Ethereum (ETH) co-founder Vitalik Buterin has outlined proposals for restructuring decentralized autonomous organizations (DAOs) in the cryptocurrency ecosystemEthereum (ETH) co-founder Vitalik Buterin has outlined proposals for restructuring decentralized autonomous organizations (DAOs) in the cryptocurrency ecosystem

Ethereum DAOs face overhaul as Vitalik warns token voting has failed

3 min read
Summary
  • Buterin argues most DAOs have devolved into token-controlled treasuries that are inefficient, vulnerable to whales, and far from Ethereum’s original governance vision.​
  • He highlights five core DAO use cases: robust oracles, on-chain dispute resolution, shared “safe lists,” rapid short-term funding, and long-term project maintenance.​
  • Vitalik proposes a convex/concave framework, private ZK voting, AI assistance (not control), and better communication tools to reduce capture, popularity contests, and decision fatigue.

Ethereum (ETH) co-founder Vitalik Buterin has outlined proposals for restructuring decentralized autonomous organizations (DAOs) in the cryptocurrency ecosystem, according to statements published by the developer.

Buterin stated that the Ethereum ecosystem requires more DAOs but argued that current implementations have diverged from the original design goals that informed the network’s development. According to his analysis, contemporary DAOs primarily function as treasuries controlled through token-holder voting mechanisms, a structure he characterized as inefficient and vulnerable to influence by large token holders.

Early Ethereum development incorporated DAOs as code-based systems operating on decentralized networks, intended to manage funds and decisions through automated protocols. The current token-voting model has led some users to question the effectiveness of DAO governance structures, according to Buterin’s statements.

The developer identified several areas where collective decision-making remains necessary for decentralized finance operations. Oracles, which supply external data to blockchain networks, represent a critical component for stablecoins, prediction markets and other DeFi applications, according to the analysis.

Current oracle designs face limitations, Buterin stated. Token-based oracles allow large holders to influence outcomes, particularly on subjective questions. The cost of attacking such systems cannot exceed their market capitalization, creating challenges for protecting large amounts of capital without imposing high fees, according to the assessment. Human-curated oracles reduce some vulnerabilities but compromise decentralization principles.

Additional challenges exist in on-chain dispute resolution for complex smart contracts such as insurance products, where subjective judgment is required. DAOs also maintain shared lists of trusted applications and verified contract addresses, which risk fragmentation without proper coordination mechanisms, according to Buterin.

The developer outlined five core use cases for improved DAO systems: enhanced oracle systems for stablecoins and prediction markets; on-chain dispute resolution for complex smart contracts; shared lists to protect users from fraudulent applications; rapid coordination for short-term community-funded projects; and ongoing maintenance when original development teams discontinue involvement.

Buterin proposed a “convex versus concave” framework for evaluating DAO designs. Concave problems benefit from compromise and averaged inputs, requiring systems resistant to capture and financial attacks. Convex problems reward decisive action and clear direction, where leadership can function effectively with decentralized oversight to prevent abuse, according to the framework.

Privacy emerged as a significant concern, with Buterin stating that lack of privacy can transform governance into popularity contests. Decision fatigue represents another challenge, as frequent voting reduces participation over time, according to the analysis.

The developer identified several technological approaches worth pursuing, including zero-knowledge proofs for private participation; limited deployment of multi-party computation or fully homomorphic encryption; software tools to reduce voting frequency; artificial intelligence systems to assist human judgment; and communication platforms designed for consensus-building.

Buterin cautioned against granting full control to large AI models, stating that AI should support human decision-making either at the organizational level or through user-controlled tools that execute votes on behalf of individuals.

Projects developing new oracle or governance systems should treat such work as a core priority rather than a secondary feature, according to Buterin, who stated this approach is necessary for maintaining decentralization across applications built on the Ethereum network.

Market Opportunity
Ethereum Logo
Ethereum Price(ETH)
$1,903.17
$1,903.17$1,903.17
-2.96%
USD
Ethereum (ETH) Live Price Chart
Disclaimer: The articles reposted on this site are sourced from public platforms and are provided for informational purposes only. They do not necessarily reflect the views of MEXC. All rights remain with the original authors. If you believe any content infringes on third-party rights, please contact [email protected] for removal. MEXC makes no guarantees regarding the accuracy, completeness, or timeliness of the content and is not responsible for any actions taken based on the information provided. The content does not constitute financial, legal, or other professional advice, nor should it be considered a recommendation or endorsement by MEXC.

You May Also Like

Top NYC Book Publishing Companies

Top NYC Book Publishing Companies

New York City has been the epicenter of American publishing for generations, but “NYC publishing” isn’t just one lane. Today’s landscape includes two very different
Share
Techbullion2026/02/06 14:02
Sensorion Announces its Participation in the Association for Research in Otolaryngology ARO 49th Annual Midwinter Meeting

Sensorion Announces its Participation in the Association for Research in Otolaryngology ARO 49th Annual Midwinter Meeting

MONTPELLIER, France–(BUSINESS WIRE)–Regulatory News: Sensorion (FR0012596468 – ALSEN) a pioneering clinical-stage biotechnology company which specializes in the
Share
AI Journal2026/02/06 14:45
AI Crypto Trading Secrets: What They Won’t Tell You About Profits and Pitfalls|9-Figure Media

