937: How to Design AI-First Products, with Marc Dupuis

22:48And all of a sudden, as a data scientist, the work you've done is actually being used and actually being consumed. And maybe the last thing I can say on this topic is I will always, always tell a data team, if you have a choice between creating an automated workflow and creating a dashboard, you should always pick the workflow. Like there's always this debate about ROI on data teams and ROI on dashboards is just incredibly hard to prove. But behavior change is even harder because like you're counting on someone remembering that dashboard, going to dashboard, figuring out what they need to take from the dashboard and go and take an action.

23:22But if you can go and like automate things where like the human's not even in the loop, then you're gonna have a much bigger impact as a data scientist in the organization. Especially if you can even flat out skip the, sharing the insight and just go and take that action directly, which you can also do for Fabi, then sort of like massive ROI unlock for the data scientist.

23:46Jon Krohn:That sounds really good. And I like your clear guidance there for all of us, whether we're product people or data analytics or data science people, that you should always be doing a workflow automation instead of a dashboard. Yeah, maybe it's a little overly simplistic and there's like, you know, nuances to this. But yeah, as a rule of thumb, I think that's a fairly good rule to live by. Yeah, it makes a lot of sense. Let's shift gears here a little bit now to the topic that seemingly everyone wants to talk about these days, which is AI agents. Yep. And so Fabi also has an agentic element. So it enables businesses to build, deploy, and share specialized data analyst agents.

24:28Jon Krohn:And so in posts that you've written, you've talked about a distinction between code generation or assistant capabilities and an AI being able to take on like a collaborator type role. Do you want to elaborate on these two kinds of roles, like the code generation versus the collaborator? Yeah, I think there's two ways to, in the data world, I think AI has two very distinct roles to play. One is more of an assistant, more of like a code, just to kind of overly simplify, maybe it's like a code generator. So it can help you write your SQL. It can help you write your Python or debug it or optimize it or whatever.

25:07And obviously like in Fabi specifically, like that's a very core component. So like, you know, 90 to 95 % of the code that's run in Fabi is actually AI generated, but you can also sort of manually correct the code. So there's that aspect of AI for data. But the other sort of like orthogonal use case is actually embedding the AI in your workflows. And it's not so if you take an example, you could in the past, for example, just automate either you build dashboard or you could to some degree automate a chart being sent or a table being sent. But the next step is actually having the AI interpret that table, interpret that chart and maybe even tell you what the next action, next action, next, next, sorry, next best action agent could be and embed the agent as part of that workflow.

25:56So that's not like generating code. It's actually interpreting results for you and telling you where to go. It's like one, you know, we'll talk about the smart books. Like one thing that we have is we have like these AI summarization cells where you can actually give the AI any number of data frames and say, okay, please go and create like an AI, you know, summary for, or please create an executive summary for me. And it will create your executive summary using whatever LLM you want. And you can like take that summary and then send it off. So those are like the two ways that we think about AI for data analysis.

26:25Thanks.

26:25Jon Krohn:I like your description there of these two different kinds of AI systems. Even more broadly, Mark, how would you describe an AI first platform? Like when you're designing a product AI first, what does that mean to you? Yeah. So there's a few big things that I think really are really important when you think about building a product that has AI at its center. The first is, I don't know, actually, John, have you heard of Andre Karpathy's sort of like autonomy scale? Have you heard him talk about that? I am embarrassed to say that I don't, actually. I feel like I follow him pretty closely, but the autonomy scale is not ringing a bell.

27:05It's totally fine. Okay. So no worries. He talked about it at the, I think it was like the YC, like AI day or whatever that was like a few months ago. And really fun talk, you know, kind of like in sort of like classic Andre Karpathy style, uh, very entertaining. Um, and he talks about the autonomy scale and I highly recommend, um, anyone who's like building product to actually go and like, listen to like that snippet about him talking about it. Um, and he actually like uses like cursor and yeah.

