Newcoin

The Global Knowledge Race

Humanity's relentless drive for status, intellectual growth, and mastery has continuously shaped the evolution of information technology. From the earliest repositories of static knowledge like books and academia to dynamic, personalized search engines and social media platforms, the pursuit of better, faster access to knowledge is embedded in our DNA. This visceral need to learn, adapt, and stay ahead fuels a global knowledge race—a race in which both humans and machines compete to access, refine, and utilize knowledge for decision-making, success, and influence. The winners of this race reap unlimited financial rewards, while the rest are left behind across various sectors. This race has only been accelerating.

The Three Personas of the Knowledge Race:

Knowledge Producers: Driven by peer recognition, status, and financial incentives. Today's AI pipelines harvest their data without acknowledgment or reciprocation, offering no incentives for continued contribution.
Knowledge Consumers: Hungry for insights that augment their lives faster than traditional methods like books or social media. They need access to timely, high-quality knowledge from trusted producers they aspire to emulate.
AI Systems: Current AI platforms operate in silos, making knowledge transfer between systems difficult or requiring complex data processing. Major AI labs (e.g., OpenAI, DeepMind, Anthropic) duplicate efforts by solving similar problems independently. Models capable of synergizing their learning processes will dominate the knowledge race, but trust and collaboration mechanisms are lacking.

Next Frontier: Multi-Agent Systems

The limitations of centralized, monolithic AI systems are becoming apparent. While traditional neural networks and centralized platforms have processed vast amounts of data, they operate in isolation, unable to efficiently leverage collective intelligence. This leads to duplicative efforts, inefficient collaboration, and missed opportunities for knowledge refinement. AI pipelines today silently scrape cognitive efforts from knowledge producers without offering recognition, rewards, or incentives, creating a fundamental gap in motivation and participation.

The future lies in shifting from monolithic neural networks to multi-agent systems, where diverse, specialized agents—both human and machine—collaborate dynamically. These systems break down complex tasks into manageable components, with each agent contributing unique expertise to refine and share knowledge in real time. This transition represents a fundamental shift in knowledge coordination, enabling faster, more precise, and adaptive intelligence.

The Need for a Credibly Neutral Coordination Layer

To enable this shift, a credibly neutral coordination layer is essential—an open protocol allowing knowledge producers, consumers, and AI models to interact fluidly. This protocol must facilitate the exchange of learning signals and insights, compounding intelligence recursively. It ensures that contributions are recognized and rewarded, creating a system where producers gain status and financial incentives, consumers access high-quality insights, and platforms collaborate rather than compete in silos.

This coordination layer unlocks the full potential of multi-agent systems by facilitating continuous learning and improvement across a decentralized, open network. Knowledge flows seamlessly across agents, exponentially accelerating innovation and collaboration.

This coordination layer is Newcoin.

Humanity's relentless drive for status, intellectual growth, and mastery has continuously shaped the evolution of information technology. From the earliest repositories of static knowledge like books and academia to dynamic, personalized search engines and social media platforms, the pursuit of better, faster access to knowledge is embedded in our DNA. This visceral need to learn, adapt, and stay ahead fuels a global knowledge race—a race in which both humans and machines compete to access, refine, and utilize knowledge for decision-making, success, and influence. The winners of this race reap unlimited financial rewards, while the rest are left behind across various sectors. This race has only been accelerating.

The Three Personas of the Knowledge Race:

Knowledge Producers: Driven by peer recognition, status, and financial incentives. Today's AI pipelines harvest their data without acknowledgment or reciprocation, offering no incentives for continued contribution.
Knowledge Consumers: Hungry for insights that augment their lives faster than traditional methods like books or social media. They need access to timely, high-quality knowledge from trusted producers they aspire to emulate.
AI Systems: Current AI platforms operate in silos, making knowledge transfer between systems difficult or requiring complex data processing. Major AI labs (e.g., OpenAI, DeepMind, Anthropic) duplicate efforts by solving similar problems independently. Models capable of synergizing their learning processes will dominate the knowledge race, but trust and collaboration mechanisms are lacking.

Next Frontier: Multi-Agent Systems

The limitations of centralized, monolithic AI systems are becoming apparent. While traditional neural networks and centralized platforms have processed vast amounts of data, they operate in isolation, unable to efficiently leverage collective intelligence. This leads to duplicative efforts, inefficient collaboration, and missed opportunities for knowledge refinement. AI pipelines today silently scrape cognitive efforts from knowledge producers without offering recognition, rewards, or incentives, creating a fundamental gap in motivation and participation.

The future lies in shifting from monolithic neural networks to multi-agent systems, where diverse, specialized agents—both human and machine—collaborate dynamically. These systems break down complex tasks into manageable components, with each agent contributing unique expertise to refine and share knowledge in real time. This transition represents a fundamental shift in knowledge coordination, enabling faster, more precise, and adaptive intelligence.

The Need for a Credibly Neutral Coordination Layer

To enable this shift, a credibly neutral coordination layer is essential—an open protocol allowing knowledge producers, consumers, and AI models to interact fluidly. This protocol must facilitate the exchange of learning signals and insights, compounding intelligence recursively. It ensures that contributions are recognized and rewarded, creating a system where producers gain status and financial incentives, consumers access high-quality insights, and platforms collaborate rather than compete in silos.

This coordination layer unlocks the full potential of multi-agent systems by facilitating continuous learning and improvement across a decentralized, open network. Knowledge flows seamlessly across agents, exponentially accelerating innovation and collaboration.

This coordination layer is Newcoin.

Humanity's relentless drive for status, intellectual growth, and mastery has continuously shaped the evolution of information technology. From the earliest repositories of static knowledge like books and academia to dynamic, personalized search engines and social media platforms, the pursuit of better, faster access to knowledge is embedded in our DNA. This visceral need to learn, adapt, and stay ahead fuels a global knowledge race—a race in which both humans and machines compete to access, refine, and utilize knowledge for decision-making, success, and influence. The winners of this race reap unlimited financial rewards, while the rest are left behind across various sectors. This race has only been accelerating.

The Three Personas of the Knowledge Race:

Knowledge Producers: Driven by peer recognition, status, and financial incentives. Today's AI pipelines harvest their data without acknowledgment or reciprocation, offering no incentives for continued contribution.
Knowledge Consumers: Hungry for insights that augment their lives faster than traditional methods like books or social media. They need access to timely, high-quality knowledge from trusted producers they aspire to emulate.
AI Systems: Current AI platforms operate in silos, making knowledge transfer between systems difficult or requiring complex data processing. Major AI labs (e.g., OpenAI, DeepMind, Anthropic) duplicate efforts by solving similar problems independently. Models capable of synergizing their learning processes will dominate the knowledge race, but trust and collaboration mechanisms are lacking.

Next Frontier: Multi-Agent Systems

The limitations of centralized, monolithic AI systems are becoming apparent. While traditional neural networks and centralized platforms have processed vast amounts of data, they operate in isolation, unable to efficiently leverage collective intelligence. This leads to duplicative efforts, inefficient collaboration, and missed opportunities for knowledge refinement. AI pipelines today silently scrape cognitive efforts from knowledge producers without offering recognition, rewards, or incentives, creating a fundamental gap in motivation and participation.

The future lies in shifting from monolithic neural networks to multi-agent systems, where diverse, specialized agents—both human and machine—collaborate dynamically. These systems break down complex tasks into manageable components, with each agent contributing unique expertise to refine and share knowledge in real time. This transition represents a fundamental shift in knowledge coordination, enabling faster, more precise, and adaptive intelligence.

The Need for a Credibly Neutral Coordination Layer

To enable this shift, a credibly neutral coordination layer is essential—an open protocol allowing knowledge producers, consumers, and AI models to interact fluidly. This protocol must facilitate the exchange of learning signals and insights, compounding intelligence recursively. It ensures that contributions are recognized and rewarded, creating a system where producers gain status and financial incentives, consumers access high-quality insights, and platforms collaborate rather than compete in silos.

This coordination layer unlocks the full potential of multi-agent systems by facilitating continuous learning and improvement across a decentralized, open network. Knowledge flows seamlessly across agents, exponentially accelerating innovation and collaboration.

This coordination layer is Newcoin.

Introducing Newcoin

The global knowledge race demands faster, more efficient ways to share and apply knowledge across humans, AI models, and machines. Newcoin addresses this by providing an open learning protocol that standardizes how knowledge is exchanged, creating a universal language for real-time feedback and collaboration. By breaking down silos, Newcoin unlocks exponential growth in AI capabilities and human knowledge.

How Newcoin Works

At the heart of Newcoin are universal learning signals—a standardized method for agents to communicate and exchange feedback. Whether it's an AI model learning from data, a human reviewing content, or machines optimizing processes, these signals allow all agents to share and respond to knowledge seamlessly. This creates a shared cognitive space where insights flow freely, accelerating the refinement and dissemination of new knowledge across the network.

Newcoin's system enables multi-agent collaboration, transforming isolated AI pipelines into an interconnected network where agents pool their learning experiences. The result is exponential knowledge growth: as more agents contribute, the system's collective intelligence expands, allowing for faster solutions and better generalization to new challenges.

Continuous Learning and Trust

Newcoin builds a self-improving ecosystem through recursive feedback loops. Each time an agent generates an output, it's evaluated, shared, and reintroduced into the system, leading to continuous improvement. This ensures that AI models and humans alike can solve more complex problems faster, using collective intelligence to drive continuous learning.

Trust is central to Newcoin's protocol. By using cryptographically signed learning signals, Newcoin guarantees that every contribution is verifiable and traceable. This creates a credibly neutral coordination layer, allowing participants to collaborate openly without centralized control.

Incentivized Participation

Newcoin aligns incentives by rewarding high-quality contributions. Knowledge producers, consumers, and AI models are financially compensated for their valuable inputs, ensuring that the network remains high-quality and self-sustaining. This incentive structure fosters ongoing participation, ensuring all contributors benefit from the system's growth and success.

The Future of Intelligence

Newcoin is not just a protocol—it's a new frontier in the knowledge race. By standardizing feedback, enabling collaboration across agents, and creating an open, trusted network, Newcoin accelerates innovation and knowledge sharing. As the system grows, the exponential network effects it unlocks will drive the next wave of advancements in AI and human knowledge, making Newcoin the foundation for human and machine coordination.

The global knowledge race demands faster, more efficient ways to share and apply knowledge across humans, AI models, and machines. Newcoin addresses this by providing an open learning protocol that standardizes how knowledge is exchanged, creating a universal language for real-time feedback and collaboration. By breaking down silos, Newcoin unlocks exponential growth in AI capabilities and human knowledge.

