I’m going to say something that might get me in trouble. Most of what you’ve read about “AI culture” is garbage. It’s written by consultants who’ve never built a company or by large corporations so bloated they think adding an “AI ethics” slide to their onboarding is culture. I almost gave up on the whole idea myself.
For four years, I’ve been obsessed with this problem. At my last company, RemoteTeam, we were building the future of work, and I knew AI had to be at the core of it. But it felt like pushing a boulder uphill. We’d buy the latest tools, run the workshops, and talk a big game, but nothing really changed. The team was hesitant, the tools gathered digital dust, and our efficiency gains were marginal at best. It was frustrating. I have to admit, I was close to throwing in the towel.
Then, I had a conversation that changed everything. It was with the CEO of a small, scrappy startup that was running circles around its competitors. They were a team of 30, but they were shipping product like a team of 100. Their secret? They weren’t “bolting on” AI. They were building their entire company around it. It was a simple shift in perspective, but it was profound.
I went back to the drawing board. I spent months analyzing successful remote companies, from bootstrapped startups to unicorns. I looked at my own portfolio of 200+ investments, including companies like Anthropic, OpenAI, and Scale AI. The pattern was clear. The companies that were winning with AI weren’t just using AI tools; they were thinking like AI. They had one thing in common when it came to AI culture building. They were building an AI-native culture from the ground up.
The AI-Native Framework: From “Using AI” to “Thinking AI”
So what does an AI-native culture look like? It’s not about having the most expensive AI tools or the biggest data science team. It’s about a fundamental shift in how your team approaches problems, makes decisions, and creates value. Here’s the framework that we developed and implemented at RemoteTeam, which ultimately led to our acquisition by Gusto.
1. Everyone is a “Prompt Engineer”
This is the most important shift. In an AI-native company, the ability to communicate effectively with AI is a core competency for everyone, not just the tech team. We stopped thinking about “prompt engineering” as a niche skill and started treating it like writing or basic math. We ran workshops on how to write effective prompts, how to chain commands, and how to critically evaluate AI-generated output. We even made it a part of our performance reviews. The result? Our team started using AI for everything from writing marketing copy to debugging code. It was a force multiplier.
2. Data-Driven by Default
AI thrives on data. An AI-native culture is obsessed with data. We made a conscious effort to track everything we could, from customer interactions to internal workflows. We built a centralized data warehouse and gave everyone on the team access to it. We also invested in tools that made it easy to visualize and analyze data. This wasn’t about micromanaging. It was about empowering our team to make data-driven decisions. Instead of relying on gut feelings, we could point to the data and say, “This is what’s working, and this is what’s not.”
3. Radical Experimentation
AI is a field of rapid experimentation. What works today might be obsolete tomorrow. An AI-native culture embraces this uncertainty. We created a culture where it was safe to fail. We encouraged our team to try new things, to experiment with different AI models, and to challenge the status quo. We celebrated our failures as learning opportunities. This might sound like a cliché, but it’s incredibly powerful in practice. Some of our biggest breakthroughs came from experiments that we thought were long shots.
4. Human-in-the-Loop, Always
This is the part that most companies get wrong. They either try to automate everything and remove the human element, or they’re so afraid of AI that they don’t automate anything. The sweet spot is in the middle. An AI-native culture understands that AI is a tool to augment human intelligence, not replace it. We designed our workflows with a “human-in-the-loop” model. The AI would do the heavy lifting, but a human would always be there to review, refine, and make the final call. This not only improved the quality of our work, but it also made our team more comfortable with AI.
Real-World Examples from the Trenches
This framework might sound good in theory, but does it actually work? Let me give you a few examples from my own experience.
At RemoteTeam, we used this framework to build an AI-powered performance review system. Instead of relying on subjective feedback, we used data from our project management tools, our communication platforms, and our code repositories to generate a holistic view of each employee’s performance. The AI would identify patterns and trends that a human manager might miss. But the final review was always delivered by a human manager, who could add context and nuance to the data. It was a game-changer for our team.
I’ve also seen this framework work wonders at some of my portfolio companies. One of my investments, a digital nomad AI tools company, used the “everyone is a prompt engineer” principle to build a customer support team that could handle 10x the volume of a traditional team. Their support agents were trained to use AI to answer common questions, troubleshoot problems, and even write personalized responses. The result was a better customer experience and a more efficient team.
The Hard Truth About AI Culture
Building an AI-native culture is not easy. It requires a long-term commitment and a willingness to challenge the way you’ve always done things. It’s not a one-time project; it’s a continuous process of learning, adapting, and evolving. But if you’re serious about building a company that can thrive in the age of AI, it’s the only way to go.
So, if you’re a founder or a leader who’s struggling to make AI work for your team, I want you to ask yourself a question: Are you just “using AI,” or are you building an AI-native culture? The answer to that question will determine your success in the years to come.
Frequently Asked Questions
Do all experts agree with this view?
No, and that's fine. The best ideas in business are often contrarian. I share my perspective based on my experience and data, but I encourage you to seek out opposing viewpoints and form your own conclusions.
What experience informs this perspective?
This perspective comes from over a decade of building companies in Silicon Valley, two successful exits (RemoteTeam to Gusto, MovieLaLa to Gfycat), and investing in 200+ startups including Anthropic, OpenAI, and Scale AI. I write about what I've lived.
How has this view evolved over time?
My thinking on most topics has changed significantly over the years. Early in my career, I held many conventional views that experience proved wrong. I try to update my beliefs when the evidence changes.