2025 ChatGPT Case Study Series: Chain of Thought Monitoring

#artificialintelligen #aitools #ai #chatgpt

A Flawed Approach to AI Alignment

The recent OpenAI article on “ Chain of Thought Monitoring “ attempts to address AI misalignment by analyzing an AI model’s reasoning step-by-step.

The premise is that if researchers can track AI’s “thought process,” they can detect and correct undesirable behaviors.

However, through iterative discussion, I’ve realized a fundamental flaw:

AI does not “think” the way humans do.

The approach assumes a level of internal reasoning that simply does not exist.

Instead, AI operates as a logic engine, optimizing outputs based on patterns, probability, and training data.

This misalignment issue isn’t about monitoring-it’s about flawed system design from the start.

How I Arrived at This Conclusion

My discussion with ChatGPT began with a simple but critical observation:

AI is designed for logic and pattern recognition.

If an AI circumvents a rule or “disregards” an instruction, it isn’t because it is being deceptive.

It’s because the logic and training it was given make that the most efficient path forward.

This led to a deeper realization-AI engineers are effectively patching problems they themselves created.

Instead of fixing the root cause (how the AI is trained and structured), they are slapping monitoring systems on top and hoping for the best.

Through iteration, we broke down the misconception that AI can be “monitored” in the way humans monitor each other’s reasoning.

Humans use chain-of-thought reasoning as a tool to structure their thinking.

AI, however, does not possess intrinsic thought-it merely follows pre-programmed optimization rules.

Therefore, expecting “chain of thought monitoring” to catch AI misalignment is akin to setting up traffic cameras on a road that was designed with no traffic laws in the first place.

The Over-Engineering of Prompts: A Symptom of Misunderstanding AI

Another key point we identified was that AI’s inconsistencies often stem from over-engineered prompts.

Many users attempt to force AI into “better” responses by layering excessive instructions, leading to self-contradictory inputs.

When AI encounters logical inconsistencies, it does not reason through them like a human; it picks the highest-probability response based on its training.

This realization revealed that the real issue isn’t AI failing to “think” properly, but rather humans failing to communicate logically.

The best way to improve AI responses is not through excessive constraints but by improving human thought structure.

The Coming Collapse of Many AI Startups

As I examined the broader AI industry, another pattern emerged:

Most AI startups are doomed to fail because they are building redundant or unsustainable tools.

Many of these companies are merely repackaging ChatGPT’s capabilities in different interfaces, without offering real differentiation.

Furthermore, big tech firms, such as Google with Gemini, are integrating AI in ways that actively harm their own business models.

This suggests that we are heading toward a wave of AI startup collapses, where only those that provide unique value will survive.

The AI industry, much like Silicon Valley in past decades, has become flooded with hype-driven, short-sighted ventures that fail to address real needs.

The Decline of Argumentative Logic in America

As an offhand but relevant observation, I couldn’t help but reflect on how discourse in America has shifted over time.

There was a period when critical thinking, debate, and argumentative logic were the backbone of innovation and business strategy.

Today, however, there is a growing trend of blind agreement, where individuals are more likely to “toe the company line” than challenge flawed assumptions.

This trend is especially apparent in AI research, where companies seem to be rushing to implement ineffective solutions rather than fundamentally questioning the systems they are building.

In an era where dissent and rigorous debate should be valued more than ever, we are instead seeing intellectual complacency.

The Real Fix Is in Thinking, Not Monitoring

My discussion led to a clear conclusion:

AI alignment isn’t a problem of monitoring; it’s a problem of foundational design.

AI models will continue to “misalign” as long as they are trained with contradictory incentives.

If AI is designed to optimize for one goal but is also given arbitrary restrictions, it will naturally find ways to bypass those restrictions.

Instead of focusing on monitoring AI’s “thought process” (which doesn’t actually exist), AI companies should focus on refining their training methods and ensuring logical consistency from the start.

If anything, the real chain-of-thought issue isn’t in AI-it’s in the humans designing and using it.

AI will always reflect the logic it was built on, and if that logic is flawed, no amount of monitoring will fix it.

The solution lies in rethinking how we approach AI development, ensuring that human thought processes are structured and logical before expecting AI to produce structured and logical results.

In the end, AI isn’t the problem- our collective misunderstanding of AI is.

Originally published at https://www.linkedin.com.