The AI Development Lifecycle: How to Thrive in the SaaS Apocalypse

Code is getting easier to produce, but the cost of building the wrong thing is about to get louder. Jason Rome sits down with Cory Voglesonger, Chief Product and Technology Officer at Aaron's, to unpack what’s actually changing as teams move toward AI-native delivery and an AI development lifecycle filled with coding agents and agentic workflows.

We use Cory’s culture framework safety, clarity, urgency as a simple way to diagnose why some teams thrive with AI while others drown in noise. Urgency is not “move fast at all costs.” It is outcome thinking: knowing what business result you’re chasing, measuring it, and avoiding the trap of shipping a mountain of features just because AI makes output cheap. We also dig into why lean thinking, flow, and the basics of continuous delivery still matter, plus how a solid developer experience with a golden path and standard toolchain prevents AI-driven sprawl.

Clarity is where AI turns the dial up to 1000. When you’re directing humans and eventually armies of agents, the work lives or dies on context engineering: guardrails, constraints, domain knowledge, and clear intent. We talk about getting closer to users, making knowledge less tribal, and using AI as adviser, assistant, and adversary to challenge bias, stress test ideas, and sharpen decisions before production feedback takes time.

Safety closes the loop, from psychological safety around job fears to environmental safety around customer data and blast radius. If you want your team to adopt AI without burnout, this is the leadership work. Subscribe, share this with a teammate, and leave a review with the culture lever you’re focusing on next.

Episode Resources:

Jason Rome on LinkedIn: /jason-rom-275b2014

Cory Voglesonger on LinkedIn: in/cory-voglesonger-81907628

Method Website: method.com

The Aaron’s Company Website: aarons.com

Transcript

Welcome And Guest Setup

Jason Rome 0:00

Welcome back to another episode of Build What's Next. I'm your host, Jason Rome. Continuing the conversation we've been having around how AI is changing how we build, really excited to welcome back to the podcast today, Corey Vogel Slanger. Corey was back on in 2024 talking about culture, and we're going to revisit that theme and the framework that he leverages and how it's changed and how it's evolved during the age of AI. Really enjoy talking to Corey. He's someone I'm going to for ideas, you know, that he implements and what he puts in on the ground. Corey is the chief product and technology officer for ARINS. He owns the full technology estate there across product engineering, design, infrastructure, and IT, supporting over a thousand stores as well as a 180-person organization. Corey comes from a software engineering background, but uh, you know, as he'll say today, he'll he'll talk about how good of an engineer he was always. But I I think I really appreciate how much Corey focuses on culture because we're seeing in this AI age how important that is and how a lot of organizations have a lot of baggage around how they've been able to empower their people. And as AI is is unbundling or shifting roles and expectations, companies with a strong culture are going to have a clear advantage. So I'm excited to uh to learn from Corey today and have a conversation.

Josh Lucas 1:17

You are listening to Methods Build What's Next, digital product perspectives, presented by Global Logic. At Method, we aim to bridge the gap between technology and humanity for a more seamless digital future. Join us as we uncover insights, best practices, and cutting-edge technologies with top industry leaders that can help you and your organization craft better digital products and experiences.

Jason Rome 1:45

Really excited for this one today. I'm Jason Rom, your host, and I've got Corey Vogelslanger with me doing a bit of a thematic on where AI development lifecycle is, AI native delivery. Um I'll let Corey introduce himself, but uh plays a dual role across product and technology for his organization. And we're gonna talk about what's changed, what's happened, uh, what hasn't, and we're gonna dive into the people element today. So culture, environment, individual coaching, uh, and what he's seen, what he's learned, and some practical advice for leaders and their team. So uh, Corey, welcome back. Uh, do you want to give a little bit of a background, uh refresh? I think we we were just debating if it's been three years or five years, but you know, COVID and having families, who knows? Uh so uh welcome back if you want to do an intro. Yeah, thank you. Some amount of years for sure.

SPEAKER_02 2:30

Exactly. I'm Corey Vogelsonger. I am the chief product and technology officer at Aaron's. I've been there an eternity, 18 years or so. Um I started in uh software engineering um and through lots of fortune and good opportunities and good people around me, have landed in a spot uh that I'm in now, which is um the chief product and technology officer, as I mentioned. And really that scope is most simply put where technology and um uh the customer intersect, you know, through digital business, e-commerce, but also um indirectly through the um uh team members, the customer service team members uh interacting with the customer. So we enable those those parts of our experience and journey. And I've got a team of product designers, product managers, engineers that do the real work.

Jason Rome 3:23

And I've always appreciated in our conversations, you know, going back to our last one. A lot of people talk about culture, but you know, you you do things that exemplify it. You know, I I think I've shared the story many times of you'll send engineers into stores. Uh and I think a lot of people look at an engineer as how much code, how many tickets, how many PRs can can can we do? And and you view them as a member

Where Aaron’s Uses AI Today

Jason Rome 3:45

of the team, which um now with AI becomes even more important. So um I'd love to hear, you know, as AI adoption has come along, where are your teams right now and um maybe what's changed and and what hasn't changed uh for you?

SPEAKER_02 3:59

Sure. Yeah so where we are, I would say we're very we're very early. I I I don't even think we were um, I wouldn't call this bleeding edge, cutting edge. Um we did a lot, we've done a lot of exploration around proof of concepts and um uh trying things out through various stages of the life cycle. And um, some of those have borne fruit to where we've changed the way we work across some of those dimensions. We use AI assisted engineering a lot, um, coding agents to help us build code. Um, our product managers and designers heavily leverage um AI to um uh you know uh create better outcomes in their work. Um we've got a few production use cases of agentic flows that are, you know, um call center related. Um and uh but we're still very, very early. I think um anybody that's not, you know, uh some massive uh big six uh company uh would is probably very early as well. So I feel like we're kind of in the sweet spot where we're figuring it out, but we're doing it in a deliberate way that I think will build a foundation for us.

Jason Rome 5:08

Yeah. Yeah. Yeah. I I I talked to some companies, they're still figuring out if they can turn it on or not. So you guys are later than them, that's for sure. Um and and having uh having agentic flows in the call center is not something that everyone can say. A lot of people are still thinking about how to secure that. Um, I I want to dive right into the topic today. You know, I've all always appreciated the the way you talk about culture. Um can you introduce that framework and then we'll use that to dive into, you know, maybe how that's had to evolve and how it hasn't had to evolve AI.

