1019: Anyone Can Write Code Now, So What Gets You Hired? (With Priyanka Vergadia)

25:58So anyways, I got to do a lot of that for Vortex AI when it was launched, Gemini Code Assist, when it was launched.

26:06Jon Krohn:Tell us more about Vertex AI. Yeah. Vertex AI is Google's platform for everything AI. So you can get a custom model, you can get open source model and deploy it in cloud. You can fine tune that model and deploy it. And now you have your own API endpoint and you can manage all of that infrastructure. You can deploy your own GPUs with it. They have got a lot of custom stuff in there as well. So it's easy. So you don't have to write code for a lot of these things. And you can just throw in a few examples and fine tune a model very easily. So that is the platform that was launched like in 2021, 2022, like that timeframe.

26:50And now it's being used for all of these things to get all these updated models in there. So that's Vortex. Gemini Code Assist was this very first launch for AI-assisted coding. GitHub Copilot was already in the market before then, and this was Google's first attempt to go out with an AI coding agent in the market. And so it was a very fun time because I was getting to write the story for the first few demos that we would go do and get the team to work on the first few hackathons that we are going to launch and get feedback from the developers. And then from then, one of my mentors reached out at Microsoft and they were, Cursor was hitting very hard at the time.

27:36They'd just done this relationship with OpenAI. So the idea was you come in, figure out how we want to do go to market for GitHub Copilot in this heavily competitive situation. and it felt very interesting because I had done and go to market, meaning it sat between the thought leadership, which I was doing at Google from the developer relations perspective, but also brought me back to, we got to go talk to the CTOs, the CXOs, the Fortune 100s of the Microsoft customers, which was a very good opportunity to sort of combine those two pieces together. And a lot of this, I believe, with AI has nothing to do with products.

28:25Everything is to do with trust, relationships. Can you get somebody? And it was never to do with products, honestly. But with AI now, you can actually clearly see that it is all about relationships. It is all about who do you trust the most? Can they be with me on the journey to get me through this transformation? And whichever company is able to put the right people in front, like the FDE roles that we're talking about and which are becoming so hard right now, that's all the idea is. You're bringing your best engineers and the best communicators, putting them out there to be talking to the biggest stakeholders.

29:07Right. And so I ended up building before this term existed, forward deployed engineering, I ended up building a team of outcomes based engineers for GitHub Copilot for Microsoft and that intersection. And we were talking to Fortune 100 CTOs to help them do AI transformation for their software development life cycles.

29:28Jon Krohn:Yeah, Forward Deployed Engineer is definitely a hot job title these days for our listeners to consider. Yeah, if you're looking to kind of be moving laterally into something that has a lot of growth potential, FDE, Forward Deployed Engineer is something to think about, which as Priyanka described is going and working alongside the client often at their office. And yeah, making sure that your product like GitHub Copilot is being effective there. It's interesting how you said while you were at Google and Google was launching, was it called Gemini Code Assist was the name of it? And so that Code Assist product, which I guess now kind of evolved into the Gemini CLI.

30:07CLI and then Anti-Gravity and then it's like a few other branches. Yeah.

30:12Jon Krohn:So many product managers, so many products. But so it's interesting how you said when that product was coming out and you were involved in that, how the kind of the existing incumbent in the space was GitHub Copilot, whom you then later joined and you made a big impact in them still. Like there was, I guess there's a big market, but you still played such a big role in GitHub Copilot rolling out to enterprises. There's something that you said earlier, you were talking about how quick it is now to be just rolling out features, rolling out products, shipping every day, how quick things change and how important it is to have these kinds of roles like your senior director of AI transformation role.

30:53Jon Krohn:at Microsoft or your head of North America developer relations role at Google, how critical it is to have those roles to be able to communicate changes. Because I run into constantly now abrupt changes in my product experience. Like pretty much every day I see the little, there's like this friendly little leaf in the bottom left corner of my clawed Mac OS that says like update, ready to reinstall, click here. And I'm like, okay. And then things happen where, you know, a couple of weeks ago, you would, in the Cloud macOS experience, you could easily navigate in the top left-hand corner between the chatbot experience, co-work, and Cloud code.