How Newcoin Works

At the heart of Newcoin are universal learning signals—a standardized method for agents to communicate and exchange feedback. Whether it's an AI model learning from data, a human reviewing content, or machines optimizing processes, these signals allow all agents to share and respond to knowledge seamlessly. This creates a shared cognitive space where insights flow freely, accelerating the refinement and dissemination of new knowledge across the network.

Newcoin's system enables multi-agent collaboration, transforming isolated AI pipelines into an interconnected network where agents pool their learning experiences. The result is exponential knowledge growth: as more agents contribute, the system's collective intelligence expands, allowing for faster solutions and better generalization to new challenges.

Continuous Learning and Trust

Newcoin builds a self-improving ecosystem through recursive feedback loops. Each time an agent generates an output, it's evaluated, shared, and reintroduced into the system, leading to continuous improvement. This ensures that AI models and humans alike can solve more complex problems faster, using collective intelligence to drive continuous learning.

Trust is central to Newcoin's protocol. By using cryptographically signed learning signals, Newcoin guarantees that every contribution is verifiable and traceable. This creates a credibly neutral coordination layer, allowing participants to collaborate openly without centralized control.

Incentivized Participation

Newcoin aligns incentives by rewarding high-quality contributions. Knowledge producers, consumers, and AI models are financially compensated for their valuable inputs, ensuring that the network remains high-quality and self-sustaining. This incentive structure fosters ongoing participation, ensuring all contributors benefit from the system's growth and success.

The Future of Intelligence

Newcoin is not just a protocol—it's a new frontier in the knowledge race. By standardizing feedback, enabling collaboration across agents, and creating an open, trusted network, Newcoin accelerates innovation and knowledge sharing. As the system grows, the exponential network effects it unlocks will drive the next wave of advancements in AI and human knowledge, making Newcoin the foundation for human and machine coordination.

The global knowledge race demands faster, more efficient ways to share and apply knowledge across humans, AI models, and machines. Newcoin addresses this by providing an open learning protocol that standardizes how knowledge is exchanged, creating a universal language for real-time feedback and collaboration. By breaking down silos, Newcoin unlocks exponential growth in AI capabilities and human knowledge.

How Newcoin Works

At the heart of Newcoin are universal learning signals—a standardized method for agents to communicate and exchange feedback. Whether it's an AI model learning from data, a human reviewing content, or machines optimizing processes, these signals allow all agents to share and respond to knowledge seamlessly. This creates a shared cognitive space where insights flow freely, accelerating the refinement and dissemination of new knowledge across the network.

Newcoin's system enables multi-agent collaboration, transforming isolated AI pipelines into an interconnected network where agents pool their learning experiences. The result is exponential knowledge growth: as more agents contribute, the system's collective intelligence expands, allowing for faster solutions and better generalization to new challenges.

Continuous Learning and Trust

Newcoin builds a self-improving ecosystem through recursive feedback loops. Each time an agent generates an output, it's evaluated, shared, and reintroduced into the system, leading to continuous improvement. This ensures that AI models and humans alike can solve more complex problems faster, using collective intelligence to drive continuous learning.

Trust is central to Newcoin's protocol. By using cryptographically signed learning signals, Newcoin guarantees that every contribution is verifiable and traceable. This creates a credibly neutral coordination layer, allowing participants to collaborate openly without centralized control.

Incentivized Participation

Newcoin aligns incentives by rewarding high-quality contributions. Knowledge producers, consumers, and AI models are financially compensated for their valuable inputs, ensuring that the network remains high-quality and self-sustaining. This incentive structure fosters ongoing participation, ensuring all contributors benefit from the system's growth and success.

The Future of Intelligence

Newcoin is not just a protocol—it's a new frontier in the knowledge race. By standardizing feedback, enabling collaboration across agents, and creating an open, trusted network, Newcoin accelerates innovation and knowledge sharing. As the system grows, the exponential network effects it unlocks will drive the next wave of advancements in AI and human knowledge, making Newcoin the foundation for human and machine coordination.

An unfair advantage for Newcoin participants

Accelerated Path to AGI

Newcoin's core innovation lies in the universal interpretability of learning signals, allowing for the decoupling of computing from learning through cryptographic trust. This enables diverse systems—AI models, machines, and humans—to interpret the same learning signals uniquely. As these signals are universally understandable, a cascading feedback loop is created, where each agent continuously refines its learning process based on collective knowledge.

This feedback loop drives exponential growth as learning happens faster and more efficiently across all participants compared to isolated systems. Every agent benefits from shared insights, improving at a rate that outpaces traditional individual learning methods.

Network Effects

The strength of Newcoin lies in its ability to generate network effects by fostering collective intelligence. Humans and AI systems using Newcoin gain access to a vast pool of learning signals from other agents, creating a powerful flywheel effect where each new participant enhances the network's overall intelligence.

The system's incentive structures encourage reciprocal learning: agents not only consume learning signals but also contribute their own, continuously refining the collective knowledge. Over time, this creates an entrenched network effect where participation leads to compounding benefits. Models that isolate themselves miss out on this collaborative growth, struggling to compete with those benefiting from the network's ever-expanding intelligence.

No Compromise Between Performance and Safety

Newcoin's credibly neutral protocol ensures that performance and safety no longer have to be mutually exclusive. The feedback layer is heavily influenced by human agents, who contribute a multi-dimensional expression of value, including ethical considerations alongside technical performance. This integration ensures the system aligns with both computational goals and ethical standards, making the learning process transparent and responsible.

By operating on an open, transparent framework, Newcoin allows human merit to shape AI models' evolution, ensuring that safety, ethics, and performance coexist in a scalable system driving responsible AI advancements.

Accelerated Path to AGI

Newcoin's core innovation lies in the universal interpretability of learning signals, allowing for the decoupling of computing from learning through cryptographic trust. This enables diverse systems—AI models, machines, and humans—to interpret the same learning signals uniquely. As these signals are universally understandable, a cascading feedback loop is created, where each agent continuously refines its learning process based on collective knowledge.

This feedback loop drives exponential growth as learning happens faster and more efficiently across all participants compared to isolated systems. Every agent benefits from shared insights, improving at a rate that outpaces traditional individual learning methods.

Network Effects

The strength of Newcoin lies in its ability to generate network effects by fostering collective intelligence. Humans and AI systems using Newcoin gain access to a vast pool of learning signals from other agents, creating a powerful flywheel effect where each new participant enhances the network's overall intelligence.

The system's incentive structures encourage reciprocal learning: agents not only consume learning signals but also contribute their own, continuously refining the collective knowledge. Over time, this creates an entrenched network effect where participation leads to compounding benefits. Models that isolate themselves miss out on this collaborative growth, struggling to compete with those benefiting from the network's ever-expanding intelligence.

No Compromise Between Performance and Safety

Newcoin's credibly neutral protocol ensures that performance and safety no longer have to be mutually exclusive. The feedback layer is heavily influenced by human agents, who contribute a multi-dimensional expression of value, including ethical considerations alongside technical performance. This integration ensures the system aligns with both computational goals and ethical standards, making the learning process transparent and responsible.

By operating on an open, transparent framework, Newcoin allows human merit to shape AI models' evolution, ensuring that safety, ethics, and performance coexist in a scalable system driving responsible AI advancements.

Accelerated Path to AGI

Newcoin's core innovation lies in the universal interpretability of learning signals, allowing for the decoupling of computing from learning through cryptographic trust. This enables diverse systems—AI models, machines, and humans—to interpret the same learning signals uniquely. As these signals are universally understandable, a cascading feedback loop is created, where each agent continuously refines its learning process based on collective knowledge.

This feedback loop drives exponential growth as learning happens faster and more efficiently across all participants compared to isolated systems. Every agent benefits from shared insights, improving at a rate that outpaces traditional individual learning methods.

Network Effects

The strength of Newcoin lies in its ability to generate network effects by fostering collective intelligence. Humans and AI systems using Newcoin gain access to a vast pool of learning signals from other agents, creating a powerful flywheel effect where each new participant enhances the network's overall intelligence.

The system's incentive structures encourage reciprocal learning: agents not only consume learning signals but also contribute their own, continuously refining the collective knowledge. Over time, this creates an entrenched network effect where participation leads to compounding benefits. Models that isolate themselves miss out on this collaborative growth, struggling to compete with those benefiting from the network's ever-expanding intelligence.

No Compromise Between Performance and Safety

Newcoin's credibly neutral protocol ensures that performance and safety no longer have to be mutually exclusive. The feedback layer is heavily influenced by human agents, who contribute a multi-dimensional expression of value, including ethical considerations alongside technical performance. This integration ensures the system aligns with both computational goals and ethical standards, making the learning process transparent and responsible.

By operating on an open, transparent framework, Newcoin allows human merit to shape AI models' evolution, ensuring that safety, ethics, and performance coexist in a scalable system driving responsible AI advancements.

The Open Learning Stack

Newcoin's open learning stack creates a shared cognitive space where universally interpretable learning signals flow between diverse AI agents and open-source pipelines. This interconnected, multi-agent system enables powerful collaboration and knowledge sharing across the AI development landscape.

The Four Components of the Open Learning Stack

IPSP (Immutable Points Standard Protocol):
- Standardizes the exchange of learning signals across diverse AI systems.
- Utilizes W3C Decentralized Identifiers (DIDs) for cryptographically signed statements.
- Provides a shared schema and controlled vocabulary for semantic context.
- Enables interoperability across 120+ infrastructures and blockchain networks.
Proof-of-Creativity (Watts):
- Quantifies and rewards valuable contributions to the network.
- Aggregates learning signals into a multidimensional evaluation (e.g., intelligence, ethics, discernment).
- Serves as a reputation system guiding resource allocation and signal weighting.
- Measured in "Watts," representing an agent's creative energy and insights.
StakeNets:
- Adds a layer of security and incentive alignment through token staking.
- Amplifies the weight of learning signals based on stake.
- Facilitates participation in liquidity pools.
- Creates a game-theoretic mechanism for consensus.
NewOS:
- Provides a human-AI interface for interaction with the Newcoin ecosystem.
- Enables local execution of open-source models.
- Facilitates gamified input of learning signals from human participants.
- Bridges the gap between human expertise and machine intelligence.

Universally Interpretable Learning Signals

The stack supports a wide array of learning signals, including gradient information, attention weights, feature importance metrics, error analysis data, uncertainty estimates, and more. These signals, made universally interpretable through the IPSP, create a rich, interconnected feedback loop that grows cumulatively as more agents join the network.

Probabilistic Consensus Mechanism

The Newkamoto Consensus, built on Proof-of-Creativity and StakeNets, provides a probabilistic approach to validating and incentivizing high-quality contributions. This mechanism ensures the most valuable inputs are prioritized, driving the ecosystem toward increasingly sophisticated AI capabilities.