The Safety Clarity Urgency Framework

SPEAKER_02 5:40

Yeah. Um so the framework uh is as you mentioned, is called safety clarity and urgency. It's been something that um well, the origin, I'll just give you the origin story is I have um uh been at this craft for a while now, and through that have uh tried to be a student reading and learning and applying um all the latest methodologies, frameworks, practices um that help us and our teams do our best work, and whether that's continuous delivery, agile, lean DevOps, design thinking, OKRs, what have you. Um there's a litany of um uh tools and practices out there to help us do our craft better. Um and after struggling myself with trying to make sense of it all and how to apply it, what should I be doing first, how to think about it, I um kind of arrived at this insight. And you know, in my mind, it's this like really clarifying moment, but uh that's provision, it's history, I'm sure, that happened over the course of years, that um really all the those three things, or excuse me, um, all of those uh practices, modern practices in the canon of modern product development, are really trying to do one or more of three things for the people doing the work. And that is to make them feel safe uh doing the work, to make the work more clear and how they do their work, and to create urgency and working towards an outcome. And if you really distill down all of those um those practices, that's the core thing they're trying to do. And and the why is that you know, the result is you know, people who feel safe and have the appropriate guardrails will take risks, they'll tell you the truth, and they'll raise issues before they're a big issue. People who um have a lot of clarity in what they're trying to do, uh, how they can go about doing it and what's important, um can move forward and adapt the plan without waiting for permission. And then people who um have a lot of urgency uh really give a damn about what they're doing and run hard towards achieving that goal. And so if you think about all those practices, that's sort of this unifying construct or organizing construct that helps me think about um, I don't have to be a master at all those things if I can break it down to those three elements. Um it's really a diagnostic tool and a lens on which I think about how teens operate. I'll go in and teens and ask them to talk to me about if there's some new practice they want to explore, how does it check the box on one of these three? Um so uh arrived at that, you know, a few years back, started talking about it, uh it was valuable to me and teams and some others. And um and so yeah, um that that's really the framework. Uh and it turns out that you know, teams that work that way um typically stand a pretty good chance of competing and thriving in the in the modern world today and the way work's done, where bits, digital bits, are really the means of economic production. So um that's the framework I've you know, as you would expect, and where I think we're gonna go today, is in recent months, um, I've been spending a lot of time thinking about what applies, what doesn't, uh, as we start to think about enabling our teams with more AI um technologies. And um it's really interesting that it really still all works pretty well. Um and you know, it turns out that those things that um while while AI certainly has changed a ton of things at a core principle level when you think about how to organize your culture, um not a lot's changed.

Jason Rome 9:33

Yeah. It it it's interesting. I I like the framework uh for many reasons. Uh I think it simplifies things. I've I've got uh when we do our assessments of companies, we actually we look at seven different things on culture because you know we're consultants, so we have to make it a little bit more complicated, obviously.

SPEAKER_02 9:48

Three's always the right number, though.

Jason Rome 9:50

It really is. Um and then, you know, but one of the things I like, I like the word urgency. Um I I personally like to use the word agency as well. But I think a lot of people, you know, the word empowerment has kind of been the zeitgeist for a long time in product. And it's it's one of those words that's kind of come to mean everything and nothing, and it's not very certain. And you talk to five different people and they have very different definitions of what they mean by feeling empowered within an organization, a lot of people fighting for that without clarity. Um, but I think urgency,

Urgency Means Outcomes Not Output

Jason Rome 10:20

it's narrower and it's clear exactly what that means of you know, being, you know, being actionable, taking ownership of something versus empowerment is is this a little bit more of a softer word. I'd be curious your thoughts on how you landed on the urgency word. And and then maybe we go into that one first in the age of AI and how it's changing.

SPEAKER_02 10:38

Yeah, how I landed on it. I don't know. It just fell right. I don't think I can make up a story, but I don't think it's I've been stealing it. Yeah, it's great. Um because I think it um uh if to reflect on that a little more, uh I think it is um representative of it does a good job at distinguishing between like frantic hectic, we've gotta hit this deadline, we've gotta we've just gotta run fast at all costs. Urgency aligns better with um what I call what the world calls outcome thinking, um, which is you know, I can be told I have to get something done within a time frame and that I'll have to move fast to do it, but it won't be um it won't be targeted and directed towards an outcome often uh when we're handed those deadlines. And so urgency just better fits the idea of in my mind, somebody who is um self-motivated, has a lot of intrinsic motivation, understands the mission that they're up against and is running hard towards it, uh, you can do that urgently with with control. Um, but that's that's I think where I got to with it. And so when you think about do you want me to I'm I'm gonna jump into the AI part. How do you what do you think? Let's do it. Okay. So when you think about you know, stress testing this urgency concept against what we're doing or uh what we're trying to do with AI uh in today's world, it's easy to go to, well, like I can I it it just got really cheap to create stuff um through a certain lens and we can create a lot a lot more stuff. Um I think that's the wrong lens, uh, and it has been the wrong lens for a while. This is where urgency made sense before. If you think it the the orientation needs to be outcome, not to a specific output. So um it's I want to be on the other side of the river and not wet versus build me a bridge. That that kind of thinking, you need to be working towards that. You need to have some metric that moves or something that is different in your business rather other than I just built a thing. So AI allows us to build a lot of things really, really quickly. And I think that's actually um we're gonna have to be more disciplined um in that world around how do we arrive at what's the right thing. Yeah. And so uh it's so hard not to talk about the other topics when you get here because clarity is the thing that helps here.

