Claude Engineering Lead: "Stop Using Claude Like It’s 2024"

Felix Rieseberg has led engineering for Claude.ai, Cowork, and Claude Code Desktop at Anthropic. He thinks many of us are still giving AI tasks it has already outgrown. If you’re only asking Claude to summarize emails, what are you missing? How to hand AI bigger goals. Why “you’re an expert” isn’t enough аnd how to catch mistakes when the answers sound convincing. Felix shares how to start trusting AI with more – and stop being the bottleneck in your own company. 📩 Your first AI agent in 20 minutes — get the guide through my free newsletter: https://siliconvalleygirl.beehiiv.com/p/your-first-autonomous-agent-in-20-min-5429?utm_source=spotify&utm_medium=video&utm_campaign=futureproof-sub&utm_content=first-agent&utm_term=felix-rieseberg https://siliconvalleygirl.beehiiv.com/p/your-first-autonomous-agent-in-20-min-5429?utm_source=spotify&utm_medium=video&utm_campaign=futureproof-sub&utm_content=first-agent&utm_term=felix-rieseberg ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://www.instagram.com/siliconvalleygirl/𝕏 : ⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://x.com/siliconvalleymm⁠https://x.com/siliconvalleymm⁠⁠⁠⁠⁠⁠⁠⁠ https://x.com/siliconvalleymm 🔗 Instagram: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://www.instagram.com/siliconvalleygirl/⁠https://www.instagram.com/siliconvalleygirl/⁠⁠⁠⁠⁠⁠⁠⁠ https://www.instagram.com/siliconvalleygirl/ 💼 LinkedIn: ⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://www.linkedin.com/in/marinamogilko⁠https://www.linkedin.com/in/marinamogilko⁠⁠⁠⁠⁠⁠⁠⁠ https://www.linkedin.com/in/marinamogilko 📌 My Companies & Products: ⁠⁠⁠⁠⁠⁠⁠⁠https://Marinamogilko.co https://Marinamogilko.co

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

• 0:00Speaker 00

  UKG is the workforce operating platform with unified HR, payroll and workforce management, built to solve the unique challenges of frontline work. Powered by People First AI and decades of labor data, UKG turns workforce intelligence into real-time decisions around staffing, pay, compliance and productivity, enabling every worker to confidently take action as work happens. Because when work works, everything works. Learn more at ukg.com/work. UKG, HR, Pay and Workforce Management.

• 0:31Speaker 01

  Into the wild, the Caltier Panther encounters a shimmering crocodile, a lonely lovebird, a breathless zebra and a restless tiger.

• 0:45Speaker 02

  If all we're doing is summarize this email for me, you're not really benefiting from the new intelligence.

• 0:49Marina Mogilko?

  This is Felix Reesberg. His team built Claude Co-Work in about a week and a half. Now he leads engineering for Claude at Anthropic and he doesn't write code anymore.

• 1:00Speaker 02

  I'm just a conveyor belt for Claude. Claude needs to have access to all the crash data and then needs to like figure out itself what it needs to fix, what it doesn't need to fix.

• 1:07Marina Mogilko?

  Is there still anything you would not trust an AI agent with?

• 1:10Speaker 02

  I rarely want the current session to judge itself. That I think is still really iffy.

• 1:15Marina Mogilko?

  How do I protect myself from AI just confidently presenting mistakes to me?

• 1:20Speaker 02

  It happens to a lot of us. I have like a very practical, easy answer.

• 1:26Marina Mogilko?

  Felix, thank you so much. I am very excited to have you. As a user of Claude, like an avid user, we're using Claude for all the operations on this channel. Every team member has their own project for the things they're responsible for. And as someone who runs it, it's a pretty big team for a creator. I have 35 people and I want them to be as efficient as possible. I want to produce as much as possible, but also something that I've noticed this year, we were able to launch, I think, three new social media channels. So we launched a newsletter. We launched LinkedIn a year and a half ago because, you know, it's text based. So AI has enabled me in many, many ways.

• 2:06Marina Mogilko?

  And the goal of this conversation is to make me even more advanced and all of the viewers and all the listeners.

• 2:13Speaker 02

  I mean, from what I've seen you do, it seems like you're pretty advanced as it is.

• 2:15Marina Mogilko?

  But there is no borderline, right? To being as ambitious as possible.

• 2:19Speaker 02

  Yeah, I think I'm discovering that about myself every single day. And like, I'm sort of, one of the most fun things of working on Claude by far is this notion of I built this product and in comparison to my other products, with my other products, it was pretty clear to me what you're going to use this for, right? Like the example I always go with is I worked on Slack and like, I kind of have a feeling of like, what are you going to do with this? The thing that's magical about working on Claude is that I see people really fly and do amazing things that I didn't consider possible. And it's like, that's like for a product builder, that's such a treat, you know, to see people fly with this thing that you made.

• 2:50Marina Mogilko?

  Is there something you've seen that's super ambitious and you're like, oh my God, I want to copy this?

• 2:54Speaker 02

  I'm not sure about copying, but two days ago, there's, I think he's actually a product manager by trade. His name is Pavel and he pointed Claude at a large amount of NASA data and asked Claude to like find them a new planet and it did. So apparently it is not confirmed yet. NASA is working on actually confirming it. I'm on a little shaky ground here because I'm not an astrophysicist of any kind, but apparently Claude may have found a new planet we didn't know of before.

• 3:22Marina Mogilko?

  This is fascinating. Okay.

• 3:24Speaker 02

  And it's the kind of thing that I would have never considered. But if you hear the story of, I think for us to work maybe on more knowledge worky things, right? The idea of like point AI and a large amount of data and you're going to find interesting things, that in like, in isolation seems pretty reasonable, right? Like something we do every single day. But the idea of like, looking at all of the public data we have about space and considering it a similar problem of like, go and see if you find anything in here we haven't noticed before. So beautiful.

• 3:50Marina Mogilko?

  Can you talk to me about your day? You helped build Claude Co-Work, but you said you're not coding anymore. So what happens in the morning? What do you do? Is it like your agents are briefing you on what's coming or what is it?

• 4:02Speaker 02

  That is true. My agents do brief me a little bit on what's coming for the day. I've really leveled up in sort of altitude on the problems that I'm thinking about, right? Like sometimes, sometimes I feel like these days I'm much more concerned with human emotion, empathy, trying to understand how people work in a way that I think I was less so as a classic engineering manager a couple of years ago. So I think for me, what I'm really discovering is that the amount of times I have to reach out to other professions to get their help has really dwindled down. I'm now this weird mix between like a product manager and engineering manager, just a traditional people manager, a data scientist, right? To answer your question very specifically, most days I'm busy with a lot of prototyping, right? We just try a lot of things. We sort of like think about stuff.

