Latest news with #trustless
Malay Mail
11 hours ago
- Malay Mail
Is a trustless society the future? — Noor Ismawati Jaafar
JUNE 8 — In today's fast-moving world, a strange new term is now making waves: 'trustless society'. At first glance, it sounds like a dystopian nightmare which refers to a world where no one trusts anyone. But the truth is more complicated and, in some ways, already a part of our lives. A trustless society doesn't mean people are more dishonest. In fact, it means we are beginning to depend on systems and technology that don't require trust at all. Instead of trusting each other, we trust machines, apps, and coded rules to keep everything fair and square. Trustless doesn't mean untrustworthy In traditional societies, trust is a key part of peoples' lives. We trust our neighbours, our teachers, and our communities. Trust makes us feel safe, belong and connected. But as the world becomes more digitalised and automated, many of our interactions no longer depend on trust between people. We depend so much on machines and computers. And that's where trustless systems come in. These systems are designed so that transactions and interactions can happen without personal trust. They rely on clear rules, automatic processes, and transparent data. You don't have to worry whether the other person is honest because the system will take care of everything. It sounds great and convincing right? And believe it or not, we're already surrounded by trustless systems. Here are just a few examples: Online shopping: We don't personally know the sellers, but we rely on platforms like Shopee or Lazada, backed by secure payment systems, buyer protection policies, and customer reviews. Ride-hailing apps: You don't know your driver, and they don't know you — but both parties trust the app (like Grab) to handle the process fairly and safely. Cryptocurrency & blockchain: Bitcoin and other digital currencies are based on blockchain technology. This system records every transaction in a public ledger, making it nearly impossible to cheat or change the data. All of these examples remove the need for personal trust and replace it with system reliability. In Malaysia, we're also seeing signs of a shift toward a trustless society, especially with the rise of cashless payments, e-wallets and digital public services. — Picture by Hari Anggara The good and the bad There are some major benefits to this new way of doing things: Less room for corruption: Automated systems follow rules strictly. They don't play favourites or accept bribes. Faster processes: Trustless systems remove delays caused by paperwork, middlemen, or negotiations. Global reach: You can do business with someone across the world without ever meeting them because the system handles everything. This kind of efficiency has huge value in a fast-paced, global economy. But as we rely more on trustless systems, there's a danger of losing something important: human connection. In a fully trustless society, people become more like users than neighbours. Instead of building relationships, we build profiles. Instead of offering a handshake, we scan a QR code. Everything becomes about the transaction not the trust behind it. And while machines and computers may be fair and fast, they can't be understanding or forgiving. A system can't show empathy if you miss a payment. It can't recognise good intentions when a mistake happens. It follows rules, nothing more. In some cases, this can lead to cold and rigid outcomes, especially for people in difficult situations. Malaysia's cashless and digital governance In Malaysia, we're also seeing signs of a shift toward a trustless society, especially with the rise of cashless payments, e-wallets, and digital public services. For example, apps like Touch 'n Go eWallet, GrabPay, and Boost have made it easy for people to buy groceries, pay bills, ride public transport, and even donate to charity, without carrying a single ringgit in their pockets. You don't need to trust the hawker stall uncle or the parking attendant you just scan the QR code, and the system handles the rest. Similarly, the MySejahtera app during the Covid-19 pandemic was a clear example of digital governance. It allowed the government to trace contacts, manage vaccine appointments, and monitor health status through automation, minimising the need for face-to-face checks or personal trust in reporting. Even JPJ's MySikap system and KWSP's i-Akaun now allow Malaysians to manage road tax, EPF contributions, and withdrawals online, reducing human involvement and relying instead on automated rules and systems. These tools increase efficiency and transparency, but they also raise questions about privacy, data security, and whether we are trading away human interaction for convenience. It's a powerful reminder that while technology helps us do more, it shouldn't replace the values that hold our society together. Let's be clear: there's nothing inherently wrong with using systems that help us avoid fraud, speed things up, or simplify life. But we must also ask: At what cost? If we depend too much on systems, we may lose the skills and values that come from personal trust: patience, forgiveness, loyalty, and kindness. These are things that no app or algorithm can replace. For example, a community that helps each other during hard times isn't built by rules, it's built by people who care and trust one another. The way forward for us The key isn't to reject technology or trustless systems but to balance them with the human side of society. We can use trustless systems for what they do best: securing transactions, protecting data, enforcing fairness. But we should also invest in relationships, build strong communities, and teach values like honesty and empathy. A world run by code may be efficient, but a world run by compassion is what truly makes life worth living. As we move toward a more digital future, the idea of a trustless society will continue to grow. But we shouldn't let technology replace the human heart of our communities. After all, no system, no matter how smart can hug a child, help a friend, or offer a second chance. Let's build a future where trustless systems support us but where real trust still brings us together. * Prof. Dr. Noor Ismawati Jaafar is a Professor in Information Systems at the Department of Decision Science, Faculty of Business and Economics, Universiti Malaya, and may be reached at [email protected] ** This is the personal opinion of the writer or publication and does not necessarily represent the views of Malay Mail.
