Embrace Early Adopters - Build for early adopters, they provide leeway for experimentation. - This allows you to keep up with the rapidly evolving AI landscape. Transcript: Dan Shipper Their problem is like, does anyone even want this? I’m just, yeah, I’m curious how you, you came to that or like why that’s your that’s your methodology yeah i’m glad you asked this because um i and you were right you know there’s different Vicente Silveira Ways and i have other friends founders and they took these other paths right um i think one is you know what what works for you right and us, this whole thing started as a side project, you Know, with me and Kartik, and we’re just like packing over the weekend because we just love the stuff, right? That’s one. But the other thing that I think about it now, and I think it’s still a reality for us, is that we are, even though we may feel like, oh, you know, AI is kind of already sort of happened, right? And we are so early in this AI cycle, in the ground. And I’m sure you feel that way as well, Dennis, like the steady state of this technology or the productivity state of this technology, it’s still not very clear what that’s going to look Like, right? So just in this short time that we’ve been doing this stuff, you start with like, okay, you have these, first you have these like specialized models, right, that people had in a bunch Of companies. We talked to i just recently talked to uh uh a company where they had a model for pdf extraction that they built on watson and all of that and then they tried it with you know gpt4 and it just Blew that thing that they worked on for years out of the water right so so that that was like a big phase shift there where a lot of people had like a rude awakening there. There’s a great paper by Microsoft when they did a big off between the Microsoft PII, the private information detection model that was built over years with Microsoft resources. And they baked that against GPT-4 and it destroyed it, right? That so that was the first the big first phase shift but the reality is that that continues to happen right so we we build this thing and you know at first chat barely worked now chat works Pretty well right and we have multimodal chat and all those kinds of things and now you know the if you look at what Sam Altman and a bunch of people building now are saying, building the Foundation, they’re pointing to like, oh, the AIs are going to become more and more capable so they can take more of the task and become these agents, right? Where everyone’s talking about this kind of, okay, we’re going to move to agents. So the ground is shifting as we go along, right? We have something like computer use now. So Claude did a little right, a little demo of that. So what I mean by that is by building this for an early adopter crowd with an actual problem to solve. Right. They have day jobs. That’s very important. Right. We are actually capable of tracking the evolution of this market, right? So that we don’t get stuck on a kind of like a early internet type thing, like, oh, there was ICQ, there was, you know, a bunch of different things that eventually became irrelevant once You got into the productivity state. So in a way, it’s like for us, doing this is a little bit also, It’s both strategic and also defensive as well. Well,

Startup Advantage in AI - Large companies struggle to take risks, creating opportunities for small, agile startups. - Startups serving niche user needs with AI can offer better experiences than large companies. Transcript: Dan Shipper Let me unpack a little bit of what you said, because the question I’d ask is sort of like that tinker mentality where you’re going out and you’re just building like the thing and you’re Getting to market super quick. And it sounds like what you’re saying is that by serving an early adopter market, you’ll be able to, and you’re incentivized to kind of like keep up with the latest and greatest so that You don’t get left So behind. How do you bridge the two? How do you bridge the gap between the two? Like what’s the bridge from how you got to market to not being left behind because you’re serving early adopters? Yeah. Vicente Silveira I think the early adopter, what they give to you is some leeway to experiment more, right? So, for example, we now introduced the agent into our product. Before, if you look at our product, right, and it’s still there. So that’s the main way. You can see this menu, right? And basically, I clicked on this menu in the chat prompt. And you have the models from the main families here on Tropic OpenAI and Google Gemini. So you can just go into this and just do a regular chat, which most users, that’s what they’re used to. But then we have our kind of like the users are pushing us forward. They want to go into the agent and be able to do more of that task, right? So we’re trying to basically work with the core of our early adopter users and also listen to the ones that are already moving forward to the next thing. And what this early adopter users, what they give you is more tolerance for the experimentation. Because of course, and something like an agent today still doesn’t work great, right? You may have a moment where it’s like amazing and the next run that you do, it may not, it may get stuck, right? But they actually want to see where that thing is going. So that’s why these types of users, they help us kind of like bring the, both like the main use case and the leading use case at the same time forward. Dan Shipper I think this is like an important related point to kind of this competitive thing that we’ve been unpacking together. Like, how do you compete if you’re like a two person team or I don’t know how big you are, but we’re eight people. So like, how do you compete where, you know, against OpenAI or Google or Notion, whatever. And I think like the thing that comes to mind for me that i that i think you’re saying which i think is true for us as well is um people forget that when you’re a big company you have to serve A lot of users it’s really hard to take risks and for a while there was this like feeling about a ai where it was like well the AI is going to be smart enough that it’s never going to make mistakes. So like, uh, big companies are going to be able to do anything that startups can do. And I was always just like, no, big companies like always find a way to fuck things up. Um, it’s just like, it just, it’s not because they’re not smart. It’s just innovators dilemma stuff. It’s basic stuff that you just like, can’t take. And, and I think like, I think that’s why when you look at a lot of the AI stuff, like, for example, and I won’t name names, because I don’t, I don’t like shitting on people directly. But for example, I got a fitness tracker app recently. And it’s really great. Like, I love actually the way it works and the app and whatever, but like, they have an AI feature in it. And the AI is just like, it’s so milquetoast. Like it just like, doesn’t say anything useful basically. It’s like, and the reason is they have to, they have to make it work for like the lowest common denominator user. They have to make it not confusing and they don’t want to take any risks because they’re like a big company and it would, it would be bad if it said something that was risky, which makes A lot of sense, but it means that the experiences you’re able to do as a bigger company are less good in a lot of ways than the experiences that you can do as a small company that you can just Decide, okay, yeah, we’re going to serve these users that don’t care if there are rough edges. And we’re going to explore on the boundaries of what’s possible. And our users are going to understand if we return a result that’s like not so great. And that allows us to experiment and they understand because they’re, they’re like, want the greater power. And they understand that there’s a trade-off there, which I think, I think that’s really important. I think people miss that all the time.

