“Build for where the industry is going, not for where it is.” This mantra has fueled disruptive innovations for decades — Microsoft capitalized on microprocessors, Salesforce leveraged the cloud and Uber thrived in the mobile revolution.
The same principle applies to AI — Generative AI is evolving so rapidly that building for today’s capabilities risks obsolescence. Historically, Web3 has played little role in this AI evolution. But can it adapt to the latest trends reshaping the industry?
2024 was a pivotal year for generative AI, with groundbreaking research and engineering advancements. It was also the year that the Web3-AI narrative transitioned from speculative hype to glimpses of real utility. While the first wave of AI revolved around mega-models, long training cycles, vast compute clusters and deep enterprise pockets — making them largely inaccessible to Web3 — newer trends in 2024 are opening doors for meaningful Web3 integration.
On the Web3-AI front, 2024 was dominated by speculative projects such as meme-driven agentic platforms that reflected bullish market sentiment but offered little real-world utility. As that hype fades, a window of opportunity is emerging to refocus on tangible use cases. The generative AI landscape of 2025 will be vastly different, with transformative shifts in research and technology. Many of these changes could catalyze Web3 adoption, but only if the industry builds for the future.
Let’s examine five key trends shaping AI and the potential they present for Web3.
1. The reasoning race
Reasoning has become the next frontier for large language models (LLMs). Recent models like GPT-01, DeepSeek R1, and Gemini Flash place reasoning capabilities at the core of their advancements. Functionally, reasoning allows AI to break down complex inference tasks into structured, multi-step processes, often leveraging Chain of Thought (CoT) techniques. Just as instruction-following became a standard for LLMs, reasoning will soon be a baseline capability for all major models.
The Web3-AI opportunity
Reasoning involves intricate workflows that require traceability and transparency — an area where Web3 shines. Imagine an AI-generated article where every reasoning step is verifiable on-chain, providing an immutable record of its logical sequence. In a world where AI-generated content dominates digital interactions, this level of provenance could become a fundamental need. Web3 can provide a decentralized, trustless layer to verify AI reasoning pathways, bridging a critical gap in today’s AI ecosystem.
2. Synthetic data training scales up
A key enabler of advanced reasoning is synthetic data. Models like DeepSeek R1 use intermediate systems (such as R1-Zero) to generate high-quality reasoning datasets, which are then used for fine-tuning. This approach reduces dependence on real-world datasets, accelerating model development and improving robustness.
The Web3-AI opportunity
Synthetic data generation is a highly parallelizable task, ideal for decentralized networks. A Web3 framework could incentivize nodes to contribute compute power toward synthetic data generation, earning rewards based on dataset usage. This could foster a decentralized AI data economy in whichsynthetic datasets power open-source and proprietary AI models alike.
3. The shift to post-training workflows
Early AI models relied on massive pretraining workloads requiring thousands of GPUs. However, models like GPT-01 have shifted focus to mid-training and post-training, enabling more specialized capabilities such as advanced reasoning. This shift dramatically alters compute requirements, reducing dependence on centralized clusters.
The Web3-AI opportunity
While pretraining demands centralized GPU farms, post-training can be distributed across decentralized networks. Web3 could facilitate decentralized AI model refinement, allowing contributors to stake compute resources in return for governance or financial incentives. This shift democratizes AI development, making decentralized training infrastructures more viable.
4. The rise of distilled small models
Distillation, a process in which large models are used to train smaller, specialized versions, has seen a surge in adoption. Leading AI families such as Llama, Gemini, Gemma and DeepSeek now include distilled variants optimized for efficiency, enabling them to run on commodity hardware.
The Web3-AI opportunity
Distilled models are compact enough to run on consumer-grade GPUs or even CPUs, making them a perfect fit for decentralized inference networks. Web3-based AI inference marketplaces could emerge, in which nodes provide compute power to execute lightweight, distilled models. This would decentralize AI inference, reducing reliance on cloud providers and unlocking new tokenized incentive structures for participants.
5. The demand for transparent AI evaluations
One of the biggest challenges in generative AI is evaluation. Many top-tier models have effectively memorized existing industry benchmarks, rendering them unreliable for assessing real-world performance. When you see a model scoring extremely high on a given benchmark, it’s often because that benchmark has been included in the training corpus of the model. Today, no robust mechanisms exist for verifying model evaluation results, leading companies to rely on self-reported numbers in technical papers.
The Web3-AI Opportunity
Blockchain-based cryptographic proofs could introduce radical transparency into AI evaluations. Decentralized networks could verify model performance across standardized benchmarks, reducing reliance on unverifiable corporate claims. Additionally, Web3 incentives could encourage the development of new, community-driven evaluation standards, pushing AI accountability to new heights.
Can Web3 adapt to the next wave of AI?
Generative AI is undergoing a paradigm shift. The path to artificial general intelligence (AGI) is no longer dominated solely by monolithic models with lengthy training cycles. New breakthroughs — such as reasoning-driven architectures, synthetic dataset innovations, post-training optimizations and model distillation — are decentralizing AI workflows.
Web3 was largely absent from the first wave of generative AI, but these emerging trends introduce fresh opportunities where decentralized architectures can provide real utility. The crucial question now is: can Web3 move fast enough to seize this moment and become a relevant force in the AI revolution?