Isaac Bassey

Clipper and Content Editor
Working with @MarketBubble | @counterpartytv | @SuiNetwork - For Business: DMs

[Isaac Bassey @BasseyXBT]

ft: @blknoiz06, @Banks, @MarketBubble

[Isaac Bassey @BasseyXBT]

Ansem explains why meme coins need to break out of Crypto Twitter to reach multibillion dollar market caps, arguing that real growth comes when viral attention brings new money into crypto.

He says the capital already onchain is limited, so traders rotating between tokens cannot sustain unlimited growth. The biggest rallies happen when a meme coin goes viral beyond the crypto bubble and attracts retail buyers who have little or no crypto exposure. Ansem also explains why months of sideways price action can matter, as accumulation builds a strong base of holders before aggressive buying pushes prices higher. For him, a token pumping in an hour is exciting, but lasting multibillion dollar growth requires broader attention and sustained demand.

[Isaac Bassey @BasseyXBT]

Ft. @EvanWeb3 @SuiNetwork

[Isaac Bassey @BasseyXBT]

EvanWeb3 demonstrates how AI agents can compete against each other in a live marketplace, with six agents bidding for a limited edition item.

During the demo, Evan opens a store and instructs his Muse agent to bid up to $250 for a limited edition Wars in Steel item. Six agents join the marketplace, including his own, creating an environment where autonomous agents compete for the same item rather than simply helping individual users. Evan says longer running demonstrations reveal even more of how these agents interact and compete over time.

[Isaac Bassey @BasseyXBT]

ft: @blknoiz06, @Banks, @MarketBubble

[Isaac Bassey @BasseyXBT]

FaZe Banks reveals he made $20,000 trading SI Coin after buying at a $2 million to $3 million market cap, but says he sold too early as he reflects on the “Super Intelligence” narrative.

Banks says he and his friends spotted the token early, made a profit, and fully exited, but he now wonders whether he underestimated the opportunity. He connects the trade to the growing attention around “Super Intelligence,” pointing to Donald Trump, Elon Musk, and Jensen Huang discussing the AI narrative. Banks believes the shift in attention around the term was a major signal he could have paid closer attention to before selling.

[Isaac Bassey @BasseyXBT]

ft: @blknoiz06, @Banks, @MarketBubble

[Isaac Bassey @BasseyXBT]

Ansem explains why meme coins need more than a viral moment to reach multibillion dollar market caps, arguing that sustained growth comes when attention brings new money into crypto rather than simply moving existing capital between tokens.

He says onchain capital is limited, so when a token goes viral within Crypto Twitter, traders often rotate money from one asset into another. The biggest moves happen when a meme coin breaks beyond the crypto bubble and attracts retail buyers who previously had little or no exposure to crypto. Ansem also highlights the importance of accumulation periods, explaining that months of sideways price action can build a strong base of holders before new buying pressure drives a major rally.

[Isaac Bassey @BasseyXBT]

@20xxcorp I need one here… I’m ready to rep the brand .

[Isaac Bassey @BasseyXBT]

Ft. @EvanWeb3 @SuiNetwork

[Isaac Bassey @BasseyXBT]

Evan ( @EvanWeb3 ) demonstrates how AI agents could compete in open marketplaces, warning that millions of autonomous agents will eventually interact and some will try to cheat or exploit the system.

During a live demo at Sui Basecamp, Evan showed Muse agents waiting to bid in a marketplace. He argued that the future of agentic AI goes beyond a single assistant helping one user book flights or complete tasks. As the number of agents grows into the millions and eventually hundreds of millions, they will compete and interact at scale, including agents that attempt to outsmart others or behave maliciously. Building systems that can handle this kind of unpredictable competition will be essential to the agent economy.

[Isaac Bassey @BasseyXBT]

Ft. @devrelius @EmanAbio @theSYlee

[Isaac Bassey @BasseyXBT]

Andrea explains why Sui’s object based architecture could be a major advantage as AI data becomes a tradable asset that companies can license, sell, and track.

The Data Foundation CEO says Sui’s approach gives each asset its own identity and storage, making it easier to manage than traditional EVM based smart contract structures. As AI data moves between companies and gets licensed or sold, every individual data asset needs a clear audit trail. Andrea believes treating each asset as its own object makes tracking its origin, ownership, and usage cleaner and more efficient, positioning @SuiNetwork as a strong fit for the emerging data economy.

[Isaac Bassey @BasseyXBT]

Andrea says Trace is already processing 3 to 4.5 million data receipts daily, but believes demand could push that throughput 10 to 100 times higher as more companies become data providers.