Jason Rome 13:10

But yeah, I mean, uh at the end of the day, you know, you if if you look at the intersection of of safety, clarity, and and urgency, you have that concept of ownership of uh, you know, and talked about this in my podcast on one day, but you know, everyone's big on loops right now and getting over the hill and like that, you know, kind of that that vibe. And that's really what we're talking about is, you know, can you point someone at an outcome and you know, can they get there? And one of the things I look at with a lot of teams is, you know, do you have teams that um and you see this a lot in enterprise where the team lets a a road bump become a blocker? Um, or do they know how to get around like blockers and things that get in their way? Or is it like, all right, I'm stopped. I'll bring it up in stand-up, I'll bring it up in PI planning, I'll bring up the problem to someone, or do they figure out a solution how to work around it and bring that and put it in business context? And so one of the one of the big things for for urgency about me is, you know, do you raise a risk or do you bring a solution for the risk? And are you someone that can see around corner and predict the next thing, next thing that's going wrong and take accountability for it, which is where that clarity of that outcome comes in and aligns here. And I think, you know, at the end of the day with AI, it hasn't fundamentally changed the problem at all, the process at all. You know, we have to frame problems, we have to explore solutions, we articulate a plan, we build it, we evaluate what we've built, and then we learn from putting it into production. And you know, at the end of the day, you know, I see a lot of articles right now, and you know, and maybe people are using inventing a lot of new words because they don't want to use the word agile anymore. Right. Um, but it you know, it's essentially they're rediscovering flow engineering, and it's just Kanban with agents in a context layer. Um and and but a lot of organizations, you know, if you threw AI out of the picture and said, you're not allowed to run do PI plannings anymore, and your teams have to run Kanban and they have to just coordinate with each other versus having an RTE coordinating for them, they wouldn't be able to do that. And so I think that's that's one of the problems is a lot of teams are and a lot of have and a lot of organizations have built teams to be gears in machines versus people who are machines. And and that's one of the tough things. You know, I I I worked with a company and they were we were helping them with their product organization. I said, well, you know, you've taken the role of product and you have a PM, you have a PO, you have a Scrum Master, you have a data team that looks at the actual data, and you have a research team. And so you've taken what might be in one person's head at other companies and you've put it in five heads, and now they have to coordinate to all understand and all have judgment. But instead you end up with a little bit of an elephant in the blind man where everyone's got a piece of the puzzle, but it's very hard to coordinate and bring that across. And when code is abundant and when it's code is fast, you don't have time for that level of coordination, especially because one of the other, you know, talking about culture, you know, a lot of what we're talking about is cultural baggage that organizations are bringing into this new AI DLC. A lot of organizations were not really good at knowledge management in the first place. It's very tribal, it's very tacit versus explicit structured knowledge that is shared out. Um, and they relied on the slowness of their process to slowly exploit that knowledge and expl and slowly make decisions and create clarity versus having to have clarity at the front end of the process. And so I think I just segued us into uh clarity accidentally, but uh I'll take credit for it.

SPEAKER_02 16:28

It's hard to it's hard to to separate them, frankly, because they they work so well together, they're self-reinforcing. But before we one quick thing on urgency, because you hit on it um uh that uh I didn't mention, but you know, you the the Kanban and and flow is the essence of lean applied to product development. You know, we did this. Uh we were we were trying to do this seven, eight, ten years ago, um, and even be before for some, that still matters. And it matters more because if you can't flow, and flow means uh and lean, really the principle I'm talking about is figure out what's valuable to you in the marketplace um and strip away all the things that don't drive towards that value. And if you think about a world where producing is no longer the constraint, um you've got to be really razor sharp uh and apply those lean principles uh a lot more deliberately to get to the right outcome, or or else you will end up with a mountain of a bunch of features and and um things that make you feel good but actually just add complexity to your environment.

Jason Rome 17:39

And that's one of the problems that that I see is a lot of organizations, they still hadn't quite figured out what type of seat they wanted to give product and engineering at the table for leadership. Now, now SaaS companies are one thing, right? Because the product is what you're selling. But even some of those companies, you know, you're hiding a product team behind a business strategy team, potentially. And so the but but there's been so much work to do that, you know, a lot of product organizations became very much, you know, requirements, project manager, BA type roles of, you know, hey, I I I I take them the requirements from the business and I give them to the engineers, kind of that office space-esque thing. And now the business is like, we need to go faster, you need to be commercially minded, you need to be out talking to customers. And it's this new playbook, and not that they don't have the capabilities, but they haven't had the permission, they haven't had the time, and they don't have the support system around that. And so that's the only other thing I'd say about urgency is you can't just put urgency on your people. You have to figure out what are the things that block their time and cause friction and block flow and how do we unlock that. So thinking Yeah. For and a great example for AI to work well, you need a strong standardized tool chain tool chain and developer golden path, and you need a developer platform because that's the stuff that's gonna block teams is hey, I I need I need to know the enterprise standard way of doing this so I don't reinvent the wheel. And if you don't have that, like know them, like if you have people that have urgency, they're gonna be pulling in new tools and new things into the stack that's probably not gonna get approved. And then you have to go pull that out later and you're gonna end up with sprawl. And so thinking about how to support that developer experience or product experience, like supporting those folks enables urgency versus forcing uh destructive creativity where they're solving problems, but you're actually adding to the problem because you haven't been able to make those decisions for teams.

SPEAKER_02 19:35

Yeah, you just described the whole continuous delivery and DevOps movement. And that's why this idea of like everything's changed, nothing's changed, is is so sort of resonant to But a lot of people aren't there still. That's what I'm getting at. Yeah. Like if you didn't do those things, if you didn't, if you didn't apply lean thinking to the way you work, if you if you truly weren't agile, and that doesn't mean you run safe, but like you, you, you adapt the plan based on results. Uh, if you truly didn't make the investment to make it really drop dead simple to deploy into production and detect if it's working well or not, like you gotta do those things still.

Jason Rome 20:09

Does AI uh you know, I I haven't seen as much of it lately, but is is AI the thing? Does it kill Safe? Like is is Safe, I mean it was already kind of falling out of uh, you know, a lot of organizations had slowly moved away from it or they're running a version of SAFE, which which really just meant they were doing PI planning or some kind of big room planning, but you know, it it it feels like it potentially kills it. Now you still need coordination and some kind of scrum of scrums, and you need that sharing and you need those ceremonies, but it I don't know.

SPEAKER_02 20:38

No, I it's I I have no personal experience with Safe. Um I never actually tried to apply it. Um but what I

AI Costs Change The Rollout Model

SPEAKER_02 20:45

would say is is is is probably you're right, but but you're also a consultant and know that there'll be a new thing that we'll package up to sell to solve that problem.

Jason Rome 20:53

Aaron Powell Well, I I think actually that's one of the dangers that I worry about is the, you know, early on with AI, we're going off script, but we that's why we didn't really write a script. So early on with AI, people treated this like a tool they were turning on, right? And they they treated it like a part of the tool chain that they were enabling, and then you train people on how to use that tool, like a new observability tool that you would roll out or a new automated testing suite, right? That was kind of the mental model people had. Um but you know, your your testing suite, you know, I at least, you know, again, I'm not in QA, you'd probably know better than me, but you know, I've never heard of someone having to turn off their testing suite because they burned through too much usage too quickly at the beginning of a month type situation.

SPEAKER_03 21:39

No.

