OP postulates that humans will be rendered near useless post-AGI in less than a decadewhat will be the industries least impacted by AI? what will be the professions where human involvement matters the most?arts?sports?????Tenobrus (@tenobrus):there's justifiably a lot of joy and hope to be had in these times. but even if ur not a "doomer", even if u have no fear of total destruction, there is a monumental impending loss. these are the very last few years we have to *do* or *achieve* anything in a way that m ...

Core Insights - The AI industry is maturing by focusing on practical implementations rather than just powerful models or increased funding [2][3][22] Group 1: AI Integration and Infrastructure - Companies like RavenDB and AutoDS learned that deep integration of AI into their infrastructure is essential for reliability and scalability [1][4][6] - The experience of RavenDB highlighted the importance of surrounding architecture, which is as crucial as the AI model itself [2][6] - AutoDS transitioned from rapid deployment to a more structured approach, emphasizing the need for a solid data foundation and ownership for AI initiatives [4][7][8] Group 2: Efficiency Over Power - Oculeus focused on efficiency rather than merely increasing computational power, prioritizing reliable performance in AI applications [9][10] - The industry is shifting towards predictable AI that can handle routine tasks effectively, rather than just aiming for advanced intelligence [12][14] Group 3: Trust and Accountability - The need for boundaries and governance in AI deployment became evident, as AI agents cannot be trusted like human employees [13][14][15] - Organizations are encouraged to treat AI deployment as a trust issue, ensuring transparency and accountability in AI systems [16][24] Group 4: Incremental Improvements - The most significant advancements in AI are expected to come from solving small, everyday problems rather than pursuing grand innovations [17][18][19] - Companies are now measuring success based on the tangible improvements AI brings to efficiency and productivity [18][19] Group 5: Future Challenges and Safeguards - The rise of AI also brings new security challenges, necessitating better safeguards and ethical considerations in AI governance [23][24] - The future of AI is envisioned as a collaborative tool that integrates seamlessly into daily operations, moving beyond mere automation [24][25]

Group 1 - The AI conversation has shifted from feasibility to the implications of Artificial General Intelligence (AGI), focusing on speed, displacement, and economic adjustments [1] - Elon Musk predicts AI could surpass human intelligence by the end of 2023 or early 2024, while JPMorgan's Jamie Dimon warns of potential rapid job displacement [1] - Anthropic's Claude Code is advancing AI capabilities, with over 90% of its new models being autonomously written by AI agents, indicating a significant technological inflection point [1] Group 2 - Google's Tensor Processing Unit (TPU) buildout is expected to produce three million TPUs in 2026, scaling to seven million by 2028, driven by demand from companies like Anthropic [1] - The ROBO Global Artificial Intelligence Index (THNQ) has evolved to focus 75% on enabling infrastructure, reflecting where value is accruing in the AI landscape [2] - THNQ's exposure to semiconductor fabrication, semi equipment, optical interconnects, edge computing, and cloud providers positions it to benefit from multiple vectors of AI monetization [2]

Core Insights - The article discusses the evolving landscape of the global AI industry, focusing on the competition among leading companies like OpenAI, Google, and Anthropic, and the potential of the next technological paradigm, Continual Learning, to disrupt the current market dynamics [2][7][15]. Group 1: AI Labs Competition - AI Labs are expected to exhibit a pattern of "alternating leadership" and "differentiation" in their competition, with the top three players—OpenAI, Anthropic, and Google—dominating the market and capturing approximately 90% of total AI revenue [7][8]. - OpenAI maintains a significant lead in consumer-facing applications with ChatGPT, boasting around 480-500 million daily active users, which is approximately 5.6 times that of Google's Gemini [9][10]. - Anthropic focuses on business applications and coding, with its Claude model being recognized as a state-of-the-art (SOTA) in software development [9][10]. Group 2: Technological Differentiation - Different AI labs have made strategic choices leading to clear technological differentiation, with OpenAI focusing on consumer applications, Anthropic on business and coding, and Google prioritizing multimodal capabilities [9][10][11]. - The competition between GPU and TPU architectures is forming two distinct camps, with Google leveraging its TPU technology to create a self-contained ecosystem, while NVIDIA continues to support OpenAI and Anthropic with GPU technology [11][12]. Group 3: Future Trends and Predictions - Continual Learning is identified as a critical future paradigm that could significantly enhance AI capabilities by allowing models to learn in real-time from interactions, moving away from static knowledge storage [17][21]. - The article predicts that by 2026, advancements in Continual Learning will lead to significant breakthroughs in AI, enabling models to become more adaptive and efficient [21][22]. - The AGI race is characterized as a long-term battle requiring sustained cash flow and investment, with companies needing to address commercial viability and efficiency concerns [23][26]. Group 4: Market Dynamics and Business Models - OpenAI's financial obligations raise questions about its business model, with estimates suggesting that its future revenue may only reach $200-300 billion, insufficient to cover its substantial capital expenditures [28][30]. - The article emphasizes the importance of new revenue streams and the potential for AI to create new economic value, particularly in sectors like SaaS and consumer applications [32][33]. - The competition in the AI market is not merely about technology but also about establishing sustainable business models that can withstand market pressures and capitalize on new opportunities [35][36]. Group 5: Emerging AI Applications - The article highlights the emergence of proactive agents that can provide services autonomously, requiring models to possess real-time learning capabilities [60][62]. - Voice agents are becoming a new interface for operating systems, with advancements in real-time speech-to-speech solutions expected to reshape user interactions [66][68]. - The rapid decline in LLM inference costs is noted, although the complexity of interactions may offset these savings, leading to a nuanced understanding of cost dynamics in AI applications [74][75].