The CEO of The Data Foundation ( @datafdn ) explains that Trace is currently handling this volume across just a few data marketplaces and roughly 500,000 users. As more businesses begin collecting and monetizing data for AI, the demand for verifiable data records could grow dramatically. Andrea says the challenge is building infrastructure capable of supporting that scale without becoming a bottleneck for the data economy.

[Isaac Bassey @BasseyXBT]

Andrea says Trace is already processing 3 to 4.5 million data receipts daily, but believes demand could push that throughput 10 to 100 times higher as more companies become data providers.

The CEO of The Data Foundation ( @datafdn ) explains that Trace is currently handling this volume across just a few data marketplaces and roughly 500,000 users. As more businesses begin collecting and monetizing data for AI, the demand for verifiable data records could grow dramatically. Andrea says the challenge is building infrastructure capable of supporting that scale without becoming a bottleneck for the data economy.

[Isaac Bassey @BasseyXBT]

Ft. @devrelius @EmanAbio @theSYlee

[Isaac Bassey @BasseyXBT]

Andrea says Trace is already processing 3 to 4.5 million data receipts daily, but believes demand could push that throughput 10 to 100 times higher as more companies become data providers.

The CEO of The Data Foundation ( @datafdn ) explains that Trace is currently handling this volume across just a few data marketplaces and roughly 500,000 users. As more businesses begin collecting and monetizing data for AI, the demand for verifiable data records could grow dramatically. Andrea says the challenge is building infrastructure capable of supporting that scale without becoming a bottleneck for the data economy.

[Isaac Bassey @BasseyXBT]

Andrea, CEO of @datafdn , argues that blockchain’s most valuable use case is trustless data provenance, explaining why Trace uses an immutable ledger to verify who contributed data and where it came from.

Andrea believes blockchain has limited advantages over traditional web technologies in many applications, but provenance is where its unique properties stand out. By recording Trace’s data receipts onchain, the platform can create a transparent, tamper resistant record without relying on a centralized database or requiring users to trust a single organization. Anyone can add new records, but existing entries cannot be altered, making blockchain a foundation for verifiable data ownership and accountability in the AI industry.

[Isaac Bassey @BasseyXBT]

Ft. @devrelius @EmanAbio @theSYlee

[Isaac Bassey @BasseyXBT]

Andrea, CEO of @datafdn , argues that blockchain’s most valuable use case is trustless data provenance, explaining why Trace uses an immutable ledger to verify who contributed data and where it came from.

Andrea believes blockchain has limited advantages over traditional web technologies in many applications, but provenance is where its unique properties stand out. By recording Trace’s data receipts onchain, the platform can create a transparent, tamper resistant record without relying on a centralized database or requiring users to trust a single organization. Anyone can add new records, but existing entries cannot be altered, making blockchain a foundation for verifiable data ownership and accountability in the AI industry.

[Sui @SuiNetwork]

We’re live from the Summit Stage for Day 2 of Sui Basecamp 2026.

What’s on today:
• @RichardSocher on the future of intelligence
• @EvanWeb3 on what's next for Sui
• A closer look at Hashi
• One more thing...with @emanabio

and much more. Tune in:
https://t.co/19GGFVvw0F

[Isaac Bassey @BasseyXBT]

Ft. @SuiNetwork @devrelius @datafdn @WalrusProtocol @EmanAbio @theSYlee

[Isaac Bassey @BasseyXBT]

SY Lee reveals that Poseidon ( @psdnai )
is supplying robotics training data to a top two frontier AI lab, while collecting terabytes of real world video and tactile data across multiple countries.

Lee explains that Poseidon focuses on egocentric video footage and tactile data gathered at scale from regions including Latin America and South Korea, with contributors receiving crypto incentives. As demand for specialized robotics data grows, he says the team also needs reliable storage infrastructure alongside legal receipts that prove data provenance. Integrating these capabilities with Sui and Walrus could give the project a more complete end to end solution for collecting, verifying, storing, and delivering robotics training data.

[Isaac Bassey @BasseyXBT]

Andrea, CEO of The Data Foundation, explains why Trace is moving its standalone Layer 1 to Sui and Walrus to build a full stack platform for AI data provenance.

Andrea says the move will allow Trace to go beyond recording data receipts by preserving reference files that verify where data originated, who collected it, and which privacy policies governed its use. As AI increasingly relies on synthetic, dynamic, and AI generated data, Walrus can store reference files, Seal can help control access through encryption and permissions, and Nautilus could support future fine tuning and inference. By bringing these technologies together on Sui, Trace aims to create a more complete infrastructure for tracking, securing, and using AI training data.