I Cubed Litepaper

d/acc-driven open-source modelverse for the world’s ML model developers and AI creators to approach AGI by decentralized intelligence.

0. What is this litepaper?

This document is the Lite Paper for I³, an intelligence-native market and world-building platform for AI.It is designed to continuously evolve — to communicate the core principles of the system, the capabilities of its builders, and the expanding possibilities for human and agent co-creation within the I³ ecosystem.This Lite Paper exists to explain what kind of system I³ is, how it is meant to evolve, and how people and agents can participate in building it.It is written for a public audience: builders, collaborators, and observers who want a clear mental model of the I³ world as it takes shape. Its role is not to lock specifications, but to provide a shared narrative foundation as the system grows.

1. WHAT IS I CUBED?

I Cubed (I³) is a decentralized intelligence market. It turns AI from something controlled by a few platforms into something that can be openly created, combined, used, and rewarded. Instead of treating AI models as closed tools or subscription products, I³ treats intelligence as a market resource that can be priced through real-world usage, composed of contributors, and sustained economically over time.In I³:

• AI models are delivered as services, not locked products

• value is measured by actual usage, not hype or access control

• Contributors are rewarded through transparent ownership and revenue sharing

I³ is not another AI platform or model provider. It is a market-based system where intelligence can be discovered, composed, and allocated without relying on centralized gatekeepers.As AI creation shifts toward multi-model and multi-generation collaboration, I³ provides the economic infrastructure that allows contributors at every level — from base model builders to downstream combiners — to be recognized and rewarded.The long-term vision of I³ is to form the foundation of a global intelligence capital market, where intelligence becomes a public, composable, and economically sustainable resource.

2. The Problem We Are Actually Solving

The core problem we are solving is the structural misalignment of today's AI economy. Built around large-scale centralized foundation models delivered through subscription platforms, it fails to meet the technical realities, economic logic, and ecosystem demands of modern decentralized AI. As applications penetrate real-time agents, specialized workflows, and long-tail domains, large language models are hitting fundamental structural limits. End-users are trapped in expensive fragmented subscription bundles, while creators face legacy payment infrastructure incapable of supporting real-time, usage-based value exchange.This reflects a deeper systemic issue: the existing market was designed for monolithic software services, not for a global multi-model intelligence economy where millions of specialized models must continuously interact, earn, and evolve.

2.1 Technical, Economic, and Ecosystem Limits of the LLM Paradigm

• Technical Limits: Large models are constrained in latency-sensitive environments like real-time interaction, financial trading, and robotic control due to inference speed, memory footprint, and architectural complexity. Their hallucination issues in specialized or high-stakes domains raise reliability concerns, while research shows diminishing returns to model scale.

• Economic Limits: Inference costs for frontier models can be 100 to 1000 times higher than smaller task-specialized models. Users bear these costs through subscription fees typically ranging from $20 to $300 monthly, hindering broad adoption. Concentrated GPU supply among a few cloud providers further inflates costs and reinforces centralized control.

• Ecosystem Limits: No single model can simultaneously serve scientific research, software engineering, creative production, and domain expertise with high fidelity. LLMs underperform in long-tail and niche domains, while centralized training fails to capture distributed expertise, creating persistent supply-demand mismatches.

2.2 The Trust and Verification Gap

In decentralized multi-model environments, economic incentives require enforcement without centralized oversight. Verifying genuine model usage and output value remains a core challenge. Without effective verification, gaming behavior, low-quality submissions, and reward manipulation undermine market trust and sustainability.

2.3 Identity and Attribution Gaps in N-Creation

Modern AI development is inherently compositional. Models are fine-tuned, merged, wrapped, and embedded into workflows, creating chains of derivative contributions called N-creation. Current Web3 systems lack robust provenance and attribution mechanisms to track these relationships at scale. Attribution often reduces to wallet-level ownership, obscuring intellectual lineage and preventing systematic royalty distribution across contributor generations.

2.4 The Failure of Conventional Benchmarking

Traditional AI benchmarks evaluate models in isolation using static offline tasks. This approach fails in multi-model ecosystems because benchmark accuracy doesn't reflect real deployment performance, benchmarks ignore composability and peer compatibility, and static tests provide no feedback loop for iterative improvement or market-based pricing.Consequently, high-performing niche models are systematically undervalued while general-purpose models dominate visibility despite inferior specialized performance.

