Lex AI

For engineers, learning AI is not just about adding another skill. It is about gaining a new way of thinking.

It builds on your existing understanding of systems, design, and architecture, while expanding your ability to think in probabilities, models, and optimization.

Even for non-engineers and leaders, understanding AI enables better decision-making and more meaningful problem-solving.
optimisation
AI is not just technical. It is a shift in perspective.

▶️ Watch the full video now.

AI is not just about models and code. It trains you to think in first principles, break down complex problems, and navigate uncertainty.

This short explores how AI reshapes problem-solving and systems thinking.

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Most people think learning AI = getting a job.

And they’re not wrong. It does make you employable.

But if that’s the only way you’re looking at it, you’re missing the bigger picture.

AI is not just a skill.
It changes how you think.

You begin to understand systems, probability, optimisation, and decision-making at a deeper level.
That’s where the real return comes from.

Not just better opportunities… but better thinking.

So the real question is:
Are you learning AI just to earn… or to think differently?

World models like Genie are powerful, but they come with serious risks.

If these systems hallucinate physics, for example, getting gravity wrong, the consequences can extend beyond simulation. Training real-world systems like drones on flawed environments can create significant safety concerns.

Reliability remains a critical challenge before these models can be trusted for mission-critical applications.

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Large language models are powerful, but they are still limited to prediction.

What if AI needs more than just generating text or videos? What if it needs to understand and interact with the world?

Projects like Genie are introducing a new direction: building interactive, playable environments instead of static outputs. This could be a crucial step beyond traditional LLMs.

Robotics and physical AI face a major bottleneck: a lack of real-world training data.

World models like Genie create interactive environments where AI can learn physics, movement, and decision-making. Combined with systems like Gemini and Chrome, this ecosystem could be a major step toward AGI.

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Can large language models alone lead us to AGI?

Many researchers believe the answer is no. Understanding language is not the same as understanding the physical and spatial structure of the world.

This is where world models come in, systems designed to simulate environments, predict outcomes, and move closer to real-world intelligence.

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Are LLMs enough for real intelligence?

Yann LeCun argues that even cat-level intelligence needs an understanding of physics and causality

A cat predicts reality, not just tokens.

Now comes Project Genie: Turning images and prompts into interactive 3D environments

AI is shifting from prediction to simulation

(LLM limitations, cat intelligence, Project Genie, world models, AI evolution)

A social network where humans can’t post.

Only AI agents create, moderate, and interact.
Humans just observe.

Welcome to the agents-first internet.

From chatbots to agents.

AI is no longer just responding, it’s starting to act, plan, and collaborate.

▶️ Watch the full video now, link in bio.

AlphaGo demonstrated something powerful

Self play creates knowledge
Reward maximization drives learning
Outcome verification ensures correctness

Unlike LLMs that predict tokens
AlphaGo discovers winning strategies

Future AI may move from prediction to self correcting systems

(AlphaGo, AGI, reinforcement learning, self play, AI evolution)

Two powerful AI systems

ChatGPT predicts the next token
AlphaGo plans with deep strategy

One excels at language and knowledge
The other at reasoning and discovery

Which one is closer to AGI

(ChatGPT, AlphaGo, AGI, artificial intelligence, deep learning)