AI Writes Your Code. The Market Writes Your Obituary.

I’m at a virtual CTO gathering hosted by 7CTOs. The energy is palpable. A founder is practically vibrating with excitement as he demos his team’s new AI-assisted development workflow. “Watch this,” he says, fingers dancing across the keyboard. “What used to take our engineers three hours now takes thirty minutes.”

The zoom room erupts. CTOs are exchanging glances like they’ve just witnessed the second coming. Someone whispers reverently about “10x productivity gains.” Another is already calculating how many fewer engineers they’ll need to hire.

I take a long sip of my hipster-friendly kombucha and resist the urge to scream from my 4 person WeWork office tucked away somewhere in Salt Lake City.

Because here’s what I’m watching: A room full of brilliant technologists celebrating their ability to put on armor faster while completely ignoring the dragon that’s about to eat them alive.

And the dragon? The dragon doesn’t give a damn about your deployment velocity.

The Knight Who Perfected His Armor

Let me paint you a picture that might feel uncomfortably familiar.

There’s a knight preparing for battle. He’s spent the last six months obsessing over a revolutionary new mechanism—a system that lets him don his full plate armor in under thirty seconds. Previously, it took forty minutes. He’s absolutely giddy about this innovation.

“This changes everything!” he proclaims to anyone who’ll listen. “Dragon slaying will never be the same! I’ll be able to respond to dragon attacks 10x faster!”

There’s just one small problem.

The armor was never the bottleneck. The dragon is the bottleneck. And while our knight was perfecting his armor-donning technique, he never bothered to learn where dragons live, what they eat, or why the last seventeen knights who fought this particular dragon are now decorative carbon deposits on a cave wall.

Welcome to the modern CTO’s dilemma. You’re the knight. AI code generation is your quick-release armor. And the dragon? The dragon is market complexity, customer needs, and organizational dynamics: the actual things that will determine whether your startup joins the 92% that fail or the 8% that somehow don’t.

The $29.5 Billion Dollar Confession

Here’s a statistic that should make every CTO question their life choices: 80% of software features are rarely or never used.

Let that sink in. Eight out of every ten features your team lovingly crafts, debates in stand-ups, and deploys with pride? They’re digital ghost towns. Tumbleweeds rolling through unused UI. Features so ignored they might as well not exist.

Pendo studied over 615 companies and found that just 12% of features generate 80% of user engagement. The waste is so staggering that public cloud companies alone burn $29.5 billion annually on features nobody wants.

But wait, it gets worse.

The Standish Group discovered that in enterprise software, 45% of features are NEVER used. Not rarely. Never. As in, you could have literally shipped empty divs and had the same business impact.

Now tell me again how making your developers code 55% faster is going to solve your problems?

You’re essentially announcing, with great pride, that you can now produce unused features at unprecedented velocity. Congratulations. You’ve revolutionized waste.

Why Perfect Code Can’t Save Bad Ideas

CB Insights analyzed 111 startup post-mortems, the black boxes of failed companies. You know what killed 42% of them? “No market need.”

Not “our code was too slow.” Not “we couldn’t scale our infrastructure.” Not “our technical debt crushed us.”

No. Market. Need.

These weren’t companies that built products poorly. These were companies that built the wrong products perfectly. Their code was clean, their deployments were smooth, their sprints were productive. They just built things nobody wanted to buy.

Paul Graham, who’s seen more startups die than a Civil War surgeon, puts it even more bluntly: startups fail because they don’t make something users want. Period. Full stop. End of story.

Yet here we are, an entire industry of CTOs convinced that if we just make the coding part faster, somehow everything else will magically work out. It’s like believing that if you can type faster, you’ll automatically become a better writer. Spoiler alert: Hemingway didn’t need GitHub Copilot.

The Complexity You’re Not Seeing

Let me share something that’ll make your imposter syndrome feel justified: organizational complexity grows at O(n²) with team size, while code complexity grows linearly at best.

Brooks’ Law isn’t just some theoretical concept from a dusty textbook. It’s mathematical reality. Every developer you add to your team doesn’t just bring their coding skills. They bring n-1 new communication channels, where n is your team size. By the time you hit 100 developers, you’ve got 4,950 potential communication paths. Good luck managing that with a Jira board.

McKinsey found that senior executives now spend 23 hours per week in meetings. That’s up from 10 hours in the 1960s. We’ve more than doubled our coordination overhead while claiming we’re more efficient than ever.

But here’s the real kicker: Conway’s Law means your messy organizational structure inevitably becomes your messy code architecture. MIT and Harvard researchers found “strong evidence” for this mirroring effect. Microsoft’s Vista failure? Primarily caused by organizational complexity, not technical incompetence.

So while you’re teaching your developers to prompt engineer their way to faster code generation, the real complexity—the human, organizational, market complexity—is eating you alive from the inside.

The 10,000 Useless Apps Problem

“But Scott!,” I argued, adjusting my mic nervously, “if we can generate code 10x faster, we can experiment more! We can try more things! Surely some will succeed through sheer probability!”

“Oh, geeezzzz” is all I could see on his face. He was super polite about it though.

