Artificial Intelligence has made some incredible leaps in recent years. We now have chatbots that can write essays, generate code, and even pass tough exams. But despite all this, AI is still far from being truly intelligent. It struggles with common sense, understanding the world, and planning ahead—things that even a dog can do effortlessly.

So, what’s missing? And what should we focus on next? Let’s break it down.

Why Current AI Models Are Limited

1. They Lack Common Sense
   • AI can solve complex math problems but struggles with basic real-world reasoning. A child learning to ride a bike doesn’t need a manual to understand balance—AI, on the other hand, needs millions of examples to grasp even simple concepts.
   • It doesn’t understand the world the way humans or even animals do. A dog knows how to nudge open a door when it’s slightly ajar; AI doesn’t truly “get” how physical objects interact.
2. They Generate Words, Not Ideas
   • Most AI, like ChatGPT, works by predicting the next word in a sentence, but this leads to hallucinations (making up false facts) and inconsistencies.
   • It lacks the ability to reflect, check its work, or understand the deeper meaning behind words.
3. AI Trains on an Unnatural Diet
   • AI is fed trillions of words from the internet, but a child learns from a mix of sight, sound, and experience.
   • Real-world intelligence doesn’t come from just reading—it comes from observing, interacting, and experimenting.
4. No Long-Term Thinking
   • Humans plan at multiple levels: “I need to get to work,” becomes “I should catch the train,” which becomes “I need to leave in 10 minutes.”
   • AI can’t break tasks down like this—it struggles with multi-step reasoning and planning ahead.
5. Too Much Data, Too Little Understanding
   • AI has read more than any human ever will, but it still doesn’t truly understand.
   • The way AI is trained is inefficient—it needs millions of examples to learn what a baby picks up from just a few experiences.

The Future of AI: What Needs to Change?

Instead of just improving today’s models, we need to rethink AI entirely. Here’s where things should go:

1. AI Needs to Predict, Not Just Generate
   • Instead of just guessing the next word in a sentence, AI should predict what will happen next in the real world.
   • This means training AI on video, images, and interactions—helping it build a sense of how objects, people, and events work.
   • Tesla’s Full Self-Driving (FSD) takes a step in this direction by recognizing states in real-time, understanding the movement of vehicles, pedestrians, and traffic signals to make driving decisions rather than just reacting to inputs.
2. Smarter, Not Just Bigger
   • AI models are getting bigger and bigger, but that’s not the solution.
   • Future AI should learn like humans do—by observing, experimenting, and understanding cause and effect.
3. Planning and Memory
   • AI should be able to remember past interactions and plan ahead, not just respond to prompts one by one.
   • This would allow AI to break down complex tasks into smaller steps, just like we do.
4. AI That Thinks Before It Speaks
   • Right now, AI answers instantly, even for tough problems.
   • Future AI should pause, reflect, and reason before responding—like a person thinking through a tough question.
5. Safer and More Controllable AI
   • Instead of fine-tuning AI after it’s built, safety should be built-in from the start.
   • Future AI should have “guardrails” that prevent it from going off-track, making it more reliable and trustworthy.

Final Thoughts

We’re still far from true human-level AI, but we know what’s missing. The next big step isn’t just making today’s AI bigger—it’s making it smarter, more interactive, and more intuitive. The future of AI isn’t about generating more text—it’s about understanding the world and making thoughtful decisions.

That’s the AI we really need.