Jason Rome 21:40

And uh, you know, I I would hypothesize most testing suites are not as expensive as the uh tokens that our engineers are burning through right now. No. Um and so you know, you that that's the other mindset shift that I'm seeing about rolling AI is yes, in some places it's a tool that you train people on, but in other places it's an investment where you have to ask yourself, Where in our organization, if we added multiple new engineers and new capabilities, would the ROI be worth it so that we're applying it appropriately? Because I think a lot of people, like, you know, processes and tools tried to peanut butter the AI approach across. And the costs are very dynamic and the ROI is going to be very dynamic as well. So complete aside there, but I think that's a key thing is like, what's your mental model for rolling this out? And most people think about it like rolling out agile or rolling out automated testing, but it is a capital deployment of where we can have ROI and where we need to invest in the business. 100%.

SPEAKER_02 22:34

Um, and that's a whole nother podcast, probably. Uh, but what I would say to that is um the best analogy, although it's not perfect, is when we started um rolling out and and and working with public cloud. Um, and my old boss used to have this saying is like, um, I don't want a Canadian cell phone bill. There was some story where he was in Detroit and his phone was roaming and he got like a $2,000 cell phone bill. So he he he taught us, I mean, that was a a phrase that went through our head about having good hygiene about how you think about managing that infrastructure. And so you mentioned that QA um can't run through a budget. Uh they can if they don't, if your automated testing suite doesn't manage your your cloud infrastructure well. Um and so some of that discipline, a lot of what I try to do is just say, like, hey, this new thing, it's a lot like the old thing. Yeah.

Jason Rome 23:29

Um I mean, and maybe QA is a good example, right? Because there was a lot of people that, you know, if you know 80% test coverage was good, then 90% was better, and then 100% was best. And that's not true. And something you did. Yeah. You know, so I I think a lot of people, again, kind of going back to this concept of of urgency if we wrap this one, is you know, don't confuse activity with progress. Uh and so, you know, ur urgency is, you know, it's gonna involve activity, but not activity for activity's sake.

SPEAKER_02 23:59

Like don't gotta be outcome-oriented. What's the what are you trying to solve for in your business and your customer experience? And how do you and how do you determine quickly that you did or you didn't?

Clarity And Context For Humans

Jason Rome 24:08

Yeah, which is a great segue into clarity. So let's let's talk about clarity. Has clarity changed at all? Um I think this is the I'm excited to jump in on this one because I think it's the most important.

SPEAKER_02 24:19

And so that's why I like talking to you. This is my craft, man. Exactly. Do this for a living. Um the uh clarity one, I think it's like it slaps across the face uh how obvious it is. Um the whole point of clarity is to give your teams the context. That's a key you you you might have been hearing something about that word today, but give the teams the context, um, the ability to go out and do their best work and achieve the outcomes that you as a leader uh see are the things that are gonna drive your business and your strategy and all the things. Um and so much and and that mapped to that if we think about that, that um hey, I want to get across telling a team I want to get across the river and not be wet on the other side instead of building me a bridge, it's like applying that, but but it's way more acute if you get it wrong when you've got an army of agents running in your environment. Um so think about take that analogy and say, um, well, really, like what if we were telling an agent that? Um and you go, well, here's your goal. I want to be on the other side of the river. Um I don't want to be wet. Well, here I want to be on the other side of the river. Um you got a compass and uh a map and some some logs and some twine or or whatever. You're essentially MacGyver. Those are your tools. And um I need to be there by tomorrow, and I don't want you to spend more than a couple hundred bucks to do it. Like I just described the agent an agentic loop. Yeah. Um the difference is if I don't get it exactly right with a human, there's a good chance somebody's gonna catch that. Now, if you've created safety, foreshadowing, uh, they'll say something. If not, you're you're out of luck. But um so it is, I think clarity is the one that if we start thinking about agentic um flows and agents being a better part of our workforce, a bigger part of our workforce and the work we do. This stuff is so vital. And and uh you hear about prompt, you know, prompt engineering was a thing. I I and I hear you on the loops. If you're not doing loops today, you're you're missing out. Yeah, it came out like last Friday. I know. Um prompt engineering. What's that about like what makes a good prompt? Give it a lot of context. Um uh make sure that you give it the guardrails and the context that it can do its job well. Uh context engineering is I guess it's not, I don't know if it's still the buzzword, but like that that was the next thing.

Jason Rome 27:03

I think harness engineering is now the Oh, is that the thing? Okay, that's the buzzword uh as we sit here today in the middle of June. And so by the time this podcast comes out, it'll be the wrong one. But that's okay.

SPEAKER_02 27:14

Fair. Um so you get it. I mean, like we we've been I it's it's I I've worked personally. This is something clarity is something that I try really hard to work on because I think it allows teams to go do their best work, and like I really believe in that. Like you guys know better than me. There's certain things that I know that I think will be helpful from my perspective for you to know. And I try to be real intentional and deliberate about that, to the point that um my team members, when they listen to this, will roll their eyes about how like uh much I talk about some of this stuff and explain things, but it's because that one person in the room that maybe hasn't heard me talk about it, here's the thing that helps them unlock something that is gets us to a different solution than we even think about. All of that matters like um 10x, 100x, 1,000x more when you're talking to an agent, which we're gonna be talking to agents.

