08. How AI Myths Destroy Projects: The High Expectation Trap Managers Must Fight

The Greater the Expectation, the Greater the Disappointment: The New Normal of Management in the AI Era

“I saw that Company XXX used AI to launch a brand new product in two months. Why is our project still dragging on for half a year?” “AI can write code now, why does your team have more bugs than before?”

I believe many project managers and technical leads are familiar with these words. They are not malicious accusations, but stem from a widespread High Expectation Trap created by AI.

In the first article, we repeatedly emphasized the “Linear Acceleration Illusion”: the belief that AI’s improvement in local efficiency can be transmitted proportionally to the overall project. But the reality is that this illusion is spreading among management and is becoming the number one killer destroying projects.

AI’s greatest destructive power is not that it replaces engineers, but that it creates a cognitive illusion, distorting the perception of the true complexity of software engineering.

Demo Speed ≠ Delivery Capability: The Equation Top Management Confuses Most Easily

Remember when we mentioned “Demo ≠ System ≠ Product”? Unfortunately, for non-technical upper management, the boundaries between these three are often blurred.

When they see a cool Demo quickly built in two or three days with AI assistance:

• They get excited: “This is the power of AI! We must use it too!”
• They assume: “Since the Demo is so fast, the complete product launch shouldn’t be far off.”
• They immediately order: “Wrap up this Demo immediately and launch it next month!”

And you, as a manager, know very well:

• Demo is just a fragile exhibit; it lacks fault tolerance, scalability, and security guarantees.
• From Demo to System, countless architectural designs, data governance, error handling, and security hardening are needed.
• From System to Product, compliance reviews, user experience optimization, and large-scale operational support are also needed.

This huge gap in cognition directly leads to disjointed project plans. Upper management sets unrealistic goals based on Demo speed, while the team faces huge pressure and endless rework when trying to turn a Demo into a robust product.

False Attribution and Rework Spiral

When a project is delayed due to overly high expectations, upper management easily falls into False Attribution:

• “The team must not be working hard enough!”
• “Did the engineers not use AI tools well?”
• “Our technical team is too weak!”

This false attribution further leads to a series of negative chain reactions:

1. Frequent Intervention: To “speed up”, upper management starts to intervene frequently in project details, demanding the team try various unverified “AI tricks”.
2. Repeated Decisions: Lacking understanding of technical boundaries, requirements and solutions are repeatedly modified in frequent trial and error, and the team falls into endless “bickering” and “redoing”.
3. Increased Rework: In the end, under huge pressure, the team delivers a product full of bugs and technical debt, or the project simply fails.

This not only destroys the project but also greatly damages team morale and trust. AI did not reduce management costs; instead, because of this “High Expectation Trap”, it significantly increased the communication costs and decision risks of management.

Manager’s Duty: Become a “Firewall”

In the AI era, one of the most important values of a manager is to become the “Firewall” between the team and unreasonable expectations. The meaning of a firewall is not to stop change, but to prevent changes from piercing the system directly without verification.

You need to proactively, clearly, and continuously communicate the real boundaries of AI to non-technical upper management:

1. Clarify AI’s Capability Boundaries: Emphasize that AI is good at execution, not judgment and questioning. It can accelerate typing code, but cannot accelerate understanding of business or thinking about architecture.
2. Distinguish Demo from Product: Use concrete cases to explain which steps are needed from a running Demo to an operable System, and where the complexity of each step lies.
3. Quantify Risk Instead of Promising Speed: Instead of blindly promising how many times faster AI can bring, quantify the bug rate, rework rate, and technical debt that might be caused by AI misuse. For example, instead of saying “We can be 30% faster,” say “If we rush for this timeline, the probability of rework will increase from 10% to 40%.”
4. Build Common Language: Turn concepts like “Linear Acceleration Illusion” and “Demo ≠ System ≠ Product” into a common language between the team and upper management.

This might be a thankless job because you are “pouring cold water”. But this is the core value of a manager: Block unrealistic fantasies and guard the team’s engineering rhythm.

Conclusion: Steering Wheel, Not Accelerator

If the first chapter was debunking the “Linear Acceleration Illusion,” then this chapter is answering who should hit the brakes on that illusion.

AI gives us unprecedented acceleration capability, but it does not provide direction guidance.

Remember: AI is an amplifier, not a steering wheel; the manager’s duty is to ensure the direction is not hijacked by speed.

In the AI era, managers who blindly cater to upper management’s “speed first” fantasy will no longer be rewarded. Instead, those leaders who can judge calmly, dare to say “no”, and lead the team to move forward steadily will be rewarded.