Core Insights - Salesforce executives have acknowledged a decline in trust towards large models over the past year, leading to a strategic shift in their AI product Agentforce, which will now rely more on deterministic automation techniques rather than generative AI [1][2] Group 1: Strategy Shift - The adjustment aims to address technical failures such as "hallucinations" that large models experience when handling precise tasks, ensuring that critical business processes follow consistent steps [1] - Agentforce is now utilizing predefined instruction-based deterministic automation to eliminate the inherent randomness of large models [1] Group 2: Technical Reliability Challenges - Salesforce has encountered multiple technical challenges with large models, including the issue where models begin to omit instructions when given more than eight commands, which is problematic for tasks requiring precision [2] - The experience of Vivint, a home security company using Agentforce for customer support, highlights reliability issues, such as the failure to send satisfaction surveys despite clear instructions [2] Group 3: Addressing AI Drift - AI "drift" is another significant challenge, where AI agents lose focus on primary objectives when users ask unrelated questions [3] - To mitigate this, Salesforce has developed the Agentforce Script system, which identifies tasks that can be handled by non-large model agents to minimize unpredictability [3] Group 4: Operational Adjustments - Salesforce has also reduced its reliance on large models in its operations, despite previous statements about using OpenAI's models for customer service inquiries [4] - Recent responses from the company have shifted to providing links to blog articles instead of engaging in further inquiries, resembling traditional basic chatbot interactions [4] - The company has improved its topic structure and protective measures, leading to a projected 90% increase in resolved conversations by the end of the fiscal year [4] Group 5: Industry Trends - This trend reflects broader industry challenges, as evidenced by issues faced by other companies, such as a chatbot from Gap Inc. that provided inappropriate responses, highlighting the common problem of large models deviating from expected use [5]

Core Insights - Surge AI, founded by Edwin Chen, achieved over $1 billion in revenue within four years without external funding, employing fewer than 100 staff members, and has been profitable since inception [4][6][7] - The company focuses on high-quality AI data training, emphasizing the importance of data quality over quantity, and aims to create AI that benefits humanity rather than merely optimizing for engagement [6][11][12] Company Overview - Surge AI is a leading AI data company that supports model training for cutting-edge AI labs, achieving rapid growth and profitability without venture capital [4][6] - The company employs a unique approach by prioritizing product quality and customer alignment over traditional Silicon Valley practices of fundraising and marketing [9][10] Business Model and Strategy - Surge AI operates with a small, highly skilled team, believing that efficiency can be achieved without large organizations, which is facilitated by advancements in AI technology [7][8] - The company avoids typical Silicon Valley promotional tactics, relying instead on word-of-mouth and the intrinsic value of its products to attract clients [9][10] Data Quality and Evaluation - Surge AI defines data quality in a nuanced way, focusing on the emotional and intellectual resonance of outputs rather than just meeting superficial criteria [11][12] - The company employs a comprehensive signal system to assess the quality of data contributions, ensuring that only high-quality outputs are used for model training [13][14] AI Industry Trends - The conversation highlights a growing concern that many AI models are optimized for benchmark tests rather than real-world applications, leading to a disconnect between model performance and practical utility [18][19] - There is a belief that the future of AI will see a shift towards more diverse and specialized models, driven by the unique characteristics and goals of different research labs [42]

So talk to us about that blurring. How much are investors institutional. Anyone really wanting to get more exposure to the private markets right now.I think it's a core theme that we're seeing across asset classes, both in the equity market and in the credit markets, of course. But as it relates to the equity lens, companies are staying private a lot longer. It's well understood from 1997 being five years on average and $20 million in sales to 2025, where it's 220 million of sales and 14 years of being a pr ...

