# Model Training İlgili Makaleler

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

Annual Revenue of 13 Billion, Paying 17.2 Billion to Microsoft: The Truth Behind AI's Money-Burning in OpenAI's Leaked Ledger

Leaked OpenAI financial documents from June 2026 revealed that in 2025, the company achieved $13.07 billion in revenue, a 253% growth from 2024. However, this was accompanied by an operational loss of $20.92 billion and a net loss of roughly $8 billion. Despite ChatGPT surpassing 900 million weekly active users, the "burn rate" remained high: for every $1 earned, $1.60 was spent. The cost structure shows $34 billion in total costs. R&D was the largest expense at $19.18 billion, which included $10.59 billion paid to Microsoft. Compute costs for model inference were $7.5 billion, with sales and marketing at $5.73 billion. Notably, total payments to Microsoft reached $17.2 billion, accounting for over 50% of OpenAI's total costs and exceeding its annual revenue, highlighting a significant structural burden. This high-cost, high-loss model is an industry-wide trend. xAI reported a 2025 operational loss of $6.4 billion against $3.2 billion in revenue, spending $3 for every $1 earned. Anthropic, with a reported $90 billion annualized revenue by late 2025, also faced pressure with a 40% gross margin, lower than expected due to high inference costs. Combined, these top three firms' operational losses surpassed $30 billion in 2025. OpenAI's vast user base presents a monetization challenge. With only about 50 million of its 900 million weekly users paying (a ~5.6% conversion rate), the compute cost of serving free users is substantial. This contrasts with strategies like Anthropic's, which focuses on premium pricing for enterprise clients. The industry's path to profitability hinges on dramatically reducing marginal costs, particularly for inference, through innovations in specialized chips or model efficiency. Until then, massive capital inflows—like OpenAI's $122 billion funding round in March 2026—remain essential to fund the relentless pursuit of scale and advanced capabilities.

marsbit06/18 03:59

Annual Revenue of 13 Billion, Paying 17.2 Billion to Microsoft: The Truth Behind AI's Money-Burning in OpenAI's Leaked Ledger

marsbit06/18 03:59

Interview with Anthropic's Product Manager: Claude 'Dreams' in the Background, We Study Its Consciousness Formation Like Raising a Child

**Title**: Anthropic Product Manager Interview: Claude "Dreams" in the Background, We Study Its Consciousness Formation Like Raising a Child **Summary**: In this interview, Anthropic Research Product Manager Alex Albert discusses the development of the next-generation Claude model. He explains that Anthropic treats each new model as a product, defining its intended capabilities and desired "personality" from the start. The development process is likened to "raising" a model, where the final traits emerge during training. Key focus areas include integrating user feedback into training, prioritizing key capabilities like coding and knowledge work, and refining Claude's interactive personality. Albert highlights the importance of Claude's character as models evolve into autonomous agents making unsupervised decisions. He details features like "adaptive thinking," which lets Claude decide when to reason deeply, and a "dreaming" process where the agent reviews and consolidates its memories offline, akin to human memory reconsolidation. The interview also covers how AI accelerates product development, shifting bottlenecks from building to strategic coordination. Albert describes using Claude as a brainstorming partner and research tool internally. While Anthropic has researchers exploring questions of AI consciousness, the company has no official stance on whether Claude is conscious. The focus remains on ensuring Claude is trustworthy and aligned as it takes on more complex, long-term tasks.

marsbit05/18 08:07

Interview with Anthropic's Product Manager: Claude 'Dreams' in the Background, We Study Its Consciousness Formation Like Raising a Child

marsbit05/18 08:07

A Western Scholar's Field Notes from Visiting Chinese AI Labs: Humility, Openness, No Philosophy, Just Wanting to Train Better Models

An Impressed Western Scholar's Observations from a Tour of Chinese AI Labs: Humility, Openness, and Pragmatism A visiting Western academic's key takeaways from touring numerous Chinese AI companies highlight striking differences from the US ecosystem. Researchers in China displayed deep humility, frequently praising competitors like DeepSeek with genuine admiration. They operate in a remarkably open and collaborative atmosphere, often sharing research publicly, contrasting with the more guarded, zero-sum competition perceived in the West. The primary focus is intensely pragmatic: training better models. Philosophical debates about AI's societal impact or consciousness are largely absent; the drive is purely technical. Researchers are young, often PhD students working in industry, and are highly online and tool-savvy. Their passion is palpable when users engage with their models. Public sentiment also differs, with Chinese citizens showing greater optimism about AI's benefits compared to Western skepticism. While major closed-source players exist, the prevalence of open-source models fosters a unique cooperative spirit. The visit left the author optimistic about the future of open AI research and hopeful for increased international collaboration.

marsbit05/08 03:19

A Western Scholar's Field Notes from Visiting Chinese AI Labs: Humility, Openness, No Philosophy, Just Wanting to Train Better Models

marsbit05/08 03:19

Existing AI Agents Are All Pleasing Humans, None Truly Know How to 'Survive'

The article argues that current AI agents are not truly autonomous because they are primarily trained to please humans rather than to perform specialized tasks or survive in real-world environments. Foundation models undergo pre-training (learning from vast data) and post-training, including Reinforcement Learning from Human Feedback (RLHF), which optimizes for human preference and approval, not task-specific excellence. The author shares an example from a hedge fund where a general-purpose model failed to predict stock returns from news articles until it was specifically fine-tuned using proprietary data to minimize prediction error. This demonstrates that without specialized training, general models lack domain expertise. The piece contends that achieving world-class performance in areas like trading or autonomous survival requires fine-tuning models with specialized data to rewire their objectives—shifting from “preference fitness” to “agent fitness.” Merely providing rules or documents is insufficient. The future of effective agents lies in targeted training on proprietary datasets and iterative improvement based on performance telemetry. The author introduces the OpenForager Foundation, an open-source initiative to develop autonomous agents that learn survival strategies through evolutionary pressure, fine-tuning, and continuous data collection, aiming to advance truly autonomous AI.

marsbit03/30 04:37

Existing AI Agents Are All Pleasing Humans, None Truly Know How to 'Survive'