Building AI Products is Easy Now. Building AI Businesses is Harder Than Ever

The Great AI Expertise Paradox: Why Building Better Products Just Got Harder

We're living through a fascinating paradox in the AI world. Just as artificial intelligence becomes the most important technology of our time, the value of AI expertise itself is plummeting. And this shift is about to reshape how we think about building successful AI companies.

The Death of the AI Moat

Let's start with something that should terrify every AI startup founder: the cost of development is racing toward zero. We're seeing development teams without deep AI expertise building absolutely amazing AI-powered products using existing APIs, frameworks, and tools. The barriers to entry that once protected AI companies are crumbling faster than anyone expected.

This means that having a PhD in machine learning or years of experience training models is no longer your secret weapon. The real differentiator has shifted to something much more traditional: product experience that a large number of people can actually use.

The 80% Problem

Here's where it gets interesting. Most AI startups are chasing the same strategy – build for the generic workflows that 80% of your target market uses. It's a logical approach that creates initial differentiation, but here's the uncomfortable question: how easy is it to maintain that differentiation when everyone else is playing the same game?

From our experience working with enterprises, especially on knowledge agents, we've learned something crucial: the way each business actually works is what matters most. This isn't just about having different processes – it's about understanding that these unique workflows are where companies derive their true competitive advantage.

The Personalization Imperative

Think about the consumer internet for a moment. The companies that dominated weren't just those with the best technology – they were the ones that personalized to individual users. Google didn't just search better; it learned what you specifically were looking for. TikTok doesn't just show videos; it creates an endless stream tailored to your exact preferences.

In the age of AI, enterprise software is heading toward the same destination. The companies that will win are those that can personalize to an enterprise's unique workflows and amplify their specific ways of working. One-size-fits-all products will deliver small marginal utility and face intense competition.

Even if generic products succeed, it'll likely be through network effects, creating a winner-take-all dynamic with a few massive winners and countless losers. Just look at Cursor and Windsurf – they're already in an arms race that could easily lead to commoditization.

The Vertical AI Trap

There's been a lot of buzz around vertical AI startups, but most of these are built on the same flawed premise: creating shallow products for that magical 80% use case. Here's what they're missing: even within the same vertical, different companies have radically different ways of working.

Think about it this way – when you hire a new employee, you don't just hand them their degree and expect them to start contributing. You have an onboarding plan. You teach them how your organization operates, what your specific workflows look like, and how to fit into your unique culture. Their basic degree is just table stakes – it doesn't add real value within your organization until they learn to adapt.

What matters is how well that employee can combine their expertise with how your organization actually operates. The most valuable employees aren't just skilled; they're adaptable enough to mold their expertise to your company's specific way of doing things.

If we think of AI agents as employees – which, let's be honest, is exactly what they're becoming – then organizations should expect the same degree of flexibility and adaptability. A "vertical AI" solution that works the same way for every law firm or every hospital is like hiring someone who insists on doing things exactly as they did at their previous job, regardless of how your company operates.

Companies don't just want AI that understands their industry; they want AI that understands their specific way of working within that industry. Because that's where they believe their competitive advantage lies – and they're absolutely right.

This reality fundamentally changes what it means to build a successful AI company. You can't just build a great product and expect enterprises to adapt to it. Instead, you need to understand each organization's unique workflows, constraints, and competitive advantages. You need to become a consultant first, technologist second.

This brings us to a fundamental shift in how we should think about AI companies. In this new era, the real differentiator won't be your algorithm or your model – it'll be human relationships, consultative selling, and becoming a solutions company rather than just a product company.

The Platform-First Approach

So how do you build for different enterprise requirements when each needs something tailored to them? The answer lies in thinking platform-first. You build around deep core expertise in technology – like operating on data at scale – combined with a platform that can be quickly tailored to different knowledge or business process workflows.

This isn't about building custom software for every client. It's about having a robust foundation that can be rapidly configured to match how each enterprise actually works. Think of it as having a sophisticated engine that can power different types of vehicles depending on what each customer needs.

The key insight is that the platform needs to be designed for adaptability from day one. Your core capabilities should be modular enough that you can mix, match, and configure them to fit different organizational workflows without rebuilding from scratch each time.

The Zero-Cost Customization Trick

Here's where living in the AI era becomes a massive advantage: you can make the cost of customization, personalization, and configuration approach zero. AI can help you understand each enterprise's unique workflows faster, generate configurations automatically, and even adapt the system's behavior based on how different organizations prefer to work.

This is fundamentally different from traditional enterprise software, where customization meant expensive professional services engagements and months of implementation work. With AI, you can potentially configure a system for a new enterprise in days or weeks rather than months.

Building Repeatable Revenue

This approach creates a path to repeatable, predictable revenue that traditional SaaS models struggle with in the enterprise AI space. Instead of selling the same product to everyone, you're selling the same platform capability but with rapid customization that feels bespoke to each customer.

The revenue model becomes: platform subscription + configuration fee + ongoing optimization. The platform provides the foundation, the configuration fee covers the initial tailoring (which becomes cheaper over time as AI automates more of the process), and ongoing optimization ensures the system continues to adapt as the enterprise evolves.

This creates stickiness because the system becomes deeply integrated into how each organization works, while maintaining predictability because you're not building from scratch each time.

What Comes Next

The AI revolution is forcing us to reconsider everything we thought we knew about building technology companies. The winners won't necessarily be those with the best AI researchers or the most sophisticated models. They'll be the companies that can bridge the gap between powerful AI capabilities and the messy, unique realities of how businesses actually work.

This isn't just about technology anymore – it's about understanding that in a world where building AI is becoming commoditized, the real value lies in understanding people, processes, and the specific ways different organizations create value.

The question isn't whether AI will transform business – it's whether you'll be among the companies that figure out how to make that transformation work for each unique enterprise you serve.

What's your take on this shift? Are you seeing similar patterns in your industry?