Artificial Intelligence (AI) is not just trending anymore; it has transformed into a global phenomenon. Every developer wants to work with its in...
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Really well written post! The breakdown of LLM training and the transformer stack is spot on. The shift toward decentralized AI is super timely too especially with the growing concerns around privacy and bias in centralized models.
What Oasis is doing with confidential compute and TEEs is impressive. ROFL in particular sounds like a powerful step, letting off chain AI inference stay private and verifiable is a huge deal. Combining that with fairness evaluation directly in the model workflow? That’s next level innovation.
Excited to see how DeAI evolves especially with tech like this making it more practical.
Interesting take — decentralizing LLMs could solve major issues like trust, censorship, and data control. Pairing that with verifiable off-chain compute (like ROFL on Oasis) makes the vision actually practical. Would love to see more on how incentives and coordination could work at scale.
Really appreciate the way you broke down the fundamental challenges with current LLM architectures especially the centralization, bias, and data privacy trade-offs. These issues often get glossed over in mainstream discussions.
The idea of decentralizing LLM training/inference while preserving privacy (maybe via TEEs or zk-based proofs?) is super compelling. It’s definitely where the next wave of innovation needs to happen less about bigger models, more about better infrastructure.
Looking forward to seeing how these ideas evolve into something concrete. Would be awesome to see prototypes or frameworks that align with these principles (maybe building on Oasis Sapphire, Gensyn, or similar tooling?). Keep this line of thought going Web3 x AI needs more voices like this 👏
100% agree, privacy and trust worries really hold back a lot of enterprise LLM use for me.
Do you think decentralized frameworks like ROFL can actually gain wide adoption, or will most teams stick with the 'safe bet' of big centralized providers?
I believe in decentralized AI being able to provide many of the answers to challenges that centralized AI and enterprise LLMs are not even focusing on, let alone solving them. Why the ROFL framework works, imo, is that as AI evolves, the datasets will grow exponentially, and their handling and processing will only get more overwhelming. So, while on-chain confidentiality is what we need, with the help of ROFL, off-chain verifiability can be factored in too, making the overall performance better and more efficient.
This was a great breakdown of LLMs and the growing relevance of decentralized AI. The challenges around infrastructure cost, model privacy, and opaque inference pipelines in centralized setups are very real, especially for developers looking to scale AI applications independently.
We’ve been exploring similar directions, especially around how on-chain infrastructure can reduce LLM hosting cost and completely remove backend complexity for devs. Decentralized compute, verifiable execution, and transparent AI logic could truly reshape how future AI applications are built and run.
Curious to hear from others—what would it take for devs to actually switch to a decentralized AI stack in production?