Summary of Box (NYSE:BOX) 2026 Conference Call Company Overview - **Company**: Box (NYSE:BOX) - **Date of Conference**: February 03, 2026 Key Industry Insights - **AI Adoption in Engineering**: AI is becoming an essential tool in engineering, with predictions that by 2026, it will be impossible for the average engineer to build software without AI. Companies like Claude and OpenAI are already producing software entirely through AI [38][40]. - **AI in Knowledge Work**: The integration of AI in knowledge work (e.g., marketing, legal, sales) is lagging behind coding due to the complexity and variability of these fields compared to software development. Knowledge work involves more context and human interaction, making it harder to automate [41][44]. - **Enterprise Software Transformation**: Companies need to adapt their workflows to effectively integrate AI agents. This includes re-engineering business processes to support AI, which can lead to significant productivity gains and new revenue opportunities [46][58]. Core Company Perspectives - **NotebookLM**: The emergence of AI agents is creating a new economy where agents can interact and build their own systems, leading to innovative business models [34]. - **ROI and Adoption Challenges**: While there is rapid innovation in AI, enterprise adoption is slow. CIOs are still grappling with how to effectively implement AI in their organizations [36][38]. - **Future of Workflows**: The future will require organizations to create systems that provide context for AI agents, which will be crucial for their effectiveness. This may involve significant changes in how work is structured [47][52]. Financial and Market Considerations - **SaaS Market Dynamics**: The cost of software development is expected to decrease, leading to more competition and potentially lower prices. However, the value of systems that manage AI agents will increase as the number of agents grows [74][80]. - **Pricing Models**: There will likely be a shift towards consumption-based pricing models as companies experiment with AI. As they scale, they may prefer fixed pricing to stabilize costs [89][91]. Additional Insights - **Contextual Data Utilization**: Companies are encouraged to leverage their unstructured data (e.g., contracts, financial documents) to unlock value through AI agents. This requires a shift in how data is accessed and utilized [60][62]. - **Ambitious Projects**: The reduction in costs associated with AI allows organizations to pursue more ambitious projects that were previously deemed too complex or resource-intensive [92]. Conclusion - The conference highlighted the transformative potential of AI in both engineering and broader enterprise applications. Companies that are willing to adapt their workflows and embrace AI will likely gain a competitive edge in the evolving market landscape [92][93].

新浪科技讯 1月28日下午消息，昆仑万维（维权）今日发布Mureka V8音乐大模型，宣布该模型在音乐 旋律、人声表现力，编曲/结构和情绪表达与渲染能力方面，均取得了超越国际知名AI音乐创作平台 Suno的水平。 发布会上，知名音乐人高晓松在谈及AI音乐创作对音乐产业乃至于自己的影响时指出："我是一个来自 上个世纪的音乐人，但并不担心AI会追上或超越自己。"他指出，人类创作音乐有数千年的历史，但真 正以机构化进行音乐商业化是近百年间才有的事情。在高晓松看来，人类对于音乐的追求在很长一段时 间内都是非功利或非商业化导向的，这也是音乐对于人类的重要性与独特性。但近年来，随着音乐产业 化、商业化进程的推进，开始出现了许多功利的做法。 "我自己是做音乐的，之前做音乐的时候，行业里流行的一种做法：如果一张专辑里面没有一首好的音 乐，这张专辑可能不会火，不会有好的商业价值，但如果有好的歌曲，不管是两首还是四首，其实商业 价值都差不了太多，所以这也会导致大家会把好的音乐只少量的放出，可能会配可能80%的普通（垃 圾）音乐，另外的好音乐则放在下一张专辑里。"高晓松流露了过度商业化对于音乐产业化扭曲乃至于 异化的担忧。 谈及于 ...

Group 1 - The article discusses the evolution of music creation in the AI era, highlighting the limitations of current AI tools in producing high-quality music compared to human creativity [4][7][20]. - Zhang Chu, a musician, expresses dissatisfaction with AI-generated music, describing it as "second-rate" and lacking the depth and complexity found in human compositions [13][17][20]. - The conversation emphasizes the importance of personal experience and emotional depth in music, which AI fails to replicate, leading to a homogenized output that lacks individuality [24][26][27]. Group 2 - Zhang Chu plans to create an animated film, aiming to explore themes of existential loneliness and the relationship between individuals and the universe, which he believes cannot be fully expressed through music alone [65][66][67]. - He appreciates the structured storytelling found in European animation, contrasting it with the emotional-driven narratives often seen in other cultures [65][66]. - The article concludes with Zhang Chu's positive experience using AI animation tools, indicating a potential shift in his creative process [71].