• 4:46Speaker 02

  We sleep over it for a couple of nights, try to figure out if it's a good idea or a bad idea. And then we just go tell Claude to like build all the various variations and ideas we had. And then we need to do critique, try to figure out which ones of these have good ideas, which ones are bad. But I think prototyping is really what I spend most of my day on.

• 5:01Marina Mogilko?

  How do you decide what is a good idea? Like, what are the signals you're looking at?

• 5:06Speaker 02

  This has been difficult for people for a long time now. And I'm not sure that I have all the answers to like, what is a good idea? What is a bad idea? I think generally speaking, the way we look at good or bad ideas, the calculus has changed, right? Like if you think about products we've built in the past for the last 10, 15 years, we always made implicit cost decisions. Right? Like we sort of had to figure out, okay, especially for software, can I build this? What is the maintenance cost? What is the ongoing cost? How much, how much time do I invest in this? What is the opportunity cost? All of these decisions that engineering managers make to figure out, do I go and build this? Yes or no? And I think now we can fast forward to the end state much quicker and be like, all right, just build me a product, let me play with this, let's try it out. I think human attention is still rather limited. Let's say you, for instance, you have a lot of taste in AI, right?

• 5:50Speaker 02

  Like I can probably show you maybe one thing a day. I can easily build a thousand. I can't show you a thousand, even if you're willing to sit down with me for all day. Like at some point, your eyes are going to glaze over and you're like, okay, I'm kind of done with this. So what makes a good product? I think especially now that we live in this AI world, there's this old saying that like perfect is sort of when you can't take anything away anymore. And less so about like building a product that has every single feature under the sun. Like we can make as many buttons as you want us to, but I think good products actually have fewer buttons. I kind of know whether or not a product really works. If we put it in front of a few people and you see things click for them rather quickly and they can do things that they couldn't do before, that's really the signal I'm looking for.

• 6:30Marina Mogilko?

  And how do you think about the underlying problem that you're solving? So for example, if you're, came to my company now, how would you think about the tasks that you would fully automate with AI versus just leave it to humans versus maybe just automating parts of it?

• 6:42Speaker 02

  The first thing I want to say is that I think about automation and augmentation in slightly different ways. Automation can be really empowering if you automate away tasks that sort of keep you from doing work that, that you enjoy. Or if it's like expensive and automating it away like saves you a lot of, saves you a lot of money or time. In those cases, automation is useful. And I think it's really where AI currently probably provides most of the economic value, right? Like when we evaluate, is this worth it or not? We usually look at automation. The area I find most interesting though, and I think where people really do things that they weren't possible with AI before is augmentation. It's this idea of AI is helping you do things that you couldn't do by hand because maybe you're lacking a skill or techniques or just the raw knowledge to do something.

• 7:26Speaker 02

  And when I go to a company, the thing that is often most impressive to me is less so what the tool does and more so how you change your workflow. Does that make any sense?

• 7:37Marina Mogilko?

  Yeah.

• 7:37Speaker 02

  AI is an interesting product because to really harness it, you kind of have to rethink how you do things. You have to like think about your outcomes. You have to figure out what does good mean, what does bad mean? Like, how do I evaluate whether or not this thing that I'm asking for, like whether the result is good? And the people who impress me the most are usually the ones who are very good at coming up with, like very good at reflecting on their own workflows.

• 7:58Marina Mogilko?

  Yeah, but it's so hard. When I'm thinking about my work, for example, and a lot of people use AI for drafting texts. Like, how do you define a good text? Like, is it still possible for like knowledge work? Cause for coding, I guess it's possible, right? Cause you know what you want, you don't want any bugs. But when it comes to knowledge work and like the result of human creativity, how do you even define what's good for the agent?

• 8:21Speaker 02

  Yeah, it's such a tricky problem. Like the, the example I often go with is, let's say your job is to like report on the weather, right? And you're like, agent, please figure out what the temperature is outside. There's like two ways to look at that because you have to rethink what good means, right? Like if the model just guesses the temperature and is correct, that should not be a passing grade. That should be bad. We should like rethink what we're doing here. But if the model is actually off by like half a degree or something, but it like really figured out the right location, figured out some reputable data sources and like sort of the, the way it got there was reputable and good, that should actually be better than like a guessed good result, if that makes any sense. But for writing and like all the other outputs where you feel like a lot of taste is involved and it's like a little hard to like prove or verify whether or not the result is like better than another.

• 9:06Speaker 02

  It is tricky, but I think I really enjoy talking to very good writers who are also a little AI friendly.

• 9:12Marina Mogilko?

  What do they say?

• 9:14Speaker 02

  It is, it is fascinating to watch them work. My friend, Adam Davidson, who was a staff writer at a number of like pretty good publications, he has all of these various prompts for Claude where he's like, okay, like take my argument and put it to like magnify it by a hundred, just mess with my text a little bit, like what are the things you disagree with the most? Like what is the strongest counter argument? It's interesting because I think to me, as someone who's not a writer, I think the naive approach would be given a text, Claude, make this good. And I think what I'm discovering more and more is that for knowledge work, the secret sauce is like less than asking for a good output and sort of doing one step in between, which is the best in the field, how do they work? And can we automate those steps?

• 9:56Marina Mogilko?

  This would kind of mimic their workflow, but still the taste isn't there? That's why I think you still need a lot of.

• 10:03Speaker 02

  Yeah, I think that's true. I think that's true. But the interesting thing is that if you talk to really good writers, most of them would disagree that the text is really good just because they have like the magic touch. They would often say, no, no, this is the result of hard work.

• 10:16Marina Mogilko?

  Interesting.

• 10:17Speaker 02

  Right? Like it's this idea of like, most artists have a process and many of them would actually probably find it, I don't want to say insulting, but like if you, the more I talk to artists and the more I explore this idea of like, is your stuff just good because it comes natural to you and it is like, happens to be good? Or do you have a process that you like refined over the years? A lot of them are quite proud of their process and like, this is very, very hard work. Like there's a lot, like a lot of like manual individual steps going to making this good. It's not like I sit down and suddenly there's a book that's great. And if you go to like writer studios, right? There's like a lot of whiteboards and like a lot of information. And I'm noticing this across a number of different domains where Claude is quite receptive to being told, not, I want a result that looks like, huh, huh, huh, but Claude, I want you

• 11:01Speaker 02

  to take the same steps that a professional in this field would take. This is like very high minded, so let me bring this down low. But I think we're currently in the public scene this quite a bit with like motion video and 3D, where I think Opus 5.5 has made a pretty big jump. But the big jump that the public has made is to just tell Claude to use Blender, right? Just like Claude, please use the same tools that professional animators use. And now the results are quite good.

• 11:25Marina Mogilko?

  Yeah.

• 11:25Speaker 02

  The unlock really is here in the media and the technique and the tools and this less sound like raw understanding of what makes a good animated movie.