AI Crypto Trading Secrets: What They Won’t Tell You About Profits and Pitfalls|9-Figure Media

AI crypto trading is everywhere, and every YouTube guru claims their bot mints money while they sleep. Sounds dreamy, right? However, most don’t discuss the full story, the wild profits possible, and the lurking pitfalls. As someone obsessed with the intersection of artificial intelligence and digital assets, let me pull back the curtain on the realities of algorithmic trading in the crypto jungle. Here’s what nobody tells you: 87% of retail traders using automated systems lose money within their first year. The marketing materials show cherry-picked results. The testimonials come from paid affiliates. But here’s the twist. The remaining 13% who succeed aren’t just lucky. They understand something the majority misses entirely. The Reality Behind the Hype The crypto world loves success stories. You’ve probably seen them. “I made $50,000 in three months using this bot.” What they don’t mention? The $200,000 they lost by testing seventeen other systems first. Real talk: most trading algorithms fail because they’re built for perfect market conditions. Crypto markets are anything but perfect. Think about it like this. Would you trust a Formula 1 car to handle rush hour traffic? That’s essentially what most people do with their trading bots. Why Smart Money Uses Crypto AI Tools Differently Professional traders approach crypto AI tools with surgical precision. They don’t expect miracles. They expect consistent, measured results. The difference lies in understanding what these tools actually do well: • Risk management automation • Pattern recognition at scale • Emotional bias elimination • 24/7 market monitoring • Portfolio rebalancing Notice what’s missing from that list? Get-rich-quick schemes. The smartest crypto AI tools focus on protecting capital first. Profits come second. This mindset separates winners from losers. Here’s something interesting. 9-figure media companies track these patterns religiously. They know which crypto AI tools produce sustainable results versus flashy short-term gains. Professional traders using crypto AI tools typically target 15–25% annual returns. Not 500% monthly moonshots. The Startup Connection Most People Ignore AI for startups isn’t just about building the next ChatGPT. Many successful companies use AI to optimize their crypto treasury management. Smart startups integrate crypto AI tools into their financial operations early. They automate routine decisions. They reduce human error. They scale their trading operations without hiring armies of analysts. But here’s where it gets interesting. The best AI for startup applications in crypto aren’t the obvious ones. Consider automated tax reporting. Or real-time compliance monitoring. Or treasury optimization across multiple blockchains. These unsexy applications generate more consistent profits than flashy trading algorithms. AI for startups in the crypto space succeeds when it solves boring problems efficiently. Not when it promises unrealistic returns. The most successful AI for startups implementations focus on operational efficiency. They reduce costs. They minimize risks. They free up human resources for strategic decisions. Learning from Top AI Start-Ups Top AI start-ups in the crypto space share common characteristics. They prioritize transparency over marketing hype. Look at successful top AI start-ups like Chainalysis or Elliptic. They don’t promise easy money. They provide essential infrastructure. The best top AI start-ups focus on solving real problems: • Market data analysis • Security monitoring • Regulatory compliance • Portfolio analytics • Risk assessment These top AI start-ups understand something crucial. Sustainable businesses solve actual problems. They don’t just ride hype cycles. 9-figure media outlets consistently highlight these fundamental companies. They ignore the noise. They focus on substance. Many top AI start-ups actually discourage retail trading. They know the odds. They’ve seen the casualties. Instead, successful top AI start-ups build tools for institutions. Banks. Hedge funds. Companies with proper risk management systems. The Hidden Costs Nobody Discusses Using crypto AI tools costs more than subscription fees. Much more. First, there’s the learning curve. Most people spend months figuring out proper settings. During this time, they’re paying tuition to the market. Second, there’s infrastructure. Reliable crypto AI tools require stable internet, backup systems, and proper security measures. Third, there’s opportunity cost. Time spent tweaking algorithms could be spent learning fundamental analysis. The real cost? Most people using crypto AI tools trade more frequently. Increased trading usually means increased losses. Think about 9-figure media companies again. They understand that technology amplifies existing skills. It doesn’t replace them. Smart Implementation Strategies Successful crypto AI tools users follow specific patterns: • Start with paper trading • Use position sizing rules • Set strict stop losses • Monitor performance weekly • Adjust strategies quarterly They treat crypto AI tools like any other business tool. With respect. With caution. With realistic expectations, startup applications work similarly. They augment human decision-making. They don’t replace it. The most successful AI for startups implementations in crypto involve human oversight at every level. Algorithms suggest. Humans decide. What Actually Works Here’s what separates successful crypto AI tools users from everyone else: They focus on consistency over home runs. They understand that small, regular gains compound better than occasional big wins followed by devastating losses. They apply AI principles to their approach for startups. They iterate quickly. They fail fast. They learn constantly. They study top AI start-ups for inspiration. But they don’t try to replicate their exact strategies. Most importantly, they never risk money they can’t afford to lose. The crypto market will humble anyone. AI doesn’t change this fundamental truth. Your success with crypto AI tools depends more on your discipline than the sophistication of your algorithms. Remember: the house always has an edge. Your job is to find where that edge doesn’t apply. That’s the secret they won’t tell you. AI Crypto Trading Secrets: What They Won’t Tell You About Profits and Pitfalls|9-Figure Media was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story
Share
Medium2025/09/18 23:20