Latest news with #Faiā
Coin Geek
3 days ago
- Business
- Coin Geek
Could foundation models make RAG obsolete?
Homepage > News > Tech > Could foundation models make RAG obsolete? Getting your Trinity Audio player ready... This post is a guest contribution by George Siosi Samuels , managing director at Faiā. See how Faiā is committed to staying at the forefront of technological advancements here . Even the smartest systems can become outdated if the paradigm shifts. Reid Hoffman recently argued that it's not the end of RAG—Retrieval Augmented Generation. But for those of us watching the evolution of large language models (LLMs) through a sharper lens, the writing might already be on the wall. Just as Yahoo's exhaustive web directory model was outpaced by Google's (NASDAQ: GOOGL) probabilistic search engine, RAG may soon find itself outdated in the face of increasingly powerful foundation models. It's not about whether RAG works. It's about whether it will matter. From Yahoo to Google: A signal from the past To understand the trajectory we're on, we need only look back. Yahoo believed in curating the Internet. Directories. Taxonomies. Human-reviewed indexes. But Google introduced a radically different idea: don't catalog everything—just rank relevance dynamically. Instead of organizing knowledge beforehand, Google inferred what mattered most through algorithms and backlinks. That wasn't just a technological improvement—it was a shift in philosophy. A move from structure to signal. From effortful storage to elegant retrieval. RAG, in many ways, feels like Yahoo. It's a bolted-on system that tries to enhance LLMs by grafting in 'clean,' retrievable knowledge from databases and vector stores. The goal is noble: improve the factuality and trustworthiness of artificial intelligence (AI) responses by injecting it with curated context. But what if that need disappears? Why RAG feels like a transitional technology RAG solves a real problem: hallucination. LLMs, especially in their earlier versions, had a tendency to fabricate facts. By adding a retrieval layer—pulling in external documents to ground the generation—RAG helped bridge the gap between generative flexibility and factual precision. But in solving one problem, it introduces others: Latency and complexity : RAG pipelines require orchestration between multiple components—vector databases, embedding models, retrievers, and re-rankers. : RAG pipelines require orchestration between multiple components—vector databases, embedding models, retrievers, and re-rankers. Data management burden : Enterprises must constantly update and maintain high-quality corpora, often requiring labor-intensive cleanup and formatting. : Enterprises must constantly update and maintain high-quality corpora, often requiring labor-intensive cleanup and formatting. Hard to generalize: RAG systems perform well in narrow domains but can break or return noise when facing edge cases or unfamiliar queries. It feels like scaffolding. Useful during construction—but not part of the finished architecture. Inference is eating Search Recent breakthroughs in LLM capabilities suggest that we're entering a new paradigm—one where inference can increasingly replace retrieval. With the emergence of models like GPT-4o, Claude 3 Opus, and even Google Gemini Pro 2.5, we're witnessing: Longer context windows : These models can now ingest and reason over hundreds of pages of content without needing external retrieval mechanisms. : These models can now ingest and reason over hundreds of pages of content without needing external retrieval mechanisms. Better zero-shot performance : The models are learning to generalize across vast domains without needing hand-fed examples or fine-tuned prompts. : The models are learning to generalize across vast domains without needing hand-fed examples or fine-tuned prompts. Higher factual accuracy: As foundation models train on more comprehensive data, their inherent 'memory' becomes more useful than brittle plug-ins or patched-on sources. In other words, the model itself is the database. This mirrors Google's dominance over Yahoo. When Google proved you didn't need to manually catalog the Internet to find useful content, the race was over. In the same way, when LLMs can consistently generate accurate answers without needing retrieval scaffolding, the RAG era ends. Enterprise blockchain implications So why does this matter to the blockchain and Web3 space? Because the architecture of how we store and surface data is changing. In the past, enterprise blockchain projects focused heavily on data provenance , auditability , and structured information flows . Naturally, RAG-like systems seemed appealing—pair a