• 11:32Speaker 00

  UKG is the workforce operating platform with unified HR, payroll and workforce management. Built to solve the unique challenges of frontline work. Powered by people first AI and decades of labor data, UKG turns workforce intelligence into real time decisions around staffing, pay, compliance and productivity, enabling every worker to confidently take action as work happens. Because when work works, everything works. Learn more at ukg.com/work. UKG, HR, Pay and Workforce Management.

• 12:04Speaker 01

  In deepest jungle, Cartier's panther spies a wandering bird that circles the skies. A lovebird, lost and missing his mate, is far from his past in lonely state. Dry your tears now, there's a start. Follow my steps to heal your heart. Panther stalks through the leaves with silent grace. Through the power of scent, she finds the trace. Behold, two lovers reunite in the skies. A most precious thrill sparkling in their eyes. Together forever, they shine each day, bound by the beauty of Cartier.

• 12:48Marina Mogilko?

  If you're listening on Spotify and this conversation was useful, please, please, please rate our show. I see all your views, but I'm not getting the actual feedback. This can really help our team to decide what to fill next. And it can help someone who's never heard of us decide whether to give us a listen. Thank you so much. But I wonder for people, like you mentioned this prompt, act as if you're a professional in this field, right? And it's gonna go on the internet and research, but a lot of information is just not there. How do you trust the output? Like, for example, if I want to start another medium or like do Discover Stars, right? And I have no idea about that, but I just tell it, act like an NASA researcher. How do I protect myself from AI just confidently presenting mistakes to me?

• 13:31Speaker 02

  Yeah, it's a good point. I think it still happens to a lot of us. I have, I have like a very practical, easy answer, which is adversarial reviews, which has proven quite effective in coding, math and like various other areas where we can give you numerical results on like how effective it is. Adversarial reviews basically boil down to the idea of you've given, given one Claude a task, spin off another 10 that try to like break this down. Their whole task is to attack this and prove to you why it's wrong. It's essentially the same concept as retiming or short selling.

• 14:01Marina Mogilko?

  Are you using the same model for that or you want to use another model?

• 14:04Speaker 02

  Kind of depends on the task. This is a little context dependent, but usually the adversarial review, it is, it is sometimes easier to find flaws than to create new things, much like as it is with humans, right? It's much easier for me to say whether or not the new phone is good. It's much easier for me to do that than to design a new phone from scratch. So usually you can like take a step down in intelligence and still get very good results. That's the very practical advice.

• 14:26Marina Mogilko?

  How would you prompt that? Like if there is a process already, what's the additional prompt that you add?

• 14:30Speaker 02

  So you have some kind of output, right? Like let's say it's a slide deck or a document. Or in my case, it's, you talked about this blog post that I wrote this morning. I just tell Claude, I'm using Claude projects, which is this new product that we have that allows you to do, to work on multiple threads with a little bit more ease. And I basically tell Claude, please fire off five threads and task each of them with like proving that this, this post is wrong, that I'm thinking about it the wrong way, that I have my facts wrong, that I misunderstood something. And then they go off with a given goal of like, break this down, you know, prove that I'm wrong.

• 15:04Marina Mogilko?

  How do you then deal with all the output? Cause I do the same, but I use, I just use different models when I'm prepping for the interview, like tell me if I'm digging too deep into this or that. But then I'm giving so much different feedback and I'm like, okay, now I have to decide because this model gave me this, this gave me something else and gave, this gave me something completely different. How do you then decide what to use?

• 15:27Speaker 02

  I think you're gonna enjoy this because you can see where this is going. The answer is once again Claude. What we use in coding context is something we call a judge, where you get the output from the people who are like the, the agents whose job it was to attack your output. And then they're like, all right, here's our main points of criticisms. And then you use yet another Claude and their job is, your, your job is to validate and verify and like opinionate whether or not this is valid feedback. And then what you can sometimes do is that ultimately, depending on how you set up your workflow, the feedback that is valid should probably go back to the Claude that actually made the thing. And that Claude too, that agent too, can be discerning in whether or not to act on feedback, right? Because sometimes, and we see this in a coding context fairly often, I also see it in like

• 16:11Speaker 02

  the knowledge work output that I make, sometimes the feedback is like maybe reasonable, but has missed a point or is actually not that actionable or is not that useful. And that's usually when I know that my output is pretty good. That's one way. That's when you are really quite uncertain whether or not the output is good yourself and you can't easily check. But for a lot of, a lot of things that I create with AI, on the other hand, I actually don't need to fully understand the entire flow. Because as I mentioned, like to me as a person, it is, I do have opinions about almost anything I use every single day. I remade my website with all of these 3D assets. I have no idea how to use Blender. I've never used it, but I can sort of judge whether or not this looks good, feels good. And that might be enough, right? Like you sort of put yourself much more in a client position. For so many other things in our life, we don't really have a good way of verifying

• 16:55Speaker 02

  whether or not we get a good output other than like giving feedback to the person we're using, right? Like for a contractor or a lawyer or like for many people, a doctor, right? So you sort of just put it in one of these two buckets. Like either you really have no idea what goods like, good looks like. There, my answer is more AI, just throw more tokens at it. Or you do have, you do have an understanding what good looks like. And then maybe that's enough and you don't really need to like have opinions about the process itself.

• 17:21Marina Mogilko?

  Yeah, I think it comes down to making sure you select only the top projects where you want to understand every single step instead of just want to control everything.

• 17:31Speaker 02

  Yeah, yeah. It's like the old problem of like sort of being willing to let go, right? Like how to not micromanage? It's really hard.

• 17:37Marina Mogilko?

  Comes, now we have this problem with agents. Is there any knowledge process that you have nailed with agents? Like you completely trust them. They just go, I don't know, maybe it's.

• 17:47Speaker 02

  Yeah, fact checking, like editorial fact checking. I fully trust now the output. I get a hundred percent. If I ask Claude to verify that all the facts that I'm stating in the slide deck and this presentation, it doesn't even need that many rounds, right? Like, it's not like I have 50 agents doing that. I really trust one agent. I'm like, all right, I need you to find primary sources for every number I mentioned. I want you to verify that that is a good primary source. I fully trust that a hundred percent.

• 18:11Marina Mogilko?

  When you say agent, do you have a special agent for fact checking that you've trained? Or is just one agent that you're using for everything?

• 18:17Speaker 02

  It's just, just Claude, right? I feel like this is going to be a spicy take, which I think you'll like. I think we're kind of over rotating on this agents thing. And I can back this up with a little bit, like I think models are really good generalists. And we've seen this now time and time again, like when we first started looking at math as an industry, I think the, the first company to really look at math like with, with a very serious lens was Google DeepMind and they trained a custom model and a custom harness. And they unleashed it on the Math Olympiad and they got a silver medal, which at the time was state of the art. And then four, five months later, all the major generalist models come along and win a gold medal. I understand why people use agents. I have empathy because it's easier for us humans to rationalize what AI can do if we

• 19:00Speaker 02

  sort of like put on a little hat, like you're a data scientist, right? But that same data scientist has all the intelligence as like a field leading biologist or mathematician or what have you. And I think we're, we're sometimes limiting ourselves and our use of AI. If we sort of force ourselves to first cut, like make up this cast of characters. So for a lot of these things that I just meant, I didn't really have any agents who are being told who they are.

