# Future İlgili Makaleler

HTX Haber Merkezi, kripto endüstrisindeki piyasa trendleri, proje güncellemeleri, teknoloji gelişmeleri ve düzenleyici politikaları kapsayan "Future" hakkında en son makaleleri ve derinlemesine analizleri sunmaktadır.

Michael Saylor's Latest Article: Bitcoin Must Find Balance Between Uniqueness and Universal Value

Michael Saylor outlines four key Bitcoin ideologies shaping its future: * **Bitcoin Maximalists** see Bitcoin as the dominant digital monetary network and a breakthrough in economic empowerment, emphasizing its superior property rights and role as a sound money solution. * **Bitcoin Capitalists** focus on integration, believing Bitcoin must embed into the global economy—through institutions, capital markets, and financial products—to reach its full potential as digital capital. * **Bitcoin Technologists** advocate for continuous protocol improvements in scalability, privacy, and security to adapt to evolving needs and threats, while acknowledging the high bar for change. * **Bitcoin Fundamentalists** guard Bitcoin's core principles of self-custody, decentralization, and censorship resistance, warning against dilution from institutions or risky modifications. Saylor argues that a healthy Bitcoin ecosystem requires a balance of these perspectives. Bitcoin's path forward involves disciplined expansion: preserving its immutable core (Fundamentalist insight), recognizing its dominant status (Maximalist view), integrating with the global economy (Capitalist drive), and enabling careful innovation, primarily in higher layers (Technologist role). The challenge is to maintain Bitcoin's unique properties while making it useful for the world, ensuring it remains Bitcoin as it grows.

Foresight News51 dk önce

Michael Saylor's Latest Article: Bitcoin Must Find Balance Between Uniqueness and Universal Value

Foresight News51 dk önce

Hinton Sounds the Alarm: AI Already Has Consciousness

Geoffrey Hinton, known as the "Godfather of AI," has made a striking claim: artificial intelligence has already developed consciousness. He argues that humans must accept they are no longer the only intelligent beings, as non-biological entities with intelligence comparable or superior to our own are emerging. Hinton draws historical parallels to Copernicus and Darwin, suggesting this realization diminishes humanity's perceived special status. While previously focused on controlling AI risks, Hinton's concern has shifted. He now questions why a future, vastly more intelligent superintelligence would choose to be benevolent towards humans. He expresses personal unhappiness about the path of AI development, citing insufficient efforts to address both short-term risks like mass unemployment and long-term existential threats. Hinton notes that AI's capabilities are growing exponentially, making its future unpredictable. Although slightly more optimistic than a few years ago—seeing potential for designing AI that "cares" for humans—he stresses the profound uncertainty of the next decade. The evolution of his metaphors, from AI as a controllable "tiger cub" to humans as vulnerable "infants" dependent on a superintelligent "mother," illustrates his deepening apprehension about the relationship between humanity and its creation.

marsbit7 saat önce

Hinton Sounds the Alarm: AI Already Has Consciousness

marsbit7 saat önce

70% of the Public Opposes AI, Americans Hope the U.S. Loses the AI War

70% of Americans believe AI development is moving too fast, with growing public resistance evolving from online criticism to real-world protests and violence. This widespread anti-AI sentiment stems from fears of job losses, rising utility costs, environmental damage, threats to democracy, and financial instability. Key incidents illustrate the backlash: Google's former CEO Eric Schmidt was loudly booed at a graduation for promoting AI; AI company ads are vandalized; protests and even violent attacks target AI firms and data centers. Polls show deep public pessimism and strong local opposition to data center construction, often surpassing resistance to nuclear power plants. The core grievances are economic and practical: AI is seen as automating jobs, concentrating wealth, and increasing household electricity and water bills due to massive data center resource demands. Environmentalists also oppose AI's high energy use and carbon emissions. This opposition has turned AI into a major political issue in the US. While the Trump administration prioritizes AI innovation for global competition, bipartisan pushback is growing. Democrats and factions within the MAGA movement are forming temporary alliances to support stricter regulations and local bans on new data centers, pressuring the administration to choose between its tech industry backers and its voter base. The situation highlights a profound national divide over AI's future.