blockchain ledger (structured, secure) with a retriever that could pull trusted data into AI responses. But if inference can outpace retrieval—if models become so strong that they infer trustworthiness based on deep pretraining and internal reasoning—the value of these data layer bolt-ons will shift. It could go three ways: Legacy enterprise solutions double down on RAG-like hybrids, bolting AI onto databases and chains for compliance reasons. Next-gen startups skip RAG entirely, trusting LLMs' inference power and layering blockchain only for verifiability , not retrieval. A new form of 'self-attesting' data emerges, where models generate and verify their own responses using on-chain signals—but without traditional RAG scaffolding. Blockchain, in this context, becomes a reference point , not a library. The foundation model becomes both the interface and the reasoner. Is clean data still necessary? One of the assumptions keeping RAG alive is this: clean data = better output. That's partially true. But it's also a bit of an old-world assumption. Think about Gmail, Google Drive, or even Google Photos. You don't have to organize these meticulously. You just type and Google finds . The same is starting to happen with LLMs. You no longer need perfectly labeled, indexed corpora. You just need volume and diverse context —and the model figures it out. Clean data helps, yes. However, the new AI paradigm values signal density more than signal purity . The cleaner your data, the less your model has to guess. But the better your model, the more it can intuit even from messy, unstructured information. That's a core shift—and one that should change how enterprises think about knowledge management and blockchain-based storage. RAG's final role: A stepping stone, not a standard So, where does this leave RAG? Likely as a valuable bridge—but not the destination. We'll probably still see RAG-like systems in regulated industries and legacy enterprise stacks for a while. But betting on the future of AI on retrieval is like betting on the future of maps on phonebooks. The terrain is changing. Foundation models won't need retrieval in the way we think about it today. Their training and inference engines will absorb and transmute information in ways that feel alien to traditional IT logic. Blockchain will still play a role—especially in authentication and timestamping—but less as a knowledge base, and more as a consensus layer that LLMs can reference like a cryptographic compass. Conclusion: The search for simplicity RAG helped patch early AI flaws. But patchwork can't match the architecture. The best technologies don't just solve problems—they disappear . Google didn't ask users to understand PageRank. It simply worked. In the same way, the most powerful LLMs won't require RAG—they'll simply respond with clarity and resonance. And that's the signal we should be tracking. In order for artificial intelligence (AI) to work right within the law and thrive in the face of growing challenges, it needs to integrate an enterprise blockchain system that ensures data input quality and ownership—allowing it to keep data safe while also guaranteeing the immutability of data. Check out CoinGeek's coverage on this emerging tech to learn more why Enterprise blockchain will be the backbone of AI. Watch | IEEE COINS Conference: Intersection of blockchain, AI, IoT & IPv6 technologies title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen="">
Coin Geek
5 days ago
- Business
- Coin Geek
Google's Quantum: How Willow rewrites blockchain's security rules
Homepage > News > Tech > Google's Quantum: How Willow rewrites blockchain's security rules Getting your Trinity Audio player ready... This post is a guest contribution by George Siosi Samuels, managing director at Faiā. See how Faiā is committed to staying at the forefront of technological advancements here. Google (NASDAQ: GOOGL) recently announced its Willow quantum computing chip, breaking records by completing tasks in under five minutes—tasks that would require classical supercomputers 10 septillion years. At 105 qubits, Willow surpasses its predecessor, Sycamore, not only in computational speed but notably in stability, reducing errors as qubit numbers increase. For enterprise leaders navigating the intersection of emerging technologies, Willow's implications stretch beyond mere computational records—they strike at the heart of blockchain security. Quantum computing's challenge to blockchain trust At the core of blockchain security—especially for Bitcoin—is cryptographic certainty. Bitcoin's security hinges on algorithms like ECDSA, which are traditionally resilient against classical brute-force attacks. But quantum