Jason Rome 28:07

Yeah, we're uh we might have to slice this into its own podcast because we're gonna talk about this for a while. Um I mean, clarity, you know, for me being a consultant, um, you know, clarity is one of the most important parts of my job because, you know, I I have to, I work in small batches with a client. And so like chartering work very clearly of what their outcome is and what they want us to do and the role they want us to play is is extremely important. And, you know, a big part of what I'm normally doing, I always tell my teams, like the the first deliverable to every client is the proposal that states their problem really well and helps them give the process for solving it. And I think a lot of times internally, and to your point, because our processes moved slow enough, we could kind of hack our way through it through through bureaucracy and conversations where you know business hands something off, we give it a PRD, we go through X iterations, we go through Y iterations of design, but that's okay because the the backlog was huge uh and the development team was was busy anyways. And so we had time to work towards that. And so now we have a lot less time for that stuff. And uh, you know, one of my clients uh, you know, he says one of the best parts about working with us is at the beginning of our project, we force his whole team to get in a room and I ask them questions that they aren't usually allowed to ask each other, and it forces them to like get on the same page about clarity for what they're doing. Um, and so being able to look at a problem, decompose it into multiple problems, track it back to business outcomes, understand the assumptions that we're making and where we need additional evidence is so critical. And and there's a lot of there's a lot of documents that are called strata or masquerading as strategy, which is really a set of activities instead, versus a clear outcome. And I think, you know, so I think a lot of people asking for bridges versus trying to get to the other side of the river. Um, and with AI, you know, what we've seen right now is engineers are getting so much more leverage initially because the tooling is built for them. Um and and the engineering, the market for coding uh is hundreds, if not thousands of times the size of the market for writing better PRDs. And so, like, yeah, yeah, we've you know, we've checked the box on like writing a PRD, though there's probably a question of whether we need PRDs at all. Um, and then, and I know you're gonna want to answer that. So we we we could talk about it in a minute. Um, but the you know, just the amount of throughput from the product team hasn't gotten as much leverage and and hasn't increased. And the if you if you take this whole AI thing to its logical conclusion and say, what is our what's our bottleneck to flow? It is the proximity of teams to the problems that they need to be able to solve where they can go find their own clarity and tie it back to a business outcome. Uh, you know, I was talking to um someone this morning and I've got a client that's that's done this actually. And one of the things I've proposed is, you know, rethink your seeding chart and org chart. Put your product and engineering team as close to the team that they are building technology for. So if you're building technology for the back office team, put them next to the back office team and let them have water cooler conversation. If you're you know building technology for the call center, put them in the call center and let them interface with those people and see them using it every day. You know, if you're building store technology, like the team needs to go out and be in the store every day. And because I I think that's that's gonna be the biggest gap is how quickly can you get access to your user? Uh and you and I talked about this when we were prepping for this is yes, you can now make 10 A-B tests, but you're not increasing the traffic through those A-B tests. And so now you build synthetic data and run them through those A-B tests, but you know, there's still gonna be a gap of that true empathy. And so that that clarity on the front end, I think it's not only that top-down commander's intent, but it's do you have people that understand your domain deeply where they are accumulating knowledge about their users and their problems? And do they have access to the data and the telemetry needed to make those decisions? And then do they have the permission to act urgently within a set of bounds and budget to solve those problems? Um, and that is the true measure of clarity. Not, you know, do we talk about our strategy once a quarter in the town hall?

SPEAKER_02 32:27

It's it's very true. Yeah, and I'm listening to you, and and this is right on theme where um I'm certainly not an expert at any of that, but it's something that we've been, I haven't mastered it, but it's something we've been striving for for quite some time now. Nothing you said has anything to do with AI other than it just amplifies if you aren't good at it, uh much uh

PRDs AI Slop And Adversarial Thinking

SPEAKER_02 32:50

uh shines a much brighter light on it.

Jason Rome 32:52

Yeah. And and I think that's actually one of the anti-patterns I worry about, where um the one of the reasons that I I don't love uh AI for writing PRDs is uh, you know, I think if if you're an organization that's still writing some kind of PRD, you know, AI can definitely assist you. But the the act of writing is the act of codifying your own thoughts and understanding of something. And if you outsource that first task, now you're just hanging on. And and if you let AI take that first step, even with a great context layer, AI is not gonna fully understand your business, what you've built, what you've tried in the past, and who your users are. And, you know, again, the the tools are gonna get better. You can do adversarial things, but the the tools do have this kind of sycophantic nature. They do confuse quantity for quality quite a bit. And and so I think it's very dangerous to introduce AI slop at step one when that's that moment you need clarity. There's a great book called How Big Things Get Done. And it's about program management, project management for everything from IT to like large building projects. And uh I love this uh quote that talks about um thinking slow and acting fast, uh, and a little bit of a play on condom in there. Yeah, um, the SEALs have their, you know, uh their their version of that, um, you know, make haze slowly, that type of thing. And so taking the time to get in a room, get really well aligned early. And when we're not aligned, figuring out what evidence we need to acquire to be aligned, uh, because now it's easier to prototype, it's easier to test, it's easier to go acquire that evidence is critical at the beginning of the process. And then once you get that right, everything can flow from there.

SPEAKER_02 34:36

I got a lot to respond to there.

Jason Rome 34:38

But before we Yeah, do you want to talk about the PRD for anything first? No, what are you?

SPEAKER_02 34:41

The first thing I want to hit on is I think you drop book reference one and two. There are first book reference one and two.

Jason Rome 34:46

So Well, you mentioned how many books you read earlier, so that was kind of like a book flex already. So I figured I would uh jump in with some speci specific ones.

SPEAKER_02 34:54

Um No, I'm gonna pass on the PRD thing. What I will say is um I have a slightly different perspective on you on the using AI to write um uh your initial sort of product strategy and um what you describe as real from a risk perspective in terms of getting it wrong. And I do think um uh if you can't if you're not critically thinking about what it's producing uh and not giving it really good context uh that and you just sort of pass through the output, um that's a fail and not something that I would uh recommend you do at all. But I think there's a I get you use the term AI slop, which is I everybody knows what that is. I think there's a I think you can not get AI slop and still have um AI assist you in the writing piece. It's hard, it's interesting for me because what you said is to me is true, which is that's a the the the writing process is a thought clarifying exercise. It has been for me forever. It still is. I just do that through writing the prompts really clearly and then adapting them. Uh for me is how I've landed that. And turns out I get something usually better in half the time. Yeah.

Jason Rome 36:07

And I think that's a key thing, though. And and early on there were some studies on this of the difference between like a senior person in our career versus a junior person using these tools of you know, senior people generally get the productivity boost um while maintaining quality. You know, they don't necessarily get a quality improvement, you know, except for the fact that there's a lot of people that couldn't make slides and Claude immediately makes their slides look better. Um but then junior folks, they get the productivity, but they get a quality drop. Are you mad about that as a consultant who prides himself on making slides? Uh no. Uh I'm I'm I'm all in on markdown files now. Uh so uh you know, the markdown file is the is the new output. Um but uh you know, and so I think because you have expertise and you can critically look at what the AI says because you deeply understand your domain, like it's an accelerator for you. I I think you know, when people use it as a shortcut for a domain they're not familiar with, um it's obvious pretty quick too. Yeah, it's obvious very quickly. And so, you know, I I I the practical advice I give my team is is twofold. Um, is one, own your outline. Even if you're not writing everything, like outline all the things you need to say and what you want to learn and how you want to do that. And then two is you know, think of AI in thirds. One third advise you, one third assist you, one third be an adversary to you. And so just as important as helping you write it, know what your weaknesses are when it comes to a product. You know, like for example, you know, I my MVP definition can is usually a little bit uh too ambitious. And so I I know that. I need to cut anything that I put out there by 20% and kill 20% more features. I can have an adversary go through and say, let's talk about which 20% we're gonna cut. Because I know that's something that I'm probably gonna do and it needs to be done. So, like, know what your weaknesses are. Yeah, that's a good point. Uh, you know, if you don't, if you're not good at thinking about edge cases, have an adversarial agent that goes through and pushes you on edge cases. And so learn about who you are as a product person and build the right agentic frameworks to support you as you're going through to help you with your own bias. Because one of the things that I'm seeing is as team sizes get smaller, you know, big teams where you have a diversity of people with different backgrounds, different thought processes, they eliminate some of that bias because there's so many different people looking at something. As a team gets smaller, you know, you potentially concentrate bias within that team and you need to use AI to look at the problem through lenses that your team might not be looking at it. So um, you know, I I think it it hits on that clarity of, you know, using AI to challenge your own thoughts to get to clarity. And that that's an uncomfortable thing at first, but that's a such a huge unlock for me, is that adversarial type view.

