How to Build an AI-Native Company Today

Nathaniel Whittemore (0:00)

A year ago, it was a very different time in Enterprise AI. Companies were still talking about things like how many use cases they had for AI. Now, a year on, we are no longer talking about use cases. Everything, it turns out, is a use case for AI. And in fact, in 2026, the long-awaited, much-discussed transition to agentic AI actually began. Surrounding that, companies have undergone a significant transformation process, one that pretty much everyone is still in the midst of. And yet, as companies try to become more AI-native, the question is, what does that actually mean? What are the hallmarks and characteristics of companies that are not just glomming AI and agents onto old processes, but are really doing things in new ways, redesigning from the ground up? While it's all still emerging, I think we're at the point where we are starting to see a set of features and characteristics that define AI-native companies.

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Welcome back to the AI Daily Brief. Today we have a fun one. I feel like at this point pretty much all of you are either at big companies who are trying to adapt and become the next version of themselves in this AI-enabled world, or are the people who are being hired by those companies to help them with that adaptation. Whichever side of that table you find yourself on, a core question that exists underneath all of that transformation is, what are we actually transforming into? A term that gets thrown around a lot is AI-native. Part of the attraction of the term is that it separates companies that have simply glommed on AI and agents to old processes from those who have actually rethought from the ground up to take advantage of this new era. But what is the substance of AI-nativeness? Part of what makes it great for a podcast is that there are a lot of things and many of them are debatable. Enter Alex Lieberman. Alex is the founder of 10xLabs, which is a company that helps transform existing companies into AI-native companies. And before that, he was a founder at Morning Brew. As you might imagine, that experience at Morning Brew means that he is often a great source of content. And the post that inspired this episode is actually a direct crib from his X account recently, 30 features of an AI-native company. What I thought would be fun after turning it into a beautiful 1950s retrofuturist-themed presentation with the help of Codex and GPT Image 2 is to go through these features one by one where I will share the aspect of AI-nativeness that Alex posted about and then add any thoughts, qualifications, disagreements, although I don't think that I necessarily disagree in a lot of places, and other observations that I've seen in my work with enterprises as well. Now, I should note that I don't think that this is in any particular order. In fact, you can very much tell that this is not AI-generated as it reads much more like a stream of consciousness than, frankly, most of the content that you're used to seeing these days, which is kind of a breath of fresh air.

First feature of an AI-native company is to blueprint every process, to create, in Alex's words, a function-by-function process blueprint of the entire business. Now, I said I wasn't going to disagree much, and I'm not exactly going to disagree here, but this is one area where, although I don't disagree with doing this, I think the reasoning behind it for a lot of companies is actually leading them down the wrong path. Some of the reasons that it's valuable to blueprint processes, in other words, to map out how work actually gets done, is that a lot of that information right now lives locked inside people's heads. There are a lot of nuances and edge cases that people have been handling on their own forever, and which are perhaps transmitted person-to-person through random spoken meetings in the hall or side chats on Slack, that don't ever find their way into actual operating manuals. That puts the AI and agents that you're bringing into the work at a disadvantage, because they're recreating things from the ground up. And so having better maps of how work currently gets done is an incredibly valuable piece of context as you redesign your organization around AI and agents. So all that part I agree with. Where I get concerned is around an inherent assumption of process mapping that I see pretty often. The assumption is that agents are going to do things the same way that humans do. I think that that's very unlikely to be true. And in fact, I think artificially constraining agents to do things along the pattern of an old workflow is in many cases the wrong approach. As opposed to, for example, giving them the goal in articulating the guardrails of what they can and can't do and letting them figure it out from there. Now again, that doesn't make process mapping not valuable, but we have to understand and prepare for the reality that the best way to do something in the future will not necessarily just look like an efficient version of the way that we did it in the past.