I’ve seen it a dozen times. A founder reads a few blog posts about AI product management, gets excited, but their product goes nowhere. Why? Because most of what you read is generic advice that misses the point entirely.
This isn’t another one of those posts. This is for founders in the trenches who need to build, move fast, and get results without a ten-person data science team. I’ve built and sold two companies, RemoteTeam to Gusto and MovieLaLa to Gfycat, and I’ve invested in over 200 startups, including some of the biggest names in AI like Anthropic, OpenAI, and Scale AI. I’m telling you what I’ve seen work, and what’s just noise.
Let’s get real. The “perfect” AI stack doesn’t exist. What matters is having a setup that lets you iterate quickly, test ideas, and not get bogged down in complexity. Forget about building everything from scratch. Your goal is to find product-market fit, not to win a science fair.
The Counterintuitive Truth About Building AI Products
Everyone thinks you need a massive team of PhDs and a supercomputer to build an AI product. That’s wrong. When we started MovieLaLa, we were just a small team with a crazy idea. We didn’t have a huge budget or a fancy office. What we had was a deep understanding of our users and a relentless focus on shipping.
We knew that movie studios were struggling to connect with younger audiences. So, we built a platform that used GIFs to market movies. It sounds simple now, but at the time, it was a new idea. We didn’t spend months building complex models. We used existing tools and APIs to get a prototype out the door as fast as possible. And it worked. We were acquired by Gfycat, and later Gfycat was acquired by Snap Inc.
My next company, RemoteTeam, was a similar story. We saw that companies were struggling to manage their remote teams. So, we built a platform to solve that problem. We didn’t try to build the most advanced AI on the planet. We focused on solving a real-world problem for our customers. And again, it worked. We were acquired by Gusto, a $10 billion company.
What’s the lesson here? Focus on the problem, not the technology. The best AI products are the ones that solve a real pain point for users. The technology is just a means to an end.
The 2026 Stack: Simple, Fast, and Effective
So, what does the tech stack for a modern AI-first product team look like? It’s not about having the latest and greatest of everything. It’s about having the right tools for the job. Here’s what I recommend:
1. The “Good Enough” Model
You don’t need to train your own models from scratch. For 99% of use cases, a pre-trained model from a provider like OpenAI, Anthropic, or Hugging Face is more than good enough. These models are incredibly powerful and can be fine-tuned for your specific needs.
I’ve invested in all three of these companies, and I can tell you that they are pushing the boundaries of what’s possible with AI. Don’t try to compete with them. Instead, stand on their shoulders.
- Start with an API. Don’t even think about hosting your own model until you have a proven use case and are hitting the limits of what the API can do.
- Fine-tuning is your friend. Fine-tuning a pre-trained model is much easier and cheaper than training a model from scratch. It’s also a great way to improve the performance of the model on your specific task.
- Don’t be afraid to switch. The AI landscape is moving fast. The best model today might not be the best model tomorrow. Be prepared to switch models as new and better ones become available.
2. The Data “Pipeline” That’s Really Just a Few Scripts
Everyone talks about data pipelines like they are these complex, magical things. For most early-stage startups, a “data pipeline” is just a collection of scripts that pull data from a few different sources, clean it up, and feed it into a model. That’s it.
- Keep it simple. Don’t over-engineer your data pipeline. Use simple tools like Python scripts and cron jobs to get the job done.
- Focus on data quality. The old saying “garbage in, garbage out” is especially true for AI. Make sure your data is clean and accurate.
- Don’t worry about big data. You probably don’t have a big data problem. You have a “get me some data” problem. Focus on getting the data you need to validate your idea.
3. The “No-Code” Backend
Your backend should be as simple as possible. For many AI products, you don’t even need a traditional backend. You can use a combination of serverless functions and a database to get the job done.
- Serverless is your secret weapon. Serverless platforms like AWS Lambda, Google Cloud Functions, and Vercel allow you to run your code without having to worry about managing servers. This is a huge time and money saver.
- Choose a database that’s easy to use. I’m a big fan of PostgreSQL, but there are plenty of other great options out there. The important thing is to choose a database that is easy to use and that you are comfortable with.
- Don’t build a custom admin interface. Use a tool like Retool or Appsmith to build your admin interface. It’s much faster and easier than building it from scratch.
Putting It All Together
So, what does this all look like in practice? Here’s a hypothetical example of how you could build an AI-powered product using this stack:
The Idea: An AI-powered tool that helps people write better emails.
The Stack:
- Model: OpenAI’s GPT-4 via API
- Data Pipeline: A Python script that pulls email data from the user’s Gmail account (with their permission, of course).
- Backend: A few serverless functions on Vercel and a PostgreSQL database on Supabase.
- Frontend: A simple React app built with Next.js.
The Process:
- Build a simple prototype. The first step is to build a simple prototype that validates your core idea. In this case, the prototype would be a simple web app that allows users to paste in an email and get suggestions for how to improve it.
- Get feedback. Once you have a prototype, get it in front of users and get their feedback. What do they like? What do they not like? What features are they missing?
- Iterate. Based on the feedback you receive, iterate on your product. Add new features, improve the user experience, and make the product better.
This is the process that I’ve used to build and sell two companies. It’s not glamorous, but it works. Forget the hype and focus on building something that people want. That’s the real secret to success in the world of AI.
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.
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 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.