I'm trying to understand where our viewpoints differ, because I suspect we have fundamentally different mental models about where programming difficulty actually lives.

It sounds like you believe the hard part is decomposing problems - breaking them into subproblems, managing the "big picture," keeping the architecture in your head. That this is where experience and skill matter.

My mental model is the opposite: I see problem decomposition as the easy part - that's just reasoning about structure. You just keep peeling the onion until you hit algorithmically irreducible units. The hard part was always at the leaf nodes of that tree.

Why I think decomposition is straightforward:

People switch jobs and industries constantly. You move from one company to another, one domain to another, and you're productive quickly. How is that possible if decomposition requires deep domain expertise?

I think it's because decomposition is just observing how things fit together in reality. The structure reveals itself when you look at the problem.

Where I think the actual skill lived:

The leaf nodes. Not chipping away until you are left with "this is a min-cut problem" - anyone off the street can do that. The hard part was:

- Searching for the right algorithm/approach for your specific constraints

- Translating that solution into your project's specific variables, coordinate system, and bookkeeping

Those two things - search and translation - are precisely what AI excels at.

What I think AI changed:

I could walk into any building on Stanford campus right now, tap a random person (no CS required!) on the shoulder, and they could solve those leaf problems using AI tools. It no longer requires years of experience and learned skills.

I think this explains our different views: If you believe the skill is in decomposition (reasoning about structure), then AI hasn't changed much. But if the skill was always in search and translation at the leaf nodes (my view), then AI has eliminated the core barrier that required job-specific expertise.

Does this capture where we disagree? Am I understanding your position correctly?

I watched a youtube video showing off Orlog https://www.youtube.com/watch?v=3rW3jtbsxZk.

Challenge accepted. If you get back to me I'll livestream this on Saturday.

I want to be crystal clear about what I'm claiming

My Claim: AI assistance has effectively eliminated specialized job skills as a barrier. Anyone can now accomplish what previously required domain expertise, in comparable time to what a pre-AI professional would take.

Specifically:

- I've never written a game. I've never used a browser rigid body physics library. Never written a WebGL scene with Three.js before. Zero experience. So I should fail write.

- I think I could recreate the full 3D scene, hand meshes, rigging, materials, lighting - everything you see in that screenshot - using AI assistance

- I'm not going to do ALL of that in a couple hours, because even a professional game developer couldn't do it all from scratch in a couple hours. They would have assets, physics engine, rendering engine, textures, etc all because they were creating Orlog inside a larger game that provides all these affordances.

- But I could do it in the same timeframe a professional would have taken pre-AI

My interpretation of your challenge:

You're claiming that writing the multiplayer networking and state management for a turn-based dice game is beyond what AI can help a -- what you called me "run of the mill coder camp wanna be programmer" -- accomplish in a reasonable timeframe. That even with a simple 2D UI, I lack the fundamental programming skills to write the multiplayer networking code and manage state transitions properly.

So here's what I'll build:

A multiplayer Orlog game with:

- Full game logic implementing all Orlog rules and mechanics

- two players can connect and play together

- observers can join and watch

- game state properly synced managed across clients.

- Real dice physics simulation (because otherwise the game feels boring and unsatisfying - I'll grant you that point). But I'll have a server pick the dice roll values to avoid cheating. (Easiest trick in the book, just run the physics offscreen first, find the face that lands up, remap the textures, replay on screen this time), but use a library for simple rigid body physics engine because you couldn't write one from scratch in 3 hours either.

- Visual approach: Simple 2D/indie game cartoon style UI, with dice rolling in a separate physics area (just compositing dice roll app at bottom of screen, results animate onto 2D board, reality is they are totally separate systems)

What I need from you:

1. Is this the right interpretation? You're claiming the networking/state management is beyond what AI can help me accomplish?

2. Time predictions:

   - How long would a competent game developer take to build what I've described?

   - How long will it take me?

3. At what point do I prove my point? What's the minimum deliverable you'd accept as having completed the challenge?

Are you willing to make concrete, falsifiable predictions about this specific challenge?

Go ahead and do it. Put the code at your convenience on github. I don't care about your livestream, you can do it whenever. What I want is an app that I can install on my smartphone, Android, and that I can have my son do it as well and we play together. I am giving you a generous one week, should be more than enough for your "AI can do it easier". I did it in a month, and I put like 120 hours during that time. You get close to those 120 hours or over, you fail this "challenge accepted". Have fun.