Discovering Use Cases - Users discover AI use cases through general-purpose tools, creating demand for specialized products. - AI PDF targets users with specific document-heavy workflows, not just early adopters. Transcript: Dan Shipper Really interesting. I mean, I think it dovetails with some of the things that I think about. Like one of the things that I have in my mind when we build things internally at Every is that ChatGPT and Claude, I think like you said, rightly, they’re building for the most broad use Cases possible. Like they just want anyone to go on and be able to do whatever they want, basically. And it’s sort of like, in that way, it’s a little bit like Excel, where anyone can go to Excel and you have a blank, you know, blank new sheet full of lots of cells, and you can just like start Typing numbers and anyone can do that. And then people are going to discover as they’re using ChatDubt and Claude that they have more specific, like they’re going to discover use cases for themselves that they didn’t know Existed. So for example, we have a product called Spiral that lets you automate a lot of creative work. Like it helps you do headlines and come up with tweets and all this kind of stuff. And that’s a thing you can do with Claude. But Claude is not purpose built for it. That’s right. So our kind of like thesis is people will discover use cases for AI and discover problems to solve with AI by using these more general purpose tools. And then that will create demand for other players to sort of peel off some of those use cases for particular kinds of people for particular kinds of workflows like for us it’s like marketers And creators who have like a very specific need for that kind of workflow and and having a product that’s purpose-built is going to serve those people better and i’m i’m sort of curious For you because a key part of my thesis there is like you need to have a particular persona in order to like be powerful enough for that kind of workflow but it sounds like you’re you’re Kind of going a level up which is like more general but do you have a particular persona in mind or like how do you think about it that’s and i we get this question quite a bit

The Allocation Economy - Managing AI agents requires allocating intelligence and skills similar to human management. - The allocation economy emphasizes the increasing importance of management skills. Transcript: Dan Shipper Love that. I think that makes a lot of sense. Like I’ve been writing a lot about, you know, what I’ve been calling the allocation economy. And I think this is like right on that, on that train, which is like in an allocation economy, you’re, um, instead of doing a lot of the IC work, you’re, you’re, you’re doing a lot of more Management work, um, where you’re managing the allocation of intelligence, um, managing agents. And, um, and in that world, like the skills of managers become more important, um, than they are now and they need to be more widely distributed. So I think, I think that makes a lot of sense. That’s really interesting. I’m curious for you, do you have any sense, like quantifiable sense of, you said earlier, the things you can get done with a smaller team with less capital, you can get a lot more done now. Do you have any quantifiable sense of what that is versus like 10 or 15 years ago? Vicente Silveira Oh, for sure. And you can see this everywhere, right? I can give an example, like working at Meta, right? Of course, Meta has a ton of money, right? And pre-Gen AI, and now they’re deploying it very aggressively internally, as I hear from the outside. But pre-Gen AI, you would have things like a product manager, and I was a product manager there. They would want to know like, okay, what’s going on with this particular feature? What are the top issues that our customers are having in this particular area? And so the PM would basically talk to a person in the support area, which their was only to go collate all this feedback and create this report. And it will take like maybe a day or, you know, if they’re busy, maybe a little bit more, depending on how priority is your problem, how much of a priority. So all of that now, right? It’s like you can get done with with AI. That’s that’s just there. Right. You can get done directly with AI. So that’s just one example of how with the tools that we have now, we should be able to be a lot more efficient and do things that would only be available to larger companies.