Forbes
09-05-2025
- Business
- Forbes
Decentralized AI: Moving Beyond Big Tech's Walled Gardens
Artificial intelligence is evolving rapidly, but the narrative is dominated by a few Big Tech players. While OpenAI, Google, and Meta make headlines, a quieter, potentially more fundamental shift is underway: the move toward decentralized AI (DeAI). This isn't just about new algorithms; it's a reaction against centralized control. Users are growing wary of opaque systems, hidden data agendas, and the power concentrated in a few hands – but escaping these walled gardens requires rebuilding AI's foundations. Yet, several projects are tackling these challenges head-on, laying the groundwork that could redefine AI's role. Understanding this evolution is critical for anyone building or investing in the decentralized space because the next wave of AI innovation hinges on getting these alternative foundations right. What Makes Decentralized AI Different? Deploying AI in a trustless, decentralized environment fundamentally changes the game. Every inference might need cryptographic verification. Data access often involves navigating complex blockchain indexing. And unlike centralized giants, DeAI projects can't simply autoscale resources on AWS or Google Cloud when computational demand spikes – not without compromising their core principles. Consider a DeAI model for community governance. It must interact with smart contracts, potentially cross-chain, ensure privacy through complex cryptography, and operate transparently—a vastly different computational challenge than typical AI analytics. This complexity explains why early visions of DeAI often stumbled. They either sacrificed decentralization for efficiency or buckled under the processing demands. The real progress began when teams stopped retrofitting traditional AI into blockchain settings and started architecting systems specifically for the challenges of decentralization, transparency, and user control. Real Applications From the Whiteboard to Mainnet DeAI projects are finally moving beyond theoretical ideals. Several teams have deployed working systems that demonstrate practical applications, particularly addressing the shortcomings of centralized alternatives. Sean and Scott meeting in Hong Kong Sean Lee Leading the push for transparency against centralized AI, Kava has emerged as a significant force, demonstrating how decentralized models can successfully challenge Big Tech. Their platform incorporates decentralized AI elements; as Kava Co-Founder Scott Stuart detailed during our recent discussion in Hong Kong, its tangible user demand for accountable systems is underscored by a user base surpassing 100,000. This growing adoption serves as potent evidence of Kava's challenge to the prevailing 'black box' AI, as its community-governed and transparent operations offer a clear alternative. NEAR Protocol offers scalable infrastructure for high-throughput decentralized applications, enabling efficient DeAI processes. Internet Computer (ICP) pioneers platforms for AI applications to operate fully on-chain, ensuring end-to-end decentralization and security. Building the Backbone The unique demands of DeAI exposed critical gaps in existing Web3 infrastructure. Akash Network recognized this early. Their solution, a DePIN (decentralized physical infrastructure network), taps into underutilized computing resources globally, creating a marketplace for computation that offers resilient and cost-effective alternatives to centralized cloud providers for AI workloads, enhancing censorship resistance. Data accessibility is another piece of the puzzle. The Graph streamlines indexing and querying data from blockchains, making it feasible for DeAI applications to access and process the vast amounts of on-chain information needed for meaningful analysis and decision-making without overwhelming individual nodes. Across the ecosystem, teams feel the impact of these infrastructure upgrades. DeAI can now handle more sophisticated tasks – from managing complex DeFi strategies to powering decentralized social platforms – without fatally compromising on the core tenets of decentralization. The growing viability of projects like Kava, running elements on decentralized rails enabled by platforms like Akash, stems directly from these infrastructure advances. The Path Forward Web3's evolving infrastructure unlocks unique possibilities for DeAI deployment. Take DeFi usability. AI agents, like those Kava is working to deploy later this year, aim to automate complex cross-chain strategies or optimize yield farming, abstracting away the intimidating complexity that keeps mainstream users out. This requires not just AI logic but also seamless interaction with diverse protocols and robust data feeds, facilitated by infrastructure like The Graph. Community governance is another frontier. Projects like Dexe explore community-driven frameworks aligning AI development with user consensus and regulatory needs, potentially using AI agents to simulate policy impacts or manage DAO treasuries if infrastructure is robust. Looking Beyond the Buzzwords The success of DeAI hinges on more than just clever models or ideological appeal. Infrastructure providers and application developers face persistent challenges around computational bottlenecks, cross-chain communication standards, data veracity, and true decentralization. Theoretical models often break upon contact with mainnet realities. Ask any team deploying DeAI about the edge cases encountered – unexpected market volatility, network congestion spikes, governance exploits – that current models struggle with. The next crucial phase involves standardization and interoperability. As more DeAI applications emerge, the need for common frameworks for data, computation, and governance becomes paramount. Long-term success depends on creating an ecosystem where decentralized components work together seamlessly, rather than a collection of isolated, competing solutions. These foundational elements – robust infrastructure, accessible data, adaptable governance – might not grab headlines like breakthroughs in model training. But they are what will ultimately determine whether decentralized AI fulfills its promise of a more transparent, accountable, and user-empowered future, or remains confined to niche applications. The teams solving these fundamental challenges today are shaping the trajectory of AI for tomorrow.