• 19:24Marina Mogilko?

  But does the data matter? Cause for every agent, you upload certain data set or just because your Claude has access to all your tools, it doesn't really matter what you feed to each particular agent.

• 19:34Speaker 02

  Exactly.

• 19:35Marina Mogilko?

  But then isn't it a lot of data for it to consider when answering each particular question?

• 19:39Speaker 02

  That is true, but it's quite discerning in what kind of data it wants to use, right? Like I think the right approach here is not to make these slices of data and then give every single agent the slice of data that it wants. Because many of us, if we want to be quite effective at our job, we do actually need the full view on the entire business, right? We're not quite effective if we only get a small slice of data. Like the, as an engineering manager, if you only give me code access, I'm probably going to be much less useful to you as opposed to if I also have customer data, access to customer calls. Sometimes I do want to sit in like a, like a customer call, like when we're trying to sell something, I want to have deep usage information. I tell you, get business data about what is working, what isn't. And you want your agents to have the same range of data. Now, how do you deal with the fact that it's probably a lot of data?

• 20:22Speaker 02

  Tools, you give them tools and you document really well, what kind of data is behind this tool so that they don't actually have to ingest the whole thing, right? You're just like, if you do want business data, here's the tool to get business data.

• 20:34Marina Mogilko?

  So when you're setting this up, because I'm thinking like when you mentioned this, should I move away from all my projects that have access to all the like specific data? For example, I don't know, there's a project for ranking a guest and we have the whole description of the process and then all the old scripts there so that we know how each guest performed. But now that you're saying this, does that mean that I should move away from that project? Tell my Claude, okay, we have, this is a folder with all the scripts. If you want to look at them in Notion, we have all the intelligence on how every podcast guest performed. And this is how we're in guests and just leave it there. And whenever I want to rank a guest, I just start a new chat without telling it anything and just tell it, rank the guest and it's gonna.

• 21:14Speaker 02

  Yeah, I'm careful to be prescriptive in workflows. Like I always say, if it works for you, it works for you and I don't think you should change it.

• 21:21Marina Mogilko?

  The problem is the models are getting better and I want to get better. So this has been working for this year, but a year is a lot.

• 21:27Speaker 02

  Yeah, okay. Well, I mean, if, if you're looking for the recommendation that I give for like, what do I do? Again, everyone should do whatever makes them happy and comfortable and what makes them powerful. But it is true that the models have gotten so much better at tool calling and discerning tool calling. And tool calling is this process of like the model knows when to use a tool, how to use a tool, doesn't like get stuck in tool call loops, doesn't accidentally say, oh, I found the data tool call, let me call this tool until there's no more data left, right? Like the models have gotten particularly good at efficiently and effectively using tools.

• 22:00Speaker 02

  And for that reason, I don't really make this a priori decision anymore on what kind of data do you need?

• 22:04Marina Mogilko?

  Hmm.

• 22:05Speaker 02

  Um, it also allows me to like have more threads going and more sessions going.

• 22:09Marina Mogilko?

  Yeah, it's an easier process, much easier process. Do you still use skills?

• 22:12Speaker 02

  We do use skills quite a bit still, although um, our official Anthropic guidance, as well as my personal guidance, is be really careful with skills. We often tell models things that were sort of relevant for like the last generation of models, which time is so compressed in AI, right? The half-life of a good skill is very short. And sometimes what we see is if you completely remove the skill, you actually get better output.

• 22:35Marina Mogilko?

  Yeah, I think Verisim said that.

• 22:37Speaker 02

  Yeah.

• 22:38Marina Mogilko?

  Like, just forget about it, just prompt.

• 22:40Speaker 02

  Just prompt and see what happens.

• 22:41Marina Mogilko?

  And see what happens.

• 22:42Speaker 02

  Yeah, it is, it is very effective. This is obviously most useful to people who have some kind of eval and have an idea of what good and bad looks like, but I'm pretty careful and very discerning in when I use skills and when I don't.

• 22:51Marina Mogilko?

  Okay.

• 22:52Speaker 02

  You know, you know what a, what, what a great way to think about this is?

• 22:55Marina Mogilko?

  Hmm.

• 22:56Speaker 02

  I like to consider the model experience as opposed to like user experience. And model experience is you pretend for a second that you're the model, you're Claude, right? What kind of things would you want if you had to fulfill whatever task you're being given? And you very quickly discover that you actually do want access to the raw data, you want access to a computer, you probably want to be able to install some software.

• 23:13Marina Mogilko?

  I want to make some decisions myself without somebody telling me what they think already. I just want to see exactly what you said, raw data, and make my own decisions.

• 23:21Speaker 02

  Like, I think the upfront cost is higher. Like, if you, like, even a year and a half ago, I was in this world of like telling Claude, I found another crash, please go and fix the crash, right? But it very quickly dawned on me, I'm just a conveyor belt for Claude, right? Like, I go look at our crashes, pick one up, move it over, drop it off in front of Claude. I'm like, please fix this. I'm like, okay, this is, this is, no, I'm just gonna, Claude needs to have access to all the crash data and then it's like figure out itself what it needs to fix, what it doesn't need to fix. The upfront cost is really worth it, but it's also like important, I think, to consider that there is upfront cost. But once you set this up, um, it, it opens up so many doors for you. And that's where I find a lot of like real hard value coming out of AI.

• 24:01Marina Mogilko?

  If you've been hearing about AI agents and wondering how you'd actually set up one, I put together a guide that walks you through your first one in about 20 minutes. It helps you choose what to automate and get it working with no coding required. You can get it through my free newsletter. The link is below. For someone who just, who's listening to this conversation and who thought they would need like a major setup to start using AI correctly, would you say, uh, if somebody's starting just today, they're using it for knowledge work? They don't need all these tone of voice things, like my business strategy, you just give it access to your email, it learns from it, from your tone of voice, you give access to your newsletters or whatever. So you don't need any of that setup anymore.

• 24:41Speaker 02

  The best way to get started with Claude today, I think, is step one, download the desktop app. It has a built-in browser where you can log Claude into some system you're using and forget about connectors, forget about like downloading all the data, forget about like all kinds of things, just let Claude use your computer a little bit, sort of give away the keyboard. That gets you so far and really shows you what works and what doesn't work. And then at some point, you might discover, oh, Claude is really helping me with whatever tool you're using, whatever internal or external tool you're using. And once you see that a workflow really works for you, you might consider, okay, can we give Claude something better than like having to manually click in this dashboard around? Can we give it some kind of connector?

• 25:17Marina Mogilko?

