Generative AI Revolution: Big Opportunities for Web3 in Changing the Digital World!

Updated

February 26, 2025

Gambar Generative AI Revolution: Big Opportunities for Web3 in Changing the Digital World!

Jakarta, Pintu News – The development of generative artificial intelligence has entered a new era with the emergence of more sophisticated and efficient models.

Recent trends in generative AI show a paradigm shift from monolithic models that require long training cycles, towards more distributed and optimized architectures. Web3, which was previously less involved in the first wave of generative AI, now has the opportunity to play a key role in this revolution.

Reasoning Ability Race

Reasoning capabilities have been a major focus for recent large language models such as GPT-01, DeepSeek R1, and Gemini Flash. These capabilities allow AI to break down complex inference tasks into structured multi-step processes, often using Chain of Thought (CoT) techniques. Just as instruction following became standard for earlier models, reasoning capabilities are expected to become a foundational capability for all major models in the future.

Web3 has the potential to increase transparency and reliability in AI reasoning processes. With each step of reasoning verifiable on-chain, it creates an immutable record of its logical sequence. This is especially important in an era where AI-generated content dominates digital interactions, making this level of provenance a basic necessity.

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Synthetic Data Training Increases

Synthetic data plays a key role in enabling advanced reasoning. Models like DeepSeek R1 use an intermediate system to generate high-quality reasoning datasets that are then used for fine tuning. This approach reduces reliance on real-world datasets, accelerates model development and improves robustness.

In the context of Web3, synthetic data generation is a highly parallel task, ideal for decentralized networks. The Web3 framework can incentivize nodes to contribute to synthetic data generation, with rewards based on dataset usage. This could foster a decentralized AI data economy, where synthetic datasets power open-source and privately-owned AI models.

Changes to Post-Training Workflows

Early AI models relied on large pre-training workloads that required thousands of GPUs. However, models such as GPT-01 have shifted focus to mid-training and post-training, allowing for more specialized capabilities such as advanced reasoning. This change dramatically alters computational requirements, reducing reliance on centralized clusters.

Web3 can facilitate the refinement of decentralized AI models, allowing contributors to stake computing resources in exchange for governance or financial incentives. This change democratizes AI development, making decentralized training infrastructure more viable.

Conclusion

Generative AI is undergoing a significant paradigm shift. With the advent of reasoning-driven architectures, synthetic dataset innovation, post-training optimization, and model distillation, the opportunity for Web3 to provide real utility in decentralized architectures is wide open. The important question that remains is whether Web3 can move fast enough to seize the moment and become a relevant force in the AI revolution.

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