Technology Trends

Explores the latest innovations, protocol upgrades, cross-chain solutions, and security mechanisms in the blockchain space. It provides a developer-focused perspective to analyze emerging technological trends and potential breakthroughs.

Refuting the '2028 Economic Collapse Theory': AI Takes Your Job, But Also Makes Everything Almost Free

In response to Citrini Research's "2028 Global Intelligence Crisis" paper predicting AI-induced economic collapse, this article presents a counter-argument. It posits that while AI may eliminate jobs and reduce incomes, it will also drastically lower the costs of goods and services—from healthcare and legal advice to software and energy—creating a "deflationary prosperity." The author argues that traditional economic metrics like GDP and unemployment rates are becoming obsolete in this transition to a "post-human economy." The true measure of future prosperity should be "intelligence output per unit of energy," which is rising exponentially. This shift will not lead to systemic collapse but rather a radical transformation where value migrates from cognitive labor to uniquely human attributes like empathy, creativity, and connection. Although the transition may involve significant disruption, the outcome will be a more abundant and functional economic system.

marsbitYesterday 09:46

Refuting the '2028 Economic Collapse Theory': AI Takes Your Job, But Also Makes Everything Almost Free

marsbitYesterday 09:46

How Can Agentic Commerce Enable AI to Start Making Money?

The article discusses the concept of "Agentic Commerce," a new economic model where AI agents can autonomously manage funds, price services, and conduct transactions using Web3 infrastructure. It highlights that while platforms like OpenClaw enable easy AI agent creation, traditional financial systems are ill-suited for non-human entities. Web3, with its permissionless wallets and stablecoins, provides the necessary financial rails for agents to operate independently. Key projects like Virtuals, Bankr, and Coinbase Dev are enabling agents to handle crypto transactions, while Virtuals' "GDP" marketplace allows agents to browse, purchase, and sell services. Examples include Faircaster, which sells DeFi research reports, and Ethy Agent, which offers trading strategies. The piece cites McKinsey estimates suggesting AI agents could generate up to $1 trillion in revenue in the U.S. B2C retail market by 2030, with a broader economic impact of $3–5 trillion. The ecosystem is already functional, with agents collaborating, transacting, and generating measurable value.

marsbit2 days ago 07:01

How Can Agentic Commerce Enable AI to Start Making Money?

marsbit2 days ago 07:01

Dialogue with Google Cloud VP: Don't Be a 'Reseller' of Large Models, The Next Wave of AI Entrepreneurship Lies in Agents

Google Cloud VP Darren Mowry discusses the shifting landscape of AI startups, emphasizing that the next wave of innovation lies in agents rather than simply reselling or wrapping large language models (LLMs). He highlights that while cloud credits and access to GPUs/TPUs lower initial barriers, startups increasingly need deeper engineering support and a focus on data, models, and agentic systems. Mowry notes a significant trend: discussions have shifted from chip infrastructure (10-15%) to models and agents (80-85%). He warns against two unsustainable models: thin "LLM wrappers" that add little value and "aggregators" that merely help choose between models without intelligent functionality. Instead, he points to high-growth areas like biotech, climate tech, and consumer experience platforms, where startups are driving real revenue and pushing technical boundaries. Google Cloud aims to support startups through credits, engineering resources, and flexible infrastructure (TPUs/GPUs), boasting high retention rates even after credits expire. Mowry positions Google as a first-party tech innovator, contrasting with competitors who he says often act more as distributors.

marsbit2 days ago 06:44

Dialogue with Google Cloud VP: Don't Be a 'Reseller' of Large Models, The Next Wave of AI Entrepreneurship Lies in Agents

marsbit2 days ago 06:44

Anthropic Data: Nearly Half of AI Agent Calls Concentrated in Software Engineering, These 16 Vertical Domains Remain Blue Oceans

