I still remember the day we sold RemoteTeam to Gusto. It was a blur of paperwork and congratulations, but one feeling cut through the noise: the ground was shifting beneath my feet. The tech world I knew was changing, and fast. Today, that change has a name: the AI Engineer.
This isn't just another LinkedIn buzzword. It's a fundamental rewiring of how we build companies. When I look at my angel investments—200+ companies now, including some you might know like Anthropic, OpenAI, and Scale AI—I see a clear dividing line. The winners aren't just sprinkling AI on top of their old products. They're rebuilding from the ground up, with AI at the core. The people leading this charge aren't your classic software engineers or data scientists. They're a new breed.
From Full-Stack to AI-Stack
Remember the good old days? To build a SaaS app, you'd hire a full-stack developer. Someone who could handle a bit of everything: wrangle a database, write some backend code, and make the frontend not look terrible. Then we all got specialized. Frontend, backend, DevOps, data. Everyone in their own lane.
AI blew that model up. Suddenly, software wasn't just about storing and retrieving information. It was about understanding, generating, and interacting with it in a way that felt... well, intelligent. Your average engineer, trained on deterministic systems where if x, then y, was lost. AI is a world of probabilities, of messy, unpredictable outputs. It demands a different way of thinking.
This is where the AI Engineer comes in. They're a hybrid. They have the machine learning chops to understand how these models work, their quirks, and how to fine-tune them. But they also have the engineering discipline to build solid, scalable systems around them. They live in the messy middle, bridging the gap between the lab and the real world.
Why You Need an AI Engineer Yesterday
If you're running a SaaS company, you might be thinking, "Do I really need one of these AI Engineers?" My answer is an unequivocal yes. And if you wait too long, you'll be playing catch-up for a long, long time.
Here's the hard truth:
- Your Features Are a Commodity: Every SaaS product has a dashboard and reports. That's the baseline. The new differentiator is intelligence. Can your product predict what a user needs before they do? Can it automate the boring stuff? That's the game now.
- Integration is Deceptive: Slapping an LLM into your product via an API call is easy. Making it work well is hard. You're dealing with latency, token limits, prompts that go off the rails, and models that just make things up. An AI Engineer has been through these wars. They know how to build the scaffolding to make the magic happen reliably.
- AI Will Bankrupt You If You're Not Careful: I've seen it happen. Startups get a big funding check, get excited about AI, and then burn through their cash on GPU costs because they didn't think about optimization. An AI Engineer understands the unit economics of AI. They can build something that's both powerful and doesn't require taking out a second mortgage.
The Pricing Puzzle
This brings me to pricing. The moment you put real AI into your product, the classic subscription model gets wobbly. Why? Because your costs are no longer predictable. If one user decides to generate a million images with your new AI feature, your AWS bill goes to the moon, but your revenue stays flat.
This is why I'm a big believer in hybrid pricing. It's a simple idea: a flat fee for the platform, and then you pay for what you use on the AI side. It's fair, it's transparent, and it aligns your incentives with your customers'. But figuring out the right way to meter usage, how to price it, and how to explain it to customers is a serious challenge. It requires someone who gets both the business side and the technical side. It requires an AI Engineer.
Building an AI-Native Company
So, where do you find these unicorns? And how do you build a team around them? It's not about just adding a new role to your org chart. It's about changing your company's DNA.
When I talk to founders, I tell them to treat AI as a core competency, not a feature. That means your AI people need to be in the room when you're making strategic decisions. It means you have to be willing to invest in the right infrastructure. And most importantly, it means you have to be okay with being wrong. A lot.
AI is moving at a breakneck pace. The hot new model from six months ago is already a dinosaur. You need a team that loves to learn, to experiment, and to throw away their old work when a better way comes along.
The Future is Being Built Now
We're at the very beginning of a tectonic shift. The companies that are being built today, the ones that are AI-native from day one, will be the giants of tomorrow.
The AI Engineer is at the heart of this transformation. They are the architects of this new world. If you're a founder, an investor, or just someone who wants to stay relevant, you need to understand this new reality.
It's not going to be easy. It's going to be messy and unpredictable. But I've been doing this for a long time, and I can tell you one thing for sure: the founders who run towards the chaos, who embrace the uncertainty, are the ones who end up building the future. So, what are you waiting for?
Frequently Asked Questions
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.
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 can I apply this thinking to my own situation?
Start by identifying the core principle behind the opinion, not the specific example. Then ask yourself: does this principle apply to my context? If yes, test it in a small, low-risk way before going all in.
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.