2.5 The Broken Legacy Payment Stack

Existing financial infrastructure was built for SaaS products, not for an AI economy where millions of models generate micro-royalties in real time. Traditional payment stacks involve multiple intermediaries, resulting in high fees, 7-14 day settlement delays, and significant cross-border friction. Regulatory complexity raises barriers to entry. Without real-time programmable payments, fair compensation for creators, downstream usage tracking, and fine-grained revenue sharing across AI workflows become impossible.These interconnected structural deficiencies spanning technical limits, broken incentives, opaque attribution, flawed evaluation, and outdated payments collectively define the fundamental problem. They prevent the emergence of a scalable, composable, and fair intelligence economy. This is the precise problem the I³ Modelverse is designed to solve.

3. Solution: The I³ Decentralized AI Modelverse

3.1 Overview: The NASDAQ for AI Models

I³ is building the world's first decentralized, market-driven NASDAQ for AI models—the Modelverse. We move beyond closed, monolithic LLM silos by creating a unified economic and technical framework where millions of specialized AI models can coexist, compete, and collaborate.At its core, the Modelverse transforms AI models into two simultaneous realities:

1. Model-as-a-Service (MaaS) Endpoints: Invokable on a pay-per-request basis.

2. Liquid Digital Assets: With tradeable ownership shares.

This dual nature enables real-time, usage-based pricing, compositional innovation, and allows market signals—not centralized gatekeepers—to determine discovery, valuation, and capital allocation. Our solution rests on three foundational pillars:

1. Decentralized Model-as-a-Service Infrastructure

2. AI Models as Liquid, Tradeable Assets

3. Market-Driven Ranking, Pricing & Incentives

• Prompt a picture: A clean, modern pyramid diagram with three distinct layers. The bottom layer is labeled "1. Decentralized MaaS Infrastructure", the middle "2. AI Models as Liquid Assets", and the top "3. Market-Driven Ranking & Pricing". Use a gradient color scheme from stable blue (bottom) to dynamic orange (top). Icons: server stack for layer 1, token/coin for layer 2, trending graph for layer 3.

3.2 Core Features

3.2.1 Model-as-a-Service (MaaS) & On-Chain Execution

The Modelverse operates as a decentralized routing network. Each model is an independent service endpoint. Users' requests are dynamically routed to the most suitable model for a given task, paying per inference via the x402 payment layer, which ensures:

• Fine-grained, per-request pricing

• Instant settlement to model creators

• Transparent, tamper-proof linkage between usage and revenue

This creates a direct feedback loop: model utility translates immediately into economic value, rewarding quality and fostering competition.

• Prompt a picture: An illustration showing dynamic routing. On the left, a "User Request" arrow enters a "Smart Router" cloud. From there, multiple colored arrows fan out to various icons (chat bubble, code bracket, painting palette, molecule, graph) labeled "Specialized Model". All arrows then converge on the right into a "x402 Settlement Layer" block that outputs "Real-Time Royalties". Style: clean lines, network diagram aesthetic.

3.2.2 AI Models as Liquid Assets: The Initial Model Offering (IMO)

In I³, models are not just services—they. They are liquid, tradeable assets. Creators can tokenize ownership of their models through an Initial Model Offering (IMO), selling shares to early supporters while retaining partial ownership and earning ongoing usage royalties.IMO Process Flow:

1. Submission & Valuation: A creator uploads a model and sets an initial token price, guided by a recommendation engine analyzing predicted demand and comparable models.

2. Staking Phase: Users stake funds to reserve ownership shares at the fixed price. An anti-rollback rule allows trading among participants but prevents withdrawal, ensuring committed capital.

3. Open-Sourcing Trigger: Once 51% ownership is staked, the model enters a brief lock period before its weights are released on IPFS under an open-source license.

4. Secondary Market: Post-IMO, shares trade freely on a secondary market, with prices reflecting model utility, demand, and community consensus.

This mechanism aligns incentives: creators get upfront capital and royalties, investors gain exposure to high-potential models, and the ecosystem steadily expands its open-source foundation.

Prompt a picture: A four-stage horizontal flow chart. Stage 1: "1. Submit & Value" with upload and bar chart icons. Stage 2: "2. Staking Phase" with wallet and lock icons. Stage 3: "3. Open-Source at 51%" with a key unlocking an open-source symbol. Stage 4: "4. Secondary Market" with trading graph icons. Connect with clear arrows. Use a cohesive color palette with a highlight (e.g., green) on stage 3.