  Hmm.

• 25:18Speaker 02

  But just starting with a browser gets you so far.

• 25:20Marina Mogilko?

  Okay. And when it, just to wrap up with the agents, when you say agents, that all, that means you have just basically chats, right? And that's it. There, there is no like specific agent who does research, no specific agent who does your tax returns.

• 25:33Speaker 02

  No, no, I, I mean, I do have projects, so I do think about, I do think about a different work items, but I don't think about different roles.

• 25:41Marina Mogilko?

  Hmm.

• 25:42Speaker 02

  Right? So I don't, I, I never tell Claude, you're an excellent whatever.

• 25:47Marina Mogilko?

  Yeah.

• 25:48Speaker 02

  Um, I think this was, this was very in vogue, sort of like 2024, where we're like, Claude, you're an excellent engineer, please go fix whatever problems are here.

• 25:56Marina Mogilko?

  But you still use that in fronting now. Act like, something that we started with, act like a top whatever.

• 26:02Speaker 02

  I specifically implore Claude to like, tell me what tools someone top of the field in this field would use. And then I'm like, I want you to use the same fields. Um, a problem most models still have today is that if you simply tell it, data science is maybe a good example, or like accounting. A lot of the models still have the problem that if you tell them to act like a very good accountant, that they engage in a little bit of role play.

• 26:23Marina Mogilko?

  Hmm.

• 26:24Speaker 02

  And role play is not really what we're going for. We want the techniques, the tools, the knowledge that a professional would use. We don't really want just the vocabulary, right? Like, I don't, I don't need Claude to sound like.

• 26:33Marina Mogilko?

  Yeah.

• 26:34Speaker 02

  I don't need to put on a costume. I actually wanted to talk to me like I'm five, but I wanted to use all the top tools in the field.

• 26:41Marina Mogilko?

  Is there still anything you would not trust in a agent with?

• 26:44Speaker 02

  It's a tricky question. We can just keep layering more validation on top. So I think the question for me is much more, it, it's less about, it's just about what do I not trust Claude with, and much more about, uh, what things do not trust one single session with. And I think the, the list of things where I don't trust one single session is actually much larger than the list of like things I wouldn't trust Claude with. I rarely want the current session to judge itself. That I think is still really iffy. Like, the current Claude session that is running, wherever you have it, in Claude code, in Claude cowork, in chat, and even in a project, the current session tends to be quite bad at being a, being a judge of whether or not the work it did was quite good. So as much as I can, I try to find that outside signal.

• 27:27Speaker 02

  And the outside signal in a pinch can be in a Claude session. That has proven to be quite effective, but my, my preference is to find some hard outside evidence for what works or what doesn't work. And sometimes you need to like trust the process a little bit and give Claude a little bit of time. Like, for instance, in your field, I'm kind of making this up, but I assume that maybe like the number of users, subscribers is like an important metric that.

• 27:49Marina Mogilko?

  Oh, like the number of views, that's, that's what we're, whenever I give a task to Claude, I tell it like, we need to hit this number of views.

• 27:55Speaker 02

  Yeah.

• 27:56Marina Mogilko?

  Go and try. But you, that, and that's, that's something that we're still like struggling with is this feedback loop, right?

• 28:04Speaker 02

  Yeah.

• 28:05Marina Mogilko?

  Cause it's me giving feedback to my team and then my team actually going and posting something else, not what Claude gave us. It's like maybe 70% the same, but then the 30% that I've added is actually my taste.

• 28:17Speaker 02

  Yeah.

• 28:18Marina Mogilko?

  How do you close that loop?

• 28:19Speaker 02

  I think that has worked for us is there's probably some kind of place where this feedback usually happens. Is it usually in person or does it usually happen?

• 28:25Marina Mogilko?

  For, for us, it's on Telegram. There isn't a native Claude connection. We have a bot that scrapes every chat, but it's still very manual. I have to trigger Claude to look at the bot, look at the feedback.

• 28:36Speaker 02

  Yeah.

• 28:37Marina Mogilko?

  And it's still not, it's, it's not there, even like looking at something we've sent, right?

• 28:42Speaker 02

  Yeah, because we do, we do, obviously, again, in a coding context, we have a lot of engineers who, you sort of know which engineer you want to review a certain thing that you're working on. And the kind of things that they look for, Claude is very good at figuring out from the feedback. It's pretty good at like, given all of this feedback that I've read.

• 29:00Marina Mogilko?

  Feedback on Slack, right?

• 29:02Speaker 02

  Yeah, on GitHub, Slack, wherever else the feedback might happen.

• 29:05Marina Mogilko?

  And you didn't tell it to look at feedback specifically just because you gave it access to Slack? It's looking for feedback?

• 29:10Speaker 02

  Yeah, that is, that is much easier to do. Like, if it has the tools and you don't have to like, as a human yourself, figure out, okay, let me assemble a list of all the feedback I've ever given. Um, but if you do this like three-step process of step one, assemble all the feedback you've ever given. Step two, tell Claude to like condense that down into a list of rules or principles that you care about. Sometimes it's useful to like, then again, do a third round of like, okay, what are the principles that maybe you might believe, but you don't actually know you believe? And then take that list as like an input for your ongoing reviews. That is, that has been quite successful in our attempts to, I want to say, like specialize the feedback that individual sessions give.

• 29:48Marina Mogilko?

  Hmm.

• 29:49Speaker 02

  Um, that, that has really worked. I think for, for work that is a little different than raw coding or not very text-based, I think the problem is still bigger. Um, but I think similar things can be done, right? About like, give me feedback on a customer call. How did that go? If you read the transcript or like, maybe you're, I'm doing a lot of interviews for Anthropic, which is like a typical knowledge book problem, but also like quite interesting.

• 30:13Speaker 00

  UKG is the workforce operating platform with unified HR, payroll, and workforce management. Built to solve the unique challenges of frontline work. Powered by People First AI and decades of labor data, UKG turns workforce intelligence into real-time decisions around staffing, pay, compliance, and productivity, enabling every worker to confidently take action as work happens. Because when work works, everything works. Learn more at ukg.com/work. UKG, HR, Pay, and Workforce Management.

• 30:46Speaker 01

  In deepest jungle, Cartier's panther spies a wandering bird that circles the skies. A lovebird, lost and missing his mate, is far from his path in lonely state. Dry your tears, now there's a start. Follow my steps to heal your heart. Panther stalks through the leaves with silent grace. Through the power of scent, she finds the trace. Behold, two lovers reunite in the skies. A most precious thrill sparkling in their eyes. Together forever, they shine each day, bound by the beauty of Cartier.

• 31:29Marina Mogilko?

  But then it's teaching me. I want it to teach itself. And I think a lot of people who started using AI experienced this. You want to do more. You see how productive you are. But you, I don't, there is no agent that I trust 100%. Every output goes through my team and then eventually through me. So I've become the biggest bottleneck in the company.

