The Moat is Dry

(Photo credit: Brad Helmink)

Remember when building software was a major capital expense? When custom codebases and engineering headcount were your moat? The moat is dry. Anyone can build now.

Timelines have collapsed. What used to take me months, I now complete in an afternoon. It's not 💩-code either; it's well-designed, follows best practices, and is ready to serve. The barrier to entry collapsed along with timelines, letting in hoards of vibe coders. Generative AI brought high-leverage software to the masses. Execution no longer sets you apart. So how do you stand out?

Standing Out in a Sea of Vibe Coders

Fit. Fit has always mattered. But now, with more entrants capable of getting something built, fit is the filter that separates tools people try from tools they rely on. Fit is how tightly your system binds to a real problem in a real environment—one painful enough that people are willing to pay to make it go away.

Domain. It's not enough to OCR documents. It’s not even enough to perform entity resolution. If vibe coders can do all that too, then you need to go deeper—synthesizing critical business concepts, surfacing novel insights, or inferring outcomes that others haven’t yet figured out how to extract. You need domain fluency. Tooling alone won't do. Experience pays dividends.

Integration. If everyone’s building similar features, then the winners will be those who focus on integration—making their tools easier to adopt, easier to plug in, and harder to rip out. The first to fit cleanly into the mess of real-world systems will win.

Welcome the Agentic Workforce

Agentic AI is becoming the new workforce. A well-scoped agent replaces a small function or task carried out by a human. It can read, reason, and adapt. It tolerates ambiguity. It executes with consistency. It learns from context. Unlike traditional systems, which were rigid and failed at the edge, agents built with generative AI are more adaptable. Those who learn to hire, train, and manage them will pull far ahead.

Agents are then orchestrated like a team to carry out more sophisticated tasks. This is the exact abstraction CrewAI uses in their core design.

Agents don’t sleep. No burnout, no PTO, no “forgot to follow up.” Once trained, they operate with relentless precision. And, let’s be honest—fewer HR issues.

To demystify what’s happening here, let's draw a metaphor to onboarding. Integrating agentic AI is a lot like bringing on a new employee. They need infrastructure (a place to sit), access to tools, a defined task, and a training plan. Today’s forward deployed engineers are configuring, integrating, and embedding domain fluency into agents. They're onboarding them.

But agents still need a manager. An agent is just an IC—it needs goals, scope, and coordination. Good systems aren’t built around “super agents” (or "jumbo prompts"); they’re built like teams, with clearly defined roles, escalation paths, and orchestrated handoffs. Tasks get distributed. Someone is accountable. Someone removes blockers and owns the outcome. It looks a lot like management because it is.

Most business processes aren’t optimized—they’re patched together to accommodate human or IT limitations. That’s not a criticism; it’s just the truth. “I click here to export the PDF, convert it to CSV over there, upload it into this tool, click ‘Go,’ and then… and then…” You know the drill. Agentic systems can execute those duct-taped workflows exactly. But they can also suggest improvements, run simulations, and evolve the flow without getting tired or distracted.

Even pricing can follow the same metaphor. Companies pay people for time and effort, hoping for outcomes. Agentic pricing follows that logic: speculative, ambiguous work is charged by effort—tokens consumed, models run. Clear, well-defined tasks with measurable success get billed by outcome. It's cost-plus vs. value-based pricing all over again, just in a new wrapper.

The New Arbitrage

Offshoring moved work across geography. Agentic AI moves it across abstraction layers. Companies with well-documented, well-scoped processes can now scale faster than ever. If your operations are explicit, you can plug in agents—internal or vendor-supplied—and scale output with near-zero marginal cost. If your ops are fuzzy, you’re stuck doing it all by hand.

Documentation—knowledge transfer—isn’t busywork anymore. It’s your spec. It’s your leverage. Start writing things down. Keep them updated. Better yet—have agents do that for you.

The ground is shifting. My thoughts on staying ahead:

• Know your domain. Live close to the work. Understand the pain. Be observant. Pay attention. Integrate deeply. Work where your users already do. Document everything. Not just for handoff—for leverage. Design for teams. Don’t build tools. Build roles. Then wire them together. Price to fit. Align your billing model to the shape and certainty of the task.

• This is a labor story more than a technical one. People will be displaced—especially in rules-based, repetitive roles. But they won’t disappear. Teams will get leaner. Repetitive tasks will shrink. Ideally, this creates space for more creativity and deeper thinking. The people closest to the pain are also closest to the opportunity. We’re going to see a wave of niche, domain-specific tech startups, built by former operators who know the workflows cold and can go from zero to prototype in a weekend. Agentic AI opens the path. Founders flood in behind it.

• The best person to automate a task is the one who used to do it. Expect a new wave of roles where former ICs become product owners for agent systems. They’ll define flows, write specs, train models, monitor behavior. They won’t be replaced—they’ll be elevated. The value doesn’t vanish. It just shifts toward design and oversight.

The moat is dry. The advantage now is fit, speed, and fluency in the domain. Your next hire might not have a desk. But it still needs onboarding. It still needs scope. It still needs leadership.

Are your processes ready?