computing, exemplified by Willow, operates fundamentally differently. Qubits exist in multiple states simultaneously, exponentially accelerating complex computations. In practical terms, this means quantum technology could eventually decrypt private keys, dismantling blockchain's foundational trust. The immediate risk isn't a quantum-powered hack tomorrow—it's the erosion of trust today. Leaders and cybersecurity strategists must anticipate this shift in perception. If stakeholders begin to question the robustness of blockchain cryptography, the ripple effects on adoption and investment could be profound. Cybersecurity under quantum pressure Expect to see a strategic recalibration in cybersecurity practices: Heightened vigilance: Anticipation of quantum threats will drive enterprises towards more proactive security postures. Organizations might preemptively migrate to quantum-resistant cryptographic standards. Anticipation of quantum threats will drive enterprises towards more proactive security postures. Organizations might preemptively migrate to quantum-resistant cryptographic standards. Strategic intuition on security shifts: Businesses will lean into strategic intuition, rapidly interpreting quantum signals and adjusting their risk frameworks accordingly. Businesses will lean into strategic intuition, rapidly interpreting quantum signals and adjusting their risk frameworks accordingly. Rise of quantum-resistant solutions: The market may see accelerated growth in blockchain platforms proactively designed with quantum resistance as a foundational feature. BTC vs. BSV: Divergent paths in quantum adaptability Quantum threats also magnify the existing scalability challenges within blockchain networks. BTC remains encumbered by rigid structures, small block sizes, and inherently slow consensus mechanisms. Its inflexible design is ill-suited to swift, quantum-driven shifts in the technological landscape. In contrast, BSV, particularly through initiatives like Teranode, positions itself as a scalable, adaptive infrastructure. By supporting near-unlimited block sizes and facilitating millions of transactions per second, BSV isn't merely addressing today's scalability requirements—it's preparing for tomorrow's quantum contingencies. For enterprise leaders, this distinction is critical: BTC: Cultural drift towards centralization if quantum mining emerges, exacerbating existing structural limitations. Cultural drift towards centralization if quantum mining emerges, exacerbating existing structural limitations. BSV: Culturally congruent, scalable foundations designed to evolve with emergent threats, maintaining operational harmony and stability. Navigating the quantum era Willow's unveiling by Google is a clear signal. It's a wake-up call, not a panic button. For enterprise professionals and consultants advising organizations in blockchain and cybersecurity, now is the time to: Conduct quantum-readiness audits: Assess current cryptographic practices and identify gaps in quantum resilience. Assess current cryptographic practices and identify gaps in quantum resilience. Champion cultural congruence: Align cybersecurity strategy with the organization's adaptive rhythm and readiness to embrace quantum-era upgrades. Align cybersecurity strategy with the organization's adaptive rhythm and readiness to embrace quantum-era upgrades. Stay ahead through informed instinct: Embrace quantum-resistant solutions early, positioning businesses as proactive rather than reactive. The quantum age demands recalibration—not panic. Google's Willow moves beyond discussions of raw computing power. It's a signal that the rules of blockchain security are about to evolve significantly. Now's the time for enterprises to listen closely and respond strategically. Watch: Want to develop on BSV? Here's how you can build with Mandala title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen="">
Coin Geek
7 days ago
- Business
- Coin Geek
Beyond 'Code is Law'
Homepage > News > Editorial > Beyond 'Code is Law' Getting your Trinity Audio player ready... This post is a guest contribution by George Siosi Samuels, managing director at Faiā, founder of CStack and a strategic advisor at the intersection of culture, AI, and blockchain. He helps leaders align tools with culture for systems that scale and resonate. Want to explore how your stack reflects your culture? Take the CSTACK audit → See how Faiā is committed to staying at the forefront of technological advancements here. What LLMs, Babel and blockchain reveal about modern governance Most tech narratives are stuck in silos. But today's real signals are cross-domain. Take the rise of Large Language Models (LLMs). At first glance, they seem