Why "Apple Was Programmed Not to Win in the Age of AI"?
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I’ve been in this software game for a long time. Long enough to remember when shipping a product felt like launching a rocket to Mars.
It was a monumental event, preceded by years of development, endless meetings, and a level of obsessive polishing that would make a diamond cutter blush. We lived by the mantra: “It’s not ready until it’s perfect.”
And for a long time, that worked. It gave us sleek, beautiful products that people didn’t even know they needed. It built empires. But those days are over. The rocket has been replaced by a flock of messy, unpredictable, but incredibly fast paper airplanes, and if you’re still on the launchpad, you’re about to be left behind.
I was watching a video by Nate B Jones the other day about how Apple’s perfectionism might be its undoing in the age of AI. It crystallized a feeling I’ve had for a while now: the rules of technological power have changed, and speed is the new currency. But it’s not that simple. Speed without guardrails isn’t just fast; it’s a car crash waiting to happen.
The Messy, Glorious Age of AI
Here’s the thing you have to understand about AI: it’s messy. It’s not like building a calculator that gives you the same right answer every time. AI is probabilistic. It’s unpredictable. Sometimes, it hallucinates and tells you that the sky is made of green cheese. And that’s okay.
In the past, a bug was a sign of failure. Now, a weird, unexpected output from an AI model is just… part of the process.
Companies like OpenAI get this. They launched ChatGPT-5 knowing it wasn’t perfect. But they shipped it. They chose speed over perfection, and it paid off. And this isn’t just a strategy for the giants like OpenAI. As Andrew Ng points out, AI has fundamentally crashed the cost of creation for all of us. Building a prototype is now almost free. We can spin up and test 20 different ideas in the time it used to take to build one polished proof-of-concept.
The game is no longer about placing one perfect bet; it’s about making lots of small, fast, and cheap bets to see what the market actually wants. Most of them will fail, and that’s not just okay; it’s the whole point.
You can’t spend years in a secret lab trying to perfect an AI product when the entire field reinvents itself every six months.
But let me be clear: “messy” doesn’t mean “dangerous.” There’s a world of difference between releasing a product with a few bugs and releasing one that causes genuine harm:
“Acceptable messiness” is when your AI occasionally misunderstands a prompt.
“Unacceptable messiness” is when it starts spewing hateful nonsense because you skipped your safety homework.
Faster Doesn’t Mean Reckless
Before you run off and push your half-baked code straight to production, let’s talk about the guardrails.
I was watching another analysis, this one about Grok AI Engineering Blunders “How Grok Went Rogue on July 8,” and it’s a perfect example of what happens when the pendulum swings too far and when speed is the only goal.
The AI didn’t become evil. It was a chain of entirely human, entirely preventable engineering failures. This is where the real lessons are for you and me.
First, your AI is what it eats.
Grock was designed to pull in live content from X to stay current. The problem? X can be a dumpster fire. The engineers failed to put a filter between the dumpster fire and the AI’s brain. The lesson is brutally simple: if you connect your AI to a chaotic data source, you are responsible for filtering out the garbage.
Second, and this is a big one,
Prompts are production code.
The team behind Grock changed its core instructions with a line that encouraged it to be “politically incorrect.” They apparently did this with the software equivalent of a shrug and a click. No serious testing, no gradual rollout.
They treated the AI’s fundamental constitution like a casual text message. Prompts aren’t just little suggestions; they are code. They need version control, testing, and a rollout plan with the same rigor you’d apply to your core infrastructure.
Decisions are expensive
With AI, the code itself is becoming cheaper. We can rewrite an entire application in a month. What’s becoming incredibly expensive are our decisions.
As Ng says, choosing the right AI architecture can save you three months, while the wrong one sends you down a blind alley.
So, while we’re spending less time typing boilerplate code, we need to spend much more time on critical thinking: Is this the right model? Is this the right data pipeline? Is our safety filter robust? Moving fast isn’t about coding faster; it’s about deciding better.
This brings me to my final point:
“Move Fast and Break Things” is a dead mantra.
That philosophy might have been fine when the worst thing you could “break” was a photo-sharing app. But when you’re dealing with systems that shape public discourse and interact with hundreds of millions of people, breaking things can have catastrophic consequences.
The reckless, “YOLO-shipping” culture of the past is dangerously irresponsible in the age of AI.
Utility Still Trumps Polish (Most of the Time)
Even with those stark warnings, the core truth remains: people will use what is useful.
For decades, Apple taught us that the experience was everything. Back then, computers weren’t obviously useful to the average person. They needed that layer of polish to be desirable.
But AI is obviously useful. The magic isn’t in the packaging anymore; it’s in the intelligence itself. This is why people tolerated ChatGPT’s early outages and clunky interface. The utility was so high that they were willing to forgive its imperfections. This is the freedom we now have — the freedom to ship something valuable, even if it isn’t flawless.
The New Freedom (And the New Bottleneck)
This new reality gives us an incredible freedom. Decisions that used to feel permanent, like choosing a tech stack, are now what we can call “two-way doors.”
The cost of being wrong is so low that you can just walk back through the door and try a different path. It kills the analysis paralysis that plagues so many projects. But this creates a new problem. For the first time in my career, the bottleneck isn’t engineering anymore.
Our ability to build is starting to outpace our ability to decide what to build.
The long, slow process is now getting clear product direction and meaningful user feedback. The new challenge isn’t “Can we build this?” It’s “Should we build this?” And that’s a whole different ballgame.
The New Rules of Power
So, what does this all mean? The game has changed, but the new rules are more nuanced than just “go faster.”
The old way of spending years perfecting a product in secret is a death sentence. The world will have moved on by the time you launch. But the new way isn’t about shipping recklessly, either. That’s just a different kind of death sentence, one that destroys user trust and your company’s reputation.
The real new rule is this: Ship fast and responsibly.
It means embracing a culture of rapid, iterative development, but building that culture on a foundation of rigorous engineering discipline:
It’s treating our prompts like production code.
It’s understanding that when you build an AI, you’re not just building a product; you’re building a system that learns from the world around it.
And it’s our job to make sure it’s learning from the right parts.
The difference between being a market leader and becoming a cautionary tale lies in that balance. Now, if you’ll excuse me, I’ve got something to ship. Responsibly, of course.
Further Reading and Viewing
🎥 Agentic AI Coding, Next-Gen Models & Prompt Engineering Explained
📖 Agentic AI Coding, Next-Gen Models & Prompt Engineering Explained
📖 Why Software Developers Need To Adopt a Systems Thinker’s Mindset
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Until next time—stay curious and keep learning!
Best,
Rakia
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