3.2.3 Verifiable Lineage & Attribution via Watermarking

A trustless multi-model economy requires indisputable proof of origin and contribution. I³'s solution is a cryptographically verifiable watermarking and provenance framework.

• Embedding, Not Appending: A statistically detectable, imperceptible watermark is embedded into the model's outputs (text, image, etc.) during generation.

• On-Chain Scheme Registry: Each watermarking method is registered on-chain with a cryptographic commitment to its detection parameters, ensuring reproducibility and preventing manipulation.

• Signed Attestations: Every generation event is signed by the model's private key and recorded on-chain, binding the watermark to a specific model identity and timestamp.

• Recursive Attribution: Derivative works (fine-tuned models, merged models) maintain and combine watermarks, enabling automatic royalty distribution across an entire lineage graph.

This transforms attribution from a heuristic claim into a verifiable, protocol-level primitive, essential for enforcing "create-to-earn" economics in a compositional AI world.

Prompt a picture: A detailed 2D digital illustration showing recursive watermarking and verifiable model lineage:

• Left: "Base Model A" outputs content embedded with a subtle, semi-transparent watermark pattern labeled "WA" (not appended, imperceptible). Include a small icon for cryptographic signing.

• Center: A "Fine-tune / Merge" process block, showing an arrow from A entering it, labeled with on-chain registry / protocol verification icons.

• Right: "Derivative Model B" outputs content with overlapping watermarks "WA + WB", layered semi-transparently. Include a small timestamp & signature icon.

• Below: An Attribution Graph, showing royalty splits: 70% to A, 30% to B, with arrows tracing contributions back to the original model.

• Style: clean, network-diagram-like, layered semi-transparent patterns for watermarks, subtle cryptography/ledger motifs, color-coded for model lineage (A in blue, B in orange, overlapping area purple).

• Emphasis: trustless, verifiable recursive attribution, highlighting how derivative models preserve and combine watermarks for automated royalty distribution.

3.2.4 Democratic Dynamic Benchmarking

We replace opaque, static benchmarks with a live, market-informed evaluation system that measures what truly matters: real-world utility and collaborative performance.

• Micro-Scores: Individual models are scored based on user ratings, real usage frequency, staking activity, and security signals.

• Macro-Indices: Models are aggregated into vertical indices (e.g., Vision Index, Finance Index), functioning like ETFs to track sector performance and demand.

• Peer-Workflow Compatibility (PWC): The key innovation. This metric evaluates how well a model performs when composed of others in real workflows, measured via on-chain execution logs. It rewards both "sharpshooter" models (high standalone accuracy) and critical "glue" models (essential for orchestration).

This system ensures niche excellence is discovered and valued, breaking the dominance of general-purpose models in visibility rankings.

3.2.5 Democratic Pricing via x402

Pricing is continuous and market-driven. The x402 payment layer facilitates real-time micropayments for inference calls. Model valuation floats based on:

• Usage flow and revenue generation.

• Benchmark scores and community sentiment.

• Ownership market activity.

This creates a self-balancing economy where useful models appreciate, inactive ones depreciate, and value flows directly from users to creators and contributors.

3.3 Platform Architecture: A Unified Stack

The I³ platform integrates all actors into a single, trust-minimized loop:

• Creators: Launch models via IMO, earn royalties via verifiable usage.

• Users: Discover models via democratic benchmarks, pay per use via x402.

• Investors: Provide liquidity, trade model shares, signal market confidence.

• Co-Creators: Remix and extend models, automatically participating in derivative revenue.

The technical stack comprises:

1. Control Layer (On-Chain): Smart contracts for ownership (IMO), royalties, and governance.

2. Service Layer: Orchestration engine for composing multi-model workflows (MCP).

3. Execution Layer: Decentralized GPU network (DePIN) for running inference, with optional TEEs/zk-proofs for verification.

4. Storage Layer: IPFS and decentralized storage for model weights and data.

Prompt a picture: A four-layer architectural diagram. From top to bottom: "Control Layer (On-Chain)" with smart contract icon. "Service Layer (Orchestration)" with workflow icon. "Execution Layer (DePIN)" with GPU cluster icon. "Storage Layer (IPFS)" with storage icon. On the left, an arrow labeled "User/Creator Interactions" flows in. On the right, an arrow labeled "Value Flow (Royalties/Payments)" flows out. Use a consistent, tech-blue color scheme.

