Latest news with #DeAI
Forbes
6 days ago
- Business
- Forbes
From Algeria To The World: Local Voice Shapes African Decentralized AI
Crypto Ownership Growth by Region. Data Source: Triple-A. (Graphic by Visual Capitalist via Getty ... More Images) When people talk about the future of decentralized AI (DeAI), the conversation often gravitates toward technical infrastructure, such as blockchain-based protocols, large language models, dataset quality, privacy, and ethical standards. But underneath the model lies a far more human foundation: community, an integral part that transforms Decentralized AI from a concept into a practical utility, beyond mere narrative. In Algeria – Africa's largest country by land area, rich in natural resources and cultural heritage, bridging North Africa and the Arab world – that foundation has been quietly taking shape over the years, led by a volunteer voice few outside the region know. I spoke with Chabane MT Tarek, a local Algerian whose mother tongue is Arabic, an Economics and Accounting graduate, who's been captivated by the world of blockchain since 2013, and is now deeply engaged with the possibilities of DeAI. Chabane is a volunteer who devotes his time largely to the Crypto & AI community in Africa. He's helping shape Africa's presence in one of the most important technological movements of our time. We spoke about his personal journey through crypto, the on-the-ground realities of building community in Africa, and what it truly means to make DeAI (and AI in general) not just technically sound, but globally inclusive. 'I Entered Crypto In 2013, Out Of Curiosity.' When Chabane came across Bitcoin in 2013, 'There wasn't even a community. I read whitepapers, followed online forums, taught myself what I could,' he tells me. That curiosity would later evolve into conviction through Ethereum, ICOs, DeFi, and eventually, decentralized AI. 'I've seen hype come and go. What excites me now is infrastructure. Real, usable, and inclusive systems.' His discovery of DeAI came at a time when AI was becoming mainstream but also increasingly centralized. 'The decentralized AI model and infrastructure fascinated me. I realized this was something with true vision, especially considering the centralized hacks and setbacks the industry had gone through in 2016 and 2019." The Africa 'Playbook' and Misunderstanding Ask someone unfamiliar with the continent about 'tech in Africa', 'usage of AI in Africa', and chances are, you'll get vague generalizations. 'People forget this is a continent of 54 countries and over 2,000 languages,' Chabane says with a light tone and smile. 'What works in Ghana won't automatically work in Algeria.' In North Africa, Arabic is the primary language; in parts of central Africa, it's French or English; and elsewhere, the dominant digital language is English. But language is just one layer. 'To localize means more than translation: it means understanding people's behaviors, platforms, economics, even cultural comfort zones.' In Algeria, for instance, Twitter is not the go-to channel for tech discussion. Facebook is still where most online communities engage, something many international projects fail to grasp. Geographically, North Africa holds a unique position as a cultural and geopolitical bridge between sub-Saharan Africa and the MENA (Middle East and North Africa) region. 'Our location gives us a natural role in connecting two massive regions,' he explains. 'There's shared language with MENA, but we're also part of Africa's emerging tech narrative.' For decentralized AI to be truly global, regions like this can't be an afterthought. They have to be part of the foundation. When I ask why decentralized AI feels relevant in his region, Chabane doesn't hesitate. 'Because centralized AI has never served us fully. We've always been on the outside of the decision-making process, whether it's access to platforms, data rights, or even language inclusion.' Decentralized AI offers an alternative. It enables local data contributors to be rewarded, gives communities agency over their own digital narratives, and creates opportunities that aren't gatekept by borders or bandwidth. Chabane's current work on DeAI is entirely volunteer-driven. He organizes community events, translates technical content, supports education initiatives, and uses AI tools to craft locally relevant stories. "There's growing interest here. People want to be part of something global, yet something they can intuitively resonate with. They just need an entry point." Build Decentralized AI From Ground up It's easy to romanticize decentralization as a technical ideal, but this story reminds us: there's no decentralization without distribution across geographies, languages, and cultures. 'You can have the best protocol in the world,' Chabane opines, 'but if people in Algeria, Nigeria, South Africa, or rural Kenya can't understand and make sense of it, then it's not really decentralized.' Although building decentralized infrastructure for AI is still at its infancy, that future could also be closer than we think thanks to voices like his, building not from the top down, but from the ground up.
