HubSpot Cofounder and CTO, Dharmesh Shah, joins Cameron Adams to explore how AI agents are reshaping work, leadership, and creativity. From building culture as a product to tech-powered parenting, Dharmesh shares insights into why he’s still betting on humans, even in the age of powerful AI tools.
HubSpot Cofounder and CTO, Dharmesh Shah, joins Cameron Adams to explore how AI agents are reshaping work, leadership, and creativity. From culture-as-a-product to tech-powered parenting, Dharmesh shares insights into why he’s still betting on humans, even in the age of powerful AI tools. From building culture as a product to tech-powered parenting, Dharmesh shares insights into why he’s still betting on humans, even in the age of powerful AI tools.
Cam’s notes on Substack: promptedwithcam.substack.com
Key Quotes:
“Culture is the product you build for your team.”
“I think of an AI agent as a very adept intern… But when they show up for their first day of work, they don’t know about your business, they don’t know about your policies, they don’t know about their teammates. They don’t know anything other than all the external training that they’ve had.”
“In a way, creativity is combining primitives that already exist in novel and unique ways… AI is good at connecting the dots ”
“Money is still on the humans. I’m sorry. Humans powered by AI — if they want to use AI, fine — but I want someone that’s got that taste.”
Timestamps
00:40 Welcome + Intro to Dharmesh
01:28 The Prompt: The Casio keyboard that changed Dharmesh’s life
03:47 The spark behind Agent.ai and its growth to 2M users
06:13 Dharmesh’s process for building agents
07:04 Can AI truly be creative?
11:47 Hybrid teams: will agents ever be true teammates?
15:12 The “adept intern” analogy and why onboarding matters
17:57 How AI amplifies creativity across writing, design, and music
20:01 Meta prompting: Dharmesh’s trick for better results
23:56 Culture is a product — lessons from HubSpot
28:19 Tech-powered parenting and raising the next generation with AI
32:00 Sorry, Must Pass: Dharmesh’s philosophy on focus
33:15 Outcome-based pricing: can you charge for creativity?
37:10 Money still on the humans — why taste and judgment matter
40:04 All-star creative team picks
43:00 Designer reveal with Paolo
44:52 Rapid-fire questions
Links
See Paolo, Dharmesh and AI’s creative collaboration
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Cam on LinkedIn: https://www.linkedin.com/in/themaninblue/
Dharmesh on LinkedIn: https://www.linkedin.com/in/dharmesh/
Dharmesh Shah: AI still cannot write even a decent original bad joke, despite my best prompt engineering attempts. I love AI as much as anyone. I have a huge amount of respect for the technology and what's going be capable of, my money is still on the humans, I'm sorry.
Cameron Adams: Welcome to Prompted, a podcast about AI, people, and the creative spark. I'm Cameron Adams and I'm fascinated with leaders who are actually using AI to be more creative, whether that's building better products, coaching their teams, or harnessing AI for their personal creativity. In this show, we dig into the details of how they use AI that we all want to know. Today, we are joined by the amazing Dharmesh Shah, co-founder and CTO of HubSpot. If you followed his career, you know that Dharmesh has always taken the unexpected path from starting his first company with $10,000 on credit cards to writing a regularly updated culture code for HubSpot and publishing it for the whole world to see. He's got a refreshingly simple approach to leadership.
He runs a massive company without a single direct report. In true entrepreneurial spirit, he has recently started some exciting new projects focused on AI agents, which we'll get into on this episode. Dharmesh, welcome to the show.
Dharmesh Shah: Thanks for having me, Cameron.
Cameron Adams: It is so good to have you here and I'm keen to dive into AI agents and tech-powered parenting with you, but to help us get to know you a little better, let's kick off with the prompt. This is the part of the show where we get an idea out of your head and onto the page, and then we get one of our designers here at Canva to take it even further with AI. So, to start, we would love you to draw something from your childhood that inspired you in your adulthood. While you draw that, you've got to tell us the story about it as well. You get 90 seconds to talk and draw. Do you think you can multitask?
Dharmesh Shah: Wow, the multitasking is going to be hard, but I'll try it. So, I'll tell you the story. When I was in my teens, my parents visited Singapore for the first time. I didn't go and they brought back this electronic device, which ended up changing my life. Funny thing is when I tell people this story, which I haven't told that many times, it's like they automatically assume it was a computer and it was not a computer because I didn't get a computer until I was in my 20s, much later. The device I did get though that they brought back, which I'll sketch out here-
Cameron Adams: I'm excited to see what it is.
Dharmesh Shah: ... looks like this. So, it's a tiny little Casio keyboard. This is probably almost drawn two sides. I had not shown any inclination from music. I'm not exactly sure why they got that for me as a gift, but I became obsessed with it even though I didn't know how to play it at all. I taught myself through brute force, which is randomly pressing keys and seeing if notes sounded right or not and then pressing one of the other keys until something sounded right. It would take me weeks to learn a single song. I've been doing that for about 30 years now ever since.
Cameron Adams: Amazing. I've never heard anyone apply brute force for piano before, but sounds like it worked for you.
Dharmesh Shah: It worked.
