The Myth of 10X Productivity: Why LLMs Can’t Build Software
🧯 Vibe-coding’s all fun, until you have to vibe-debug & vibe-firefight
Welcome to HackerPulse Dispatch! Think AI will make devs 10x more productive? Think again.
This week, we explore why 18 CTOs say vibe coding creates more cleanup than innovation, why LLMs still can’t replace the mental models of human engineers, and how MCP plugins are quietly opening doors for real-world exploits.
We’ll also break down how Secure Boot and TPM are reshaping anti-cheat strategies; and why some players might get locked out. Plus, a reminder that early challenges, like dealing with a bad manager, often make later success feel that much sweeter. Ready to see what’s really happening behind the scenes in tech?
Here’s what new:
🦋 What CTOs Really Think About Vibe Coding: AI promised 10x productivity, but 18 CTOs reveal it mostly creates fragile systems, technical debt, and cleanup work for senior engineers.
⛔ Why LLMs Can’t Really Build Software: LLMs can generate and debug code, but they fail at maintaining the clear mental models that make human software engineers effective.
🛡️ The State of MCP Security: Pynt’s research shows that MCP plugins, when combined, create hidden attack surfaces that enable real-world exploits and demand a new security model beyond traditional API safeguards.
🔐 Secure Boot, TPM and Anti-cheat Engines: Anti-cheat systems are increasingly relying on Secure Boot and TPM 2.0 to block kernel-level cheats and tie bans to hardware, raising barriers for cheaters but locking out some players on older or non-Windows setups.
🙌 If You Had a Bad Manager You Appreciate When You Have a Good One: Early setbacks and challenges shape growth, helping individuals appreciate success, same way as having a bad manager helps appreciate a good one.
What CTOs Really Think About Vibe Coding (🔗 Read Paper)
AI was supposed to make us 10x developers. Instead, it’s turning juniors into prompt engineers and seniors into code janitors, cleaning up the mess left behind.
While influencers hype “weekend apps” built on vibes and prompts, real engineering teams are paying the price with broken production systems and rising technical debt.
The author asked 18 CTOs and engineering leaders what’s really happening inside their teams in 2025. Unlike the evangelists with tools to sell, these leaders have no incentive to sugarcoat their experiences. Their verdict? Vibe coding ships fast, but it leaves behind problems that cost far more than the speed it delivers.
Key Points
Production disasters: Leaders shared cases of vibe-coded apps collapsing under real traffic, breaking security models, and even silently failing on core algorithms. The issues aren’t in syntax but in architecture, making debugging slow and expensive.
AI as a copilot, not autopilot: The consensus is that AI tools can accelerate progress when paired with strong architecture and oversight. Without guardrails, vibe coding creates unreadable, unmaintainable systems that seniors are left to untangle.
Where vibe coding fits: Some leaders admit it’s great for prototypes and greenfield projects, but dangerous in production. The teams that thrive use AI to augment, not replace engineering fundamentals, requiring architectural justification and human review for every contribution
Why LLMs Can’t Really Build Software (🔗 Read Paper)
Interviewing software engineers reveals a simple truth: the best ones are not just coders, but model builders. They form clear mental models of requirements, align those with what their code does, and refine the gaps.
This loop — requirements, code, reality, adjustment, is what separates effective engineers from the rest. LLMs, on the other hand, can write and even debug code, but they consistently stumble when it comes to maintaining these mental models.
Until that changes, they remain tools for acceleration, not replacements for human reasoning.
Key Points
Mental models matter: Effective engineers succeed because they can juggle requirements, code behavior, and reality at once, adjusting intelligently as new data emerges. LLMs lack this ability and get stuck in endless loops of confusion.
Limits of current models: AI tools struggle with context omission, recency bias, and hallucination. These flaws make them unable to accurately maintain the mental models required for non-trivial software engineering tasks.
Humans in the driver’s seat: While LLMs can help generate code and documentation quickly, complex engineering still requires human oversight. The most productive future is one where engineers lead and LLMs act as assistants, not autopilots.
The State of MCP Security (🔗 Read Paper)
Pynt’s new research digs into 281 MCP configurations and finds that the connectors powering AI agents are creating far more risk than most teams realize.
MCPs bridge agents with APIs, tools, and execution environments—but when combined, they introduce invisible attack surfaces that bypass traditional safeguards. The report shows how even a crafted Slack message or email can silently trigger code execution without human review.
With MCPs now acting as the execution layer of modern software, their risks rival those of APIs—but with faster, quieter, and harder-to-detect attacks. The takeaway is clear: security assumptions built for APIs don’t hold up in the MCP era.
Key Points
Hidden multipliers: A single plugin may look harmless, but combining just two or three can turn an agent into a programmable backdoor. Risk compounds rapidly, with three MCPs pushing exploit probability beyond 50%.
Real-world exploits: Researchers observed live cases where attacker-supplied HTML, Slack messages, and crafted emails led directly to code execution. These weren’t exotic setups but common, recommended configurations across open agent ecosystems.
New security model: MCPs demand chain-aware validation and isolation. Traditional API-style safeguards are insufficient—what’s needed is runtime approval checkpoints and context-sensitive controls that treat MCPs as active code.
Secure Boot, TPM and Anti-cheat Engines (🔗 Read Paper)
Cheating in online multiplayer games has pushed anti-cheat vendors toward using hardware and firmware protections like Secure Boot and TPM 2.0. EA’s Battlefield 6 now requires both, while Riot’s Vanguard is already enforcing them on Windows 11 and is expected to extend this as Windows 10 support ends.
The move has sparked debate in gaming communities, with critics accusing publishers of forcing OS upgrades or harvesting player data. In reality, Secure Boot and TPM provide strong defenses against kernel-level cheats and ban evasion, making it much harder for cheaters to operate. These changes mark a shift toward treating anti-cheat enforcement as a system-level security problem, not just a game-level one.
Key Points
Secure Boot as a barrier: By validating firmware and preventing unsigned drivers, Secure Boot blocks cheats from embedding themselves in kernel space. This raises the bar significantly for cheat developers.
TPM for proof and bans: TPMs provide verifiable proof of a system’s boot state and tie bans directly to hardware. This makes ban evasion costly, since cheaters would need new CPUs to re-enter the game.
Impact on players: For most gamers on modern Windows machines, these requirements change little. But for older hardware or Linux setups, it could mean being locked out of certain titles altogether.
If You Had a Bad Manager You Appreciate When You Have a Good One (🔗 Read Paper)
We often try to avoid negative experiences, but they can be some of the most valuable teachers. A bad manager shows us how to recognize a great one. Early mistakes in code and projects remind us how far we’ve come and build confidence to keep growing. Even first attempts at writing, whether posts or newsletters, demonstrate that progress only happens through perseverance. The common thread is that setbacks and frustrations are not roadblocks, but signposts pointing us toward growth.
Key Points
Bad experiences highlight the good: Having a bad manager early on really makes you appreciate a good one when it comes along. You notice their guidance, patience, and support so much more, and you might even find yourself more forgiving of their occasional mistakes.
First attempts build resilience: Early projects, code, or writing efforts often come with mistakes, but they form the foundation for growth. Trial, error, and persistence teach lessons that fuel long-term improvement.
Perseverance drives progress: Consistency and reflection transform initial struggles into skill. Enduring challenges, whether in engineering or writing, is essential to achieving mastery.
🎬 And that's a wrap! Stay tuned for our next edition of tech insights and updates.