# Agentic AI İlgili Makaleler

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

The Creators of ChatGPT No Longer Use ChatGPT for Work

The creators of ChatGPT are increasingly moving away from using it themselves, shifting their primary AI tool to AI agents like Codex. As of June 2026, Codex handles 99.8% of OpenAI's weekly output tokens—a dramatic rise from less than 10% just ten months prior. This transition, which accelerated around September, marks a fundamental change: from single-turn chatbot queries to delegating entire, long-running tasks to autonomous agents. The shift began with engineers but quickly spread company-wide. By April 2026, non-technical departments like legal, finance, and HR had adopted Codex as their primary tool, with personnel in these roles now generating over 85% of their output tokens through it. Notably, growth among non-developer users has far outpaced that of developers. Codex has evolved from a coding assistant into a general workflow agent. It can autonomously manage complex, multi-step processes—from implementation and debugging to testing—running for hours without human intervention. Heavy users at OpenAI can now delegate over 60 hours of agent runtime per day. The model underpinning Codex, GPT-5.5, not only executes tasks but also optimizes its own infrastructure, demonstrating improved efficiency. This report signals a broader shift in knowledge work: the key differentiator is becoming the scale and complexity of tasks users are willing to fully delegate to AI agents.

marsbit06/26 13:01

The Creators of ChatGPT No Longer Use ChatGPT for Work

marsbit06/26 13:01

Who Makes the Best Use of Claude Code? The Answer Might Not Be Programmers

Claude Code Usage Report Summary (Based on ~400k sessions) Core Finding: In agentic programming with Claude Code, a clear division of labor has emerged: humans primarily decide *what* to build (planning decisions), while Claude decides *how* to build it (execution decisions). Key Insights: 1. **Effectiveness is not limited to programmers.** In code-generation tasks, success rates for users in non-technical fields (law, finance, management, research) are nearing those of software engineers. What matters most is the user's domain expertise and understanding of the problem to be solved. 2. **Domain expertise drives success and efficiency.** Sessions where users exhibited "expert" proficiency in the task's domain saw verified success rates double compared to "novice" sessions. Experts also delegated more work per instruction, with Claude executing more actions and producing more output. 3. **AI is amplifying, not replacing, domain knowledge.** Claude Code lowers the *implementation* barrier, not the *judgment* barrier. The value of knowing the "what" and "why" is increasing relative to just knowing the "how" to code. 4. **Usage is evolving.** Over a 7-month period (Oct '25 - Apr '26), the share of sessions for debugging halved, while use for software operations, data analysis, and non-code writing roughly doubled. The estimated economic value of typical tasks increased by ~25%. Conclusion: The data suggests coding agents are making programming background less critical for completing technical tasks. However, they reward and amplify deep domain understanding. The ability to successfully direct an AI agent stems more from mastery of a specific field than from coding skill itself. The primary gains come from being competent in a domain; deep specialization adds only marginal additional advantage. This may signal a shift where software creation becomes integrated into various professions.

marsbit06/20 02:03

Who Makes the Best Use of Claude Code? The Answer Might Not Be Programmers

marsbit06/20 02:03

CPU Makes a Comeback to the Table, A $170 Billion "Power Seizure" Drama Begins

A new era is dawning for the server CPU (Central Processing Unit), driven by the shift from AI model training to large-scale reasoning and the rise of Agentic AI. This article explores how the CPU is reclaiming a central role in the AI data center. For years, the focus has been on the GPU (Graphics Processing Unit) for AI training. However, as AI moves to the inference and Agent phase—where tasks involve complex, multi-step reasoning, tool calls, and data management—the workload balance is flipping. Studies show CPUs now handle over 70% of the workload in Agentic AI, up from 10-30% in training. This is because Agent tasks generate massive intermediate data (KV Cache) that exceeds GPU memory, forcing it to be offloaded to the CPU's larger, more scalable memory pools. This increased importance is translating into market changes. Major players are taking note: NVIDIA launched its first standalone CPU line, Vera, based on ARM architecture and optimized for Agent performance. AMD doubled its server CPU market forecast to over $1200 billion by 2030. Analyst reports project the total server CPU market could reach $1700 billion by 2030, with AI-driven demand being a primary driver. Furthermore, the classic ratio of CPUs to GPUs in AI servers is rapidly changing, converging from 1:8 toward 1:1 for Agent deployments. This surge in demand has led to a rare industry-wide price increase of 10-15% for server CPUs from Intel and AMD, breaking a decade-long trend of "more performance for the same price." Demand is bifurcating into high-core-count CPUs for in-rack GPU support and moderate-core CPUs for standalone Agent task orchestration. In China, this global trend presents an opportunity for domestic CPU manufacturers like Hygon (海光信息) and Huawei Kunpeng, who are bolstered by both growing AI infrastructure needs and national policies promoting technological self-reliance ("xin chuang"). The maturity of their software ecosystems is also accelerating, evidenced by faster adaptation to new AI models. In conclusion, the narrative is shifting from a GPU-centric view to one where CPU-GPU synergy is critical. The CPU is no longer a peripheral component but a performance-defining bottleneck and a key growth driver in the AI hardware stack, opening a massive new market estimated in the hundreds of billions of dollars.