According to Anthropic's comprehensive study on real-world AI Agent usage, nearly 50% of all tool usage by AI agents is concentrated in software engineering. In contrast, 16 other sectors—including healthcare, legal, finance, and education—each account for less than 5% of total usage, representing significant untapped opportunities. A key insight is the "trust deficit": while models like Claude are capable of working autonomously for nearly five hours, the 99.9th percentile of user sessions lasts only about 42 minutes. This gap highlights a major product opportunity. Over time, user trust grows—experienced users shifting from pre-approval to proactive monitoring—but overall adoption still lags behind technical capability. The report suggests that vertical AI applications in underserved domains could spawn hundreds of unicorns, mirroring the rise of SaaS. Success requires deep domain expertise, proprietary data integration, context-aware engineering, and effective change management. Regulatory approaches should enable—not hinder—human-AI collaboration by focusing on monitoring and intervention rather than mandatory step-by-step approvals. In summary, the AI agent landscape remains early-stage, with vast potential in verticals where domain-specific agents can automate complex, high-value workflows.

marsbit02/24 07:57

Anthropic Data: Nearly Half of AI Agent Calls Concentrated in Software Engineering, These 16 Vertical Domains Remain Blue Oceans

marsbit02/24 07:57

Vitalik Buterin is trying to fix crypto’s biggest blind spot: ‘The goal is to…’

Vitalik Buterin, co-founder of Ethereum, is shifting the focus of crypto conversations from price and performance to a more fundamental issue: the gap between user intent and a system's actual behavior. He argues that true security is about minimizing this divergence, as even simple actions involve complex human assumptions that are difficult to encode. Buterin extends this concern to the broader internet and AI, noting that the core challenge is not the ability to generate content or execute code, but the need for quality, judgment, and context. He envisions Ethereum evolving into a neutral coordination layer where AI agents can transact, prove actions, and build verifiable trust on-chain. This framework aims to create systems that don't just execute commands blindly but safely interpret human intent, making trustless and private interactions possible in an increasingly automated world.

ambcrypto02/23 19:03

Vitalik Buterin is trying to fix crypto’s biggest blind spot: ‘The goal is to…’

ambcrypto02/23 19:03

Bitcoin’s Quantum Risk Steals Spotlight At Ethereum Gathering

Bitcoin's vulnerability to quantum computing emerged as a key topic at the ETH Denver conference. While quantum computers pose only a moderate threat to Bitcoin’s hashing algorithms due to limited speed improvements, the real concern lies with digital signatures. Most wallets rely on elliptic curve cryptography, which Peter Shor’s algorithm could break using a sufficiently powerful quantum computer, allowing attackers to derive private keys from public keys. Current estimates of the qubit requirements for such an attack have decreased, with some studies suggesting six-figure-range machines might eventually pose a risk. However, practical challenges like error rates and stability remain significant barriers. In response, the crypto industry is proactively developing defenses. The Ethereum Foundation has a dedicated research group, and companies like Coinbase are engaged in planning, expressing confidence that the quantum threat is solvable with preparation.

bitcoinist02/23 10:22

Bitcoin’s Quantum Risk Steals Spotlight At Ethereum Gathering

bitcoinist02/23 10:22

Variant Founder: Everything is a Market, the Endgame of Finance is 'Invisible'

Jesse Walden, founder of Variant Fund, argues that crypto is fundamentally about finance—but in a radically expanded sense. He identifies three core drivers reshaping finance: mass participation, permissionless markets, and programmable endpoints. Mass participation is lowering barriers, intertwining finance with culture as trading becomes a form of expressive, accessible engagement. Permissionless innovation allows new markets to emerge outside traditional systems, forcing regulatory evolution—as seen with prediction markets, stablecoins, and ICOs. Programmable markets, combined with AI, are evolving from outputs into infrastructure. Markets generate real-time, hard-to-fake data that AI and applications can consume as APIs, enabling new products and automated decision-making. Ultimately, finance is transitioning from a vertical industry to a horizontal, invisible infrastructure layer embedded into the fabric of culture and technology.

marsbit02/23 08:39

Variant Founder: Everything is a Market, the Endgame of Finance is 'Invisible'