SPEAKER_02 38:39

Yeah. Yep. I know who that person is in my organization, and they just don't have to be sitting with me. I can do, I can do it without them there.

Jason Rome 38:46

Exactly. I mean, I I I do that with some of the executives I work with of, you know, when it comes to clarity, I had this conversation this week. I said, three pages, let's build a markdown file that codifies your commander's intent for what the strategy is. And then with that, actually, the other thing I'm finding really important is to hold anti-positions. What is our strategy not? Um, because I think AI has decreased the cost of ideation and pivoting so much that it's, you know, it's so easy to drift from that clarity over the course of the project and follow AI where it takes you or end up with feature proliferation, that type of thing.

SPEAKER_02 39:26

Never make or decisions, just make and decisions.

Jason Rome 39:28

And and so, you know, synthetic personas, synthetic buyer personas, like advert adversarial strategy relationships. So if I'm working with someone who's an executive who I know is busy, is like, hey, let's codify your beliefs and heuristics and strategy into a skill that your team can run so that you know it's like you're you're you know, you're looking over their shoulder as they write a PRD, but you don't actually have to be there.

unknown 39:50

Yeah.

SPEAKER_02 39:51

Yep. We're still on clarity, right?

Jason Rome 39:53

We're still on clarity if if it wasn't clear.

SPEAKER_02 39:56

Yeah. This is my this is my favorite one. They're all my favorite kids, but this is my this is your favorite one.

Jason Rome 40:01

Gotcha. Wink wink. Um yeah, anything else to close on clarity before we move to safety?

Bottlenecks Production And Learning Loops

SPEAKER_02 40:07

I think the just something and and it again, they kind of they borderline something that you mentioned that I um I think it's maybe a something that I've been doing in this space that I think is maybe a little more practical than the theory here is um you and you touched on this, which is if we're if we're thinking about flow, which is delivering you know, left to right val value in line with how the customer in your business sees it um in an optimal way or an increasingly optimal way, if that's how we're thinking about things, you know, the the the concept there that you that you apply, applying lean thinking is like where's your bottleneck and how do you increase throughput through the bottleneck? And when I think about that, you know, you can only we can create like the cost to create things, as we've talked about, has just um you know exorbitantly gone up. Sorry, the cost has come way down, we're creating way more. Um but you still can't, your your your your production environment, your users um can't consume that level of change. Uh and so you're making yourself feel good that you're building a lot of things and going, why can't we get this in production when really it probably shouldn't be in production, or even if it should, you won't be able to get a clear signal on if it's the right thing or the wrong thing. And so you're really constrained there. That's a bottleneck. I don't know that you can do a whole lot uh to change that one, uh, other than um, you know, use a science-based approach where you need it, do good enough in other spots, you know, what's an A-B test, what what isn't, those kind of things. Um but in general, it takes a little while to know if the thing worked or not. It just does. And so there so that where that pushes what I think is the biggest bottleneck is um how we figure out what are the right bets to go after, knowing that we've got a lot of bullets, but we can't take a lot of shots. And so we really want to aim and calibrate those. And and this is where I'm pushing my teens the most to leverage AI is like, how can we gain increasingly more confidence that we've got a pretty good answer to this problem before we get in production and have to play the game of statistical significance is going to take as long as it's gonna take. I can't speed that up. AI can't speed that up. So that gets you thinking about the synthetic personas to get early reads and prototyping and uh uh on the product side, it's you know, using AI to go stress test and um your ideas and all the ways we talked about. And it also includes stop thinking, and this isn't new to me and not new, stop thinking about engineers and utilization terms and think about them as uh just a team member and how to and them being a part of helping to understand and shape that bet um is better than them piling up a bunch of code that can't get into production. So I'm sort of force-fitting this one into clarity because there's some clarity in here, but I I wanted to pull on the thread that you mentioned about the production piece for moved on.

Jason Rome 43:03

Yeah, no, uh, you know, working a lot of the organizations, um it's always funny looking at what their definition of done is, uh, because you know, at a team level, their definition of done is like feature complete and like test past. And then there's usually another organizational level of done, which is you know, hardening, SID testing, you know, security testing, right? Which is like, hey, it's fully tested, it's it's you know, fully deployed, ready to release, right? It's on master. And then sometimes they have a third definition of done, which is like, hey, it's it's GA, it's released, it's whatever it is. But rarely do I see someone with the definition of done of like we've achieved our goal. So again, it's a lot of people that done is the bridge, not done someone's walked across it. And you know, with AI, we should be spending a lot more of our time admiring our work and seeing if it's if it's if it's you know, if it's doing what it needs to do. And again, that comes back to the proximity of users. And that that's one of the fun things, you know, for me, like using this on personal projects, is I I'm my first user. And so I immediately go from code to UAT, uh, to code to UAT and eventually bring someone else in. And you know, I've always said my favorite two moments in the product development lifecycle are uh user feedback and demos. And with AI, if you're doing it right, like that's all there is. Right. Uh and the whole thing is awesome. And so like that, you know, it it it it should be, you know, it should be really fun again. And like that, that's one of the coolest things with AI.

SPEAKER_02 44:26

So it is very fun. I get to I get to build again in ways. I mean, that's why I got into this game is I like to create stuff.

Jason Rome 44:32

And I I hear that from so many executives of, you know, I I got into this game to build things and what I turned into was a budget manager. Uh and, you know, I I think that's this is it it's fun again in in a lot of ways. And to close out the point on clarity, you know, one of my favorite questions when it comes to like release and thinking about releases is making sure you have clarity on, you know, at what point do we have, will we have something that is better than what we have today, where it's worth it for our users to adopt it? I think that's a really clarifying question for me, because as people start working, it's just human tendency, people start getting obsessed with their future state vision and they lose track of where their users are today. And really knowing like what is a minimum kind of adoptable, better experience for our users, uh, so that one, you don't undershoot it and just release stuff that is that you find valuable, but that's not really valuable for your users, or you you put you keep product on the shelf for too long where it could be valuable to your users because you're pursuing perfection. So um let's pivot to safety.