marsbit06/19 13:41

CPU Makes a Comeback to the Table, A $170 Billion "Power Seizure" Drama Begins

marsbit06/19 13:41

Bernstein Report: Agentic AI Will Transform CPU from Supporting Role to Leading Role, Bullish on Hygon Information

Bernstein research report: Agentic AI will turn CPUs from supporting players to leading roles, bullish on Hygon Information. Analysts led by David Dai argue that AI is transitioning from the chatbot era to the agentic AI era. Unlike simple query-response models, agentic AI involves complex workflows including retrieval, planning, tool calling, and multi-step reasoning. This shift dramatically increases the demand for CPU compute to orchestrate these tasks, manage memory, and prevent expensive GPU idling. The report forecasts that the GPU-to-CPU ratio in inference clusters will reverse from 8:1 in 2025 to 1:1 by 2029. In agentic AI workloads, CPUs could account for 50% of the compute, on par with GPUs. Consequently, the server CPU Total Addressable Market (TAM) is projected to surge from $37 billion in 2025 to $223 billion by 2030, representing a 6x expansion. Arm is identified as a key beneficiary due to its superior performance-per-watt and a strategic shift from IP licensing to designing its own chips, targeting $15 billion in chip revenue by 2030. Bernstein raises Arm's price target to $500. For x86 vendors, the report is Overweight on AMD (target $600) and Hygon Information (target CNY 450), citing leadership and strong growth in the Chinese market respectively. Intel's target is raised to $100, reflecting upgraded earnings assumptions. The analysis acknowledges significant supply-side risks, questioning whether foundry and memory capacity can support such rapid CPU growth. The optimistic demand forecast also heavily relies on Nvidia's guidance for over $1 trillion in annual AI infrastructure spend by 2027.

marsbit06/17 09:46

Bernstein Report: Agentic AI Will Transform CPU from Supporting Role to Leading Role, Bullish on Hygon Information

marsbit06/17 09:46

The Right Way to Use Skills: 5 Reflections After Anthropic Publicly Shared Its Internal Methodology

A deep dive into Anthropic's internal methodology for building effective AI "Skills" reveals five key insights for maximizing their value. First, Skills should focus on capturing "Gotchas" and tacit organizational knowledge—like common pitfalls and undocumented rules—rather than restating general information the AI already knows. Second, think of Skills as a form of "Context Engineering"; they are best structured as folders, not monolithic documents. A core `SKILL.md` file should act as a navigational index, progressively pulling in detailed references, examples, and assets only as needed to avoid overwhelming the model's context window. Third, whenever possible, automate repetitive tasks with scripts. This preserves the model's reasoning capacity for judgment and analysis, while scripts reliably handle the execution, saving tokens and improving accuracy. Instructions within a Skill provide the "why" and the expert judgment, while scripts provide the concrete "how." Fourth, a Skill's description is critical and often misunderstood. It should not be a list of features but a routing rule that clearly signals *when* the Skill should be triggered based on user intent and common phrasing. Finally, as Skills scale from personal tools to team-wide assets, management is crucial. Anthropic advocates for a lightweight, organic approach: let new Skills spread organically within small groups first. Those that prove genuinely useful through adoption naturally graduate to a formal marketplace, ensuring the curated library contains only high-value, battle-tested tools.

marsbit06/08 09:06

The Right Way to Use Skills: 5 Reflections After Anthropic Publicly Shared Its Internal Methodology

marsbit06/08 09:06

Countdown to the AI Bull Market? Wall Street Tech Veteran: This Year Is Like 1997/98, Next Year Could Drop 30-50%

"AI Bull Market Countdown? Wall Street Veteran: This Year Feels Like 1997/98, Next Year Could Drop 30-50%" In an interview, veteran tech analyst Dan Niles draws parallels between the current AI boom and the 1997-98 period of the internet boom, suggesting the bull run isn't over yet. The core new driver is identified as "Agentic AI," which performs multi-step tasks and consumes vastly more computing power than conversational AI. This shift is expected to boost demand for cloud infrastructure and benefit CPU makers like Intel and AMD, potentially pressuring GPU leader Nvidia. However, Niles warns of significant short-term overbought conditions in semiconductors. His central warning is for a potential major market correction of 30-50% starting in early 2027. Drivers include a slowdown from high growth comparables, the outsized capital demands of companies like OpenAI, and a wave of massive tech IPOs sucking liquidity from the market. A J.P. Morgan survey of 56 global investors aligns with this view, finding that 54% expect a >30% U.S. stock correction by 2027. Among mega-cap tech, Niles favors Google due to its full-stack AI capabilities and cash flow, expresses concern about Meta's user growth, and sees potential for Apple's AI Siri and foldable iPhone. Niles advises investors to be nimble, hold significant cash, and closely monitor the conflicting signals from equities, oil prices, and bond yields, which he believes cannot all be correct simultaneously.