27:29Jon Krohn:It sounds like it would be from his software is changing again. Why combinator talk? I'll have that in the, in the show notes for everyone. Yeah. And I can dig it up too and, and, and, and make sure you can share it. So if, if it's not that one, I'll, find it for you. But yeah, and it's interesting because he talks about this autonomy scale. And the idea is, and he uses two examples that are sort of fun. One is Cursor and one is the Iron Man suit. And the idea is that you can have, I'll pick the Iron Man suit as the sort of example here. He talks about how an Iron Man suit can literally go and do its own thing.

28:01It can operate without Tony Stark in the suit, or it can literally just be this thing that supercharges the human because Tony Stark is not a superhero, right? Technically, he's just a human and it allows him to do things that he simply couldn't do before. And the idea is that there's this autonomy scale where you can choose how autonomous you want the AI to be. And that's one really big thing that we believe in at Fabi. So we believe that you should be able to pick your own adventure. You should be able to have the AI do as much or as little as you want. You should be able to literally just code everything by hand, sort of like old school style if you want.

28:38or you could have the AI do 100 % of the work when it comes to building data apps or workflows. And that's really, really important because if you wanna build a tool that's, or I'll take our example, but obviously you're building your own product here, but like if you are, in our case, we wanna be as appealing to a data scientist as we are to a product manager, the product manager is gonna spend most of their time in the fully autonomous mode, whereas the data scientist is gonna spend more of their time in like the semi-autonomous mode. And from an architectural standpoint, when you're designing a product, it requires a lot of thinking about how you create an AI that can actually act on any element of the interface.

29:15And so we've put a lot of thinking into like, how do we build an AI that can go and like create the chart, it can go and add an automatic grid table, it can go and create the filters and do all that stuff. So that's kind of like one big principle that we believe in when you're building an AI first product. The other one that's normally we believe in is every action should be reversible. So I don't know, John, how much you've used like a, you know, Lovable or Replit or some other sort of like bi-coding tool?

29:40Jon Krohn:I have never used Lovable or Replit. I have had the experience of automatically creating apps in like a chat GPT or a quad interface where like the artifact layer allows me to have the app right there. Yeah. So same idea, right? But, and if you do it enough, everyone who does it at some point will have this experience where they're like doing their thing or kind of happy with where they are. and then they'll ask like, you know, four or five more questions and they're like, ooh, hold on, wait, I took like a wrong turn here. Like I want to sort of back up and go to where things were actually working and maybe like just hit reset.

30:15And so we, and that's just like, I think just a factor of how the AI works and how like vibe coding, vibe analyzing works. So we believe also like every action should be fully reversible. And so, you know, one big thing that we do, for example, is in the smart book, like we'll snapshot versions as you go. So like as you accept the AI's suggestion, we'll actually take a snapshot there and you can kind of rewind at any point and view a diff of like, okay, what happened? Like, what's the question I asked? What's the before and after? And if I don't like where things kind of like forked off, I can hit revert and I revert back to that.

30:46So everything has to be fully reversible in an AI, in an AI native solution.

30:51Jon Krohn:I like the forking off verb. Yeah. Yeah. Well, yeah, you took that wrong turn. You're like reverse, get back to it to where. we've gotten forked again. Fork off. That's exactly right. That is great insight into how to build an AI first product. Something specific that a lot of people want when they're thinking about an AI first product is a conversational interface, right? So you were just talking about it there with Lovable or whatever you're using to Vibecode an app where in that case, You're entirely, you know, you're going right from natural language conversation to working application. You're not even necessarily seeing the code in between in any way.

31:37Jon Krohn:Um, when you're kind of doing the opposite. So with Fabi, you are in a lot of cases, data are coming out of the platform and you want to be converting data or insights into natural language that can be provided to the user. And I think you might call these smart reports. Um, and so how do you ensure that that part of your application includes explanations that are correct, reproducible, consistent, maybe well-cited so that people can dig further? Uh, is, is that tricky to get all of that right? That kind of comes down to, we talked about the guardrails, uh, you know, sort of like the outset here and, uh, that kind of goes back to the guardrails.