Markets don’t have infinite capacity to evaluate solutions. Your potential customers aren’t sitting around thinking, “Gosh, I really wish there were 10,000 more SaaS products to evaluate this quarter.” They’re drowning in options already.

The App Store has 1.96 million apps. Know how many the average person uses regularly? Thirty. That’s a 0.0015% usage rate. You’re not competing for development speed; you’re competing for attention in an attention economy that’s already bankrupt.

Creating more bad products faster doesn’t increase your odds of success. It just increases the noise. It’s like believing that if you buy more lottery tickets faster, you’ll somehow change the underlying mathematics of probability. The house still wins, you just lose money more efficiently.

The Inconvenient Truth About AI Coding

Now, about those impressive AI coding stats you’ve been flaunting at board meetings.

GitHub says Copilot makes developers 55% faster. Microsoft and Accenture claim 26% productivity gains. Impressive, right?

Here’s what they don’t advertise: A 2024 ArXiv study of experienced developers found AI tools actually INCREASED task completion time by 19%. Not decreased. Increased. These weren’t juniors struggling with syntax, these were seasoned developers working on real projects.

Stack Overflow’s 2024 survey is even more damning. Two-thirds of developers report their biggest AI frustration is “solutions that are almost right, but not quite.” Trust in AI-generated code accuracy has plummeted from 43% to 33% in one year.

You know what “almost right” code is? It’s technical debt with compound interest. It’s bugs that pass initial tests but explode in production. It’s that subtle incorrectness that takes three senior engineers two days to debug because nobody actually understands what the AI generated.

But sure, celebrate those velocity metrics. I’m sure your customers will be thrilled to know their data corruption happened 55% faster than usual.

What Actually Kills Companies

Let’s get brutally honest about what actually destroys software companies:

• 42% die from no market need
• 29% run out of cash (usually because of no market need)
• 23% don’t have the right team
• 19% get outcompeted (rarely on technical merit)
• 17% have pricing/cost issues
• 14% ignore their customers

Notice what’s missing? “Coded too slowly” isn’t on the list. Neither is “insufficient AI assistance” or “needed more story points.”

Yet walk into any CTO gathering and what are we talking about? Kubernetes orchestration. Microservice architectures. AI-assisted development. The latest JavaScript framework that’ll definitely, absolutely, pinky-promise solve all our problems this time.

We’re optimizing the 20% that doesn’t matter while ignoring the 80% that determines life or death.

The Real Dragon

Here’s what the dragon actually looks like:

It’s the enterprise customer whose procurement process takes nine months and involves seventeen stakeholders, none of whom care about your test coverage.

It’s the market that shifts beneath your feet because a competitor you’ve never heard of just raised $50 million to solve the same problem with a business model you didn’t consider.

It’s the feature you spent six months building because it was technically interesting, while customers were screaming for a simple CSV export that would have taken two days.

It’s the organizational dysfunction where your backend team and frontend team haven’t actually talked in three months but are definitely “aligned on the roadmap.”

It’s the 80% of your features gathering dust while the 20% that matter are held together with technical debt and prayer.

No amount of AI-assisted coding will slay these dragons. You can’t prompt-engineer your way out of product-market fit. ChatGPT won’t tell you that your entire business model is fundamentally flawed.

The Path Forward

So what should you actually be doing instead of salivating over the latest AI coding assistant?

First, acknowledge the brutal truth: You’re probably building the wrong thing. Statistically speaking, you almost certainly are. The question isn’t whether you’re wrong, but how quickly you can figure out exactly how you’re wrong and pivot.

Second, recognize that code is the easy part. It always has been. The hard part is everything else—understanding customers, navigating markets, building organizations, creating sustainable business models. These are irreducibly human problems that require judgment, empathy, and the ability to synthesize qualitative feedback into strategic decisions.

Third, stop measuring the wrong things. Lines of code, deployment frequency, sprint velocity—these are vanity metrics. What matters is customer retention, revenue per feature, time to product-market fit. One customer interview is worth a thousand unit tests.

Fourth, embrace the uncomfortable truth that most of what you build will be waste. The goal isn’t to eliminate waste through perfect planning, that’s impossible. The goal is to minimize the cost of waste through rapid learning. And learning doesn’t come from coding faster; it comes from talking to users faster.

The Bottom Line

Look, I get it. AI code generation is seductive. It’s tangible, measurable, and makes for great demos. It feels like progress. It lets you tell yourself and your board that you’re innovating, that you’re on the cutting edge.

But while you’re celebrating your ability to generate pull requests at superhuman speed, your competitors are out there actually talking to customers. They’re figuring out what to build, not just how to build it faster. They’re solving real problems, not optimizing their solutions to imaginary ones.

The companies that win aren’t those that code the fastest. They’re the ones that learn the fastest. They’re the ones that understand the dragon before they start forging armor.

So by all means, use AI to write your code. Use every tool available to make the easy parts easier. But remember: the code is not the challenge. The code has never been the challenge.

The challenge is everything else.

The challenge is the dragon.

And the dragon doesn’t care how fast you can put on your armor.

Next time you’re in a meeting where someone’s breathlessly explaining how AI will revolutionize your development process, ask them this: “How does this help us figure out what to build?” Watch them squirm. Then send them this article.

And them send them our book LIQUID.

The armor is not the problem. It never was.