to sit far from the blockchain: one predicts words, and the other validates transactions. But both are shaping new governance realities—in language, law, and trust. One reflects. The other enforces. And both are confronting an old idea made new: what happens when we turn systems into scripture? LLMs as echo chambers of collective memory LLMs don't just autocomplete text—they echo the cultural archive. Trained on internet forums, literature, open-source codebases, and the messiness of human discourse, they return our voices back to us. But it's an average voice. Smoothed. Predictable. In biblical terms, we may have built a new Tower of Babel—a vast, multilingual archive made of code and tokens, reaching upward without a clear north star. LLMs mirror what we say. But they also shape what we believe is sayable. Over time, that subtly compresses imagination, narrowing the space for dissent, edge cases, or strategic deviation. This isn't just a technical issue—it's a cultural one. Blockchain: Immutable execution vs. adaptive reality On the other side, blockchain technologies promise finality. 'Code is law,' early pioneers declared. But lived experience shows the cracks: 2016 DAO hack : Code executed, but not as intended. : Code executed, but not as intended. Forks and governance clashes: Communities split over interpretation, not infrastructure. In human terms, law has always allowed room for interpretation, context, and evolution. Code, by contrast, is binary. It does not debate—it deploys. This is where blockchain hits its cultural limit. Not in transaction speed. Not in scalability. But in the rigidity of machine-defined justice. From 'Code is Law' to 'Code as Ritual' It's time for a more nuanced frame. Not code as law , but code as ritual: a precise, repeatable action that reflects a shared intention—but one that remains open to revision, reinterpretation, and human override. This isn't about slowing down tech. It's about building alignment into its execution layer. LLMs can then act not as authorities, but as mirrors with memory—tools for insight, not decision. Blockchains become tools of collective enforcement, but tethered to real-world feedback loops. Consultants, enterprise leaders, and technologists are no longer just implementers—but stewards of digital culture, shaping not only what these tools do but also what they mean . Strategic Takeaways for Enterprise Leaders Design for friction – Not all automation is of value. Build in intentional pauses, human overrides, and ethical inflection points. Code clarity ≠ cultural clarity – Technical precision without cultural resonance leads to brittle systems. Ensure your smart contracts reflect not just logic, but intent. Treat AI and blockchain as co-arising forces – LLMs and blockchain aren't rivals—they're two sides of modern infrastructure: one governing semantics, the other enforcement. Align them. Governance is your differentiator – In commoditized AI and decentralized systems, how you govern—adaptively, transparently, and with cultural congruence—is what compounds. Closing Frame: From tools to teachings We are no longer just building tech. We're encoding values. Whether deploying smart contracts, training LLMs, or orchestrating hybrid systems, your stack becomes your story. Your architecture becomes your ethics. The question is no longer: What can we build? It's: What are we normalizing? And who gets to revise the script? Watch: Want to develop on BSV? Here's how you can build with Mandala title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen="">
Coin Geek
21-05-2025
- Coin Geek
Why human-in-the-loop still matters in AI + blockchain future
Getting your Trinity Audio player ready... This post is a guest contribution by George Siosi Samuels, managing director at Faiā. See how Faiā is committed to staying at the forefront of technological advancements here. Even in a world increasingly shaped by automation, the human signal still matters. As artificial intelligence (AI) accelerates and blockchain infrastructures mature, the temptation is to assume we're on the verge of a fully automated future—self-learning machines issuing smart contracts, AI DAOs negotiating with each other in real-time, trustless systems orchestrating commerce at scale. But beneath this sleek, autonomous vision lies a truth we can't ignore: without intentional human input, these systems don't evolve—they drift . In the convergence of AI and blockchain, Human-in-the-Loop (HITL) may be the linchpin that ensures these systems stay aligned with human values, cultural integrity, and real-world complexity. Let's break this down. Automation without attunement: The risk of AI alone Most current AI systems, including the