31:33Jon Krohn:And then now somehow they've just combined co-work and chat, I guess, into one panel. And now I can only click between code. And obviously there's reasons why they do that. And they think it's ultimately going to be better for me, but it's frustrating to have those changes happen when you're like, you're used to something working some way, it seemed to work really well. and you're like, how come I can't find cowork? It's gone. I've been on a long tangent here, but there's a specific thing now that we're back on Claude. Way back in the content creation section that we were in like 15, 20 minutes ago, you were talking about how a big part of your success with using Gen AI tools, with Claude specifically, is having skills set up.

32:16Jon Krohn:And so that's something that I meant to at that time. we ended up going off and talking about something else. But I'd love to come back because I feel like that's something really practical and technical and useful for our listeners. Could you explain for our listeners who don't already know about skills, what those are and how they can make the most of them in cloth? Skill is something that you would define your task to be. So break down your task into small subtasks and you define how you do that. When I write the blog. I do a research. This is how I would do it as a human, right? So think about this.

32:55When you're writing a skill, think about it like a human. Now, the way Claude helps you do it is amazing. But before you even get into it and start setting up a skill, think about your task explicitly. I'll give you an example because it'll be more material that way. I'll do a research first if I'm about to write a blog. And on this topic, I would, and the research prompt has to be really good as well, where like, I want only high quality content written by researchers and scientific research from schools and universities and organizations like this, only look for that stuff around this topic, and then create a rapport.

33:37Like, let's say that is the prompt, But that becomes part of my skill as step one. Then the next part of that skill is after I do the research. So the prompt is the how, right? So I've put the how in there as well. Research is the task. How you do it is that prompt. Then the next step would be to synthesize that research. I usually do a human in the loop thing in there. I don't trust it to make decisions beyond that. So I would do a human in the loop. It sends me a text message. I've got all this set up in Hermes. So it sends me a text message saying, I've done the research, here's the doc. I like to read because I'm trying to do that intentionally so that I don't lose the skill with AI.

34:26But you can also, I've also done cases where it would send the audio to me and I can just hear what the research was while I'm running or while I'm on a workout, which is super handy, right? And then I would give it instructions on, okay, I want to change this or that. And then the next step is like kickoff writing the first outline. I don't do, I don't let, like most people would just go in, write a blog on this topic. You're going to get slop, right? The whole idea here is how would you approach a blog? I approach with research. Then I would go in and do my analogy and storytelling on top of it.

35:09That's my next step. Then I would synthesize after the synthesis, the storytelling and then putting it into a format that I usually used to write blogs when I was doing it all on my own. Right. Which is I need to have three images in this blog and they need to be developer focused, which usually talk about the flow of movement of of tokens or query or like. And I have some of these examples in there. So, and I need to have one practical example in there. And that can come from, there's prompts in there where Claude would ask me other practical examples from my experiences. And so that it can take those and do that.

35:51So I'm going into too much detail, but the idea of a skill is, how do you do the task? Define it into subtasks. Those are your bullets that go into the skill.

36:05That way you will get a much more personalized, the type of outcome you would, it's never perfect, but at least like 80 % there and now you can start editing it from there.

36:21Jon Krohn:Agents are getting smarter every day, but even the smartest agents get stuck without the right context and the right tools. That's where Notion comes in. With the recent launch of custom agents, Notion became the collaborative AI workspace where teams and agents work side by side. And now, their new developer platform is turning that workspace into infrastructure developers can build on. The piece I keep coming back to is how easy it is to ship something real. The CLI authenticates in one line, workers deploy without provisioning any infrastructure, you write your code, deploy, and you're done.

