From Vibes to Signal: The Case for Human-Directed AI Building

You’ve hit the wall. Maybe not yet, but you know it’s coming.

The app worked beautifully for the first few weeks. You described what you wanted, the AI generated code, you refined and adjusted, and suddenly you had something real. The demo impressed people. You could click through it and it did the thing.

Then you tried to add a feature, or scale past a handful of users, or hand it to someone else to maintain. And now you’re staring at error messages that don’t make sense, or behavior you can’t explain, or a codebase that fights you every time you try to change something.

This is where most AI-built software projects stall or die. Not at the beginning, where everything feels magical. At the 70% mark, where the magic runs out and the real questions begin.

Why do some projects push through while others collapse? The answer isn’t better prompts or smarter AI models. It’s something the builder either provided or didn’t, long before the problems became visible.

I call it signal.

What Vibe Coding Actually Is

Let’s be honest about what vibe coding does well. It’s fast. It’s accessible. It put real software creation tools in the hands of people who couldn’t build before. A founder with an idea can have a working prototype by the end of the weekend. A designer can test an interaction pattern without waiting for engineering resources. A product manager can explore possibilities that would have stayed theoretical.

That matters. The democratization of software creation is genuinely valuable, and vibe coding made it happen.

But let’s also be honest about what vibe coding actually means in practice. When you prompt an AI to “build me a task management app,” you’re not just asking for code. You’re asking the AI to answer dozens of questions you never asked.

Where should data be stored? The AI picks something. How should users be handled? The AI decides. What happens when two people edit the same thing at the same time? The AI makes a choice. How should the code be organized so you can change it later? The AI has opinions, and now those opinions are your architecture.

The AI isn’t grabbing power. It’s filling a vacuum. You provided the “what” (a task management app), and the AI filled in every “how” because it had to. Those choices might be fine for your situation, or they might be time bombs. You don’t know, because you didn’t make them. They were made for you, silently, by defaults.

This works for prototypes. It fails for production. Not always, not immediately, but reliably.

Why Vibes Don’t Scale

The pattern is becoming well-documented now. Analysts are calling it the “scaling wall”: startups that reach product-market fit through fast vibe-coded MVPs, then die because their codebase becomes a black box no human can manage. The excitement phase of AI-assisted building has given way to a quieter, more sobering recognition. As one industry analysis put it: “The spike is the headline; the plateau is the signal.” The initial frenzy of fast prototyping was noise. What matters is what survives.

The cost curve tells the story. AI-only development starts cheap. Scaffolds appear, endpoints materialize, screens light up. But the curve bends upward as the project acquires mass. Integration drag. Ambiguous intent baked into code paths. Security patching after the fact. Tests arriving late and thin. Refactors that were optional becoming unavoidable.

The first developer you hire will probably want to burn everything down and start over. And fixing someone else’s AI-generated decisions is often harder than writing from scratch, because at least when you write from scratch you know what decisions you’re making.

Some teams are tracking a new metric: “comprehension margin,” meaning how much of the system the team actually owns mentally. When AI generates code without human architectural direction, you end up with a codebase that works but that nobody understands. That’s a new kind of debt. Traditional technical debt is a shortcut you consciously took. This is debt you didn’t know you were incurring.

The teams hitting this wall aren’t bad at building. They’re discovering something counterintuitive: speed without direction isn’t actually fast. The 40% you saved on MVP development gets swallowed by a 5x increase in maintenance costs. Senior engineers end up spending 80% of their time trying to understand AI-generated modules rather than building new features. Speed is not a competitive advantage if it leads to a dead end.

Here’s the key insight: this isn’t a code quality problem. The code is fine for what it was asked to do. It was never asked to do the right things. The decisions underneath the code were made by default, and defaults don’t know your users, your constraints, your future, or your actual goals.

The problem isn’t bad code. It’s absent decisions.

What the Human Brings

The projects that survive have something the crashed projects lack. It’s not better AI models or more sophisticated prompts. It’s something the human provided before and during the build: direction that shapes what the AI produces.

