This post is part of a series on the future of work, AI, and entrepreneurship. I share my unfiltered thoughts and experiences from building and investing in tech companies for over a decade.
The Rise of AI Culture Building: A 2024 Prediction
I almost gave up on AI culture building. Seriously. After two successful exits and over 200 angel investments in companies like Anthropic and OpenAI, I thought I had a pretty good handle on building great teams. But integrating AI felt… different. It was like trying to solve a puzzle with half the pieces missing.
We had a team, a brilliant team, working on a new AI-powered product. We gave them the best tools, the biggest budgets, and the most ambitious goals. And they failed. Spectacularly. The product was a mess, the team was burned out, and I was left with a very expensive lesson: culture eats AI for breakfast.
We were struggling. Our efficiency was dropping, and the team felt more disconnected than ever. I was about to throw in the towel and go back to the old way of doing things. But then, a simple shift in perspective changed everything. And I’m not exaggerating when I say it led to a framework that has since transformed not just our team, but many of the startups I advise.
Forget everything you've read about AI culture building. I'm about to share a secret that will change how you work forever.
The "Async-First" AI Revolution
For years, the startup world has been obsessed with real-time collaboration. We’ve been told that the key to innovation is to have everyone in the same room, brainstorming and whiteboarding together. And for a long time, that was true. But the rise of remote work and AI has changed the game.
I’ll be honest, I was a skeptic at first. I’m an investor in companies that are at the forefront of AI, but I still believed in the power of in-person interaction. I thought that the spontaneous conversations and creative energy of a shared office were irreplaceable. But the pandemic forced us to adapt, and what we discovered was surprising.
Our team at RemoteTeam, which was later acquired by Gusto, was fully remote from day one. We had to figure out how to build a strong culture without ever meeting in person. And what we found was that async communication wasn’t just a workaround – it was a superpower. It forced us to be more intentional in our communication, to document everything, and to trust our team members to work autonomously. We created a “handbook-first” culture, where every process, every decision, and every piece of knowledge was documented in our company wiki. It was our single source of truth. And it was the foundation of our success.
And when we started integrating AI into our workflows, we realized that our async-first culture was the perfect foundation. AI thrives on data, and our handbook-first culture was a goldmine of structured information. We were able to train our AI models on our own data, to create custom tools that were perfectly tailored to our workflows. We built an AI-powered “virtual teammate” that could answer questions, summarize meetings, and even draft documents. It was like having a super-smart intern who never slept.
The Problem with "AI" Onboarding
Most companies are getting AI onboarding all wrong. They’re treating it like a software update, a one-time training session where they show employees how to use a new tool. But that’s like giving someone a hammer and expecting them to build a house. AI is not just a tool; it’s a new way of working. And it requires a fundamental shift in your company’s culture.
I remember talking to a founder who had spent a fortune on a new AI-powered sales tool. He was so excited. He thought it was going to be a silver bullet, a magic wand that would instantly double his revenue. He rolled it out to his sales team with a one-hour training session and then sat back and waited for the magic to happen. And of course, it didn’t. The sales team went back to their old way of doing things, and the expensive new tool sat on the virtual shelf, gathering dust.
The founder was baffled. He couldn’t understand what had gone wrong. The tool was amazing, the team was smart, so what was the problem? The problem was that he had focused on the tool, not the culture. He had tried to bolt AI on to his existing workflow, instead of rethinking the workflow from the ground up.
The successful ones, on the other hand, are the ones that are building an AI-native culture from the ground up. They’re rethinking everything, from how they hire and onboard new employees to how they manage performance and communicate as a team. They’re not just using AI; they’re thinking like AI.
My "AI-Native" Culture Framework
So, what does an AI-native culture look like? It’s built on three pillars:
1. Radical Transparency: In an AI-native company, information is not a currency to be hoarded; it’s a resource to be shared. Everything is documented and accessible to everyone. This is not just about a shared Google Drive; it’s about a culture of open communication and knowledge sharing. At MovieLaLa, which was acquired by Gfycat, we had a rule: if it’s not in the wiki, it doesn’t exist. This forced us to document everything, from our product roadmap to our sales process. It was a pain at first, I’m not going to lie. But it paid off in spades. It made it incredibly easy for new employees to get up to speed, and it created a culture of accountability where everyone was on the same page.
We even took it a step further and made our product roadmap public. People thought we were crazy. They said our competitors would steal our ideas. But we believed that the benefits of transparency far outweighed the risks. And we were right. Our users loved it. They felt like they were part of our team, and they gave us invaluable feedback that helped us build a better product.
2. Algorithmic Decision-Making: In an AI-native company, decisions are not made based on gut feelings or personal opinions; they’re made based on data and algorithms. This doesn’t mean that you remove the human element from decision-making. It means that you use AI to augment human intelligence, to help you see patterns and make predictions that you would otherwise miss. At Scale AI, one of my portfolio companies, they use their own dogfooding to an extreme. They use their own data labeling platform to improve their own operations. It’s a virtuous cycle of continuous improvement. They are constantly collecting data on their own performance, and using that data to train their AI models to be more accurate and efficient. It’s like having a built-in R&D team that is constantly working to make the company better.
Another company I advise, a fintech startup, uses a similar approach to make lending decisions. They’ve built a machine learning model that can predict with a high degree of accuracy whether a borrower is likely to default on a loan. This has allowed them to approve loans to people who would have been rejected by traditional banks, and to do it at a lower cost. It’s a win-win for everyone.
3. Continuous Learning: In an AI-native company, learning is not a one-time event; it’s a continuous process. The world of AI is constantly evolving, and you need to be constantly learning and adapting to stay ahead of the curve. This means investing in training and development, but it also means creating a culture where it’s safe to experiment and fail. At Hugging Face, another company I’m proud to back, they have a culture of “blameless post-mortems.” When something goes wrong, they don’t look for someone to blame; they look for lessons to learn. This creates a culture of psychological safety where people are not afraid to take risks.
I remember a time when we were working on a new feature for RemoteTeam. We were so excited about it. We were convinced it would be a huge hit. We launched it with a big marketing campaign, and it was a total flop. Nobody used it. We could have easily blamed the product manager or the marketing team. But instead, we held a blameless post-mortem. We looked at the data, we talked to our users, and we figured out what had gone wrong. And we learned a valuable lesson that helped us build a much better product in the long run.
The Future is AI-Native
Building an AI-native culture is not easy. It requires a fundamental shift in your mindset and your company’s DNA. But it’s the only way to survive and thrive in the age of AI. The companies that are able to make this transition will be the ones that dominate their industries for years to come.
I’ve seen it happen time and time again. The startups that embrace this new way of working are the ones that are able to attract the best talent, build the best products, and create the most value. The ones that don’t are the ones that get left behind.
So, I challenge you to take a hard look at your own company. Are you just using AI, or are you building an AI-native culture? Are you just automating the old way of working, or are you inventing a new one? The future is coming, and it’s AI-native. Are you ready? The choice is yours. But don’t say I didn’t warn you.
Frequently Asked Questions
What's the most common pushback you get on this?
People often push back by citing exceptions or edge cases. And they're usually right that exceptions exist. But building a strategy around exceptions rather than patterns is a losing game for most founders.
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.
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.