Cameron Adams: But if you want to see that image and you are listening to this on the audio, head to the link in the show notes to see Dharmesh's picture. We're actually going to send this picture to Paolo, our designer in residence who's going to team up with AI to whip it into something new. We'll reveal Dharmesh, Paolo, and AI's creation at the end of the episode. So, stay tuned for that. But while Paolo works, let's go deep on AI with you, Dharmesh. Now I want to get into HubSpot a little bit later. I first wanted to touch on agents. You've recently launched Agent.AI, a professional network for agents. What was the spark that led you to build this platform?
Dharmesh Shah: I've had this thesis that in the future we're going to have agents and humans working together in what we would think of as modern hybrid teams. I'm like, "Okay, well, if that's true, if we're going to be putting AI agents on teams along with humans, then we're going to need all the things that we have in the analog human world." It's like how will people find these agents? What will their resumes look like? How do we know if they've had experience and all these things? And so the original spark was there should be a professional network for agents because that's going to need to exist some. So, that was the original genesis of the Agent.AI idea.
We launched it in beta about a year ago at about 50,000 users back then. It's now up to two million. So, there's been some resonance with the idea. What's happened now is people are building their own agents, creating them on the platform, and we have about 2,000 publicly shared agents now on the platform. So, it's been a lot of fun.
Cameron Adams: Many of the agents on Agent.AI are actually ones you've built yourself. I figure that's a cold start problem. You needed to get some agents on there to start, but can you walk us through one of your favorite agents and what it does?
Dharmesh Shah: One of my favorite agents, one of the most widely used ones is a company research agent where you can type in the company name or the website domain and then we'll do this deep research from all across the web, hitting a bunch of proprietary APIs. But what's cool about it is just like if you had a researcher on your behalf, you would say, "Okay, well, here's the parts that I'm actually interested in. Here's the business that I'm in. Here are the questions that I have. So, I want you to answer these five questions, focus on these particular areas, and give me a customized report every time I ask you for a research report on a company."
That's one of them, but I've probably built a dozen or so agents from relatively sophisticated ones to really simple ones. I'll tell you the one that I've been trying to build, which is like a dad joke agent to be able to generate a dad joke, which one needs. One of the last remaining frontiers, despite all the reasoning models and all the advancements that I played with all of them, AI still cannot write even a decent original dad joke. That's what I've discovered. Despite my best prompt engineering attempts.
Cameron Adams: That's going to be the new cheering test. Can you pick whether this is a dad or an AI dad?
Dharmesh Shah: Yeah, there you go. Yeah.
Cameron Adams: What's the actual process for building an agent that you are using at the moment? Is it mostly just wiring up APIs or what does it look like when you're building from scratch?
Dharmesh Shah: here's a spectrum of it. When we started the platform, we had a low-code agent building platform, which primarily deterministic flows that says, "Here are the steps I want to take and the multistep flow. Here are the inputs. Here's a minimalist UI." And then it would call the LM or call APIs and we have a bunch of data connectors to get social media data and pull all that in. Now we also have the ability to have agents that are built off platform, pick your agentic or vibe coding tool of choice and build an agent that way and just wire it into the platform, which I've been spending a lot of time on building these much more sophisticated UI agents that still plug into Agent.AI.
Cameron Adams: We see a lot of data mining and research and going out to the web and fetching stuff, but do you think AIs can possibly be creative? Could you have a truly creative agent?
Dharmesh Shah: For some definition of creativity, I use AI for what I think would be classified as relatively creative use cases. They're not just data research. It's not just writing or wordsmithing something. One of these tricks I want to learn from my son who's 14 is around creating what I'll call a simulation for lack of a better term. So, what he does is he's an aspiring fantasy author. So, he'll do world building inside of ChatGPT.
So, he's got a 2,000-word prompt to say, "Oh, here's the world, here are the characters, here's the power structure, here are the things that people can do." Then he'll pressure test the ideas. What happens if I do this and it'll come back and it's this very iterative process. I do something similar with business ideas. Here's what I'm going to tell you and I've got this very long knowledge base that I can pull information in from and say, "What would happen if HubSpot launched a product X or we did this change to it or whatever or if I were to ask five people across different disciplines to give me feedback on this thing from different regard, what would that be? What would that look like?" In a way, creativity is combining primitives that already exist in novel and unique ways. Not like we're building things always from whole cloth.
I don't use it for writing full articles or help with talks and things like that, but it's really good at connecting dots that might be hard to connect and doing transitions. Oh, I'm trying to wire this up and I can't quite find a way to get to this thing that I want to get to from where I am right now. Can you give some options? It's a great critic. It's a great brainstorming tool. It's a great way to just spark some ideas and add some dots to this mix of dots you're trying to wire up.
Cameron Adams: Yeah, I love that possible concept of what creativity actually means in terms of connecting dots and bringing ideas that maybe you didn't think should exist together into proximity to one another and seeing what comes out of it. In my life, a lot of creativity has come from that, and I can truly see the value of doing that through an AI model that has all these connections in it that you might not possibly actually think of.
Dharmesh Shah: One of the neat things you can do, it does it really well, is imagine you had a spreadsheet or just the two axis thing, whatever, and you have your products or product features down one side and the same product features along the top. So, it's the exact same thing along X and Y dimension, and then what you do is you run the simulations. I want you to combine this product with this product or this feature with this product in novel ways and come up with a description of what would that look like.
