Can you show us how it's actually built? Like, where do you get this feed? It sounds like you run it locally. How does this all work? I wrote this thing called the Parakeet podcast processor. And this podcast processor basically takes in a file.

And what it will do is it will read the file, it'll download it, and then it'll convert it via FF MPEG. And that will take the audio and convert it to text. So here's the podcast summaries for today. There's Lenny's podcast, the host, the guests, a comprehensive summary. So here's a conversation with Bob Baxley, key topics, and then key themes. The part that's most invaluable for me are these quotes. And those quotes, I'll read them.

It will suggest a bunch of actionable investment thesis for a venture capital firm, which is put into the prompt, like, okay, maybe we should be looking at AI-assisted design tools. You've gotten not only the content you want, but the user experience you want. You control it end-to-end, and you can build this hyper-personalized software experience. Welcome back to How IAI. I'm Clairevote, product leader and AI obsessive, here on a mission to help you build better with these new tools. Today, I have Tom Tungu's, a legend in the enterprise software business and founder of theory ventures, which invests in early stage enterprise AI, data, and blockchain companies.

Tom is followed by over a half a million folks on his blog and LinkedIn, and he's going to show us today how he uses AI to keep up with all the podcasts, including this one, and draft blog posts that would be approved by your AP English teacher. Let's get to it. This episode is brought to you by Notion. Notion is now your do-everything AI tool for work. With new AI meeting notes, Notes, Enterprise Search, and Research Mode,

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by leaving a rating and review on your favorite podcast app and subscribing to YouTube. To enter, simply go to How IAIIPOD.com slash giveaway. read the rules and leave us a review and subscribe. Enter by the end of August and we will announce our winners in September. Thanks for listening. Okay, Tom, I'm so happy to have you here because you're going to show us how you are solving a problem I'm creating for you. The problem I'm creating for you is I am creating yet another piece of interesting content

that you have no time to consume, certainly the format that we get it out. And I know to you, content is a really interesting source of ideas, of trends, of companies. So tell us what you built and why. Absolutely. Well, thanks for having me on. So I prefer to read than to listen because I can skip ahead. And I think there's a lot of information inside of podcasts that people share that I would love to know. And so I built, I guess what I call a podcast Ripper.

And the idea is I have a list of. 36 podcasts, this one included, that I really admire and I want to learn from, but I don't have 36 hours every week to listen to 36 podcasts, right? So what I did is I created a system that go through each of those podcasts every day and downloads the podcast files and then transcribes them using initially it was open source or open AIs, open source whisper, which takes audio and converts it to text. And then there's a new version called Parakeet, which InVedia released, that runs really well on a Mac. And so I'll take that text, and then I'll run it through a prompt, and it will spit out a whole bunch of different things. It will spit out a high-level summary

or whatever I ask it to. Okay. Can you show us how it's actually built? Like, where do you get this theme? It sounds like you run it locally. How does this all work? So I initially downloaded the Whisper app, and what I did is I wrote this thing called the Parakeet podcast processor. and this podcast processor basically takes in a file and what it'll do is it will read the file, it'll download it, and then it'll convert it via FFMPEG, which is a library that converts different kinds of files, and that will take the audio and convert it to text.

And then I use Gemma 3, which is really good at this, to actually clean up the transcript. So if we search for the Olamma model, basically what I'm doing is I'm just cleaning up the file here. Your transcript editor clean up this podcast while preserving all the content, keep the same lengths, remove the uns and the azz, preserve all technical conversations. And that returns a clean transcript.

And so on a given day, there might be five or six different transcripts that need to be transcribed. and then what I'll do is it runs through the Parakeet podcast orchestrator, actually it's just a podcast orchestrator, which is here. And so I'm storing each of the files that I'm transcribing in a local duct TV, which is a little database that says, I process this particular podcast on this particular day. And then I save the transcripts,

and I take all the transcripts on that particular day from the database, which is here, And then I send them through a prompt, which, see if you can find it, summarizes. Here, the daily summarizers. So it generates a daily summary document, which is here. It'll produce a file that looks like this. So here's the podcast summaries for today, June the 13th. So there's Lenny's podcast, the host, the guests, a comprehensive summary.

