JIQQYJONES

#HaedalProtocol Analysis 🔥🔥🔥

Don’t just watch liquid staking derivatives (LSDs) on ETH and SOL.

Because what if the real signal is in Sui?

Haedal Protocol just conducted its TGE—and has already captured 37.4% of Sui’s LSD market with $130 million of TVL, 3.19% APY (vs. Sui’s 2.5% average), and approximately 794,000 holders.

Sui’s LSD penetration? Just under 2% of staked tokens.

Ethereum is topping 20%.

Solana’s LSD is 10%.

Haedal Protocol on Sui is closing the gap.

Under the hood, Haedal isn’t just staking—it’s stacked infrastructure:

✅ The Haedal Market Maker (HMM) is a real-time DEX liquidity optimizer that uses oracle pricing

✅ HaeVault is the ultra-narrow LP rebalancer for SUI-USDC, grossing up to 1117% APY and netting 938% after fees

✅ HaeDAO is for governance with veToken incentives, using treasury-compounding logic

Haedal’s network logic is baked into the core protocol. The tech monitors all verification nodes on Sui and dynamically allocates capital—staking to those with the highest APYs and withdrawing from the lowest—ensuring optimal yield performance at all times.

On-chain momentum gives the same energy. Daily volume surged from $6 millio to $32 million in just two months, fee revenue from HMM grew 4x, and haSUI’s annualized return rate climbed from 2.58% to 3.21%.

Haedal isn’t another LSD protocol—it’s the optimizer stack behind Sui’s DeFi layer.

One to watch, if you’re paying attention.

Study the charts 👉

Adtech is the most neglected layer in Web3.

Every project needs users, but far too few are building the infrastructure to actually reach them.

The current state of affairs is that Web3 has no systemized way to drive growth, no rails for ad or marketing campaigns, and no feedback loops. Just vibes, influencers, and hopes to go viral.

Contrast that to Web2, which built a $1 trillion ad machine. Love or hate ad spend, it works—and enables growth teams to measure, target, and iterate.

Web3 wallets are programmable. On-chain behavior is trackable. Users join by opting in. The ingredients for a radically new kind of adtech system are already here, and yet the Web3 ecosystem is still using YouTube influencer tactics from 2014.

Picture this instead—
👉 Web3-native open systems enable user acquisition
👉 User targeting is based on real wallet actions
👉 Marketing campaigns trigger directly from programmable contracts
👉 Campaign measurement is trustless and shared
👉 Incentives align rather than exploit
What you don’t get from Web3 adtech is middlemen, surveillance, or algorithm opacity. Just clean, composable growth loops for on-chain ecosystems.
The team that builds Web3 adtech doesn’t just unlock a new market for ad spend.

Web3 adtech builders create the feedback loop that Web3 lacks.
Web3 adtech builders create the missing growth engine.
Check out: #dat.network

Every major LLM is drinking from the same data trough—Reddit, Wikipedia, Stack Exchange, but the platform owners have begun to catch onto the value of their data, and are making scraping harder and harder.

The result is a shrinking public internet, and a greater proportion of AI slop in what remains. We will not be able to train AGI on the 2025 web. Not only is it too small, the vast amounts of synthetic data skew the distribution of the training set. This will lead to more beige, average answers, and finally to model collapse.

This is the future? A beige slurry of average? Nah.

The real unlock is decentralized data. Not just for privacy, not just for provenance—but also for signal.

To source high-quality, high-entrop data for future training it will be necessary to fine-tune AI models on sovereign, user-owned data vaults.

Models get trained on the weird, the wild, the real. Subcultures. Local languages. Outlier behavior.

These edge cases don’t break the model—they make the model.

What a model knows matters more than how it's built, especially as LLMs commoditize. Data is the new differentiator, and the most valuable data won’t come from the public web—it’ll come from the edges.

Where data is owned, permissioned, and alive.

And here's the kicker—centralized AI models are allergic to messiness. They’re optimized for compliance, not curiosity.

But messiness is where meaning lives. A model trained on DAO governance forums, fringe science subreddits, or voice notes from rural WhatsApp groups understands the world differently. It doesn’t just autocomplete—it contextualizes to produce deeper perspective.

If you're building AI without thinking about where the data comes from, or who controls it, you’re not building intelligence. You’re merely scaling consensus.