Oh, we happen to have this and we also have that. Is there some unique blend of things that some of them or a lot of them are not going to make any sense but sometimes it comes up with things that are actually useful? Now it's like, "Oh, yeah, I hadn't thought about how we could take this feature that's in our sales product and connecting with this customer service product, and that would be cool actually."
Cameron Adams: And that's an emerging skill, isn't it, in the age of AI? It's like picking out the gold. You being that editor that is taking in everything that the AI is spitting at you, which might be terrible, might be brilliant that you need to figure out which one's which.
Dharmesh Shah: Yes. Yeah, it's weird. Back on the dad joke thing, not only is it not able to write dad jokes well, it actually struggles with even when you tell. So, I have a database, which is what I call it, a database of curated jokes that I've collected over the years, and so it can actually explain to you what makes a joke a joke and why it's funny. So, it actually does a very good job at that. But then if you have it rate the jokes on a scale of 0 to 10 that says, "I'm going to give you 100 jokes, you tell me which ones and give me a quantitative score based on the joke." It does actually not that good at that, and I'm not exactly sure why.
Even though I can tell you what makes the joke funny, the underlying mechanics of it, it's not good at actually quantifiably measuring the quality of something, which is I don't know if that's just a function of the way they work or something. Anyway, I haven't been able to figure that out yet.
Cameron Adams: Do you think actually is an objective measurement of humor though?
Dharmesh Shah: That's a good question. Maybe I'm asking the wrong question. Maybe the question I should ask is I want you to simulate an audience of 1,000 people and estimate how many people would laugh at this joke.
Cameron Adams: Well, they've done this before, haven't you? Because you have optimized some of your talks for laughs per minute.
Dharmesh Shah: I have, but I actually use real life humans and human audiences to test that prior to actually delivering it on stage, but it's an interesting idea. Maybe just run a mock simulation of an audience with a certain makeup and say, "Okay, well, I'm going to test this joke or this humor and see if it gets a reaction or not."
Cameron Adams: So you've talked about AI agents possibly being teammates, and you are actually all in on hybrid human-AI teams at HubSpot. Can you actually share a real moment at HubSpot or one of the companies you advise where that human-AI collaboration has created something unexpected, something that now the humans nor AI could have pulled off alone?
Dharmesh Shah: I'll be honest, the answer is not yet. I think we're very early in this notion of agents being more than just tools. So, there are sophisticated tools now as AI is because they can actually carry on conversations. We have a customer support agent. We have a prospecting agent. The thing we haven't quite figured out yet, and I think we're right on the brink, is I think the really interesting things start to happen not when you have an agent that's purpose built for a specific thing, which is what our agents currently do.
It's when agents have knowledge of each other and can actually bring in, it's like, okay, I'm trying to resolve this customer support issue, I'm going to bring in the prospecting agent to try and resolve this issue for whatever reason, bring in some other agent that's disconnected from the core mission of the agent that's currently running. I think part of this is going to get resolved. We have things like the agent-to-agent protocol from Google and MCP where the large language models can be exposed to tools and other agents. So, we have some discoverability, but that dynamic of unexpected things.
Now, it will do unexpected things and be able to answer questions that we didn't think it would be able to answer, but it doesn't really draw outside the lines, right? It's like, okay, well, so within the system instructions and the rag-based knowledge that we provided, it will do.
They'll mostly stay within the lines. We see this with humans as well. It's very, very interesting things happen when the number of humans exceeds one, right? It's like, okay, there's a certain number of creativity I can draw in a brainstorming session with myself, but then when I put other people in there is when the truly interesting things start to happen. The other thing, I think this is more on the technology side and AI is getting better and better and that's great. The protocols will get better, but I think we need a better way to weave agents into all the same tools that we use, be it email or Slack or whatever it is.
They need to be part of the same systems that we use for doing the regular work that we do. I think that'll probably happen probably next year sometime.
Cameron Adams: Yeah, I think sitting around waiting for a prompt to come in is probably the least interesting thing you can do. Getting a WhatsApp notification or a Slack notification is definitely going to be the future.
Dharmesh Shah: Yeah.
Cameron Adams: Do you see agents really being part of a team? You mentioned they're at the tool stage now, or do you think they'll always be tools? Will they bridge that gap to truly being a teammate that you trust, that you converse with, that you're happy to delegate to?
Dharmesh Shah: Yeah, I think so. This is not just the optimist in me. From just a raw IQ perspective, models already have the intelligence to do a lot of the things that we do that we would ascribe and say those are human-oriented tasks. There's a couple of things where we can continue to do work. One is around EQ, around understanding despite its lack of human experiences, can it actually develop true, at least a proxy for empathy? And it's starting to get better at that because it fakes it pretty well and there've been some studies that are done.
But the thing I think will really unlock it is not the technology's problem, it's our problem, it's organizations. The analogy I use for agents is So let's say you were hiring what I think of as a very adept intern. That's the way to think about an AI agent. Let's say this adept intern was really adept, had a PhD in everything, was very, very knowledgeable, super smart, but when they show up for their first day of work, they don't know about your business, they don't know about your policies, they don't know about their teammates, they don't know anything other than all the external training that they've had.
We're like, "Oh, we hired this really smart agent to do X and we unleashed it on the world, unleashed it within our organization and expect it to really perform. I think that's a unreasonable expectation." So once we start truly treating them as potential teammates, well, they're going to need onboarding.