32:27And so when you're building, whether it's like an automated workflow that shares AI generate insights or you're building, you know, a smart report or data app in Fabi, ultimately, as a data practitioner, you have full control about the data that's going in. So, you know, what we're not necessarily doing here at Fabi, at least not today, is we're not giving you an AI that will look at all your data and answer any question about all your data ever. What we're saying is we're going to give a platform that you as data scientists can go into and you can go and take the time to actually write a SQL query and pull the data and structure and clean it up and do all that kind of stuff and ultimately sort of data frame.

33:07And then you can pass that very sort of succinct curated data frame that you as data scientist or data practitioner have gone taking time to curate. And then you can pass that to the AI that will then either generate the insights on schedule or when you're publishing a smart report in Fabi, for example, we have AI embedded directly there. The AI in the smart report is there to help you answer follow up questions. But it's only going to look at the data that you've the data frames, actually, that you've given access to in that specific report. So like one very common thing that we hear is, you know, as data, as a data scientist or an analytics engineer or whoever's building these dashboards and these data apps, you get a lot of follow-up questions.

33:46And so why not just let the AI answer that? But again, the AI in that specific scenario in Fabi is not going to go and like write a raw SQL query and pull new data. It's only going to answer questions off of the data that you as a data scientist said, I want the AI to be able to answer questions off of this. And if it doesn't have the data to answer a specific question, it's just going to say, I don't have the data. So those are like the types of guardrails that we create for the data professional and data scientists to be able to leverage AI without running the risk of the AI either hallucinating or going rogue.

34:20Jon Krohn:Nice. That's a good explanation. And it does help me understand better, you know, what Fabi is offering there. And it makes sense to constrain responses to data that are available. and I could see how that, it obviously deals with a lot of the issues that I was describing around, you know, what's the source of this information and are these data reliable? Makes a ton of sense. Speaking of data, data provenance, data quality, in a tutorial that you provided online, you urge data teams to spend most of their time on the data cleaning step. But in social media posts, you've cautioned against having trying to have perfect data first and then dashboards later.

35:05Jon Krohn:So you've stated that your best hires, for example, are those that lean into the mess. They explore, improvise and ship something useful without waiting for the perfect setup. So how do you get that balance right between these, these, you know, obviously on one end of spectrum, there's some kind of data cleaning that may need to happen in a lot of scenarios. but simultaneously if you try to make everything perfect, you know, you, that the, you know, there's that saying done is better than perfect. Yeah. Yeah. Yeah. It's a great question. Yeah. And it's always, um, it's funny, you know, when you write like social media posts, you're always trying to like create like these sound bites that, that sort of explain a concept, but it's always like the underlying concept, always like much more nuanced, much more complicated.

35:47Um, and so I'll, I'll just take the time to kind of like explain here how I think about this. So I think that first of all, um, I, I think there's kind of like a spectrum, like all sorts of nuances. So you can clean and prep the data at the like data warehouse database layer. And so you can spend a lot of your time there, or you can also do it like at completely upstream. Right. And, and I think that there's like this whole like shift left thing where you want to do as much data cleaning and data prep as possible in the data sort of layer database, data warehouse layer. But really what I'm trying to say is that you don't need the perfect data model to make AI useful for you and your workflow.

36:21Like it's great. Listen, If you have a data engineering team, review yourself, our data scientist who knows DBT and SQL Mesh or whatever, and you want to go and build these incredible data models and you can maintain them. And the AI has access to fantastic, wide, clean tables that are always up to date with the latest metrics. That's great. But that's not usually how things play out in the corporate world and in the enterprise, right? Like typically like you have some tables like that, but then you also have like a whole sort of like constellation of data that lives around it that's in spreadsheets or much more messy.

