Scenario: The Prototype Credit-Grabber — "I Built a Demo With AI"
You're holding back in the meeting, but your colleague just excitedly presented the prototype he built with AI at the weekly standup: "This process used to take three days — AI did it in ten minutes!" The manager nods approvingly, praising his "AI-forward attitude."
You know what actually happened: he fed a core team workflow's judgment logic to an AI, then showed the output to the person who decides your team's headcount.
What is this person actually doing? What should you do?
The Sharper Question First
"If nobody does it, doesn't this kind of person benefit even more?"
Short term: yes. Within a single performance review cycle, the prototype credit-grabber genuinely gains: management attention, the "embraces innovation" label, possible performance bonus. Everyone else looks "not proactive enough."
But this is what makes the prisoner's dilemma so vicious — short-sighted self-interest leads to long-term collective disaster, including for the credit-grabber.
Don't call this "individual rationality." A truly rational person runs the long-term numbers. This is short-sighted self-interest — a game horizon that only sees the next performance review, not the next round of layoffs.
And credit-grabbers compete against each other too: someone writes a skill, then someone writes a better skill; someone builds a prototype, then someone benchmarks the prototype. More precisely, this is a race to the bottom — everyone competing to more thoroughly prove "my work can be replaced by AI." Everyone accelerates. Everyone sinks.
What Credit-Grabbers Don't Know: What They're Actually Demonstrating
He thinks he's showing: "I'm capable. I know how to use AI."
What management actually receives: "This team's work can be done by AI."
Real cases have already been documented:
Case 1 (Economic Times report): An e-commerce developer spent enormous effort building automation systems for the company, saving thousands of hours of labor. Result: no promotion, no raise — instead, more work piled on. Eventually the company hired a $16/hour new hire to take over and demanded he hand over admin access to all his code. He suspects he's being replaced on the cheap.
Case 2 (Economic Times report): A startup employee spent 10 months training his manager to use the automation system he built. After training was complete, he was laid off. Then the company brought him back as a freelancer to maintain the system — paid per task, no benefits.
Case 3 (georgesonfirst.com.au report): A 26-year-old engineer at his "dream job" worked 80-hour weeks building AI Agents designed to replace human labor. He was the most proactive, most capable person on the team. He got laid off.
The pattern is consistent: builders get their value extracted, then get discarded. "Thanks for your contribution" and the layoff notice are often just weeks apart.
People Are Already Catching On
In Chinese tech communities, clear-eyed voices have been showing up:
"Write it for local use. Local stuff saves your own time. Public stuff is selling yourself out."
"Keep yin-yang versions of your published skills and your personal skills. Don't ask why — just say your AI isn't tuned right. How to tune it? I'll hold a training session and walk you through it slowly — and there's your performance metric."
"You want machines? Invest and buy them. But you can't steal. You want skills? Buy them out at 5 years' salary, not just two weeks of garden leave."
"You could actually write skills that contradict each other, then nest skills inside skills, hiding contradictions across different skills. The agent runs slow and can't handle real problems. CTO looks at the bill and declares the whole skill thing is a scam."
"Whoever first proposed defensive programming was a genius."
These people haven't read Zion, but they're already practicing Zion's core logic — protect your judgment, control the depth of what you hand over. Their instincts are right. All Zion does is systematize and frame that instinct so more people can think it through.
Game Analysis: Why the Credit-Grabber's Strategy Is Self-Destructive
Round 1 (Short Term): Credit-Grabber Scores
You: Stay measured, do your job
Him: Show off prototype, get good review
Score: Him > YouRound 2 (Medium Term): Management Reassesses the Team
The credit-grabber's prototype "proves" the work can be done by AI. But management won't only cut other people — they'll reassess the entire team's headcount.
Result: Team goes from 8 to 4
Credit-grabber stays (maybe), but picks up 3 people's workloadRound 3 (Long Term): Credit-Grabber Gets Replaced
Once his prototype matures enough, the company no longer needs "an engineer who understands the business" — they need "someone who can maintain AI systems." That person is younger, cheaper, easier to hire.