They're going to need some training, they're going to need some feedback. They're going to need some objective, measurable way to know whether they're making progress. They're going to get some things right, they're going to get some things wrong. Then over time, I think we'll have much more feels like a teammate person that just so happens that they're digital, but they're sitting in my Slack, they're responding to emails, they're watching what's going on. They don't have to always be prompted to do something just like... The distance between how much you have to look over their shoulder, it grows over time just like we have with a regular person. The trust builds. They get more awareness and more context about the organization. I'm hopeful we'll get there.
Cameron Adams: I wonder what those relationships are going to be like because you've talked about onboarding, you talked about feedback, possible coaching of AI agents, which are all very human things. You have conversations about performance. You identify growth areas. You have growth plans that roll out over six months, a year, all very human things. Are we going to treat AI agents in the same way or is it going to almost be a servant relationship?
Dharmesh Shah: I still think of it as a tool, right? So I don't like personifying tools. It's like I love AI as much as anyone. I have a huge amount of respect for the technology and what's going to be capable of, I don't think it actually replaces a human in the literal sense. I think it replaces individual tasks that humans should have been investing their calories in because they're capable of so much more. The reason we did those things that we have the same thing with the industrial age is as we invented machines and automated things like, "Okay, now we can work at a higher level as humans work at a higher level of abstraction." I think these will still continue to be tools for a while, but that doesn't mean that they can't be accountable and responsible and overtake things with a high degree of trust.
But I think ultimately the tastemaker, the thing that's going to hold it all together, the glue is going to still be carbon-based life forms. I think there's still something particularly special about humans. I may be biased being a human.
Cameron Adams: I've seen you described as a creative CTO. To what extent does technology unlock your creativity and how do you think this might be changing in the AI era?
Dharmesh Shah: I understand the arguments that argue that AI squashes or suppresses creativities because it replaces the need for that creativity. I think the exact opposite. Creativity is like, okay, you have some concept, some idea in your brain. The challenge that many of us have... I'll talk like my son has, so he's an aspiring fantasy writer someday, but when he was 9 and 10 and even now, his skillset in terms of being able to put words together into sentences and actually write and do it was lacking. So, it's going to take him years to develop that to even a rudimentary level to where he can craft a story for his target audience.
But now with AI, it's like, okay, well, he still has the ideas. He's always had the ideas in his head. From a design perspective since we're here on the Canva podcast, it's like, okay, well, Canva did this for me back when the first folks first came out and now AI does this for me. I've never thought of myself because I'm not a designer, but it's not because I don't have visual ideas. It's like I have things that are in my head that I want to accompany a blog post or something like that. Now with AI, it lowers the bar in terms of the entry level for people that do a particular thing.
So, I think it amplifies creativity because now it takes the ideas that you have in your head, the creative ideas and helps you manifest them, express them, be it music, be it visual design, be it writing, whatever it happens to be as your skills catch up. On the whole, I think people are going to be more creative. We're going to see more output, more creative things from people because the bars just lower. More people can do it.
Cameron Adams: Yeah, I love getting that insight into how the next generation's using AI. You mentioned your son is building worlds to express his fantasy writing and he's putting in 2,000 word prompts, which is amazing. I think your pretty experience with tools like ChatGPT. Is there a way that you use it that would surprise people?
Dharmesh Shah: Yeah, a couple of things. I just launched a very simple agent. So, we've heard of prompt engineering, which is a craft of writing a prompt in order for you to get good results from AI back. But there's this step up from that and it's called meta prompting. The idea behind meta prompting is using AI to actually improve your prompt. The agent that I have is metaprompt.com, which is completely free.
So, what you do is you type in the prompt that you have been using on a recurring basis and what it'll do is to say, "Okay, here's what you're trying to do." It'll come back and ask you questions. It's like, "Oh, you're doing this but you haven't told me this. You haven't told me this. You haven't told me this." Answer these questions. When you do, it comes back with a well-structured prompt that's actually written to... It's like a one-time investment to take your prompt that you've been using all the time into something that will demonstrably prove to give you better results.
I use that all the time, and this is one of the nice things about AI now in agentic or vibe coding, is that these are the kinds of things I would build for myself back in the day. It wasn't the calories necessary to be able to launch something and put it out there for the world to use. I could just never make that trade-off. But now it's like, okay, not that hard to build, not that hard to share. I can just put it out there.
Cameron Adams: metaprompt.com, you heard it here. It's actually one of my favorite pieces of devices. It's turned into my Let Me Google app for you, just telling people to go meta prompt that is my automatic response when they're asking for how to deal with AI.
Dharmesh Shah: Totally.
Cameron Adams: So you actually write simple.ai, which is a newsletter with more than a million subscribers now. I'm going to guess that you actually use AI to help get this out the door. So, how do you personally use AI in your writing workflow?
Dharmesh Shah: I tend not to use it for writing per se. What I use it for is editing. I use it for visuals. I use it for what I think of as less about writing and more about copywriting. I'll use it for headline ideas or subject line ideas, and I'll use it for coming up with punchy ways to capture something. It's like, okay, I've got this thing.
Write around here on this particular concept. I need a pithy way that maybe uses alliteration or a figure of speech that makes it more memorable, more remarkable, and improve and take it up a notch. I've made multiple attempts at this and I think it's pretty good, but it's still can't capture my voice and it's not good at that yet. I say yet, maybe it will be some day.