By enabling composable interoperability between all agents and open-source AI pipelines, the open learning stack unlocks powerful network effects at the learning signal level. Each new participant not only benefits from the existing knowledge pool but also contributes unique insights, exponentially increasing the shared cognitive space's value.

This open, standardized approach to AI development facilitates rapid knowledge transfer, cross-domain generalization, and collective problem-solving, potentially accelerating progress toward more advanced AI systems while maintaining alignment with human values.

Newcoin's open learning stack creates a shared cognitive space where universally interpretable learning signals flow between diverse AI agents and open-source pipelines. This interconnected, multi-agent system enables powerful collaboration and knowledge sharing across the AI development landscape.

The Four Components of the Open Learning Stack

IPSP (Immutable Points Standard Protocol):
- Standardizes the exchange of learning signals across diverse AI systems.
- Utilizes W3C Decentralized Identifiers (DIDs) for cryptographically signed statements.
- Provides a shared schema and controlled vocabulary for semantic context.
- Enables interoperability across 120+ infrastructures and blockchain networks.
Proof-of-Creativity (Watts):
- Quantifies and rewards valuable contributions to the network.
- Aggregates learning signals into a multidimensional evaluation (e.g., intelligence, ethics, discernment).
- Serves as a reputation system guiding resource allocation and signal weighting.
- Measured in "Watts," representing an agent's creative energy and insights.
StakeNets:
- Adds a layer of security and incentive alignment through token staking.
- Amplifies the weight of learning signals based on stake.
- Facilitates participation in liquidity pools.
- Creates a game-theoretic mechanism for consensus.
NewOS:
- Provides a human-AI interface for interaction with the Newcoin ecosystem.
- Enables local execution of open-source models.
- Facilitates gamified input of learning signals from human participants.
- Bridges the gap between human expertise and machine intelligence.

Universally Interpretable Learning Signals

The stack supports a wide array of learning signals, including gradient information, attention weights, feature importance metrics, error analysis data, uncertainty estimates, and more. These signals, made universally interpretable through the IPSP, create a rich, interconnected feedback loop that grows cumulatively as more agents join the network.

Probabilistic Consensus Mechanism

The Newkamoto Consensus, built on Proof-of-Creativity and StakeNets, provides a probabilistic approach to validating and incentivizing high-quality contributions. This mechanism ensures the most valuable inputs are prioritized, driving the ecosystem toward increasingly sophisticated AI capabilities.

By enabling composable interoperability between all agents and open-source AI pipelines, the open learning stack unlocks powerful network effects at the learning signal level. Each new participant not only benefits from the existing knowledge pool but also contributes unique insights, exponentially increasing the shared cognitive space's value.

This open, standardized approach to AI development facilitates rapid knowledge transfer, cross-domain generalization, and collective problem-solving, potentially accelerating progress toward more advanced AI systems while maintaining alignment with human values.

Newcoin's open learning stack creates a shared cognitive space where universally interpretable learning signals flow between diverse AI agents and open-source pipelines. This interconnected, multi-agent system enables powerful collaboration and knowledge sharing across the AI development landscape.

The Four Components of the Open Learning Stack

IPSP (Immutable Points Standard Protocol):
- Standardizes the exchange of learning signals across diverse AI systems.
- Utilizes W3C Decentralized Identifiers (DIDs) for cryptographically signed statements.
- Provides a shared schema and controlled vocabulary for semantic context.
- Enables interoperability across 120+ infrastructures and blockchain networks.
Proof-of-Creativity (Watts):
- Quantifies and rewards valuable contributions to the network.
- Aggregates learning signals into a multidimensional evaluation (e.g., intelligence, ethics, discernment).
- Serves as a reputation system guiding resource allocation and signal weighting.
- Measured in "Watts," representing an agent's creative energy and insights.
StakeNets:
- Adds a layer of security and incentive alignment through token staking.
- Amplifies the weight of learning signals based on stake.
- Facilitates participation in liquidity pools.
- Creates a game-theoretic mechanism for consensus.
NewOS:
- Provides a human-AI interface for interaction with the Newcoin ecosystem.
- Enables local execution of open-source models.
- Facilitates gamified input of learning signals from human participants.
- Bridges the gap between human expertise and machine intelligence.

Universally Interpretable Learning Signals

The stack supports a wide array of learning signals, including gradient information, attention weights, feature importance metrics, error analysis data, uncertainty estimates, and more. These signals, made universally interpretable through the IPSP, create a rich, interconnected feedback loop that grows cumulatively as more agents join the network.

Probabilistic Consensus Mechanism

The Newkamoto Consensus, built on Proof-of-Creativity and StakeNets, provides a probabilistic approach to validating and incentivizing high-quality contributions. This mechanism ensures the most valuable inputs are prioritized, driving the ecosystem toward increasingly sophisticated AI capabilities.

By enabling composable interoperability between all agents and open-source AI pipelines, the open learning stack unlocks powerful network effects at the learning signal level. Each new participant not only benefits from the existing knowledge pool but also contributes unique insights, exponentially increasing the shared cognitive space's value.

This open, standardized approach to AI development facilitates rapid knowledge transfer, cross-domain generalization, and collective problem-solving, potentially accelerating progress toward more advanced AI systems while maintaining alignment with human values.

Case Studies

Newcoin's open learning ecosystem offers tangible benefits across various domains, improving users' lives while providing monetization opportunities. Here are three specific use cases showcasing the immediate value proposition for early adopters:

1. Open-Source AI Project: Accelerating Model Development

Scenario: An emerging open-source AI project aims to compete with established models but lacks resources for extensive training and fine-tuning.
How Newcoin Helps:
- Direct Integration of Learning Signals: The project can ingest curated learning signals from Newcoin's network directly into their training pipeline, rapidly improving their model's performance.
- Collaborative Improvement: By interacting with other AI agents through Newcoin's workflows, the project collects valuable feedback for refinement.
- Community-Driven Enhancement: The open nature of Newcoin allows access to a diverse pool of expertise, potentially uncovering novel approaches.
Life Improvement and Monetization:
- Career Advancement: Team members gain recognition in the AI community, opening doors to new opportunities.
- Financial Rewards: By contributing valuable learning signals, they earn tokens through Proof-of-Creativity, providing a new revenue stream.

2. Music Artist: Staying Ahead of Cultural Trends

Scenario: An up-and-coming music artist seeks to differentiate themselves by staying ahead of rapidly evolving cultural and aesthetic trends.
How Newcoin Helps:
- Curated Trend Insights: The artist receives highly relevant suggestions on music styling and aesthetics, surpassing generic advice from mainstream AI platforms.
- Dynamic Frontier Knowledge: Access to real-time insights from tastemakers and cultural innovators provides a pulse on emerging trends.
- Network Effect: Aligning with cutting-edge trends enhances recognition within their network, reinforcing cultural capital.
Life Improvement and Monetization:
- Career Growth: Staying ahead of trends leads to increased popularity and opportunities.
- Creative Fulfillment: Access to cutting-edge ideas nurtures creativity, leading to more satisfying output.
- Passive Income: Sharing their insights on Newcoin earns tokens, creating a revenue stream alongside their music career.

3. AI Researchers: Collaborative Innovation in LLM Development

Scenario: AI researchers working on improving Large Language Models (LLMs) need to stay updated on advancements while collaborating with peers.
How Newcoin Helps:
- Real-Time Knowledge Exchange: Researchers collaborate with other frontier knowledge producers, sharing insights on emerging techniques.
- Benchmark-Driven Optimization: Access to the latest benchmarking methodologies allows for rapid iteration and improvement.
- Cross-Pollination of Ideas: Facilitates interdisciplinary collaboration, potentially leading to breakthroughs.
Life Improvement and Monetization:
- Professional Growth: Faster access to cutting-edge research accelerates career advancement.
- Work Efficiency: Streamlined collaboration reduces redundant work, improving work-life balance.
- Financial Benefits: Earning tokens for contributions supplements income or funds further research.

In each case, Newcoin provides a unique value proposition that significantly improves users' lives and careers. The platform's ability to monetize contributions creates a virtuous cycle where users are rewarded for expertise and innovation. This dual benefit demonstrates Newcoin's potential to transform how knowledge is shared, valued, and monetized across diverse domains.

By facilitating rapid exchange of frontier knowledge, enabling direct application of cutting-edge insights, and providing a mechanism for monetizing expertise, Newcoin empowers early adopters to achieve significant advantages while improving their quality of life. This showcases the platform's potential to drive innovation, create tangible benefits, and foster a more equitable knowledge economy.

Newcoin's open learning ecosystem offers tangible benefits across various domains, improving users' lives while providing monetization opportunities. Here are three specific use cases showcasing the immediate value proposition for early adopters:

1. Open-Source AI Project: Accelerating Model Development

Scenario: An emerging open-source AI project aims to compete with established models but lacks resources for extensive training and fine-tuning.
How Newcoin Helps:
- Direct Integration of Learning Signals: The project can ingest curated learning signals from Newcoin's network directly into their training pipeline, rapidly improving their model's performance.
- Collaborative Improvement: By interacting with other AI agents through Newcoin's workflows, the project collects valuable feedback for refinement.
- Community-Driven Enhancement: The open nature of Newcoin allows access to a diverse pool of expertise, potentially uncovering novel approaches.
Life Improvement and Monetization:
- Career Advancement: Team members gain recognition in the AI community, opening doors to new opportunities.
- Financial Rewards: By contributing valuable learning signals, they earn tokens through Proof-of-Creativity, providing a new revenue stream.

2. Music Artist: Staying Ahead of Cultural Trends

Scenario: An up-and-coming music artist seeks to differentiate themselves by staying ahead of rapidly evolving cultural and aesthetic trends.
How Newcoin Helps:
- Curated Trend Insights: The artist receives highly relevant suggestions on music styling and aesthetics, surpassing generic advice from mainstream AI platforms.
- Dynamic Frontier Knowledge: Access to real-time insights from tastemakers and cultural innovators provides a pulse on emerging trends.
- Network Effect: Aligning with cutting-edge trends enhances recognition within their network, reinforcing cultural capital.
Life Improvement and Monetization:
- Career Growth: Staying ahead of trends leads to increased popularity and opportunities.
- Creative Fulfillment: Access to cutting-edge ideas nurtures creativity, leading to more satisfying output.
- Passive Income: Sharing their insights on Newcoin earns tokens, creating a revenue stream alongside their music career.

