The Convergence of Big Data, AI, the Hypergraph, and Cryptocurrency

Ben jorgensen
8 min readJul 19, 2023


We started the Constellation journey in 2017 under the idea that there could be a new infrastructure layer, outside of Ethereum, that was more compatible with developer tools for big data processing and management. Big data was the buzz word around machine learning and it is where the “titans of tech” were spending all of their money and research. Infrastructure companies like Palantir, Cloudera, Mongo DB, RedHat, and Databricks were the juggernauts in the space that pushed out a plethora of use cases to sell the data management stack and the utility of data. Applications like Facebook, Google, Apple, Amazon, and Netflix were the closed data gardens that produced results from advanced data management. Artificial Intelligence (AI) was the marketing term for machine learning models and only represented a nascent application of use cases that applied to organizations outside of FAANG.

At Constellation, we saw an opportunity to define our own category by taking data management tools, web frameworks, and combining that with blockchain technology: managing your data pipeline by leveraging federated and decentralized networks to verify data. As 2018 started the first crypto winter, 2019 had crypto investment funds pivot to focus on a technology-first approach with the leading use case around, “supply chain management”. As the SEC made their way through the tranches of regulating business practices of crypto fundraising, the supply chain management use case was the guiding hope for a tech forward approach in the industry.

By 2020, governments and companies failed to adopt blockchain for supply chain management use cases as it failed to provide a clear financial value proposition. Constellation flipped the script in 2022 by showcasing a working demo, funded by the US government, that orchestrated secure communications leveraging our “blockchain-in-a-box” networking while connecting to existing technology. We prototyped how our customizable blockchain, scalable network, and our approach to consensus, would allow us to address man-in-the-middle attacks that occurred between multiple parties (federal and commercial entities) that were passing mission communications between one another. We successfully added our blockchain’s immutability to the data management tech stack, providing a clear value proposition solving a gap in secure communications.

Fast forward to 2023, regulators are now knocking on the door of the cartels of crypto, or those that enable the exchange of cryptocurrencies: new story, same actors but this time DeFi is holding the space together with all the value locked up. We are now returning to the tech forward approach where cryptocurrencies main utility is to access an open network while providing a more efficient accounting mechanism to orchestrating node operators that are maintaining decentralized networks.

While cryptocurrency is rediscovering its utility, blockchain technology is due for a new use case and capability.

Productizing Hypergraph Capabilities

It is no secret that much of our development of the protocol has been behind closed doors. However, our work with the US Federal Government has allowed us to receive critical and constructive peer feedback from a customer that has a budget and marketable reach larger than 99% of any private sector entity. This has enabled us to prioritize certain features and functionalities that will pave a path to scalable commercial deployments with functionality that will transfer over to the public network. Thus, the work paid for by the federal government, both research and engineering, allows us to build, test, and deploy features to our core protocol and enable the vision of a true blockchain-in-a-box solution that caters to multiple frameworks and use cases. All of our learnings, research, and developments are being directly transferred over to the public network and open source code base which will ultimately drive wider adoption of blockchain technology far beyond cryptocurrency.

Our approach to a “blockchain-in-a-box”, or Metagraphs, solutions are more complex (and different from other infrastructure layers) but allows our Network to adapt to different use cases (i.e. application specific blockchains). Developers have access to low level tooling for building blockchain networks (PRO consensus, validation, and state management) and can use multiple frameworks aimed at their use cases. For example, the IoT example in our Data API documentation, is something that has demonstrable value for many departments in the federal government as well as commercial applications, like Dor Technologies.

By being able to to demonstrate infrastructure capabilities for both the Department of Defense (public sector) and commercial sectors, we have been able to structure multiple products and avenues for adoption of our network, technology, and utility of DAG:

  1. Metagraphs (Private Networks and Managed Solutions): This requires extensive budgets to recreate the Hypergraph and the necessary components (Layer0, Layer1, consensus, node management, and staff to support). This also includes supporting ongoing software updates, data schemas for specific use cases, and network specific features.
  2. Metagraphs — (Public Network, Hypergraph, PRO Consensus, Frameworks, and DAG): This leverages our open source software on GitHub, Euclid SDK, Currency Framework, Data API, PRO consensus and DAG (currency) to create an application specific blockchain (blockchain-in-a-box). Metagraph’s deployed on the public ledger get the additional security of the Hypergraph Layer 0 validation by paying DAG or incentivizing nodes with a currency. This is more financially feasible and provides additional security using a decentralized network. Furthermore, it is more financially feasible for companies to deploy using the Hypergraph as they explore strategies around data validation and want to incentivize the distributed servers that make up the decentralized network.
  3. Hybrid: This is a mix of managed solutions and open source technology where Constellation Network, Inc acts as the data clearing house using the public network, DAG as the currency used to secure throughput and prioritize snapshots of data to the network. Additionally, this might include deploying nodes and scaling node operations to accommodate the use case and client. In the Hybrid approach, the client doesn’t handle DAG and thus pays Constellation Network Inc a flat fee for services while Constellation procures the needed DAG through exchanges.