Cameron Adams: I'm sure you are very data forward and you've got an amazing amount of content that you can feed it. Surely, it could find your voice somewhere in there.
Dharmesh Shah: It can replicate my voice. What it is likely unable to do, and maybe this will change the thing that it doesn't have those, it doesn't actually have human experience. So, it's not had the day that I just had going back to comedy, which I'm not a comedian either, but I've heard comedians say it's like their best material just comes from just observation. This was nothing particularly remarkable, but that was quirky or had this or whatever. Then they pull on that thread and that turns into a skit or a joke or something like that. I think it's similar for writing.
It's like, okay, I was solving this particular problem or I had this thing where I had this debate on a call and it's like, "Oh, then I thought about it. Oh, there's something broken or something noteworthy about that. I just put it out there."
Cameron Adams: Yeah, so what I'm hearing is that the creative spark always needs to come from your brain from a person.
Dharmesh Shah: For the kinds of things that I'm doing, right? It's like if I'm doing a LinkedIn post, the purpose of it is to put my brain on speakerphone in a way that says, okay, well, I think what people are there following me for, it's like, "Ah, what's going on in Dharmesh's head?" You're not curious about what's going on in my head, then you're likely not following me. That's because I have a grab bag of things that I post out there and it's like, "Okay, well, that's why the audience is here. My purpose is to serve the audience. Yeah."
Cameron Adams: All right. Well, AI agents might be teammates, they might be, they could be tools, but you also famously took on the culture project at HubSpot despite feeling that you were absolutely the worst person to do it. So, I'm interested in a truly hybrid team where maybe we've got AI agents sitting next to people. How do you instill culture in the AI half?
Dharmesh Shah: I'll summarize my dozen plus years in working in culture involuntarily. I'll just deal it down. The big lesson, big analog I've come, I've only said maybe three smart things in my life. I think this is one of them, which is culture is a product. What I mean by that is as companies, we build a product for our customers. Everybody knows that.
Whatever your offering happens to be, culture is the product you build for your team. I'm just talking about human teams right now. Well, you would never build a product without asking your customers how the product was doing. So, we get product feed. So, we do an NPS for our employees in the same way. So, we carry as much as we can from our product sensibilities over into building this second product, which is what culture is.
The other big lesson on culture was in the same way that a product is never really done, culture is never really done. I think the mistake a lot of founders and entrepreneurs make is like, "Oh, my job is to preserve our startup cultures. What we had when we were like 5 people, 10 people, 50 people." As long as I can preserve and maintain that, then we're good. It's like, no, that's not the answer because the company needs and the people need is going to be different at 50 people, 500 people, 5,000 people.
So, now I'm going to continue to pull on that thread and maybe there's a point at which the analogy breaks, but it's like, okay, well, if we continue to think about culture as a product, what's changed now is who the customers are. So, now we have a combination of humans and AI agents, and if the goal for the product is to deliver for those customers, now we have two constituents of customers just like you might have for your regular product.
So then how would we get feedback from the other constituent of customers? Is the culture serving AI agents and making the most? Is it helping them live their best life, so to speak? Not that they're conscious. But that's the way I've been thinking about it's how do we replicate the things that have worked for us in thinking about culture into this hybrid agentic world?
Cameron Adams: How far do you take it though? Does the AI get a vote on what the values of the team are?
Dharmesh Shah: It's a good question. I don't think so. The reason is, I'm trying to think off the top of my head right now. So, we may have multiple product constituents for our regular product, but there's the constituents that we're focused on. It's like, oh yeah, this is for instance, HubSpot serves SMBs. That's our target.
That's what we're passionate about. We've been passionate about that for 19 years, but we have segments of enterprise. We have other adjacent customers, but the ones we solve for are the SMB constituent. Real-time evolving thesis here is that humans will continue to be the primary customer, but we have this other constituent that we need to be mindful, but that's not who we're building the culture for. So, they may have a voice in terms of we want to know what they're thinking and how they're responding, but they may not get a vote in the way in terms of shaping what the product looks like. We're not solving for them in that way.
Cameron Adams: Do you think leadership, maybe management is going to radically shift in a world where AI can take on so much of that role and some of those management tasks and what a leader normally does?
Dharmesh Shah: Yeah, I think so. I think the managers and leaders of the future are going to be the ones that embrace this idea of hybrid teams because I think that's going to be inevitable. Wherever on the spectrum you place the agents in terms of their capability, whether it's a tool or to what degree do they become a full teammate, but they're in there just like managers have to understand technology today. So, you have to understand that we have HR systems and CRMs and things like that in order to be a good manager.
I think what agents will start to show up and manifest is that great managers will know how to manage a diverse team, which includes both carbon-based life forms and agents, and they'll just know here's what agents how to get the most out of them and here's what people expect and how to get the most out of them is going to be a combination. I think there's going to be a whole new generation of managers and leaders that have that skill set.
Cameron Adams: Yeah, it's going to be an interesting dual skill set of managing two different types of organisms really.
Dharmesh Shah: Yeah.
Cameron Adams: I'm going to take us down a slightly different path. Your LinkedIn lists one of your favorite topics as tech-powered parenting. Does this mean you just put your kids in front of camera for 12 hours a day?
Dharmesh Shah: I don't, and even if I wanted to, my wife would never let me, but I don't think that's a good idea. So, I've exposed my son to technology very, very early, including, so he had access to GPT-II before ChatGPT was even launched because I got an early access to the API adopter.
