These changes are in the best interest of the project and will position Cherry AI Robotics as one of the first mover in robotics data infrastructure. Complete documentation on why the team believes these changes are necessary has been provided below.

Rationale

• Robotics is becoming a multi-trillion-dollar industry that depends on specialized data models.

• Our ecosystem already generates valuable user and trading data, which can now be parsed into robotics-ready datasets.

• Consolidating tokens on BSC strengthens liquidity, security, and ecosystem alignment.

Voting Options

• FOR: Approve the expansion into robotics data products and the migration of all tokens to BSC.

• AGAINST: Reject the proposed changes and maintain the current structure.

• ABSTAIN: No opinion expressed.

A full report along with data and analyses on the robotics market is detailed below. This report will help you better understand why this proposal was brought forth and how it can help shape the future of the project.

Appendix

• Robotics Market Overview
• Cherry AI Robotics: Competitor Analysis
• Cherry AI Robotics: Building the Data Layer for Robotics
• $AIBOT is placing a Major Emphasis on BSC
• BSC is Hyper Focused on Utility
• Cherry AI Robotics Has a Major Network of Partners on BSC
• Revenue and Cost Models
• Future Objectives

Robotics Market Overview

The autonomous robotics industry represents one of the fastest-growing frontiers in technology, and is expected to become a $1.8T industry by 2033. According to IMARC, the global autonomous robotics market is led by autonomous vehicles, mobile robots, drones, and defense systems, with each segment demonstrating unique growth dynamics. Together, these technologies are reshaping mobility, logistics, defense, and industrial automation, positioning autonomous robotics as a cornerstone of the future economy.

Market Segments

Autonomous Vehicles (AVs): form the dominant segment, valued at USD 109.0 billion in 2024 and projected to reach USD 1.73 trillion by 2033, reflecting a CAGR of 31.85% (IMARC). AVs—including passenger cars, logistics fleets, and ride-hailing platforms—are driving the largest transformation, underpinned by heavy investment in AI, safety, and regulatory frameworks.

Autonomous Mobile Robots (AMRs): are gaining rapid traction across warehouses, factories, and healthcare environments. The market reached USD 4.08 billion in 2024 and is expected to expand to USD 16.87 billion by 2033, at a CAGR of 15.7%. AMRs are increasingly vital for addressing labor shortages and improving operational efficiency in logistics and manufacturing.

Autonomous Aerial Robots (Small Drones): represent a fast-scaling niche, valued at USD 13.0 billion in 2024, with forecasts reaching USD 37.6 billion by 2033. Growing adoption in agriculture, surveying, security, and last-mile logistics drives this segment, supported by improvements in payload capacity and autonomous navigation systems.

Military Robotics and Autonomous Systems: contribute a significant defense-oriented layer, valued at USD 10.8 billion in 2024 and projected to hit USD 24.6 billion by 2033, at a CAGR of 9.6%. These include unmanned ground and aerial systems, enhancing surveillance, logistics, and combat readiness in increasingly complex security environments.

Regional Dynamics

North America currently leads the adoption of autonomous vehicles and AMRs, driven by strong R&D ecosystems and early commercial deployments. Asia-Pacific, led by China, Japan, and South Korea, dominates manufacturing robotics and is emerging as a key hub for AV testing and commercialization. Europe follows with strong regulatory frameworks and emphasis on safety and sustainability, while emerging markets are accelerating drone and AMR adoption in agriculture and infrastructure.

Investment and Adoption

Investment in autonomous robotics is surging, fueled by capital flowing into AV and drone technologies, alongside AI-enabled logistics platforms. Robotics-as-a-Service (RaaS) models are lowering adoption barriers, enabling scalable and flexible deployment across industries. By 2033, autonomous robots are expected to evolve from specialized tools into collaborative partners, augmenting human productivity and reshaping sectors from healthcare to transportation.

In summary, IMARC forecasts the autonomous robotics market to expand at a rapid pace, led by AVs and supported by AMRs, drones, and military systems. With the AV segment alone projected to exceed USD 1.7 trillion by 2033, autonomous robotics is set to become a transformative force in the global economy.

