Joining us now with more is Aaron Levy, the co-founder and CEO of Box. Erin, it's good to see you again. >> Hey, good to be here.Thanks for having me. >> So, I I don't even like this term of AI agents. What is What is an agent. Like, is Chad GPT an agent. Is Grock an agent.What's an agent. Well, um I I think if you think about AI when it first came out, um really, you know, big on the scene a couple years ago with chat, the chat GBT moment, this was really a moment when you would talk back and forth with AI ...

Financial Data and Key Metrics Changes - Total company organic revenues rose by 7%, with the big three segments growing by 9% [4][10] - Adjusted EBITDA increased by 10% to $672 million, reflecting a margin increase to 37.7% [10][26] - Adjusted EPS was $0.85 for the quarter, compared to $0.80 in the prior year [26] Business Line Data and Key Metrics Changes - Legal Professionals segment saw organic revenue growth of 9%, up from 8% in the first half of 2025 [6][11] - Corporates segment organic revenues grew by 7%, driven by offerings in Legal, Tax, and Risk portfolios [11][23] - Tax and Accounting organic revenues grew by 10%, supported by strong performance in Latin America and the U.S. [11][24] - Reuters News organic revenues rose by 3%, primarily due to growth in the agency business [12][25] - Global Print organic revenues declined by 4% year on year [12][25] Market Data and Key Metrics Changes - The percentage of annualized contract value from GenAI-enabled products increased to 24%, up from 22% in the previous quarter [25] - The company expects organic revenue growth of approximately 7% in Q4, including about 9% for the big three segments [31] Company Strategy and Development Direction - The company is focused on leveraging AI innovations to enhance product offerings, particularly in the Legal Professionals and Tax and Accounting segments [5][21] - A balanced capital allocation approach is maintained, with a commitment to assess additional inorganic opportunities [8][9] - The company completed a $1 billion share repurchase program and remains well-capitalized with a net leverage of only 0.6 times [8][9] Management's Comments on Operating Environment and Future Outlook - Management reaffirmed the full-year 2025 revenue and profit outlook, expecting approximately 9% organic revenue growth for the big three segments [4][29] - Temporary factors affecting growth include slower commercial print volumes, U.S. Federal government cancellations, and softer bookings trends in Corporates [5][29] - The company is optimistic about the long-term value proposition in government despite recent downgrades and cancellations [44][45] Other Important Information - The company is updating its 2026 financial framework, expecting organic revenue growth of 7.5% to 8% and margin expansion [32] - Free cash flow outlook for 2026 is raised to approximately $2.1 billion, reflecting confidence in operational efficiency [33] Q&A Session Summary Question: What are the recurring impacts of government and corporate headwinds? - Management remains confident in the corporate segment's growth potential despite temporary sales softness, expecting 9% to 11% organic growth next year [40][43] Question: How is customer reaction to AgenTik AI? - Customer feedback has been very positive, with significant changes in behavior noted among users, indicating strong adoption of the new tools [45][46] Question: What is the impact of the government shutdown on contracts? - Cancellations occurred prior to the shutdown, which has minimal impact on current revenue [52] Question: How does the company view competition in the AI assistant space? - New players have entered the AI assistant space, but the company is confident in its position and product development plans [68] Question: What is the pricing strategy in light of AI product value? - The company follows a price-to-value principle, ensuring pricing aligns with the efficiencies provided by AI products [93][94] Question: How does the company see AI affecting the tax business? - The tax business is expected to benefit from AI advancements, enabling more efficient processes and advisory services [114][115]

@Tether_to @GoKiteAI Addendum 2: @caesar_data adds x402 payment supportAgents can now make micropayments to access one of the best deep research models availableThis is gonna snowball real quick; AI szn imminenthttps://t.co/y3jwyPmf7D https://t.co/1Iwlpn8XimCaesar (@caesar_data):Caesar x402 support is now live.Developers and AI agents can now access the best deep research on demand, using trustless, instant pay-per-query payments powered by the x402 protocol. https://t.co/V4StlUeZ82 ...

Product Assessment - The product "Caesar" is considered strong after testing [1] - The product is recommended as a Deep Research tool, especially for crypto analysis/research [1] Team & Venture - The venture is backed by experience and data engineering expertise from the team [1]

Product Assessment - Caesar is a strong deep research tool, particularly for crypto analysis/research [1] - The product's strength has impressed early testers [1] Team & Expertise - The team possesses wealth of experience and data engineering expertise [1] - Backing the venture was a logical decision due to the team's expertise [1]

Product Assessment - The product "Caesar" is considered strong after testing [1] - The product is recommended as a Deep Research tool, especially for crypto analysis/research [1] Team & Venture - The venture is backed by experience and data engineering expertise from the team [1]

Core Insights - MiroMind aims to build a self-aware digital agent ecosystem, focusing on the continuous evolution of Artificial General Intelligence (AGI) through community collaboration and open-source principles [2][4]. Group 1: Open Source Ecosystem - MiroMind has developed a comprehensive open-source ecosystem that includes the Agent framework (MiroFlow), models (MiroThinker), data (MiroVerse), and training infrastructure (MiroTrain/MiroRL), all of which are open for learning, reuse, and further development [1][8]. - The MiroFlow framework achieved a state-of-the-art (SOTA) score of 82.4 on the GAIA validation set, surpassing existing commercial model APIs [1][12]. - MiroThinker, the core model, reached a SOTA performance of 60.2% on the GAIA-Text-103 dataset, nearing the performance level of OpenAI's Deep Research [1][15]. Group 2: Community Collaboration - MiroMind fosters a developer-centric environment that encourages community participation through data requests, feature customization, and technical challenges, with feedback directly influencing project development [2][22]. - The project organizes various community activities such as competitions, leaderboards, and hackathons to enhance developer engagement and contribution [22]. Group 3: Key Personnel - The project is led by Chen Tianqiao, a renowned entrepreneur known for his strategic vision and significant contributions to brain science and AI [4]. - Dai Jifeng, a key figure in the project, is a professor at Tsinghua University with extensive experience in computer vision and deep learning, having published over 80 papers with significant citations [5][6].

