Everybody has dashboards now. That is the part that actually changed.

Fifteen years ago the argument in most rooms was that we needed more information. We got it. There is now more information about a song in its first week than there used to be about an artist in a year. And the arguments in the room are exactly as long as they used to be. They just have charts in them.

I have sat in a lot of those rooms. Radio, label, management. The thing I keep noticing is that the number is almost never what people are disagreeing about. Two people look at the same curve. One says the song is working. The other says the song is being pushed. They are both looking at the same chart, and neither of them is wrong about the chart.

What they disagree about is what the chart is evidence of.

That is a context problem, and no amount of additional data touches it.

What context actually means

Context is everything that was true around the number.

A record adds forty stations this week. Is that good? It depends on which forty. Whether they were already in on the last one. Whether somebody called in a favor that costs something in six months. Whether the adds are in markets the artist can actually get to. Whether the same forty adds happened for somebody else in the spring and went nowhere.

None of that is in the number. All of it is in somebody’s head.

And that is the real shape of the problem. The context exists. It is just distributed across people, and it decays. The person who knew why a station made a call in March has changed companies by August. The number stays in the system with none of the reason attached to it, and a year later somebody builds a model on it.

Why more data made it worse

Here is the part I did not expect.

When there were three numbers, everyone knew they were working off almost nothing, so they argued about the thing itself. Someone in the room had been doing it for twenty years and their read got weight, because there was nothing else to give weight to.

Now there are three hundred numbers, and the numbers feel like the argument. So the experienced person’s read gets treated as an anecdote. It gets called a gut call, which in most rooms is now a polite way of saying you do not have a slide.

But that read was not a feeling. It was compressed context. It was fifteen years of watching what happened after this exact pattern, and the fact that it cannot be sourced does not mean it is not evidence. It means we have not figured out how to keep it.

That is the loss nobody is measuring. We replaced a small amount of information with a lot of it, and along the way we quietly downgraded the only thing that told us what any of it meant.

And now anybody can ask

The thing that changed recently is that the analysis got cheap.

Somebody can drop a spreadsheet into a chat window and have a competent read in about forty seconds. I do it. It is genuinely useful and I am not going to pretend otherwise while building with the same tools.

But it moved the bottleneck rather than removing it. The model does the arithmetic correctly and then tells you a story about the arithmetic, and the story is where the trouble is. It does not know that the bump in week three was one station in Dallas going into heavy rotation. It does not know the artist was on a stadium run through the south that month. It does not know that the record it is quietly comparing this one to had a completely different setup behind it. It gives you a confident answer built on the numbers you handed it and nothing else, because the nothing else was never in the file.

That is not a complaint about the tools. It is a description of what they have to work with.

What it does mean is that the scarce thing moved again. It used to be information. Then it was analysis. Now it is being able to look at a plausible, well written, internally consistent answer and know that it is wrong.

What I think this actually is

I do not think this is a tooling problem, exactly, and I am suspicious of anyone who tells you their product solves it. Most of what gets sold as a solution here is another dashboard, which is more of the thing that caused it.

What I think is closer to true: the industry got very good at capturing what happened and stayed bad at capturing why anybody thought it happened. Those are different jobs. The first one is a database. The second one is harder, because the answer lives in a person, changes over time, and is sometimes wrong.

I do not have a clean answer. I have spent a while now building against the edges of it, and the honest position is that the interesting work is in the second job and almost all of the money has gone into the first.

The tell, if you want one: next time somebody presents a number in a meeting, count how long it takes before someone asks what it means. If the answer is instant and everyone agrees, you did not need the number. If the answer takes twenty minutes, the twenty minutes was the valuable part, and nobody wrote any of it down.

Everyone in this business knows this. Say it out loud in a room and you get nods.

Then look at what any of us actually measure.

The song has a chart position, a spin count, a streaming curve, a playlist footprint, a first week, a peak, and a story that everyone can recite. The artist has a follower count and a vibe.

We have instrumented the song to three decimal places and left the career mostly to memory.

The two things come apart constantly

I have watched a song do everything right and leave nothing behind. It went where it was supposed to go, in the order it was supposed to go, and eighteen months later the artist was in the same place they started, except now with a song people liked and no particular reason to care who sang it.

I have also watched artists with nothing that looked like a hit build something that held. Slower, uglier on a chart, obviously working if you were standing close enough to see it.

Both of those are common. Neither of them is visible in the way we count things.

The gap is usually the name

The mechanical version of this is simple enough to say out loud. A lot of people can sing a song and cannot tell you who sings it.

That sounds trivial. It is not. Almost everything an artist needs next depends on the audience having attached the song to a person. A second single getting a fair hearing. A ticket getting bought. A room getting bigger in a market where nothing else happened. None of that runs on the song. All of it runs on the name.