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Persistence Pays Off - When OpenAI allowed PDF uploads to ChatGPT, some AI PDF competitors gave up. - AI PDF persisted, focusing on features like multi-file upload and folder structures. Transcript: Vicente Silveira Yeah. It’s funny you mentioned the whole wrapper thing, right? We almost have like a wrapper death countdown clock, right? Which is like this many days since we died the last time that we keep track of. From time to time, we’re pronounced dead, right? In reality, the business keeps growing. It’s kind of interesting. At some point, it was when OpenAI, for the first time, they allowed to upload PDFs to ChatGPT, right? It was like, oh, all this stuff is dead, right? Including some of our competitors actually just gave up during that time. But we kept plugging at it. And I think at some level, you know, this is an interesting industry because everyone is pointing at each other saying you are a rapper, right? It’s like, you know, I don’t know if NVIDIA is a rapper around math, everything goes, goes from there. We started on, on this because we tried different things actually. When we first started, when ChatGPT just was coming out. And I was basically trying to do like probe injection against ChatGPT and Sydney back then. And I found some stuff. I sent it to an email to Greg Brockman at OpenAI. And he replied. He’s like, oh, that’s kind of interesting. You should talk to this guy here. Right. And at the time, I was actually still kind of like i just you know got out of meta and i was angling for a job at uh at open ai right i talked to this guy he’s like well you know you have an interesting Background uh you know right now we’re not hiring for this maybe in a couple months so keep you in mind and i’m like well maybe you can give me like an api key or let me into this new developer Program. And that’s how we started. Right. And, and we tried different ideas, but the PDF one really took off, took off immediately. And in reality is because this is one of the first things that people are trying to figure out with AI. It’s a lot of pain around dealing with lots of documents and PDF is the main kind of document across platforms. So people just, just to that. Dan Shipper That’s really interesting. And so people gravitated to that. And you’ve said people have declared your death multiple times. I mean, if a PDF reader is in ChatGPT, why are people using you? Vicente Silveira Yeah, yeah. Great question. So the thing is, when we looked at this and we started building it, we actually at first, we didn’t even have a place for people to upload PDFs, right? We just like, okay, you know, you can just give us a link, and we’ll our server would go go there and fetch the content. And because we’re growing so fast at the time, people were giving us like Google Drive links and Dropbox links. And Google and Dropbox started rate limiting our IPs, right? Because they saw us like as an aggressive bot. And this was a bad experience for our users because it would get an error. And we were like, well, you know, what are we going to do? And we’re like, well, I guess we could try to play the cat and mouse game with those guys. But, you know, we actually know how that works. And that would be kind of pretty bad. So we’re like, well, what if we just let them upload their files? And we thought no one would do it, right? And so the first version of our website, my co-founder, he was like, it looks scary. And to our surprise, like in a week, that domain for our website became the number one domain for the links, you know, passing the Google domain and the Dropbox domain. So that was a lesson for us because it told us that the users that were gravitating to ChatGPT and going to all the trouble to enable plugins, that those people were actually risk takers And early adopters. Right. So we built that for them. And then kind of going back to your questions, like why do they kept using us even after this was created on ChatGPT proper is because they don’t want to just upload one file. When it works, they basically want to upload their whole collection of files. And even to this date, I think on ChatGPT, you’re limited to like 20 files, right? And for us, it’s like we have people with more than 150,000 files in one account we have people with multi-level folders right no one else supports that kind of stuff but it’s important Because people need that low friction and even respecting the intelligence they put in creating their folder structure uh to be part of this onboarding right so this part of the product Experience.