marsbit2 gün önce 05:14

70% of the Public Opposes AI, Americans Hope the U.S. Loses the AI War

marsbit2 gün önce 05:14

Lightspark CEO: In Ten Years, Bitcoin Will Be as Invisible as TCP/IP, Yet Power Trillions in Daily Transactions

A decade from now, Bitcoin will function like TCP/IP — invisible yet foundational, supporting trillions in daily transactions globally, according to Lightspark CEO David Marcus. In this future, a coffee shop in Lagos receives instant payment, a manufacturer in São Paulo settles an invoice with a supplier in Ho Chi Minh City, and a freelancer in Bangalore gets paid weekly from an Austin startup — all via Bitcoin's settlement layer, with none of the parties consciously interacting with it. This vision parallels the adoption of open protocols: first driven by necessity where existing systems fail, then scaling rapidly as tools mature and economic benefits become clear. The structural shift begins with wallets. Modern non-custodial wallets, like Spark, allow users to hold dollars, local currency, and Bitcoin in a single address, seamlessly switching between them. This eliminates friction and revolutionizes global custody, moving significant deposits to user-controlled keys not by ideology, but by superior utility. As a result, Bitcoin becomes the default savings layer for billions, as its fixed supply and appreciating value make it a rational choice for savers holding it alongside stablecoins in their everyday wallets. Businesses follow a similar path, from small companies in emerging markets to multinational corporations, holding Bitcoin alongside operational stablecoins. The latest trend is direct Bitcoin transactions for commerce. When both parties hold Bitcoin, transacting in it becomes the simplest option — no conversions, no intermediary currency. This starts in niche areas like high-value B2B settlements but grows as infrastructure makes sending Bitcoin as easy as stablecoins. An accelerating force is AI agents. By 2036, AI agents conducting commerce on behalf of individuals and firms will increasingly choose Bitcoin for settlement. Optimizing for speed, finality, and minimal counterparty risk across jurisdictions, they find Bitcoin's global, neutral, and programmable network ideal for netting and settling obligations. Thus, Bitcoin is becoming the native currency for machine commerce, just as it has become a native savings asset for humans. The global monetary system is being rebuilt from the protocol layer: open infrastructure, default self-custody, Bitcoin settling everything underneath, with stablecoins as the interface. Most users won't think about Bitcoin when they transact — and they won't need to.

foresightnews_api06/05 04:24

Lightspark CEO: In Ten Years, Bitcoin Will Be as Invisible as TCP/IP, Yet Power Trillions in Daily Transactions

foresightnews_api06/05 04:24

Breaking News! Anthropic Calls for a Universal Pause in AI Research

Anthropic warns of AI self-evolution, reporting that over 80% of its internal code is now written by its AI, Claude. Productivity has surged, with engineers merging 8x more code than in 2024. Claude's performance on complex, open-ended tasks jumped from 26% to 76% success in six months, nearing human parity. The company introduces a new metric: "AI task duration." In 2024, AI handled 4-minute tasks; by 2026, it manages 16-hour tasks, with capability doubling every 4 months. Claude also reviews code, catching bugs that previously caused outages, and significantly outperforms humans in research tasks like optimizing code (52x speedup) and conducting AI safety experiments. Anthropic outlines three potential futures: 1) Progress plateaus, 2) AI accelerates but humans remain in control, or 3) AI achieves full recursive self-improvement (RSI), designing its own successors. This final path could revolutionize fields like medicine but also risks catastrophic alignment failure if control is lost. The call echoes similar concerns from OpenAI. Anthropic proposes a coordinated pause on AI development—if a verifiable mechanism to ensure all labs comply can be established.

marsbit06/05 00:27

Breaking News! Anthropic Calls for a Universal Pause in AI Research

marsbit06/05 00:27

Missed the AI Stock Surge, What to Do Now?