Result: Credit-grabber discovers his biggest contribution (that prototype)
has become the evidence that he's no longer neededA 2026 deep analysis in The Atlantic stated the logic directly: "If the result of efficiency gains is layoffs, employees won't bring their best innovations to the table." This isn't a moral failing of workers. It's a rational response — when an organization can't make a credible commitment that "innovators won't be replaced," hiding innovation is the equilibrium strategy.
You Can't Stop the Credit-Grabber — and You Don't Need To
This is reality you need to face clearly: you don't control other people.
Some people genuinely don't care whether the market stays healthy or whether society improves. Their game horizon is the next performance review, not the next five years.
But this doesn't change your strategy. Here's why:
1. The Credit-Grabber's Prototype Is Usually a "How" Layer Demo
"I used AI to shrink this process from three days to ten minutes" — this proves efficiency gains at the operational layer. It doesn't include your deep understanding of the system: why the process is designed this way, under what edge cases it fails, which exceptions need human judgment.
What the credit-grabber can demo, AI really can do. But the part AI can't do, the credit-grabber hasn't demonstrated either — because he might not even have that knowledge.
2. When the Prototype Hits Real Production Use, Deeper Problems Surface
Between a 10-minute demo and a system that runs reliably in production, there's an enormous gap. Management will realize this after the first incident — and at that point, they don't need someone who can build demos. They need someone who understands why the system broke.
That person is you, not the credit-grabber.
3. Your Protective Behavior Doesn't Create an Obvious Contrast
You're not "rejecting innovation." You use AI too — you just choose to apply it differently. You've automated repetitive operations, improved code quality, sped up testing — but you haven't handed core judgment logic to AI for a demo.
From the outside, you're both "embracing AI." The difference: you controlled the depth. He didn't.
Practical Strategy
When a Colleague Presents a Prototype in a Meeting
Don't oppose it. Don't question it. Don't show any dismissiveness.
You can:
- Show acknowledgment: "Good idea, the efficiency gain is clearly there."
- Ask about technical details (naturally): "How does this demo handle edge-case data? What happens if it hits [specific boundary condition]?"
- This question isn't to embarrass him — it's to naturally surface for management that between demo and production, there's a massive amount he hasn't considered
When Your Manager Asks You to Build a Similar Prototype
Do it. But pick work you're willing to automate.
Good choices: Automate your testing workflow, auto-generate reports, speed up data processing Dangerous choices: Use AI to replicate your architecture decision process, demo AI replacing your code review judgment
Principle stays the same: How can be automated. Why doesn't get demoed.
When the Credit-Grabber's Prototype Gets Rolled Out to the Team
Join the rollout. Help improve it, even. But in the process of improving it, you naturally become the person who understands the system's limitations. You know where it'll fail, you know when it needs to fall back to humans — that knowledge makes you more irreplaceable than the credit-grabber once the system is actually running.
Calibrating Your Mindset
The credit-grabber makes you anxious because you're measuring yourself against him on the same dimension — "who's more active on the AI front."
Switch dimensions: whose value is harder to replicate.
Someone who can build a demo in ten minutes? The market is full of those — AI tools lowered that bar. Someone who knows "under what conditions this demo breaks, why it breaks, and how to fix it"? The market has very few — because that requires real system experience.
Don't race the credit-grabber to see who runs faster. You're not on the same track.
Section Summary
| Dimension | Credit-Grabber | You |
|---|---|---|
| Short-term performance optics | Good | Normal |
| Image with management | "Embraces innovation" | "Stable and reliable" |
| What they're actually doing | Proving work can be automated | Normal work + maintaining depth |
| Position 12 months later | Post-downsizing: 3x workload or replaced by someone cheaper | Needed when the system breaks |
| Difficulty of being replaced | Low — anyone can build what he demoed | High — your value is at the judgment layer |
The credit-grabber isn't your enemy. He's someone making a strategic mistake. You don't need to stop him. You just need to not make the same one.