Safety Fears Compliance And Identity

Jason Rome 45:40

Let's do it. Uh so start us off.

SPEAKER_02 45:43

Yeah, I'll set it up again. Um, there's really two aspects to the, as I originally thought about the safety side, two sides of a of a of a related coin, and it's psychological safety, which is the do people feel free to speak their mind, to tell the truth, um, to to to um to take risks. Uh this is not my research. There's a wealth of research out there, obviously, that speaks to the value of this. And um, that's one side. So, how do you get people to feel comfortable to do those things in your environment? And um that's key to this overall what you said, ownership that sits in the middle of all this. Without that, you're never gonna own the problem. They're never gonna own the own the opportunity and problem. And then the other side of the coin is how do you make it safe, more safe for the teens to do their work where they um are are well protected against uh big impact, high impact, I call it large blast zone type problems. And um so I so I think both of those have this is the one that that I think maybe bent the most. Everything else pretty held pretty firm uh and and uh and amplified. This is one that bends a little bit, but I still think it works really well. When you talk about safety, you know what I'm finding is rightly so, people have a healthy bit of um concern, angst about what this does for them uh over over time. I do too, frankly. Um, you know, I've got mixed feelings on the grant, you know, what it means to humanity. Um and there's good and bad. That's true, uh, I think for a lot of folks, and I'm finding that a lot of folks in my teams. And so that leads to some hesitancy, I think, in adopting some of these tools sometime because there's concern about it's gonna replace my job. Um there's concern about how will we not screw up uh how will we do it in a way that we don't screw up customer data somehow or or have some big compliance issue. And it's there's a lot of fear around it. Um and so I think finding ways, so that psychological safety around those issues is still really, really vital. It's incredibly vital in a in a world where AI sits at the center of our work. Um but the ways you go about it is maybe a little bit different. Um and I think the biggest thing that I'm doing on that front is just being empathetic to that and and and leaning into that. Uh, I don't know where all this ends up. Yeah. Nobody does. And um, I think just letting people like people hearing that and listening to concerns, hear, hearing, feel feeling heard. So I've done over the past month or so, I've gone to every product team and hell just a let's talk uh about it session. Um where I'm I try to be candid and honest with them about my feelings. Um and then kind of put a reframe on it of you know, we all have mixed feelings, there's good and bad in this. Uh but like AI doesn't care and it's here to stay. And so the position I'm taking is that um circle of influence, circle of control. Um let's get really damn good at using it. Yeah, and that's been empowering. So I think that's an example of how psychological safety maps to this world um just as well as it did when we were talking about all the things stepping on people's toes. The only difference now is like this is the first time in Union I'd said this where like engineers kind of did this to themselves, yeah, which I think is interesting.

Jason Rome 49:22

But um Yeah, I the the example I always use was you know, Taylor wasn't working on a factory floor when he came up with Taylorism and scientific management. Um solid reference. Um You can't you can't do a modern product podcast without a tailorism reference. So yeah, I I mean a lot of things there, you know, I'll I'll say first on the on the psychological safety and the intersectionalism of these values. I I think the intersection of safety and clarity is very um underrated because I think when you have really high safety but really low clarity, you spend a lot of time with opinionated debates that go nowhere. Um, because it's a lot of people that feel safe to share their opinion, but it's not grounded in a clear strategy.

SPEAKER_02 50:04

And the urgency that like we got to decide and move.

Jason Rome 50:06

Exactly. And and you know, and especially when code is abundant, you know, I I've been in design debate reviews that, you know, is five, six, seven people takes half hour, an hour. And I'm sitting there and I said, you know, I could have coded both of these options. Um, and I could be going out and finding the user in the time it took us to have this conversation. And so like that clarity and then also the culture of let's use these tools to like find evidence. And, you know, one of my other favorite questions to to ask each other is always, you know, what would have what would have to be true for this to be the right decision? And like, how do we go find evidence versus, you know, I I I think there there's a lot of people with very strong opinions and very different taste in in in this space, um, and a lot of people with with perspectives on on what is right um and and what looks good. You know, it's it's the reason when you know an engineer goes look at another engineer's code, they always want to refactor something. A designer goes look at another designer's thing, they always want to, you know, change something, that type of thing. So um I I I I want to talk about the intersection. On this environmental thing, I'm same as you, uh I think really empathize with people, especially because you know, and and the reason I got into this space, right, is it's one of the coolest jobs in the world. Uh and and it should be one of the most fun jobs in the world because you never have the same day twice. Uh and you get to make an impact on people and you get to solve problems and you get to work with a lot of really other smart people. And a lot of people that get into this, and and this is true of most jobs in America and complete tangent, but uh, you know, as we've seen decline in different kinds of community organizations, like work has become a big part of identity for people. And so, you know, a lot of people that, you know, oh, you know, AI is going to take jobs. Great, we'll put basic income in place and we'll tax people. And I think it misses the larger point of like identity and the difference of that and and and all of that, which is definitely another podcast because we'd have to talk about a whole lot of different stuff for the to tackle that issue. But that empathy is key. And I don't think we helped it where the people that were so close to this technology uh in in Silicon Valley were you know prognosticating a hundred, you know, 90% of code will be automated and 50% of jobs will be eliminated. And you know, I don't I don't know if those ended up on polymarket, but they definitely would not have been winners uh at the time. And so I think you know, as this technology is suffused, you know, we're seeing that, okay, it's gonna be a minute. There's gonna be more jobs. I think actually the you know, it's the the classic, you know, as a as a good becomes cheaper, there becomes more demand for there's actually gonna be more opportunities. We're we're seeing a need for a lot more product folks. And so, but we definitely didn't help ourselves with that uh at the time. And so I think that environmental safety and helping people, like that being said, you know, this is not, again, this is not just a technology you roll out and you give someone a training and they understand. So for all of us in this space, there there does have to be personal ownership of you're playing with these tools on the side, you know, you're you're learning them, you're you're keeping up with your education. It's your employer is not going to be able to keep up with the technology change to be able to help you keep up with it. Um, but then also, you know, for me, working across a ton of clients, you know, you can see the same data in the Wall Street Journal or wherever you read, but I'm not seeing AI allow people to do tons of layoffs or anything. If if anything, it's it's hesitancy maybe to hire until they understand the value of it. But I think there was a couple of really big high-profile situations. Uh, but then the counterpoint is always, you know, were those companies that had just overhired um compared to the amount of innovation capacity they needed. And this was a correction, and AI was the excuse versus the actual. And so um, but you know, AI not disrupting jobs is not an interesting headline grabbing story.