Specialized vs. General AI - OpenAI and other large language models (LLMs) prioritize broad use cases. - AI PDF focuses on specific user needs with document workflows, similar to Loom’s success. Transcript: Dan Shipper Interesting, but I want to push you a little more. So it sounds like one of the reasons people are still using it is because you’re sort of staying one step ahead of what ChatGPT will do. Do you have a theory about why they won’t eventually do a multi-folder upload? An example might be Notebook where it’s like it does let you it does let you open a lot of files like are you worried about that at all or is there some strategic reason why why you think um Vicente Silveira You’re gonna go deeper than a chat gpt or like a gemini or or whatever yeah i mean this is this is interesting right because i feel like that these guys uh especially if you look at uh chat Gpt, their focus is to kind of race towards AGI and create a product that’s good enough to have enough usability there so that lots of people use, they can collect the training data and Then feed their machine. I don’t think they’re going into one particular specific direction. They’re mostly kind of like touching on kind of like what is the minimum for core use cases right um so i think you know is it is there a possibility that something like uh chat gpt or claude Or something like that can actually compete with us and and and basically there’s there’s no need for this kind of platform yes but that is always the question for startups when things Start, right? So when you think about a startup like Loom, why does Loom exist? And was sold, I think, almost like a billion dollars to, I think, a classic. Loom is just recording video, right? But they did that use case so well that, you know, even though YouTube had all the technology to do it, they didn’t do it. Vimeo had technology to do it, they didn’t do it. All of the major providers, they had the technology, they didn’t do it. But Loom actually nailed the use case. So for us, we’re nailing the use case of getting a collection of documents that you have and being able to do end-to workflows with those documents.

Diverse User Base - An 80-year-old lawyer uses and advocates for AI PDF within his law firm. - AI PDF serves various professionals, united by their need to process many documents. Transcript: Vicente Silveira And it’s uh it is interesting because our our persona is an early adopter of technology a risk taker that has an actual job to be done involving lots of documents right so so that’s like All of those things are important right so one is like they’re not just an early adopter right because an early adopter and a lot of actually chat gpt and claude use is people that sign Up to just see what’s possible they don’t have an actual job they’re going to do there but they just want to get that you know familiar with technology be able to talk to someone about it Those kinds of things so a lot of that is that kind of use case. So that’s one component of our user base. The other component of our user base is they have an actual job that they need to do today, and they have a lot of documents. So this is the combination of things that creates our… So who are these people? So we have like, there’s a law firm, the partner is an 80-year lawyer, right? And he found us, he’s like, I’m using this every day and we’re gonna have it within our firm and I’m a decision maker, we’re gonna have it adopted here. And we have people that are like researchers, we have accountants, we have writers. So there’s a range of these different types of profiles. But what you find in common is that they bring a lot of documents to the platform, and they are basically trying to get some job done today.

Minimum Viable Product (MVP) and Market Discovery - When deciding what to build, start with the simplest thing possible, similar to creating a plugin, which involves minimal effort. - This approach allows you to discover the market and understand user needs. - Initially, AI PDF was just a ChatGPT plugin with no independent web app or direct user relationship. - This limited their functionality and made them reliant on ChatGPT’s platform. Transcript: Vicente Silveira So if you go back, when we built this, and I think this is actually a good, I give this advice to people when they’re thinking about what they’re going to do. It’s like what we did with the plugin, that was the least effort thing that we could have done at the time, right? It’s just like an API. At the time, I had a server running on Replit, right? Replit’s amazing. And that was everything. And with that, we discovered the market, right? But then at that point, we’re just a plugin. So we couldn’t operate independently, right? Dan Shipper What do you mean by it was just a plugin? Vicente Silveira There was no web app, no place for you to create an account. We had no direct relationship with the user. So you would go to ChatGPT, you would enable plugins, you would find us. I see. What was the ChatGPT plugin? Okay, I just forgot about that whole era of ChatGPT. Dan Shipper It wasn’t that long ago, but it feels so. Vicente Silveira It feels like that. Yeah, so like, so at that point, right, we were a product that only made sense as an add-on to ChatGPT, right? But what we realized is,

Start Small, Iterate Quickly - Start with minimum effort products to test the market. - AI PDF initially launched as a ChatGPT plugin, helping them discover the demand for PDF tools. Transcript: Vicente Silveira Yeah. And we’re flipping that because our trajectory is going through this transformation, right? So if you go back, when we built this, and I think this is actually a good, I give this advice to people when they’re thinking about what they’re going to do. It’s like what we did with the plugin, that was the least effort thing that we could have done at the time, right? It’s just like an API. At the time, I had a server running on Replit, right? Replit’s amazing. And that was everything. And with that, we discovered the market, right? But then at that point, we’re just a plugin. So we couldn’t operate independently, right? Dan Shipper What do you mean by it was just a plugin? Vicente Silveira There was no web app, no place for you to create an account. We had no direct relationship with the user. So you would go to ChatGPT, you would enable plugins, you would find us. I see. What was the ChatGPT plugin? Okay, I just forgot about that whole era of ChatGPT. Dan Shipper It wasn’t that long ago, but it feels so. It