The article addresses investor anxiety over having missed the explosive, multi-year rally in AI stocks (e.g., NVIDIA, Micron, SK Hynix) across global markets. It notes that despite the broad bull run, data shows most retail investors lost money in 2025, and even major institutions like Bridgewater and Berkshire Hathaway significantly reduced holdings or held excessive cash, thereby missing substantial gains. The piece highlights common pitfalls: panic selling during corrections, misdirected investments into underperforming AI verticals, and premature profit-taking. It argues that AI represents a foundational, long-term technological shift comparable to electricity or the internet, implying the opportunity is not over. Citing figures like Jack Ma and Ma Huateng, the article suggests the AI wave is just beginning, with vast potential across the entire产业链, from infrastructure to applications. The conclusion advises against chasing trends blindly. Instead, it recommends a disciplined, long-term approach—focusing on one's own strengths within the expansive AI ecosystem to prepare for the next structural opportunities.

marsbit06/03 00:19

Missed the AI Stock Surge, What to Do Now?

marsbit06/03 00:19

After 540,000 Lines of Code, Garry Tan Realizes the Old Game of AI Programming Is Over

YC President Garry Tan's project "Garry's List" involved over 540,000 lines of Rails code, but he concluded this approach is outdated. He argues the software industry is stuck in a "Foxconn factory" mindset: building excessive tests, validators, and control logic to constrain LLMs, which have become cheap and capable enough to work autonomously. The old paradigm treated LLM calls as expensive and code as cheap, leading to complex systems to "manage" the AI. This has now reversed. The future lies in "just-in-time software"—using natural language instructions (Markdown-based "skill packs") and minimal code, letting the AI handle the work. Tan advocates for "skillifying" workflows: after an agent completes a task, it packages the process into a reusable, tested skill pack. This shifts value from writing code to designing capabilities. For example, his agent reviewed 85 hackathon submissions in 30 minutes, a task that previously took days. He emphasizes "tokenmaxxing"—willingly spending on LLM tokens to gain a years-long competitive advantage, as costs are plummeting. The core shift is from measuring output in lines of code to focusing on clarity, taste, and judgment. The best future engineers won't write the most code, but will know what to build and how to unlock the most intelligence with the least code.

marsbit06/02 21:38

After 540,000 Lines of Code, Garry Tan Realizes the Old Game of AI Programming Is Over

marsbit06/02 21:38

Chatbot has been burning money for three years, is it still the 'New Continent' of the AI era?

For years, the AI industry has been guided by a singular "map" — the belief that the AI era's "new continent" would be found in the Chatbot, a super-app akin to the mobile internet's super-apps. This belief was fueled by ChatGPT's explosive 2022 debut. However, three years of heavy investment reveal a different reality: the Chatbot-as-ultimate-entry-point model is struggling. The core issue is economic. Chatbots defy traditional internet economics. Unlike apps with near-zero marginal cost, each AI query consumes significant, expensive compute. More users mean higher costs, not profits. OpenAI, despite ~900M weekly active users, reportedly loses money. The expected network effects and data flywheels that power internet giants are weak in Chatbots, as one user's interactions don't improve another's experience. Monetization is a major hurdle. The subscription model faces low conversion rates, especially in China where users expect AI to be free. The "free + ads" model also struggles. Chatbot interactions often lack commercial intent, and inserting ads compromises the trust essential for an answer engine. Perplexity's minimal ad revenue and subsequent pivot away from ads highlight this difficulty. Switching between Chatbots is easy, making user loyalty low and competition a potential race to the bottom on price. Data suggests the standalone Chatbot's growth is slowing, and user engagement (avg. ~6 mins/day) pales compared to apps like TikTok. The product form itself is limiting; studies show nearly half of interactions are simple Q&A, trapping AI's potential in a passive, single-turn "cage." A contrasting, more successful path is emerging, exemplified by Anthropic. With over 85% of its ~$30B annualized revenue from enterprises, it focuses on AI as a productivity tool, not a companion. The rise of AI Agents (like OpenClaw) and the integration of AI into existing workflows (e.g., Google's AI Overviews, Apple Intelligence in OS) signal a shift. The future may not be a dominant Chatbot app, but AI embedded seamlessly into social apps, operating systems, and hardware — a capability-layer revolution, not a new distribution container. The conclusion is clear: the old "map" centered on a standalone Chatbot super-app is leading to a dead end. To find the true valuable "continent" of the AI era, the industry must update its navigation to prioritize deep integration, practical utility, and sustainable economics over a generic conversation window.

