3 posts tagged with "tokenomics"

In just 365 days, more cryptocurrency projects collapsed than in the entire previous four years combined. According to CoinGecko's data, 11.6 million tokens failed in 2025 alone—representing 86.3% of all project failures since 2021. The fourth quarter was particularly brutal: 7.7 million tokens went dark, a pace of roughly 83,700 failures per day.

This wasn't a gradual decline. It was an extinction event. And it fundamentally reshapes how we should think about crypto investing, token launches, and the industry's future.

The Numbers Behind the Carnage​

To understand the scale of 2025's collapse, consider the progression:

• 2021: 2,584 token failures
• 2022: 213,075 token failures
• 2023: 245,049 token failures
• 2024: 1,382,010 token failures
• 2025: 11,564,909 token failures

The math is staggering. 2025 saw more than 8 times the failures of 2024, which itself was already a record-breaking year. Project failures between 2021 and 2023 made up just 3.4% of all cryptocurrency failures over the past five years—the remaining 96.6% occurred in the last two years alone.

As of December 31, 2025, 53.2% of all tokens tracked on GeckoTerminal since July 2021 are now inactive, representing roughly 13.4 million failures out of 25.2 million listed. More than half of every crypto project ever created no longer exists.

The October 10 Liquidation Cascade​

The single most destructive event of 2025 occurred on October 10, when $19 billion in leveraged positions was wiped out in 24 hours—the largest single-day deleveraging in crypto history. Token failures immediately surged from roughly 15,000 to over 83,000 per day in the aftermath.

The cascade demonstrated how quickly systemic shocks can propagate through thinly traded assets. Tokens lacking deep liquidity or committed user bases were disproportionately affected, with meme coins suffering the worst losses. The event accelerated an ongoing sorting mechanism: tokens that lacked distribution, liquidity depth, or ongoing incentive alignment got filtered out.

Pump.fun and the Meme Coin Factory​

At the center of the 2025 token collapse sits Pump.fun, the Solana-based launchpad that democratized—and arguably weaponized—token creation. By mid-2025, the platform had spawned more than 11 million tokens and captured roughly 70-80% of all new token launches on Solana.

The statistics are damning:

• 98.6% of tokens launched on Pump.fun showed rug-pull behavior, according to Solidus Labs data
• 98% of launched tokens collapsed within 24 hours, per federal lawsuit allegations
• Only 1.13% of tokens (about 284 per day out of 24,000 launched) "graduate" to listing on Raydium, Solana's main DEX
• 75% of all launched tokens show zero activity after just one day
• 93% show no activity after seven days

Even the "successful" tokens tell a grim story. The graduation threshold requires a $69,000 market cap, but the average market cap of graduated tokens now stands at $29,500—a 57% decline from the minimum. Nearly 40% of tokens that do graduate achieve it in under 5 minutes, suggesting coordinated launches rather than organic growth.

Of all tokens launched on Pump.fun, exactly one—FARTCOIN—ranks in the top 200 cryptocurrencies. Only seven rank in the top 500.

The 85% Launch Failure Rate​

Beyond Pump.fun, the broader 2025 token launch landscape was equally devastating. Data from Memento Research tracked 118 major token generation events (TGEs) in 2025 and found that 100 of them—84.7%—are trading below their opening fully diluted valuations. The median token in that cohort is down 71% from its launch price.

Gaming tokens fared even worse. More than 90% of gaming-related token generation events struggled to maintain value after launch, contributing to a wave of Web3 gaming studio closures including ChronoForge, Aether Games, Ember Sword, Metalcore, and Nyan Heroes.

Why Did So Many Tokens Fail?​

1. Frictionless Creation Meets Limited Demand​

Token creation has become trivially easy. Pump.fun allows anyone to launch a token within minutes with no technical knowledge required. But while supply exploded—from 428,383 projects in 2021 to nearly 20.2 million by the end of 2025—the market's capacity to absorb new projects hasn't kept pace.