36:54And so my sort of general recommendation there is, you know, don't try to get everything in your data warehouse and clean your data model perfectly clean before you start actually analyzing or leveraging AI. But if you are going to leverage AI to analyze some of this messier data, take the time to actually prep your data in SQL or Python before you do any sort of like further exploratory analysis or just data science work. So let's take an example. Let's say you have this like revenue spreadsheet that was given to you by your go to market team or rev ops team. And you're asked to go and build some forecast or whatever on it.

37:32if you actually want to use AI to help you generate the code and move much faster, much more efficiently, take the time, spend 90 % of your time just cleaning up that spreadsheet. Again, using whatever tool you want, whether it's literally the spreadsheet, Excel or Google Sheet or SQL or Python, remove the merged cells, remove the charts, clean up the names, do all that stuff. And then the AI is going to be much more effective downstream in your analysis. So that's what I mean when I say like, you know, again, I think you do need sort of good data depending on what you're trying to do with it.

38:07But you don't need perfect data. And it sort of depends on your situation and what you're working with. And if you are going to be using AI, just take the time to like frame up your data properly.

38:18Jon Krohn:All right. Well, with that one, you've covered the topics around Fabi that I really wanted to dig into. And I want to take some time now to get into your broader experience. So you have a decade of experience prior to co-founding Fabi as a product manager for lots of great companies, Trasada, Clari, Assembled. I'm probably mispronouncing all of those company names. No, sorry. And so at those companies, you build platforms, APIs, self-service analytics. Drawing from that experience, when you're managing metrics, platforms like Fabi would allow product managers, like if you think about yourself in those previous roles, if you had a tool like Fabi, you would be able to very quickly spin up complex engagement metrics for your platform or your APIs on the fly.

39:10Jon Krohn:Would you be worried then about, or how would you prevent like metric sprawl where all of a sudden you're, you're, you just have tons of different measures flying around. You're not really like maybe people on the team that aren't going to be clear about what we're really building towards. Does that question make sense? Do you see that as a potential problem or? Yeah, it makes a lot of sense. Yeah, the question makes perfect sense. And I do think that like there's always kind of this risk and also fear. And I think like justified fear that if you give everyone AI, then the AI is going to go and sort of like reinvent the metric every single time you ask a question and so we could talk about semantic layers that's probably oh yeah that that i hadn't even thought that's a huge problem too yeah exactly where you could end up you could have each person in the organization say you know just ask their ai enhanced bi tool you know how are we doing on this metric and every time it's coming up with a new way and pulling different data yeah oh my goodness yeah that's an even bigger potential problem here yeah so that's maybe that's maybe another episode for us talk about like semantic layers and and kind of that sort it ties back to like the guardrails as well that we talked about fabi and like make sure the team's actually like supervising and collaborating and work with the business stakeholder um but to go back to your your maybe original question if that's not 100 what you had in mind which is like um how do you make sure that and actually let me make sure i ask the question back is the question about how we um actually let me let me let me ask you for clarification the questions make sure i understand because like that's what i was thinking about when you asked the question.

40:48Jon Krohn:Yeah, I mean, we should definitely answer the question that you brought up. But what I was what I was thinking about, you would end up with the problem that you just brought up in what I described, where if if project managers of people across the organization, executives, product leads on individual products, if everybody can be can be using automated BI tools to be creating metrics on usage, you could end up having a lot of, it could start to muddy the water around what the organization at a whole is building towards. But even if there was agreement, so what I think is more interesting about the question that you heard is that even if you have agreement across everyone and humans are being consistent, with their definitions and the humans all kind of have a clear idea of what they're building towards, what the metrics that they're trying to optimize are with the product that they're building, the AI could be surprising them by recalculating things a different way.

41:54Jon Krohn:So does that help kind of clarify what I was originally asking? Yeah, I think it does. And when you ask that question, I actually have two things that come to mind for me. The first is that the role of the data team, I don't think changes in the sense that like data team still needs to be there to help make sure there are consistent metrics and that we're all working towards the same North Star as a whole, as an organization, as a department or whatever. And like that's not changing, right? You don't, what you don't want is you don't want AI that like, this is why we talk about dashboards. I don't think dashboards are disappearing in that sense.

