The Build That Worked—And Why It Wasn’t Enough

How to differentiate AI products is the real question today, because everyone has access to frontier AI—and baseline practical AI coding efficiency is table stakes. Just being able to ship something impressive isn’t the differentiator anymore.

The real stall came from picking a low-leverage problem. I iterated endlessly, mistaking busy release cycles for actual traction, hoping users would wander in because the tech was cool. I wish I could say it was clear in the moment—but it never is. If you’re like me, you might confuse momentum with progress—the “loop” feels active, but never proves lasting value. We all have the same tools now. The only edge left is in choosing a problem that matters, looping fast enough to validate if it’s useful, and standing out when attention’s scarce.

Here’s how you actually get ahead: focus hard on problem selection, loop to prove and cement durable value, and make engineered distribution your own. Access is the new zero. The moat is where you pay attention.

How to Differentiate AI Products When Access Isn’t the Moat

Frontier models and no-code tools mean turning an idea into a product is now the new zero. The time from concept to working demo shrinks to hours, sometimes minutes. Fast shipping used to matter, but now everyone—teams, solo builders, hobbyists—can sprint just as quickly. Speed alone doesn’t set you apart anymore.

But here’s the catch: when you build inside someone else’s garden, like launching an app atop GitHub, you’re giving up leverage whether you admit it or not. Microsoft owns GitHub, and they’re not shy about putting their own AI features front-and-center. Ask anyone who built a Twitter client. When Twitter changed API terms, most third-party apps disappeared, which shows how fast platform risk can turn into obsolescence. I knew the asymmetry was there, but kept building anyway, half-convincing myself I’d outpace the giant. I didn’t. The question isn’t “is your tech impressive?” It’s: when everyone’s got frontier models, instant branding, and global access, how does AI product differentiation become the moat?

So when you ask how to differentiate AI products, you stop fixating on novelty and pure speed. Instead, we pick better problems, loop fast to prove there’s durable value, and design distribution we actually control—and keep evolving teams for scaled AI as complexity scales. That’s how we build real moats—right where the leverage lives.

How to Spot High-Leverage Problems Fast

Start with the pain, not the prompt; AI problem selection starts there. High-leverage problems show up where work is stuck every day, not once a quarter. There’s a budget owner whose neck is on the line, and the problem fits right into what people are already doing—no behavior change required. The best ones connect to unique data or set you up for compounding advantage later. I admit, I defaulted to what I could build, not what would accrue advantage, and it absolutely slowed me down.

Before you get swept up, run three fast filters. Who owns the underlying platform—and could they subsume your solution overnight? Is there a wedge that actually earns trust with a specific, sticky group, not just broad “users”? I wish I’d asked early on, was GitHub the right battlefield for me, now, or was I signing up to play someone else’s long game?

Picking a problem is like choosing a climbing route. The first few moves feel easy—it’s the crux in the middle that shows whether your strengths and gear match the route.

Here’s my most durable shortcut: block off two hours for a sprint. Map exactly who buys and where your tool fits—literally draw their workflow, not just the “feature.” Talk to five target users—split across real roles, not just friendly faces. Shadow one in-session if you can. Then, write the kill criteria—clear rules that help you decide when to build vs. solve. What will force you to drop this next week if it’s not clearly worth chasing? Writing my own kill criteria instead of moving the goalposts would have saved me weeks and erased the sunk cost trap before it ever started.

How to Prove and Compound Value—Stop Thrashing, Start Validating

To validate AI product value, look for recurring time saved, fewer errors, or habits changed directly in the user’s workflow—not just in your dashboard. If users feel it in how they work, you’re onto something. If the only “impact” is your app showing a green tick, you’re not.

Make the loop observable. For each release, pick a single metric you want to improve, capture three user sessions—real usage, not simulated—and require at least one before/after artifact, while deciding POC vs production up front. Skip this and you risk engineering a feedback halo where you celebrate progress that isn’t landing for anyone but you.

Concrete patterns stick. One well-built integration that removes a daily copy/paste from Slack to Jira, or one automation that prevents this week’s inevitable 2AM on-call page, does more to cement value than any suite of “potential” features. Look for spots in your users’ week that vanish—those are the moats worth digging.

Here’s where I still get stuck: even when I know all this, there are weeks I catch myself stretching the definition of “user impact” just to feel like something shipped. I know I shouldn’t. But that pull—to call the new metric or dashboard “value,” even if it’s not felt by the actual user—never really goes away. Maybe that tension is just how building works now.

Replace Hope With Design—Engineered Distribution Wins

Here’s the rule I live by now. Never trust platform benevolence. Engineer your distribution from the start. Don’t assume access or audience will magically follow a cool tool. If you ship and hope, you’re guaranteeing nobody will see it.

If you want owned reach, design an AI distribution strategy that goes where your users already are—and build ties you control. That means integrating into the tools and workflows your audience uses daily, not inventing new ones for them. Show up in their Slack channels, their community forums, their newsletters. Start collecting your own list from the first sign-up, so you have permission to check back, share updates, and actually learn what’s landing.

The secret is to build a trust flywheel. Publish something useful—a script, a template, a how-to—then open the door for feedback. Ship the next improvement based on what people tell you. Return with “here’s the update, thought you’d like it,” not just blasting noise. Send it back to your list, to partners, to anyone who used what you posted. I missed a huge opportunity by keeping my code reviewer learning private; if I’d turned those lessons into public artifacts and cycles, curiosity could have become compounding trust.

Let’s answer the doubt head-on: can you build distribution without the blessing of a big platform? Yes. Start narrow. Find micro-communities, zero in on one ideal user type, and make an artifact that actually solves a job they need done today—and build buy-in for unproven bets by tying it to measurable pain. Forget waiting for “scale” or a platform shoutout. You’ll earn leverage one tight loop at a time.

This isn’t a feel-good pep talk. When you ship with engineered reach, trust compounds and your distribution grows as an actual asset. Nobody’s coming to save your product. Design so it never needs saving.

Turn Speed Into Adoption: Your Four-Week Start Plan

Here’s how to actually begin. No theory, just a concrete starter plan. Week 1: pick one high-leverage problem, but write down clear kill criteria before touching code. If it’s not promising midweek, drop it with zero guilt—what matters is that you prove the opportunity, not just fill a backlog.

Weeks 2 and 3: ship two fast loops, each scoped ruthlessly to a metric you can track. Hold yourself to capturing some “before/after”—real artifacts, not just vibes. Week 4: launch two trust assets (e.g., a how-to guide, a sample workflow, a sharable template) and push one live integration that collects emails or sign-ups directly. Don’t wait for users to stumble onto your project—kickstart an owned list from day one. The question isn’t who has the tools. It’s who wields them with speed and focus when everyone starts from the same baseline.

You’re not sacrificing quality for motion here—you’re compressing the time it takes to surface what works (and what doesn’t), so nothing precious ages out in private. When access is free, you differentiate without frontier models by making focus and trusted distribution the way speed becomes adoption. You don’t need a platform’s permission. Start building your moat now.