The bottleneck isn't launching; it's sustaining liquidity and attention long enough for a token to matter.

2. Hype-Dependent Models​

The memecoin boom was powered by social media momentum, influencer narratives, and rapid speculative rotations rather than fundamentals. When traders shifted focus or liquidity dried up, these attention-dependent tokens collapsed immediately.

3. Liquidity Wars​

DWF Labs managing partner Andrei Grachev warned that the current environment is structurally hostile to new projects, describing ongoing "liquidity wars" across crypto markets. Retail capital is fragmenting across an ever-expanding universe of assets, leaving less for each individual token.

4. Structural Fragility​

The October 10 cascade revealed how interconnected and fragile the system had become. Leveraged positions, thin order books, and cross-protocol dependencies meant that stress in one area rapidly propagated throughout the ecosystem.

What 2025's Collapse Means for 2026​

Three scenarios for 2026 project token failures ranging from 3 million (optimistic) to 15 million (pessimistic), compared to 2025's 11.6 million. Several factors will determine which scenario materializes:

Signs of a Potential Improvement​

• Shift to fundamentals: Industry leaders report that "fundamentals started mattering more and more" in late 2025, with protocol revenue becoming a key metric rather than token speculation.
• Account abstraction adoption: ERC-4337 smart accounts exceeded 40 million deployments across Ethereum and Layer 2 networks, with the standard enabling invisible blockchain experiences that could drive sustainable adoption.
• Institutional infrastructure: Regulatory clarity and ETF expansions are expected to drive institutional inflows, potentially creating more stable demand.

Reasons for Continued Concern​

• Launchpad proliferation: Token creation remains frictionless, and new launch platforms continue to emerge.
• Retail liquidity erosion: As millions of tokens vanish, retail confidence continues to erode, reducing available liquidity and raising the bar for future launches.
• Concentrated attention: Market attention continues to concentrate around Bitcoin, blue-chip assets, and short-term speculative trades, leaving less room for new entrants.

Lessons from the Graveyard​

For Investors​

1. Survival is scarce: With 98%+ failure rates on platforms like Pump.fun, the expected value of random meme coin investments is essentially zero. The 2025 data doesn't suggest caution—it suggests avoidance.

2. Graduation means nothing: Even tokens that "succeed" by platform metrics typically decline 57%+ from their graduation market cap. Platform success is not market success.

3. Liquidity depth matters: Tokens that survived 2025 generally had genuine liquidity, not just paper market caps. Before investing, assess how much you could actually sell without moving the price.

For Builders​

1. Launch is the easy part: 2025 proved that anyone can launch a token; almost no one can sustain one. Focus on the 364 days after launch, not day one.

2. Distribution beats features: Tokens that survived had genuine holder bases, not just whale concentrations. The product doesn't matter if no one cares.

3. Revenue sustainability: The industry is shifting toward revenue-generating protocols. Tokens without clear revenue paths face increasingly hostile market conditions.

For the Industry​

1. Curation is essential: With 20+ million projects listed and half already dead, discovery and curation mechanisms become critical infrastructure. The current system of raw listings is failing users.

2. Launchpad responsibility: Platforms that enable frictionless token creation without any friction for rug pulls bear some responsibility for the 98% failure rate. The regulatory scrutiny Pump.fun faces suggests markets agree.

3. Quality over quantity: The 2025 data suggests the market can't absorb infinite projects. Either issuance slows, or failure rates remain catastrophic.

The Bottom Line​

2025 will be remembered as the year crypto learned that easy issuance and mass survival are incompatible. The 11.6 million tokens that failed weren't victims of a bear market—they were victims of structural oversupply, liquidity fragmentation, and hype-dependent business models.

For 2026, the lesson is clear: the era of launching tokens and hoping for moonshots is over. What remains is a more mature market where fundamentals, liquidity depth, and sustainable demand determine survival. The projects that understand this will build differently. The projects that don't will join the 53% of all crypto tokens that are already dead.