• 31:47Speaker 02

  Honestly, I feel like our products would do that for you pretty well today. I'm actually quite confident. Like if Claude has the ability to watch you give this feedback for a while, like the first thing he would probably do is he would sort of run it side by side. This is an experiment I recommend to almost everyone. It's like really fun to do. If you currently have a trust problem, you will probably not be super excited to immediately give over the reins, right, like from one day to the next. But sort of run it side by side, like you read something, you write down your feedback and you do that for a few rounds. And then just to check what kind of feedback Claude would have given. And like, where do they differ, where are they the same? That also gives you something where you can actually sharpen the workflow a little bit. Because I think models will still be imperfect, right? There's still going to be things that where, where the feedback is like a little different.

• 32:30Speaker 02

  And like even for most humans, if we take the same human and we ask them to review the same document, like on a Monday versus a Friday, the feedback will be different. But this like side by side process is rather powerful because it shows you so much.

• 32:41Marina Mogilko?

  And how do I build this agent that talks to my team? Is it just something that like a website where they upload stuff and it gives feedback?

• 32:48Speaker 02

  I would recommend Claude Tag, which is Claude inside Slack.

• 32:52Marina Mogilko?

  You build, you give it access to my taste.

• 32:55Speaker 02

  Yeah, it's like, it's like currently our core multiplayer experience, right? Where multiple people can talk to Claude at the same time.

• 33:03Marina Mogilko?

  This is all getting very interesting when it comes to building things, right? Because you can build so many things easy. What becomes valuable when you're building software? And I'm talking about people who are beginner entrepreneurs. I'm thinking about myself. What is it? To 2015, we came here with a booking platform for steady broad experiences. Amazing, like a mode was building this whole platform because we hired a lot of engineers, was done hundreds of thousands of dollars. We automated stuff. Now I can do this in two days by myself. Like what is my entrepreneurial mode when you think about building software? Is there any?

• 33:41Speaker 02

  Spicy opinion, I think it actually hasn't changed that much. I think the cost of investment, like sort of the upfront capital cost was much higher. That is true. And like, I think we're now entering this interesting area where like single person teams or like two person teams can build billion dollar companies. But I think the secret to building a good product has actually not changed. And I believe it's empathy. Empathy for the user, empathy for the use case, empathy for like trying to figure out, is this actually valuable for the person who's ultimately going to pay for it? I think that has, again, and I think this is maybe a spicy opinion, but I think this has always been the key ingredient in any entrepreneurial team, right? And I think especially in startups, like before we started taping, you and I talked a little bit about 500 startups. I think this is even like sort of common knowledge within VCs

• 34:24Speaker 02

  that whatever you show up with doesn't really matter. Probably going to change anyway. But you as the founder, you're the key ingredient. And we can, we can go one level deeper. What does that mean? Is it like your talent, your skills? I think empathy and like an ability to really build something of value is probably the core thing that matters in a founder. And I think that has not changed. It has just gotten much easier to tap into that empathy and like to, to execute on it.

• 34:46Marina Mogilko?

  I totally agree with that. But also sometimes I'm thinking like, I have this idea, but then I see something on X where we'll say, oh, this new model just killed this. And we see people vibe coding their smaller tools, et cetera. Do you think it's a trend that we're going to vibe code a lot of tools versus paying for them? Like what would us make us pay for a certain tool if we can spend an hour just building something similar?

• 35:08Speaker 02

  I think software that is hopefully going to go away is bad software. Like the software that doesn't really serve your use case and you're only using it because you don't have anything better. I think all of us have some tools that come to mind where you're like begrudgingly, okay, I guess I'm going to use this because I have to and I don't have the time to like look for something else or use something else. I think that will come to an end. However, at the same time, we talked about how hard it is to, to build good products. I think there's still going to be tremendous value in like figuring out how to best deliver value and like to have empathy in a way where most of the products I use that I'm like a big fan of, I wouldn't have come up with myself, right? Like I'm on record as like having written blog posts about how the iPhone is stupid because obviously you need a keyboard, right? Of course.

• 35:51Speaker 02

  I think for most of the products that I really enjoy, someone else came up with an idea, executed on it and then gave it to me. And I think the main reason I use those products is rarely because it was very expensive to build or because it took them a long time or because they needed a big team. The reason I use the product is because it solves a need I have really, really well, probably in a way that was better than I thought of. The gap that I think we're going to completely fill is this garbage software and I only have to use it because I have no other choice. I think that will hopefully go away and I'm very excited about that future.

• 36:17Marina Mogilko?

  Before we keep going, I'd love to get to know you a little better. I share a lot about my work here and I'm curious about yours. What do you do and what you'd like more help with? We've put together a little survey. Your answers will help me decide what to cover in future conversations and what resources to put together for you. I'll leave the link in the description. Please, please, please take a couple of minutes and tell me about you, about your business, about your ambition, so I can make better content for you. I really, really appreciate you. Thank you so much for doing this. You mentioned something about Windows computers that quietly run work and that they are a big business opportunity. Can you talk to me about that?

• 36:57Speaker 02

  I build AI products. I work on Claude. When I go out into the world and I look at what are the computers and the machines that are really running the economy? I think it's really easy, especially for like us in Silicon Valley to like sort of consider your typical startup with like a bunch of latest generation MacBooks and that's all that exists. And then you go to an airport and you sort of like peek around the corner and you see that they have not actually yet moved to graphical operating systems. It's still like the black and white.

• 37:21Marina Mogilko?

  Oh, yeah, the transportation authorities, the.

• 37:24Speaker 02

  Yeah. And I'm doing this for fun now sometimes that I just go to all of these places that I, that, that runs so much of the economy. And like the last place I did this was a hotel. Like I love doing this at a check-in where I'm like, hi, I work in software, I build tools. Can I just like, can you just show me real quick like what you do on your little machine right there to check me in? Harrowing. Like all of the, all of these devices have software that is so clunky, so bad, truly awful. And I think it is probably an untapped opportunity for many businesses to go in there and like try to clean that up. Well, before AI became a thing in 2010, I, I joked that if I ever wanted to retire, I would build really good software for dentist offices.

• 38:05Marina Mogilko?

  Now you can build it in a day, I guess. It's, it's the relationship part.

• 38:08Speaker 02

  Yeah, exactly.

• 38:09Marina Mogilko?

  That's becoming, well, that is the hardest, right? Not, not just the software. Any other businesses you wish existed?

• 38:16Speaker 02

  I still think we're barely getting started on software that feels a little bit more personal and has a little bit of a touch of AI that is not just, here's a, here's a sidebar with a chatbot on it. I think we've, we've sort of permeated the entire industry with low intelligence, cheap models that operate sort of at the level of, I've read the FAQ page and if you give me a question, I'm sort of like, I can spit out the answer. We haven't really like figured out what can we do with top line intelligence across many of our applications. Businesses that I specifically wish existed, I'm just going to double down on this empathy thing. The industry moves slowly, right? Like even VCs move kind of slowly and it's like really hard to adjust to a new world. And I don't think we've properly adjusted to execution is cheap. We've always said ideas are cheap, that was true.