most powerful large language models, operate on probabilistic inference. They predict what comes next based on the most likely patterns—trained on massive but uncurated datasets from across the internet. Left unchecked, they generate content and actions that are generic at best and subtly biased or inaccurate at worst. Now imagine this output codified into immutable smart contracts. Or injected into decentralized governance. Or used to trigger blockchain-based financial flows. Without intentional HITL checkpoints, we risk hardcoding misunderstanding into systems that are designed to be trustless—but not infallible. The result? Technically precise, but culturally tone-deaf and ethically brittle systems. Blockchain isn't immune either Blockchain gives us permanence and provenance. It's the architecture of trustless interaction, distributed consensus, and data immutability. But without thoughtful calibration, it can be just as blind as AI. Code is law—but the law needs interpretation. Culture. Context. Evolution. Take smart contracts, for instance: Automated execution sounds elegant until it hits a gray area—something only humans could have foreseen. DAOs sound democratic until you realize not all votes carry equal weight in practice. Tokenized incentives are powerful until they incentivize the wrong behavior in the name of growth. Blockchain secures what it is . HITL ensures we're still questioning what should be . The convergence is already happening We're not speculating about a distant future here. The AI + blockchain convergence is already underway: AI-powered oracles are feeding real-time data into on-chain contracts. are feeding real-time data into on-chain contracts. Generative agents are managing tasks within DAOs. are managing tasks within DAOs. LLMs are being trained on-chain for transparency and auditability. This is where the line blurs—and HITL becomes essential not just for quality control but also for ethical and cultural steering. Because once AI starts interacting directly with value—automating decisions about money, governance, or identity—the stakes multiply. HITL: Not just a checkpoint, but a compass Human-in-the-loop isn't just a technical mechanism. It's a design principle. It forces us to embed discernment into systems that otherwise chase efficiency. It reminds us that data ≠ wisdom and that not every pattern is worth repeating. In the context of blockchain and AI, HITL roles could include: Curation : Guiding which data sets train on-chain AIs, avoiding cultural erasure or disinformation. : Guiding which data sets train on-chain AIs, avoiding cultural erasure or disinformation. Interpretation : Decoding edge cases that smart contracts can't resolve autonomously. : Decoding edge cases that smart contracts can't resolve autonomously. Signal injection : Ensuring generative outputs (content, governance, financial decisions) are aligned with community values, not just majority rules. : Ensuring generative outputs (content, governance, financial decisions) are aligned with community values, not just majority rules. Legacy safeguarding: Embedding indigenous, ethical, or historical nuance into systems that might otherwise flatten context. These roles aren't about slowing down progress—they're about guiding it from the inside. Toward a HITL-conscious tech stack A future-ready AI+Blockchain ecosystem doesn't mean removing the human. It means placing the human more consciously within the system. Designing for HITL might look like: AI-generated governance proposals that still require human debate before execution Transparent edit trails where humans can review and revise LLM-generated smart contracts Middleware that lets human validators audit AI decisions on-chain, not just off-chain And just as blockchain brought us the concept of proof-of-work (PoW) and proof-of-stake (PoS), perhaps we now need proof-of-wisdom—evidence that a human's insight has shaped a critical path in the system. Building With, Not Just For, Humans The real frontier isn't building tools for humans. It's building tools with humans—in the loop, logic, and legacy. The most powerful systems of the future will not be the most automated. They'll be the most attuned. AI will draft. Blockchain will record. But it is the human who will decide. Closing thoughts As we enter this hybrid age, one thing becomes clear: the future doesn't need more automation. It needs more orientation. Human-in-the-loop isn't about clinging to the past—it's about steering the future. It's the compass inside the code. The wisdom between the layers. The signal within the system. If we want our decentralized, intelligent systems to serve humanity—not just efficiency—we must keep humans in the loop. In order for artificial intelligence (AI) to work right within the law and thrive in the face of growing challenges, it needs to integrate an enterprise blockchain system that ensures data input quality and ownership—allowing it to keep data safe while also guaranteeing the immutability of data. Check out CoinGeek's coverage on this emerging tech to learn more why Enterprise blockchain will be the backbone of AI . Watch: Turning AI into ROI title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen="">