SPEAKER_02 54:05

So I think it's one of those that it's like as most things, the truth's somewhere in the middle. And that um and this is this is what I share with my team is like you, you know, if if you uh really enjoy writing rackets, curly braces, semicolons, uh and uh and the way you um do your job, like this might not be for you in a while. If if the actual act of writing machine langu uh uh code is your thing and where you really root your identity to, this won't be probably something you're gonna enjoy. Um however, if you can flip your your brain or reframe a little bit to say, I still do my craft, I still write code, but I do it in plain language, and I I understand um how to engineer and architect um a large-scale digital service better than a vibe coder that's uh wrote a habit tracking app.

Jason Rome 55:07

Yeah. So so Which we needed a hundred thousand habit tracking apps.

SPEAKER_02 55:12

So that um you know, that I think uh is is representative of this change is you've gotta you've gotta rethink your identity and embrace this technology and the ways of working with it. Um what my message is like hey, software is still the thing that's driving um the economy. And uh it will be. It just happens to be through large language models now too. Um and you sit you have first movers advantage of people who know how to craft software. Uh you should take advantage of that and learn to harness these tools. Because businesses will, and this is where I say the truth in the middle. I've worked in a business for a while. They will, where there are opportunities to create efficiencies, they're gonna take them, and they should. Um, that means jobs are gonna change, jobs are gonna get eliminated, new jobs are gonna get created. How all that nets out, nobody knows. But what I do know is that if you don't know how to harness this technology two years from now, you're gonna have a harder time finding a job.

Jason Rome 56:18

Which isn't a new thing. So um, you know. What do you think about the the fall of the semicolon and the rise of the M-dash as a name for this podcast, by the way? I love it. And people will just think it's a grammar podcast on punctuation uh in the age of AI. Perfect. That is the nerdiest joke I've ever heard. Um I've had worse, um, which is, you know, for those who know me, is the most believable statement that will be uttered on this podcast. It's the only thing that, you know, we're not gonna have to fact check uh after after we come to this. Yeah, I I think with safety, like the environment is key. And there's a lot of great articles. I haven't checked them lately, but when this first came out about you know, this technology having an uneven impact on people of different ages, people of different genders, and like the socio impact of this, the impact of on cognitive load and and how people think about this. I do think to your point around you know, people that like to understand the code at a deep level, uh, you know, probably five, 10 years from now, there's gonna be a lot of companies looking for people that like really understand computer science and like how code works and how it's written to be able to like debug and go deep into some of these systems. Again, you know, the the models are getting better though. I I think for most companies, you know, we're at a diminishing return of like how much they need the models to get better for a lot of what they have to do on a day-to-day basis. And, you know, the you know, who who knows where the the cost of all this stuff is gonna go. And obviously that's gonna have a huge impact after some of these companies start going public and and we figure out how much they want to value margin versus market share and and how they prioritize that and what's subsidized and what's not once once it's publicly traded. So um there's gonna be a lot of change. And you know, anyone who says that they have it figured out perfectly uh is definitely not telling the truth. And so I I think it's one of those things. It's gonna be a busy, it's gonna be a busy couple months, and it's gonna continue being that way. And couple months. Yeah, a couple years. Um, you know, and I do think that's the last thing I'll say about safety is you know, being empathetic of your people, being empathetic of what these like tools do to folks, and the amount like there's always been this upper limit, anyways, around the amount of thought work that someone can do in a day. So I think it's like six hours or something. We will have to fact check that one. Um and you know, AI takes away a lot of the rote toil and tasks, but but that means you're always on. And you know, I've ended up in situations where I've got, you know, I'm working on, you know, one project with Claude. I've got two other agents working on, you know, coding something and I'm switching back and forth between that, and it's exhausting. And so I think the AI brain fry is real. Um and and you know, I think again, one of the shames is this would be a great time for us to look at the 40-hour work week, I think, real hard. Um and and that, you know, that that's one of the nice things, you know, if I had a wish list of what would come out of some of this. But I I think there's still a lot of people that are being told to do more with less right now. And hopefully AI helps with that. And I hope people are able to benefit from the productivity that comes with AI, not just the companies, and and and we're able to to leverage that as well. And I think that'll help a lot with safety. And I think, you know, employers and and people like yourself that are making people feel comfortable and can retain talent, especially talent that knows their domain and has the knowledge of how things work extremely well. I actually think that's gonna be a pretty big competitive advantage um because they they see, you know, they're able to look at an engineer and see what they're able to do from a business value perspective and not just the number of PRs that they have. And and they have that mindset and they're able to make that switch. So I think that's gonna be critical.

SPEAKER_02 59:58

Absolutely. Yeah. The the um the thing that hasn't changed here is while AI is an incredible tool at production, humans still have to make judgment around is it the right thing? Um and that will continue. I think uh if we talk about software engineers specifically or product people, um they have domain expertise around their craft that they need to overlay on top and will need to. Companies need to recognize that that you know, and they and they do. Um and so I think that that's gonna play out to where maybe the the demand supply works out to where we create more demand. I think there's there's there's likely a scenario over that. But um what I what I am trying to do is to unlock people. Uh it's basic change leadership. That's what it is. It's it's unlock people to unfreeze and just start moving forward in a direction. And and it's what can you do today? All that's big and scary stuff is out there. Yeah. What can you do today that gives you a better chance?

Jason Rome 1:01:04

Uh great safety to unlock urgency.

SPEAKER_03 1:01:06

Yes.

Jason Rome 1:01:08

Close the loop. I think that's a I think that's a uh a great end message to uh wrap it. Um so thanks, Corey. Ton for the time today. This was a lot of fun. Yeah, appreciate it. Thanks for inviting me. Awesome.

Final Takeaway And How To Follow

Josh Lucas 1:01:19

Thank you for joining us on Build What's Next Digital Product Perspectives. If you would like to know more about how Method can partner with you and your organization, you can find more information at method.com. Also, don't forget to follow us on social and be sure to check out our monthly tech talks. You can find those on our website, and finally, make sure to subscribe to the podcast so you don't miss out on any future episodes. We'll see you next time.