This two pronged approach of creating private networks and enabling the use of the public network, both relay learnings, while expanding use cases, all the while improving capabilities of the tech stack for both channels. This also paves the way for diversification of Constellation and wider adoption while allowing us to be agile during different industry cycles that impact both the technology sector and crypto sector.

For example, Dor Technologies, a revenue generating entity that collects IoT data, has given us the flexibility to experiment with big data, revenue, customers, and hardware that collects data at the source. Because of this, the Dor Metagraph has provided a framework for other IoT use cases that have more security needs around the collection and transportation of IoT data; conversely, our work with the DoD has allowed us to develop more robust capabilities that are deployed into the public ledger. The two pronged approach creates multiple entry points for different ecosystem partners, all of which are using our technology but have different needs (security, support, and technical experience) and different use cases.

Where do we go from here? The Convergence.

The convergence of decentralization, crypto, and big data will be the next major utility, or tech forward approach, to blockchain technology at large. It opens up an avenue for more scalable data governance, zero trust, and robust applications for the technology as a whole. As the AI revolution kicks off, consumers are being exposed to AI beyond the walled gardens of FAANG. At the same time, society is going through a massive social transition where we are questioning the source of the data inputs, the data exchange, and programmatic outputs associated with the usage of data. AI is already impacting every aspect of our daily life from marketing, cybersecurity, home automation, sales, and the automation of digital operations and tasks.

During this AI revolution, decentralized tooling will be leveraged to provide a “proof of source” or the auditability, assurance, and attribution of data that reveals proper governance of data. As such, “AI Proof of Source” productizes decentralized Zero Trust Networks, federated databases, consensus mechanisms, and cryptocurrency that rewards global validators of nodes that validate data packages. AI Proof of Source becomes essential to scaling, governing, and realizing the possibility of the $2T industry that AI is to become in 2030.

The Hypergraph, as an open network, is positioned to be a public utility that validates this data and provides a means of exchanging and augmenting data sets to create a data library at scale. Our tools uniquely allow full access to the code base and the ability to use web frameworks to build decentralized applications that secure data pipelines without constraint. For example, Dor deploys light nodes as data producers on the hardware which signs the data, upon collection, and verifies the data collection by leveraging the Hypergraph. The notarized data at the source assures that the data hasn’t been spoofed.

Our vision is that the Hypergraph will enable a network of networks (or a network of application specific networks) to communicate across customizable blockchains. These networks can safely interoperate with each other through a common global network, the Hypergraph. Today, each independent blockchain is free to implement their own unique data types, business logic, and execution environments. These networks will soon interface with each other through global consensus to orchestrate heterogenous data processing. Ultimately, the Hypergraph, decouples the dependencies seen in existing blockchain architectures by creating a microservices framework that separates the execution, settlement, consensus, and data availability layers into a scale free hierarchical network of networks.

Sound familiar?

We architected the Hypergraph, and Metagraphs, to emulate neural networks, which is the basis for AI: complex networks (i.e. Metagraphs) perform operations and validate data while processing the inputs (i.e. Hypergraph) to create an output. These neural networks are mesh networks that form a system as a result of complex networks creating interdependent mathematical relationships. This means that the Hypergraph can effectively scale to meet the needs of a new data-hungry technology paradigm, while providing proof of source for diverse data sets. In the future, this will enable the ability to create dependencies, references, statistical relationships, and augmentations of data with verifiability — a verifiable neural network for AI.

The social movement of crypto was needed to question the integrity of centralized systems, starting with banking; the increase in processing power was needed to see the emergence of AI/machine learning. Now the convergence of big data, traditional tech, AI, and crypto is creating an unprecedented time for a technology as a whole and for the next wave of digital innovations. It is time we evolved the potential of blockchain technology and scale beyond simple use cases, merge with the broader technology industry, and de-silo infrastructure frameworks.

The Future is Here. Think out of the “blockchain-in-a-box”.

Thank you,

Ben Jorgensen

CEO of Constellation Network, INC

Follow me on Twitter @BenJorgensen



Ben jorgensen

Building big data on blockchain and experiential dining: CEO of Constellation Network; Co-Owner of MZ Dining Group (Ittoryu Gozu); Owner of A5 Meats.