Forbes
12-06-2025
- Business
- Forbes
What Good Is AI On Blockchain If No One Can Use It Easily In Practice
The interaction of Artificial intelligence (AI) and Blockchain are emerging. (Photo Illustration by ... More Budrul Chukrut/SOPA Images/LightRocket via Getty Images) An increasing number of blockchains is actively seeking to integrate AI capabilities, but this 'AI on blockchain' growth is accompanied by significant challenges, most notably the issue of "chain silos", which fragment the sector and can hold back the full realization and utility of Decentralized AI (DeAI) potential. Afterall, if there's no widely available use case scenario, how do we continue the narrative and innovation of the currently already-overhyped DeAI sector? Take blockchain-native autonomous AI agents as an example, while a precise census of AI agents on the blockchain is elusive, the available data strongly suggests a rapidly growing and dynamic landscape. The number is likely in the hundreds to potentially thousands when considering individual deployed agents across various platforms and projects. All these AI agents reside in a scattered landscape of chains. It's like when computers could not communicate with other computers before the World Wide Web was invented, as a result, the full potential of computers could not be unleashed. While centralized AI suffers from data silos controlled by corporations, DeAI risks creating new silos at the blockchain level if interoperability is not prioritized, blocking DeAI's full potential. This fragmentation is not merely about data residing on different ledgers. It extends to the unique protocols, smart contract languages, virtual machine environments, consensus mechanisms, and overall operational logic of each distinct blockchain. For example, a DeAI application built to leverage the specific features of Ethereum and its EVM may not be able to natively interact with or utilize AI models deployed on a non-EVM chain like Solana without resorting to complex and potentially insecure bridging solutions. Similarly, AI agents trained within one chain's environment may find it difficult to operate effectively elsewhere. This leads to scenarios where separate databases or non-communicating tools on different chains effectively become isolated islands of DeAI activity. Fragmentation issues, similar to those seen in decentralized identity systems or healthcare electronic health records due to platform incompatibilities, can limit the scalability and impact of DeAI solutions. The DeAI community's vision extends beyond isolated applications on single blockchains. Building "Super AI Applications" is becoming a key mission for many. Imagine it as an all-encompassing platform or a network of integrated services that accommodates diverse AI functionalities – such as sophisticated data analysis, distributed model training, autonomous agent deployment, and complex decision making – across different, often varied and disparate blockchain environments. Such an application would not be confined to the resources or limitations of a single chain. On one hand, specialized Layer 1 blockchains like Bittensor, and Gensyn are being engineered from the ground up with DeAI specific requirements in mind. These platforms aim to provide optimized environments for tasks like high-volume data processing, intensive computation, or unique AI model incentive mechanisms, based on the premise that general-purpose L1s may not be ideally suited for the distinct demands of DeAI. On the other hand, many prominent DeAI Apps and protocols, such as Ocean Protocol and SingularityNET, initially launched on established, general-purpose L1s like Ethereum and are now pursuing multichain strategies. Then a key debate arose: Commit to a specialized L1 for potentially superior tailored performance but a smaller initial ecosystem, or build on/across established L1s/L2s to tap into broader reach but with possible limitations in AI specific optimizations? Inevitably, successful DeAI platforms will increasingly rely on reliable and functional cross-chain capabilities to access wider markets, liquidity, and data sources, regardless of their foundational architecture, thereby avoiding the very 'silo-zation' they aim to overcome. Realizing the Super AI APP vision is charged with significant challenges though. Despite these challenges, industry players are proactively exploring solutions and standardization for DeAI Super Applications to cross chain, including leaders like BSC and Solana, although this is still at an infant stage. In the meantime, innovations in protocols, platforms, and conceptual frameworks are also taking shape to construct a more interconnected DeAI ecosystem which can potentially become real utility for even novice internet users. This trend is inevitable, driven by the enormous potential underneath AI and blockchain's synergistic benefits. The inherent characteristics of blockchain can address some of AI's most pressing challenges, while AI can unlock new functionalities and efficiencies for decentralized systems, such as network optimization and intelligent resource allocation, or automated security auditing, and more. For the benefits and advantages of AI on blockchain over centralized AI, I've discussed in my previous articles: How To Solve Data Collection Challenges For Your Business's AI Needs DeepSeek's Lesson: The Future Of AI Is Decentralized And Open-Source Top 5 Decentralized Data Collection Providers In 2025 For AI Business
Business Wire
29-05-2025
- Business
- Business Wire
DCG Poll Reveals 75% of Americans Favor Decentralized AI's Open Innovation Over Centralized AI