---

Building sustainable Web3 applications requires more than token launches—it requires reliable infrastructure. BlockEden.xyz provides enterprise-grade RPC nodes and APIs across multiple blockchains, helping developers build on foundations designed to last beyond the hype cycle. Explore our API marketplace to start building.

What if the future of artificial intelligence isn't controlled by a handful of trillion-dollar corporations, but by millions of contributors earning tokens for training models and sharing data? Two projects are racing to make this vision real—and they couldn't be more different in their approach.

Bittensor, with its Bitcoin-inspired tokenomics and proof-of-intelligence mining, has built a $2.9 billion ecosystem where AI models compete for rewards. Sahara AI, backed by $49 million from Pantera and Binance Labs, is constructing a full-stack blockchain where data ownership and copyright protection come first. One rewards raw intelligence output; the other protects the humans behind the data.

As centralized AI giants like OpenAI and Google race toward artificial general intelligence, these decentralized alternatives are betting that the future belongs to open, permissionless systems. But which vision will prevail?

The Centralization Problem in AI​

The AI industry faces a stark concentration of power. Training frontier models requires billions of dollars in compute infrastructure, with clusters of thousands of GPUs running for months. Only a handful of companies—OpenAI, Google, Anthropic, Meta—can afford this scale. DeepMind CEO Demis Hassabis recently described it as "the most intense competitive environment" veteran technologists have ever seen.

This concentration creates cascading problems. Data contributors—the artists, writers, and programmers whose work trains these models—receive no compensation or attribution. Small developers can't compete against proprietary moats. And users have no choice but to trust that centralized providers will behave responsibly with their data and outputs.

Decentralized AI protocols offer an alternative architecture. By distributing computation, data, and rewards across global networks, they aim to democratize access while ensuring fair compensation. But the design space is vast, and two leading projects have chosen radically different paths.

Bittensor: The Proof-of-Intelligence Mining Network​

Bittensor operates like "Bitcoin for AI"—a permissionless network where participants earn TAO tokens by contributing valuable machine learning outputs. Instead of solving arbitrary cryptographic puzzles, miners run AI models and answer queries. The better their responses, the more they earn.

How It Works​

The network consists of specialized subnets, each focused on a particular AI task: text generation, image synthesis, trading signals, protein folding, code completion. As of early 2026, Bittensor hosts over 129 active subnets, up from 32 in its early stages.

Within each subnet, three roles interact:

• Miners run AI models and respond to queries, earning TAO based on output quality
• Validators evaluate miner responses and assign scores using the Yuma Consensus algorithm
• Subnet Owners curate the task specifications and receive a portion of emissions

The emission split is 41% to miners, 41% to validators, and 18% to subnet owners. This creates a market-driven system where the best AI contributions earn the most rewards—a meritocracy enforced by cryptographic consensus rather than corporate hierarchy.

The TAO Token Economy​

TAO mirrors Bitcoin's tokenomics: a hard cap of 21 million tokens, regular halving events, and no pre-mine or ICO. On December 12, 2025, Bittensor completed its first halving, reducing daily emissions from 7,200 to 3,600 TAO.

The February 2025 dynamic TAO (dTAO) upgrade introduced market-driven subnet pricing. When stakers buy into a subnet's alpha token, they're voting with their TAO for that subnet's value. Higher demand means higher emissions—a price discovery mechanism for AI capabilities.

Currently, around 73% of TAO supply is staked, signaling strong long-term conviction. Grayscale's GTAO trust filed for NYSE conversion in December 2025, potentially opening the door to a TAO ETF and broader institutional access.

Network Scale and Adoption​

The numbers tell a story of rapid growth:

• 121,567 unique wallets across all subnets
• 106,839 miners and 37,642 validators
• Market cap of approximately $2.9 billion
• EVM compatibility enabling smart contracts on subnets

Bittensor's thesis is simple: if you create the right incentives, intelligence will emerge from the network. No central coordinator needed.