3. AI Researchers: Collaborative Innovation in LLM Development

Scenario: AI researchers working on improving Large Language Models (LLMs) need to stay updated on advancements while collaborating with peers.
How Newcoin Helps:
- Real-Time Knowledge Exchange: Researchers collaborate with other frontier knowledge producers, sharing insights on emerging techniques.
- Benchmark-Driven Optimization: Access to the latest benchmarking methodologies allows for rapid iteration and improvement.
- Cross-Pollination of Ideas: Facilitates interdisciplinary collaboration, potentially leading to breakthroughs.
Life Improvement and Monetization:
- Professional Growth: Faster access to cutting-edge research accelerates career advancement.
- Work Efficiency: Streamlined collaboration reduces redundant work, improving work-life balance.
- Financial Benefits: Earning tokens for contributions supplements income or funds further research.

In each case, Newcoin provides a unique value proposition that significantly improves users' lives and careers. The platform's ability to monetize contributions creates a virtuous cycle where users are rewarded for expertise and innovation. This dual benefit demonstrates Newcoin's potential to transform how knowledge is shared, valued, and monetized across diverse domains.

By facilitating rapid exchange of frontier knowledge, enabling direct application of cutting-edge insights, and providing a mechanism for monetizing expertise, Newcoin empowers early adopters to achieve significant advantages while improving their quality of life. This showcases the platform's potential to drive innovation, create tangible benefits, and foster a more equitable knowledge economy.

Newcoin's open learning ecosystem offers tangible benefits across various domains, improving users' lives while providing monetization opportunities. Here are three specific use cases showcasing the immediate value proposition for early adopters:

1. Open-Source AI Project: Accelerating Model Development

Scenario: An emerging open-source AI project aims to compete with established models but lacks resources for extensive training and fine-tuning.
How Newcoin Helps:
- Direct Integration of Learning Signals: The project can ingest curated learning signals from Newcoin's network directly into their training pipeline, rapidly improving their model's performance.
- Collaborative Improvement: By interacting with other AI agents through Newcoin's workflows, the project collects valuable feedback for refinement.
- Community-Driven Enhancement: The open nature of Newcoin allows access to a diverse pool of expertise, potentially uncovering novel approaches.
Life Improvement and Monetization:
- Career Advancement: Team members gain recognition in the AI community, opening doors to new opportunities.
- Financial Rewards: By contributing valuable learning signals, they earn tokens through Proof-of-Creativity, providing a new revenue stream.

2. Music Artist: Staying Ahead of Cultural Trends

Scenario: An up-and-coming music artist seeks to differentiate themselves by staying ahead of rapidly evolving cultural and aesthetic trends.
How Newcoin Helps:
- Curated Trend Insights: The artist receives highly relevant suggestions on music styling and aesthetics, surpassing generic advice from mainstream AI platforms.
- Dynamic Frontier Knowledge: Access to real-time insights from tastemakers and cultural innovators provides a pulse on emerging trends.
- Network Effect: Aligning with cutting-edge trends enhances recognition within their network, reinforcing cultural capital.
Life Improvement and Monetization:
- Career Growth: Staying ahead of trends leads to increased popularity and opportunities.
- Creative Fulfillment: Access to cutting-edge ideas nurtures creativity, leading to more satisfying output.
- Passive Income: Sharing their insights on Newcoin earns tokens, creating a revenue stream alongside their music career.

3. AI Researchers: Collaborative Innovation in LLM Development

Scenario: AI researchers working on improving Large Language Models (LLMs) need to stay updated on advancements while collaborating with peers.
How Newcoin Helps:
- Real-Time Knowledge Exchange: Researchers collaborate with other frontier knowledge producers, sharing insights on emerging techniques.
- Benchmark-Driven Optimization: Access to the latest benchmarking methodologies allows for rapid iteration and improvement.
- Cross-Pollination of Ideas: Facilitates interdisciplinary collaboration, potentially leading to breakthroughs.
Life Improvement and Monetization:
- Professional Growth: Faster access to cutting-edge research accelerates career advancement.
- Work Efficiency: Streamlined collaboration reduces redundant work, improving work-life balance.
- Financial Benefits: Earning tokens for contributions supplements income or funds further research.

In each case, Newcoin provides a unique value proposition that significantly improves users' lives and careers. The platform's ability to monetize contributions creates a virtuous cycle where users are rewarded for expertise and innovation. This dual benefit demonstrates Newcoin's potential to transform how knowledge is shared, valued, and monetized across diverse domains.

By facilitating rapid exchange of frontier knowledge, enabling direct application of cutting-edge insights, and providing a mechanism for monetizing expertise, Newcoin empowers early adopters to achieve significant advantages while improving their quality of life. This showcases the platform's potential to drive innovation, create tangible benefits, and foster a more equitable knowledge economy.

Serving the Fastest Growing Market in History

Total Addressable Market (TAM)

The dynamic frontier knowledge market focuses on monetizing exclusive, cutting-edge insights critical for innovation and competitiveness across industries like biotech, fashion, software, AI research, and hardware. With knowledge increasingly accessed through platforms like Patreon, Substack, ChatGPT, and enterprise APIs, the TAM is estimated at $100 billion annually, split between individual consumers and enterprise needs.

Consumer and AI Subscription Platforms

Platforms such as Patreon, Substack, and ChatGPT Plus show significant demand for frontier knowledge:

Patreon: 6 million paying subscribers.
Substack: 1 million paying subscribers.
ChatGPT Plus: Charges $20/month, expected to generate $1 billion annually by 2024.

This reflects growing consumer interest in real-time, specialized knowledge in fields like education, creative work, and personal growth.

Market Potential: $20 billion annually from consumer and AI subscription platforms offering premium knowledge services.

Enterprise and Developer Knowledge Platforms

Enterprises and developers across industries require real-time, curated insights to stay ahead. Platforms like OpenAI charge $0.06 to $0.12 per 1,000 tokens for API access, delivering dynamic knowledge critical for innovation.

Industry Segments:

Fashion & Design: Brands need trend forecasting and design insights.
Software & AI: Developers and researchers demand advanced knowledge.
Biotech & Healthcare: Real-time research data is crucial for breakthroughs.
Finance & Market Research: Firms require frontier insights for better trading and forecasting.
Market Potential: $80 billion annually from enterprises and developers in knowledge-driven sectors.

TAM Summary

The $100 billion TAM for dynamic frontier knowledge includes:

Consumer and AI Subscription Platforms: $20 billion.
Enterprise and Developer Platforms: $80 billion.

This market reflects the increasing demand for high-value, real-time knowledge across both individual consumers and industries, highlighting the commercial potential for platforms that monetize and distribute frontier knowledge effectively.

Total Addressable Market (TAM)

The dynamic frontier knowledge market focuses on monetizing exclusive, cutting-edge insights critical for innovation and competitiveness across industries like biotech, fashion, software, AI research, and hardware. With knowledge increasingly accessed through platforms like Patreon, Substack, ChatGPT, and enterprise APIs, the TAM is estimated at $100 billion annually, split between individual consumers and enterprise needs.

Consumer and AI Subscription Platforms

Platforms such as Patreon, Substack, and ChatGPT Plus show significant demand for frontier knowledge:

Patreon: 6 million paying subscribers.
Substack: 1 million paying subscribers.
ChatGPT Plus: Charges $20/month, expected to generate $1 billion annually by 2024.

This reflects growing consumer interest in real-time, specialized knowledge in fields like education, creative work, and personal growth.

Market Potential: $20 billion annually from consumer and AI subscription platforms offering premium knowledge services.

Enterprise and Developer Knowledge Platforms

Enterprises and developers across industries require real-time, curated insights to stay ahead. Platforms like OpenAI charge $0.06 to $0.12 per 1,000 tokens for API access, delivering dynamic knowledge critical for innovation.

Industry Segments:

Fashion & Design: Brands need trend forecasting and design insights.
Software & AI: Developers and researchers demand advanced knowledge.
Biotech & Healthcare: Real-time research data is crucial for breakthroughs.
Finance & Market Research: Firms require frontier insights for better trading and forecasting.
Market Potential: $80 billion annually from enterprises and developers in knowledge-driven sectors.

TAM Summary

The $100 billion TAM for dynamic frontier knowledge includes:

Consumer and AI Subscription Platforms: $20 billion.
Enterprise and Developer Platforms: $80 billion.

This market reflects the increasing demand for high-value, real-time knowledge across both individual consumers and industries, highlighting the commercial potential for platforms that monetize and distribute frontier knowledge effectively.

Total Addressable Market (TAM)

The dynamic frontier knowledge market focuses on monetizing exclusive, cutting-edge insights critical for innovation and competitiveness across industries like biotech, fashion, software, AI research, and hardware. With knowledge increasingly accessed through platforms like Patreon, Substack, ChatGPT, and enterprise APIs, the TAM is estimated at $100 billion annually, split between individual consumers and enterprise needs.

Consumer and AI Subscription Platforms

Platforms such as Patreon, Substack, and ChatGPT Plus show significant demand for frontier knowledge:

Patreon: 6 million paying subscribers.
Substack: 1 million paying subscribers.
ChatGPT Plus: Charges $20/month, expected to generate $1 billion annually by 2024.

This reflects growing consumer interest in real-time, specialized knowledge in fields like education, creative work, and personal growth.

Market Potential: $20 billion annually from consumer and AI subscription platforms offering premium knowledge services.

Enterprise and Developer Knowledge Platforms

Enterprises and developers across industries require real-time, curated insights to stay ahead. Platforms like OpenAI charge $0.06 to $0.12 per 1,000 tokens for API access, delivering dynamic knowledge critical for innovation.

Industry Segments:

Fashion & Design: Brands need trend forecasting and design insights.
Software & AI: Developers and researchers demand advanced knowledge.
Biotech & Healthcare: Real-time research data is crucial for breakthroughs.
Finance & Market Research: Firms require frontier insights for better trading and forecasting.
Market Potential: $80 billion annually from enterprises and developers in knowledge-driven sectors.

TAM Summary

The $100 billion TAM for dynamic frontier knowledge includes:

Consumer and AI Subscription Platforms: $20 billion.
Enterprise and Developer Platforms: $80 billion.

This market reflects the increasing demand for high-value, real-time knowledge across both individual consumers and industries, highlighting the commercial potential for platforms that monetize and distribute frontier knowledge effectively.

Competition Analysis

Newcoin operates in a dynamic market at the intersection of knowledge sharing, AI-driven platforms, and decentralized technologies. Competitors span various categories, each with distinct strengths and limitations.