So here's a conversation with Bob Baxley. key topics. So here he's talking about his philosophy, a company culture. And then key themes. And the part that's most valuable for me are these quotes. And those quotes are then,

you know, I'll read them. It will suggest a bunch of actionable investment theses for a venture capital firm, which is put into the prompt, like, okay, maybe we should be looking at AI-assisted design tools. And then that might kick off a market map. We're really thesis-driven.

So maybe that starts a conversation on the Monday and we decided to staff a market map. Then it'll produce these noteworthy observations, which are actually put into tweets. So here are the Twitter post suggestions. So I haven't done this yet. I'm still working on the prompt. But the idea is like, could we actually automate linking back to people who we really like? And then another part, this is a little out of order. But another part here is, are there startups that are mentioned within these podcasts that we should know?

right so here's Airbnb Google Amazon Stripe we know all these guys I don't know what this company is and so this might go into our CRM right to be enriched and and then the last is we'll actually generate prompts for blog posts in the style that I write and then this will go into a Python pipeline to actually machine generate blockposts so before before we get to the the machine automated AI blog post pipeline I have a couple of questions. questions about this process because I think you did a couple interesting things. One, I have a question is if you found higher quality by cleaning up the transcripts, like how much did that incremental input quality piece actually help your output?

So it helped. So initially I was trying to get the answer was initially a lot and then over time less. Because initially what I was trying to do was to find these companies, I was used. using named entity extraction algorithms from Stanford or the Python library. And it was having a really hard time. And so I was cleaning up, cleaning it up to try to get the performance to improve. And then I just put it to a really large, large language model. And it could spit it out much better.

And so the cleaning is not that useful anymore. Yeah, I was looking, because I was looking at it and you were focusing on like proper nouns, company names. And so I'm assuming if you want to extract something like Stripe, which has many, many meanings, meaning getting it into a proper noun format, for example, would help with that extraction. But you're saying as you could just use as opposed to these kind of package libraries for specific machine learning use cases, instead just send it to an LLM, that ended up just meaning you could worry less about the input quality of your transcripts and more about

that kind of prompting and structure here of the output. Yeah, that's exactly right. So my goal initially was to do everything locally. And so I was using Olamma, I was using that Stanford library, parakeet is run locally. And then what I realized is particularly for the named entity extraction, more powerful machines are much better. Yeah. And so, and then I have to ask another question, which is everybody's going to look at this and they're going to go, what the hell is he typing in? Like we have a couple people that are like, why in the terminal?

So I'm just curious, you know, did you ever think about putting a UI on top of this? you just seem very comfortable in the terminal so it seems to work for you. I'm just curious about where you decided to focus your user experience efforts on this personal. I love the terminal. I read this blog post by Dan Liu with two youths

where he was talking about latency and the latency between like the keyboard and the computer. And it turns out that the terminal is actually the application of the lowest latency and the lower the latency, the less frustration you have using a computer. So during COVID, I decided to learn

how to use a terminal. And since that, then I've sort of lived in it. And so like my email client is a terminal-based email client. And I use that because it's really fast. And then I can also script different things. So I can lead 10 messages at once. So I can call an AI to actually automatically respond to an email or add a company to a CRM.