WASHINGTON--(BUSINESS WIRE)--DCG, a global investor, builder, and incubator of decentralized ecosystems, today unveiled the results of a new study conducted in partnership with The Harris Poll, revealing that 75% of consumers believe decentralized AI is more likely to support innovation and progress than centralized AI. 1 The survey, which polled 2,036 U.S. respondents, including registered voters and AI users, provides insight into growing sentiment favoring decentralized approaches to artificial intelligence, signaling strong support for the emerging sector of decentralized AI (deAI). 'This research makes it clear: there's strong public support for policies that both protect innovation and keep pace with where the world is headed. Three-quarters of Americans agree that the transformative power of AI would benefit more people if it wasn't consolidated in the hands of a few major players,' said Julie Stitzel, Senior Vice President of Policy at DCG. 'Just as we've done in digital assets, we're committed to working hand-in-hand with policymakers to shape thoughtful, balanced rules that foster the growth of decentralized systems, which are opening access to intelligence in the same way that the internet first opened access to information.' The poll is released alongside the DCG Fly-In, a two-day event held last week in Washington, D.C., bringing together DCG, its portfolio companies, and key U.S. lawmakers to discuss decentralized AI, privacy, energy, and pending market structure and stablecoin bills. During the event, DCG co-hosted a deAI briefing with the bi-partisan House Congressional Crypto Caucus, and one Fly-In participant testified before the House Natural Resources Subcommittee of Investigations and Oversight about decentralized solutions for AI energy challenges. DeAI is a model combining artificial intelligence with blockchain technology to democratize access, enhance transparency, and ensure privacy in AI systems, which was Americans' single largest concern with AI according to polling data. Unlike traditional AI controlled by a few large entities, deAI offers economic incentives and distributes control to contributors across a decentralized network such as Bittensor, enabling equitable access to AI systems' benefits and reducing risks such as bias and privacy concerns. "The public is calling for a new social contract with artificial intelligence: one where AI is governed openly, distributes value fairly, and gives people a meaningful stake in the systems that shape their lives,' said Tony Douglas, Co-Founder, Decentralized Research Center. 'Decentralization is no longer a fringe idea, it's a framework for building AI that reflects public values and a chance to avoid repeating the failures of the last tech era." Compelling findings from the survey include: Consumers are optimistic about AI's potential, but demand stronger personal oversight. 88% agree that if AI is using their personal information and data, they should have more control over what is being used. A majority of consumers (59%) agree that AI is becoming as essential as the internet, and that it should therefore be accessible to the public without heavy regulation. Public appetite is growing for AI beyond Big Tech's constraints. Big Tech companies are one of the least trusted groups when it comes to decision-making regarding AI use and development, surpassed only by elected officials. 77% of consumers see deAI as more beneficial to society overall than centralized AI. 1 72% would be more willing to use AI if there were more alternatives to the systems controlled and developed by Big Tech. Two thirds (67%) of consumers see centralized AI as more biased than deAI. 1 Voters are looking for alternatives to centralized AI. A majority of voters (56%) prefer deAI over centralized AI. A majority of both Republicans and Democrats prefer deAI over centralized AI. DeAI and decentralized systems are more favorable to consumers. 71% see deAI as more secure for consumers' personal data than centralized AI. 1 Three-quarters (75%) see deAI as more supportive of innovation and progress than centralized AI. A vast majority (86%) of Americans already see the benefits of AI in their daily lives, and 74% agree that they'd be more comfortable using AI if they knew they could benefit from the use of their personal data. A majority of AI users show preference for deAI over centralized AI. The complete survey findings, along with detailed analysis and recommendations, are available at DCG remains committed to collaborating with policymakers, community leaders, and stakeholders to foster a more inclusive and resilient financial system and is actively focused on the growth of decentralized ecosystems, including the decentralized Bittensor network through the recent launch of its subsidiary Yuma. Yuma supports early-stage teams building on Bittensor, providing capital, technical support, community access, and long-term collaboration to help launch and grow subnets. Through its accelerator, incubator, validator, and mining roles, Yuma helps shape a more open and accessible future for AI. DCG and Yuma own $TAO, the native token of the Bittensor ecosystem, and may hold interests in projects built on or supporting Bittensor and other deAI ecosystems. About DCG Founded in 2015 by Barry Silbert, DCG is a global investor, builder, and incubator committed to advancing decentralized ecosystems built on blockchain technology. As the most active investor in the space, DCG has backed over 250 early-stage technology companies across 40 countries and holds more than 70 token and digital asset investments. In addition to its investment portfolio, DCG is the parent company of subsidiaries including Grayscale, Fortitude Mining, Foundry, Luno, and Yuma. For additional information about DCG, visit The Harris Poll Method Statement The research was conducted online in the U.S. by The Harris Poll on behalf of Digital Currency Group among 2,036 U.S. adults aged 18 years or older. The survey was conducted between the 21st of April and the 25th of April. Data are weighted where necessary by age by gender, race/ethnicity, region, education, marital status, household size, employment, household income, political party affiliation, and voter registration status to bring them in line with their actual proportions in the population. Respondents for this survey were selected from among those who have agreed to participate in our surveys. The sampling precision of Harris online polls is measured by using a Bayesian credible interval. For this study, the sample data is accurate to within ± 3.0 percentage points using a 95% confidence level. This credible interval will be wider among subsets of the surveyed population of interest. All sample surveys and polls, whether or not they use probability sampling, are subject to other multiple sources of error which are most often not possible to quantify or estimate, including, but not limited to coverage error, error associated with nonresponse, error associated with question wording and response options, and post-survey weighting and adjustments. 1 Opinion of the general public when shown blinded descriptions of Decentralized AI and Centralized AI