AI Technology Scale - AI represents a technological revolution larger than the internet, comparable to electricity and microprocessors, and is still in a "very early" stage [2] - The unit cost of AI is decreasing at a rate that far exceeds Moore's Law, leading to explosive demand growth [2] Market Dynamics - Following the historical pattern of "shortage leads to surplus," the large-scale construction of GPUs and data centers will eventually result in oversupply, further driving down AI costs [3] - The future AI market structure will resemble the computer industry, with a few "god-level models" at the top and a vast number of low-cost "small models" proliferating at the edges [3] US-China Competition - The competition between the US and China is characterized as a dual hegemony, with Chinese companies like DeepSeek and Kimi making remarkable progress in speed, open-source strategies, and chip self-research [3][10] - The emergence of DeepSeek has surprised both Washington and Silicon Valley, indicating a shift in global price competition that may influence US regulatory approaches [10] Business Model Evolution - AI applications are transitioning from "pay-per-token" to "value-based pricing," with startups moving beyond being mere wrappers to integrating their own models [4][12] - High pricing can benefit customers by supporting better research and development, as AI startups demonstrate more creativity in pricing compared to SaaS companies [12] European AI Landscape - The EU's inability to lead in innovation has led to a focus on "regulatory leadership," which has stifled local AI development and caused major companies like Apple and Meta to withhold new features in Europe [5] AI Democratization - Advanced AI technologies are now accessible to anyone, breaking down barriers and allowing immediate use and validation of previously expensive technologies [6] - Public sentiment shows fear of AI replacement, yet actual behavior indicates a rapid adoption of AI technologies [6] Cost Deflation and Investment Outlook - The extreme deflation of AI input costs is expected to drive demand growth beyond expectations, with significant investments anticipated in the coming years [8] - Historical cycles suggest that shortages will lead to oversupply, resulting in a dramatic decrease in AI companies' unit costs over the next decade [8] Model Competition - The future of AI will not be a zero-sum game between closed large models and open-source small models, but rather a clearly defined "intellectual pyramid" [13] - The industry structure will feature a few supercomputer-like "god models" at the top, with numerous smaller models extending to embedded systems [13]

Core Insights - AI represents a technological revolution larger than the internet, comparable to electricity and microprocessors, and is still in its early stages [2][3][11] - The cost of AI is decreasing at a rate faster than Moore's Law, leading to explosive demand growth [4][41] - Historical patterns suggest that shortages in GPU and data center capacity will eventually lead to oversupply, further driving down AI costs [5][12][41] Group 1: AI Market Dynamics - The future AI market structure will resemble the computer industry, with a few "god-level models" at the top and numerous low-cost "small models" proliferating at the edges [6][19] - The competition between the US and China is intensifying, with Chinese companies like DeepSeek and Kimi making significant strides in open-source strategies and chip development [6][15][59] - AI applications are shifting from "pay-per-token" models to "value-based pricing," allowing startups to integrate and build their own models rather than merely acting as wrappers [7][17] Group 2: Public Perception and Regulatory Landscape - Public sentiment towards AI is mixed, with fears of job displacement coexisting with rapid adoption of AI technologies [8] - The EU's regulatory approach, focusing on leading in regulation rather than innovation, is hindering local AI development [8][60] - The US regulatory environment is shifting towards supporting innovation, with less interest in imposing strict regulations that could hinder competitiveness against China [14][64] Group 3: Economic Implications - The rapid decline in AI input costs is expected to create significant demand elasticity, leading to unprecedented growth in AI applications [41][42] - The economic landscape for AI companies is promising, with many experiencing unprecedented revenue growth as they effectively monetize their offerings [32][39] - The ongoing construction of data centers and GPU production is projected to lead to a significant reduction in AI operational costs over the next decade [41][50]

