Welcome back, my fellow digital warriors ⚔️!

Today, we’re diving into a hot topic that’s been making waves lately. You might have heard about those eye-opening interviews with ex-Google CEO Eric Schmidt and Amazon’s top brass Jeff Bezos. If you haven’t, well, let’s just say they’ve been stirring the pot (and maybe, just maybe, getting a bit too real for some folks). Schmidt’s chat at Stanford, which you can’t easily find on the usual channels (wonder why), and the Bezos’ talk, both dropped some serious bombshells about the future of software development and the role of AI.

The bottom line?

In just 24 months, developers might not be coding anymore. 🙀

Yeah, you heard that right. The big brains are predicting a world where developers are speaking to AI like it’s the next-gen Siri on steroids, and voila—code just writes itself. And to get there, they’re talking about investing TRILLIONS globally. The promise? A revolutionary shift where AI context windows beef up AI’s brainpower, AI agents test this newfound knowledge, and complex development tasks get distilled into simple English commands from AI text-to-tasks prompts.

But hold up—before we all start prepping our AI-driven future, doesn’t this sound like a modern twist on Nostradamus? Sure, it’s a tech prophecy, but it’s got me raising an eyebrow (or two). Because while the world gears up for this AI revolution, let’s not forget the little issue of energy consumption as highlighted by Forbes:

The IEA is projecting global electricity demand from AI, data centers and crypto to rise to 800 TWh in 2026 in its base case scenario, a nearly 75% increase from 460 TWh in 2022. The agency’s high case scenario calls for demand to more than double to 1,050 TWh.

The missing link 🔗

As an old-school, hardcore developer and tech lead, there’s something in these AI projections that just doesn’t sit right with me. Writing code isn’t just about knowing a programming language or mastering the latest tech stack from development to delivery. It’s about something far more fundamental—something that AI, with all its power, might just miss entirely.

1. ☝️ First off, it’s about understanding the problem. It’s not enough to throw code at a wall and see what sticks. You need to slice that problem into cognitively digestible pieces. You’ve got to know how to break it down so that each part can be tackled effectively. Mark Seemann nailed this concept in his book “Code That Fits in Your Head.” The idea is simple: your code should be small enough, clear enough, that you can keep it all in your head at once. Only then can you really understand it—and only then can you write something that won’t come back to bite you later.

2. ✌️ Then there’s the art of keeping your code clean. My friend J. B. Rainsberger , in his legendary talk “7 minutes, 26 seconds, and the Fundamental Theorem of Agile Software Development,” showed us how vital it is to avoid tech debt like the plague. Accidental complexity piles up, making your Agile processes about as predictable as a game of Jenga. Skip the refactoring today, and tomorrow, you’ll be staring at a monolith of spaghetti code that nobody dares touch.

3. 🤟 And let’s not forget testing. Adding new features without breaking the old ones is the golden rule. Mike Cohn laid out the importance of a healthy test pyramid back in 2009 with his book “Succeeding with Agile.” Martin Fowler further broke it down in his “Test Pyramid” blog, and Jez Humble and Dave Farley built on that foundation in “Continuous Delivery.” They didn’t just preach about testing—they made it clear that aiming for a CI build and test process that wraps up in 5 to 10 minutes is key to maintaining velocity without sacrificing stability.

Here’s the thing:

the AI hype is just shifting the root problem of sloppy software elsewhere.

Sure, AI will be powerful—no doubt about that—but it’s also going to be a power hog, potentially sparking another global energy crisis or exacerbating the scavenging for precious metals in poorer countries. But you know what AI won’t save you from?

Failing to create the right product the right way.

Handing over development to poorly trained workers chatting in plain English with an AI won’t cut it. Without a solid foundation in software engineering, these folks won’t know how to properly slice a complex problem, craft understandable solutions, or ensure that everything is fully tested from day one.

The result? A trillion-dollar investment in AI that churns out brown code from day zero. And considering that, in the past, the so-called costly IT departments managed to keep enterprise products alive for about 10 years before the tech debt became too much, this doesn’t seem like progress. It seems like a recipe for disaster.

Why Should We Care? The High-Stakes Gamble of AI and Brown Code

Let’s cut to the chase—why should we care about this AI revolution that promises to turn software development into a conversation with a machine, spewing out code like a vending machine spits out candy? Because the stakes are higher than most people realize, and the consequences of getting it wrong could be catastrophic.

Think about it. AI-generated software without proper testing💥? Brown code from day zero 💩? High cognitive complexity that even seasoned developers struggle with🤯? This isn’t just some academic exercise or a cool experiment in machine learning. This is the future of the systems that run our hospitals, manage our finances, control our transportation, and keep our data safe. When those systems fail—and let’s be clear, they will fail if they’re not built right—people get hurt. Lives are disrupted ☠️.

Now, imagine a world where this “new trend” of AI-driven development becomes the norm. A world where shortcuts are the standard, where testing is an afterthought, and where the understanding of a problem is left to an AI that might not fully grasp the nuances of real-world applications. We’ve already seen what happens when software is rushed, when corners are cut, and when profit is prioritized over quality.

So, why should we care? Because the future being sold to us—where AI takes over the heavy lifting of coding—comes with a dark side. If we’re not careful, we’ll be trading short-term gains for long-term disasters. And if history has taught us anything, it’s that these mistakes are often paid for in the most tragic ways.