Cameron Adams: Early, early adopter.
Dharmesh Shah: Yeah, very, very early adopter. What I want him to do is I want him to understand the technology that's available and use it as a tool because it's going to be in there, it's going to be part of his life. So, I don't want to keep him from that, but I want him to over the fullness of time understand what responsible use looks like and the schools will help out here as well. He's got getting this message from all over, but I will sit down. It's like, "Oh, here's how I use AI and here's why I use it for this. I don't use it for that." What I love about collaborating with him on that front is one thing kids are really good at is coming in with a blank slate.
They assume the technology will actually work. That's their default assumption that he will type things into ChatGPT that you would never [inaudible 00:29:32]. Why would you even think he could do that? He's like, "Why wouldn't it be able to do that? It's like it's supposed to be able to do everything." They don't know of a pre-internet world. It's like, "Okay, well, yep, there are dark uses and ways you don't want use the internet."
I think we're going to have the same thing with AI. We just have to use it responsibly, but I don't think it's a responsible thing to keep our kids from it because they're going to be out in a world where it's going to be there. That's just the reality of life, whether we like it or not. So, yeah.
Cameron Adams: Giving our kids access to technology is one part of it, but with tech-powered parenting, is there something that you particularly do to help them come to grips with it or ways that you use the tools to give your kid a better life?
Dharmesh Shah: I think what AI is really good at is taking in context, and we only have the one child. This is our first time parents, obviously billions of people have gone through that exercise, but it's actually really good at providing advice and we will collaboratively sometimes like, okay, well, here's the situation. How do we think through this? And sometimes it's just me and my wife, sometimes it's just me, but in terms of helping me navigate or helping us as a family navigate what are common issues, but they're uncommon for us because we haven't encountered them yet. Obviously, every child is different, but there are definitely patterns and it turns out AI is actually very good at that. It's like having read all that there is to read out there on the internet is actually quite helpful.
Even before the reasoning models came out, they were actually really good, but now the reasoning models that they can do deep research can do inference, time, compute, the quality of the responses you get on very, very deep, sometimes philosophical questions. This may be atypical because my son is wired a certain way, but he likes the AI's arbitrary objective third party also because kids sometimes don't trust their parents.
Ah, you're just saying that because you're my parents. No, it's not just us. Let's see what AI has to say, and if nothing else, it may not give us the answer, but it does often at least give us the spark of what the dialogue should be and at least possibly a next step or something like that. So, it's been very useful on that.
Cameron Adams: You need to give me access to the special LLM that's only trained on great parenting tips from Dharmesh. Anything he asks or just agrees with you?
Dharmesh Shah: Maybe put that in my will and pass it down to him when he has his child or something like that. Yeah.
Cameron Adams: You have a fairly well-known philosophy that you've written about called Sorry, Must Pass, and for those who haven't read it and you should, it's essentially a way of gently letting down all the people who ask you for your time, but who would distract you from the things that are important to you. You've also paired it with a quote from Derek Sivers saying, if you are not saying hell yeah about something, say no, but as AI amplifies capabilities, he's your definition of a hell yeah shifted.
Dharmesh Shah: I'll say it's shifted a little bit. More accurate thing might be it's changed. I have this defined set of three things that I'm working on and the things will change out, but there will be three things. It helps me narrow my focus. What has shifted though is that within those three things, the number of things I can accomplish now that I will say yes to is actually grown. I can take on more now as a result of having these power tools and the assistance of AI than I would've likely. So, I can say yes to more things, but I still don't say yes to more than the things outside of three. So, I haven't gone from three to five things as a result of AI. Still those three things, but more things will filter in terms of number of those things within those buckets that I'll work on.
Cameron Adams: That's awesome. You're living the AI dream, getting more done.
Dharmesh Shah: Yes, I am actually literally getting more done. I really am.
Cameron Adams: When it comes to creative or strategic work, the kind that's subjective and hard to measure, what new ways do you see emerging to value and monetize AI's contribution?
Dharmesh Shah: Yeah, this is a tough one. I think there's been lots of discussion within the business circles around how you charge for software and how you build products and charge for them and we've gone from user-based, receipt-based things to consumption-based things to now outcomes like AI does the actual work and you charge for the work that was done or the outcome that was achieved. My sense is that although people are excited about this, especially the outcome-based thing, I think partly the reason they're excited is you look at the early use cases. For instance, customer service is a really good example.
This is one of the most common use cases for which it's like, oh, if AI can resolve the ticket, the software can charge Y dollars per ticket as long as wise considerably less than what the human cost of the manual cost would've been. My sense is that there's a reason why that particular use case, the outcome-based pricing worked, and it's because you have an objective measure.
Did it resolve the ticket or did it not? You have an objective measure to some degree of quality. Was the CSAT at or above the levels it was with human resolution? So we know, and you also had the economics. It's like, oh, we've been solving tickets for months and years. So, we know what the actual cost of a ticket is. So, we have all those three things. Most of those things have to be true in order to have outcome-based pricing. I don't think that works for logo design because it's like, okay, well, let's say good versus a bad logo. You create a hundred iterations versus... What does that mean? This classic approach to how do you value outcomes, I think that's still going to be a little bit human driven, which is the way to limit the degree to which you can just charge products based on outcomes.
