fire

Hybrid

DeFi

Opinion by: Paige Xu, OpenMind
As the race for the best humanoid robot heats up, with global teams integrating autonomous systems across workflows in healthcare, manufacturing and defense, choosing the best robot for the job is becoming one of the top challenges to solve in robotics. Whether it’s a drone delivering medical supplies, a robot inspecting hazardous sites or an AI agent managing cybersecurity threats, optimal task allocation between human and machine coordination can determine the success or failure of the mission. Poor choices waste resources and increase costs and, in high-stakes environments, can lead to catastrophic outcomes.&nbsp;
Hybrid teams using robots to achieve greater efficiency need tools to ensure the most capable participants complete tasks. That requires understanding the task, the environment and how machines will work with humans. Decentralized finance (DeFi) offers a surprising solution. DeFi’s core<a data-ct-non-breakable="null" href="https://cointelegraph.com/learn/articles/roots-of-defi-artificial-intelligence-big-data-cloud-computing-and-distributed-ledger-technology" rel="noopener noreferrer nofollow" target="null" text="null" title="null"> principles</a> — decentralization, transparency and automation — lay the groundwork for more intelligent, more efficient systems that connect humans and machines. Using tools like auctions, bidding and reputation systems, we can create fairer ways to match the right agents or robots to the right tasks, making collaboration more seamless and effective and tackling significant staff shortages across key industries.
<h2 id="strong-efficiency-through-competition-strong">Efficiency through competition</h2>
Task allocation in robotic and agentic systems is inherently complex. These systems involve multiple agents with varying capabilities, costs and resource requirements. Traditional, centralized approaches to task allocation do not scale well across multiple companies and countries and introduce single points of failure.&nbsp;
Bidding mechanisms offer a market-driven approach to task allocation. Tasks in this context are treated as resources that agents compete to “win” and are allocated based on measurable criteria such as cost, timeliness and quality.&nbsp;
The most common are reverse actions, where sellers compete to offer the lowest price for a service, and maximal extractable value (MEV) auctions. MEV auctions allow “searchers” to bid for their transactions to be included in a block. They do this by offering validators or miners a portion of the value they extract. These auctions often use a second-price model, where the highest bidder wins but only pays the amount of the second-highest bid. This approach encourages honest bidding while keeping the process fair.
Recent: <a data-ct-non-breakable="null" href="https://cointelegraph.com/news/ai-robots-hacked-to-cause-real-world-harm" rel="noopener noreferrer nofollow" target="_self" text="null" title="https://cointelegraph.com/news/ai-robots-hacked-to-cause-real-world-harm">Researchers hack AI-enabled robots</a>
Flashbots took this idea further by introducing private bidding layers. These layers help reduce network congestion and make the system more efficient. By managing limited resources such as block space transparently and effectively, these systems create a strong foundation for decentralized decision-making.
This approach aligns with principles of competition and self-optimization, much like how DeFi platforms optimize liquidity and transaction inclusion through auctions.
<h2 id="strong-a-new-approach-for-robots-and-agents-strong">A new approach for robots and agents</h2>
In systems where thinking machines operate, the concept of auctions is flipped. Instead of bidding to pay for inclusion, machines compete to win tasks by offering the best value. This is called reverse bidding.
When a task is announced, eligible agents evaluate their ability to complete it and submit bids based on cost, time and quality. The system then reviews these bids and assigns the task to the agent (or group of agents) who offers the best balance of efficiency, speed and reliability.
Unlike MEV auctions, where the highest bid wins, reverse bidding focuses on finding the agent who can complete the task most effectively and at the lowest cost. This ensures the process is cost-efficient and performance-driven.
<h2 id="strong-teamwork-and-collaboration-strong">Teamwork and collaboration</h2>
Many tasks are too complex for a single human or machine to handle. For example, to extinguish a fire, a drone might team with a human firefighter and a ground-based robot to complete a mission — the drone handles aerial reconnaissance, the human holds the fire hose, and the robot ensures regular firefighting supplies. In such cases, humans and machines can form dynamic teams, combining their skills to submit joint bids.
Once selected, these teams work together using decentralized communication systems. They share information, coordinate actions, and adapt to real-time changes, ensuring the best possible results. This collaborative approach adds a layer of complexity and efficiency, similar to MEV auctions but tailored to the needs of robotic systems.
Just like in human-only teams, incentives also play a key role. Agents earn reputation points or tokens for successfully completing tasks, which improves their chances of winning future bids. That creates a cycle where agents are motivated to keep improving, driving innovation and competition within the system.
<h2 id="strong-betting-on-bidding-strong">Betting on bidding&nbsp;</h2>
Bidding offers robotics a much-needed, decentralized approach to problem-solving. It removes the need for centralized systems to assign tasks, allowing robots and agents to organize themselves and work together dynamically. By embracing competition, transparency and adaptability, bidding opens up new possibilities for scalable, decentralized collaboration.
The similarities to DeFi are striking. Just as MEV auctions optimize how block space is used, reverse bidding ensures the most capable and cost-effective agents handle tasks. Reverse bidding goes even further, enabling multi-agent teamwork, real-time adaptability and continuous improvement through reputation systems.
By applying the economic ideas of DeFi to the challenges of robotic ecosystems, we can create a future where machines and humans work together seamlessly. These decentralized, trustless systems prioritize efficiency, fairness and innovation, paving the way for a new era of collaboration.
DeFi is about breaking down financial barriers, the free movement of capital and more intelligent resource allocation. Those principles are naturally suited for autonomous agents and robots working in a decentralized ecosystem. This is only the beginning of a new,<a data-ct-non-breakable="null" href="https://cointelegraph.com/news/the-future-of-trust-is-onchain" rel="noopener noreferrer nofollow" target="null" text="null" title="null"> onchain</a> economy where machines and humans work together hand in hand, executing payments, handling tasks and running errands more transparently and efficiently. That is where crypto and artificial general intelligence come together.
Opinion by: Paige Xu, OpenMind
This article is for general information purposes and is not intended to be and should not be taken as legal or investment advice. The views, thoughts, and opinions expressed here are the author’s alone and do not necessarily reflect or represent the views and opinions of Cointelegraph.

DeFi can help us choose the best robots for the job

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