They seem relentlessly determined to inflate the AI bubble — right up until it bursts.

Engineers — whether in software, ML, AI, or a hybrid of all three — must stay plugged into real-world human experience. A project doesn’t begin with clean-cut requirements; it starts with half-baked ideas, vague business goals, and gaps nobody notices — until they turn into disasters.

Then comes that moment. The cold shiver. The sinking realization: “Oh damn, this is gonna be a mess.” And if the project keeps scaling? You better be on the right team. Otherwise, welcome to the brutal climb up Chart 1 below.

Because let’s be completely honest — wrangling vague, ever-changing expectations is basically the job. And the number one reason projects crash and burn? A fundamental disconnect between the code and the real human problem it’s supposed to solve.

At the finish line, our job isn’t just to ship code — it’s to transform a vague business idea into something tangible. Something that generates revenue, solves real problems, or, if we’re lucky, at least gets used. And the path there? It’s a relentless mix of classic software engineering, ML, and AI — balancing logic with intuition, data with gut instinct — all while wrestling with constraints. And at the heart of those constraints? Two unyielding forces that define everything: pricing and quality. Like ruthless gatekeepers, they dictate deadlines, tech choices, and whether we build with the right tools or just duct-tape together a workaround.

And so, we fight for those workarounds — finding ways to sneak in the best tech stack without blowing the budget. We sit across from over-optimistic CEOs, trying to make them see that stretching the budget just a little could save them from disaster later. And more often than not, we fail to convince them.

I know that feeling all too well. I’ve been there — losing the argument, watching a project march toward inevitable failure. Until I learned. And never stopped learning. Because, at the end of the day, everything is a negotiation — trade-offs, compromises, and constant self-discovery in a tech market that’s anything but predictable. I learned to blend engineering with small-group psychology, sales tactics, financial strategy, and a few legit financial tricks — wrapping it all up in improvised, barely-held-together rhetoric, just to keep a doomed project from collapsing under tight budgets or rigid mindsets.

Now, after all such circling around the reality of the tech underworld, can anyone with a shred of sanity truly believe that this fuzzy, messy, deeply human work can be digested and processed by an AI that lacks even the world experience of a five-year-old (see Chart 2 above)?

Well… no one — except big tech CEOs.

Unfortunately, they rarely see the real work — only snapshots of product development, a service in progress, and finally, the shiny delivery. And that’s exactly why they fall for the illusion that tossing some vague specs at an AI can replace a chunk of their engineers. Even ex-developer CEOs aren’t immune to this trap. They’ve long forgotten the hidden complexities, the blind spots, and just how fragile today’s hyper-connected, distributed systems really are — especially now, with cloud and AI piling on layers of abstraction, turning the landscape into a minefield of vulnerabilities and dysfunctions, where every gap and misstep spawns failures far more intricate than the bugs of the past.

The hubris-driven imagination of those in power — be they politicians or CEOs/CTOs detached from the technical battlefield — often creates a well-known dissociation between two realities. One exists in their heads, reinforced by stakeholders, investors, and the executive team — a collective hallucination fueled by overblown expectations of AI’s potential. The other? The harsh, unforgiving reality where seemingly minor, overlooked details determine everything.

And when those visions inevitably collide — despite the tech crew’s continuous warnings about logistics, immaturity, feasibility, and the ever-present chaos of real-world deployment — they don’t just fail. They burst on impact.

CEOs, Want to Run Successful AI Projects? Listen to Your Engineers — Logistics Will Make or Break You.

And so, the cycle repeats — leaders, swept up in their own vision, convinced that ambition alone can override reality. But reality isn’t shaped by boardroom optimism — it’s dictated by logistics, infrastructure, and the cold, immovable limits of execution.

History delivers brutal lessons. At Stalingrad (see Fig. 1 below), the German high command, obsessed with conquest, stretched its forces past their supply limits. Two advancing fronts, divided and overextended, bleeding resources faster than they could be replenished. Supply routes collapsed, flanks were left exposed, and when the Red Army struck, it wasn’t just a defeat — it was annihilation. Why? Because the Soviets mastered logistics.

While the Germans pushed deeper into disaster, the Red Army stayed anchored to its supply hubs, stockpiling resources, moving with precision, and striking exactly where the enemy was weakest. It wasn’t ideology or sheer numbers that won Stalingrad — it was engineering, logistics culminating in the cold, calculated execution of a plan that respected real-world constraints.

Tech’s Stalingrad: CEOs Marching into Defeat

And here’s the parallel: Big Tech CEOs keep making the same mistake. They stretch their companies too thin, bet everything on AI-driven automation, and dismiss logistical concerns as mere “engineering problems.” But just like in Stalingrad, logistics isn’t an afterthought, it’s the battlefield itself.

The engineers warning them? yep, they’re the modern logisticians — the strategists who see the real picture, who understand the constraints, who know exactly how fragile these hyper-connected systems really are.

But when leadership ignores them — when they dismantle engineering teams in favor of a misguided vision — they are marching straight into an encirclement, setting themselves up for tech defeat.

Examples of Tech’s Stalingrad Syndrome

• AWS outages (2020, 2021): Hidden dependencies and weak failovers, dismissed as minor concerns, until they brought down global platforms.
• TSB Bank migration disaster (2018–2019): A catastrophic infrastructure overhaul that locked out customers for days and burned through millions, all because leadership underestimated complexity.
• Microsoft’s Tay chatbot: Turned toxic within hours due to a lack of human oversight — an AI system blindly unleashed without logistics in place.
• Self-driving car companies: Overpromised, then crashed, sometimes literally, into regulatory walls.
• ChatGPT-like models: Despite their power, they still “hallucinate” facts, a fatal flaw in real-world applications unless carefully controlled.

The Pattern is Clear

Executives believe they can bulldoze through complexity with vision alone. Engineers see the disaster coming. Logistics gets ignored, and the march forward turns into a slow-motion collapse.

Investing in Hybrid Engineers Instead of Laying Them Off

And here we are — at yet another AI overreach frontline.

AI isn’t rewriting the fundamental principles of engineering — it’s expanding what we can do. But let’s be clear: AI is a tool, not a replacement. It allows us to break past old limitations, working across multiple domains by blending AI and ML with classical frameworks, existing data models, and pre-trained architectures (see Figure 2 below).

At the same time, it serves as the perfect excuse to finally push hardware beyond the rigid von Neumann architecture of traditional CPUs and GPUs — toward open, adaptive systems. We’re no longer just writing software. We’re stepping into a world where the latest FPGAs, custom ASICs, and even neuromorphic hardware are optimized for AI workloads — bringing software, machine learning, and hardware closer together than ever before.

Yet, in the middle of this transformation, something is deeply wrong.

We urgently need more multidomain engineers — not just to drive innovation but to rescue AI projects from the very recklessness of the companies investing in them. Big Tech is pouring billions into AI while simultaneously gutting the very talent that could make it work. Many of those who should be building the AI future — including maybe you — are the same engineers being laid off.