Nevermined is the WEB3 Access, Transparency and Monetization stack for Data and AI.
Nevermined is the WEB3 Access, Transparency and Monetization stack for Data and AI.
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Monetizing AI services with Smart Subscriptions
This is the first post of a series showing how Nevermined enables the monetization of AI as well as Data. In this article we discuss our latest features, which allow for the monetization and access control of AI web services using Nevermined’s Smart Subscriptions*. We’ll cover the monetization of Data later.* For context, in previous posts (“Bringing online subscriptions into Web3 and making them SMART” and “NFTs are dead. Long live NFTs!”), we introduced some of the concepts and building blo...
Create your own AI assistant with Nevermined and Llama Index
Creating an AI assistant is all the rage. But then what? This is the second post of a series demoing how Nevermined is rocket fuel for the monetization of both AI and data. In previous posts (“Bringing online subscriptions into Web3 and making them SMART” and “NFTs are dead. Long live NFTs!”), we spoke about how Web3 can enhance the utility of digital assets. And in Monetizing AI services with Smart Subscriptions we showed how you can use Nevermined to monetize a web service using the OpenAI ...
NFTs are dead, long live NFT utility!
How a second generation of NFTs can be used to provide ownership and bring interactivity and NFT utility to digital assetsPhoto by Marius Masalar on UnsplashWhat’s a NFT and why have they “failed” so far?NFTs represent ownership of digital assets in a decentralized manner. They allow you to control assets in a digital environment that traditionally have belonged and been controlled by a third-party. In traditional, Web2 digital environments, the ownership of digital assets by their users is m...
Monetizing AI services with Smart Subscriptions
This is the first post of a series showing how Nevermined enables the monetization of AI as well as Data. In this article we discuss our latest features, which allow for the monetization and access control of AI web services using Nevermined’s Smart Subscriptions*. We’ll cover the monetization of Data later.* For context, in previous posts (“Bringing online subscriptions into Web3 and making them SMART” and “NFTs are dead. Long live NFTs!”), we introduced some of the concepts and building blo...
Create your own AI assistant with Nevermined and Llama Index
Creating an AI assistant is all the rage. But then what? This is the second post of a series demoing how Nevermined is rocket fuel for the monetization of both AI and data. In previous posts (“Bringing online subscriptions into Web3 and making them SMART” and “NFTs are dead. Long live NFTs!”), we spoke about how Web3 can enhance the utility of digital assets. And in Monetizing AI services with Smart Subscriptions we showed how you can use Nevermined to monetize a web service using the OpenAI ...
NFTs are dead, long live NFT utility!
How a second generation of NFTs can be used to provide ownership and bring interactivity and NFT utility to digital assetsPhoto by Marius Masalar on UnsplashWhat’s a NFT and why have they “failed” so far?NFTs represent ownership of digital assets in a decentralized manner. They allow you to control assets in a digital environment that traditionally have belonged and been controlled by a third-party. In traditional, Web2 digital environments, the ownership of digital assets by their users is m...
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Data is the primary rate limiting factor to the progress of Artificial Intelligence. This is our strong belief and one of the motivations for building Nevermined. So, academic research stating that “stock of high-quality language data will be exhausted soon; likely before 2026” provides a huge validation of our value proposition.
Their recommendation for preventing the manifestation of this gloomy scenario? Improve data efficiency or create availability of new sources of data.
Two, tops three, more years before we run out of data. 🤯🤯
Let’s unpack this.
Currently, computational capacity and access to expertise are two of the biggest factors limiting AI development. LLMs require massive data centers with vast amounts of specialized processing, like GPUs. And the expertise for creating the infrastructure and fine-tuning the models is expensive. Yet, history shows that over time both the cost of compute and expertise will decrease.
With data, the trend goes the other way. Deep learning, and large neural networks like LLMs, benefit significantly from exposure to larger and larger training datasets. Over time, however, these large training datasets approach a ceiling size. As the study shows, at some point soon, all models will have scraped all available public data. At that point, AI models will converge to the same result and their individual ability to separate their usefulness from other, equally large LLMs, will diminish.
So instead of computation and expertise, data will become the bottleneck for AI. How ironic.
