I still remember the feeling in 2014. We had just sold MovieLaLa to Gfycat. Before that, RemoteTeam was a wild ride that ended in an acquisition by Gusto. People see the exits, they see the headlines. What they don’t see are the thousands of small bets, the gut feelings, and the constant, nagging feeling that you might be missing the next big thing.
That feeling is stronger than ever today. The AI market isn’t just moving fast; it’s rewriting the rules of software, and it feels like we're living a year of progress every month. If you’re a founder right now, you can’t afford to be surprised. That’s why I’m opening up my playbook. This isn’t some generic report. This is my personal analysis of the cloud AI market, the trends I'm betting on, and the opportunities I believe will define 2026. This is the research guiding my own investment and product strategy.
For years, the game in SaaS was horizontal. Build a tool that can be used by anyone, from a small marketing agency to a Fortune 500 company. Think Salesforce, think Slack. These are phenomenal businesses. But the ground is shifting. The massive, general-purpose AI models, like the ones my friends at OpenAI and Anthropic are building, have created a new foundation. The biggest opportunities are no longer in building the next giant, horizontal platform. The real gold is in going vertical.
Think of it like this: the first wave of the internet gave us massive, open highways. The second wave gave us the cars—the browsers and search engines—to navigate them. This new wave of AI is about building specialized vehicles for specific destinations. We're not just building cars anymore; we're building combines for agriculture, specialized drills for mining, and high-precision scalpels for surgery. We're building AI for specific industries, for specific workflows. This is the era of Vertical SaaS.
The Old Guard is Dead: Usage-Based Pricing is King
One of the clearest signals of this shift is the death of the old pricing model. The days of charging a flat $50 per seat per month are over. It just doesn’t make sense in an AI-powered world. When the value your product delivers is directly tied to an AI's output—a generated report, a processed transaction, a completed analysis—your pricing has to reflect that.
I learned this lesson the hard way. In the early days, we experimented with all sorts of pricing tiers. What we found was that value wasn't about how many people were logged in. It was about what they were doing. The companies getting the most value were the ones running the most payrolls, not the ones with the most HR managers on the platform.
This is why usage-based pricing is no longer a niche strategy; it's the only strategy that makes sense for AI-powered SaaS. It aligns your revenue directly with your customer's success. When they grow, you grow. It’s that simple. Look at companies like Snowflake. Their entire business is built on consumption. You pay for what you use. This model is filtering down into the application layer. AI pricing models are not about seats; they are about results.
Founders who get this right will win. It forces you to have an honest conversation about the value you create. If your AI is truly making a difference, customers are happy to pay for the outcome. If it’s not, well, you have a bigger problem than your pricing model. Your product isn't working.
Your Unfair Advantage: The Serverless AI Stack
So, how does a startup compete in this new world? How do you build a sophisticated, AI-powered vertical SaaS product without the resources of a Google or a Microsoft? The answer lies in the stack.
Ten years ago, you needed a team of PhDs and a mountain of servers to even think about building an AI product. Today, you can build a world-class AI application with a small team and a credit card. This is thanks to the rise of the serverless AI stack.
I’m an investor in companies like Scale AI and Hugging Face because they are building the critical infrastructure for this new generation of AI developers. You don't need to build your own models from scratch. You can leverage powerful foundation models and fine-tune them for your specific vertical. You don't need to manage your own GPU clusters. You can use serverless platforms to deploy and scale your models on demand.
This is a profound shift. It means that the barrier to entry for building AI products has collapsed. Your competitive advantage is no longer about who has the biggest server farm. It's about who has the deepest understanding of a specific industry's problems. It's about who has the most unique dataset. It's about who can build the best, most intuitive workflow for a specific user.
This is your unfair advantage as a founder. You can be faster, more focused, and more nimble than the incumbents. While they are stuck in endless meetings trying to figure out their AI strategy, you can be in the market, talking to customers, and shipping product.
Where Are the Untapped Opportunities?
Every time I meet with founders, I ask them the same question: “What’s a broken, inefficient process in your industry that everyone just accepts as ‘the way things are’?” The answers are always a goldmine.
That’s where you’ll find the next billion-dollar vertical SaaS companies. They won’t be building another CRM. They’ll be building AI-powered systems for:
- Automated underwriting in niche insurance markets: Think crop insurance, or insurance for classic cars. These are complex, data-intensive fields where a specialized AI can outperform a human underwriter.
- Supply chain optimization for perishable goods: The amount of waste in the food supply chain is staggering. An AI that can predict demand, optimize routing, and minimize spoilage is a massive opportunity.
- Personalized education platforms for skilled trades: The world needs more electricians, plumbers, and welders. An AI-powered platform that can create personalized learning paths and provide real-time feedback could revolutionize vocational training.
These are just a few examples. The key is to find a niche that is big enough to be interesting, but small enough to be overlooked by the big players. Go deep. Become the expert. Build the indispensable tool for that industry.
My Bet for 2026
I’m putting my money where my mouth is. My investment thesis for the next 18 months is simple: I’m backing founders who are building AI-powered vertical SaaS companies. I’m looking for teams that have a unique insight into a specific industry, a clear vision for how AI can solve a critical problem, and the grit to build a great product.
This isn’t just about the financial returns. It’s about building the future. The last decade of software was about connecting us. The next decade will be about making us smarter, more efficient, and more capable. AI is the engine of that progress, and vertical SaaS is the vehicle.
Don’t get me wrong, the road ahead is not easy. There will be hype cycles, there will be failures, and there will be moments of doubt. But for the founders who are willing to go deep, to solve real problems, and to build for the long term, the rewards will be immense. The future isn’t just bright; it’s vertical.
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 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.
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.