Coin Geek
19-05-2025
- Business
- Coin Geek
When the East buckled, blockchain whispered
Homepage > News > Editorial > When the East buckled, blockchain whispered Getting your Trinity Audio player ready... This post is a guest contribution by George Siosi Samuels, managing director at Faiā. See how Faiā is committed to staying at the forefront of technological advancements here. What a Japanese hedge fund collapse reveals about enterprise blockchain's next move. On April 8, 2025, a Japanese hedge fund collapsed under the weight of a 60x leveraged position in 10-year U.S. Treasury bonds. The timing wasn't coincidental. Just days prior, Trump's revived tariff announcements sent markets into a spiral. Bond yields surged. Liquidity evaporated. And then—like a signal too loud to ignore—a domino fell. The result? A cascade sell-off in Treasuries, surging Japanese 30-year yields, and enough global pressure to force Trump into pausing his plans—at least for now. But beneath the geopolitical theatrics lies a quieter question: what does this moment signal for enterprise blockchain? Let's examine the fallout—through an east vs. west lens. The West: From transparency theatre to on-chain accountability In the U.S. and Europe, blockchain has long been marketed as the next evolution in trust. But in practice, it's often been relegated to pilot programs, glossy white papers, or internal innovation teams far removed from real risk. This hedge fund's collapse may change that. Why? Because it wasn't some meme-stock gamble or crypto blowup—it was a traditionally structured, institutionally respected fund betting on U.S. government debt—the safest of the safe. And it still cracked. This sends a signal to Western enterprises: If the global financial plumbing can buckle under legacy assumptions, then maybe 'blockchain for auditability' isn't a side quest. It's survival infrastructure. Expect a rise in: Tokenized treasuries with real-time on-chain proof of collateral Smart-contract-driven leverage thresholds for hedge funds and asset managers Auditable risk registries that allow governments and counterparties to monitor systemic exposure before it's too late Enterprise blockchain here stops being theoretical. It becomes tactical. The East: Circuit-breakers and sovereign stack recalibration Japan's role in this story is more than geographical—it's philosophical. In a culture known for precision, resilience, and long-term stewardship, the idea that a local fund imploded from exposure to foreign debt will land hard, especially in a region where U.S. monetary policy still casts a long shadow. This may accelerate two moves in Asia: 1. Rethinking risk transparency through public-private DLT collaborations – Japan and South Korea were already exploring blockchain-based finance tools—but now, expect movement toward real-time leverage indexing, cross-border asset tracing, and circuit-breaker layers powered by distributed consensus. 2. Regional financial sovereignty via blockchain rails – The shock of U.S.-led volatility will likely boost projects like: mBridge (multi-CBDC settlement) RCEP-backed digital trade corridors Intra-Asian DeFi infrastructure with enterprise compliance baked in Asia won't decouple from the west—but it will want more say in how systems operate. Blockchain gives them the levers. A moment of pattern recognition This isn't just about one hedge fund or one policy move. It's about what this moment reveals: The world's most secure assets are no longer 'safe' Rate shocks in one country ripple into another's retirement funds Old pipes are leaking—and everyone's still using them Enterprise blockchain doesn't solve everything, but it offers a new type of resilience: Programmable trust Distributed oversight Real-time calibration East vs. West: How they're likely to move Closing thought Blockchain isn't here to save the system. It's here to show us where it's broken—and build what comes next. For enterprise leaders still on the fence, this moment isn't just a warning. It's a window. And it's closing fast. Watch | Mining Disrupt 2025 Highlights: Profitable trends every miner should know title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen="">