Careful Fundraising - Be cautious and intentional with fundraising; focus on product development initially. - Delaying VC funding allows startups to stay lean and avoid complacency. Transcript: Dan Shipper So that’s definitely a metaphor that we use a lot internally. I’m curious for you, like you raised only a friends and family round. I assume with the kind of traction you have, you could have gone and raised a venture round. Why didn’t you do it? Yeah. I think first our experience raising was kind of very interesting, right? Vicente Silveira The beginning was like, oh, this is amazing. We’re just going to be, you know, we’re going to raise a ton of money now and maybe we should raise more. But the beginning was fast and the process then later started dragging along, right? And I felt like I was, you know, back working ata, doing PowerPoints and tweaking PowerPoints and prepping with a friendly VC to talk to another one. And when we had users who were basically asking us to do stuff, and at the time it was just Karthik and I, right? So I’m like, I’m hating this, right? And if we can just go and monetize, let’s just do that, right? And then we’ll come back. So I think that was the main reason. And it was a time as well where with AI, the whole kind of like hangover of the first wave of investments was what set in. So people were really worried about this. It was a time also for us product-wise that the product was very much dependent on OpenAI and ChatGPT specifically, which is not the case now anymore. So for those reasons, we’re like, yeah, let’s just focus on the product, which I think it was the right thing to do. That’s

AI-Driven Onboarding - Delegate tasks like user onboarding to AI agents, even if imperfect initially. - Hire AI managers to oversee and improve agent performance. Transcript: Vicente Silveira We think about, let’s a job like onboarding on our product when you sign up for our product our onboarding sucks your onboarding is beautiful i did the spiral one it’s beautiful thank You it worked no hard yeah i i love it our sucks right and uh we’re like okay we need to make our onboarding better um i do think that the way i want to do that is basically giving AI a job to do That onboarding of that user, right? So what does that mean to us is like, instead of having sort of a, either a product that we attach to our app that will kind of be configured for that onboarding in some way, or build that Ourselves, we are going to basically lend you an agent, right, that knows about the product, knows where you came from. Oh, you know, we have a landing page for lawyers, right? You came from the landing page for lawyers, so you’re likely a lawyer. Hey, you know, this is what the product does. Do you want to, you know, upload one of your files and we can, I can show you what it can do for you. And it basically gives that AI a job. And what we want to do is, so we have a person that we just hired that is going to be responsible for this area. So that person’s job, the actual human that we hire is to basically manage that little agent, right? And be responsible for the delivery that that little agent does, right? And of course, as the agent gets better, there will be more and more that it will be able to do. So it’s kind of an interesting way to think about these things where we feel like that as we hire people, they will end up be responsible for certain agents on the product that have specific Jobs both for the user and for the company as well.

Increased Productivity with AI - Generative AI significantly increases productivity for everyone, from engineers to managers. - Vicente Silveira, with a software engineering background, found he could code again thanks to AI. Transcript: Vicente Silveira And for us, it’s like productivity at all levels, right? So I have a software engineering background. I coded like early in my career and then I stopped, pretty much went to the business side. I thought I was never going to be able to code again. And when this came back with Gen AI, he was like, he was kind of, you know, you can’t mountain bike anymore because you can’t go up the mountain. Now you have an electric bike and, you know, up you go, right? It was incredible. And then I say I have like two mentors. One is, you know, AI. The other one is my co-founder. And I see that even for him, kind of world-class engineer, you know, former Google AI, it makes him so much more productive. So it’s like everyone goes up. Like at whatever level you are at, you become a lot more capable. So that’s absolutely true. And we think that there is a huge lack of awareness overall in the population.

AI Management as Upskilling - If you have a phone, you have access to AI, and you can practice being a manager by specifying tasks for the AI. - If the AI doesn’t deliver what you want, refine your prompt or question, as that’s a key aspect of management. - Evaluate the AI’s output: Is the quality acceptable? Would you endorse it? - This process of prompting, refining, and evaluating helps to improve management skills. Transcript: Vicente Silveira You talked about like this this like learning to be a manager well if you if you have access to an ai you are practicing being a manager if you have a phone you have access to ai so you can you Can start like specifying a task the ai doesn’t do what you want you’re like well actually my question wasn’t good enough that’s a lot of what being a manager is so you get your prompt, Your question better. Now you have to look at the result, what the AI brought back. Is this quality good? Am I willing to put my name on this thing, right? That the AI came up with? So, yeah. Dan Shipper I think that makes a lot of sense. I mean, you know, obviously there’s a lot of difficult social and economic issues to like, you know, broad AI rollouts. But I do think that point is totally right is people tend to miss how powerful