Data Market In Robotics Economy

The rise of autonomous robotics is inseparable from the rise of data. Robotics is shifting from hardware-driven automation toward data-centric systems, where AI, machine learning, and sensor fusion define autonomy. Autonomous vehicles, mobile robots, drones, and defense systems all depend on vast datasets for perception, navigation, and decision-making, positioning the data layer as the central enabler of growth.

Market Size and Growth

The global robotics market is estimated at $110 billion in 2025, projected to reach US$1.8 trillion by 2035 at a CAGR of 31.85%. Service robotics leads the segments, driven by consumer and healthcare applications, while industrial robotics focuses on manufacturing efficiency.

Neural networks in robotics are heavily reliant on data integration, and is valued at US$23.01 billion in 2025, expected to grow to US$29.6 billion by 2026 and beyond, with a CAGR of approximately 28-29% through 2030. This growth stems from over 1,700 funding rounds and investments surpassing US$1.5 billion in AI-robotics startups.

Key Trends and Segments

• AI and Generative Integration: Physical, analytical, and generative AI are enhancing robotics, enabling natural language processing and real-time data analysis from sensors, akin to a "ChatGPT moment" for the field.
• Human-Machine Collaboration: Cobots and humanoid robots are rising, with collaborative models expected to account for 35% of sales by 2027, leveraging data for safe, adaptive interactions.
• Digital Twins and Sustainability: Virtual simulations using big data reduce downtime and promote eco-friendly designs, with trends in swarm robotics (6.36% growth) and human-robot interaction (14.48% growth).
• Agentic AI and Unstructured Data: AI agents handle complex tasks autonomously, processing unstructured data for broader applications in biotech, manufacturing, and consumer electronics.

Challenges and Opportunities

Data-driven robotics faces several hurdles, including high implementation costs, data quality issues, and integration complexities with legacy systems. Ethical concerns, such as workforce displacement, cybersecurity risks, and data privacy in sensor-heavy environments, pose significant challenges. Opportunities lie in AI advancements overcoming these, like improved actuators and vision AI for affordability, and real-time analytics for ROI maximization. Vast data ingestion and analysis tools present growth in scalable workflows.

Key Players

• Boston Dynamics: Advanced mobility robotics

• ABB & Fanuc: Industrial automation leaders

• NVIDIA, AMD, Intel: AI computing chips powering robotics

• Intuitive Surgical: Pioneer in healthcare robotics

• Tesla: Emerging in humanoid robotics

• AMD: Expanding presence in humanoid tech

• NVIDIA: Leading in AI integration across robotics

Cherry AI Robotics: Competitor Analysis

Cherry AI Robotics is a unique hybrid project that combines Web3-native community infrastructure with AI-driven data modeling for robotics. Unlike most Web3 projects, which tend to focus on decentralized finance, NFTs, or infrastructure protocols, Cherry AI positions itself as a data layer. Its core thesis is that Telegram interaction data, such as: buy bots, raids, community sentiment, and trading flows—can be systematically gathered and transformed into AI models that benefit future robotics systems.

Cherry AI Robotics has no direct competitors but there are quite a few indirect competitors. Almost all of herryAI’s indirect competitors are within the Data DAO and AI niches. Both these niches also focus on gathering, parsing and building data models, but Cherry AI Robotics has numerous advantages over these projects, such as: specialization and built in data funnels.

Cherry AI Robotics VS Data DAOs

Cherry AI Robotics shares surface similarities with data-focused Web3 DAOs such as Ocean Protocol, Numeraire, and Grass, which tokenize datasets for industries like finance and machine learning. Yet these initiatives typically act as open marketplaces where data is aggregated and exchanged, which gives them a huge bottleneck because they need others to provide them with data. These data DAOs have a huge weakness: they provide access to data without owning or even curating the data, and their impact remains confined to digital industries.

Cherry AI, in contrast, is a vertically integrated data engine. It directly captures proprietary Telegram-native behavioral datasets from its own product ecosystem, and channels them into the robotics AI sector. Cherry AI Robotics leverages its ecosystem of Telegram products to combine ownership of the data collection layer with control over application. Cherry AI isn't just a data intermediary but is actually a data originator since it owns the products that gather the data.

