Tavily raises $25M to connect AI agents to the web | TechCrunch https://t.co/OSmU6Cv7Ec ...

Summary of Key Points from the Conference Call Industry Overview - The conference call discusses the China Internet sector, particularly focusing on mega-cap companies and their upcoming Q2 earnings reports. - It is anticipated that aggregate profits for the China Internet sector will decline by 10% year-over-year (YoY) for the first time since Q2 2022, primarily due to challenges in eCommerce and local services [1][1]. Core Insights and Arguments 1. **AI and Cloud Revenue Growth**: - There is an expected sequential acceleration in AI/cloud hyperscaler revenue growth, with Alibaba Cloud projected to grow by 23% YoY, up from 18% in the previous quarter. This growth is attributed to rising demand for AI inference and applications [1][1]. - Comparatively, other cloud services like Google Cloud, Azure, and AWS are expected to grow by 32%, 39%, and 17% respectively during the same period [1][1]. 2. **Profit Declines in Transaction Platforms**: - Significant profit declines are anticipated across major transaction platforms, with Alibaba's EBITA expected to drop by 16% YoY, and Meituan and JD projected to see declines of 58-70% YoY due to increased competition in food delivery and merchant support measures [1][1]. - In contrast, sub-segments such as gaming and mobility are expected to show healthy profit growth, with Tencent's adjusted EBIT growth estimated at 15% YoY [1][1]. 3. **Government Policies and Competition**: - The intensity of food delivery competition is expected to peak in Q3, with a potential for a more fragmented market in the long term. ECommerce players are positioning food delivery as a customer acquisition channel [1][1]. - The report suggests that while competition may moderate in the near term, it will likely extend longer than anticipated, affecting the overall landscape of food delivery services [1][1]. 4. **Company-Specific Expectations**: - **Tencent**: Expected to report Q2 revenue growth of 11% YoY, with adjusted EBIT growth of 15% YoY, driven by solid performance in games and marketing services [1][1]. - **Alibaba**: Anticipated to see a 3% YoY revenue increase in Q1 FY26, with a significant decline in adjusted EBITA by 16% YoY due to investments in food delivery and instant shopping [1][1]. - **PDD**: Projected revenue growth of 11% YoY in Q2, but adjusted EBIT is expected to decline by 38% YoY [1][1]. - **Meituan**: Expected to report a 16% YoY revenue increase, but adjusted EBIT is projected to decline by 58% YoY due to competitive pressures [1][1]. - **JD**: Anticipated revenue growth of 16% YoY, but adjusted EBIT is expected to decline by 70% YoY [1][1]. - **DiDi**: Expected to see revenue growth of 8% YoY, with adjusted EBIT growth of 32% YoY, driven by operational leverage [1][1]. Other Important Insights - The report highlights the ongoing competition in eCommerce, particularly in food delivery and on-demand shopping, with Alibaba's instant shopping volumes reaching 15 million daily [1][1]. - Geopolitical developments and their implications on cross-border business models are also discussed, particularly in light of expanded tariffs and potential delisting risks for ADR companies [1][1]. - The report emphasizes the importance of AI investments and the expected increase in capital expenditures for AI applications in the second half of 2025 [1][1]. This summary encapsulates the key points discussed in the conference call, providing insights into the current state and future outlook of the China Internet sector and its major players.

@eastdakota What are your thoughts?If users couldn't delegate their actions to AI agents, and all agent traffic was forbidden by robots.txt, then agents wouldn't be able to log in on behalf of users & perform actions.Perhaps robots.txt should get a new section for AI agents. ...

RT mayowa 🍀 (@bigZUKO_)Okay, I found one product that falls into this Venn diagram well.→ Giza: the name is Giza and @S4mmyEth mentioned it under the tweet belowThey announced that the product has done a total of $750m in agentic volume since its start after announcing a milestone of $500m 4 days before that day.This brought a lot of attention to the product itself and the announcement post.To be honest, this is the first time I’ll be seeing these guys, and after a quick run-through of what they offerIt’s D ...