So that was really important. But at a high level, like I think it's, I've just become really comfortable with it. It's really fast. And then the last thing I'll say is I think Claude Code is an amazing product. and the great part about what Claude does is I have about 2,000 blog posts. I can just go into Cloud Code and say, modify the files in this way,

or change the blog post theme, or recently I launched a blog post generator, which takes all of the content that I have on the blog, and you can ask you a question, and we'll write a blog post for you about your particular question, and I did that all using CloudCode. Yeah, I mean, I have two sort of thematic things

that I think of while observed, this workflow and your love for the terminal. I agree. Claude code is an amazing product and it's a really well-designed terminal-based product. I love it. I love that you have this constrained surface area in which to like communicate progress and latency and changes. And I think it's really thoughtfully designed. So for anybody out there building dev tools in particular, learn how to design in the terminal. And it's so so important. Because you make really fabulous products for, I guess, people like you and me that say things like I picked up the terminal over COVID as my hobby. The second thing that I was thinking about is since generative AI has become mainstream,

every single person has said, somebody make a podcast digest application. Every single person I know is like it was one of the first projects I made. I made my kids a podcast digest, their favorite podcast. And it made little quizzes about the topics that they could answer a super. cute. So I think it was a very common use case. But what I was thinking is no startup is going to be like, you know, it's going to be a huge TAM company, a terminal based podcast, transcript, processor, and thematic extraction generation engine. And I think this is such a perfect example of like, yeah, there's probably something off the shelf that could do something like this. But you've

gotten not only like the content you want, but the user experience you want, you control it end to And you can build this hyper-personalized software experience, which I just, it was not possible or it wasn't efficient to do, I would say, until very recently. Yeah, it fits the workflow, my workflow like a glove, right? And any time something comes up and changes, like maybe there's a section that's out of order, like we found, I can just go into plot code and updated. And it'll be done in 15 to 30 seconds, right? And, you know, I really wanted an email of this every day, and that was straightforward.

So I agree with you. I think we're at a place where the marginal friction to achieving a glove-like fit with little utilities that maybe you wouldn't have paid for in the past is now it's just so it's so quick, right? You're just entering a couple of emails and it'll be done. Yep. You've seen the doom and gloom headlines. AI is coming for your job.

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We will see if those actually get posted, you know, in production. And then let's talk about your second workflow, which is you extract insights that might be interesting for you to write about or add your perspective on. And then you actually turn those into drafts using AI. There's a lot of stuff that happens in the ecosystem. And every once in a while I like to write about what somebody said in a podcast, right? And I think today, like I was looking, the GitHub CEO is actually interviewed. And so Matt Turk interviews, who's at another venture firm, interviews Thomas. and he talks about how AI encoding is the future.

And so what I really want to do here is let's suppose I really wanted to have a blog post that was tied to this. So what I can do is I say like, okay, I have this podcast generator and I'll show it to you in a second. And what I'll do is I'll take as context the transcription of that podcast, which is here. And then I'll define an output file and then I'll give it a little prompt, which is like, you know, he said this quote which is actually within the podcast summary everything that i can easily replace with a single prompt is not going to have any value it will have the value of the prompt and the inference in the tokens but that's often a few dollars and i'll tell it okay go look for podcasts that are related to this and i've categorized them uh as a i and then here actually there's a bug so shish

phintem fail i was trying to fix it before i got on the video but the searching for the relevant blog post is failing and i need to figure it that out. It's run through Lance D.V. Vector embedding his database. And then it'll generate a blog post and I'll share the prompt in a second. One of the techniques that I found

the most effective when generating blog posts is to ask it to grade it like an AP English teacher. And it goes back to my history. I remember not really loving to write until I took a class with an Army veteran and she taught me to really love to write. He was my AP English teacher.

And so I really like receiving feedback in that way. I grade it on a letter grade and then tell me what I could improve. And then I'll iterate with the model until I get to an A-minus. Got it. And so just before we go into the actual writing, and I'd love to see a little bit of this AP English prompt, are these two pieces connected? Your podcast summaries, do those go into this vector DV that can then be searched through for relevant other podcasts if you're writing on a topic?

Like, how does this all come together? Yeah. So right now, it's just the blog posts that I've written in the past, a 2000 blog post or so, that go in. And the major reason I add those as context is I'm trying to capture my style. And I have to tell you, like, that's really hard. Like, I have fine-tunes, open AI, I have fine-tuned Gemim models, and getting the voice, and you'll see it in the output.