And that attachment is not automatic. It happens when somebody hears the artist talk, sees them somewhere, hears the song enough times somewhere that also tells them whose it is, or finds a reason to care past the three minutes. A song can get very large without ever doing any of that. Then the next one arrives with nothing to inherit, and everybody is confused, because on paper the first one worked.

Why the mix-up is structural

It is not that people are stupid about it. It is that the song is the unit almost every system was built around.

Radio works in records. Promotion works in records. The chart is a record. The trade story is a record. The budget cycle attaches to a release. Almost every number that arrives on a Monday morning is a number about a piece of music rather than a number about a person’s career.

So the song is what gets optimized, because the song is what gets scored. And then everyone is surprised when a very good score does not produce the thing they actually wanted.

What a career looks like when you look at it directly

The honest version is that career movement shows up in slower and less flattering places.

Whether the second single does better than the first with the same push. Whether the room gets bigger in a market where nothing specific happened. Whether people who came in on one song will sit through five. Whether the audience the artist has now looks like the audience they will still have in four years, or whether it is a crowd that arrived for a moment.

None of that reports weekly. All of it matters more than the peak position, and I would say most of it is knowable if anyone were pointed at it.

The part I am less sure about

I do not think the answer is to stop caring about songs. Songs are how anybody finds out an artist exists, and a business that loses interest in the record is going to be very principled and very broke.

The thing I would push on is smaller than that. When a song works, somebody should be asking what it did to the career, and asking it separately, using different evidence, out loud, in a meeting where the answer is allowed to be “nothing much.”

Right now that question mostly gets asked two years late, by someone deciding whether to renew.

Your barber runs his business on texts and a paper book.

The indie golf course down the road manages tee times in a spreadsheet. The food truck owner tracks inventory in her head. The church worship team coordinates setlists in a group chat.

These aren’t edge cases. This is how most of the world actually works.

And if you’ve ever wondered why better software doesn’t exist for these people, the answer is surprisingly simple: traditional software economics won’t allow it.

The Scale Problem

Building a production-quality web app used to cost $50,000 to $200,000. Minimum. That’s before you hire a designer, before you market it, before you support it. Just the code.

To recoup that investment, you need thousands of users. To justify those thousands of users, you need a market big enough to attract venture capital or at least generate meaningful recurring revenue.

So the software industry optimized for scale. We got Salesforce, not barber software. We got HubSpot, not food truck software. We got generic tools designed for everyone, which means they’re perfect tools for no one.

The local golf pro doesn’t need a CRM with 47 integrations. He needs something that remembers which member prefers morning tee times and sends a text when a slot opens up. But nobody’s going to raise a Series A to build that.

Until now.

What Changed

In the last 18 months, the cost to build a production-ready web application dropped from six figures to effectively zero.

I’m not talking about prototypes. I’m not talking about no-code landing pages. I’m talking about real products, with authentication, payment processing, databases, responsive design, and a UI that doesn’t look like it was built at a 48-hour hackathon.

AI coding tools (Claude, Cursor, Codex) fundamentally broke the economics of software development. The bottleneck was never the idea. It was always the building. And that bottleneck is gone.

I know this because I’ve lived it.

My Proof

I’m a creative director. I’ve spent 20 years in the music industry designing brands, directing campaigns, and producing content for artists like Luke Combs. I’m not an engineer. I didn’t go to a coding bootcamp. I don’t have a CS degree.

In the last six months, I’ve shipped:

• A news intelligence platform that processes 400+ sources and surfaces relevant stories for radio professionals
• A content engine that generates a week’s worth of marketing content in minutes
• A lead generation tool that finds and qualifies prospects automatically
• A full operational dashboard that runs my entire company: tasks, CRM, finances, agents, newsletters

More products than most seed-stage startups ship in two years. With a team of zero engineers.

The tools I used cost less than a Netflix subscription.

The Math That Changes Everything

Here’s the part nobody’s talking about:

When building costs drop 100x, the minimum viable market drops 100x.

Traditional SaaS math: spend $200K building, charge $50/month, need 4,000 paying users just to break even over a year. That means your total addressable market better be in the millions.

New math: spend effectively $0 building (your time + $20/month in AI tools), charge $39/month, need 2 users to cover your costs. Ten users and you have a real business. Two hundred users and you’re making more than most junior developers.

Suddenly it is worth building that barber CRM. It is worth building that golf course booking tool. It is worth building that worship team coordinator.

Not for millions of people. For 200 people who need it desperately and currently have nothing built for them.

