Most Tech Companies Putting All Their Eggs in the Overhyped AI Basket Will Regret It Soon

We are now in the middle of another creatively destructive cycle in the tech world. If you haven’t been living under a rock since 2020, you’ve probably noticed — or worse, experienced — the wave of massive layoffs sweeping across the industry like a plague. As I write this, IT unemployment is hitting levels we haven’t seen since the dot-com crash. What sets this cycle apart is the eerie contradiction at its core: record-breaking profits for many companies, yet unprecedented job cuts.

This combination of high profits and mass layoffs is a new chapter in economic history. Why? Because, for the first time, we’re seeing returns on capital and investments far outstrip the value placed on so-called “human capital”. And with the latest AI boom, this trend has only intensified.

The chart below drives the point home: tech giants like Amazon, Google, and Microsoft are dumping billions into tech capital (mainly AI-related) while allocating comparatively minimal funds to labor investment. What makes this even more striking is that only a few years ago, the trend was the opposite — just a few years ago, companies were investing more in workers than in tech capital. The gap between the two bars isn’t just large — it’s reshaping how companies prioritize growth.

The financial sector — banks, venture capitalists, hedge funds — is betting big on replacing human workers with tech-driven solutions. The more companies automate, the more attractive they become to investors blinded by AI overhype.

The Disappearing Programmers: How Tech’s Blind Spot Is Destroying Its Greatest Asset

As companies double down on automation to attract investors, the critical voices of those who truly understand the technology are being drowned out.

And here’s where things get messy: the very people who understand technology best — engineers, software developers, hardware geeks, and programmers grinding daily in the trenches — are rarely asked for their opinion. Instead, decision-makers lump all technical roles into a single, reductive caricature: just coders translating specifications into lines of Python, Java, Rust, or Go. They miss the bigger picture, and the consequences are devastating.

The industry has a blind spot. The ‘real’ programmer — what’s currently labeled a software engineer and might soon be called a cross-domain engineer— spends only 10–20% of their time coding. Why? Because coding is merely the tip of the iceberg, the visible part that non-technical managers recognize. The real magic happens behind the scenes, where programmers take wild, often impractical ideas and turn them into implementable, scalable business logic. It’s a process that requires problem-solving, domain knowledge, and creativity.

But by conflating real programmers with the caricature the industry itself invented, companies are sabotaging their own future survival. The best programmers, the ones who excel at translating chaos into structure, are disappearing like dodo birds — once thriving in Mauritius before being wiped out by careless human actions. Their value was only understood after extinction, but by then, it was too late. Today, these programmers are fading from a corporate worldview that no longer recognizes their unique contributions. Once the stars of tech, they’re now sidelined just when they’re needed the most — as we face multiple crashing tech waves, one after another, as you’ll see next.

Companies Will Soon Desperately Need the Programmers They Dismissed.

Let’s acknowledge AI’s evolution, but we must also confront its absurd hunger for energy and resources. Entire data centers consume staggering amounts of power for achievements that still fall short of what a human brain can do on the energy budget of a modest LED light. Without real programmers, AI will collapse under its own complexity.

Why? Because software and hardware engineering are aimed at human goals and purposes. It’s always been about satisfying human needs, and that requires the mindset and world experience of real programmers, software engineers, or developers — whatever you want to call them.

But here’s the problem: the tech industry is shooting itself in the foot. It’s blocking fresh talent and shrinking the market for future programmers. Instead of nurturing the next generation — the people who will need to handle AI’s challenges, new architectures, and evolving GPU competitors — companies are throwing money at AI as if it were a magic wand.

Here are the 5 successive waves — though not strictly chronological — of the tech nemesis meteors heading straight for the industry. Most companies should make every effort to support the cross-domain programmers they’ll soon desperately need to thrive in the immense new tech opportunities ahead.

But I don’t know about you — I just don’t see how these corporations can dismantle their own culture. It’s the same story many of us know too well: clogged with hyper-bureaucratic HR gatekeepers and a suffocating hierarchy of middle managers, all the way up to the untouchable super-bosses in their corporate sanctuaries, falsely feeling safe after yet another mass layoff round while pouring millions into their shiny new AI toy. Many of us fear that this level of complacency will come soon with a very steep cost.

You know that Moore’s Law is fading, and traditional CPUs and GPUs just can’t keep up with the exploding demands of AI, massive data loads, and real-time processing.

The future is all about programmable-synthesizable hardware — advanced FPGAs, ASICs, and custom chips that don’t just run software, but are configured to handle tasks at the hardware level.