Summary of Key Takeaways from the Corporate Day on AI and LLM in Beijing Industry Overview - The LLM (Large Language Model) market competition remains intense, but there is optimism for AI monetization, particularly in the vertical enterprise market [1] Company-Specific Insights iFlytek (002230 CH, Neutral) - The consumer business is identified as the core growth engine, contributing about one-third of total revenue in 1H25, driven by strong momentum from learning machines [5] - Management expects the consumer business and education sector to be the main growth drivers, supported by the "Spark" LLM platform [5] - iFlytek is noted as potentially the only large LLM in China trained on domestic AI chips, allowing it to secure orders from government and state-owned enterprises (SOEs) [5] - The "Spark" LLM platform is projected to generate approximately CNY1 billion in revenue for FY25E [5] Kingsoft Cloud (3896 HK, Buy) - AI computing power demand is accelerating, with Xiaomi's LLM training and inference demand in smartphones and electric vehicles (EVs) being major growth drivers [4] - Management anticipates FY25F revenue growth to exceed 20% year-on-year, although gross profit margin (GPM) will be under pressure due to increased leased computing [5] - The company expects computing power assets to increase by approximately CNY9 billion, including both self-purchased and leased assets [5] Fourth Paradigm (6682 HK, Not rated) - Aiming for CNY20 billion in revenue by 2029E, with a focus on enterprise-level AI service platforms [6] SenseTime (20 HK, Not rated) - Generative AI is the main revenue growth driver, contributing 70%-80% of total revenue [8] - The company is committed to building full-stack AI capabilities and has released multimodal LLMs [9] Yonyou Network (600588 CH, Neutral) - Domestic substitution and large enterprises' AI migration are key growth drivers, with AI agents integrated into ERP systems [12] - Management expects steady revenue growth and significant cash flow recovery in 2025 [15] Qihu 360 (601360 CH, Not rated) - Revenue for 9M25 reached CNY6.1 billion, up 8% year-on-year, with a focus on "AI + Security" [13] - The company is shifting towards SaaS security, which now accounts for 30% of security revenue [17] Youdao (DAO US, Not rated) - Positioned as an AI-powered solutions provider, with online marketing services as the largest revenue contributor at 45% [18] - The company has achieved 5x growth in online marketing services over the past 3-4 years, driven by programmatic advertising [19] Additional Insights - The public cloud and enterprise cloud segments account for approximately 70% and 30% of revenue, respectively, with AI cloud being the fastest-growing segment [5] - Management noted that the demand for AI products is expected to drive revenue growth across both consumer and enterprise segments [11] - The overall macro environment remains challenging, but the penetration rates for AI solutions in enterprises are still low, indicating potential for growth [10] Conclusion The corporate day highlighted the competitive landscape of the AI and LLM market in China, with various companies showcasing their growth strategies and revenue projections. The focus on consumer and enterprise applications of AI, along with the integration of LLMs into existing business models, presents significant opportunities for growth in the coming years.

Core Insights - The Glimpse Group, Inc. has entered into a six-figure contract with a New York City-based higher education institution for the design, deployment, and integration of a local Large Language Model (LLM) infrastructure [1] - This LLM infrastructure is specifically configured to run Foretell AI in immersive environments, which will be utilized across various campus and community systems and programs [1] - The partnership highlights Glimpse's capability to integrate AI with immersive platforms, facilitating natural interactions with smart AI non-human virtual characters [1] Company Developments - The contract represents a significant step for Glimpse in expanding its offerings in the immersive technology sector [1] - The collaboration with the higher education institution underscores the growing demand for AI-driven solutions in educational settings [1] - The integration of AI in immersive environments is expected to enhance user experience and engagement within the institution [1]

Joining us now, Mark Mahaney, Everore, senior managing director of internet research. Mark, great to see you in person. Um, we mentioned the Google 60%.Most of that move was in the second half of the year. So, it's it's sort of an interesting dynamic here where it's no longer a top pick because of the recent run, but you still think it is firmly in the hold in the buy camp, I should say. >> It's a quality compounder.So, we've had a stock that went from 15 times earnings to almost 30 times earnings in a six- ...