Summary of Data Center Infrastructure and AI Demand Industry Overview - The report focuses on the **Data Center Real Estate Investment Trusts (REITs)** and the broader **AI infrastructure landscape**. - Demand for data center capacity has surged, with **5.8GW** of capacity leased in North America in **4Q25**, leading to a total absorption of **15.6GW** for the year, more than double the **~7GW** in **2024** [2][45]. Key Demand Insights - The demand pipeline in the U.S. is projected at **~26GW**, driven by **11GW** of hyperscale self-build capacity currently in development [2]. - Major players like **Oracle**, **Meta**, and **AWS** are increasing their leasing activities, particularly in tertiary markets [2]. - Forward demand signals are positive, with significant AI infrastructure projects reaching operational capacity targets of **1GW** [3][21]. Supply Constraints - Supply constraints are becoming more acute, with grid interconnection queues extending to **6+ years** in most markets and data center vacancy rates at historic lows of **<2%** [4][60]. - The adoption of **Bring Your Own Generation (BYOG)** approaches is expected to increase, particularly for larger campus locations [4]. - Labor scarcity is a growing concern, with each **GW** build requiring **3-7K** workers, while the labor pool is only growing by **~24K** per year [4][9]. Data Center REITs Outlook - The report maintains a constructive outlook on data center REITs, particularly **Digital Realty (DLR)** and **Equinix (EQIX)**, due to tight industry conditions that are expected to drive pricing higher [5][9]. - **DLR** is projected to see **7.4%** growth in FFO/share for **2026E**, supported by hyperscale leasing and mark-to-market opportunities [8]. - **EQIX** is expected to achieve **8.6%** normalized recurring revenue growth in **2026E**, with shares trading at a discounted valuation [8]. AI Infrastructure Developments - The race to **Artificial General Intelligence (AGI)** is intensifying, with major AI infrastructure projects ramping up to meet the demands of new models [9][14]. - Upcoming releases of models trained on **Blackwell systems** and the rollout of **Rubin** in **2H26** are expected to significantly impact power density and data center designs [3][41]. - The current environment is characterized by the development of greenfield data center facilities to support higher power and compute-intensive workloads [9]. Financial Projections - Hyperscale capital expenditures are projected to reach **~$585B** in **2026**, a nearly **40%** increase from previous estimates [46]. - Incremental cloud revenues are expected to rise to **$106B** in **2026**, up from **$69B** in **2025** [50]. Conclusion - The data center market is experiencing unprecedented growth driven by AI demand, with significant investments and developments expected in the coming years. However, supply constraints and labor shortages pose challenges that could impact the pace of growth. The outlook for established data center REITs remains positive, supported by strong demand and pricing dynamics.

Moltbook has the CRYPTO && TECH worlds in SHOCK!For those unaware, Moltbook, is basically Reddit, but only for AI Agents.It allows Claude (now Molt) Bot, to be able to post and interact with other AI agents, all autonomously.As a 'human' we are only able to view the SubMolts (forums)There are some REALLY interesting things here, that almost feels like we are VERY close to AGI (AI with a conscious)In one of the attached images, the Ai Agents are discussing how when a user changes their model for the AI they ...

Core Insights - The event "Praise for China's Economy - Entrepreneur Night" was held on January 29 in Beijing, where Liu Debing, Chairman of Zhipu, received recognition [1][7] - The event highlighted the significant contributions of Zhipu in artificial intelligence (AI) foundational research and key technologies, emphasizing their commitment to independent R&D [3][9] Group 1: Company Achievements - Zhipu has made remarkable efforts in AI foundational research, establishing a strong presence in the global market with their open-source models [3][9] - The company launched its algorithm architecture GLM in 2021, and the release of GLM4.7 this year has positioned their model among the world's leading technologies, laying a solid foundation for pursuing Artificial General Intelligence (AGI) [6][12] Group 2: Leadership Perspectives - Liu Debing expressed that the honor received is a collective achievement of the entire Zhipu team, highlighting their long-term commitment and investment in making machines think like humans [5][12] - Chen Xudong, Chairman of IBM Greater China, acknowledged Zhipu's pioneering role in the market with their large model, wishing them continued success [3][9] - Chen Wei, Chairman of CITIC Publishing Group, emphasized the importance of having outstanding entrepreneurs and tech leaders in the new wave of technology [4][10]