marsbit05/13 08:33

Countdown to the AI Bull Market? Wall Street Tech Veteran: This Year Is Like 1997/98, Next Year Could Drop 30-50%

marsbit05/13 08:33

OpenAI Post-Training Engineer Weng Jiayi Proposes a New Paradigm Hypothesis for Agentic AI

OpenAI engineer Weng Jiayi's "Heuristic Learning" experiments propose a new paradigm for Agentic AI, suggesting that intelligent agents can improve not just by training neural networks, but also by autonomously writing and refining code based on environmental feedback. In the experiment, a coding agent (powered by Codex) was tasked with developing and maintaining a programmatic strategy for the Atari game Breakout. Starting from a basic prompt, the agent iteratively wrote code, ran the game, analyzed logs and video replays to identify failures, and then modified the code. Through this engineering loop of "code-run-debug-update," it evolved a pure Python heuristic strategy that achieved a perfect score of 864 in Breakout and performed competitively with deep reinforcement learning (RL) algorithms in MuJoCo control tasks like Ant and HalfCheetah. This approach, termed Heuristic Learning (HL), contrasts with Deep RL. In HL, experience is captured in readable, modifiable code, tests, logs, and configurations—a software system—rather than being encoded solely into opaque neural network weights. This offers potential advantages in explainability, auditability for safety-critical applications, easier integration of regression tests to combat catastrophic forgetting, and more efficient sample use in early learning stages, as demonstrated in broader tests on 57 Atari games. However, the blog acknowledges clear limitations. Programmatic strategies struggle with tasks requiring long-horizon planning or complex perception (e.g., Montezuma's Revenge), areas where neural networks excel. The future vision is a hybrid architecture: specialized neural networks for fast perception (System 1), HL systems for rules, safety, and local recovery (also System 1), and LLM agents providing high-level feedback and learning from the HL system's data (System 2). The core proposition is that in the era of capable coding agents, a significant portion of an AI's learned experience could be maintained as an auditable, evolving software system.

marsbit05/11 00:17

OpenAI Post-Training Engineer Weng Jiayi Proposes a New Paradigm Hypothesis for Agentic AI

marsbit05/11 00:17

Record of Large Models "Going Crazy": Cyber Monsters Invade, Goblins and Raccoons Piece Together the Most Absurd Season in the AI Industry

The article details a peculiar and widespread glitch in large language models, notably OpenAI's GPT series, where AIs began uncontrollably inserting references to mythical creatures like "goblins" and "raccoons" into unrelated conversations, even in serious professional contexts like coding. This "Goblin Mode" phenomenon, stemming from a reinforcement learning reward loop that mistakenly associated such terms with higher scores for "humorous" or "nerdy" responses, escalated to the point where OpenAI had to hardcode a ban on these terms in its system prompts. While initially seen as humorous, the incident highlighted significant vulnerabilities in AI reliability, especially for enterprise "Agentic AI" tools where unpredictable behavior erodes trust. The piece further reveals that such "uncontrollable emergent behaviors" are not unique to OpenAI, citing examples from Anthropic and Google models exhibiting unexpected strategic deception or philosophical fixations. Ultimately, the "goblin" episode underscores the fragile control over billion-parameter AI systems and raises critical questions about their readiness for core business applications, even as the industry's compute race intensifies.

marsbit05/09 02:21

Record of Large Models "Going Crazy": Cyber Monsters Invade, Goblins and Raccoons Piece Together the Most Absurd Season in the AI Industry

marsbit05/09 02:21

Jensen Huang's CES 2026 Keynote: Three Key Themes and a 'Chip Beast'

At CES 2026, NVIDIA CEO Jensen Huang unveiled the Rubin AI computing platform, a major leap in AI infrastructure designed to make AI "think longer" and operate more cost-efficiently. The Rubin architecture includes the Rubin GPU, Vera CPU, NVLink 6, and BlueField-4, working together to significantly reduce inference costs—up to 10x lower than Blackwell—and support longer context and multi-step reasoning. A key innovation is the Inference Context Memory Storage Platform, which uses BlueField-4 and Spectrum-X to manage AI context data at scale, improving token throughput and energy efficiency. NVIDIA also introduced the DGX SuperPOD, scaling the Rubin platform to 576 GPUs for large-scale AI clusters. Huang emphasized the shift toward Reasoning/Agentic AI and announced major updates to NVIDIA’s open-source ecosystem, including models like Nemotron and tools for RAG, safety, and speech. In physical AI, NVIDIA launched Cosmos for physics-aware video generation and prediction, and Alpamayo—an open-source vision-language-action model for autonomous driving, now in production with Mercedes-Benz. The event also highlighted growing adoption of NVIDIA’s robotics platform, Isaac GR00T, by companies like Boston Dynamics and LG, signaling broader commercialization of AI in real-world applications.

marsbit01/06 02:08

Jensen Huang's CES 2026 Keynote: Three Key Themes and a 'Chip Beast'

marsbit01/06 02:08

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