The Automation Trap: Why AI Products Fail by Copying Humans

‍How breaking free from human-centric design leads to breakthrough AI solutions

I've been building LLM-powered tools since GPT-3 was released in 2020, and I've watched countless AI products launch with the same fundamental flaw. They're all trying to be better humans instead of being better solutions.

The Human Template Problem

The most common trap in AI product development is what I call "human template thinking." Teams start with a simple question: "How would I personally solve this problem?" Then they attempt to automate that exact process with AI.

On the surface, this approach makes perfect sense. We understand human workflows. We know what works. We can easily explain the logic to stakeholders and users. But this tendency to build digital replicas of human processes anchors our thinking in ways that limit breakthrough innovation.

Consider these popular examples:

Customer Support: Most companies stick an LLM in a chat window and call it AI-powered support. But what if there's a fundamentally better way to resolve customer issues that doesn't involve chat at all? What if AI could predict and prevent problems before customers even know they exist?

Sales Outbound: Teams feed existing email templates into AI and ask it to personalize the content. But what if the most effective sales approach isn't email-based? What if AI could identify exactly when a prospect is ready to buy and reach them through the optimal channel at the perfect moment?

Content Creation: We prompt AI to write blog posts the way humans would write them. But what if AI could create content that adapts in real-time based on reader behavior, personalizing not just the message but the entire structure and format?

The Robotics Analogy

Robotics provides the perfect analogy for this thinking trap. When most people imagine robots, they picture humanoid figures with two arms, two legs, and human-like dexterity. Hollywood has trained us to think of C-3PO or the Terminator as the pinnacle of robotic design.

But robots already exist everywhere around us, and they look nothing like humans:

• Factory robots are massive arms that move with precision no human could match
• Autonomous vehicles are cars with sensors, not walking machines
• Robot vacuums are flat discs that navigate under furniture

Each of these designs is optimized for its specific task, not for resembling humans. They work extremely well precisely because they abandoned human constraints.

The same principle applies to AI products. The most successful solutions will be those that abandon human workflows entirely and optimize for outcomes instead.

Non-Human AI Design

Several domains have already demonstrated the power of thinking beyond human approaches:

Algorithmic Trading: High-frequency trading systems don't mimic human traders. They analyze market patterns and execute trades at speeds and scales impossible for humans. They've created entirely new approaches to financial markets.

Recommendation Systems: Netflix doesn't recommend movies the way a human friend would. It processes viewing patterns across millions of users to surface content you never would have discovered through human recommendations.

Fraud Detection: Modern fraud detection doesn't work like human investigators. It analyzes thousands of variables simultaneously to identify suspicious patterns that would be invisible to human analysts.

Supply Chain Optimization: Companies like Amazon don't manage inventory the way traditional retailers do. They use AI to predict demand, optimize warehouse locations, and coordinate logistics in ways that transcend human supply chain management.

The Breakthrough Mindset

If you're building an LLM-powered product today, you're better off throwing human constraints out the door and experimenting with novel approaches to achieving your desired outcomes.

Instead of asking "How would I solve this problem?" try asking:

• "What's the optimal way to achieve this outcome now that I have an all-knowing intern that never sleeps?"
• "What would this process look like if it were designed by an intelligence that reasons via next token prediction?"
• "What constraints am I accepting simply because they're part of the human experience?"

Now write down your current human-based approach, then deliberately design three alternative approaches that would be impossible for humans to execute. One of those alternatives might just be your breakthrough solution.

Final Note

Companies that successfully break free from human-template thinking gain a massive competitive advantage. While their competitors are building slightly better versions of human processes, they're creating entirely new categories of solutions.

The breakthrough products won't look like better humans. They'll look like something entirely new. Whether you're motivated by commercial success or simply the joy of building something that outperforms millions of years of evolution, you'll need to think like an alien to build the future.

The market is still wide open. The most transformative AI products are waiting to be built by teams brave enough to embrace "alien" intelligence.