Sometimes maybe it's okay for a tool to be charged as if it were a tool. It's like, okay, well, you have a human that's spending X amount of time doing whatever it is they're doing. If this can increase that productivity and this is age-old pitch for software products, but it happens to still be true, if we can deliver a product that cuts down costs or makes the humans that are super valuable even more valuable and allows them to accomplish much more, I think that's great.
Cameron Adams: We've seen data come into all sorts of decision-making and you can put a metric behind anything. So, logo design, yes, you can put it in front of 100 people and the one that gets a 95% response is the one that you go with. So, you can measure things like that and then feed that to the AI and say, "Job done," and then pay it to that output.
Dharmesh Shah: You could. This is another dimension of it. There are things where the outcomes can be reduced as I'll call them semi-fungible things. It's like, okay, let's say the fungible thing in this example is a logo design that gets 95% approval rating among some breadth of people, yes, but my sense is there's a large amount of human activity where the outcome is non-linear that says, okay, if you produce the one thing that happens to actually have that breakthrough, breakout outcome, that pays for a million of the mediocre, modest, average ones or whatever. You're not going to actually hit those by simply just solving for the mean or the average or something like that. You look at the publishing industry and look at writing and books and things like that.
It's like, okay, well yes, can an AI produce a book and maybe it will sell, but will it produce something that's actually going to be this thing that you could not have measured in a test case or whatever. Maybe it will, but I think it's going to be really hard to know a priori that this set of outcomes, these hundred logos that were produced were actually worth $1,000. That's the actual economic value of those logos. It's like, okay, well, but for a brilliant one, for the right brand, for whatever, it's like my money is still on the humans. I'm sorry, humans powered by AI. If they want to use AI, fine, but I want someone that's got the taste that has that certain je ne sais quoi.
Cameron Adams: I think that's the takeaway. My money is still on the humans. That's everything we need to know. Is there anything that keeps you up at night when it comes to AI? What's one thing you think everyone needs to be doing more of now to prepare for the new age?
Dharmesh Shah: Yeah, there's two things that keep me up at night, one on the light side and one on the dark side. We'll do the dark side first. I'm not a doomsday thinker at all around AI in terms of it's going to overtake the world and we're going to cease to exist as a species. That I don't worry about all that much. What I do worry about is on the consumer side of the fence. AI is going to become indistinguishable in almost every modality that there ever was.
It'll be able to write what a human speak, a human produce videos, produce images that are indistinguishable from real life. In that world, so I'm not worried about AI taking over the world or doing really bad things. I think that's possible, but relatively unlikely. What is possible and more likely is you have malintent humans as we've always had, that now have power tools that they didn't have access to before, and they can actually wreak more havoc than they would've been able to do. I think the answer to this is just more education around just how we consume AI. For my parents and everyone's parents to know, it's like when you get that call or something like that saying, "Your son's been in accident," whatever, okay, you need to know what's possible now with this technology, whatever.
So, we don't get taken advantage of and things like that. So, I think we just need to raise the level of education and people need to understand how it works, what it's capable of. On the lighter side of it, what keeps me up at night is that this feels a lot like the internet back when I was first starting HubSpot, which is back then it was like, "Oh, there's a thing called the internet."
Millions of businesses and business owners and entrepreneurs should be benefiting from this thing called the internet, and very few were. The problem was they just didn't understand it and the tools weren't there then it's like they just couldn't bring it together and that was the genesis for HubSpot. I'm having that same feeling in a positive way. It's like there's this new thing called AI that millions of people and businesses and entrepreneurs should be benefiting from.
Not enough people actually are benefiting. Yes, they'll use ChatGPT, they'll type in a prompt, but they really are not using even 2% or 5% of what AI is capable of and I think that needs to change. That's partly the motivation behind simple that AI is like, okay, demystify all these things going on. Try to put some simple tools out there. HubSpot's doing a bunch of good work in terms of democratizing access to AI for hopefully millions. That's the part I'm optimistic about, but it does keep me up at night. Put it in a good way.
Cameron Adams: I love the light side, and thank you for going deep on me. I want to change pace just a little bit. I would love to know who you would have on your all-star creative team. You get four seats. You can pick anyone from any era, any universe, dead or alive, put together your dream creative team.
Dharmesh Shah: So I'm going to pick people that are alive because I like constraints and also because it's like, "Oh, well maybe I have a shot of working with these people someday. Maybe they'll hear this." So since we've talked about music before, Jacob Collier who's a singer-songwriter.
Cameron Adams: Amazing.
Dharmesh Shah: He's amazing. I think what Jacob Collier would be really great at is deep down inside, he's a product guy. I'll say it right here. He's won lots of Grammys, produced lots albums, done live shows. He solves for the customer, he solves for the audience, but he brings himself into it. I think parachute him in to your company, to my company, to pretty much anything, and I think he would be a top death file performer, whatever the thing happens to be.
I'll say Jack Butcher who's a visual designer, he does visualize value. There's a really good tweet, but what I love about Jack Butcher, you should look him up, he's one of the best that I know of what I think of as visual abstraction and my life is about abstractions in differing ways and different levels. That's what software engineering is actually all about and he does that with visuals, be able to take a concept and reduce it down to a monochromatic just lines and circles image that captures the essence of that idea, right? And he is world class at that. So, I think he would be awesome. I'll say Jason Fried from 37Signals/Basecamp, an awesome entrepreneur.