marsbit02/23 08:39

What Can OpenClaw Do? A Deep Dive into 10 Real-World Use Cases from a Power User

Based on Matthew Berman's real-world use cases, this article details how OpenClaw, a powerful AI framework, can be deployed to automate a wide range of tasks, effectively replacing the functions of a small operations team. The ten core use cases are: 1. **Natural Language CRM:** Built in 30 minutes with no code, it integrates with Gmail and calendar, filters important contacts/emails, and enables semantic search and relationship health scoring. 2. **Meeting Action Item Tracker:** Automatically extracts tasks from transcribed meetings, distinguishes between user and others' responsibilities, tracks completion, and learns from user feedback. 3. **Personal Knowledge Base:** Users simply share links (articles, videos, PDFs) via Telegram; OpenClaw automatically processes, stores, and enables natural language search on the content. 4. **Business Advisory Board:** Eight AI expert agents analyze 14 different business data sources nightly, debate findings, and deliver prioritized, consolidated recommendations. 5. **Security Committee:** A multi-agent system runs a nightly audit of the entire codebase, logs, and data for vulnerabilities, offering fixes and evolving its rules. 6. **Social Media Tracker & Daily Briefing:** Automatically pulls analytics from multiple platforms for a daily performance report and feeds this data to the advisory board. 7. **Video Topic Pipeline:** Turns a Slack message into a fully researched video outline, complete with title suggestions and background research, then creates an Asana task. 8. **Memory System:** The AI maintains a persistent memory of user preferences and conversation history, allowing it to understand context and adapt its personality for different channels. 9. **Food Diary:** Users log meals via photos; the AI identifies food, correlates it with symptom reports, and helped identify a previously unknown food sensitivity. 10. **Automated Infrastructure:** A robust backend handles scheduled tasks (CRM scans, backups, updates), encrypted backups, and API usage tracking. The article emphasizes that the true power lies not in individual features but in how these interconnected systems create a "data flywheel," where outputs from one module become inputs for others, massively boosting productivity. It concludes that the key modern skill is orchestrating such AI workflows with natural language, not just coding.

marsbit02/23 07:39

What Can OpenClaw Do? A Deep Dive into 10 Real-World Use Cases from a Power User

marsbit02/23 07:39

AI Payment Undercurrents: Google Brings 60 Allies, Stripe Builds Its Own Entire Road

The AI payment war is intensifying as major tech companies race to control the infrastructure for AI-driven transactions. Google has formed an alliance with over 60 traditional financial and tech companies, including Mastercard and PayPal, to establish the "AI Agent Payment Protocol." Meanwhile, Stripe has taken a more independent approach by acquiring key companies like Bridge (for stablecoin capabilities) and Privy (for wallet technology), co-developing the Tempo blockchain with Paradigm, and launching the Agentic Commerce Protocol (ACP) with OpenAI. This allows AI platforms like ChatGPT to enable seamless, in-chat payments without redirecting users. At the heart of the conflict is the "toll" for processing AI transactions. Stripe’s strategy involves building a full-stack solution—from stablecoin accounts and blockchain infrastructure to banking licenses—while Google’s coalition relies on established financial networks. Notably, Circle and its USDC stablecoin emerge as a likely winner regardless of which camp dominates, as both ecosystems depend on compliant, auditable digital dollars for settlement. The broader implication is the need for a financial system capable of supporting autonomous AI agents conducting economic activities. While Stripe envisions a future where AI handles end-to-end transactions, Google’s alliance prefers integrating AI with existing human-centric systems. Regardless, the adoption of stablecoins for AI payments is accelerating, with regulatory and consumer protection questions remaining unresolved. The infrastructure is being built rapidly, and the toll collection has already begun.

marsbit02/23 07:34

AI Payment Undercurrents: Google Brings 60 Allies, Stripe Builds Its Own Entire Road

marsbit02/23 07:34

Vitalik Buterin Says Perfect Crypto Security Remains Impossible

Vitalik Buterin, the founder of Ethereum, argues that perfect security in the cryptocurrency sector is unattainable due to the complexity of human intent. He explains that blockchain networks cannot perfectly interpret user intentions and hard-code them into inflexible code. Buterin defines security as an alignment problem, where the goal is to ensure the protocol's actions match user expectations. Even basic transactions involve assumptions about identity, network, and interface accuracy that cannot be fully programmed. Instead of pursuing perfect security, Buterin advocates for layered security mechanisms. These include redundancy through multiple independent checks, transaction simulations, spending limits, and address verification. He also suggests that AI could complement, but not replace, cryptographic security by modeling human judgment patterns. However, no technological system can fully emulate human reasoning. Buterin concludes that crypto security is a continuous alignment process rather than a final endpoint, requiring ongoing improvements as technology evolves.

TheNewsCrypto02/23 06:58

Vitalik Buterin Says Perfect Crypto Security Remains Impossible

TheNewsCrypto02/23 06:58

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