I’ve started calling this “signal.”

Signal is the architectural judgment, the production requirements, the understanding of why one database choice matters over another for this specific use case. It’s the constraints you set before code is generated. It’s the decisions you make consciously rather than letting the AI make by default. It’s the direction that turns a pile of working features into a coherent system.

You’ve probably heard “signal” used differently in the AI discourse. People talk about “signal-to-noise ratio” as a way to measure output quality: how much of the AI’s code review feedback is actually useful versus how much is nitpicking? That’s a valid usage. But it’s measuring something downstream.

The usage I’m proposing goes upstream. Signal isn’t just a quality you measure in output. It’s something the human provides as input. And the reason AI code review produces so much noise is often that insufficient human signal went in at the beginning. The AI generated code without clear direction, so now another AI is generating feedback without clear standards to measure against. Noise compounds.

The 2026 discourse has been circling this idea using a lot of different words. Intent. Judgment. Architecture. Specification. Direction. Constraints. People are talking about “spec-driven development” and “architecture-first approaches” and “human-in-the-loop governance.” One researcher put it simply: “Intelligence isn’t our bottleneck; intention is.” Another framed it as the human becoming a “master architect” while the AI serves as a “highly skilled executor.” The teams at Amazon working on this problem found that the highest returns come not from maximizing AI output speed, but from minimizing the necessary human rework cycles. Spending time up-front on clarity yields better results than spending time later on fixes.

All of these are describing the same underlying phenomenon. But “signal” captures something the other words don’t. It carries a systems-thinking quality: something transmitted into a system, with a sender, a receiver, a medium, and the possibility of degradation or loss. When you think about it this way, the question becomes clearer. Not “did the AI write good code?” but “did the human transmit enough signal for the AI to write the right code?”

The future of AI-assisted development, as one engineer put it, lies not in “prompt-and-pray vibe coding” but in “augmented workflows where AI amplifies human design and humans rein in AI’s excesses.” That’s a description of signal flowing in both directions: the human provides architectural direction, the AI provides implementation options, the human evaluates and decides, the AI executes within those decisions. Meaning remains human. Enforcement becomes automated.

The Spectrum

Here’s where the conversation usually goes wrong. People frame vibe coding and serious software development as opposing camps. Either you’re vibing with the AI and shipping fast, or you’re doing things the slow, proper way. Pick a side.

That framing misses what’s actually happening. Vibe coding and what I’ll call “AI building” aren’t binary categories. They’re ends of a spectrum, and the spectrum is defined by a single variable: who makes the consequential decisions.

At one end, all vibes, no signal. “Build me a task app.” The AI makes every consequential decision about data, users, architecture, and organization. Fast, exciting, fragile. Good for weekend prototypes and personal tools.

At the other end, all signal, no vibes. A fully specified architecture with the AI generating code strictly within defined constraints. Every decision made by the human, documented, and enforced. Slower to start, coherent throughout. Necessary for production systems that will face real users and real consequences.

Most real work falls somewhere in between. And the right position on the spectrum depends entirely on what you’re building.

A throwaway prototype to test an idea? Lean toward vibes. The cost of architectural mistakes is low because you’re going to throw it away anyway. A tool just for yourself that nobody else needs to maintain? Vibes are probably fine. You can live with decisions you didn’t consciously make because you’re the only one affected.

But a product that will face real users, real data, and real consequences? A system that other people will need to understand and modify? Something that needs to scale, or stay secure, or integrate with other systems, or survive for more than a few months? You need signal. The cost of absent decisions is too high.

The question isn’t “are you vibe coding?” That’s the wrong frame. The question is: “How much signal are you providing, and is it enough for what you’re building?”

How to Provide Signal

Signal doesn’t require you to become a software engineer. It requires you to change how you interact with the AI.

Most people use AI like an oracle. You ask a question, you get an answer. “Build me a user authentication system.” The AI builds you something. Maybe it’s the right approach for your situation, maybe it isn’t. You have no way to evaluate because you never saw the alternatives. You asked for a solution and you got one.