I've known Jason for a long time, but I've never worked with him, but one of his many superpowers as he does idea distillation, idea abstraction really well. He can take the concept, express it visually, and in terms of product design, but almost more importantly he can reduce it into words. He's an exceptionally good writer in terms of conveying the concepts and ideas in his head. He's written lots of really good books, all of which are worth reading. I think he'd be awesome. The last one I'll say, because his name doesn't start with J as the first three did and break the pattern, I'll say Andrej Karpathy who works in the AI space and he does what I think of as technology distillation or complexity distillation in the technology front.
So, he will describe complicated AI concepts in ways that mere mortals like me can actually understand what the mechanics of a large language model is. Obviously, he's a practicing software engineer deep on AI research or whatever. The way his creativity really shows up is that ability to make things simple that are actually really, really complicated. So, those are my picks.
Cameron Adams: That is a fascinating team. We're now turning this podcast into Tinder for product people. So, let's put the call out. If anyone knows Jacob and can hook them up with Dharmesh to make the most amazing music/marketing product all brought together, please make it happen. Okay. I think Paolo has had enough time to feed your sketch into AI and work with it just a little bit.
So, let's bring him back to see the final results of this Paolo, Dharmesh, AI human creative triangle. Paolo, you there?
Paolo: Hello. I've been so excited to share this generation.
Cameron Adams: We're excited to see it.
Paolo: Awesome. I love the story because I could relate it a lot. My grandmother gave me a guitar when I was younger and I also just had to learn from scratch and stuff. I've also been obsessed with old-timey cartoons lately. I started generating these old-timey cartoons. I just ran with it and had so much fun listening to your stories and even hearing about your son who's a writer.
So, I wanted to incorporate those are fantastical elements in it. I ended up with this short, a bunch of clips just put together of you learning how to play the keyboard and just getting older and learning how to play more well. Yeah, that's pretty much it.
Dharmesh Shah: That's brilliant. That is awesome.
Cameron Adams: I love it, pal. I love the development of the narrative and seeing your brain in action being fueled with AI and what you ended up with, just telling a great story about Dharmesh's experience flowing into the next generation. It was really beautiful.
Dharmesh Shah: Thanks for doing that.
Paolo: Thank you so much. Thank you. Thank you for having me.
Cameron Adams: Thank you for joining us. Well, Dharmesh, it's almost time to close out the episode and we've got some rapid fire questions. Are you ready?
Dharmesh Shah: I think so. I'm not fast on my feet, but yes.
Cameron Adams: We normally put 60 seconds just for you. I'll put 65 seconds on the clock. Let's go. What are you reading at the moment?
Dharmesh Shah: So right now, I'm reading Superagency by Reid Hoffman, which is a book about AI and what it's actually capable of. What would happen if everything went right?
Cameron Adams: Of course, you are. What's one prompts you overuse?
Dharmesh Shah: Tell me about XYZ person. I do it in Perplexity. That's my tool of choice for those search-based prompts.
Cameron Adams: What is the first thing you built with AI?
Dharmesh Shah: The first thing with what we would now call AI, generative AI was that ChatGPT before ChatGPT, which is I had GPT-2 access and we built little chat app that I used and my son use to just run little chat things through. Always the Pioneer.
Cameron Adams: What's your favorite underrated AI tool?
Dharmesh Shah: The one I use the most right now is it's a new tool. It's called Willow Voice, and effectively, what it does, it's like voice input for everything on your desktop. So, it's a desktop tool. You can just hold a key down, you just say things, whatever app you happen to be in, and it goes beyond just transcription. We'll actually be aware and format things like, oh, well you said this thing, but you're writing an email, so I'm going to format the text in this way anyway. So, Willow Voice is the answer.
Cameron Adams: Sum up AI in one word.
Dharmesh Shah: Power tool.
Cameron Adams: You and AI are making an album. What's the genre?
Dharmesh Shah: Bollywood meets the west, so we'll call it Bollywest.
Cameron Adams: Oh, I can't wait to hear that. What's one job AI should never do?
Dharmesh Shah: Lead. So, our jobs.
Cameron Adams: And finish this sentence. Surprising thing AI taught me about myself is...
Dharmesh Shah: I have the same limitations as LLMs, which is I have limited context window. I hallucinate and I still struggle with writing dad jokes.
Cameron Adams: I'm hoping you hallucinate legally. So, that's it. That's all we have time for. Thank you so much for diving deep with me on AI, agents, and creativity, Dharmesh. It has been an absolute pleasure to chat with you.
Dharmesh Shah: Thanks for having me. This was a lot of fun.
Cameron Adams: Thank you for tuning into today's episode of Prompted. That was an absolutely engrossing conversation with Dharmesh and some of the things that stood out to me were firstly how agents are already becoming powerful tools, though the jury is still out as to whether they'll become fully-fledged teammates like many people have predicted. I was also really interested to hear about Dharmesh's idea that culture is a product you build for your team, which really emphasizes its role in creating a successful work environment.
Finally, what we're all here to learn about, creativity. This notion that creativity is about connecting the dots and the powerful way that AI can serve as a valuable partner in making those connections was one of the best insights. To keep up with future episodes, please subscribe wherever you love to listen to podcasts or head on over to YouTube.
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