42:28I think that dashboards are actually going to be much more powerful and useful because there's going to be fewer of them. But the ones that are actually built are going to be curated and managed by the data team because they are tracking the actual core metrics that matter for the business. It's going to be tracking your churn, your ARR, your retention, or whatever it is. And the data team is going to be spending a lot more time making sure that those are correct. um now when i think about ai for the business what i think about is like all the other questions that like haven't yet made your way into your your north star metrics or your okrs right so as a product manager maybe going back to your original question as a product manager i um and i still do today as a founder product manager i'm constantly exploring new ways to ask to think about the data so you know you're thinking about your user activation for example um maybe at some point if you're a mature organization that's growing, like your activation is very well defined.

43:22Okay, it's seven, you have to friend seven people on Facebook and then that's your activation metric or that's our more star, that's set, we're good. But a lot of organizations don't have that or it's evolving or you're interested in your product. And so you don't want to go and like model what's effectively a hypothesis into your data and like pull in the data, create these new tables and then feed that and go through the entire process and feed that through your BI solution before you've actually taken the time to figure out if that's actually what you want. And so that's where you can, thanks to AI, actually let someone, ideally a pair of a data scientist and a product manager or a data scientist and a CSM or whoever, work together to kind of explore that messy phase and see like, okay, does this metric actually make sense?

44:03Is this how we want to think about it? And a lot of times I think what you'll find when you do that is, again, I'll draw my own experience as a product manager. You'll kind of look at the data and be like, hmm, actually, that was the wrong question I was asking. Let me rethink about what I'm really asking and I'll get back to you. and if as a data as a product manager i can start asking you my own questions off the data that data science security for me or off the raw data um i'm going to get to my own answer much faster and then we can also just sort of experiment and sit with that metric for a minute for a month or a quarter and then if it's like okay guys like you know we're guys and gals like we're looking at this metric and um it's been the same metric for the last you know three months now maybe it's the for us to go and build this into, add this to a dashboard.

44:47That's the point where you can say, okay, well, how do we pipe this data into our data warehouse? And how do we add this to our data modeling and feed it all the way through? So I think you just need to carefully think about, okay, is this a metric that we know that we've established and that is set? And if so, let's go through the quote unquote proper channels that have the right guardrails. If not, let's actually take the room to explore that before we go and overly invest in the measurement.

45:16Jon Krohn:Cool. That's very helpful. Something that follows from your response there is, I'm reminded of Goodhart's law here. So Goodhart's law is that when a measure becomes a target, it ceases to be a good measure. And so you just described a great process there for identifying a great measure. But then, yeah, Goodhart's law is that once that becomes a target, it starts to become a poor measure because people start gaming it. And so, yeah, do you have any magic solutions for that? Yeah, I wish I did. I don't have any magic solutions, but I certainly have a thought about it, which is, first of all, incentives.

45:58I don't know how much of your audience has worked. And Clary is like a RevOps platform. And so, I worked in like the sales and marketing world for decades. I don't know how much of your audience like has lived in that world. There's certainly some folks who are listening who have probably like done some quota setting and that kind of stuff. That stuff is at its heart. Like if you think it's easy to go and like set a quota and like how you incentivize your sales team to your marketing team, let me tell you, that is a tough job because the minute you set a quota with a certain incentive, like these are smart people who are going to go and like, I'm not saying they're dishonest.

46:33Like, I don't think anyone's necessarily dishonest in this situation, but like, it's your livelihood, right? And we're talking like big dollars here, like people are going to find ways to go and like, get creative. So I don't have a good answer on that one. But I will say that, like, one thing that I've experienced, especially if you're, I think, if you're working at like, a large public company where, you know, you're kind of like cruising altitude in terms of business, like maybe it's like this crazy growth business, and the metrics are generally the same and all kind of stuff. and like your measures and dimensions, all kinds of stuff probably aren't changing that much.