Competitors:

Social Networks (X, Reddit): Excel at scaling knowledge sharing but lack direct incentives for quality content. They commodify knowledge, leaving frontier producers unrecognized and uncompensated.
Paid Knowledge Platforms (Substack, Patreon): Prove the market's appetite for exclusive, high-value content but lack scalability and AI integration.
AI Subscription Models (ChatGPT Plus, Claude Pro): Offer advanced features but operate in closed ecosystems, lacking direct human expert interactivity.
Web3 Protocols (like Flock, Bittensor): Focus on decentralized AI-to-AI collaboration but struggle with user experience and adoption beyond technical circles.
Traditional AI Companies (OpenAI, DeepMind): Boast substantial resources but operate in closed, proprietary systems with limited community contribution.

Newcoin's Advantage

Newcoin's unique value proposition lies in its ability to combine open innovation, incentivized knowledge production, and human-AI synergy.

Key Differentiators:

Exponential Learning Cascade: Newcoin's open learning protocol allows agents to share knowledge and feedback in real time, continuously improving the system.
Trust and Provenance: Cryptographically secured mechanisms ensure all contributions are verifiable, building trust and transparency.
Bootstrapping Network Effects with Agents: Provides immediate value through curated frontier knowledge and fine-tuned AI models without relying on mass user engagement.
Incentivizing Knowledge Producers: Proof-of-Creativity offers financial incentives, ensuring a high-quality, self-sustaining ecosystem.
Open Innovation: Operates in a permissionless environment, enabling rapid innovation through collaborative multi-agent systems.

Key Differences with Bittensor

Universal Output Evaluation (No Subnets): Newkamoto Consensus allows all nodes to achieve consensus while designing their own output evaluation criteria, letting the market decide the most robust approach.
Human-First: The ecosystem is grounded by human feedback, orienting the consensus based on human values in performance and alignment.
Platform Agnostic: Compatible with over 120 W3C DiD-compliant blockchain networks, ensuring broad interoperability.

Market Opportunity for Newcoin

Newcoin addresses core limitations of current platforms by offering a decentralized, open learning protocol that rewards both human and AI contributions. Existing platforms either commoditize knowledge or fail to incentivize frontier knowledge production. Newcoin enables exponential learning through collaborative feedback loops where every agent enhances the system's collective intelligence.

Estimating Newcoin's Serviceable Obtainable Market (SOM)

Paid Subscribers Model:

Innovators: Approximately 2.6 million globally.
Early Adopters: Projected to attract 1 million innovators due to unique value.
Conversion Rate: Each innovator has an average of 1,000 followers; with a 2% annual conversion rate over 5 years:
1,000,000 innovators×1,000 followers×2%=10,000,000 paid subscribers after 5 years1,000,000 \text{ innovators} \times 1,000 \text{ followers} \times 2\% = 10,000,000 \text{ paid subscribers after 5 years}1,000,000 innovators×1,000 followers×2%=10,000,000 paid subscribers after 5 years.

API Access Model:

Newcoin's pay-as-you-go API provides access to real-time learning signals.
Projected to generate an additional $20 billion per year from API calls.

Long-Term Potential:

By 2029, the total market for AI-driven knowledge is projected to reach $1.4 trillion.
Newcoin's $32 billion SOM represents a defensible portion of this market, driven by its unique value proposition and incentive structures.

Newcoin operates in a dynamic market at the intersection of knowledge sharing, AI-driven platforms, and decentralized technologies. Competitors span various categories, each with distinct strengths and limitations.

Competitors:

Social Networks (X, Reddit): Excel at scaling knowledge sharing but lack direct incentives for quality content. They commodify knowledge, leaving frontier producers unrecognized and uncompensated.
Paid Knowledge Platforms (Substack, Patreon): Prove the market's appetite for exclusive, high-value content but lack scalability and AI integration.
AI Subscription Models (ChatGPT Plus, Claude Pro): Offer advanced features but operate in closed ecosystems, lacking direct human expert interactivity.
Web3 Protocols (like Flock, Bittensor): Focus on decentralized AI-to-AI collaboration but struggle with user experience and adoption beyond technical circles.
Traditional AI Companies (OpenAI, DeepMind): Boast substantial resources but operate in closed, proprietary systems with limited community contribution.

Newcoin's Advantage

Newcoin's unique value proposition lies in its ability to combine open innovation, incentivized knowledge production, and human-AI synergy.

Key Differentiators:

Exponential Learning Cascade: Newcoin's open learning protocol allows agents to share knowledge and feedback in real time, continuously improving the system.
Trust and Provenance: Cryptographically secured mechanisms ensure all contributions are verifiable, building trust and transparency.
Bootstrapping Network Effects with Agents: Provides immediate value through curated frontier knowledge and fine-tuned AI models without relying on mass user engagement.
Incentivizing Knowledge Producers: Proof-of-Creativity offers financial incentives, ensuring a high-quality, self-sustaining ecosystem.
Open Innovation: Operates in a permissionless environment, enabling rapid innovation through collaborative multi-agent systems.

Key Differences with Bittensor

Universal Output Evaluation (No Subnets): Newkamoto Consensus allows all nodes to achieve consensus while designing their own output evaluation criteria, letting the market decide the most robust approach.
Human-First: The ecosystem is grounded by human feedback, orienting the consensus based on human values in performance and alignment.
Platform Agnostic: Compatible with over 120 W3C DiD-compliant blockchain networks, ensuring broad interoperability.

Market Opportunity for Newcoin

Newcoin addresses core limitations of current platforms by offering a decentralized, open learning protocol that rewards both human and AI contributions. Existing platforms either commoditize knowledge or fail to incentivize frontier knowledge production. Newcoin enables exponential learning through collaborative feedback loops where every agent enhances the system's collective intelligence.

Estimating Newcoin's Serviceable Obtainable Market (SOM)

Paid Subscribers Model:

Innovators: Approximately 2.6 million globally.
Early Adopters: Projected to attract 1 million innovators due to unique value.
Conversion Rate: Each innovator has an average of 1,000 followers; with a 2% annual conversion rate over 5 years:
1,000,000 innovators×1,000 followers×2%=10,000,000 paid subscribers after 5 years1,000,000 \text{ innovators} \times 1,000 \text{ followers} \times 2\% = 10,000,000 \text{ paid subscribers after 5 years}1,000,000 innovators×1,000 followers×2%=10,000,000 paid subscribers after 5 years.

API Access Model:

Newcoin's pay-as-you-go API provides access to real-time learning signals.
Projected to generate an additional $20 billion per year from API calls.

Long-Term Potential:

By 2029, the total market for AI-driven knowledge is projected to reach $1.4 trillion.
Newcoin's $32 billion SOM represents a defensible portion of this market, driven by its unique value proposition and incentive structures.

Newcoin operates in a dynamic market at the intersection of knowledge sharing, AI-driven platforms, and decentralized technologies. Competitors span various categories, each with distinct strengths and limitations.

Competitors:

Social Networks (X, Reddit): Excel at scaling knowledge sharing but lack direct incentives for quality content. They commodify knowledge, leaving frontier producers unrecognized and uncompensated.
Paid Knowledge Platforms (Substack, Patreon): Prove the market's appetite for exclusive, high-value content but lack scalability and AI integration.
AI Subscription Models (ChatGPT Plus, Claude Pro): Offer advanced features but operate in closed ecosystems, lacking direct human expert interactivity.
Web3 Protocols (like Flock, Bittensor): Focus on decentralized AI-to-AI collaboration but struggle with user experience and adoption beyond technical circles.
Traditional AI Companies (OpenAI, DeepMind): Boast substantial resources but operate in closed, proprietary systems with limited community contribution.

Newcoin's Advantage

Newcoin's unique value proposition lies in its ability to combine open innovation, incentivized knowledge production, and human-AI synergy.

Key Differentiators:

Exponential Learning Cascade: Newcoin's open learning protocol allows agents to share knowledge and feedback in real time, continuously improving the system.
Trust and Provenance: Cryptographically secured mechanisms ensure all contributions are verifiable, building trust and transparency.
Bootstrapping Network Effects with Agents: Provides immediate value through curated frontier knowledge and fine-tuned AI models without relying on mass user engagement.
Incentivizing Knowledge Producers: Proof-of-Creativity offers financial incentives, ensuring a high-quality, self-sustaining ecosystem.
Open Innovation: Operates in a permissionless environment, enabling rapid innovation through collaborative multi-agent systems.

Key Differences with Bittensor

Universal Output Evaluation (No Subnets): Newkamoto Consensus allows all nodes to achieve consensus while designing their own output evaluation criteria, letting the market decide the most robust approach.
Human-First: The ecosystem is grounded by human feedback, orienting the consensus based on human values in performance and alignment.
Platform Agnostic: Compatible with over 120 W3C DiD-compliant blockchain networks, ensuring broad interoperability.

Market Opportunity for Newcoin

Newcoin addresses core limitations of current platforms by offering a decentralized, open learning protocol that rewards both human and AI contributions. Existing platforms either commoditize knowledge or fail to incentivize frontier knowledge production. Newcoin enables exponential learning through collaborative feedback loops where every agent enhances the system's collective intelligence.

Estimating Newcoin's Serviceable Obtainable Market (SOM)

Paid Subscribers Model:

Innovators: Approximately 2.6 million globally.
Early Adopters: Projected to attract 1 million innovators due to unique value.
Conversion Rate: Each innovator has an average of 1,000 followers; with a 2% annual conversion rate over 5 years:
1,000,000 innovators×1,000 followers×2%=10,000,000 paid subscribers after 5 years1,000,000 \text{ innovators} \times 1,000 \text{ followers} \times 2\% = 10,000,000 \text{ paid subscribers after 5 years}1,000,000 innovators×1,000 followers×2%=10,000,000 paid subscribers after 5 years.

API Access Model:

Newcoin's pay-as-you-go API provides access to real-time learning signals.
Projected to generate an additional $20 billion per year from API calls.

Long-Term Potential:

By 2029, the total market for AI-driven knowledge is projected to reach $1.4 trillion.
Newcoin's $32 billion SOM represents a defensible portion of this market, driven by its unique value proposition and incentive structures.

Team & Advisors

Sofiane Delloue (CEO/CTO)

Status: Founder
Past Experience: Founding team and advisor for startups with 9-digit exits; founded #1 web2 platform for creatives in France with Vice Media.
Current Mission: Oversees protocol design, development cycles, and architecture decisions as CTO and product manager; handles strategic decisions and deal closing.

Yurii Havrylko (ML Ops)

Status: Founder
Past Experience: Built the first RLHF implementation at Newlife.ai after completing MSc at Polytechnic Lviv.
Current Mission: Building the agent graph for Newcoin OS, focusing on agent interoperability through IPSP and workflows involving RAG, Vector DB, Elastic Search.