The Micro-Product Revolution

The future of software isn’t the next Salesforce. It’s a million micro-products, each solving one specific problem for one specific group of people that the software industry has ignored for decades.

Think about your own world:

• The yoga studio owner who manages class bookings across three apps and still loses track of which members are about to churn
• The wedding photographer who spends more time on contracts and invoicing than editing photos
• The little league coach coordinating 15 families’ schedules through a nightmare of reply-all emails
• The independent mechanic who writes estimates on carbon copy paper and hopes he remembers what parts he ordered

Every single one of these people is working around software that doesn’t exist. They’ve cobbled together solutions from tools that were built for someone else. And they’ve accepted it because the alternative, custom software, was never economically viable.

That’s over.

The Builders Won’t Be Who You Expect

Here’s what makes this truly revolutionary: the people who build the next wave of software won’t be developers.

They’ll be barbers. Coaches. Operators. Creative directors. Pastors. Golf pros. Food truck owners.

Why? Because the people closest to the problem build the best solutions.

A developer building “barber software” has to research the problem. Interview barbers. Guess at workflows. Assume pain points.

A barber who can now build software? She already knows every pain point. She’s lived them for 15 years. She knows that the real problem isn’t scheduling. It’s that her best clients don’t rebook before they leave, and by the time she remembers to text them, they’ve already booked with someone else.

No developer would think to build for that specific problem. But she would. And now she can.

What This Means for You

If you’re reading this and thinking “I have an idea for software that would solve a problem in my industry”, you’re probably right. And the barrier that stopped you from building it is probably gone.

You don’t need to learn to code. You don’t need a technical co-founder. You don’t need $500K in funding.

You need:

1. A clear problem that you understand deeply because you’ve lived it
2. AI coding tools (Claude Code, Cursor, or similar)
3. A weekend to build your first version
4. 20 people who would pay for it

That’s it. The technology is ready. The economics work. The only question is whether you’ll build the software your world is missing, or wait for someone who doesn’t understand the problem as well as you do.

The world has 1% of the software it needs. What’s the 99% YOUR world is missing?

I’m Zack Massey, a creative director who started building AI-powered software products six months ago. I write about the intersection of creativity, AI, and entrepreneurship. Follow me on LinkedIn or visit zackmassey.com to learn more.

I work in the music industry. I’ve watched technology “threaten” creative jobs for twenty years. Digital photography was supposed to kill photographers. DAWs were supposed to kill studios. Spotify was supposed to kill musicians.

The creatives who adapted? They’re doing better than ever.

AI is the same story, just faster.

The Fear Is Misplaced

When people worry about AI replacing creative work, they’re usually imagining some fully autonomous system that produces finished work with no human input.

That’s not how it works. That’s not how it’s going to work.

What AI actually does is handle the mechanical parts of creative work. The tedious stuff. The scaling stuff. The “I need twelve versions of this” stuff.

The vision, the taste, the emotional resonance… that’s still you. AI can generate a thousand images, but it can’t tell you which one will make someone feel something.

What I’m Seeing

The photographers I know who use AI edit faster. They can experiment with more looks. They spend less time on technical adjustments and more time on creative decisions.

The designers who use AI prototype faster. They can show clients ten concepts instead of three. They iterate in real time instead of going back to their desk.

The musicians who use AI produce more. They can try ideas without booking studio time. They can hear what something might sound like before committing.

Nobody got replaced. The creative ceiling got higher.

The Real Divide

It’s not AI versus humans. It’s humans with AI versus humans without it.

If you’re a photographer competing against someone who can do everything you can do, plus edit three times faster, plus generate reference images instantly, plus automate their boring admin work… you’re at a disadvantage.

Not because you’re less creative. Because you’re slower. Because your capacity is lower. Because you’re doing more manual labor for the same output.

The playing field isn’t level anymore, and it’s not going back.

The Opportunity

Here’s what excites me: AI lets individual creatives operate like small studios.

You don’t need a team to produce at volume. You don’t need to turn down projects because you can’t scale. You don’t need to choose between creative ambition and business viability.

One photographer with good AI workflows can output what used to require a team. One designer can deliver at agency scale. One musician can produce across genres they never could have touched before.

The ceiling on what one person can do is going up. If you’re good at your craft and you figure out these tools, you can build something that wasn’t possible five years ago.

How to Think About It

Stop thinking of AI as a replacement. Think of it as leverage.

You still need taste. You still need vision. You still need to understand what makes work resonate with people. AI has none of that.

But AI can help you execute faster, explore more, and spend more of your time on the parts that matter.

The creatives who figure this out are going to win. The ones who pretend it’s not happening are going to struggle to understand why.

This isn’t a threat. It’s an opportunity. But only if you take it.