It sounds like your computer when it writes, even with that additional context. And it doesn't, the other thing that I have not been able to figure out is I think it's really important in one blog post to link to other block posts that I've written, just because the knowledge builds on itself, and obviously outside as well. But I haven't been able to figure out how to get it to link effectively. I think this is a common feeling with AI generated writing. No one is satisfied with style. Even when style is exceptional, I think I've seen examples, especially as something they were commercial models, actually writing really lovely prose and really lovely language. It's just, it's so personal what your style is and how you would write something, the rhythm in which you would

write it, how would you punctuate and break line, all that kind of stuff is so personal that I have, like you had a very, very hard time getting it to write like me. And I think even harder, which is why I appreciate that you're not yet posting this. It cannot, it can't tweet like me. I can't. I can't. No. The short ones. The short ones are the hardest, you know? I guess they say that about writing generally. Have you felt like any of the models have done better or worse at writing like you? Or is it just like they only get 70, 80% there? And I just accept the fact that I'm going to have to rewrite things. Well, they have different voices. I don't think any of them are close. Like, I think Gemini is more clinical. is the way that I put it.

I agree. Claude is more warm and verbose. It's very, very gallous, like just wants to keep talking and wants really long sentences and really long paragraphs. And Open AI, I think the models each have slightly different personality, so there I don't think there's like a single characterization. So I've been, I think I've been iterating to, I used to use Claude 3.5 a ton.

and I uploaded all of my blog posts in a project, and then I'd have it iterate there. Now I can kind of do it with cloud code or using this prompt, so that's a little less useful. But what I've found is you really need to add your own voice, and then you need to tell the AI to keep the things that are wrong. Right? Like, it's kind of a funny thing to say,

but as you were saying Claire before, the way that you punctuate, I really like ampersands, right? And I like adding spaces before colones, And I like starting certain sentences with or having little incomplete clauses because I think they keep the reader moving. But an AI won't do that. And AI will only deliver you a grammatically perfect specimen. Yeah, we're going to have one very nerdy English language moment, which is I like to start paragraphs with a conjunction. I love an and or a butt.

Yeah, it pulls you in. So, okay, you and I are going to work. We'll build like a microsass on good writing models and prompts that people can use. So, okay, so we accept that it's not going to write exactly like you. But you've created this grading process to say, well, is at least good. And so I'm curious, can you walk us through how it gets to an A-9-91? Yeah. As an A plus student, I don't know.

A 91 would really stress me. Tell me how you kind of wrote the prompt and then why you picked like A minus as your bar. Yeah, for sure. Okay, so the way I broke the prompt, I told it what I wanted. And I asked an AI to critique, I think I asked Gemini to critique Claude's output.

So it's kind of using a student teacher or critique model. And then what it does is we'll walk through the prompt in a second. but it goes through three grading attempts. So it reads a file, gives it a grade and the score. And then the things that are the most important that I found, particularly for readers, are the hook, which is the first few sentences or the lead, you might call it. And then the last is the conclusion

and making sure it ties back because then you have a complete, you have a complete post. And so it goes through this three times, right? And so you can actually see, like here, give itself a 90 and then a 91. And then at that point, it basically was good enough. It was satisfied with the hook. So if we read the blog post generator, you can see what it does at a high level, right?

So it finds the blog post, it generates an initial blog post, grades it like an AP English teacher, improves, and then auto-generates a URL-friendly slug. So it actually writes it in the right format. And then it can use OpenAI or Olama. And then the prompt is here. You are an expert blog writer specializing in technology and business content. And then here I add in the blog posts, and it kind of shows the patterns. What it also does is it dynamically calculates the number of paragraphs from relevant posts and uses Olamma to summarize the stylistic patterns of those related posts.

So I might write a little bit differently when I'm targeting a Web 3 or a crypto audience than say I might, when I'm analyzing the public disclosures of a company that Snowflake, just announced earnings, let's say. And so it's dynamically injecting that here. It shows a bunch of different examples. And then, you know, here's what I think makes my blog post tick, right? 500 words or less.

