AI Slop: Why Forking Isn't a Walk in the Park

Artificial Intelligence isn't just about flashing lights and futuristic dreams. It's about real-world implications. One of those is the challenge of forking AI models. In open-source software, forking is a common practice. Developers take existing code, tweak it, and voila, a new version is born. But in AI, this process is far from straightforward.

Why Forking AI Models Is Tough

AI models are complex beasts. They're not just code, they're trained on vast datasets. When you fork an AI model, you're not just copying code, you're also trying to replicate the training process. That's where the trouble begins.

Training AI models requires significant computational resources. We're talking about access to powerful GPUs and enough energy to power a small town. It's not something a lone developer can do on a laptop over the weekend. This makes forking AI models an exclusive club, limited to those with deep pockets or institutional backing.

The Cost of Entry

Let's be honest, who pays the cost when AI can't be forked easily? It's certainly not the big tech companies. They hold the cards, keeping AI development in their hands. The productivity gains went somewhere. Not to wages or democratized innovation, that's for sure.

And what happens to open-source ideals in this scenario? They're left in the dust. The promise of collaborative improvement is hamstrung by the sheer resource demands involved in AI development. Ask the workers, not the executives, about what this means for innovation.

Why It Matters

So, why should you care? Because this isn't just about techies and their toys. It's about control over technology that shapes our world. If AI development remains in the hands of a few, the rest of us stand to lose out on the benefits of innovation and improvement. Automation isn't neutral. It has winners and losers.

Will we see a future where AI development is open to all, or will it remain a walled garden? The jobs numbers tell one story. The paychecks tell another. Who will benefit from the next wave of AI advancements? That's a question worth pondering.