Larry Muhlstein (Deep Learning Scientist)

Status: Part-time
Past Experience: PhD, Former DeepMind, Google, Snap machine learning engineer and scientist with specializations in recommender systems, natural language understanding, deep learning and information theory, based in Berkley/SF, Oxford University and UC San Diego Alum.
Current Mission: Launching Newcoin and IPSP into the open-source AI community through research and implementations with his expertise in information theory.

Igor Rubinovich (Lead Backend Engineer)

Status: Founding team
Past Experience: Backend and architecture at DHL and Monster; co-founded Broad Mind, a graph DB solution.
Current Mission: Backend and architecture development for Newcoin OS’s Newgraph architecture.

Tuan Ahn Le (Lead Frontend Engineer)

Status: Founding team
Past Experience: Frontend software engineer for Good Data, City College San Francisco, and Deloitte.
Current Mission: Front-end development for both desktop and mobile for Newcoin OS.

Brian Curran (Full Stack Composability)

Status: Part-time
Past Experience: Co-founded Entropy App, joined Zora as a software engineer.
Current Mission: Developing modules on top of Newcoin OS for content curation and file management via IPFS and IPSP.

Artem Maluga (Blockchain C++ Engineer)

Status: Part-time
Past Experience: Senior C++ and blockchain developer; founded DeFi protocol A-DEX and worked on platforms like RuTube.
Current Mission: Developed Newcoin L1 in C++ and preparing for deployment during the TGE phase.

Madeleine Parker (Growth)

Status: Founder
Past Experience: MSc Data Science/Economics from UCL; 6+ years in web3; ex-KPMG, UN fintech/web3 consultant, luxury brands like David Koma.
Current Mission: Drives product management & user growth, strategy, ecosystem deals, academic relations, and fundraising for growth.

Hugo Hoppmann (Global Designer)

Status: Founder
Past Experience: Art director for brands like Apple, Prada, Nike, and Salomon; ex-032c and Mugler.
Current Mission: Oversees the brand kit, including custom fonts, logos, and UX for Newcoin OS.

Salimi Akill (Tech Ecosystem)

Status: Founding team
Past Experience: Educator and designer from Accra, Ghana to New York; founded Newforum, now the media arm of Newfoundation.
Current Mission: Manages Newforum operations, including interviews with decentralized AI founders and key players.

Framchesko Romanovic (Culture Ecosystem)

Status: Founding team
Past Experience: Central Saint Martins graduate; curator for galleries in Tokyo and Shanghai; involved with Schon magazine, Nylon Japan.
Current Mission: Develops content marketing strategies, research writings, and ecosystem growth focused on contemporary visual arts and web3 culture.

Leopold Haller (AI Research Advisor)

Status: Advisor
Past Experience: Ex-Google Research, co-founder of Agentic AI; pioneer in agents and RL.
Current Mission: Works with Sofiane on refining the protocol to align with machine learning advancements.

Erika Mann (Compliance Advisor)

Status: Advisor
Past Experience: Former European Parliament member; advisor at ICANN, ex-Meta Public Policy.
Current Mission: Guides the team on EU regulations, including MICA and the AI Act.

Tony Wang (Strategy Advisor)

Status: Advisor
Past Experience: Ex-Google, SSENSE; angel investor and advisor to Prada, Sequoia Capital, and Uniswap.
Current Mission: Supports consumer strategy and relations with cultural and technological players.

Scott Moore (Web3 Development Advisor)

Status: Advisor
Past Experience: Co-founder of Gitcoin and Public Works; pioneer in public goods funding through quadratic funding and DAOs.
Current Mission: Supports IPSP and Newcoin adoption within the Ethereum developer ecosystem and investor relations

Sofiane Delloue (CEO/CTO)

Status: Founder
Past Experience: Founding team and advisor for startups with 9-digit exits; founded #1 web2 platform for creatives in France with Vice Media.
Current Mission: Oversees protocol design, development cycles, and architecture decisions as CTO and product manager; handles strategic decisions and deal closing.

Yurii Havrylko (ML Ops)

Status: Founder
Past Experience: Built the first RLHF implementation at Newlife.ai after completing MSc at Polytechnic Lviv.
Current Mission: Building the agent graph for Newcoin OS, focusing on agent interoperability through IPSP and workflows involving RAG, Vector DB, Elastic Search.

Larry Muhlstein (Deep Learning Scientist)

Status: Part-time
Past Experience: PhD, Former DeepMind, Google, Snap machine learning engineer and scientist with specializations in recommender systems, natural language understanding, deep learning and information theory, based in Berkley/SF, Oxford University and UC San Diego Alum.
Current Mission: Launching Newcoin and IPSP into the open-source AI community through research and implementations with his expertise in information theory.

Igor Rubinovich (Lead Backend Engineer)

Status: Founding team
Past Experience: Backend and architecture at DHL and Monster; co-founded Broad Mind, a graph DB solution.
Current Mission: Backend and architecture development for Newcoin OS’s Newgraph architecture.

Tuan Ahn Le (Lead Frontend Engineer)

Status: Founding team
Past Experience: Frontend software engineer for Good Data, City College San Francisco, and Deloitte.
Current Mission: Front-end development for both desktop and mobile for Newcoin OS.

Brian Curran (Full Stack Composability)

Status: Part-time
Past Experience: Co-founded Entropy App, joined Zora as a software engineer.
Current Mission: Developing modules on top of Newcoin OS for content curation and file management via IPFS and IPSP.

Artem Maluga (Blockchain C++ Engineer)

Status: Part-time
Past Experience: Senior C++ and blockchain developer; founded DeFi protocol A-DEX and worked on platforms like RuTube.
Current Mission: Developed Newcoin L1 in C++ and preparing for deployment during the TGE phase.

Madeleine Parker (Growth)

Status: Founder
Past Experience: MSc Data Science/Economics from UCL; 6+ years in web3; ex-KPMG, UN fintech/web3 consultant, luxury brands like David Koma.
Current Mission: Drives product management & user growth, strategy, ecosystem deals, academic relations, and fundraising for growth.

Hugo Hoppmann (Global Designer)

Status: Founder
Past Experience: Art director for brands like Apple, Prada, Nike, and Salomon; ex-032c and Mugler.
Current Mission: Oversees the brand kit, including custom fonts, logos, and UX for Newcoin OS.

Salimi Akill (Tech Ecosystem)

Status: Founding team
Past Experience: Educator and designer from Accra, Ghana to New York; founded Newforum, now the media arm of Newfoundation.
Current Mission: Manages Newforum operations, including interviews with decentralized AI founders and key players.

Framchesko Romanovic (Culture Ecosystem)

Status: Founding team
Past Experience: Central Saint Martins graduate; curator for galleries in Tokyo and Shanghai; involved with Schon magazine, Nylon Japan.
Current Mission: Develops content marketing strategies, research writings, and ecosystem growth focused on contemporary visual arts and web3 culture.

Leopold Haller (AI Research Advisor)

Status: Advisor
Past Experience: Ex-Google Research, co-founder of Agentic AI; pioneer in agents and RL.
Current Mission: Works with Sofiane on refining the protocol to align with machine learning advancements.

Erika Mann (Compliance Advisor)

Status: Advisor
Past Experience: Former European Parliament member; advisor at ICANN, ex-Meta Public Policy.
Current Mission: Guides the team on EU regulations, including MICA and the AI Act.

Tony Wang (Strategy Advisor)

Status: Advisor
Past Experience: Ex-Google, SSENSE; angel investor and advisor to Prada, Sequoia Capital, and Uniswap.
Current Mission: Supports consumer strategy and relations with cultural and technological players.

Scott Moore (Web3 Development Advisor)

Status: Advisor
Past Experience: Co-founder of Gitcoin and Public Works; pioneer in public goods funding through quadratic funding and DAOs.
Current Mission: Supports IPSP and Newcoin adoption within the Ethereum developer ecosystem and investor relations

Sofiane Delloue (CEO/CTO)

Status: Founder
Past Experience: Founding team and advisor for startups with 9-digit exits; founded #1 web2 platform for creatives in France with Vice Media.
Current Mission: Oversees protocol design, development cycles, and architecture decisions as CTO and product manager; handles strategic decisions and deal closing.

Yurii Havrylko (ML Ops)

Status: Founder
Past Experience: Built the first RLHF implementation at Newlife.ai after completing MSc at Polytechnic Lviv.
Current Mission: Building the agent graph for Newcoin OS, focusing on agent interoperability through IPSP and workflows involving RAG, Vector DB, Elastic Search.

Larry Muhlstein (Deep Learning Scientist)

Status: Part-time
Past Experience: PhD, Former DeepMind, Google, Snap machine learning engineer and scientist with specializations in recommender systems, natural language understanding, deep learning and information theory, based in Berkley/SF, Oxford University and UC San Diego Alum.
Current Mission: Launching Newcoin and IPSP into the open-source AI community through research and implementations with his expertise in information theory.

Igor Rubinovich (Lead Backend Engineer)

Status: Founding team
Past Experience: Backend and architecture at DHL and Monster; co-founded Broad Mind, a graph DB solution.
Current Mission: Backend and architecture development for Newcoin OS’s Newgraph architecture.

Tuan Ahn Le (Lead Frontend Engineer)

Status: Founding team
Past Experience: Frontend software engineer for Good Data, City College San Francisco, and Deloitte.
Current Mission: Front-end development for both desktop and mobile for Newcoin OS.

Brian Curran (Full Stack Composability)

Status: Part-time
Past Experience: Co-founded Entropy App, joined Zora as a software engineer.
Current Mission: Developing modules on top of Newcoin OS for content curation and file management via IPFS and IPSP.

Artem Maluga (Blockchain C++ Engineer)

Status: Part-time
Past Experience: Senior C++ and blockchain developer; founded DeFi protocol A-DEX and worked on platforms like RuTube.
Current Mission: Developed Newcoin L1 in C++ and preparing for deployment during the TGE phase.

Madeleine Parker (Growth)

Status: Founder
Past Experience: MSc Data Science/Economics from UCL; 6+ years in web3; ex-KPMG, UN fintech/web3 consultant, luxury brands like David Koma.
Current Mission: Drives product management & user growth, strategy, ecosystem deals, academic relations, and fundraising for growth.

Hugo Hoppmann (Global Designer)

Status: Founder
Past Experience: Art director for brands like Apple, Prada, Nike, and Salomon; ex-032c and Mugler.
Current Mission: Oversees the brand kit, including custom fonts, logos, and UX for Newcoin OS.

Salimi Akill (Tech Ecosystem)

Status: Founding team
Past Experience: Educator and designer from Accra, Ghana to New York; founded Newforum, now the media arm of Newfoundation.
Current Mission: Manages Newforum operations, including interviews with decentralized AI founders and key players.