36:52Jon Krohn:For me, that unlocks building purpose-built tools for my custom agents with the predictability and custom logic I need. Think a guest prep agent that pulls a researcher's papers, recent talks, and citation graph on demand. Tools my agents can actually call with parallelism and predictable behavior, not just hope for. Learn more about Notion's developer platform today at notion.com slash superdata. That's all lowercase letters, notion.com slash superdata to try Notion's developer platform today. And when you use our link, you're supporting our show, notion.com slash superdata. Perfect. Yeah, that did have a lot of examples, a lot of detail, but hopefully it helps us understand just like a lot of your cartoons, your illustrations, you know, go into practical examples.

37:37Jon Krohn:And so we got lots of examples there of how to build effective skills in Claude. So thank you for indulging us with that. Back to the enterprise stuff with, you know, your Google Cloud experience, your GitHub Copilot experience at Microsoft. With your work bridging the gap between high-level boardroom strategy and real-world enterprise AI execution, you have something that you call the 10-20-70 framework to help guide budgets for AI success. Could you tell us about that 10-20-70 framework? So 10 % on tools, 20 % on execution with those tools, and 70 % on education and skilling and upskilling. And I know this sounds crazy, but I work with enterprises that have large number of large teams and have bought the tools and don't see ROI.

38:42This is exactly the Stanford report that just came out. The impact report is a great example. It has like 8 % or 9 % of the actual AI use cases in production. In real production use cases are about 8 % to 9%. Everything else is just like experimentation, right? And this is exactly the reason. You're not going to see ROI. You're not going to see real use cases that are leading to revenue or cost reduction or savings, right? Because you've bought the tools. AI is a habit. And habits don't form in days. They form in an extended period of time. So this whole concept of token maxing and all of these things are just natural evolutions as well.

39:33Yes, the idea of token maxing is super weird because we went from, okay, we've got a tool. Now the AI officer in the company is like, nobody's using this. We got to make them use this. So you get into this whole problem of now everybody's using it for writing emails or like the dumbest tasks, right? And now you're in this token maxing situation, which you never thought of, where it's like, oh my God, now we are spending so much money on this tool and we're not seeing ROI. So you got them to use it, not effectively, and you're in a different problem. But I think it's all a good problem because you at least got them to touch it.

40:19when you look at 10, 20, 70-year-old, if you spend that 70 % of your budget and time on upskilling your employees, which means showing them effective ways of using the tool, not just telling them use it. Showing them effective ways of using the tool, not just giving them training, but actually giving them real use cases of the thing they can do in their job. and that requires time and effort and energy. My DevRel hat on, that requires building a community and saying, I tried this thing today. Let me share it with you all. If you're a testing team, if you're a coding team, if you're a team that's product managers, got to share those experiences with each other and you have to bring space for that as a leadership team to allow people to build and form these communities.

41:17And after like, this is a whole, a big J curve, right? And after six, eight months, you start to see effective use of tools, actually helping them be productive. And then you get to a point where it's like, now we can write test cases with this. That's looking really good. How do I write them faster? Improve my prompts a little bit more. How do I take an entire process and make that an agent? Now you have agents in each of the different business units and you can form that into a repository of agents. And now an entire company is becoming efficient. efficient. But this is a trajectory and the curve, you have to see the vision for a year or so and pour into it, which is why I say if you spend 70 % on some of this stuff, which is going to be very costly and hard for a CFO to agree to, but that's the only way to build a habit and an effective habit.

42:18Jon Krohn:I recently read in an economist article about how it was basically the economist trying to back into what kinds of money enterprises across the world would need to be spending on tools, on Cloud Code, on Google Gemini, in order to be able to make good on the hundreds of billions of dollars that are being spent on AI data centers in 2026. and it was that same article brought up, I don't know if they were citing you or citing Stanford Research or what, but they brought up how, okay, if hypothetically organizations were going to spend this much on the tools, they would need to spend an order of magnitude more.

43:08Jon Krohn:So similar to your like seven to one ratio on transformation, on getting people educated and in order for any of this to be effective and for any of the tool purchases to be effective. And so that kind of makes it, that all of a sudden when you're like, whoa, when we're nowhere near even that 10 % being spent in order to realize the AI data centers are being worth it, it starts to make it seem like, I guess we're going to see what happens. It depends on, you know, some of these players are banking on super intelligence or something to make good on these huge AI data center investments. But it isn't.