The I³ Modelverse directly solves the structural problems of the current AI economy by introducing liquid asset ownership, verifiable on-chain provenance, and market-driven discovery and pricing. It shifts the paradigm from centralized, subscription-based access to a decentralized, composable, and creator-owned intelligence economy. This is not an incremental improvement but a foundational rebuild of how AI is valued, traded, and evolved.

4. Why Web3: The Decentralized Imperative

The I³ Modelverse is more than a technical solution; it is a response to a structural and moral failure in today's AI industry. Web3 is not merely an implementation choice it. It is the only viable foundation for a truly open, fair, and sustainable intelligence economy. This chapter explains why.

4.1 Web3: The Missing Primitives for Create-to-Earn

The vision of “create-to-earn” for AI is built on a simple premise: creators should own the value they generate and be rewarded transparently in real time. The existing Web2 framework is architecturally incapable of delivering this.

• Trapped Value & Opaque Control: In Web2 platforms, earnings are locked within walled gardens. Withdrawals are slow, fee-ridden, and subject to platform discretion. Creators trade their work for “exposure” or non-portable points, not for liquid, ownable assets.

• Centralized Gatekeeping: Visibility, monetization, and even the right to participate are controlled by platform algorithms and corporate policies. This reproduces the digital feudalism of the Web2 era, where a handful of giants capture most of the value.

Web3 introduces the institutional primitives that a creator-first AI economy fundamentally requires:

1. Immutable Property Rights: Tokenized identifiers turn every AI model into a tradable asset with secure, verifiable provenance.

2. Frictionless, Global Liquidity: Ownership shares and micro-royalties can be transferred and settled peer-to-peer across borders in real time.

3. Permissionless Governance (DAOs): Stakeholders—not corporate boards—collectively govern platform evolution, resource allocation, and rule-making.

4. Transparent, Tamper-Proof Ledgers: Every usage event, benchmark result, and royalty payment is recorded on a public ledger, eliminating opaque ranking bias and enabling trustless auditing.

5. Automatic, Multi-Generation Attribution: Smart contracts track lineage and distribute royalties recursively across long chains of derivative work (N-Creation), an impossibility in legacy accounting systems.

In short, Web3 provides the economic and governance operating system that Web2 lacks. It transforms AI from a service you rent into an asset you can own, govern, and build upon.

image[[x1, y1, x2, y2]]Illustration Prompt: Create a modern, clean two-panel infographic contrasting two economic paradigms.Left Panel (Web2 AI Economy):

• Title: “Centralized & Extractive”

• Visual: A dominant, central platform icon (like a tall corporate tower) at the center.

• Arrows point INWARD to the center from smaller, peripheral icons representing “Developers”, “Users”, and “Data”.

• Inside the central platform, show locked dollar signs and a question mark/gear icon for “Opaque Control”.

• Use a cool, impersonal color scheme (e.g., grays, cold blue).

Right Panel (Web3 / I³ Economy):

• Title: “Decentralized & Regenerative”

• Visual: A circular, networked diagram. Place icons for “Creators”, “Users”, “Investors”, and “Co-Creators” at various nodes around the circle.

• Show bidirectional arrows of value flowing between the nodes. Label these flows with terms like “Real-Time Royalties”, “Usage Payments”, “Governance Votes”.

• A small “I³ Protocol” icon can sit in the middle, but the emphasis should be on the peer-to-peer connections and the circular flow.

• Use a warm, active color scheme with gradients (e.g., teal to orange, purple to cyan).

Overall Style: Minimalist, professional, with clear iconography. The contrast between the two models should be immediately visually apparent.

4.2 Decentralization as an Economic and Moral Necessity

Today’s AI landscape is marked by extreme centralization, reminiscent of early industrial monopolies. A small cohort of well-funded entities controls critical resources: compute, data, model distribution, and pricing power. This centralization is not just inefficient, it is structurally hazardous.

• It Stifles Innovation: By raising barriers to entry, it systematically sidelines independent researchers and small teams, whose niche expertise and novel approaches are essential for long-tail progress.

• It Concentrates Risk: Centralized control over foundational AI creates single points of failure—be they technical, governance-related (as seen in OpenAI’s leadership crises), or ethical.

