Brian Balfour: commitment

Transcript context

…Just to quickly explain that transition so it makes sense for people, which is, I started Reforge just with the interest that there was all these incredible leaders out there growing on the front lines of some of the fastest growing companies and they have all this amazing knowledge and I wanted to encode it in useful and practical ways for others. That took the form of courses and content and product, all that kind of stuff at the beginning. Along the way, everybody kept asking us to essentially build the tools to implement what we taught. Because with anything, you can learn as much as you want. You can listen to my podcast, your podcast, Lenny, whatever, as much as you want, but if you don't actually put it into action and implement it, then it's not really going to create value. People kept asking us to really close that gap and we said no for the longest time. And then about a couple of years ago when AI really started to inflect, it really created this moment that, oh, wow, now there's this opportunity not just to encode this knowledge into content, but also into the products, the software, the tools that we use ourselves. So we started to take a really big bet on that and started to develop this new platform for AI-native product teams. The first product we launched is called Reforge Insights, which acts like your AI product researcher, aggregates all the feedback from all the sources, uses AI to analyze it, helps you explore it, but also will start to identify what are the gaps, the things that you don't have in your feedback today and auto-generate the research to go gather all those new insights, so complete the full cycle. We're going to launch two other major products as part of this platform before the end of the year, but we'll save that for some future episode. So that's been our journey. We've seen inside companies that are going through this transformation from two perspectives. One is obviously selling in that tools, but the other perspective is, for 10 years, companies have been coming to us to help them try some sort of transformation with our learning product. Most companies are not coming to us to just throw a bunch of courses in front of, they're trying to solve some big business problem, some transformation. Now that used to be things like, we've got to figure out this growth thing, or I'm going from sales-led to product-led, or I have more project managers and I need to transition to product managers, something like that. There's some business problem, they're going through some transformation and they saw us as part of that transformation and we got to partake in quite a few of those types of transformation. Now of course, the transformation that everybody's going through is, okay, how do I become more AI-native? How do I adopt this stuff? We've seen a pretty wide spectrum and from both perspectives of how companies are approaching this. that everybody's going through is, okay, how do I become more AI-native? How do I adopt this stuff? We've seen a pretty wide spectrum and from both perspectives of how companies are approaching this. I'm sure everybody's seen that AI, we've been calling them the AI manifesto memos from CEOs out there that proclaim, "We are now AI-native," in some grandiose way, but behind the scenes, there's actually some incredibly stark differences in the actual teeth of what backs up those memos and backs up those executive decrees that we should all be AI. Just to point out a few of them, which is, one is that I think the most impactful thing that you can do is form really hard constraints. There's other parts that's like, okay, you want to communicate this, you want to establish an owner of who's going to drive this, you want to build an incentives and rewards, and you see this all playing out in things like building it into your career ladders, or some people are starting to introduce this as questions into their performance reviews, all those types of pieces. But the thing that is actually moving the needle are the companies that are defining incredibly hard constraints. One company that we worked with developed this constraint that they benchmarked against other companies of their revenue size and the team sizes for those stages, and they set a benchmark that we will be one-fifth, each of our functions will be one-fifth the size. What that did is it created a constraint that you couldn't hire above that level, and it forced people to essentially find ways to adopt AI and do things to replace that. So that was one. You've seen these other ones, I can't remember from what company, that might've been Shopify or another, who was like, you are not allowed new headcount until you prove to us that you are not able to accomplish this with AI. That's another hard constraint. But you also see these other constraints on a smaller level, which is executives saying, "I will not do a product review or review a PRD unless it comes with three prototypes." Something like that. That's the hardest one. Those are the biggest constraints. I think the biggest change that I'm seeing is, and the things that separates out the top few percent making this change and everybody else is essentially making the hardest decisions, and that hardest decision is going to come down to exiting people. In every transformation, what we see is essentially three groups of folks. We call them the catalysts, the people leading the charge, the people who are experimenting, doing this on their own time, all that kind of stuff. You then have what we call your converts. These are folks that will make the transformation, they will adapt, but they need structure, they need permission, they need a clear outline, they need a clear plan. I don't say this in a negative way, it's just that that's how some people operate. That's where things like all the things that we were talking about before, which was the decree, the permission, the clear budgets, the rewards, all of those types of things. But then, inevitably you have a certain percentage that are anchors. t we were talking about before, which was the decree, the permission, the clear budgets, the rewards, all of those types of things. But then, inevitably you have a certain percentage that are anchors. They're dragging their feet, they're silently creating friction in the background and all those pieces. There's a big difference in how I think companies are treating and thinking about their strategy for those folks. One group is like, ah, we're going to work with them very passively. Others have set a hard deadline. They're either going to make the transformation by X date or we're going to exit folks. A lot of people look at this as being really harsh. I think a lot of people would think that, especially individuals, but let me explain it from more of a CEO perspective. A lot of these companies are seeing this AI transformation, the ones that are taking it more seriously, as this isn't adopting new tools, this isn't a light change. This is a fundamental culture change of how we operate as a company. You can't have 20, 30%, whatever meaningful number of it is of your company trying to operate in a completely different way, in a completely different culture. Cultures thrive on density, and that's why there's sometimes the best ones feel like cults. As a result, from that perspective, it's like, hey, for us to be successful, for this to be the best thing for all employees, we all need to be operating around the same culture of principles and stuff. If that's not you anymore, then we're defining a plan to exit it. I would say that less than 10% of companies we see are taking this hard stance, but I would say they are probably the ones that are farthest along getting the most adoption and are seeing the most results of the ones that are taking those hard stances. There's a bunch of other stuff I could talk about, but that's the high level of what we've seen across a bunch of different companies.…

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