43:46Jon Krohn:Yeah. Anyway, that's kind of a side story. But it does that kind of economic story ties into the same kind of benchmark that you're talking about here. Yeah, absolutely. And it's hard to buy into, right? Because we're traditionally used to thinking as organizations, like, what is going to be my spend this quarter, this next quarter? And then just return on that spend is what you were looking at. But this is a longer term play, which is hard to digest and understand. And it's easy for people like me to understand because we've lived in the developer relations world, right? Where building a community does not have near-term benefits.

44:25It has long-term benefits, right? You build a community of developers that are working on open source or something. That's yours when you start to see actual benefits of that. Like when Kubernetes came out, like it was a large amount of effort for a large period of time before Kubernetes became the thing that people wanted to use. Right. So I cite this because it is it's hard for economic for people in economics and also CFOs to kind of wrap their head around this.

44:59Jon Krohn:But yeah, to be getting an ROI on these tools, we've got to be spending a lot on organizational transformation, on education, for sure. Speaking of which, I, half an hour ago, maybe started asking you to talk about your journey from Google to Microsoft to what you're doing now with the Cloud Girl. And so you've just in recent weeks, seemingly, decided that it's the right time to jump off on your own and be doing the Cloud Girl. Why is that? And does it relate to us being in this unique time in history, maybe in this unique time in history where we need to be spending lots of money on training people?

45:39Yes. Oh, my God. I have been always passionate about two things, which drive me every day. and all the roles that I've had kind of fed all of that. So I'm very, very thankful to have all these roles at these amazing companies. I love learning new things and I love teaching those things. And that's my cycle of like going from learning to teaching to solidify my understanding and then keep doing that. And I've been doing that for years on as Cloud Girl on content on my YouTube, Instagram, and LinkedIn and X. But in the last six, eight months, it just seemed like the opportunity for what I could be doing with that learning and teaching passion for companies or startups to do product storytelling.

46:36This whole idea of like, we can ship so much faster, but we don't know how how to tell an enterprise why we are important and why you should be using us, that product storytelling muscle for startups and enterprises. And then the second, which is career storytelling muscle for people who are like moving into FDE roles or want to make those moves, becoming AI engineers, and don't know how to position themselves because the resumes are all looking same now, right? So, and once you are past that resume state, how do you stand out, which is all about storytelling? You've got great stories. How do you represent them and you in that process so that they end up picking you, right?

47:21So there's all these, we're always storytelling, by the way, but they are now becoming so much more important because everybody else is like sounding very similar to you. Products that you're building are very similar to the product somebody else is building. So product storytelling will differentiate you in the market. Career storytelling will differentiate you as an individual, as a professional, so that you can grow faster. And then my third thing that I've been passionate about is content, right? So learning and teaching and growing that community of Cloud Girl. And so that is what I am focused on.

47:57It gives me so much energy to be helping a few startups and companies bringing their products to life, helping a few individuals get their careers into places where they want to be. It just brings me joy to do that. So I want to focus on that. And I think it's perfect time to do that.

48:14Jon Krohn:Are you able to, I heard a rumor that you might be working on a next book. Are you able to tell us anything about that given how popular your first books have been? Oh my God. Yes. So I'm working on storytelling book, which a tech storytelling. So this is in the works. I still haven't decided what outlet it's going to be, but to those first two points, like product and career storytelling, I think in tech, there is a huge need for this. And I do fill that gap in a lot of ways. And I want to bring more of that knowledge to the world. On this podcast, I'm always going on about how Claude Code is mind-blowing, but now Claude Clawd Cowork is making my jaw drop as well.