• It Extracts Value Without Fair Return: The current model often extracts data and innovation from a broad community, while concentrating on financial returns and decision-making power within a closed loop.

Decentralization is the necessary corrective. Just as 20th-century economies evolved from robber-baron monopolies toward broader ownership models like public equity, the AI industry must evolve toward a stakeholder-owned model. In this model:

• Contributors are Owners: Developers, data providers, and users are not passive suppliers or consumers; they are economic stakeholders with aligned incentives.

• Value and Voice are Distributed: Governance is transparent, and value flows to those who create it, enabled by protocol-level rules rather than corporate goodwill.

• The System is Regenerative, Not Extractive: Value circulates within and strengthens the ecosystem, funding further innovation in a sustainable loop.

4.3 Rebuilding the Order of the AI Industry

The conversation about AI’s future cannot be limited to safety and capability. It must also address economic rights, access, and equity. The prevailing “digital feudal” structure—where a tech aristocracy sets the terms—is unstable and unjust.For AI to scale as a force for broad, sustainable progress, its economic foundation must be rebuilt. We must redefine how value is created, measured, distributed, and governed.I³ embodies this rebuild. By marrying decentralized technology with market-based incentives, we are constructing an ecosystem where intelligence can flourish as a public, composable, and economically sustainable resource. This is not a marginal improvement on the old system; it is the foundation for a new one, the power to create and benefit from AI is distributed by design.This is why we build on Web3: not to add a feature, but to rewrite the rules.

5. Why I³ Wins (Competitive Edge)

The AI platform space is crowded. Yet, most solutions are built on old paradigms—closed API silos, speculative tokens, or donation-based open source. I³ is different by design. It’s not another wrapper around a large model; it’s the first full-stack intelligence economy, where models, creators, users, and capital are aligned through transparent usage, ownership, and market signals.Here’s what sets I³ apart.

5.1 From Renting to Owning

Other platforms treat AI as a service you rent. You pay per call or per month, but you own nothing, build no equity, and have no stake in the model’s success.In I³, every model is a liquid asset. Through the IMO (Initial Model Offering), you can own a share of the models you believe in—like early-stage equity in a startup. As the model gets used and improves, its value accrues to you, the owner. You’re not a customer here. You’re a stakeholder.

image[[x1, y1, x2, y2]]Illustration Prompt: A clean, two-scene comic-style comparison.Left Scene (Renting):A person hands over coins labeled "Monthly Fee" to a large, faceless "API Cloud".The cloud returns an output (a speech bubble with text/image), but the person walks away with nothing else.Caption: "Rent access. Build nothing."Right Scene (Owning in I³):The same person uses coins to buy a "Model Token" from a transparent, community-run "I³ Market".The token grows in size and gleams as arrows labeled "Usage" and "Community Growth" flow into it.The person now holds the glowing token and also receives a dividend stream of smaller coins.Caption: "Own the asset. Share growth."Style: Friendly, modern vector art. Use a cold, impersonal blue for the left scene, and a warm, vibrant gradient (orange/teal) for the right scene.

5.2 Real Markets, Not Hype Contests

Many "AI platforms" rank models by downloads, GitHub stars, or opaque internal scores. This rewards popularity, not utility.I³ replaces artificial rankings with real market signals. Model quality is determined by:

• Real usage and revenue

• User ratings and satisfaction

• Capital committed through staking

The best models rise to the top because they’re useful, not because they’re trendy. Our democratic benchmarking and Peer-Workflow Compatibility score ensure that even a highly specialized "glue" model gets the visibility—and rewards—it deserves.

5.3 True Create-to-Earn, Not “Exposure”

On most open-source platforms, sharing your work is an act of charity. You get visibility, but no sustainable income.I³ is built for true create-to-earn economics. Creators earn from:

• Real-time royalties every time their model is used

• Ownership sales through the IMO

• Automatic revenue sharing when their model is remixed into derivatives (N-Creation)

This turns AI development from a speculative hobby into a sustainable profession.

image[[x1, y1, x2, y2]]Illustration Prompt: A 3-part value flow diagram showing "Create-to-Earn" in I³.Part 1 (Create): A builder (avatar with tools) places a model (cube labeled "Model A") onto the I³ platform.Part 2 (Earn - Direct): An arrow labeled "Usage" flows from users to the model, generating a stream of coins ("Royalties") back to the builder.Part 3 (Earn - Derivative): Another builder takes "Model A" and combines it with other elements, creating a new "Workflow B". An arrow labeled "Royalty Share" automatically splits a portion of the new workflow's earnings back to the original "Model A" builder.Visual Cue: Show the royalty streams as glowing, connected pathways. Include a small, elegant on-chain ledger icon in the background to signify this is automated and trustless.Style: Isometric or flat 2.5D. Clean, connected, emphasizing the automatic flow of value.