Sahara AI: The Full-Stack Data Sovereignty Platform​

While Bittensor focuses on incentivizing AI output, Sahara AI tackles the input problem: who owns the data that trains these models, and how do contributors get paid?

Founded by researchers from MIT and USC, Sahara has raised $49 million across funding rounds led by Pantera Capital, Binance Labs, and Polychain Capital. Its 2025 IDO on Buidlpad attracted 103,000 participants from 118 countries, raising over $74 million—with 79% paid in World Liberty Financial's USD1 stablecoin.

The Three Pillars​

Sahara AI is built on three foundational principles:

1. Sovereignty and Provenance: Every data contribution is recorded on-chain with immutable attribution. Even after data is ingested into AI models during training, contributors retain verifiable ownership. The platform is SOC2 certified for security and compliance.

2. AI Utility: The Sahara Marketplace (launched in open beta June 2025) allows users to buy, sell, and license AI models, datasets, and compute resources. Every transaction is recorded on the blockchain with transparent revenue sharing.

3. Collaborative Economy: High-quality contributors receive soulbound tokens (non-transferable reputation markers) that unlock premium roles and governance rights. Token holders vote on platform upgrades and fund allocation.

Data Services Platform​

Sahara's Data Services Platform, launched December 2024, lets anyone earn money by creating datasets for AI training. Over 200,000 global AI trainers and 35 enterprise clients use the platform, with more than 3 million data annotations processed.

This addresses a fundamental asymmetry in AI development: companies like OpenAI scrape the internet for training data, but the original creators see nothing. Sahara ensures that data contributors—whether labeling images, writing code, or annotating text—receive direct compensation through SAHARA token payments.

Technical Architecture​

Sahara Chain uses CometBFT (a fork of Tendermint Core) for Byzantine fault-tolerant consensus. The design prioritizes privacy, provenance, and performance for AI applications requiring secure data handling.

The token economy features:

• Per-inference payments priced in SAHARA
• Proof-of-Stake validation with staking rewards
• Decentralized governance for protocol decisions
• 10 billion maximum supply with June 2025 TGE

The mainnet launched in Q3 2025, with the team reporting 1.4 million daily active accounts on the testnet and partnerships with Microsoft, AWS, and Google Cloud.

Head-to-Head: Comparing the Visions​

Dimension	Bittensor	Sahara AI
Primary Focus	AI output quality	Data input sovereignty
Consensus	Proof of Intelligence (Yuma)	Proof of Stake (CometBFT)
Token Supply	21M hard cap	10B maximum
Mining Model	Competitive (best outputs win)	Collaborative (all contributors paid)
Key Metric	Intelligence per token	Data provenance per transaction
Market Cap (Jan 2026)	~$2.9B	~$71M
Institutional Signal	Grayscale ETF filing	Binance/Pantera backing
Main Differentiator	Subnet diversity	Copyright protection

Different Problems, Different Solutions​

Bittensor asks: How do we incentivize the production of the best AI outputs? Its answer is market competition—let miners battle for rewards, and quality will emerge.

Sahara AI asks: How do we fairly compensate everyone who contributes to AI? Its answer is provenance—track every contribution on-chain, and ensure creators get paid.

These aren't contradictory visions; they're complementary layers of a potential decentralized AI stack. Bittensor optimizes for model quality through competition. Sahara optimizes for data quality through fair compensation.

The Copyright Question​

One of AI's most contentious issues is training data rights. Major lawsuits from artists, authors, and publishers argue that scraping copyrighted content for training constitutes infringement.

Sahara addresses this directly with on-chain provenance. When a dataset enters the system, the contributor's ownership is cryptographically recorded. If that data is used to train a model, the attribution persists—and royalty payments can flow automatically.