I have like 49 seconds with a reader. No section headers. I ran an analysis of dwell time as a function of how many headers there were, and it turns out headers were terrible for dwell time. People just bailed. Flowing paragraphs, each paragraph

transitioned smoothly to the next. Actually, the AI consistently critiques my transitions and says they're too harsh. And going back to the a minus point that you made before, I think I lose five or six points because of my transitions because they're abrupt. And then limit each paragraph to it, most too long sentences. And then the structure of the blog posts. I think this is a really interesting story of the top and I want to make sure people don't miss it. I've seen this before, which is like take this example and describe it back to me and use it. And so you're saying, I'm writing on this topic, go find the blog host like this topic, analyze them for format.

Like what is, what is the structure, how am I writing things and match, stylistically match this subset of my blog post because I do vary style by topic. Exactly right. Exactly right. Okay. And then two sentences, I was not expecting this two sentences per paragraph thing. I like it. Yeah. I have one more question for you as somebody who did take AP English. So this is perfect.

Did you actually, do they publish the AP English like grading standards for the tests? Like, did you integrate any of that? Is it just sufficient enough to say AP English teacher? I'm just curious how deep you went. Yeah, I just said AP English teacher. I figured there are enough people leaking. Either like the scoring rubrics or essays that scored fives or whatever it was. Got it. There's good underlying data. Okay, so this is for writing it. And then what about for grading it? Do you have that prompt?

Here's the grading prompt. So you're an experienced English teacher. Here's a letter, grade, numerical score. And then here are the evaluations, the hook, which, you know, argument clarity, evidence and examples, paragraph structure, conclusion strength, overall engagement. Got it. And have you ever gotten Bs and Cs? Yeah, for sure.

I'm getting like 91%. I always wonder about this because I do think these models are positively inclined towards telling you you've done good work. I've found that consistently. I've always had to say, be more harsh, be more critical, call out where I'm doing things wrong. So I'm curious, do you actually get high variability in these, in these gratings or, you know, what has been your experience?

Yeah, absolutely. So another, so this is one pathway for, I mean, the podcast to blog post data pipeline is one pathway for generating blog post. Another one is just an idea comes to me. And so then what I'll do is I'll just literally dictate. I'll dictate, I'll put it in, and I'll pass it into the blog post generator and then have it grade.

And there, I've seen C minuses, right? Yeah. So it's easier when it's grading itself and it's a little harder when it's grading you. This is super interesting. And then you do it three loops. Do you also get high variability between the loops? You find that that three-time process

is actually additive to the evaluation? I do. I think I often see the first one, like a 91, and then the second one will dip into the BB plus range, and then it'll pop back up. Yep. So it's a little bit explore, exploit. Again, most of the time, for me, it's around those transitions,

and most of the time, the verbosity of those transitions that the AI injects is just catastrophic. I mean, it doubles the length of the blog post. And then the third iteration tends to then kind of reinforce the brevity. Got it. And my kids are too small for AP English to be something that I have to worry about yet. But meta question, you know, everybody's so worried about students using AI to write. This seems like such a more fair way to evaluate writing.

I'm curious, do you think we're going to see more and more of this type of evaluation in an academic setting? you think teachers could benefit from, you know, checking their own work when they're grading these things that are a little harder to put quantitative or qualitative feedback against. Yeah, I think it's a great first-pass filter. Like 80% of the work, what's going on grammatically, are you using sentences and conjunctions and dangling modifiers and all that stuff? I think that the wrote analysis of the logic of that language

is to be handled by an AI. Right. And then I think there's this. other part which is the stylist. I mean, you look at, I was reading E. E. Cummings poems last week, and you look at the creativity of some of those poems. And I, you know, I think it only comes after you have the mastery of the language, but you'd want, you'd want teachers to be free to champion that or encourage it. I think it's really just just as important. Yeah. So for the students listening, you know, I still think it's good to learn to write.