With that in mind, let’s take a look at what happens when cost-cutting and rushing development lead to catastrophic failure. Enter: the Boeing 737 MAX 🛩️ disaster.

The Boeing ✈️ Disaster: When Cost-Cutting Turns Deadly

Now that we’ve set the stage, let’s dive into a real-world example that shows exactly what happens when the pursuit of short-term profits trumps everything else. This isn’t just a story about bad code or a poorly managed project—it’s a story about how a culture of cost-cutting, driven by Wall Street’s demands, led to one of the most tragic and costly disasters in recent corporate history.

From Engineering Excellence to Financial Folly

Boeing wasn’t always the poster child for corporate greed and catastrophic failure. For most of its history, Boeing was the gold standard in aerospace engineering. Safety was paramount, innovation was encouraged, and engineers had the final say on critical decisions. But that all started to change after the company merged with McDonnell Douglas in 1997. This merger didn’t just bring together two companies; it brought a seismic shift in corporate culture—from one that prioritized engineering excellence to one obsessed with shareholder value and cost-cutting.

The new leadership, heavily influenced by McDonnell Douglas’s financial-first mindset, started making decisions that would eventually lead to disaster. Engineering teams were pressured to meet increasingly tight deadlines, all while their budgets were slashed and their work was outsourced to cheaper labor markets. And it was this toxic combination of rushing and penny-pinching that set the stage for the 737 MAX debacle.

The 737 MAX: A Tragedy in the Making

In the early 2010s, Boeing found itself in a race against Airbus, which had launched the A320neo—a more fuel-efficient aircraft that was quickly becoming a favorite among airlines. Boeing needed to respond fast. But instead of developing a new aircraft from scratch, they opted for a quicker, cheaper solution: modifying the existing 737 design to accommodate larger, more fuel-efficient engines.

This decision led to the creation of the Maneuvering Characteristics Augmentation System (MCAS), a software system designed to prevent the plane from stalling due to the altered aerodynamics caused by the new engines. However, in a bid to cut costs, Boeing outsourced much of the software development to engineers in India who were paid a fraction (9 $/h) of what their U.S. counterparts earned. These engineers were tasked with creating safety-critical software under immense pressure and with limited resources.

The result? A deeply flawed system that relied on data from a single sensor. When that sensor failed, the MCAS system would erroneously force the plane’s nose down, with catastrophic consequences.

On October 29, 2018, Lion Air Flight 610 crashed into the Java Sea, killing all 189 people on board. Less than five months later, on March 10, 2019, Ethiopian Airlines Flight 302 suffered the same fate, with 157 lives lost. The cause? The same MCAS system that Boeing had outsourced, rushed to develop and deployed without adequate testing.

346 lives lost due to the fatal consequences of cutting corners in software engineering and neglecting critical technical practices ⚰️

The Fallout: Billions Lost and Lives Shattered

The financial impact of the 737 MAX disasters was as severe as the human toll. Within weeks of the second crash, Boeing’s market value plummeted by around $34 billion. The 737 MAX was grounded worldwide, leading to billions in lost sales and production delays. Lawsuits piled up, with Boeing ultimately setting aside over $18 billion to cover the costs of the disaster, including compensation for airlines and victims’ families.

But here’s the thing—no amount of money can undo the damage. Boeing’s reputation, once synonymous with safety and reliability, was in tatters. And more importantly, 346 people lost their lives because a company decided that saving a few bucks was more important than ensuring their planes were safe with proper software engineering practices and ethic.

The Lesson: Cutting Costs, Cutting Corners, Cutting Lives

So, what’s the moral of the story? When you let cost-cutting drive your decision-making, you’re not just saving money—you’re putting lives at risk. Boeing’s story is a tragic reminder that there’s a real human cost to prioritizing short-term over long-term profits and quality. It’s a cautionary tale for any industry, but especially for those of us in tech, where the blind rush to embrace AI automation for software development purposes could lead us down a similarly dangerous path if we’re not careful.

Because at the end of the day, software isn’t just code. Planes aren’t just machines. These are tools that people trust with their lives. And if we keep letting short-term profits dictate our decisions, the next disaster is just a matter of time.

Call to Action: 👉 Elevate Your Software engineering Practices

Boeing’s eight-month struggle to recertify the 737 MAX software is a glaring example of what happens when software engineering practices fall short. Despite pouring in millions to patch the holes, if your organization’s DNA is built around cost-cutting and viewing IT as just another expense, then, dear CXO, you’re sitting on a ticking time bomb 💣.

Don’t let your organization face the same fate. It’s time to embrace the revolutionary advancements in software engineering and upskill your team with a continuous active-learning program like the SW Craftsmanship Dojo®. Secure your future by investing in the practices that prevent disasters before they happen.

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In the Next ⏭️ Episode: The Human Challenge and AI’s Mission Impossible

So, we’ve dug into the catastrophic consequences of cutting corners in software development, and you might be wondering, “Why is mastering these practices such a Herculean task for humans, let alone AI?” In the next episode, we’re going to dive deep into exactly why the critical technical practices we discussed today are not just difficult, but often nearly impossible to perfect. We’ll explore the cognitive challenges that make these practices so elusive for human developers, and why AI—despite all its promise—might be setting itself up for an even tougher battle.

Stay tuned, because understanding this is key to navigating the future of software development without repeating the mistakes of the past. See you next time!

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