Additionally, Cherry AI’s data collection is for a specific niche, Robotics, an arena where data scarcity is the defining constraint. Ultimately, Data DAOs pursue accessibility, whereas Cherry AI Robotics pursues strategic exclusivity and applied intelligence. This positions Cherry AI as the superior model for long-term value creation.

Comparison to AI x Web3 Projects

AI x Web3 infrastructure projects such as Bittensor, Ritual, and Autonolas focus on distributed compute and model marketplaces, enabling developers to train and deploy AI models in decentralized ways. Their advantage lies in network scale, compute efficiency, and incentive alignment. However, these projects share a critical limitation: they lack proprietary data streams. Without exclusive, high-quality datasets, their infrastructures risk becoming pipelines with no little to input.

Cherry AI Robotics, by contrast, is not a distributed compute and data modeling marketplace but a proprietary data engine. It captures unique Telegram-native interaction data and refines it into training assets for robotics AI, a domain where relevant datasets are scarce and highly valuable. This makes Cherry AI’s role both complementary and superior to almost any AI project. While platforms like Bittensor provide the rails, Cherry AI owns the fuel. In doing so, it secures a defensible moat that pure computing projects cannot replicate, ensuring its data becomes indispensable to any AI ecosystem seeking to scale beyond infrastructure.

Strategic Positioning

Cherry AI Robotics’s competitive advantage becomes clear when viewed across these categories. Compared to Data DAOs and Web3 AI projects, Cherry AI owns an ecosystem of products that can constantly supply uniquely valuable behavioral datasets. This essentially makes Cherry AI a data layer that doesn’t need input from 3rd party data providers to survive. This positioning ensures the project is anchored to a cross-industry value proposition. The project effectively translates Web3 activity into training data that can fuel the next generation of robotics AI.

Cherry AI Robotics: Building the Data Layer for Robotics

Cherry AI Robotics is more than just a suite of Telegram tools—it is the foundation of a data ecosystem. Its products serve Web3 communities with trading, engagement, and automation. These same products will evolve into large-scale data pipelines, capturing unique human interaction data that can be transformed into AI models for robotics.

Ecosystem of Products

Trading & Sniping Bots:
Currently used for real-time trading execution, token discovery, and sniping utilities. In the future, they will provide data on how humans make rapid decisions under pressure, offering insights for robotics models that must act in fast-changing environments.

Buy-Bot Alerts & Trackers:
Today, these bots deliver instant Telegram alerts for buys, sells, and whale activity. Over time, they will evolve into data streams on attention, prioritization, and event-driven reactions, helping robots learn how to filter and respond in noisy, signal-rich contexts.

Raid & Community Tools:
At present, they organize raids and amplify community sentiment through coordinated actions. In the long term, they will produce datasets on group coordination, sentiment, and collective behavior, enabling robots to better collaborate with humans in team-like settings.

Data Parsing Tool:
Cherry AI Robotics’ data parsing tool converts raw activity from its Telegram ecosystem into structured, robotics-ready datasets. It captures trading behavior, community sentiment, and interaction patterns, then cleans and organizes them for AI use. The tool ensures high-quality, domain-specific data that directly fuels robotics neural networks.

Data Funnel API: Cherry AI Robotics’ Data Funnel API allows external projects to plug their products directly into Cherry AI’s ecosystem. This allows them to leverage Cherry AI’s Data Parsing Tool to turn their user activity into structured robotics datasets. Projects are incentivized to plug the API into their products through revenue sharing and token rewards. This API creates a steady pipeline of new data streams, and expands Cherry AI Robotics reach beyond its own products.

Strategic Vision

Each product in the Cherry AI Robotics ecosystem is more than a community utility—it is a node in a growing data network. As adoption expands, the datasets will provide unprecedented insight into human intent, coordination, and decision heuristics. By transforming this interaction data into structured AI models, Cherry AI will create the world’s first Web3-native data layer for robotics AI.