Managing AI Agents Like Humans - People assume AI agents will eliminate the need for human thought, but managing intelligent agents, even AI, requires significant effort and skill. - This mirrors challenges faced by managers delegating tasks: balancing micromanagement with leverage. - Many find it quicker to do tasks themselves, but this misses the potential of delegation, like early managers struggling to find the right balance. - A future with powerful agents still necessitates defining tasks, choosing resources, and other “invisible” management skills. Transcript: Dan Shipper You’re so right. I’m literally writing an article about this today, but there’s this weird fallacy where people are like, well, agents are gonna be doing it, so you won’t have to think about it. And I’m like, have you ever managed a person who is literally a general intelligence? People are very smart. It’s hard. It’s really hard. It’s, it for sure is like a different kind of thing than doing it yourself. But even if someone else is doing it, like the skill of delegating, like the, like going from, there’s this thing that early managers, you have to figure out. It’s like, okay, how much do I delegate and how much do I micromanage? Because if I, if I micromanage, it’ll get done the way I want it to get done. But if I delegate, but then I have no leverage and I’m like just basically doing their job for them. So why did I hire them? But if I, if I delegate, then I have more time to do other things or think at a higher level, but it comes back wrong. And that’s like literally the problem that a lot of people are having with AI right now is they’re like, oh, it sucks or whatever I have to do. I can just do it quicker myself. And I’m like, that’s exactly what managers face. And so a world where we have these cool, let’s say it’s AGI, like really cool agents that do all this stuff.

Delegating Tasks to AI Agents with Planning - Vicente Silveira demonstrates delegating tasks to an AI agent within AI Drive using a planning approach. - He instructs the agent (GPT-4) to read context window logs and suggest conversation starters. - Before executing the task, the agent consults an “expert model” for a plan, which optimizes the process. - This planning stage improves the agent’s ability to complete the task effectively. - Users can monitor the agent’s actions and the tools utilized, offering transparency. Transcript: Vicente Silveira So I’m just here in AI Drive, and I’m just typing this, you know, prompt. Hi, I’m talking to Dan Shipper, so I’m writing this to the AI, which is our agent in AI Drive. And he has a series of logs, context window in the folder, And then I’m pointing it to which folder I have downloaded it. Can you read them all and suggest some interesting talking point or conversation starters for us? And I’m going to use this thing here too as well, which is use the expert to plan, right? So this is one delegation too. So the reason, and I’m going to fire this up as I explained. So now it’s kind of processing. And if you click, you can see what it’s actually doing. So first thing it’s doing is getting a tool plan from the expert model. So the main agent you’re talking to here is GPT-4 which is great, but it’s actually not the smartest at planning. Or it can be the latest cloud, which is also very good. And you can see this here, right? It says, oh, you know, this is the task. I need to read all the blogs

AI as Management Training - Access to AI, even on a smartphone, provides practical experience in management. - Specifying tasks and refining prompts to achieve desired outcomes mirrors the role of a manager. - Evaluating AI-generated results for quality and taking ownership builds responsibility and critical thinking, which are important management skills. Transcript: Vicente Silveira You talked about like this this like learning to be a manager well if you if you have access to an ai you are practicing being a manager if you have a phone you have access to ai so you can you Can start like specifying a task the ai doesn’t do what you want you’re like well actually my question wasn’t good enough that’s a lot of what being a manager is so you get your prompt, Your question better. Now you have to look at the result, what the AI brought back. Is this quality good? Am I willing to put my name on this thing, right?

• Note: .starred

Practicing Management with AI - Practice management skills with readily available AI tools, such as those on your phone. - Start by specifying tasks to the AI. - If the AI’s output isn’t satisfactory, refine your prompts or questions. - Evaluate the AI’s results for quality and whether you would endorse them. Transcript: Vicente Silveira You talked about like this this like learning to be a manager well if you if you have access to an ai you are practicing being a manager if you have a phone you have access to ai so you can you Can start like specifying a task the ai doesn’t do what you want you’re like well actually my question wasn’t good enough that’s a lot of what being a manager is so you get your prompt, Your question better. Now you have to look at the result, what the AI brought back. Is this quality good? Am I willing to put my name on this thing, right? That the AI came up with? So, yeah.

• Note: .starred