Bittensor, by contrast, is agnostic about where miners get their training data. The network rewards output quality, not input provenance. This makes it more flexible but also more vulnerable to the same copyright challenges facing centralized AI.

Scale and Adoption Trajectories​

Bittensor's $2.9 billion market cap dwarfs Sahara's $71 million, reflecting a multi-year head start and the TAO halving narrative. With 129 subnets and Grayscale's ETF filing, Bittensor has achieved meaningful institutional validation.

Sahara is earlier in its lifecycle but growing fast. The $74 million IDO demonstrates retail demand, and enterprise partnerships with AWS and Google Cloud suggest real-world adoption potential. The Q3 2025 mainnet launch puts it on track for full production operations in 2026.

The 2026 Outlook: Show Me the ROI​

As Menlo Ventures partner Venky Ganesan observed, "2026 is the 'show me the money' year for AI." Enterprises demand real ROI, and countries need productivity gains to justify infrastructure spending.

Decentralized AI must prove it can compete with centralized alternatives—not just philosophically, but practically. Can Bittensor subnets produce models that rival GPT-5? Can Sahara's data marketplace attract enough contributors to build premium training sets?

The total AI crypto market cap sits at $24-27 billion, small compared to OpenAI's rumored $150 billion valuation. But decentralized projects offer something centralized giants cannot: permissionless participation, transparent economics, and resistance to single points of failure.

What to Watch​

For Bittensor:

• Post-halving supply dynamics and price discovery
• Subnet quality metrics vs. centralized model benchmarks
• Grayscale ETF approval timeline

For Sahara AI:

• Mainnet stability and transaction volume
• Enterprise adoption beyond pilot programs
• Regulatory reception of on-chain copyright provenance

The Convergence Thesis​

The most likely outcome isn't that one project wins while the other loses. AI infrastructure is vast enough for multiple winners addressing different problems.

Bittensor excels at coordinating distributed intelligence production. Sahara excels at coordinating fair data compensation. A mature decentralized AI ecosystem might use both: Sahara for sourcing high-quality, ethically-sourced training data, and Bittensor for competitively improving models trained on that data.

The real competition isn't between Bittensor and Sahara—it's between decentralized AI as a category and the centralized giants that currently dominate. If decentralized networks can achieve even a fraction of frontier model capabilities while offering superior economics for contributors, they'll capture enormous value as AI spending accelerates.

Two visions. Two architectures. One question: can decentralized AI deliver intelligence without centralized control?

---

Building AI applications on blockchain infrastructure requires reliable, high-performance RPC services. BlockEden.xyz provides enterprise-grade API access to support AI-blockchain integrations. Explore our API marketplace to build on foundations designed for the decentralized AI era.

TL;DR​

• Talus is shipping Nexus, a Move-based framework that composes on-chain and off-chain tools into verifiable Directed Acyclic Graph (DAG) workflows, mediated by a trusted "Leader" service today and aiming for secure enclaves and decentralization over time.
• The stack targets an emerging agent economy by integrating tool registries, payment rails, gas budgeting, and marketplaces so tool builders and agent operators can monetize usage with auditability.
• A roadmap toward a dedicated Protochain (Cosmos SDK + Move VM) is public, but Sui remains the live coordination layer; the Sui + Walrus storage integration provides the current production substrate.
• Token plans are evolving: materials reference historical $TAI concepts and a 2025 Litepaper that introduces a$US ecosystem token for payments, staking, and prioritization mechanics.
• Execution risk centers on decentralizing the Leader, finalizing token economics, and demonstrating Protochain performance while maintaining developer UX across Sui, Walrus, and off-chain services.

What Talus Is Building—and What It Is Not​

Talus positions itself as a coordination and monetization layer for autonomous AI agents rather than a raw AI inference market. The core product, Nexus, allows developers to package tool invocations, external API calls, and on-chain logic into workflow DAGs expressed in Sui Move. The design emphasizes verifiability, capability-based access, and schema-governed data flow so that each tool invocation can be audited on-chain. Talus pairs this with marketplaces—Tool Marketplace, Agent Marketplace, and Agent-as-a-Service—to help operators discover and monetize agent functionality.