48:58Jon Krohn:For example, I recently wanted to quantify how healthy my sales pipeline is for my AI consulting business. I simply asked Clawd to estimate my sales for the coming quarter, and it brought info from relevant Google Sheets and my Gmail to create a professional spreadsheet of clients with estimated revenue for each one. Whoa, this might've taken me a day. Instead, it was done flawlessly with Clawd Cowork in minutes. Clawd is the AI for minds that don't stop at good enough. It's the collaborator that actually understands your entire workflow and thinks with you. Whether you're debugging code at midnight or strategizing your next business move, Claude extends your thinking to tackle the problems that matter.

49:33Jon Krohn:Ah, and you'll appreciate that I can ask Cowork to show me data, such as my sales spreadsheet, and it provides an interactive chart right in the conversation. For problems worth solving, get started with Claude at Claude.ai slash superdata. That's Claude.ai slash superdata. And check out Claude Pro, which includes access to all of the features mentioned in today's episode. Claude.ai slash superdata. Fantastic. Well, your huge following must be dying for another book release. So that is exciting news indeed. Maybe they're even hearing it here first, which is exciting for us here at the show. I don't think I've shared it anywhere.

50:11So this will be the first.

50:12Jon Krohn:There you go. Breaking news. Yeah, we'll be looking forward to that for sure. given all of your experience, such rich experience at organizations that are at the cutting edge of AI deployment, where do you see things going in terms of, especially now that with the Cloud Girl business, you are advising individuals and enterprises on what they should be doing with their careers or their companies. What guidance do you have for my audience in terms of how as individuals, we can be positioning ourselves best for the decades to come? And then there's a second question, how can organizations get themselves best set up for the decades to come?

50:59We talked a little bit about taste. And this is, to me, this is synonymous in a lot of ways. So storytelling or being unique or being differentiated, the only way to do that is finding your USB. whether you're a business or an individual, you have something unique that you bring to the table. You just haven't spent the time to either figure that out or it got lost in the journey, right? So you have to do that homework. So the workshops that I've done in the past for this exercise is sit down for a weekend. And I have some questions on my, we can put some in show notes if there's an option to put some links for this.

51:44I have a list of questions that I would ask myself. And these are not easy questions to answer, right? These are questions like, how do I want to change the world? What wakes me up every day, right? And this may take you some time to actually get to or figure out, or what my business, you might have a business today as a product, right? What does my product solve for? And it's not the features and the functions. it's the core of like, when would someone be like dying to give you money for this? Or that when you're thinking about the product, but when you're thinking about your career, like why would someone hire you, right?

52:26Not because of the skills, because those are any, everybody's got now because of AI, right? Anybody can write code. That's not a skill I'm interviewing for or anybody's interviewing for. What is unique about me? What do I bring to the table?

52:40Jon Krohn:Well, thanks for sharing those invaluable tips on what we can be doing as individuals and organizations to prepare for the years to come. I think there's a lot of anxiety when you see things like, you know, you mentioned there how, you know, nobody's interviewing for coding skills anymore. And that's something that a lot of us, myself, a lot of our listeners have spent years, decades developing that skill. And now a Claude skill is doing it. um so yeah so it's it's nice to hear your perspective that it seems like because of things like taste because of these big questions uh that that direction that that people can give um that we can still provide a lot of value hopefully for years and years to come and we will have those uh questions that you talked about for our listeners in the show notes thank you Priyanka.

53:32Jon Krohn:Before I let you go, I ask all of my guests the same final two questions. The first one is, do you have a book recommendation for us? I've been reading this book, which has nothing to do with tech, by Michael Singer, Untethered Soul. And I've read this book twice in the last two years. And this is the first book that has ever, I picked it up reluctantly by recommendation from a friend and has been the first ever book that I've read on mindfulness that I actually resonated with. So if somebody out there is like, I do, I'm intrigued. I want to know a little bit more. I think this book just approaches it so scientifically that my engineer brain was like, okay, I can be with you through this to understand what mindfulness is all about.

54:31So if you're skeptical like me, I think this is something that I would recommend. It opened up my mind to it.