Joining us now, Mark Mahaney, Everore, senior managing director of internet research. Mark, great to see you in person. Um, we mentioned the Google 60%.Most of that move was in the second half of the year. So, it's it's sort of an interesting dynamic here where it's no longer a top pick because of the recent run, but you still think it is firmly in the hold in the buy camp, I should say. >> It's a quality compounder.So, we've had a stock that went from 15 times earnings to almost 30 times earnings in a six- ...

Core Insights - By 2025, China's AI industry is at a historical turning point, with generative AI user base reaching 515 million, an increase of 266 million from December 2024 [1] - The Chinese government has outlined a clear direction for AI development through the "AI+" action plan, emphasizing six key actions and eight foundational capabilities [1] - The capital market has responded positively, with 709 investment events in the AI sector in 2025, amounting to approximately 59.145 billion yuan, which is 94.5% of the total investment in 2024 [1] Group 1: Industry Trends - The AI industry is witnessing a shift from a focus on technology narratives to practical applications, with a brutal elimination process for startups lacking financial viability [2] - Major companies are leveraging their technological advantages to attract capital and accelerate their market presence, while startups face existential challenges [2] - The AI sector is experiencing deep penetration into various industries, indicating a transition from speculative investments to more grounded business models [2] Group 2: Market Developments - New companies like Xiaoma Zhixing and Wenyan Zhixing have recently listed on the Hong Kong Stock Exchange, marking significant milestones in the autonomous driving sector [6] - Xiaoma Zhixing operates a fleet of over 720 Robotaxi vehicles, providing 24/7 service in major cities, while Wenyan Zhixing has over 1,500 autonomous vehicles licensed across eight countries [6] - The AI sector saw 435 new financing events in Q3 2025, a 99% year-on-year increase, with total financing around 37 billion yuan [7] Group 3: Competitive Landscape - The competition in the AI industry is intensifying, with both tech giants and startups vying for market share, leading to a complex competitive environment [8] - The launch of DeepSeek's app has significantly increased user engagement, with active users surpassing 240 million within a month of its release [8] - The AI app user base reached 287 million by September 2025, indicating a growing trend towards multi-model integration in applications [9] Group 4: Investment Dynamics - The investment landscape is evolving, with a focus on AI hardware and applications, as evidenced by significant funding rounds for companies like Ling Yuzhou [13] - The return cycle for AI hardware investments is shorter compared to traditional internet investments, necessitating careful selection of investment targets [14] - The relationship between technology breakthroughs, industry application, and capital investment is forming a virtuous cycle, enhancing the potential for future advancements [14] Group 5: Future Outlook - The AI industry is expected to continue its growth trajectory, with a focus on achieving a balance between technological, industrial, and commercial value [16] - Major companies like Alibaba and Tencent are significantly increasing their investments in AI infrastructure, indicating a long-term commitment to the sector [15] - The Chinese AI patent application volume reached 1.576 million, accounting for 38.58% of the global total, positioning China as a leader in AI innovation [16]

With generative AI, we create this large language model that can say anything and then we put restrictions on it to prevent it from using bad language or nudging people into harmful behavior. We try to rain them in with guard reels hoping that they will work in every situation but that is really really hard. And as these companies rais to market they're bound to miss things.And some of these things will have big consequences. So here's my question to you. When AI shifts from being a helpful assistant to a c ...

SUNNYVALE, Calif.--(BUSINESS WIRE)--Cerebras Systems, in partnership with G42's Inception and MBZUAI's IFM, today announced the release of Jais 2, the leading open-source Arabic LLM – the first frontier language model both trained and deployed for inference on Cerebras Systems. The organizations combined their expertise with leading machine learning techniques, uniquely enabled on Cerebras wafer-scale clusters, to achieve state-of-the-art quality on Jais 2, using only a fraction of compute used. ...