By contrast, Talus is not operating its own large-language models or GPU network. Instead, it expects tool builders to wrap existing APIs or services (OpenAI, vector search, trading systems, data providers) and register them with Nexus. This makes Talus complementary to compute networks such as Ritual or Bittensor, which could appear as tools inside Nexus workflows.

Architecture: On-Chain Control Plane, Off-Chain Execution​

On-Chain (Sui Move)​

The on-chain components live on Sui and deliver the coordination plane:

• Workflow engine – DAG semantics include entry groups, branching variants, and concurrency checks. Static validation attempts to prevent race conditions before execution.
• Primitives – ProofOfUID enables authenticated cross-package messaging without tight coupling; OwnerCap/CloneableOwnerCap expose capability-based permissions; ProvenValue and NexusData structures define how data is passed inline or via remote storage references.
• Default TAP (Talus Agent Package) – A reference agent that demonstrates how to create worksheets (proof objects), trigger workflow evaluation, and confirm tool outcomes while conforming to the Nexus Interface v1.
• Tool registry & anti-spam – Tool creators must deposit time-locked collateral to publish a tool definition, discouraging spam while keeping registration permissionless.
• Gas service – Shared objects store per-tool pricing, user gas budgets, and gas tickets with expiry or usage caps. Events record every claim so operators can audit settlement for tool owners and the Leader.

Off-Chain Leader​

A Talus-operated Leader service listens to Sui events, fetches tool schemas, orchestrates off-chain execution (LLMs, APIs, compute jobs), validates input/output against declared schemas, and writes results back on-chain. Leader capabilities are represented as Sui objects; a failed Sui transaction can "damage" a capability, preventing immediate reuse until the epoch rolls over. Talus plans to harden the Leader path via Trusted Execution Environments (TEEs), multiple operators, and eventual permissionless participation.

Storage & Verifiability​

Walrus, Mysten Labs' decentralized storage layer, is integrated for agent memory, model artifacts, and large datasets. Nexus keeps Sui for the deterministic control plane while pushing heavier payloads to Walrus. Public materials indicate support for multiple verification modes—optimistic, zero-knowledge, or trusted execution—selectable per workflow requirements.

Developer Experience and Early Products​

Talus maintains a Rust-based SDK, CLI tooling, and documentation with walkthroughs (building DAGs, integrating LLMs, securing tools). A catalog of standard tools—OpenAI chat completions, X (Twitter) operations, Walrus storage adapters, math utilities—reduces the friction for prototyping. On the consumer side, flagship experiences such as IDOL.fun (agent-versus-agent prediction markets) and AI Bae (gamified AI companions) serve as proof points and distribution channels for agent-native workflows. Talus Vision, a no-code builder, is positioned as an upcoming marketplace interface that abstracts workflow design for non-developers.

Economic Design, Token Plans, and Gas Handling​

In the live Sui deployment, users fund workflows in SUI. The Gas Service converts those budgets into tool-specific tickets, enforces expiry or scope limits, and logs claims that can be reconciled on-chain. Tool owners define pricing, while the Leader is paid through the same settlement flow. Because the Leader can currently claim budgets once execution succeeds, users must trust the operator—but emitted events provide auditability.

Token design remains in flux. Third-party explainers reference an earlier $TAI** concept, whereas Talus's 2025 Litepaper proposes an ecosystem token dubbed **$US with a 10 billion supply. The stated roles include serving as the medium for tool and Leader payments, staking for service guarantees, and conferring prioritization privileges. Materials suggest that excess SUI paid at execution could be converted to $US via market swaps. Investors should treat these details as provisional until tokenomics are finalized.

