Two Simple Prompts to Use When AI Feels Smart but Acts Dumb

Turn ChatGPT & Gemini into a more powerful collaborator.

It started with a feeling I couldn’t shake.

In mid-June, Sam Altman says GPT-5 might land in summer 2025 if it clears internal benchmarks. A week later, the rumors indicate a surprise July launch. This, after Altman publicly hit pause just months before, citing integration headaches and overstretched capacity.

At first, I tracked the conflicting signals like a product manager, trying to triangulate a launch date. But sitting with the noise, I realized I was asking the wrong question. The real story isn't the launch date but that AI development is visibly under strain.

If you've used these tools for any serious work, you've likely felt this Leaky Pipeline as well.

You hand the AI a brilliant strategy memo to summarize, and it returns a generic, soulless list. You ask it to analyze a dataset, and it confidently hallucinates figures. You give it clear instructions, and halfway through, it forgets the primary goal, overlooking a key constraint you never thought you’d need to repeat.

This isn’t a user error. It’s a design flaw. And understanding that flaw is the first step to overcoming it.

Why the Pipeline Leaks

To fix the leaks, we have to understand the plumbing. Today's AI is the product of two forces: 1) a simple learning method and 2) a brutal race for scale.

1. The Engine is a Prediction Machine

The breakthrough behind current AI was the discovery that you could train a massive model by having it do one simple task over and over: predict the next word. Training an LLM is like giving a teenager the entire world's library and asking them to guess the next word in every sentence, billions and billions of times.

Here’s the key takeaway: Predicting language turns out to be a proxy for modeling the statistics of human text about the world. It is not a proxy for modeling the world itself. The model isn't reconstructing fragments of our minds; it's reconstructing the statistical patterns of our linguistic output. It is a master of correlation, not causation. This is the source of the Context Leak (it forgets what's not statistically probable) and the Output Gap (it produces plausible-sounding text that lacks true insight).

2. The Fuel is Extraction

A venture capitalist recently compared AI development to Christopher Columbus’s voyage, noting that while Columbus didn't get where he intended, he "still got to a place that was highly valuable."

The comparison is more revealing than he intended. Columbus’s "discovery" inaugurated an era of exploitation justified by the pursuit of riches for a select few.

This "gold rush" mentality prioritizes speed and scale over precision and safety. The race for more data, more compute, and more energy—the megawatts and minerals for new data centers—creates a system that is powerful but inherently unstable and riddled with hidden externalities. This is the source of the Input Bottleneck and the system's overall unreliability.

The result? A brilliant, tireless, and slightly unreliable intern.

Now, let’s learn how to manage it.

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How to Make AI a Reliable Collaborator