Data is the primary rate limiting factor to the progress of Artificial Intelligence. This is our strong belief and one of the motivations for building Nevermined. So, academic research stating that “stock of high-quality language data will be exhausted soon; likely before 2026” provides a huge validation of our value proposition.
Their recommendation for preventing the manifestation of this gloomy scenario? Improve data efficiency or create availability of new sources of data.
Two, tops three, more years before we run out of data. 🤯🤯
Let’s unpack this.
Currently, computational capacity and access to expertise are two of the biggest factors limiting AI development. LLMs require massive data centers with vast amounts of specialized processing, like GPUs. And the expertise for creating the infrastructure and fine-tuning the models is expensive. Yet, history shows that over time both the cost of compute and expertise will decrease.
With data, the trend goes the other way. Deep learning, and large neural networks like LLMs, benefit significantly from exposure to larger and larger training datasets. Over time, however, these large training datasets approach a ceiling size. As the study shows, at some point soon, all models will have scraped all available public data. At that point, AI models will converge to the same result and their individual ability to separate their usefulness from other, equally large LLMs, will diminish.
So instead of computation and expertise, data will become the bottleneck for AI. How ironic.
To sidestep this AI convergence issue, access to relevant walled data becomes critical. In order to increase their relevance, their appeal and their commercial value, AI models will need access to data that is not publicly available. The real relevant data is stored in company databases, in personal healthcare records and in private data repositories. Behind walls.
With this thesis as a foundation, Nevermined has set out on a mission to transform siloed data into AI goldmines. To do this, our team has taken multiple novel approaches to addressing the barriers to data sharing and incentivization. The benefits are threefold:
Better Precision via Access Control
High-Fidelity Trust and Provenance
Easier Monetization & Incentive design
With Nevermined’s blockchain-based technology an Asset Publisher can register and tokenize any data asset or data processing workflow. In this process, the Publisher also defines the rules for access, which are consolidated into a smart contract.
Once published, a User (e.g. an AI model) can gain access to these assets, based on the rules set by the Asset Publisher. These rules are evaluated based on whether the User holds the correct access token (an NFT) and on the access conditions placed on that token. This token-gating access control can be managed in a decentralized manner.
This approach matches the trend in the AI industry, moving towards Transfer Learning and Precision AI. This is where secure access to walled data is critical. By making a smaller context available to a larger LLM, the model inference can effectively be filtered and provide more fine tuned responses. Nevermined enables federated compute on relevant, decentralized datasets, so you can run far less invasive and energy consumptive AI learning.
Provenancial integrity and data lineage remain significant challenges in the development of large scale data estates. At the core, you’re trying to answer four simple questions:
Where is the data coming from?
Where is the data going?
Who (or what) is using the data?
What are they doing with the data?
The ability to answer these questions with confidence remains difficult. There is no silver bullet but blockchain does provide an elegant solution. With the ability to record transactions immutably, and expose them to all actors in an ecosystem, blockchains provide novel transparency to the usual opaqueness of data lakes and estates. Solutions using Nevermined can now trace assets from source to destination, and record how the data was used, with detailed provenance records.
But there’s more. With high fidelity provenance comes the ability to establish fine-grained attribution: which actor did what? And lack of attribution is part of AI’s data problem. Currently, LLMs trained on publicly available source material fail to disclose how a model response was inferred, and therefore, acknowledge what source training material was used to create the response. This can be problematic when the source material was actually copyrighted or subject to licensing restrictions. Because current models lack the ability to track and trace this source material, they fail in their ability to properly attribute publishers.
Provenance and Attribution create the base for monetization. Granular attribution can improve payments such as royalties, residuals and rewards. This creates the ability to commercialize datasets (and AI services) as well as more advanced incentive schemes.
In a Data and AI ecosystem, powered by Nevermined, LLMs will acquire datasets, relevant to the use case it intends to address. Above we’ve described how access conditions are baked into the dataset. These access conditions can obviously be monetary. For example, the AI could simply pay to access the dataset and use it in its training set.
On the other hand, Nevermined also offers the ability to monetize AI’s via tokengating the web service. So as these systems evolve, we will see more advanced commercial models take root.