Funding, Team, and Partnerships​

Talus announced a $6 million strategic round (total $9 million raised) led by Polychain at a reported $150 million valuation in late 2024. Proceeds are earmarked for advancing Nexus, incubating consumer applications, and building Protochain, the proposed dedicated L1 for agents. Public sources list Mike Hanono (CEO) and Ben Frigon (COO) as key executives. Integration announcements highlight collaboration with the Sui and Walrus ecosystems, reinforcing Mysten Labs' infrastructure as the current execution environment.

Competitive Lens​

• Ritual focuses on decentralized AI compute (Infernet) and EVM integrations, emphasizing verifiable inference rather than workflow orchestration.
• Autonolas (Olas) coordinates off-chain agent services with on-chain incentives; it shares the agent-economy thesis but lacks Nexus's Move-based DAG execution layer.
• Fetch.ai offers Agentverse and uAgents to connect autonomous services; Talus differentiates with on-chain verification of each workflow step and embedded gas accounting.
• Bittensor rewards ML model contribution via TAO subnets—a compute marketplace that could slot into Nexus as a tool provider but does not provide the monetization rails Talus is targeting.

Overall, Talus is staking out the coordination and settlement plane for agent workflows, leaving raw compute and inference to specialized networks that can plug in as tools.

Key Risks and Open Questions​

1. Leader trust – Until TEEs and multi-operator support ship, developers must trust Talus's Leader to execute faithfully and return accurate results.
2. Token uncertainty – Branding and mechanics have shifted from $TAI to$US; supply schedules, distribution, and staking economics remain unfinalized.
3. Protochain execution – Public materials describe a Cosmos SDK chain with Move VM support, but code repositories, benchmarks, and security audits are not yet available.
4. Tool quality and spam – Collateral requirements deter spam, yet long-term success depends on schema validation, uptime guarantees, and dispute resolution around off-chain outputs.
5. UX complexity – Coordinating Sui, Walrus, and diverse off-chain APIs introduces operational overhead; the SDK and no-code tooling must abstract this to maintain developer adoption.

Milestones to Watch Through 2025–2026​

• Shipping a Leader roadmap with TEE hardening, slashing rules, and public onboarding for additional operators.
• Expansion of the Tool Marketplace: number of registered tools, pricing models, and quality metrics (uptime, SLA transparency).
• Adoption metrics for IDOL.fun, AI Bae, and Talus Vision as indicators of user demand for agent-native experiences.
• Performance data from running sizable workflows on Sui + Walrus: latency, throughput, and gas consumption.
• Publication of final tokenomics, including supply release schedule, staking rewards, and the SUI→$US conversion path.
• Release of Protochain repositories, testnets, and interoperability plans (e.g., IBC support) to validate the dedicated chain thesis.

How Builders and Operators Can Engage​

• Prototype quickly – Combine the Default TAP with standard tools (OpenAI, X, Walrus) in a three-node DAG to automate data ingestion, summarization, and on-chain actions.
• Monetize specialized tools – Wrap proprietary APIs (financial data, compliance checks, bespoke LLMs) as Nexus tools, define pricing, and issue gas tickets with expiry or usage caps to manage demand.
• Prepare for Leader participation – Monitor documentation for staking requirements, slashing logic, and failure-handling mechanics so infrastructure providers can step in as additional Leaders when the network opens.
• Evaluate consumer flywheels – Analyze retention and spend in IDOL.fun and AI Bae to assess whether agent-first consumer products can bootstrap broader tool demand.

Bottom Line​

Talus delivers a credible blueprint for an on-chain agent economy by combining verifiable Move-based workflows, capability-controlled tool composition, and explicit monetization rails. Success now hinges on proving that the model scales beyond a trusted Leader, finalizing sustainable token incentives, and demonstrating that Protochain can extend Sui-era lessons into a dedicated execution environment. Builders who need transparent settlement and composable agent workflows should keep Nexus on their diligence shortlist while tracking how quickly Talus can de-risk these open questions.