The volume of data collected and harnessed for AI applications is experiencing unprecedented growth. The surge, driven by the booming AI sector, is not a gradual increase but a seismic shift reshaping industries. Cere Decentralized Data Clusters are designed for the new era, ready to handle the AI Data Tsunami. We have been building Cere protocol to power Decentralized Data Clusters specifically in anticipation of this, for the past few years.

The World’s Data Trilemma

The adoption of artificial intelligence (AI) by enterprises and research organizations is rapidly accelerating, yet today’s data infrastructure and AI-data challenges (1) present barriers to implementing it successfully at scale. These challenges have been exacerbated by the rapid onset of generative AI that has defined the evolution of the AI market in 2023. (1) S&P 2023 Global Trends in AI report

A data trilemma emerges, illuminating the need for innovative data management and automated AI-related processing solutions. This trilemma is characterized by three interconnected challenges stemming from the data surge driven by the AI revolution:

• Data Overload for Cloud Solutions: Existing data-cloud solutions are ill-prepared to accommodate the massive influx of data generated by the AI revolution. “The meteoric rise of data and performance-intensive workloads like generative AI is forcing a complete rethink of how data is stored, managed and processed. Organizations everywhere now have to build and scale their data architectures with this in mind over the long term” said Nick Patience, senior research analyst at 451 Research, part of S&P Global Market Intelligence.
• Demand for Automated Data Science: The second challenge underscores the limitations of current data science teams and methodologies. The AI tsunami necessitates data science to operate with automation at an unprecedented scale and speed to derive insights from this vast dataset. According to McKinsey Technology Trends Outlook 2023 next-generation software development is key to successful automation, but the science is not ready yet, facing challenges around security, debugging, and error monitoring, as well as update coordination among other challenges.
• Lack of Edge Infrastructure: Adequate edge infrastructure for networks is currently underdeveloped and demands substantial investment to meet the requirements of the data-driven future. In the same study by McKinsey cited above, edge computing stands as a top trend, but key uncertainties remain around scaling hurdles due to lack of development.

A Strong Case For Migration From Centralized To Edge Cloud Computing For AI

As this torrential wave of data gains momentum, it unfurls a critical debate: Centralized Cloud Computing versus Edge Cloud Computing.

Forecasts indicate that centralized computing is ill-suited to fulfill the requirements of the new era of data, so much of the oncoming data collection will need to be efficiently collected at the edge and automatically processed (e.g. data continuously map-reduced or enriched) before integrating into current data cloud solutions. Not only is this a fundamental scalability need, but it also highlights the innovation slowed by existing centralized and labor-intensive cloud data solutions. According to Grand View Research, the US Edge computing market is projected to reach USD 155.90 billion by 2030.

Furthermore, this unprecedented surge in cloud data demand also presents an unyielding challenge for existing centralized cloud infrastructures such as AWS (Amazon), GCP (Google), and Azure (Microsoft). The high costs and geo-limitations of these data infrastructure facilities highlight issues of excessive concentration, fragmented silos, vendor lock-ins, and management intricacies. The once-promising centralized data architecture now struggles under the weight of data traffic. Trapped in various big data silos with high migration costs, seamless data collaboration, and meaningful insights from interconnected information are harder than ever to achieve. (1)

Here at Cere, we have been driving the decentralized and privacy-preserving data innovations that can store, deliver, and operate on user data stored on these autonomous edge clusters. This means that we are very close to being able to run automated data operations and facilitate AI data interactions directly where the data resides; near the edge.

This also means that Cere’s tools and protocol can account for the AI agent actions while providing a transparent and secure data (and payment) gateway to automate these agents. It is highly anticipated that the LLMs of today will become less “large”, and more open-source, specialized, and agile to operate near the edge close to where the users are. This deployment approach reduces latency and enhances privacy and security through individual consumer-focused encryption that we’ve been working on for years at Cere.

We aim to lead this transformative shift, as the next generation of data infrastructure must transcend geo/vendor limitations, embracing decentralization, interconnectivity, and agility as fundamental principles.

