LLMs and Agents Are Commodities. Your Moat Is Somewhere Else.

Everyone's racing to build AI agents right now. Every SaaS company is shipping an "AI assistant." Every startup pitch deck has the word "agentic" somewhere in the first three slides. VCs are writing checks to anyone with a functioning agent demo.

But here's what nobody wants to admit: agents are just as commoditized as the LLMs powering them.

The entire AI stack has become interchangeable. Claude, GPT-4, Gemini—they're converging fast. Most users can't tell them apart in blind tests. Switching costs? Change an API key. That's it. The models themselves stopped being a moat about 18 months ago.

Now we're watching the same thing happen with agents. The recipe is public knowledge. Take an LLM, give it tools, add a loop so it can plan and execute, throw in some memory. Done. You've got an agent. n8n ships agents. Zapier ships agents. Glean, Dust, every workflow tool on earth is shipping agents. The JSON definition of any agentic workflow can be replicated in an afternoon.

So if the models are commodities and the agent architecture is public knowledge, where's the actual value? Where's the moat that keeps customers from churning the second a competitor launches?

It's not in the engine. It's in the fuel.

The Thing Everyone's Building vs the Thing That Actually Matters

I've been watching companies obsess over their agent architecture. They're tweaking prompts, optimizing for latency, building fancy UI for agent orchestration. That stuff matters for user experience, sure. But it's not defensible.

The real differentiation is in what you feed the agent. Not the agent itself.

Think about it this way. Every car company has access to similar engines. Toyota, Honda, Ford—they're all working with internal combustion or electric motors built on the same physics. The engine is table stakes. What differentiates them is everything else: the design, the supply chain, the brand, the dealer network, the data on what breaks and why.

Agents work the same way. The LLM is the engine. Everyone's got the same one. What makes your agent better than mine isn't the model or the architecture. It's the context, the data, the accumulated knowledge that took time to build.

Here's what actually creates a moat:

Proprietary data that agents can act on. When a customer connects their CRM, their accounting software, their email, their calendar—that's not just data. That's context the agent needs to be useful. A generic agent with no context is like a smart person with amnesia. Technically capable, but useless for getting real work done.

Historical patterns that compound over time. If you're building an agent for plumbers, the value isn't in the agent itself. It's in knowing that plumbers in residential service average 4.2 jobs per day, that no-shows spike on Mondays, that customers who reschedule once are 3x more likely to cancel. That knowledge came from thousands of businesses using your product. It can't be copied by reading a blog post.

Domain expertise baked into the skills. A good agent for real estate works differently than one for e-commerce or one for law firms. The tools are different. The workflows are different. The metrics that matter are different. Building that domain knowledge takes time. You have to talk to users, watch them work, understand what they actually need versus what they say they need.

Network effects from aggregated data. "Businesses like yours typically see X" is a sentence that gets more valuable every time a new customer signs up. That's a moat. You can't replicate that overnight.

What This Means If You're Building in AI

Stop competing on the agent. Everyone's going to have agents. The technology is too accessible, the patterns are too well known. You can't win by having a slightly better prompt or a faster execution loop.

Start competing on what the agent knows. Build systems that accumulate context over time. Collect data that makes your agents smarter with every interaction. Develop domain expertise that takes years, not weeks.

The companies that win in AI won't be the ones with the best models or the fanciest agent frameworks. They'll be the ones that control the context layer. The ones that have data flywheels. The ones where switching costs come from accumulated history, not from technical lock-in.

Think about why people don't switch from their CRM, even when they hate it. It's not because Salesforce has better technology than HubSpot or Pipedrive. It's because all their data is in there. Their historical deals, their customer notes, their forecasting models. The switching cost is in the context, not the features.

That's the game with AI agents too.

The Uncomfortable Truth About AI Moats

Most AI companies right now don't have moats. They have nice UX and good marketing. That's fine for growth, but it's not defensible. When the next wave of AI tools launches—and it will—users will churn.

The ones that survive will be the ones that made themselves hard to leave. Not through technical lock-in or predatory contracts. Through accumulated value that compounds over time.

Your agent needs to get smarter the longer someone uses it. It needs to learn their preferences, their patterns, their weird edge cases. It needs to have context about their business that a fresh competitor can't replicate by spinning up a new instance.

This is why horizontal agents struggle and vertical agents win. A generic "do anything" agent has no moat. It's competing against every other generic agent, and the best model wins. But an agent built specifically for dental practices, law firms, or HVAC companies? That agent has domain knowledge. It has industry-specific skills. It knows what matters and what doesn't.

That's defensible.

Where to Actually Invest Your Time

If you're building something in AI, here's what I'd focus on:

Figure out what data you can accumulate that competitors can't easily replicate. Usage data, outcome data, industry benchmarks, anything that gets better with scale.

Build the context layer before you build the agent layer. Most people do this backwards. They build a cool agent, then wonder why users churn. Build the data infrastructure first. The agent is just the interface.

Pick a vertical and go deep. Horizontal is a losing game unless you're OpenAI or Anthropic. Even then, their moat is capital and compute, not technology. Everyone else needs to specialize.

Make your product more valuable over time. Every interaction should add context. Every user should make the product better for the next user. If you're not building a flywheel, you're building a feature.

The Bottom Line

LLMs are commodities. We all know this by now. But agents are commodities too. That's the part people don't want to hear.

The real question isn't "how do I build a better agent?" It's "what can my agent know that nobody else's can?"

Answer that, and you've got a business. Miss it, and you're just another AI wrapper waiting to get disrupted by the next model release.

The moat isn't in the engine. It's in the fuel you've spent years accumulating.