5.4 Built for the Multi-Model Future

Today’s AI isn’t about one model to rule them all. It’s about specialization and composition. Legacy platforms struggle here—they’re built for isolated models.I³ is natively composable. Models are designed to work together in workflows. Our Model Communication Protocol (MCP) and watermark-based lineage tracking mean you can freely combine, fine-tune, and remix models, knowing that every contributor in the chain is automatically recognized and paid.We’re not just hosting models. We’re hosting the relationships between them.

5.5 The Bottom Line

I³ wins because it aligns with the inevitable future of AI: more specialized, more collaborative, and more economically real.

Dimension	Traditional Platforms / Competitors	I³ Modelverse
Creator Incentive	Exposure, donations, or platform wages.	Ownership, usage royalties, & derivative revenue.
Model Discovery	Controlled by platform rankings or popularity.	Driven by real usage, staking, & peer compatibility.
Economic Model	Rent-seeking (subscriptions, API fees).	Stakeholder-aligned (asset appreciation, shared growth).
Composability	Limited, often siloed or unsupported.	Native, with verifiable attribution & automatic royalties.
User Role	Consumer.	User, investor, and co-creator.

The old world locks intelligence inside products. I³ sets intelligence free in a market.

6. Build With Us

I³ is built by its community. This is how you become part of it.Imagine this:

1. You find a model that’s good, but not perfect for your task.

2. You remix it—tweak it, combine it, adapt it with your own data.

3. You share your improved version back to the ecosystem.

4. You earn automatically, every time someone uses your creation, and every time someone builds upon it in the future.

Your creativity and effort are directly translated into ownership and ongoing rewards. This isn’t just sharing code; it’s starting a virtuous cycle where better models lead to more usage, which funds more innovation.

image[[x1, y1, x2, y2]]Illustration Prompt: A simple, compelling 4-step circular flow diagram.

• Step 1 (FIND): A person using a magnifying glass over a grid of different AI model icons.

• Step 2 (REMIX): The person now has a lightbulb above their head. They are connecting two model icons with a plus sign, creating a new, combined icon. A simple “upload data” arrow can be included.

• Step 3 (SHARE): The person hands the new, glowing model icon to a welcoming community (represented by a few diverse, friendly avatars).

• Step 4 (EARN): Coins automatically flow from the community avatars (when they use the model) back to the original creator. A smaller, secondary stream of coins also flows from a future remixer (hinted at in the background) back to the first creator.

• Style: Friendly, almost app-like illustration. Bright, inviting colors. Use clear icons and minimal text. The cycle should feel positive, empowering, and intuitive.

7. What’s Next

The I³ you see today is just the beginning. Our vision is a fully decentralized stack for intelligence—from the models themselves to the hardware they run on, governed by the people who use them.As we grow, you’ll see us:

• Expand how and where models run, integrating with new hardware and decentralized networks.

• Deepen community governance, putting more power and resources directly in the hands of builders.

• Bridge the gap between experimental AI and everyday, reliable applications.

This journey will be shaped by our community. The core principles—ownership, fair rewards, and open composability—will remain our guide.We’re not just building a platform. We’re growing an ecosystem. And you’re invited to help define its future.

image[[x1, y1, x2, y2]]Illustration Prompt: A “vision” image, metaphorical and aspirational.

• Title (Top, subtle but clear): “A Living Intelligence Ecosystem”

• A futuristic, lush rainforest ecosystem symbolizing a decentralized AI economy. Interconnected glowing plants and nodes represent composable AI models, linked by light-like roots and pathways. Diverse builders, creators, and market participants interact naturally within the ecosystem. Subtle integrated text labels appear in the environment: “Composable Models”, “Open Ownership”, “Automatic Rewards”, “Builders”, “Creators”, “Markets”. The rainforest expands toward a bright horizon, suggesting growth, interoperability, and shared value. Elegant, minimal typography, optimistic lighting, high-end digital illustration style.