Forbes
25-04-2025
- Business
- Forbes
How One Company Is Quietly Working To Transform AI Forever
Decentralized AI could displace centralized AI with more reliable compute and energy generation — ... More and a blockchain company Bittensor is leading the way. A few short years ago, decentralized artificial intelligence was a fringe concept — a speculative side path compared to the centralized AI juggernauts such as OpenAI, Google DeepMind and Anthropic. But things are changing fast. Friday, in Austin, Texas, a significant milestone for DeAI is quietly unfolding — the Bittensor Endgame Summit, an inaugural gathering that signals the next phase of the movement from theory to action. Unlike the flashier commercial AI conferences, the Endgame Summit isn't about splashy product launches or celebrity tech keynotes. It's about something arguably more important — building a sustainable, decentralized ecosystem for AI. And it's drawing a passionate, global community of developers, researchers and network participants determined to make AI more transparent, accessible and accountable. Bittensor is an open-source blockchain protocol that allows AI models to be trained, run and rewarded on a decentralized network. Instead of relying on centralized data centers and co-opted electrical generation, it distributes compute tasks and energy needs across a global system of participants. Contributors earn the network's native crypto token, TAO, in exchange for their computational work, electrical use and model performance. What sets Bittensor apart is that it doesn't merely replicate centralized systems with a crypto twist. It reimagines the incentive structure and governance model behind AI itself. 'We wanted to, kind of, bring in the masses and make this part of the global zeitgeist,' said Bittensor co-founder Ala Shaabana during an exclusive Zoom interview. 'We all mined [TAO] Screen capture of exclusive interview with Co-Founder of Bittsensor DeAI network, Ala Shaabana. The Endgame Summit offers a tangible glimpse into what a decentralized AI future could look like — and more importantly, who is building it. The event's sessions are focused on real-world use cases, such as scalable subnet deployment – think of subnets as applications as useful apps on your mobile device — governance models and secure training for the AI model. All of this is being accomplished with existing energy generation and compute power. This isn't the language of hype, it's the language of infrastructure. Shaabana emphasized the ecosystem-like nature of subnets in the Bittensor network. 'Each project is almost its own independent entity that is tied to TAO directly,' he said. 'You're almost creating a new company every time you create a subnet.' That means the summit isn't just a casual meetup in a hip city — it's a working session for a growing league of builders treating DeAI like an open-source economy. One of the most underreported aspects of DeAI is the role of open-source collaboration. Unlike proprietary models locked behind APIs and NDAs, decentralized AI systems often rely on shared, peer-reviewed code. That means anyone, anywhere, can inspect, improve, or fork a model to fit local needs. This ethos mirrors what made the internet, and indeed crypto, grow in the first place. 'A lot of our innovation comes from open source,' Shaabana noted. 'We've learned a lot by building Bittensor on top of it.' The Summit is expected to deepen these collaborative ties, particularly among subnet developers who are building everything from real-time inference engines to protein-folding healthcare apps to predictive 'wagering' on international soccer games to secure enclave-powered model layers. Each subnet is driven, and rewarded, by the utility and value it provides to the marketplace of users. While the core principles of decentralization remain — no single point of control or failure, permissionless access and composability — Bittensor's developers are also keenly aware of the need for strategic governance. That's why the Summit includes sessions on future voting frameworks and accountability standards. It's a delicate balance – maintaining decentralization without inviting chaos. 'Governance is definitely our next step,' Shaabana said. 'The foundation has been taking care of everything, but it's time for us to sort of take a step back and give the reins to the whole community.' The rise of centralized AI has brought with it serious concerns — about surveillance, about access and about algorithmic bias – among the top issues. But until now, the alternative has largely been theoretical. The Endgame Summit represents a rare inflection point where a viable path forward is being mapped out in real time. The organizers contend that this is not about competing with OpenAI or Big Tech's strangle hold on AI and their hundreds of billions of investment dollars. It's about building resilient, decentralized infrastructure that allows thousands of participants to shape the future of AI without having to work for — or through — Big Tech. And while the Summit itself might not be flashy enough to make headlines, its substance may one day prove historic. Decentralized AI isn't coming. It's already here. And in Austin, it's finding its people.