Cere Decentralized Data Clusters that can be run autonomously for any region or purpose

Cere Decentralized Data Clusters represent a groundbreaking leap in the automation of the edge data infrastructure, engineered to efficiently distribute data loads, accelerate data agility, and reduce latency autonomously, all powered by the Cere Protocol and $CERE tokens. Cere Decentralized Data Clusters are designed with this future vision in mind, poised to chip away at the vast, trillion dollar+ cloud infrastructure market by driving the next level of data efficiency.

Cere DDCs are dynamic hubs of automation, with each cluster being run by independent operators to optimize for specific needs or utility. Some could be used to store fast, small real-time transaction data, while some could be optimized for data streaming for specific geo-locations. Soon, they will also be able to execute automated data operations such as map-reducing, data enrichment, and even AI inference at the edge.

The core innovations of DDC follow years of hard work in overcoming the limitations of centralized data processing while embracing a strong decentralized data ethos.

We are establishing a new data approach to propel the world into an era where AI intelligence can be integrated for businesses and users effortlessly, agilely, and in real-time on the fringes of data creation.

What sets Cere Decentralized Data Clusters apart is their design for operation by any independent set of self-organizing nodes, enabling diverse local, custom use cases.

The key difference with many other Web3 data protocols (such as Filecoin) is that in the operation of decentralizing data, the data clusters are decoupled from the protocol itself, opening up a world of possibilities for localized, usage-specific implementations without limitations. Even semi-permission data use cases, such as a consortium of schools, could host their students’ behavior data on a cluster that’s based on many of the existing school computers (for cheaper costs) while hosting and running its AI cognitive insight agents where the data is already stored.

How will these Clusters be adopted?

As one of our first integrations, serving as a serverless gateway for storing user game data, CerePlay is launched as a showcase for game developers to demonstrate how to independently run decentralized leaderboard contests.  Developers using the CerePlay Toolkit can leverage user skills/achievements/content that are stored on Decentralized Data Clusters to unlock the power of direct Web3 rewards, user activation, and secondary marketplaces far beyond what the current tools can do. CerePlay plays a pivotal role in gathering essential metrics and stress-testing Cere’s storage nodes.

Once we’ve achieved our desired metrics and results for storing data, Cere will battle-test its Decentralized Data Clusters’ CDN (Content Delivery Network) infrastructure through DaVinci. A novel showcase that allows content creators to revolutionize fan engagement and monetization by offering exclusive or early-access content in the form of digital collectibles, completely white-labeled through its self-service toolkit. Content can be directly streamed from Decentralized Data Clusters, only to the holders of these collectibles, while creators can leverage user activity insights to reward and engage their top fans.

In parallel, Cere’s Data Network will soon be ready for external node participants to join our first open data cluster with early participants likely to come from our EDP (external developers program) and existing partners like Ankr Network and Republic.co. A fully independent 3rd party DDC cluster would then follow, likely from the close collaboration with enterprise clients who are all in need of AI infrastructure and strategy, to remain in control of their customer data on top of their own cluster.

Join Us on the Cere Mission, Building the Future of Data and AI

We invite you to learn more about our vision and join us on our exciting journey pushing forward innovation at the verge of the data paradigm shift.

As discussed in the latest Token2049 panel: The Convergence of AI and Web3 by Richard Muirhead, Fabric Ventures Managing Partner, Jamie Burke, Outlier Ventures Founder and CEO, Jake Brukhman, CoinFund Founder, Illia Polosukhin, NEAR Protocol Co-Founder, and Alex Blania, Worldcoin Co-Founder and CEO, the essence of why what we discussed here matters, at a high level, is about balancing the interest of sovereign individuals with free markets, access, and the collective good, and to achieve that, we need trustless, automated and distributed data systems.

Join us today and explore the Cere Hub or check the exciting career opportunities with us! 👉Careers

The time for action is now!

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The Decentralized Data & Finance Cloud for Enterprises