• 39:01Speaker 02

  And we said execution kind of expensive, but it's, it's gotten cheaper over time. And now execution has become extremely cheap. I think we need to like really double down on empathy connections. Who's capable of like going into a business and really understanding what the needs are and how to can, how can he actually optimize things here? So this might actually be a nice opportunity, like the bigger venture of the MBAs. Right? Like I think MBAs can now make a bunch of software startups that might be quite successful.

• 39:24Marina Mogilko?

  I was just talking to a Nobel Prize laureate in economics and he was telling me that we have such powerful models, but we really lack the application layer. Do you agree with him that there is this huge gap between what models are capable of and what average user is using them for?

• 39:39Speaker 02

  Oh yeah, for sure. Yeah, we definitely have like a capability overhang, right? Like the idea of you have an amazing model in front of you and if you're essentially using it to, to ask, hey, what's the capital of France again? You're probably not really tapping into what is possible there. Our founder and CEO, Dario, talks about this concept of like a data center of geniuses. My job really is to figure out, okay, like let's assume we have a data center of geniuses. How do I empower you as a user to actually make use of that? And it requires creativity. It requires like a rethinking of like your needs and like what you think is possible. It's a, it's a really hard problem that I don't actually think is new, right? It's this notion of people have always been held back by what they thought is possible rather than what is actually possible.

• 40:22Speaker 02

  Yeah, I do agree that we have like, we've just scratched the surface of what's possible.

• 40:27Marina Mogilko?

  How can one close that on an individual level?

• 40:30Speaker 02

  On an individual level? I thought about this like all weekend. I think on an individual level, the thing that you see on X a lot is this, this rallying cry of be more ambitious. We ought to be more ambitious. Like what being more ambitious really means is I think so context dependent. But I would like people to shed all the shackles of like what they believe is possible and rethink why are they doing things, right? Like, like I'm doing a certain task in my business. What I, what am I hoping for is going to happen next? And you just keep giving the model that task. Like whatever outcome you're hoping for, give the model that task. You can't play this forever. You will eventually find the limits of the current top intelligence.

• 41:12Speaker 02

  Like a good counter argument here is okay, like the purpose for most businesses is to make money. And if I just tell Claude, go make money, it's probably going to like not make it very far. So we, we haven't reached like the top level of altitude yet. It's somewhere in the middle. But once you bump against that ceiling, you do know that you're at the forefront, you're at the frontier and you're going to really benefit. You're going to benefit from the next model generations. Because if, if all we're doing is like summarize this email for me or like make this email sound less angry, you're not really like benefiting from the new intelligence.

• 41:41Marina Mogilko?

  What would be your advice to someone who's just starting out now as a software engineer? In this ever evolving?

• 41:46Speaker 02

  In this ever evolving like landscape of like how to do software engineering? I think it's, I think it really depends on what you want as a software engineer, right? Like what does it, what does it mean to be a software engineer? I think we're like, as engineers, we're currently in this like moment of crisis where we're like, what does it mean to be a software engineer? Does it mean that I'm, I've completely memorized the entire API service in React? Is that what I'm valuable for? Is that what I do? And I think that's probably less the case now. To me, this is a pretty welcome change. I've always been of the opinion that. A good engineer versus a bad engineer, I measure, I've always measured my engineers on their impact. And the impact is like, does the user like this enough to buy the software? There's always for me been relatively little value in like really elegant, beautiful,

• 42:29Speaker 02

  mastery solutions where we can be really proud of the engineering craft here, but the user doesn't feel any benefit.

• 42:35Marina Mogilko?

  How do you develop that? What do you double down on now in order to be relevant in three years? If like every position is suddenly a senior position, what would be your advice there?

• 42:45Speaker 02

  The way I got my start in this career is by like building a strong portfolio. I think that would be the same advice today. I think building a portfolio that is not just like a mock is much easier. So my recommendation for anyone young would be go and build some real things, prove that you can do it, prove that you have the empathy, prove that you have the chops, the ability to talk to customers, like figure out what is necessary versus not necessary. I do think there's like a slightly new definition of what software engineering is. I personally think that's good. I do see the young people adapt to this new style of thinking much quicker than some of the older engineers who maybe enjoyed the luxury of primarily working on on tickets without having to ask why too many times. I've seen so many people be quite successful in that. The things that I would maybe to

• 43:30Speaker 02

  contrast, not recommend anymore. It is true that I think like completely memorizing individual API services, I think has become less valuable. What is still valuable is being able to combine different systems in interesting ways. I think the value of that is amplified now because you can so quickly combine things, right? You can just like point AI, like three different systems and be like, I want these three, like I want these five ingredients mashed together, go and do it.

• 43:55Marina Mogilko?

  Is that something that you look for when you hire people?

• 43:58Speaker 02

  Yeah.

• 43:58Marina Mogilko?

  This ability to, is there something else that you're particularly looking for?

• 44:02Speaker 02

  I mean, I don't know how you hire. Do you still, do you, do you care a lot about the resume?

• 44:05Marina Mogilko?

  I do. When it goes, because when you mentioned relationships, they're very important for my industry. I am looking for someone who knows people. I am looking for someone who knows how other businesses operate because this is not public information. Like how do I learn how to, what, what do I charge as a podcaster, right? What do I charge for instance? I want someone who knows exactly what's going on in the market. So we do care about relationships, but when it comes to the creative part, actually with creative part, we still look at how many views their videos have produced because that means

• 44:33Speaker 02

  Yeah.

• 44:33Marina Mogilko?

  they know the art. I don't look at their degree, of course.

• 44:36Speaker 02

  Yeah, yeah, exactly. Exactly. And I think this is, I don't know if this is so controversial. I guess we both work like in a, you know, like tech adjacent, but I've never cared too much about which degree do you have, which school you went to. It was always much easier for me to look at what are the things you made? Are those things I can touch, feel, try? And I can feel like, oh, you either were the winning player on a team, or you at least worked on a winning team that is sometimes useful for me to know. I think that's really valuable information. And the thing is, if I didn't have that, I might go take the shortcut and look for schools, degrees. I don't really have to. I will always pick someone who has proven themselves to build something amazing over someone who hasn't built something amazing, but has an amazing degree.

• 45:14Marina Mogilko?

  What would you tell your kid when she grows up and says like, Dad, I wanted to coding? What would be your advice?

• 45:20Speaker 02

  I mean, she's nine months old right now, so I think things will change a little bit.

• 45:24Marina Mogilko?

  I think you have to be in line for your daycare already. Just saying.

