“Build where the industry is going, not where it is.” This mantra has fed the disruptive innovations for decades: Microsoft capitalized the microprocessors, Salesforce took advantage of the cloud and Uber prospered in the mobile revolution.
The same principle applies to AI: generative AI is evolving so fast that construction for today’s abilities runs obsolescence. Historically, web3 has played a small role in this evolution of AI. But can it adapt to the latest trends that remodel the industry?
2024 was a crucial year for the generative AI, with innovative advances in research and engineering. It was also the year that the web3-AI narrative went from exposing the glimpses of real utility. While the first wave of I was revolved around mega models, long training cycles, large computing groups and deep business pockets, largely inaccessible to the web3, the newest trends in 2024 are opening doors for a web3 integration significant.
In the web3-AI front, 2024 was dominated by speculative projects such as agent platforms driven by memes that reflected the feeling of the upward market but offered little use of the real world. As this pump fades, a window of opportunity is emerging to re -behered in tangible cases. The 2025 generative panorama 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 that make up the AI and the potential they present for web3.
1. The reasoning career
The reasoning has become the next border for large language models (LLM). Recent models such as GPT-01, Deepseek R1 and gemini flash place reasoning capabilities at the center of its advances. Functionally, reasoning allows AI decomposes complex inference tasks in structured processes of several steps, often taking advantage of the techniques of the chain of thought (COT). Just as the monitoring of the instructions became a standard for LLMS, the reasoning will soon be a reference capacity for all the main models.
The Web3-AI opportunity
Reasoning implies intricate workflows that require traceability and transparency, an area where web3 shines. Imagine an article generated by the where each step of reasoning is verifiable in the chain in the chain, providing an immutable record of its logical sequence. In a world where the content generated by Ia dominates digital interactions, this level of origin could become a fundamental need. Web3 can provide a decentralized and confidence layer to verify the reasoning paths of AI, joining a critical gap in the current ecosystem.
2. Synthetic data training scale
A key habilitator of advanced reasoning is synthetic data. Models such as 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 the dependence of real world data sets, accelerating the development of the model and the improvement of robustness.
The Web3-AI opportunity
Synthetic data generation is a highly parallel task, ideal for decentralized networks. A web3 frame could encourage nodes to contribute with the calculation power towards the generation of synthetic data, gaining rewards based on the use of the data set. This could foster a decentralized data economy in which systemic data sets feed open source models and patented equally.
3. The change to workflows after training
The first AI models were based on massive workloads prior to the pre -meage that thousands of GPUs require. However, models such as GPT-01 have changed the focus on medium training and post-training, allowing more specialized abilities, such as advanced reasoning. This change dramatically alters the requirements to calculate, reducing dependence on centralized groups.
The Web3-AI opportunity
While the pretrado requires centralized GPU farms, post-training can be distributed through decentralized networks. Web3 could facilitate the refinement of the decentralized AI model, allowing taxpayers to calculate calculation resources in exchange for governance or financial incentives. This change democratizes the development of AI, making decentralized training infrastructures more viable.
4. The emergence of small models distilled
Distillation, a process in which large models are used to train smaller and specialized versions, has seen an increase in adoption. The main families of AI as Call, Gemini, Gemma and Deepseek now include distilled variants optimized for efficiency, which allows them to function with basic products hardware.
The Web3-AI opportunity
Distilled models are compact enough to execute in GPU of consumption degree or even CPU, which makes them a perfect adjustment for decentralized inference networks. Web3 -based inference markets could arise, in which nodes provide computing power to execute light and distilled models. This would decentralize the inference of AI, reducing the dependence of cloud suppliers and unlocking new tokenized incentive structures for participants.
5. The demand for transparent evaluations of AI
One of the greatest challenges in generative AI is evaluation. Many top -level models have effectively memorized the existing industry reference points, which makes them little reliable to evaluate real world performance. When you see a model that writes extremely high at a given reference point, it is often due to the fact that this reference point has been included in the model training corpus. Today, there are no robust mechanisms to verify the results of the model evaluation, which leads companies to trust self -informed numbers in technical documents.
The Web3-AI opportunity
Blockchain -based cryptographic tests could introduce radical transparency in AI evaluations. Decentralized networks could verify model performance at standardized reference points, reducing non -verifiable corporate claims dependence. In addition, web3 incentives could promote the development of new evaluation standards promoted by the community, which has the responsibility of AI to new heights.
Can web3 adapt to the next wave of AI?
The generative AI is experiencing a paradigm shift. The path to artificial general intelligence (AGI) is no longer dominated only by monolithic models with long training cycles. The new advances, such as reasoning -based architectures, innovations of synthetic data sets, optimizations after training and distillation of the model, are workflows of decentralized.
Web3 was largely absent from the first wave of generative, but these emerging trends introduce new opportunities in which decentralized architectures can provide real utility. The crucial question is now: Can Web3 move quick enough to take advantage of this moment and become a relevant force in the AI revolution?