$AIBOT is Placing a Major Emphasis on BSC

Cherry AI Robotics is taking a long-term view of its ecosystem growth. The future of $AIBOT is anchored in building real products, sustainable utility, and lasting value creation. By aligning with BSC, we are positioning $AIBOT at the heart of a network that supports innovation, infrastructure, and scalability for serious projects in Web3.

BSC is Hyper Focused on Utility

BSC has become the go-to chain for projects focused on real-world utility. With its deep liquidity, strong developer community, and DeFi-first mindset, BSC provides the perfect environment for $AIBOT to thrive. While some ecosystems lean heavily toward speculative assets, BSC has established itself as the home for tokens that deliver tangible value and use cases.

Cherry AI Robotics Has a Major Network of Partners on BSC

Cherry AI Robotics is already plugged into BSC’s leading platforms. Partnerships with Binance Wallet, Binance Alpha, and PancakeSwap strengthen our reach, visibility, and liquidity. These integrations not only increase adoption but also ensure that $AIBOT is positioned within BSC’s most trusted and widely used tools.

BSC Projects as Data Partners

Cherry AI Robotics is creating a Data funnel API, which will allow any project to seamlessly integrate and become a data funnel for Cherry AI’s data parsing tool. A revenue-sharing model will incentivize partners to utilize this API, creating a win-win for data providers and Cherry AI.

The initial rollout will focus on BSC projects, as they are utility-driven, tech-oriented, and benefit from a large active user base. By targeting this ecosystem first, Cherry AI ensures rapid adoption, strong data inflows, and a foundation for scaling its role as the data layer for robotics.

$AIBOT Consolidation on BSC

To maximize growth and eliminate fragmentation, all $AIBOT tokens will be unlocked and consolidated on the BSC network. This unified approach ensures liquidity is concentrated, the community is aligned, and the project can focus its full attention on building out the robotics data ecosystem without distraction.

Building the data layer for robotics will require broad partnerships. To drive early adoption of Cherry AI’s Data Funnel API, the project will offer token incentives alongside revenue sharing, accelerating partner onboarding and ecosystem growth. This approach will require ample token supply to be readily available.

Solana Bridge Will Always be Available

While $AIBOT’s consolidation is on BSC, the Solana bridge remains in place. This provides long-term flexibility: if expansion onto Solana (or other chains) becomes strategically important in the future, Cherry AI Robotics can seamlessly re-deploy there. For now, the focus is squarely on BSC as the foundation for sustainable growth.

Revenue and Cost Models

The robotics industry is entering a period of accelerated expansion, projected to surpass $1.7 trillion by 2033 with autonomous systems as the dominant growth driver. Within this trajectory, data emerges as the most constrained and valuable input: every autonomous robot requires domain-specific datasets to power its AI models. The market for robotics-specific data and training assets is expected to compound at 30%+ CAGR, creating multi-billion-dollar opportunities for data infrastructure players.

Revenue Model

Cherry AI Robotics generates fees across its entire ecosystem of products, including trading bots, community tools, and engagement platforms. These products not only drive direct revenue through usage and service fees but also provide a continuous stream of behavioral and interaction data, forming the foundation of the company’s value proposition.

The data parsing tool transforms this raw data into structured, robotics-ready datasets. By converting product-generated activity into specialized data assets, Cherry AI Robotics unlocks a second layer of monetization. Data licensing and selling data to data marketplaces, vendors and brokers will become Cherry AI Robotics largest source of revenue. This positions the company as both a product operator and a data infrastructure provider.

The Data Funnel API expands this model further by enabling other BSC projects to contribute data directly into Cherry AI’s pipeline. Through a revenue-sharing structure and token incentives, these partnerships broaden the scope of data inflows. Once parsed, this additional data is monetized as robotics-specific datasets, creating recurring revenues while strengthening network effects across the ecosystem.

Cost Structure

On the infrastructure side, operating a robotics data layer requires significant ongoing investment. Cloud compute and storage costs typically range from $50,000–$100,000 per month depending on usage, with high-intensity parsing and model training pushing expenses higher during peak activity. For example, processing 100 terabytes of behavioral data may cost $30,000+ in cloud storage and bandwidth fees alone, while GPU compute for training robotics-specific AI models can add another $40,000–$60,000 monthly depending on scale. These costs are expected to moderate with efficiencies but remain a core part of the business.