Instead of writing code that overlooks the hardware, we should design configurations that extract every drop of performance AI demands. Yet, companies continue to cling to closed, rigid architectures, wasting time and money trying to force AI into outdated systems. Open, programmable hardware isn’t optional anymore — it’s how AI becomes scalable and profitable.

As we mentioned, programmable-synthesizable hardware is the future of AI, led by advanced FPGAs, optimized ASICs, and custom AI chips. Unlike traditional CPUs and GPUs, these open architectures can be reconfigured on the fly, dynamically aligning with interconnected tasks and business needs. AMD’s Alveo UL3524 and Achronix’s Speedster7t FPGA show how hardware can be reprogrammed in real time to optimize specific neural networks. Researchers are also working toward building ferroelectric FET-based FPGAs for ultra-fast, on-the-spot reconfiguration. This tech could redefine AI systems by retraining them dynamically, instead of wasting months and resources as we do today.

Try to get on the train with the companies or small businesses early adopting these smart chip networks, because they will be the first to automate entire areas of business, leaving competitors stuck in the past.

Neuromorphic chips, inspired by biological brains, are not a fantasy anymore — they are already among us as the next major step in AI hardware. Sure, these chips aren’t just for classical ANNs — they’re also, and mainly, built for spiking neural networks (SNNs) and integrate-and-fire neurons, processing information only when needed, unlike our current poor version of them: the power-hungry GPUs.

Intel’s Loihi chip and IBM’s TrueNorth lead in spiking neural network support. Researchers are also developing quantum leaky integrate-and-fire spiking neurons, merging quantum computing and neuromorphic hardware for ultra-adaptive, real-time AI.

If you’re developing AI, this is the train you can’t miss. Neuromorphic systems are the future of scalable, power-efficient AI. Design for spiking neural systems now, or risk being left behind while others build smarter, self-optimizing AI.

Early adopters of these chips will drive smarter drones, IoT, and autonomous vehicles, leading the next real-time AI revolution. Companies stuck on power-hungry GPUs? They’ll be struggling to catch up.

It’s your call: get on board, or watch others pass you by.

Quantum computers aren’t some sci-fi dream anymore — they’re happening. Once they scale, everything changes. Cryptography? Exposed. Simulations? Supercharged. Drug discovery? Leveled up. Logistics and financial models? Solved faster than your CI/CD pipeline breaks in production.

IBM, Google, and IonQ are racing to build machines that can break today’s encryption using Shor’s algorithm , turning RSA and elliptic curve cryptography into ancient history. Meanwhile, quantum annealing and variational quantum algorithms are kicking off revolutions in optimization-heavy fields. What takes traditional systems years could soon take hours — or even minutes.

If you’re coding anything that depends on secure communication or optimization, this is the tech you can’t ignore. You don’t need a quantum physics degree — just understanding how to work alongside quantum co-processors will give you a massive edge.

The early adopters integrating quantum systems won’t just stay relevant — they’ll lead entire industries like finance, pharma, and logistics. The rest? They’ll be stuck patching their systems and wondering how they missed the memo.

Don’t be the dev caught retrofitting old systems while competitors scale with quantum speed. Try to start learning now, so you won’t regret cleaning up the broken cryptomess later.

And finally, we reach the wave that impacts us most as programmers: the scarcity of people who blend open hardware architecture, configuration, AI engineering, and programming, bringing all these worlds together on the same new platforms.

We’re not talking about AI tools devouring routine coding jobs faster than you can commit buggy code, nor about those CRUD apps, boilerplate scripts, and basic web development that LLMs are rapidly covering. No — the real money isn’t in writing scripts. It’s in mastering hardware-software integration, AI optimization, and custom chip design.

With programmable FPGAs, ASICs, neuromorphic chips, and quantum accelerators driving the future, developers won’t just code — they’ll design AI that works directly with hardware. You’ll optimize pipelines, memory, and concurrency to push advanced chips to their limits. Think AI-driven hardware compilers and real-time reconfigurable systems.

The talent gap is already here. Universities and bootcamps aren’t keeping up, but more worrisome are the companies lost in the AI hype, firing the very developers they’ll soon beg for — the ones who can build AI systems that don’t just run on hardware — they control and optimize it. So, here’s a prediction, given the current crazy tech job market and dismissive attitudes: unfortunately, many of those crucial developers are already gone. And soon, they’ll be writing their own ticket — founding startups, leading R&D labs, and building the next wave of tech empires.