Generic content is dying. Here is what replaces it

Something is shifting, and if you are paying attention you can feel it everywhere.

YouTube recently changed its algorithm. Channels that recycle widely available information are being suppressed. The platform is now rewarding original, specific content over broad, generic coverage. Creators who built audiences by summarising what already existed elsewhere are watching their reach collapse. The algorithm has figured out what the audience already knew: if I can find this anywhere, why would I watch it here?

That same principle is about to reshape how pilots learn.

Think about it this way. Have you ever tried to fix something around the house with a tool that was not designed for the job? A hammer to tighten a wheel nut. A screwdriver to pry open a paint tin. It sort of works, but you are forcing it, and you know it. That is what generic training content has become. Broad courses covering everything and mastering nothing. Long video modules that tick a compliance box but do not sharpen the specific skill they claim to teach.

I have spent six years making training videos. I can tell you categorically that for genuine learning and retention, the era of generic training content is ending. The same way the YouTube algorithm now punishes wide, unoriginal content, learners are moving away from products that try to serve everyone. They want something built for one purpose that does that one thing properly.

That is why question banks remain popular despite being basic. They lean toward being a tool. They give the learner something specific to engage with. But they are still rudimentary. The next step is purpose-built digital tools that solve one training problem and solve it well. Not a Swiss Army knife. A precision instrument designed for a single job.

Generic is dying. Specific is winning. The platforms know it, the algorithms know it, and the learners know it. The training industry just has not caught up yet.

Here is the part that should get you thinking: you do not need to be a programmer to build one of these tools anymore.

I spent two years trying to build a training tool the traditional way. It was painful, slow, and expensive. Then AI agents reached the point where I could articulate clearly what I wanted, collaborate with the machine, and watch the thing take shape. The result is the Risk Management and Decision Making Simulator.

I built it to solve one problem. Pilots practise handling emergencies in the simulator all day long, but almost nobody practises the decision-making process itself. The thinking. The framework. The part where you weigh competing pressures and choose a course of action under time constraints. There was no tool for that. So I built one.

It pulls real flights from Flightradar24, generates dynamic scenarios using AI, walks you through structured decision-making frameworks like RMM and T-DODAR, and then gives you an AI-powered debrief on how you performed. It does one thing. It does it well. It does nothing else. That is the point.

If you see a gap in how something is trained, you can now build the tool that fills it. Learn to articulate the problem with precision, sit down with an AI agent, and start. The barrier is lower than it has ever been. You can find the simulator at bryanair.tools.

Microsoft and Anthropic launch an AI that runs your office work
An AI agent that can operate across Microsoft Office applications autonomously. If this reaches airline training and operations departments, the amount of admin that currently buries instructors and training managers could shrink dramatically. Watch this space.

Claude Code now deploys autonomous bug-hunting agent teams
Anthropic's coding tool can now send out teams of AI agents to find and fix problems in code independently. This is exactly the kind of capability that made building tools like the RMM Simulator possible for someone without a software engineering background. The barrier keeps dropping.

Robots learn to play tennis by watching amateur footage
A machine learning model taught a robot to play tennis by studying clips of regular people, not professionals. The aviation parallel: if robots can learn complex motor skills from imperfect human examples, the implications for how we model and assess human performance in training are significant.

If you know a young pilot figuring out their next move, please forward them this.

Fly safe. Think smart.

Bryan