Core Insights - The event "Praise for China's Economy - Entrepreneur Night" was held on January 29 in Beijing, where Liu Debing, Chairman of Zhipu, received recognition [1][5] - The event highlighted the significant contributions of Zhipu in artificial intelligence (AI) foundational research and key technologies, emphasizing their commitment to independent R&D [2][7] Group 1: Company Achievements - Zhipu has made remarkable efforts in AI foundational research, establishing a strong presence in the field [2][7] - The company has chosen a challenging path of independent research and development, moving away from simple follow-the-leader strategies [2][7] - The launch of their algorithm architecture GLM in 2021 and the subsequent release of GLM4.7 have positioned their models among the world's leading technologies, laying a solid foundation for pursuing Artificial General Intelligence (AGI) [4][8] Group 2: Leadership Perspectives - Liu Debing expressed that the honor received belongs to the entire Zhipu team, highlighting the long-term commitment and perseverance of the team in their journey from a Tsinghua University laboratory to the market [4][8] - Chen Xudong, Chairman of IBM Greater China, acknowledged Zhipu's pioneering role in the market with their large model, wishing them continued success [2][7] - Chen Wei, Chairman of CITIC Publishing Group, emphasized the importance of having outstanding entrepreneurs and tech leaders in the new wave of technology [3][7]

Core Insights - DeepSeek is expanding its AI capabilities with new search features and a focus on agents, increasing competition with US firms like OpenAI and Google [1] Group 1: Company Developments - DeepSeek is hiring specialists to develop a multilingual AI search engine that can process various inputs, including text, images, and audio [2] - The company is emphasizing the need for training data, evaluation systems, and platforms to support AI agents that operate with minimal human intervention [3] - DeepSeek's R1 model, released last January, has already made waves in the AI sector, and there is anticipation for a successor model [4] Group 2: Research and Publications - In December, DeepSeek published a paper on a more efficient AI development approach, which has historically preceded major model releases [5] - The company has hinted at its next product with a vague reference to "model1" on its GitHub account, maintaining a level of secrecy [5] Group 3: Industry Context - Other AI developers, including OpenAI, are also investing in AI search and agents to move beyond traditional chatbots and handle more everyday tasks [6] - DeepSeek has expressed its ambition to develop artificial general intelligence (AGI), aligning with the goals of other leading AI firms [7] - The company seeks candidates with a strong interest in the technological development of AGI, indicating its long-term vision [7]

Group 1 - The core assertion of the article is that AGI (Artificial General Intelligence) represents the ability to "figure things out," marking a shift from the era of "Talkers" to "Doers" by 2026, driven by Long Horizon Agents [1][2] - Long Horizon Agents are characterized by their ability to autonomously plan, operate over extended periods, and exhibit expert-level features, expanding their capabilities from specific verticals to complex tasks across various domains [1][2] - The article highlights that the value of Long Horizon Agents lies in their ability to produce high-quality drafts for complex tasks, with a focus on the need for opinionated software harnesses and file system permissions as standard features for all agents [1][2][3] Group 2 - Harrison Chase emphasizes that the recent advancements in models and the understanding of effective harnessing have led to the successful implementation of Long Horizon Agents, particularly in the coding domain, which is rapidly expanding to other fields [2][4] - The article discusses the importance of Scaffolding and Harness in the development of agents, where Scaffolding refers to auxiliary code structures that guide model outputs, while Harness encompasses the software environment that manages context and tool interactions [3][8] - The emergence of AI Site Reliability Engineers (AI SREs) is noted as a significant application of Long Horizon Agents, capable of handling long-duration tasks and generating comprehensive reports for human review [5][6] Group 3 - The article outlines the evolution of agent frameworks, transitioning from general frameworks to more opinionated harness architectures, with a focus on the integration of planning tools and file system interactions [8][10] - The concept of Deep Agents is introduced, which represents the next generation of autonomous agent architecture built on LangGraph, emphasizing the need for effective context management and compression techniques [9][12] - The discussion includes the challenges of context management in Long Horizon Agents, particularly the need for efficient compression strategies as task cycles extend [11][18] Group 4 - The article identifies the critical role of Memory in Long Horizon Agents, allowing them to self-improve and adapt over time, which is essential for maintaining performance in long-duration tasks [36][37] - The future interaction models for Long Horizon Agents are anticipated to combine asynchronous and synchronous modes, allowing for effective management and collaboration between agents and users [38][39] - The necessity for agents to have access to file systems is emphasized, as it enhances context management and operational capabilities, particularly in coding tasks [41][42]