47:05But the reality is a lot of us work, you know, especially here in Valley and you're in New York and New York, you're working in startups that like are constantly growing and shifting and creative out of new products. Like the actual OKRs and the measures are always changing. Like they're always, it's so hard to like, even ARR, man, it's like one of those things where you think you have it nailed and the next thing you know, the product team goes and adds a credit-based system, consumption-based pricing. And next thing you know, it's like AR means a completely different thing next quarter. And what felt like a sure value isn't at all.

47:37So all I can say is that I think if you try to set metrics in stone and you're sort of hellbent on that, you're just going to set yourself up for a heartache. And I think you have to just be willing and flexible as things go to some degree. And I think obviously you need to push back when it doesn't make sense to rethink a measure.

47:57Jon Krohn:I like that answer a lot. And it is something that I've experienced a lot. in companies that I've been in where you can kind of, in a lot of scenarios, you're like, okay, quarter to quarter, we can do a good comparison. But if we want to kind of look back to how we were doing a year on this metric, a year ago on this metric, it's like, man, that was a different, completely different time for our platform. And so like you're saying, like even something that seems so clear cut is ARR. And you'll recur revenue, by the way, for, yeah, for, for the audience. I don't know if that's a, obviously, yeah, but yeah.

48:25Jon Krohn:Yeah. Yeah. Yeah. That is a bit of an inside baseball term if you think about it. Yeah. But yeah, no, you think that that stuff would be stable, but it's not. And again, it's like, you know, interesting product or the business shifted and priorities shifted. And even by the way, you know, we talk about storytelling. It's like the way the executives want to tell the story to the board changes. Now, suddenly you want to emphasize a different sort of revenue source to tell a growth story. Like even just that small, subtle shift can turn things on its head. Pivot. So going back right to the beginning of the episode, so we've kind of we've wrapped up all the technical questions that I had for you in any way related to tech or AI.

49:05But right at the

49:06Jon Krohn:beginning of this conversation, we talked about how you did this master's in neurotechnology at Imperial College London. I'd love to just know like, you know, a brief overview of what you were doing in neurotech research back then. Yeah, so it was it was a broader biotech uh masters and um what what we were studying a lot of was the um signal process it was a lot of signal processing from brain waves for things like controlling robotic arms so you know one thing that you you're probably honestly like much better suited to actually talk about this than i am at this stage but you know you'd you have like these chip implants uh that you know would be would be implanted like monkeys and the idea was like you'd control mechanical arms eventually they help people who have disabilities.

49:51And so you'd be studying the brain waves, right? And that's incredibly complex because you're dealing with like literally billions of connections and the signal is anything but clear. And so that's a lot of signal processing where you're sort of trying to separate the signal from the noise. Like even if you, for example, have someone that's kind of like has their eyes open and you're asking to control an arm, it's like, is that signal already, is their brain reacting to what they're seeing at the time or is their brain reacting to them trying to control the arm? So there's a lot of that. And actually, but I actually especially it's been a little bit different, which was image processing.

50:30So I did my research on using image processing to try to detect early onset Alzheimer's by looking at brain scans. So one of the earliest signs you can get from physiological science, I should say, because like there are some other like markers that you can look at now that I think it's much more advanced than it was even back 10 years ago. But back then, one of the earliest times you could look at was the brain shrinking and the brain shrinking size was way too subtle for a doctor to catch early on. So you'd catch it eventually over multiple months. But by that time it was too late. It's hard to say because it's, it's hard to reverse Alzheimer's, but you can maybe like even slow it down.

51:10And so the idea was to use image processing to like try to detect like the slow, you know, the slightest nuance shrinkage in, um, in, in brain size at the very, very, uh, or in the very early days.