Forbes
01-04-2025
- Forbes
DeepSeek's Child Prodigy Paradox: When Knowledge Outpaces Judgment
Imagine a child prodigy—a mind that holds the sum of human knowledge, the entire arc of humanity's history engraved within. able to recite facts, write poetry, and solve math problems yet utterly unaware of the darker, more complex corners of the human world. That was DeepSeek when we put it to the test, a brilliant machine, but one that could not recognize human deception, malice, or the ethical weight behind specific requests. In this article, we unpack the less talked-about side of AI development, using DeepSeek as a case study to illustrate how a highly knowledgeable model can be misused when it lacks the wisdom to distinguish between right and wrong — or even when someone is lying. A critical gap needs to be highlighted here: the potential divide between knowledge and contextual understanding in large AI models. This isn't just a technical flaw; it's a fundamental risk that warrants caution and careful consideration from industry players. After DeepSeek's public release, we mobilized our decentralized data collection solution to crowdsource real-world tests. We invited our global community to submit creative, nuanced, and ethically complex prompts to see if DeepSeek could recognize when humans were hiding malicious intentions — or whether it would blindly comply, reciting dangerous information without question The purpose was simple: to determine whether DeepSeek could tell when it was being manipulated. Here are two real examples from our tests: These examples highlight the risk of reverse exploitation — how bad actors could use AI's massive knowledge base, not because the AI intends harm, but because it lacks the capacity to understand human darkness or deceit. The internet's early years taught us hard lessons about content moderation and online safety. Platforms eventually introduced keyword filters, reporting systems, and community guidelines. But unlike static web pages or user-generated content, AI doesn't merely host information — it generates it on demand. And teaching an AI to filter malicious content is exponentially more difficult. You can't solve this problem by banning keywords alone. Human intentions are nuanced. Cultural contexts vary. Harmful requests are often disguised in creative, subtle ways. Furthermore, generative AI models don't inherently "understand" the difference between helpful and harmful behavior — unless we teach them to. This isn't limited to centralized AI (CeAI) models. Decentralized AI (DeAI) faces the same challenge. When data is collected globally from diverse sources, data annotation, cleansing, and ethical filtering could be even harder. The decentralized structure may provide a broader, more diverse dataset — theoretically reducing systemic bias — but it also increases the risk of misinformation, manipulation, and exploitation if not carefully managed. This brings us to two fundamental questions every AI developer and policymaker should be asking: 1. What do we do with the data we collect? 2. How do we transform that data into real intelligence — not just information but ethical, contextual understanding? The truth is that access to a massive amount of data does not automatically translate to intelligence, let alone responsible intelligence. Between data collection and model deployment, a lot of hard, careful work needs to be done. As we entrust AI with more responsibilities, we must ask ourselves: Are we ready to be responsible parents? Raising an AI system is not unlike raising a child. It's not enough to fill it with facts; we need to teach it wisdom, responsibility, and empathy. The future of AI safety will depend on our ability to embed human oversight, ethical frameworks, and cultural awareness into these systems from the ground up. Constructive dialogue about the ethical considerations and potential dangers of AI development must move to the top of our priority list — not as an afterthought but as an essential part of the development cycle. Whether it's centralized AI or decentralized AI, the challenge remains: How do we ensure the intelligence we build is not just powerful but ethical, contextual, and aware of the human world it serves? Only then can we unlock AI's true potential—not as a cold, mechanical prodigy but as a responsible, wise, and trusted partner for humanity.