Because an AI Agent can create its own revenue (by selling access), it may negotiate a lower access payment to the Data Publisher, in return for royalties. This could be a percentage of revenue that the AI accrues based on its usage and/or output. Now we have a system in which the Publisher receives proper attribution via royalties relative to how well the AI performs in the future.
While we certainly subscribe to the academics’ view that AIs will soon run out of data, we see this as an opportunity for Web3 tech to come to the fore and help solve these core issues. We believe that access, provenance and monetization will be key enablers for a booming and aligned Data & AI Economy.
Questions, ideas, coffee? Get in touch via the website or join our Discord.
To sidestep this AI convergence issue, access to relevant walled data becomes critical. In order to increase their relevance, their appeal and their commercial value, AI models will need access to data that is not publicly available. The real relevant data is stored in company databases, in personal healthcare records and in private data repositories. Behind walls.
With this thesis as a foundation, Nevermined has set out on a mission to transform siloed data into AI goldmines. To do this, our team has taken multiple novel approaches to addressing the barriers to data sharing and incentivization. The benefits are threefold:
Better Precision via Access Control
High-Fidelity Trust and Provenance
Easier Monetization & Incentive design
With Nevermined’s blockchain-based technology an Asset Publisher can register and tokenize any data asset or data processing workflow. In this process, the Publisher also defines the rules for access, which are consolidated into a smart contract.
Once published, a User (e.g. an AI model) can gain access to these assets, based on the rules set by the Asset Publisher. These rules are evaluated based on whether the User holds the correct access token (an NFT) and on the access conditions placed on that token. This token-gating access control can be managed in a decentralized manner.
This approach matches the trend in the AI industry, moving towards Transfer Learning and Precision AI. This is where secure access to walled data is critical. By making a smaller context available to a larger LLM, the model inference can effectively be filtered and provide more fine tuned responses. Nevermined enables federated compute on relevant, decentralized datasets, so you can run far less invasive and energy consumptive AI learning.
Provenancial integrity and data lineage remain significant challenges in the development of large scale data estates. At the core, you’re trying to answer four simple questions:
Where is the data coming from?
Where is the data going?
Who (or what) is using the data?
What are they doing with the data?
The ability to answer these questions with confidence remains difficult. There is no silver bullet but blockchain does provide an elegant solution. With the ability to record transactions immutably, and expose them to all actors in an ecosystem, blockchains provide novel transparency to the usual opaqueness of data lakes and estates. Solutions using Nevermined can now trace assets from source to destination, and record how the data was used, with detailed provenance records.
But there’s more. With high fidelity provenance comes the ability to establish fine-grained attribution: which actor did what? And lack of attribution is part of AI’s data problem. Currently, LLMs trained on publicly available source material fail to disclose how a model response was inferred, and therefore, acknowledge what source training material was used to create the response. This can be problematic when the source material was actually copyrighted or subject to licensing restrictions. Because current models lack the ability to track and trace this source material, they fail in their ability to properly attribute publishers.
Provenance and Attribution create the base for monetization. Granular attribution can improve payments such as royalties, residuals and rewards. This creates the ability to commercialize datasets (and AI services) as well as more advanced incentive schemes.
In a Data and AI ecosystem, powered by Nevermined, LLMs will acquire datasets, relevant to the use case it intends to address. Above we’ve described how access conditions are baked into the dataset. These access conditions can obviously be monetary. For example, the AI could simply pay to access the dataset and use it in its training set.
On the other hand, Nevermined also offers the ability to monetize AI’s via tokengating the web service. So as these systems evolve, we will see more advanced commercial models take root.
Because an AI Agent can create its own revenue (by selling access), it may negotiate a lower access payment to the Data Publisher, in return for royalties. This could be a percentage of revenue that the AI accrues based on its usage and/or output. Now we have a system in which the Publisher receives proper attribution via royalties relative to how well the AI performs in the future.
While we certainly subscribe to the academics’ view that AIs will soon run out of data, we see this as an opportunity for Web3 tech to come to the fore and help solve these core issues. We believe that access, provenance and monetization will be key enablers for a booming and aligned Data & AI Economy.
Questions, ideas, coffee? Get in touch via the website or join our Discord.
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