Deep Dive
1. @AventusNetwork: Deflationary model goes live 🔥 Bullish
"More Aventus usage = more $AVT burned = more value for holders" – @AventusNetwork (25.2K followers · 189K impressions · 2025-07-23 10:00 UTC)
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What this means: This is bullish for $AVT because the 2.0 upgrade’s fee-burn mechanism directly ties network activity to supply reduction. With 6M circulating tokens, increased appchain transactions could accelerate deflation.
2. @AventusNetwork: Prediction markets test scalability 📈 Bullish
"Truth Network could soon become one of the highest-traffic networks [...] burning $AVT through its fees" – @AventusNetwork (25.2K followers · 213K impressions · 2025-07-24 16:00 UTC)
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What this means: This is bullish as the Truth Network partnership demonstrates real-world utility, potentially driving $AVT burns through prediction market fees while showcasing Layer-3 appchain capabilities.
3. @AventusNetwork: CEX/DEX liquidity expands 💧 Neutral
"@coinbase, @Uniswap [...] all list $AVT!" – @AventusNetwork (25.2K followers · 87K impressions · 2025-06-24 09:10 UTC)
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What this means: Neutral for price but bullish for ecosystem growth – multi-platform availability lowers entry barriers, though $1.65 price remains -17.54% below yearly highs despite recent exchange listings.
4. @AventusNetwork: Holders steer protocol 🧭 Bullish
"$AVT holders shape the future [...] via governance deep dive" – @AventusNetwork (25.2K followers · 102K impressions · 2025-06-23 08:30 UTC)
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What this means: Bullish long-term as decentralized governance aligns incentives – 6 proposals passed in 2023 show active participation, with 10K $AVT (~$16.5K) needed to meet voting quorum thresholds.
Conclusion
The consensus on $AVT is bullish, driven by deflationary tokenomics, enterprise appchain adoption, and empowered governance. While exchange listings broaden access, price action (-2.97% monthly) lags behind network developments. Watch Q3 transaction volumes through the Truth Network integration – sustained burns could test the MVP Workshop’s supply-reduction models under real load.