Core Insights - The discussion emphasizes that the current AI revolution is unprecedented in scale, comparable to electricity and microprocessors, and is still in its early stages [4][6][12] - The rapid decline in AI costs, described as "hyper deflation," is expected to drive explosive demand growth [4][6][35] - Historical patterns suggest that shortages in AI infrastructure, such as GPUs, will lead to oversupply and further cost reductions in the future [4][6][35] AI Technology Landscape - AI is viewed as a transformative technology that surpasses the internet in magnitude, with its current phase being very early [6][12] - The cost of AI is decreasing at a rate faster than Moore's Law, leading to significant demand growth [6][35] - The future AI market structure will resemble the computer industry, with a few "god-tier models" at the top and numerous low-cost "small models" at the periphery [6][10] US-China Competition - The emergence of Chinese models like DeepSeek and Kimi has surprised both Washington and Silicon Valley, indicating significant progress in open-source models from China [5][7][52] - The competition is characterized as a dual hegemony, with both the US and China being the primary players in AI development [5][7][51] Business Models and Pricing - A shift from "pay-per-token" to "value-based pricing" is occurring in AI applications, allowing companies to capture more value from productivity gains [9][34] - AI startups are moving beyond being mere wrappers around large models and are integrating their own models, enhancing their competitive edge [9][34] AI Democratization - Advanced AI technologies are becoming accessible to the public, breaking down barriers and allowing widespread use [6][21] - Despite public fears about AI replacing jobs, actual behavior shows a rapid adoption of AI technologies [6][21] Regulatory Environment - The regulatory landscape is shifting, with a reduced risk of federal-level restrictions on AI, as there is bipartisan support for innovation [7][59] - State-level regulations are emerging, leading to a fragmented legal environment that could hinder progress [59][60] AI Chip Development - The AI chip market is expected to see significant investment and innovation, with a potential oversupply in the coming years as companies ramp up production [4][45] - The competition in chip development is intensifying, with both established companies and startups entering the market [45][49]

Core Insights - The article presents a curated list of top AI tools categorized by their utility and effectiveness, emphasizing the importance of selecting the right tool for various tasks in a landscape of overwhelming options [1][2]. Group 1: S-Level AI Tools - ChatGPT, Gemini, and Claude are identified as the top-tier AI tools essential for everyday tasks such as answering questions, web searches, and writing assistance [2][5]. - Each of these tools has distinct strengths: ChatGPT excels in deep research and voice patterns, Claude is superior in writing and programming, while Gemini stands out in image and video generation [5]. Group 2: A-Level AI Tools - NotebookLM is highlighted as a valuable research tool powered by Gemini technology, capable of summarizing documents and providing answers with citations, thus minimizing inaccuracies [3]. Group 3: Specialized AI Tools - Perplexity and Comet are recommended for AI-driven browsing and search, with Comet functioning as a personal assistant for web tasks [7]. - The "Deep Research" feature in ChatGPT, Perplexity, and Gemini is noted for generating comprehensive reports with minimal errors, making it particularly useful for work reports and academic research [9]. Group 4: Presentation and Content Generation - Gamma is introduced as a tool for generating presentations based on simple prompts, while Claude is also effective in this area despite not being specifically designed for it [11][12]. - Nano Banana is recognized as the leading AI tool for image generation, with specific strengths in various scenarios [13]. Group 5: Audio and Video Generation - ElevenLabs is noted for its capabilities in generating realistic voice and sound effects, including voice cloning [14]. - HeyGen is highlighted for its proficiency in creating digital avatars and translating videos into multiple languages while maintaining the original speaker's characteristics [17]. Group 6: Automation and Workflow Tools - n8n is presented as a low-code automation tool that allows users to create custom workflows, particularly favored by technical users for its open-source nature [18][20]. - Napkin AI is introduced as a tool that converts text into visual content like mind maps and flowcharts [21]. Group 7: Music and Video Generation - Suno is recognized for generating music based on text prompts, achieving a level of quality that is often indistinguishable from human-created music [22]. - Sora 2 and Veo 3 are mentioned as excellent options for video generation, showcasing significant advancements in realism and success rates [23][24]. Group 8: Innovative Development Approaches - "Vibe coding" is introduced as a new development paradigm where AI handles most of the heavy lifting, allowing users to create applications with simple prompts [25].