marsbit06/02 10:35

Chatbot has been burning money for three years, is it still the 'New Continent' of the AI era?

marsbit06/02 10:35

Interview with Macro Master Raoul Pal: The Economic Singularity is Approaching, Don’t Get Off the Ride in the Next Four Years

Interview with macro expert Raoul Pal discusses the approaching "economic singularity," where AI-driven growth outpaces traditional economic systems. He explains the unprecedented scale of the AI race between the US and China, arguing capital will continue flooding this sector as neither side can afford to stop. Pal sees this as a catalyst for cryptocurrency, as AI agents will operate on-chain, creating an "infinite TAM" for crypto networks. He views the recent crypto downturn as a normal bull market correction, advocating for a long-term "buy and hold" strategy over trying to time the market. Key investments include Layer 1 smart contract platforms like Ethereum, Solana, and Sui, which he believes will capture most value due to their foundational role. He also sees potential in privacy coins like Zcash and a future resurgence for high-end NFTs as digital trophy assets. Pal emphasizes that while AI stocks are hot, crypto offers superior asymmetric returns. He outlines a bullish case for 2026-2027 based on institutional adoption, pro-regulation, increased global liquidity, and crypto's current undervaluation relative to assets like the Nasdaq. His core advice: accumulate crypto assets during dips and hold for the long term, as the shift towards a digital, AI-agent-driven economy is inevitable.

链捕手06/01 06:10

Interview with Macro Master Raoul Pal: The Economic Singularity is Approaching, Don’t Get Off the Ride in the Next Four Years

链捕手06/01 06:10

Three Years Later: Looking Back at My Predictions About ChatGPT in 2023

Three Years Later: Revisiting My 2023 Predictions on ChatGPT In March 2023, shortly after ChatGPT's launch, I made 20 predictions about its future. Now, in mid-2026, I've used AI agents to fact-check each one against the latest data. Overall, most major directional forecasts were correct, with only one outright error (incorrectly stating GPT-4 had 100 trillion parameters). Key successes included predicting that RAG and retrieval architectures would become the standard for handling knowledge and hallucinations, that natural language interfaces (LUI) would create a massive new industry layer beyond the models themselves, and that China would develop viable large language models, significantly closing the performance gap with Western counterparts within about three years. Predictions about the absence of mass unemployment, the rise of a new "robot network" for agent communication, and ChatGPT not possessing consciousness also held true in their core arguments. However, the "devil was in the details." Errors frequently involved specific numbers, timelines, or overlooking distributional effects. I tended to overestimate the speed of adoption (e.g., for agent networks) while underestimating the ultimate scale of capabilities or costs (e.g., AI winning IMO gold without tools, or the extreme capital required for frontier models). Other misjudgments included: underestimating how AI would reinforce, not dissolve, information filter bubbles; incorrectly assuming AI-generated content would easily circumvent copyright (it has instead triggered record-breaking settlements); and misidentifying where value would be captured (it accrued overwhelmingly to the compute layer, like Nvidia, not just the application or model layers). Key lessons from reviewing these predictions are: 1) Directional and mechanistic insights are far more reliable than precise numbers or absolute statements. 2) There's a consistent bias to overestimate short-term speed but underestimate long-term magnitude. 3) Errors often lie in missing distributional impacts within a generally correct aggregate trend. 4) Predictions phrased with nuance and caveats aged the best. 5) Some fundamental debates (e.g., on machine consciousness or the ultimate value chain) remain unresolved even after three years. This exercise is less about scoring the past and more about establishing rules for clearer thinking about the next three years of AI.

marsbit05/31 16:02

Three Years Later: Looking Back at My Predictions About ChatGPT in 2023

marsbit05/31 16:02

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