Eth Denver 2025

This was my fourth time attending, participating and hosting during Eth Denver. A lot of planning always goes into attending a blockchain conference, and this time I was hosting multiple events. This year, the booths and side events were so well organized and intentional. It looked like a free version of CES in Las Vegas. There were robots, high tech screens and stages, plus content creators galore! They took up literally 50 percent of the floor space, which totally made up for 20,000 people who claimed tickets. 

Meanwhile, I managed to hit the gym and have a few good meals at Guard and Grace Steakhouse and Matsuhisa (Nobu's sister restaurant). I stayed at the Kimpton Monaco Hotel, which actually ended up being a vibe! Any hotel that offers robes and slippers is an added luxury. They didn't have an in-room coffee bar, however they made up for it with their whiskey tastings every day at 6pm. Overall, the restaurant breakfast was fresh and the location was pretty central.  It was nice seeing my close friends, and managed to sneak 1:1 time with a few VV favorites as well. 💛 There were a few hiccups that could have led to a crash out, but I got through it and it didn't take cigarettes. The Boys Club events felt like home. The brunch and Tradwife Prom were my highlights and I even went home with an custom embroidered VV tea towel! Moreover, ya girl arrived back home healthy! 

Here are my notes from Vana, an event organized by Dani Osorio: 

Trust Assumptions in Data Storage

• In choosing a storage provider, Vana operates with no trust assumptions on privacy—trust is placed in ZK proofs.
• Data is encrypted, uploaded via elliptic key, and proof of contribution is verified through a TEE.
• TEE vs. Centralized Companies:
  • Secure enclaves: Intel started development due to security concerns.
  • TEEs as a trust assumption in cloud providers—linked to Eric Snowden revelations?
• Breaking SGX:
  • If a cloud provider is compromised, can they provide proof of secure computing?
  • Governments with significant resources could potentially break TEE security models.

Encrypted VMs & Private Key Security

• If a hack is detected, can assets be moved safely?
• Biggest vulnerabilities in decentralized data?
  • Hardware-level security.
  • TEEs depend on hardware manufacturers like AMD vs. AWS vs. Intel.
  • AWS & Intel introduce additional layers that act as intermediaries in attestation.
  • Tamper-proof software challenges—how does the network persist in an attack?

User Experience & Wallet Security

• Hot wallet vulnerabilities – Users getting rugged.
• Smart contract (SC) risk – If it's not your keys, it's not your wallet.
• Arbitrage calculations & data security risks – How to prevent leaks?

Data Sovereignty & Ownership

Defining Data Sovereignty

• Vana’s perspective: Ownership and control over where data is stored and accessed.
• From a technical level: Home servers vs. cloud servers—who holds the access keys?
• Can users export their data? If yes, they maintain some level of sovereignty.
• The right to be forgotten – Is proven deletion possible?

Google Drive & Data Sharing

• Google actively reads PDFs and targets ads—how can Web3 enable true data ownership?
• "Data exhaust" – Users generate excess, often monetizable, data unknowingly.
• Building bridges & consent – Can decentralized storage resolve these issues?

Future Breakthroughs (5–10 Years)

Key Questions

• LLMs & Data Sovereignty – Will users own their data in AI-driven systems?
• Critique of Crypto Economy – Are Web3 builders too idealistic?
• AI Agents & the Internet – Will personal LLMs act as core assistants, managing personal data?
• Livable Data & User Experience – Can data ownership be frictionless for users?

Innovative Concepts

• "Witness Encryption" – Data encrypted & accessible only to those meeting specific conditions.
• ChatGPT & Data Replication Issues – How can AI models respect self-sovereignty?

Data as an Asset Class

Key Discussion Points from the Panel

• Market Structure for Data Financialization:
  • We've mastered data collection—what’s next?
  • Quality rating & validation—how do we ensure trustworthy data?
  • Building actual products—how do we bridge data into real-world applications?

Early-Stage Examples

• Data DAOs – Incentivized data pooling & community-led initiatives.
• Real-World Use Cases:
  • Tesla Data – Audience member collecting automotive data.
  • Reddit – Selling user-generated data for $6M.

Challenges in the Data Economy

• Scarcity vs. Oversupply – Data is valuable when exclusive, but overwhelming when abundant.
  • Example: YouTube has endless videos, but only a fraction hold real value.
• "Tragedy of the Commons" – When does it make sense to share personal data?
• Separate Audiences – Data users vs. data providers—how to incentivize both?
• Economic Incentives – Most users don’t care about privacy unless there's tangible value.

Physical vs. Digital Economy

• Cost of Operations – The physical world is becoming more expensive, while digital spaces are easier to navigate.
• Luxury vs. Accessibility – Walmart is thriving; Birkin dupes are rising.
• Product-Market Fit – When does privacy become a core selling point?

The Wildest Future Predictions

• Bryan’s Take: Most data in the future will be generated without human input.
• Diminishing Human Commerce – How will AI reshape consumer behaviors?
• Parasocial Celebrity Relationships – AI will redefine engagement with public figures.
• The Hero’s Journey & AI – What used to take a lifetime may now take an hour with AI.