• 45:28Speaker 02

  It all happens quicker than you think. What would I tell my daughter? I think I would tell my daughter to very early on, try to understand how businesses operate, how people collaborate, how to really find value. I've not gone to the University of Waterloo, but I'm such a fan of the University of Waterloo's process.

• 45:43Marina Mogilko?

  All the amazing minds.

• 45:44Speaker 02

  All the amazing minds. But I think the University of Waterloo has always had this approach, which was quite unique within, within computer science engineering, which was this is an outside perspective. They might disagree with me on this, but my outside perspective was always coming from Germany where computer science is very much computer science. It's informatics. It's a lot of math and very little, like only a sliver of how do we actually build a good product, let alone computer engineering. JavaScript was in Germany for a long time universally considered to be like not what real computer scientists use. And the University of Waterloo's approach has always fascinated me so much because I would get these interns or junior engineers who've worked at five different companies before in their internships. It was pretty common, especially in San Francisco, that you get an intern and it's their like fourth internship and they previously interned

• 46:29Speaker 02

  at Tesla, Google and Apple. And they have so many interesting things they learned about, how companies operate, what are the upsides and downsides? How do you actually advocate for an idea? How do you get resources? How do you convince legal to get something done? I think those are the skills that really like for me made University of Waterloo's engineers almost immediately effective. It's their first job, maybe with me, but they're in a completely different category than someone who has maybe even gotten a PhD in computer science, but has never worked in a company. They might be very smart what they do. But as we all know, like building a good product and getting it out there, it's about much more than just knowing what code to write. You need to be able to talk to people. You need to be able to like work together with other functions. You need to have like a little bit of a, of a sense for how does this company run? So I would tell

• 47:14Speaker 02

  my daughter to probably like really invest in that.

• 47:16Marina Mogilko?

  Love it.

• 47:16Speaker 02

  What do you tell your kids? What do you think your kids should do in the ever evolving economy?

• 47:21Marina Mogilko?

  I tell

• 47:21Speaker 00

  UKG is the workforce operating platform with unified HR, payroll and workforce management. Built to solve the unique challenges of frontline work. Powered by people first AI and decades of labor data, UKG turns workforce intelligence into real time decisions around staffing, pay, compliance and productivity, enabling every worker to confidently take action as work happens. Because when work works, everything works. Learn more at ukg.com/work. UKG, HR, Pay and Workforce Management.

• 47:54Speaker 01

  In deepest jungle, Cartier's panther spies a wandering bird that circles the skies. A lovebird, lost and missing his mate, is far from his path in lonely state. Dry your tears now, there's a start. Follow my steps to heal your heart. Panther stalks through the leaves with silent grace. Through the power of scent, she finds the trace. Behold, two lovers reunite in the skies. A most precious thrill sparkling in their eyes. Together forever, they shine each day, bound by the beauty of Cartier.

• 48:37Marina Mogilko?

  Them to try all of the things. And I tell them, you can be anything. Because sometimes they're like, oh, robots. You can be the queen of, like, if I tell her, like, you can build a robot, she's like, not very excited. I tell her, you can be the queen of robots.

• 48:51Speaker 02

  Yeah.

• 48:51Marina Mogilko?

  And she's like, oh, I like queen of robots. And then she says, and we, we bought this self-driving car that you can program yourself.

• 48:58Speaker 02

  Oh, that is fun.

• 48:58Marina Mogilko?

  And she was building it with her dad. And like just telling them they can be anything. And there is no thing that they cannot touch because it's, I don't know, out of this or whatever. Because I came here with a notion that everyone here is from another planet.

• 49:12Speaker 02

  Yeah.

• 49:12Marina Mogilko?

  If you went to Stanford, for me, that was like something that I couldn't even fathom.

• 49:17Speaker 02

  Yeah.

• 49:17Marina Mogilko?

  But seeing that these are all normal people, just very passionate, hardworking, and like giving this idea to my daughters that anything is possible, it's just hard work and passion.

• 49:28Speaker 02

  Oh, man, I love that. The, I think the reason I love that so much is because in AI circles, we constantly talk about this idea of ambition. And the reason people are not ambitious enough is because we're sort of programmed as humans to like only consider certain things possible. And if we don't consider them possible, we don't even try. So it's like real, like what we believe is possible is the real limiter, like a much bigger limiter than what the models can do or what we can do. Maybe that's another reason I like really believe in the new generation is because they will never have experienced all these limitations that we have.

• 49:55Marina Mogilko?

  Okay. I think we covered so much. I just wanted like your final thought on our future. How do you think our life is going to be different in five years?

• 50:04Speaker 02

  That is such a big question. I'm sure you have the right person for like, I, I know that like going on any podcast is like, there's a, there's an occupational hazard of having to make predictions. Biggest thing that I've noticed so far is that we're all so wrong about the intelligence exponential coming out of AI. It is really hard to like wrap your finger around what does it really mean for, for the exponential to exist. And I keep going back to math, right? Like when, when we won the silver medal at the Math Olympiad, when I say we, I mean, Google as an industry. I was well aware of scaling laws. I was well aware of like how much intelligence is progressing. And I think if you'd asked me at what point will AI solve what we would otherwise consider unsolvable

• 50:48Speaker 02

  math problems, I don't think I would have given you like the middle of 2026. I would have been like, that's, that's in a year and a half. What are you talking about? So I think instinctively, having a good feeling for how well, what will the future look like is really, really tricky. That's the first part. And I think the second part that is rather tricky for me is the one thing I can tell you with a decent amount of confidence is that I think the products that we use today will all look completely different. I think, I think we're currently, we've hit this point where AI is becoming so mainstream and it's becoming so popular that it's like easy to imagine, oh, we may be like this, this is sort of the end state. But I'm quite convinced that all the AI products we use today are roughly in the same phase as all the funny phones that we had right before the smartphone came along. So I think we're still in the pre iPhone era, which makes

• 51:32Speaker 02

  it very exciting for me to work in.

• 51:34Marina Mogilko?

  Wow, this is really fascinating. It's already like from in our pre AI era, there are so many things that are changing. And you made me rethink my whole process. I won't even show you what it, what it looks like now.

• 51:45Speaker 02

  I'm sure it's fine if it works for you.

• 51:47Marina Mogilko?

  It's very outdated because, because we give it all the data and tell it exactly what to look at. But I totally understand how giving it access to everything is just so much more powerful.

• 51:56Speaker 02

  It's such a tricky thing, right? Like all the advice I've given you today, in about 10 weeks, we're probably going to have a new revision on all of it.

• 52:02Marina Mogilko?

  That is also true. So being an always on agent when it comes to new models, I think that's the, that's the thing and try new things. Thank you so much, Felix. If this conversation with Felix made you want to try Claude in your work, watch my interview with Ally Miller next. Ally works with teams on using AI. And in our conversation, she showed me her real workflows and built a Claude morning brief alive. It's a practical place to start if you're wondering what to do first.