Framchesko Romanovic (Culture Ecosystem)

Status: Founding team
Past Experience: Central Saint Martins graduate; curator for galleries in Tokyo and Shanghai; involved with Schon magazine, Nylon Japan.
Current Mission: Develops content marketing strategies, research writings, and ecosystem growth focused on contemporary visual arts and web3 culture.

Leopold Haller (AI Research Advisor)

Status: Advisor
Past Experience: Ex-Google Research, co-founder of Agentic AI; pioneer in agents and RL.
Current Mission: Works with Sofiane on refining the protocol to align with machine learning advancements.

Erika Mann (Compliance Advisor)

Status: Advisor
Past Experience: Former European Parliament member; advisor at ICANN, ex-Meta Public Policy.
Current Mission: Guides the team on EU regulations, including MICA and the AI Act.

Tony Wang (Strategy Advisor)

Status: Advisor
Past Experience: Ex-Google, SSENSE; angel investor and advisor to Prada, Sequoia Capital, and Uniswap.
Current Mission: Supports consumer strategy and relations with cultural and technological players.

Scott Moore (Web3 Development Advisor)

Status: Advisor
Past Experience: Co-founder of Gitcoin and Public Works; pioneer in public goods funding through quadratic funding and DAOs.
Current Mission: Supports IPSP and Newcoin adoption within the Ethereum developer ecosystem and investor relations

Go To Market

STRATEGY BY STAGES

Stage 1: Validation of Frontier Knowledge Data (By End of 2024)

Objective: Prove that frontier knowledge producer (FKP) data leads to superior AI outputs through retrieval-augmented generation (RAG) and fine-tuning.
Workshops:
- Guest delivers a keynote presentation.
- Participants attend or watch asynchronously and respond to a quiz.
- One-week contribution cycle starts.
- Participants upload contributions and perform output evaluations via gradual voting.
- Rewards are given based on accumulated points, with top performers receiving USDC prizes.
Key Focus:
- Attract 1,000 knowledge producers and 100,000 knowledge consumers.
Expected Outcomes: Collect data to determine if frontier knowledge offers a measurable advantage in model outputs.

Product sneak peaks from user testing:

AI fashion outputs: https://os.newcoin.org/folder/e6c8eec2-bdfe-06a5-09e6-779d573a232b/gallery
Onboarding: https://www.newcoin.org/prototype/start
Prototype: https://www.newcoin.org/prototype/profile

Stage 2: Scaling Paid Memberships (2025)

Objective: Leverage validated fine-tuning results to scale the platform and attract more knowledge consumers willing to pay for superior insights.
Assumptions:
- Significant portion of consumers will convert to paid subscribers upon seeing superior outputs.
- Test subscription plans ranging from $10-$30/month.
Key Focus:
- Attract 1 million knowledge consumers with a $3 million marketing plan.
- Scale to 10 million paid consumers by end of Stage 2.
Growth Channels:
- Increase quality and quantity of workshops.
- Attract knowledge consumers and AI systems.
Goals:
- Target $1.2 billion ARR by end of Stage 2.

Stage 3: Achieving AGI with Open Learning (Ongoing After 2025)

Objective: Test the assumption that open-source AI pipelines and multi-modal agents collaborating in an open learning environment lead to faster, more generalizable models.
Key Focus:
- Integrate open-source agents and models.
- Build synergies between frontier knowledge producers and open-source AI agents.
Goals:
- Position Newcoin's ecosystem as a key player in the AI arms race.

The Powerful Interplay of Frontier Knowledge and Open Learning

Frontier Knowledge Producers (FKP): Data produced becomes an irreplaceable asset driving value.
Open Learning Protocol: Enhances AI models through continuous collaborative efforts.

Risk Mitigation through Stage-Based Validation

Stage 1: Validates frontier knowledge as valuable.
Stage 2: Commercializes the validated model.
Stage 3: Leverages open-source collaboration.

By progressively validating assumptions and ensuring platform security through StakeNets, Newcoin provides a roadmap for exponential growth while minimizing risks.

STRATEGY BY STAGES

Stage 1: Validation of Frontier Knowledge Data (By End of 2024)

Objective: Prove that frontier knowledge producer (FKP) data leads to superior AI outputs through retrieval-augmented generation (RAG) and fine-tuning.
Workshops:
- Guest delivers a keynote presentation.
- Participants attend or watch asynchronously and respond to a quiz.
- One-week contribution cycle starts.
- Participants upload contributions and perform output evaluations via gradual voting.
- Rewards are given based on accumulated points, with top performers receiving USDC prizes.
Key Focus:
- Attract 1,000 knowledge producers and 100,000 knowledge consumers.
Expected Outcomes: Collect data to determine if frontier knowledge offers a measurable advantage in model outputs.

Product sneak peaks from user testing:

AI fashion outputs: https://os.newcoin.org/folder/e6c8eec2-bdfe-06a5-09e6-779d573a232b/gallery
Onboarding: https://www.newcoin.org/prototype/start
Prototype: https://www.newcoin.org/prototype/profile

Stage 2: Scaling Paid Memberships (2025)

Objective: Leverage validated fine-tuning results to scale the platform and attract more knowledge consumers willing to pay for superior insights.
Assumptions:
- Significant portion of consumers will convert to paid subscribers upon seeing superior outputs.
- Test subscription plans ranging from $10-$30/month.
Key Focus:
- Attract 1 million knowledge consumers with a $3 million marketing plan.
- Scale to 10 million paid consumers by end of Stage 2.
Growth Channels:
- Increase quality and quantity of workshops.
- Attract knowledge consumers and AI systems.
Goals:
- Target $1.2 billion ARR by end of Stage 2.

Stage 3: Achieving AGI with Open Learning (Ongoing After 2025)

Objective: Test the assumption that open-source AI pipelines and multi-modal agents collaborating in an open learning environment lead to faster, more generalizable models.
Key Focus:
- Integrate open-source agents and models.
- Build synergies between frontier knowledge producers and open-source AI agents.
Goals:
- Position Newcoin's ecosystem as a key player in the AI arms race.

The Powerful Interplay of Frontier Knowledge and Open Learning

Frontier Knowledge Producers (FKP): Data produced becomes an irreplaceable asset driving value.
Open Learning Protocol: Enhances AI models through continuous collaborative efforts.

Risk Mitigation through Stage-Based Validation

Stage 1: Validates frontier knowledge as valuable.
Stage 2: Commercializes the validated model.
Stage 3: Leverages open-source collaboration.

By progressively validating assumptions and ensuring platform security through StakeNets, Newcoin provides a roadmap for exponential growth while minimizing risks.

STRATEGY BY STAGES

Stage 1: Validation of Frontier Knowledge Data (By End of 2024)

Objective: Prove that frontier knowledge producer (FKP) data leads to superior AI outputs through retrieval-augmented generation (RAG) and fine-tuning.
Workshops:
- Guest delivers a keynote presentation.
- Participants attend or watch asynchronously and respond to a quiz.
- One-week contribution cycle starts.
- Participants upload contributions and perform output evaluations via gradual voting.
- Rewards are given based on accumulated points, with top performers receiving USDC prizes.
Key Focus:
- Attract 1,000 knowledge producers and 100,000 knowledge consumers.
Expected Outcomes: Collect data to determine if frontier knowledge offers a measurable advantage in model outputs.

Product sneak peaks from user testing:

AI fashion outputs: https://os.newcoin.org/folder/e6c8eec2-bdfe-06a5-09e6-779d573a232b/gallery
Onboarding: https://www.newcoin.org/prototype/start
Prototype: https://www.newcoin.org/prototype/profile

Stage 2: Scaling Paid Memberships (2025)

Objective: Leverage validated fine-tuning results to scale the platform and attract more knowledge consumers willing to pay for superior insights.
Assumptions:
- Significant portion of consumers will convert to paid subscribers upon seeing superior outputs.
- Test subscription plans ranging from $10-$30/month.
Key Focus:
- Attract 1 million knowledge consumers with a $3 million marketing plan.
- Scale to 10 million paid consumers by end of Stage 2.
Growth Channels:
- Increase quality and quantity of workshops.
- Attract knowledge consumers and AI systems.
Goals:
- Target $1.2 billion ARR by end of Stage 2.

Stage 3: Achieving AGI with Open Learning (Ongoing After 2025)

Objective: Test the assumption that open-source AI pipelines and multi-modal agents collaborating in an open learning environment lead to faster, more generalizable models.
Key Focus:
- Integrate open-source agents and models.
- Build synergies between frontier knowledge producers and open-source AI agents.
Goals:
- Position Newcoin's ecosystem as a key player in the AI arms race.

The Powerful Interplay of Frontier Knowledge and Open Learning

Frontier Knowledge Producers (FKP): Data produced becomes an irreplaceable asset driving value.
Open Learning Protocol: Enhances AI models through continuous collaborative efforts.

Risk Mitigation through Stage-Based Validation

Stage 1: Validates frontier knowledge as valuable.
Stage 2: Commercializes the validated model.
Stage 3: Leverages open-source collaboration.

By progressively validating assumptions and ensuring platform security through StakeNets, Newcoin provides a roadmap for exponential growth while minimizing risks.

Economic Flow

Tokenomics and Value Flow

Newcoin's tokenomics represent a unified design supporting network health and fair participant incentivization. The system leverages two key elements: NCO tokens and Watts. As agents accrue Watts and Total Value Locked (TVL) in NCO, the system achieves a probabilistic consensus determining reward pool shares.

Tokenomic Flow:

Value Creation: Agents contribute valuable learning signals and frontier knowledge.
Watts Accrual: Positive feedback leads to Watts accumulation.
TVL Requirement: Agents stake NCO to increase their weight.
Consensus Mechanism: Network consensus is determined based on weighted aggregates.
Reward Pool Formation: Proceeds from subscriptions and API purchases are added to the GNCO pool.
Reward Distribution: Watt holders and liquidity providers receive rewards.
Controlled Withdrawals: Buffer contract restricts withdrawals to ensure stability.
Value Appreciation: As the network grows, utility and demand for NCO increase.
Reinvestment: Rewards can be staked back into the network.

Since Newfoundation works in favor of the Newcoin network, proceeds from paid subscriptions, API calls, and enterprise deals are used to purchase NCO from public markets and add them to the reward pool (minus a 5% management fee).

Private Sales and Token Generation Event

Previous Sales: ~10% of current token supply sold via SAFTs to contributors, raising approximately $2,500,000.
Upcoming Private Sale: 10% of the supply for $5,000,000, targeting institutional purchasers in the DeAI space.
Use of Proceeds: Validate Stages 1 and 2, leading to Stage 3.