Core Viewpoint - The AI sector, particularly the "AI Four Strong" companies, is experiencing a resurgence as they pivot towards Deep Research and AI Agents to compete with larger firms and demonstrate their value to investors [1][3][21]. Group 1: AI Four Strong's Strategy - The AI Four Strong have launched their own Deep Research products, aiming to regain market presence after a period of low activity [2][9]. - These companies are focusing on vertical integration and delivering value through Deep Research and AI Agents, which are seen as safer positions amid competition from larger firms [3][4]. - The introduction of AI Agents is not only a strategy to re-enter the market but also a way to create monetizable opportunities, with reports indicating significant user upgrades to premium services [5][21]. Group 2: Market Dynamics and Competition - The AI landscape has shifted with the emergence of Deep Research as a new benchmark, prompting the AI Four Strong to innovate rapidly [8][17]. - The competition has intensified, with major players like Tencent and Alibaba also entering the fray, leading to a reassessment of strategies among the AI Four Strong [17][20]. - The AI Four Strong are now prioritizing technological advancements over user growth, reflecting a strategic shift in response to market pressures [20][21]. Group 3: Product Development and User Engagement - The AI Four Strong are adopting different approaches to product development, with some focusing on user-friendly interfaces while others emphasize high user interaction [12][13]. - Recent model releases, such as MiniMax's M1 and Kimi's K2, showcase significant advancements in capabilities, including increased parameter counts and improved efficiency [15][23]. - The need for AI Agents to deliver quantifiable value to clients is critical, as demonstrated by successful case studies that highlight efficiency improvements and cost reductions [27][28]. Group 4: Financial Outlook and Investment - The AI Four Strong are beginning to attract positive attention from investors, with reports of significant funding rounds and IPO plans [23][24]. - The financial viability of AI Agents is under scrutiny, as the costs associated with their use can be substantial, necessitating a focus on creating clear value propositions for clients [30][31]. - The overall market for AI Agents is still developing, with indications that achieving product-market fit remains a challenge for many companies in this space [31][32].

Core Viewpoint - The article highlights the launch of the free "Deep Research" feature by Metaso AI Search, which allows users to conduct comprehensive research without the need for applications or memberships, showcasing a new approach to AI-driven research capabilities [1][12][46]. Group 1: Features of Deep Research - The Deep Research function provides a complete research report by connecting various sub-questions and presenting a clear evidence chain [2][22]. - Users can input complex queries, and the system generates a research path in real-time, displaying the AI's thought process [18][19]. - The final report is structured and can be exported in formats like Word and PDF, with sources clearly cited [28][29]. Group 2: Performance and Evaluation - Metaso AI has shown superior performance in evaluation tests compared to other models, including the WebSailor model [8][10]. - The system can visualize data through charts and graphs, making it suitable for business research and everyday inquiries [39][41]. Group 3: Accessibility and Market Position - The Deep Research feature is available for free, contrasting with many competitors that require payment or limited access [48][50]. - This launch is seen as a significant development in the domestic AI search field, providing users with a low-barrier entry to advanced research tools [52].

Core Insights - The article discusses the emergence and evolution of "Deep Research" systems, highlighting their rapid development since Google's initial product launch in late 2024, with over 80 teams now involved in this field [1][2] - It emphasizes the shift in competitive focus from model capabilities to system architecture, engineering optimization, and application scenario adaptability [2] - The article outlines the core engineering challenges faced by these systems, including hallucination control, safety and privacy, and process explainability [3] Group 1: Current Landscape and System Comparison - The ecosystem of deep research systems is characterized by significant diversity, with different systems focusing on various technical implementations, design philosophies, and target applications [4] - Key differences among systems are evident in their foundational models and reasoning efficiency, with commercial giants like OpenAI and Gemini leveraging proprietary large models for superior performance [5] - Systems also differ in tool integration and environmental adaptability, with some aiming for comprehensive platforms while others focus on specialized capabilities [6][7] Group 2: Performance Metrics and Evaluation - The evaluation of deep research systems is evolving from general benchmarks to highly specialized assessments tailored to specific research or commercial scenarios [9][10] - New specialized benchmarks have emerged, such as AAAR-1.0 for research assistance and DSBench for data science, reflecting the growing need for precise evaluation metrics [11][10] - The article highlights the importance of multi-dimensional evaluation frameworks that encompass functional, performance, and usability metrics [19][20] Group 3: Technical Implementation and Challenges - The implementation of deep research systems involves strategic trade-offs across architecture design, operational efficiency, and functional integration [12][13] - Four primary architectural paradigms are identified: Monolithic, Pipeline-based, Multi-Agent, and Hybrid architectures, each with its own advantages and challenges [13][14] - Core technical challenges include hallucination control, privacy protection, and ensuring explainability and transparency in research applications [17][18] Group 4: Future Directions in Reasoning Architecture - The reasoning capabilities of deep research systems are expected to evolve significantly, focusing on overcoming limitations such as context window constraints and enhancing causal reasoning abilities [24][32] - Future systems will likely integrate neural and symbolic reasoning, allowing for more reliable and interpretable outputs [30] - The article discusses the need for advanced uncertainty representation and Bayesian reasoning integration to improve decision-making processes [36][37]