to read a lot, to learn to write, to write yourself. And if you're looking for a place to practically apply AI to your writing work, maybe it's as a first past grade. Say, if you were my teacher, how would you grade this and what feedback would you give me? As opposed to you, if you were me, how would you write this? Maybe that's the right way to get students starting to use AI in a practical way that still allows you to develop these hard skills that I think are going to continue to be super relevant. Could not agree with you more. I mean, oftentimes, I don't know about you, but I'll run into writer's block or I'll have an idea that I really want to convey, but it's just a soup in my mind. And there, and AI will help you iterate and refine. And often

it will give you the germ of an idea and then you'll take it and add your specific lens to it. But yeah, I think it's a wonderful learning tool because you have the feedback so quickly. Yep, exactly. Okay, so you have shown us just taking Zoom back, 30-something podcast, you process on a daily basis, you create summaries, you extract themes, you extract tweets, you extract topics. Those topics then go into another Python script that writes a blog post based on some other relevant blog to posts in your own blog, writes the blog post, on-demand AP English teacher to grade you three times, and then you take the final pen, and then is AI posts like, Do you have it just like an agent going, but a send hit or you?

That I don't. That would be awesome. But no, that's still done the artisanal way, point and click. You are still copying and pasting with your human fingers. Okay, this is a great super practical process. I'm even thinking about ways I can do this to identify future podcast guests or topics that people might want to see. So you've given me some inspiration.

I'm going to ask you to wrap up. questions and then get you out of here back into your terminal. First question, I was reading your 2025 predictions and you said, this is going to be the year. We see a 30 person, 100 million dollar company. And I'm curious when you, in your mind's eye, when you imagine that company, what is it? Who's in it? Like, what are they doing? How are they operating? What do you imagine that company looks like? Yeah, I think it's probably there's a CEO is a product person. There's an engineering team of 12 to 15. And then there's probably a couple of customer support slash dev rel people. And maybe there's a salesperson, maybe who's closing some of those

bigger contracts and then a solution to the kind of company. But it will be predominantly software engineering. And then I think the go-to-market motion is PLG, bottoms up, just massive adoption. And do you think those software engineers are largely still focused on product building, or do you imagine that those software engineers are also enabling the company with tooling in automations and figuring out how one salesperson can do the work of 20. I'm just curious how you think that's going to shake out. Oh, absolutely. I think that's right.

I mean, we were kind of talking about this, but the ability of a person to come up with a demo and then use AI to critique the demo and test is now so fast. And the ability to take that code and basically move it into production really quickly is also incredibly fast. So I do think there will be a pretty significant like internal platforms enablement. function. And whether that's kind of 20% time for a bunch of engineers or a dedicated team of two or three people, a huge amount of leverage there. Yeah, I completely agree. Okay. And then last

question, when your AI is grading you unfairly or writing terribly or making very long transitions that do not sound like you, what is your prompting technique to get AI to listen? I have two AI's duke it out. So I have like a little, example of like this is the input, this is the output that you gave me, this is the output that I want. And then I have Gemini and Claw duke it out and finally kind of decide on. And I'll use a little script to do that where they'll finally polish a script. It doesn't work all of the time. But I do think switching models helps a ton. It creates a level of generalize ability that I haven't been able to replicate as a human. I agree. And I will give you a how I AI tip from a previous guest,

Hillary who like negs the models to each other. So they're like, Gemini, look at this garbage. No way. How to, and then they're like, Claude, look at this trash open AI gave me. Like, surely you can do that. That's where she calls it mean girls. She's like, I mean girls, models and get them to compete with each other. And maybe you can create a Python-based terminal script to do that and then share it with

our audience. Open source that thing. Great idea for a weekend project this Saturday. Well, this is so helpful. Where can we find you? How can we be helpful to you? Oh, I'm on tomtimgoose.com, and if you're starting a company within the AI ecosystem, I'd love to hear from you. Great.

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