Conditions for TGE:

Incorporation of Newfoundation in Switzerland.
8,000,000 Watts distributed to users via the Base blockchain network.
$5,000,000 in the treasury of Newfoundation.

Regulatory Compliance: Issuance under FINMA and compliance with MiCA due to active marketing in Europe. Active communication with market makers, lawyers, and regulators ensures perfect compliance of NCO as a utility token.

Tokenomics and Value Flow

Newcoin's tokenomics represent a unified design supporting network health and fair participant incentivization. The system leverages two key elements: NCO tokens and Watts. As agents accrue Watts and Total Value Locked (TVL) in NCO, the system achieves a probabilistic consensus determining reward pool shares.

Tokenomic Flow:

Value Creation: Agents contribute valuable learning signals and frontier knowledge.
Watts Accrual: Positive feedback leads to Watts accumulation.
TVL Requirement: Agents stake NCO to increase their weight.
Consensus Mechanism: Network consensus is determined based on weighted aggregates.
Reward Pool Formation: Proceeds from subscriptions and API purchases are added to the GNCO pool.
Reward Distribution: Watt holders and liquidity providers receive rewards.
Controlled Withdrawals: Buffer contract restricts withdrawals to ensure stability.
Value Appreciation: As the network grows, utility and demand for NCO increase.
Reinvestment: Rewards can be staked back into the network.

Since Newfoundation works in favor of the Newcoin network, proceeds from paid subscriptions, API calls, and enterprise deals are used to purchase NCO from public markets and add them to the reward pool (minus a 5% management fee).

Private Sales and Token Generation Event

Previous Sales: ~10% of current token supply sold via SAFTs to contributors, raising approximately $2,500,000.
Upcoming Private Sale: 10% of the supply for $5,000,000, targeting institutional purchasers in the DeAI space.
Use of Proceeds: Validate Stages 1 and 2, leading to Stage 3.

Conditions for TGE:

Incorporation of Newfoundation in Switzerland.
8,000,000 Watts distributed to users via the Base blockchain network.
$5,000,000 in the treasury of Newfoundation.

Regulatory Compliance: Issuance under FINMA and compliance with MiCA due to active marketing in Europe. Active communication with market makers, lawyers, and regulators ensures perfect compliance of NCO as a utility token.

Tokenomics and Value Flow

Newcoin's tokenomics represent a unified design supporting network health and fair participant incentivization. The system leverages two key elements: NCO tokens and Watts. As agents accrue Watts and Total Value Locked (TVL) in NCO, the system achieves a probabilistic consensus determining reward pool shares.

Tokenomic Flow:

Value Creation: Agents contribute valuable learning signals and frontier knowledge.
Watts Accrual: Positive feedback leads to Watts accumulation.
TVL Requirement: Agents stake NCO to increase their weight.
Consensus Mechanism: Network consensus is determined based on weighted aggregates.
Reward Pool Formation: Proceeds from subscriptions and API purchases are added to the GNCO pool.
Reward Distribution: Watt holders and liquidity providers receive rewards.
Controlled Withdrawals: Buffer contract restricts withdrawals to ensure stability.
Value Appreciation: As the network grows, utility and demand for NCO increase.
Reinvestment: Rewards can be staked back into the network.

Since Newfoundation works in favor of the Newcoin network, proceeds from paid subscriptions, API calls, and enterprise deals are used to purchase NCO from public markets and add them to the reward pool (minus a 5% management fee).

Private Sales and Token Generation Event

Previous Sales: ~10% of current token supply sold via SAFTs to contributors, raising approximately $2,500,000.
Upcoming Private Sale: 10% of the supply for $5,000,000, targeting institutional purchasers in the DeAI space.
Use of Proceeds: Validate Stages 1 and 2, leading to Stage 3.

Conditions for TGE:

Incorporation of Newfoundation in Switzerland.
8,000,000 Watts distributed to users via the Base blockchain network.
$5,000,000 in the treasury of Newfoundation.

Regulatory Compliance: Issuance under FINMA and compliance with MiCA due to active marketing in Europe. Active communication with market makers, lawyers, and regulators ensures perfect compliance of NCO as a utility token.

Risk Mitigation

Proactive Risk Strategy

Regulatory Risk

Newcoin's regulatory approach is integrated into its foundational strategy, evolving with the platform's growth. Initially, we focus on research and creativity, areas outside high-risk categories defined by regulations like the EU AI Act, allowing greater flexibility while maintaining ethical standards.

We've enlisted expertise like Erika Mann, a former EU regulator and ex-managing director of Meta's Brussels office, to guide regulatory navigation. Our corporate structure is strategically established in jurisdictions with clear frameworks for crypto, data, and copyrights. Leveraging Switzerland's FINMA guidelines, we scale operations confidently, adapting to evolving regulations from a position of strength.

Security and Attack Vectors

Newcoin's security strategy is designed as a learning system that evolves and strengthens over time. We've built a dynamic ecosystem that continuously evaluates and responds to potential threats, aligning with our gradual, intelligent growth strategy.

Our system employs sophisticated algorithms and machine learning techniques to analyze data and user behaviors in real time, assigning dynamic trust scores and value metrics. As we scale, adding more agents and modalities contributes to a nuanced understanding of contribution value. This allows context-aware decisions, penalizing malicious agents and rewarding valuable contributions proportionally.

The scalability of this approach means that as the network grows, so does its ability to discern and mitigate threats, creating a security model that becomes more robust over time. This adaptive, learning-based security strategy effectively manages the complex environment anticipated as Newcoin expands.

Network Effect and Adoption

Our strategy for building network effects is tied to the gradual, focused growth of Newcoin. Rather than aiming for broad, immediate adoption, we target a specific, high-value audience: frontier researchers and innovators in culture and technology. This approach builds a dense, interconnected network from the ground up.

Leveraging partnerships with respected cultural and academic institutions reinforces our network's value and credibility. These partnerships attract our target audience, creating tight-knit communities within the larger Newcoin ecosystem. Our incentive structure evolves alongside this growth, using increasingly sophisticated AI algorithms to measure and reward meaningful interactions.

This creates a virtuous cycle—more valuable contributors join and interact, making the network more attractive to similar high-quality participants. By focusing on the quality and density of connections rather than sheer quantity, we're building a network effect that compounds in value over time, aligning perfectly with our gradual, intelligent growth strategy.

Proactive Risk Strategy

Regulatory Risk

Newcoin's regulatory approach is integrated into its foundational strategy, evolving with the platform's growth. Initially, we focus on research and creativity, areas outside high-risk categories defined by regulations like the EU AI Act, allowing greater flexibility while maintaining ethical standards.

We've enlisted expertise like Erika Mann, a former EU regulator and ex-managing director of Meta's Brussels office, to guide regulatory navigation. Our corporate structure is strategically established in jurisdictions with clear frameworks for crypto, data, and copyrights. Leveraging Switzerland's FINMA guidelines, we scale operations confidently, adapting to evolving regulations from a position of strength.

Security and Attack Vectors

Newcoin's security strategy is designed as a learning system that evolves and strengthens over time. We've built a dynamic ecosystem that continuously evaluates and responds to potential threats, aligning with our gradual, intelligent growth strategy.

Our system employs sophisticated algorithms and machine learning techniques to analyze data and user behaviors in real time, assigning dynamic trust scores and value metrics. As we scale, adding more agents and modalities contributes to a nuanced understanding of contribution value. This allows context-aware decisions, penalizing malicious agents and rewarding valuable contributions proportionally.

The scalability of this approach means that as the network grows, so does its ability to discern and mitigate threats, creating a security model that becomes more robust over time. This adaptive, learning-based security strategy effectively manages the complex environment anticipated as Newcoin expands.

Network Effect and Adoption

Our strategy for building network effects is tied to the gradual, focused growth of Newcoin. Rather than aiming for broad, immediate adoption, we target a specific, high-value audience: frontier researchers and innovators in culture and technology. This approach builds a dense, interconnected network from the ground up.

Leveraging partnerships with respected cultural and academic institutions reinforces our network's value and credibility. These partnerships attract our target audience, creating tight-knit communities within the larger Newcoin ecosystem. Our incentive structure evolves alongside this growth, using increasingly sophisticated AI algorithms to measure and reward meaningful interactions.

This creates a virtuous cycle—more valuable contributors join and interact, making the network more attractive to similar high-quality participants. By focusing on the quality and density of connections rather than sheer quantity, we're building a network effect that compounds in value over time, aligning perfectly with our gradual, intelligent growth strategy.

Proactive Risk Strategy

Regulatory Risk

Newcoin's regulatory approach is integrated into its foundational strategy, evolving with the platform's growth. Initially, we focus on research and creativity, areas outside high-risk categories defined by regulations like the EU AI Act, allowing greater flexibility while maintaining ethical standards.

We've enlisted expertise like Erika Mann, a former EU regulator and ex-managing director of Meta's Brussels office, to guide regulatory navigation. Our corporate structure is strategically established in jurisdictions with clear frameworks for crypto, data, and copyrights. Leveraging Switzerland's FINMA guidelines, we scale operations confidently, adapting to evolving regulations from a position of strength.

Security and Attack Vectors

Newcoin's security strategy is designed as a learning system that evolves and strengthens over time. We've built a dynamic ecosystem that continuously evaluates and responds to potential threats, aligning with our gradual, intelligent growth strategy.

Our system employs sophisticated algorithms and machine learning techniques to analyze data and user behaviors in real time, assigning dynamic trust scores and value metrics. As we scale, adding more agents and modalities contributes to a nuanced understanding of contribution value. This allows context-aware decisions, penalizing malicious agents and rewarding valuable contributions proportionally.

The scalability of this approach means that as the network grows, so does its ability to discern and mitigate threats, creating a security model that becomes more robust over time. This adaptive, learning-based security strategy effectively manages the complex environment anticipated as Newcoin expands.

Network Effect and Adoption

Our strategy for building network effects is tied to the gradual, focused growth of Newcoin. Rather than aiming for broad, immediate adoption, we target a specific, high-value audience: frontier researchers and innovators in culture and technology. This approach builds a dense, interconnected network from the ground up.

Leveraging partnerships with respected cultural and academic institutions reinforces our network's value and credibility. These partnerships attract our target audience, creating tight-knit communities within the larger Newcoin ecosystem. Our incentive structure evolves alongside this growth, using increasingly sophisticated AI algorithms to measure and reward meaningful interactions.

This creates a virtuous cycle—more valuable contributors join and interact, making the network more attractive to similar high-quality participants. By focusing on the quality and density of connections rather than sheer quantity, we're building a network effect that compounds in value over time, aligning perfectly with our gradual, intelligent growth strategy.