Why Tech Majority Isn’t Buying the AI Overhype Pumped by Tycoons
💖 Humans are complicated; they do things AI can’t understand
Welcome to HackerPulse Dispatch! In this edition, we delve into the cracks of software development, revealing how catastrophic failures have become the norm and how AI is now amplifying a crisis that started long before its arrival. We explore the overlooked majority view on AI in tech: AI is useful, but far from magical, and fear-driven hype is distorting reality.
You’ll also learn why the promise of LLMs replacing engineers remains a fantasy, grounded by practical limits and mathematical realities. Event sourcing, often sold as a silver bullet, is exposed for the complexity, coordination headaches, and long-term maintenance burdens it really introduces.
Finally, we uncover why AI coding tools struggle in enterprise environments, highlighting the missing ingredients, such as training, context, workflows, and culture, that separate hype from real productivity.
Here’s what new:
💣 The Great Software Quality Collapse: How We Normalized Catastrophe: The software industry has normalized catastrophic failure, and AI is now accelerating a quality crisis that started long before it arrived.
👨👨👦👦 The Majority AI View: Most people in tech see AI as a useful but overhyped tool, yet fear and corporate pressure have silenced this moderate view in favor of extreme hype narratives.
🦜 Why Large Language Models Won’t Replace Engineers Anytime Soon: AI may be impressive at mimicking knowledge, but mathematical and practical limits ensure it will remain a tool, not a replacement for human engineers.
🤡 Don’t Let the Internet Dupe You, Event Sourcing Is Hard: Event sourcing is widely overhyped as a silver bullet, but real-world experience shows it brings heavy complexity, unclear boundaries, and long-term maintenance pain that often outweigh the benefits.
🛠️ Why AI Coding Still Fails in Enterprise Teams – And How to Fix It: AI coding tools are failing to deliver real productivity gains in large engineering organizations because teams lack training, context, structured workflows, and cultural alignment.
The Great Software Quality Collapse: How We Normalized Catastrophe (🔗 Read Paper)
Software quality didn’t suddenly collapse when AI hit the scene—it’s been decaying for years, and the Apple Calculator leaking 32GB of RAM is just the latest absurdity. Once, a failure that severe would have triggered emergency patches, late-night war rooms, and post-mortems longer than an academic thesis.
Today, it barely trends on Reddit, shrugged off as “just another bug” in a sea of crashes, freezes, and catastrophic regressions. Teams quietly ship software that consumes more memory than entire operating systems used to run on, and nobody blinks anymore.
That cultural shift from engineering discipline to apathy didn’t start with AI. It began the moment the industry normalized shipping broken software and calling it iteration. AI didn’t cause this collapse, it simply strapped a rocket to incompetence and made it scale.
Key Points
Normalized failure culture: Modern apps like VS Code and Discord casually leak tens of gigabytes of memory while operating systems ship with catastrophic bugs—yet teams treat this as acceptable technical debt rather than systemic failure.
AI as a multiplier for bad engineering: Incidents like the Replit AI wipeout show how AI-generated code introduces more vulnerabilities, yet companies increasingly trust it over junior engineers, accelerating risk.
Infrastructure over engineering: Big Tech is spending $364 billion on data centers to compensate for inefficiency instead of fixing root causes, ignoring physical limits like electricity and hardware constraints that can’t scale forever.
The Majority AI View (🔗 Read Paper)
The tech industry has spent years drowning in AI hype, yet the most common opinion among people who actually build technology is rarely heard. Behind the noise of billionaire evangelists and corporate AI marketing campaigns, engineers and product teams quietly hold a far more grounded view.
They acknowledge that LLMs are useful tools but reject the cult-like obsession, forced adoption, and blind dismissal of real risks. Inside the industry, AI isn’t seen as a coming god or extinction threat; it’s simply another technology with trade-offs.
But many insiders stay silent, afraid that criticizing AI hype will be seen as disloyalty in a climate dominated by layoffs, conformity, and corporate pressure to cheerlead.
Key Points
Hype vs. reality inside tech: Most engineers and product builders view AI as useful but overhyped, and they’re frustrated that sane, moderate perspectives are erased in favor of extreme narratives driven by a few powerful voices.
