“Build where the industry is going, not for where it is.” This mantra has fueled disruptive innovations for decades – Microsoft has capitalized on microprocessors, Salesforce has exploited the Cloud and Uber prospered in the mobile revolution.
The same principle applies to AI – the generative AI evolves so quickly that today’s capacity building risks obsolescence. Historically, web3 played little role in this evolution of AI. But can it adapt to the latest trends reviving industry?
2024 was a central year for a generative AI, with revolutionary research and engineering progress. It is also the year when the web3-Ai narrative has gone from speculative media threshing to the previews of real utility. While the first wave of AI revolved around mega -models, long training cycles, large calculation clusters and pockets in deep business – which makes them largely inaccessible to web3 – the new trends in 2024 open significant web3 integration doors.
On the Web3-Ai front, 2024 was dominated by speculative projects such as agencies focused on the memes which reflected the feeling of the bullish market but offered little real utility. As this threshing has disappeared, an opportunity window emerges from refocusing on tangible use cases. The generative landscape of the 2025 AI will be very different, with transformative changes in research and technology. Many of these changes could catalyze the adoption of web3, but only if the industry is built for the future.
Let’s examine five key trends by shaping AI and the potential they have for web3.
1. Reasoning race
Reasoning has become the next border for models of large languages (LLM). Recent models such as GPT-01, Deepseek R1 and Gemini Flash place reasoning skills at the heart of their progress. Functionally, the reasoning allows the AI to decompose complex inference tasks in process structured in several stages, often taking advantage of the techniques of thought chain (COT). Just as monitoring of instructions has become a standard for LLM, reasoning will soon be a reference capacity for all the main models.
The web3-ai opportunity
The reasoning involves complex work flows that require traceability and transparency – an area where web3 shines. Imagine an article generated by the AI where each reasoning step is verifiable on a chain, providing an immutable recording of its logical sequence. In a world where the content generated by AI dominates digital interactions, this level of origin could become a fundamental need. Web3 can provide a decentralized layer and without confidence to verify AI’s reasoning routes, fill a critical gap in today’s IA ecosystem.
2. Training in synthetic data increases
An advanced key factor is synthetic data. Models like Deepseek R1 use intermediate systems (such as R1-Zero) to generate high-quality reasoning data sets, which are then used for fine adjustment. This approach reduces dependence on data sets in the real world, accelerating the development of the model and improving robustness.
The web3-ai opportunity
The generation of synthetic data is a very parallelizable task, ideal for decentralized networks. A web3 framework could encourage nodes to contribute the computing power to the generation of synthetic data, gaining rewards based on the use of the data set. This could promote an economy of decentralized AI data in which synthetic data sets feed the open-source models and AI owners.
3. The transition to post-training workflows
The first models of AI relied on massive levy workloads requiring thousands of GPUs. However, models like GPT-01 have moved attention to mid-training and post-training training, allowing more specialized capacities such as advanced reasoning. This change considerably modifies the calculation requirements, reducing dependence on centralized clusters.
The web3-ai opportunity
While the pre-training of centralized GPU requirements, post-training can be distributed on decentralized networks. Web3 could facilitate the decentralized refinement of AI models, allowing contributors to bring calculation resources in exchange for governance or financial incentives. This change democratizes the development of AI, making the infrastructure of decentralized training more viable.
4. The climb of the small distilled models
Distillation, a process in which large models are used to form smaller and specialized versions, has experienced an adoption wave. The main families of AI such as Llama, Gemini, Gemma and Deepseek now include distilled variants optimized for efficiency, allowing them to operate on basic equipment.
The web3-ai opportunity
The distilled models are compact enough to operate on basic GPUs or even processors, making it a perfect adjustment for decentralized inference networks. AI inference markets based on web3 could emerge, in which nodes provide calculation power to execute light distilled models. This would decentralize the inference of AI, reducing the dependence on cloud suppliers and would unlock new incentive structures to tokenized for participants.
5. The request for transparent AI assessments
One of the greatest challenges of the generative AI is evaluation. Many high -level models have effectively memorized the existing landmarks of the industry, which makes them unreliable to assess the performance of the real world. When you see an extremely high marking model on a given reference, it is often because this reference has been included in the model training corpus. Today, no robust mechanism exists to verify the results of the assessment of models, which leads companies to rely on auto -declared numbers in technical articles.
The web3-ai opportunity
Cryptographic evidence based on blockchain could introduce radical transparency in AI assessments. Decentralized networks could verify the performance of the model through standardized benchmarks, reducing dependence on unverifiable business complaints. In addition, web incentives could encourage the development of new community -oriented assessment standards, pushing AI responsibility for new heights.
Can Web3 adapt to the next AI wave?
The generative AI undergoes a paradigm shift. The path of general artificial intelligence (AG) is no longer dominated only by monolithic models with long training cycles. New breakthroughs – such as architectures focused on reasoning, innovations of synthetic data, post -training optimizations and the distillation of the model – are decentralized the workflows of AI.
Web3 was largely absent from the first wave of generative AI, but these emerging trends introduce new opportunities where decentralized architectures can provide real utility. The crucial question is now: can web3 move quickly enough to grasp this moment and become a relevant force in the AI revolution?