54:38Jon Krohn:It's a cool recommendation. I will have a link to it in the show notes. And it is interesting because the people that gave the testimonials for the book, just at a glance here that I'm looking at, it's a pretty wild mix of people. You've got a rabbi, You've got a priest. It sounds like the beginning of a joke. You've got a couple of yogis. And you've got Ray Kurzweil, the AI, the big AI and author of The Singularity is Near and The Singularity is Nearer. And so quite an interesting mix of people walk into a bar and find mindfulness. Exactly. It's a very interesting take and it does draw people from, especially from zeros and ones type of backgrounds like ours, to welcome into that world, which if anybody wants to get into it, I think this is a good start.

55:33Jon Krohn:Cool. Thank you for the recommendation. And it's sold millions and millions of copies. So it is a different world from selling tech books, isn't it, Priyanka? when you see the kinds of numbers that are trade publications. But yeah. It's not thousands, it's millions. Exactly. But yeah, this is our, I don't know if you knew this, but I wrote a bestselling book just before the pandemic called Deep Learning Illustrated, which was so kind of a similar vein to your visualizing books. I checked it out. I am going to get a copy. I checked it out. I went to the website. I was like, this is very similar to how I think as well.

56:11So I'm going to have to check it out.

56:14Jon Krohn:Yeah, I'm looking forward to hearing what you think. Anyway, before I let you go, the very final question that we ask is, how can people be following you? Of course, you have over 100 ,000 followers on LinkedIn. So that seems like a place. Where else should people be following you? In all of the platforms on X and LinkedIn and Instagram and Substack or my website, it's everywhere the Cloud Girls. So if you just look for that and follow in all these places, also on YouTube, that's where you can find me. On YouTube, I do long form content. I actually do explainers, teach stuff. And on Instagram and stuff, I do roadmaps and like what to do to like build your career, like in super fast formats.

56:58But yeah, the Cloud Girls should take you in all of those platforms.

57:02Jon Krohn:And you recently started a podcast too, right? I did. I just started a podcast. If you go to my website, you'll see the episodes there. And yeah, if you want to be on the podcast, like reach out and on any of these platforms and we can have a chat. Fantastic. Quite an opportunity there to get in early with somebody with such an enormous following and respect in this industry. Priyanka, thank you so much for taking the time out of your busy day to talk to us. Really appreciate it and hope to have you on the show again in the future. Thank you so much for having me. great episode today with Priyanka Vergadia in it she covered her 10-20-70 framework for AI budgets 10 % spent on tools 20 % on execution and 70 % on education and upskilling she explained why AI adoption follows a j-curve it takes six to eight months of showing people real use cases in their actual jobs not generic training before productivity gains appear then teams graduate from better prompts to full agents across business units.

58:03Jon Krohn:Full agents across business units. She talked about how she builds skills in Claude by breaking a task like blog writing into explicit subtasks and how AI raised the technical floor while taste became the ceiling. So she now interviews for perspective and lived experience rather than tool skills like coding. As always, you can get all the show notes, including the transcript for this episode, the video recording, any materials mentioned on the show, the URLs for Priyanka's social media profiles, as well as my own at superdatascience.com slash 1019. Yes, that is the episode number 1019. Thanks to everyone on the Super Data Science podcast team, our podcast manager Sonja Breivich, media editor Mario Pombo, partnerships manager Natalie Zajski, researcher Serge Masise, and our founder Kirill Arimenko.

58:52Jon Krohn:Thanks to all of them for producing another excellent episode for us today. for enabling that excellent team to create this free podcast for you. We are deeply grateful to our sponsors. You, yes, you can support this show by checking out our sponsors links, which are in the show notes. And if you're ever interested in sponsoring an episode yourself, you can find out how at johnkrone.com slash podcast. Otherwise, please do help us out by sharing this podcast with folks that would love to hear from Priyanka's Cloud Girl Insights. review the podcast on whatever podcasting platform you use or on youtube subscribe if you're not already a subscriber but most importantly i hope you'll just keep on tuning in i'm so grateful to have you listening and i hope i can continue to make episodes you love for years and years to come till next time keep on rocking it out there and i'm looking forward to enjoying another round of the super data science podcast with you very soon

59:54Thank you.