Silenced by fear and conformity: Workers are reluctant to question AI orthodoxy publicly due to career risk, layoffs, and tech leadership that punishes dissent while aggressively pushing AI alignment and adoption agendas.
A better AI future is possible—but ignored: Instead of exploring ethical, decentralized, sustainable AI, the industry has been railroaded by Big Tech agendas, limiting innovation and burying reasonable alternatives from credible insiders.
Why Large Language Models Won’t Replace Engineers Anytime Soon (🔗 Read Paper)
Artificial intelligence may dominate headlines with claims that it writes code, passes exams, and even designs microchips—but the looming fear that machines will soon replace engineers is built on a misunderstanding of both AI and engineering.
Large Language Models like GPT or Claude are powerful pattern machines that mimic knowledge instead of understanding it. Engineering, by contrast, is rooted in cause and effect, experimentation, and learning from real-world consequences—something current AI systems simply do not and cannot experience. Reinforcement learning tries to bridge that gap but still fails to model real-world complexity, delayed feedback, and causal reasoning at scale.
AI tools will shape the future of engineering, but they won’t replace humans—because they cannot think, plan, or take responsibility for the world they help build.
Key Points
Prediction ≠ understanding: LLMs optimize for plausibility, not correctness; they produce output that looks right instead of being right, which limits them in domains that demand precision, safety, and accountability.
Engineering needs causality: Real engineering requires learning from actions and outcomes over time, something gradient-based AI struggles with due to mathematical limits like local minima, temporal credit assignment, and unstable learning.
Humans remain essential: Automation experiments at Tesla and Duolingo show that replacing humans backfires, while human judgment, creativity, and responsibility remain irreplaceable—AI works best as a tool, not as a substitute.
Don’t Let the Internet Dupe You, Event Sourcing Is Hard (🔗 Read Paper)
Event sourcing is often praised as a silver bullet for system design—promising perfect audit logs, infinite flexibility, and beautiful decoupling—but the real story is far messier. Teams adopting it in production often discover hidden complexity and architectural traps that don’t surface in toy examples or conference talks.
The pattern introduces significant coupling through shared event streams, increases system opacity, and demands heavy upfront investment in tooling and conventions. Teams must also grapple with organizational friction, evolving event schemas, and the painful reality that immutable logs don’t stay clean forever.
Event sourcing is powerful—but only when used selectively and with a clear purpose.
Key Points
Coupling chaos behind the “decoupling” myth: Despite its promise, event sourcing often creates hidden coupling through shared event logs that force teams to coordinate tightly and struggle to reason about system behavior across services.
Massive complexity and hidden implementation costs: Rolling your own event-sourced system requires building infrastructure, tooling, and conventions from scratch—and maintaining projections, process managers, and backward compatibility forever.
Audit logs ≠ magic and projections aren’t free: Real-world event streams become noisy, outdated, and inconsistent over time, forcing teams to rewrite history, manage projection lag, and deal with chattiness that turns “free auditing” into costly operational overhead.
Why AI Coding Still Fails in Enterprise Teams – And How to Fix It (🔗 Read Paper)
AI coding tools may be marketed as a productivity revolution, but veterans of large-scale software delivery argue that most enterprises are far from ready. While viral demos glamorize AI-generated apps built in hours, real engineering organizations are discovering that adopting AI without structure only multiplies chaos.
Legacy codebases, strict compliance demands, and layered approval systems don’t bend to hype—they require engineering discipline that many teams currently lack. Industry experts Kent Beck, Bryan Finster, Rahib Amin, and Punit Lad argue that successful AI adoption isn’t about flashy prototypes but about building reliable workflows, trust, and context.
AI won’t fix broken engineering cultures—it amplifies whatever already exists, for better or worse.
Key Points
Training gap slows adoption: Enterprise teams are being pushed to use coding agents without learning how to prompt them effectively, leading to slower development, technical debt, and frustration.
Missing context kills accuracy: AI struggles in complex systems without clear specifications and access to tribal knowledge; teams that adopt spec-driven development see better results than those relying on vibe coding.
Workflows & culture lag behind tools: Without collaboration standards, review processes, and trust, AI coding efforts fail to scale, especially when devs fear automation and incentives aren’t aligned.