The shift is to use AI as an analyst instead. An analyst doesn’t just give you an answer. An analyst gives you options, explains the tradeoffs of each, and helps you understand the consequences of different choices. You take that analysis and make a decision based on your specific context, goals, and constraints.

The practical mechanism is what I call the options conversation. Instead of asking for solutions, you ask for alternatives. Instead of accepting the first answer, you probe for what you’d be giving up. Instead of letting the AI decide, you decide, with the AI’s help.

Here’s what this looks like in practice.

Vibes version: “Build me a task management app with teams.”

Signal version: “I’m building a task management app for small teams. Before we write any code, I need to understand my options. What are the main approaches for how team membership and permissions could work, and what are the tradeoffs of each?”

The vibes version gets you code immediately. The signal version gets you a conversation about whether teams should be hierarchical or flat, whether users can belong to multiple teams, who can see whose tasks, who can edit what, and what happens when someone leaves a team. You learn things you didn’t know to ask about. Then you decide, and the AI generates code that reflects your decision rather than its default.

Another example.

Vibes version: “Add user login.”

Signal version: “I need user authentication. What are the main approaches I could take? For each one, tell me what it would mean for security, user experience, and my ability to change things later.”

The vibes version gets you a login system. Maybe email and password, maybe social login, maybe something else. The signal version surfaces that email and password means you’re responsible for security best practices; social login means dependency on third-party providers but less security burden; magic links mean no passwords to manage but reliance on email delivery. You learn the shape of the decision, then you make it.

One more.

Vibes version: “Store the data.”

Signal version: “Where should this application’s data live? What are my options, and what happens to data availability, performance, and cost at each choice?”

The vibes version stores data somewhere. The signal version explains that local-only storage means no sync across devices but also no server costs; a cloud database means availability everywhere but introduces latency and ongoing expenses; a hybrid approach means complexity but flexibility. You understand what you’re choosing before you choose.

In each case, the signal version doesn’t take longer. It takes different. The conversation might add thirty minutes to your process. But the understanding you build in that conversation prevents the four-month detour, the scaling wall, the first developer hire who wants to throw everything away.

Every conscious decision you make is signal entering the system. Every decision you leave to the AI’s defaults is signal you didn’t provide. The sum of those decisions determines whether you’re building something that will survive contact with reality.

The Real Advantage

Code is a commodity now. Any AI can produce it. The bottleneck has moved.

In 2024, the question was: can AI write code? In 2025, the question became: can AI write good code? In 2026, the question that actually matters is: can AI write the right code?

And the answer to that question depends entirely on what the human provides. The AI has broad knowledge: patterns from millions of codebases, solutions to problems across every domain. But broad knowledge isn’t the same as specific judgment. The AI doesn’t know your users, your constraints, your goals, or your future. Only you know those things. And if you don’t transmit that knowledge into the system, the system can’t use it.

This is what the teams that survive understand. Code is now a commodity; architectural judgment is the only remaining asset. The winners in 2026 won’t be those who prompted the fastest. They’ll be those who used AI to amplify human judgment rather than replace it.

Signal is what makes AI-generated code worth something. It’s the human judgment, the architectural direction, the conscious decisions that turn generated code into a system that works for your specific situation. Without signal, you have a prototype that might work. With signal, you have something you can ship, maintain, and grow.

The ability to provide signal is what separates projects that ship from projects that stall, products that scale from products that crash, builders who grow from builders who plateau.

Some people worry that AI is making human judgment obsolete. The opposite is true. When anyone can generate code, the ability to direct that generation becomes the scarce resource. When AI can produce infinite plausible-looking output, knowing what output to produce becomes the valuable skill. The human role isn’t diminishing. It’s concentrating into the part that actually matters: the decisions that determine whether code becomes a product or joins the graveyard of abandoned prototypes.

Vibes got you started. Signal gets you there.