AI Agent Challenges & Solutions - AI agents currently struggle with basic computer tasks like clicking, typing, and scrolling [1] - Amazon AGI SF Lab aims to build general-purpose AI agents capable of performing any computer task a human can [1] - The lab proposes a new approach to agents called Useful General Intelligence [1] Amazon AGI SF Lab's Approach - The lab is focused on solving challenges in computer use for AI agents [1] - Developers can access the lab's technology in its early developmental stages [1] - Nova Act, the lab's agentic model and SDK, is being used by developers to build real workflows [1] Personnel & Context - Danielle, a cognitive scientist from Amazon AGI SF Lab, presented this information at the AI Engineer World's Fair in San Francisco [1] - Danielle previously worked at Google and Adept [1]

Key Idea - Successful startups create new lingo, not just new products, suggesting that owning the lingo can lead to owning the market [3] - Language signals market movements; money follows lingo [6] - Lingo leverage involves crafting terminology that resonates with customers and reflects deep market insights [15][16] Lingo Leverage Characteristics - First of its kind: New lingo should introduce a novel concept or perspective [7] - Customer-driven: Lingo created by customers is more valuable than marketing-driven terms [11] - Stickiness: The lingo should be essential and deeply connected to the audience's needs and understanding of reality [13][14] Practical Application - Founders should intentionally craft new terminology as part of their strategy [16] - During due diligence, investors should assess the linguistic assets a startup owns [17] - Founders should create lingo for a problem they're solving or a category they're creating, and encourage early users to adopt it [21]

AI Agents and Workflows Debate - The industry is currently engaged in a debate regarding the roles and effectiveness of AI agents versus workflows, sparked by differing viewpoints from Anthropic and OpenAI [1][2][3][4] - The industry should avoid dogmatic approaches, recognizing that there isn't one single "right" way to develop AI systems [5][6][7][8] - The industry should be cautious of overly complex APIs (like those relying heavily on graph theory), as they can hinder readability and team collaboration [9][10][11][12][13] Design Patterns for AI Systems - The industry needs a commonly accepted vocabulary and glossary for agentic patterns and agentic workflow patterns [14][15] - Agents can be viewed as turn-based systems, while workflows are akin to rules engines managing dependencies [16][17][18] - Workflows are gaining popularity in AI engineering due to the need to trace and manage non-determinism, which is more critical in AI than in traditional software engineering [19][20] - Balancing power and control is a key trade-off in designing AI systems; starting with powerful models and adding control where needed is a viable strategy [21][22] Composition and Implementation - Agents and workflows can be combined in various ways: agents can be steps in workflows, workflows can be tools for agents, and so on [23][24][25] - The agent supervisor model involves an orchestrator agent calling other agents as tools [25] - Dynamic tool injection, where agents are given a limited and relevant set of tools for a specific task, can improve performance [26] - Nested workflows, where a workflow is a step within another workflow, are also valuable [26][27] - Practical experience and community knowledge are currently more valuable than theoretical correctness in this rapidly evolving field [28][29]

Fundamental Research Labs nabs $30M+ to build AI agents across verticals | TechCrunch https://t.co/Ymx1HoIda1 ...

Fundamental Research Labs nabs $30M to build AI agents across verticals | TechCrunch https://t.co/Q2OI2uVFCC ...

Acquisition Overview - Palo Alto Networks is acquiring Cyber Arc for approximately $25 billion [1] - Cyber Arc's stock price remained relatively stable after the announcement, while Palo Alto Networks' stock decreased by about 7% [1] - The deal is largely stock-based, with only about $45 in cash per share [6][7] Rationale for Acquisition - Cyber Arc specializes in identity security, which is becoming increasingly important with the rise of AI agents [2][3] - Cyber Arc addresses the need for AI agents to verify the identities of other AI agents to prevent fraud or unauthorized actions [3] - Palo Alto Networks lacks a strong identity security component within its platform, making Cyber Arc a valuable addition [4] M&A Market Trends - The market is seeing a surge in M&A activity, including Baker Hughes buying Chart Industries for $135 billion and Union Pacific's potential acquisition of NSX for $72 billion [4][5] - High equity prices are making stock a favorable currency for companies to use in acquisitions [5][7] - The Palo Alto Networks-Cyber Arc deal suggests a trend of larger tech companies acquiring specialized cybersecurity firms [8] Cybersecurity and AI - The cybersecurity sector is drawing parallels to the energy sector, with AI foundational models (LLMs) likened to oil, and cybersecurity services likened to oil field services [9] - Companies are expected to acquire infrastructure to support AI development [9]