Core Insights - The article discusses the rise of AI-generated music, highlighting the viral success of the song "Skills Gobang" and its connection to AI music creation tools [2][4][8] Group 1: AI Music Creation - AI is significantly lowering the barriers to music creation, allowing non-professionals to produce songs easily [8][9] - Platforms like Suno and DeepSeek are enabling users to create high-quality music with minimal effort, transforming the music industry landscape [8][9] - The latest version of Suno offers studio-level sound quality, while Udio allows for style transfer in music production [8][9] Group 2: Viral Success and Cultural Impact - "Skills Gobang" became a cultural phenomenon, dominating social media and resonating with the youth's desire for shared experiences [2][4][15] - The song's catchy phrases and simple melodies make it suitable for short video formats, contributing to its viral nature [14][15] - The success of AI-generated songs like "Seven Days Lover" illustrates the potential for rapid content creation and commercialization in the music industry [11][14] Group 3: Controversies and Challenges - The rise of AI music has sparked debates about copyright and competition, with some traditional musicians expressing concerns over the legitimacy of AI-generated content [15] - Despite claims of proper licensing and rights, the controversy surrounding AI music creation continues to grow, affecting creators like Yang Ping [15] - The article emphasizes that the essence of music lies in emotional connection rather than just technical creation, highlighting the importance of human experience in music [15][16]

Core Insights - Suno has completed a $250 million Series C funding round, achieving a valuation of $2.45 billion, nearly five times higher than last year's $500 million valuation [1] - The company's annual recurring revenue (ARR) has reached $200 million, primarily from subscriptions, significantly up from the previously reported $150 million [1] - Nearly 100 million users have created music on Suno over the past two years, with many discovering their passion for music creation for the first time [1] Funding and Valuation - The Series C round was led by Menlo Ventures, with participation from Hallwood Media, Lightspeed, Matrix, and NVentures [1] - The valuation increase reflects strong market interest and growth potential in the music AI sector [1] User Engagement and Community - Suno aims to build an ecosystem involving creators, listeners, and the broader music community [2] - The platform has transformed users from listeners to creators, with many discovering Suno through community sharing [4] - The ease of creating music by simply inputting an idea has attracted users who had never engaged with musical instruments before [7] Target User Segments - Individual creators and music enthusiasts, including amateur musicians and content creators, utilize Suno for personalized songs or background music [5] - Professional content creators, such as video producers and independent artists, may opt for Pro or Premier plans for commercial use [5] - Businesses and commercial clients, including small enterprises and advertising agencies, are also significant users of Suno for generating custom music content [5] Product Development - Suno has launched Suno Studio, a professional-grade digital audio workstation (DAW) aimed at both semi-professionals and professionals, enhancing its appeal across user segments [7] - The product is designed to simplify the music creation process while incorporating AI features, catering to both casual and serious users [7]

Core Viewpoint - The article discusses the first CEO annual conference organized by Jinqiu Fund, themed "Experience with AI," focusing on the intersection of technology, capital, and creativity in the AI era [1]. Group 1: Event Overview - The conference aims to explore not just AI itself but how technology, capital, and creativity can interact in the AI age [1]. - The event is designed to be a genuine space for understanding, utilizing, and experiencing AI [1]. Group 2: Music Generation with AI - Seven representative AI music generation products were evaluated, including Suno, ElevenLabs, and Udio, with Suno being selected for the conference music due to its high success rate [4][5][6]. - The music requirements included creating entrance music for guests based on their company and personal situations, as well as warm-up music suitable for the conference theme [7][8]. Group 3: Music Production Process - The production process involved using ChatGPT to generate prompts for music creation, which were then used with Suno to produce suitable music [10][12]. - Different styles of warm-up music were created based on the agenda and desired atmosphere, with 10-20 tracks prepared for each segment [20][21]. Group 4: AI Music Generation Insights - AI can generate melodies and mimic styles but lacks deep semantic understanding, making it challenging to create emotionally resonant music [26]. - The effectiveness of AI music generation heavily relies on the precision of prompts, which can be a challenge for those unfamiliar with music [27][28]. Group 5: Future Directions - The company plans to explore a more systematic and intelligent approach to music generation in the future, potentially integrating multiple AI models for different styles [30]. - There is an aspiration to create a conference theme song that meets the satisfaction of all team members and to experiment with real-time emotional feedback for music generation [30].