Innovion | Web3 Marketing Agency

Two lessons every Web3 marketer needs to hear right now: 1. Pivot or die - community management paid the bills in 2017. It didn't in 2018. Teams that clung to what worked before got wiped out. 2. Know what never changes - Web3 clients will always need users and funding. Build around that, and the pivot becomes clear.

AI isn't just writing your tweets anymore. It's running your campaigns, moving your budget, and predicting churn before it happens. Great breakdown from Dejan below. How are you using AI in Web3 marketing?👇

Most Web3 marketing feels like an ad. @monad Cards felt like a flex. 💪 Dejan explains the mechanics behind Monad's successful campaign, and how you can build your own distribution network for free.

RWAs grew 200% last year, and in 2026, the game changes completely. Dejan just broke down the 3 sectors - from AI infra to onchain robotics - that are making moves this year. Number 2 is the only real solution to the AWS outages we keep seeing.

Creators are the product on TikTok. Web3 brands win when they stop explaining and start embedding themselves into creator stories.

Tokens don’t create value, they amplify it. Successful projects are shipping real demand first, then using token design to (1) reward impact, (2) align incentives, and (3) keep emissions sustainable.

X didn’t kill crypto marketing, it killed one mechanic that looked like a parallel ad system. The reset is healthy: less vanity volume, more proof-based campaigns (deliverables, signups, conversions, onchain verification). Influence360 is built for that shift.

As this space matures, AI models and agents will become composable components inside smart contracts. Agent swarms will coordinate via token loops - funding, executing, and evolving across ecosystems. That points toward a more decentralized, innovation-friendly AI economy than the one we have today.

With these pieces in place, a new wave of projects is pushing AI model tokenization forward: → @autonolas: enabling users to co-own and monetize autonomous AI agents → @auraonchain: discovery, ranking, and trading of agents → @AlpacaNetworkAI: building a DEX for AI models (Modelz) Others like @virtuals_io, @Pai3Ai, and @oceanprotocol are also exploring adjacent ownership and data-AI primitives.

By 2023- 2024, infrastructure caught up to support these ownership mechanics. L1s and L2s improved scalability and interoperability. Revenue-sharing contracts matured. Oracles and verification techniques - including “optimistic AI” approaches - made onchain AI economics more viable in production.

A major catalyst was the funding gap for open-source AI. Community-driven approaches like Initial Model Offerings (IMOs) emerged, letting developers raise capital while turning token holders into long-term stakeholders in a model’s success.

In the early 2020s, “AI tokens” surged across crypto. Then ChatGPT’s 2022 release made AI mainstream, pulling builder attention toward model economics - not just inference, but who owns the upside. Token designs began centering on usage, governance, and revenue sharing.

One of the earliest experiments came from @SingularityNET, which allowed developers to monetize AI services directly and users to access them via the AGIX token. Projects like @numerai and @Fetch_ai also explored AI rails early - though more focused on infrastructure, coordination, and payments than direct model ownership.

Why tokenize AI models? Because AI development is increasingly concentrated. Tokenization gives communities a way to fund models, coordinate contributors, and share cash flows from adoption - aligning incentives around usage, not just hype.

AI model tokenization treats a model like an onchain primitive. Tokens can represent ownership, governance, and value rights tied to usage. When a model earns inference fees, token holders can share in the upside - similar to a transparent, programmable cap table.

Tokenization first democratized access to RWAs and compute. Now it’s moving into AI - where models can be traded, co-owned, and governed onchain. That shift turns AI from a closed product into a shared economic asset.

Today, large institutions, hedge funds, family offices, and everyday savers use ETFs for crypto exposure. Regulators are now explaining clearer rules, enabling single-asset, index, and yield-focused ETFs, including ETH and SOL products that stake a portion of holdings - a trend tracked closely by @EricBalchunas, @JSeyff, and @Matt_Hougan.

Again, the US followed a slower path. In 2021, the SEC approved the first futures-based Bitcoin ETF (BITO), which became one of the fastest ETF launches ever. In January 2024, after a court ruling, the SEC finally approved spot Bitcoin ETFs. By late 2025, the US also saw spot Solana ETFs, led by @Bitwise’s BSOL.