A further critical category is ecosystem incentives, where Cherry AI Robotics allocates tokens and revenue shares to onboard partners into the Data Funnel API. Incentives act as the catalyst for rapid adoption, particularly in the early phases of ecosystem growth. To ensure enough supply is available for incentives all tokens must be unlocked. Even though projects integrated via the API earn a share of data monetization revenues, it needs to be supplemented by token rewards that offset integration costs and attract participation.

While this represents a near-term expense, it creates durable long-term value by expanding Cherry AI’s proprietary data streams and cementing its position as the robotics data layer.

Fundraising and TGE Costs

The project had to commit substantial funds to marketing campaigns, centralized exchange listings, and liquidity provisioning to ensure smooth trading and strong visibility at launch. These activities represented not only an operational necessity but also a strategic investment in brand awareness and accessibility. In total, these fundraising-related costs consumed approximately 5% of total token supply and over $1.5 million USD, highlighting the capital intensity of establishing a tokenized ecosystem from inception.

To build the initial suite of products within the Cherry AI Robotics ecosystem, the project deployed over $1 million USD in development costs. These funds were raised through a seed round, where early participants were allocated 5% of the total token supply. However, due to a developer oversight, 10% of total supply was mistakenly distributed to seed round investors, creating a surplus release of tokens beyond the intended design.

Profitability Outlook

Near-term financials will emphasize ecosystem growth and data acquisition, requiring capital allocation toward incentives and infrastructure. Over time, recurring licensing contracts, API integrations, and robotics enterprise partnerships are expected to yield compounding revenues with improving margins. A mature robotics data layer can position itself as a quasi-monopoly in a niche where data scarcity creates defensibility, driving both high valuations and durable cash flows.

Future Objectives

Cherry AI Robotics has established itself as the first dedicated data layer for robotics, consolidating tokens, building infrastructure, and launching its proprietary Data Funnel API. With a strong foundation in place, the next phase of growth focuses on expanding ecosystems, forging partnerships, and scaling distribution. The following objectives outline the roadmap for positioning Cherry AI Robotics as a cornerstone of the global robotics data economy.

Expansion into Ripple Ecosystem

While Cherry AI Robotics is currently consolidating its foundation on BSC, the project is actively exploring expansion into the Ripple ecosystem. Ripple’s growing network of utility-focused and technology-driven projects makes it an attractive environment for Cherry AI’s Data Funnel API. By integrating into Ripple, Cherry AI can broaden its reach, attract new partners, and align with one of the most utility-centric ecosystems in Web3.

Partnerships with Web2 Companies

A core objective moving forward is bridging Web2 and Web3 by negotiating deals with traditional technology and enterprise firms. By integrating the Data Funnel API into their products, Cherry AI Robotics can capture proprietary behavioral data from Web2 platforms and convert it into robotics-ready datasets. These partnerships will expand the scope and quality of Cherry AI’s data while validating its relevance across industries beyond crypto.

Data Vendor and Marketplace Integration

Cherry AI Robotics is also pursuing partnerships with data vendors and marketplaces to establish a seamless distribution system for robotics datasets. These integrations will make Cherry AI’s parsed data easily sellable and accessible through established data marketplaces, ensuring visibility and expanding customer acquisition channels. This step is critical to scaling revenues and embedding Cherry AI within the broader AI and robotics data economy.

Long-Term Vision

By expanding into Ripple, forging Web2 partnerships, and aligning with data marketplaces, Cherry AI Robotics is building toward its long-term vision as the global data layer for robotics. Each objective strengthens the ecosystem, enhances data inflows, and ensures that Cherry AI’s proprietary datasets remain indispensable to the next generation of autonomous systems.

Resources

https://www.mordorintelligence.com/

https://www.marketsandmarkets.com/

https://www.statista.com/

https://www.imarcgroup.com/

https://statzon.com/

https://www.cognitivemarketresearch.com/

Join Cherry AI Robotics Community

Community Channel: https://t.me/cherrycommunity

Twitter: https://x.com/cherrytgbot

Announcement Channel: https://t.me/Cherrybotannouncements