51:21Jon Krohn:Nice. And so then maybe you can have interventions, medication starting sooner for people in those kinds of scenarios. Yeah. And I, I, I love that stuff. Uh, it's, it's so hard. It's, it's, uh, but yeah, if you can, if you can do that early, uh, I'm also very passionate about Alzheimer's, uh, had a lot of, uh, I shouldn't say a lot, but I've had family members impacted. And so, um, that was very interesting research for me. And I think there's been like so much progress even since then. That was 10 years ago. And I see kind of amazing things coming out. So, you know, huge, huge kudos to anyone working on that area.

51:51I think it's a fantastic research area.

51:54Jon Krohn:Yeah, and hopefully AI can play a role in accelerating this. And just kind of generally as a society, the more and more and more that we can automate having nutrition for people on the planet, having a roof to cover our heads, having security, that's more and more and more people that can be doing research and coming up with solutions, working with AI to have solutions faster. Absolutely. I'm a big fan of anyone working on like hard tech. I mean, there's some hard problems out there to be solved around medicine, agriculture. And so again, kudos to anyone working on that. I'm also very founder friendly.

52:32So if anyone's listening to this and you're a founder working on that space, you know, I'm more than happy to connect and see what I can do to help you on your journey.

52:39Jon Krohn:Nice. Well, so I usually, my final question is how people should connect with you, but we'll just skip right to that. And I'll ask you for your book recommendation after. so uh how should people uh how should people reach out to you or follow you after today's episode mark yep so i am on uh i am on twitter so definitely follow me there um and uh i'm also very active on linkedin so you can uh you can add links to the to the show notes but i'll share it yeah nice thanks mark and then yeah your book recommendation for us yeah so uh i am a uh i'm a big big walter isaacson fan uh he writes these incredible biographies um which you know Steve Jobs probably being like one of the more famous ones.

53:19I'm sure a lot of folks have read, but big fan of the Einstein one. So the Einstein one is a fascinating read. He was an incredible character and really makes you think about like what the source of creativity is, but also the book's almost spiritual to me because it makes you realize like how little we actually know about the universe that we live in and how much more there is to be discovered. So very fun read, very interesting. Highly, highly recommend it.

53:47Jon Krohn:That sounds great. I've added it to my personal list. Nice. Thanks a lot, Mark. This has been a fun and informative episode. Hopefully we'll be welcoming you back again in the future to hear more on how the Fabi.ai journey is coming along. Absolutely, John. Thank you so much for having me. It was a real pleasure. And yeah, hopefully we'll get to connect again in the future. An inspiring episode from a founder cleverly streamlining workflows with AI. In today's episode, Mark Dupuis covered how Fabi.ai emerged from the frustration of product managers constantly asking data scientists for quote unquote quick data polls.

54:27Jon Krohn:He talked about why AI tools elevate data scientists rather than replacing them by eliminating routine coding tasks and allowing focus on business understanding, experimentation design, and statistical supervision of AI outputs. He talked about why AI workflows that push insights directly into tools like Slack or email where people actually work deliver far better ROI than traditional dashboards. And he talked about how building AI-first products requires designing for variable autonomy levels and making every action fully reversible to handle the inevitable wrong turns in AI-assisted work. As always, you can get all those show notes, including the transcript for this episode, the video recording, any materials that were mentioned on the show, the URLs for Mark's social media profiles, and my own at superdatascience.com slash 937.

55:18Jon Krohn:Thanks to the Super Data Science podcast team, our podcast manager Sonia Brejevich, media editor Mario Pombo, partnerships manager Natalie Jaisky, researcher Serge Massis, our writer Dr. Zara Karche, and our founder Kirill Arimenko. Thanks to all of them for producing another stellar episode for us today for enabling that stellar team to create this free podcast for you. We are deeply grateful to our sponsors. You can support this show by checking out our sponsors links in the show notes. And if you'd ever like to sponsor the show yourself, you can find out how to do that at johnkrone.com slash podcast.

55:52Jon Krohn:Otherwise, please do help us out any other way you can share the podcast with folks who'd love to hear this episode, review the show, wherever you consume podcast content, subscribe, and most importantly, 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. Until 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.