Core Insights - The "2026 Peking University Report" outlines the top ten trends and characteristics of the cultural industry for 2025 and forecasts for 2026, emphasizing the importance of expert opinions in shaping the annual academic theme [1] Group 1: Cultural Industry Trends - Digital governance of cultural resources will continue to deepen, driven by national digital strategies, enhancing the precision of data collection and restoration through technologies like AI and 3D modeling [2] - AI Agents will empower individuals in the cultural and tourism sectors, lowering professional barriers and enabling rural tourism influencers and intangible cultural heritage inheritors to become "super individuals" [3] - Globalization and digitalization are driving the transformation of local culture for international markets, as seen in successful cultural products that effectively communicate Chinese stories while minimizing cultural discounts [4] - The "14th Five-Year Plan" will support the cultural industry through a dual drive of policy and market forces, fostering innovation and high-quality development [5] - Cultural data will shift from being an auxiliary tool to a core driver of brand development, transforming the industry’s value chain and business models [6] Group 2: Technological Integration - Intelligent robots will become foundational infrastructure in the cultural and tourism sectors, enhancing visitor experiences and addressing labor shortages through advanced technology [7] - The integration of culture and tourism with various industries will create new business models and immersive experiences, driven by technology and ecological synergy [8] - The pet economy is emerging as a new growth point within the cultural tourism sector, creating pet-friendly services that cater to emotional consumer needs [9][10] - Cultural innovation is transitioning from conceptual exploration to practical implementation, with interactive and immersive experiences engaging younger audiences [11] - Agentic AI will revolutionize the creative process by autonomously managing tasks, leading to new business models and rapid product iteration [12]

I'll introduce myself in in just a moment, but I'd like to get to know a little bit about you. So, you know, you can see that there's there's two brands up on the screen here. There's OpenAI agents SDK in particular, and there's Temporal. I work for Temporal. I'll tell you more about myself in just a second. Um, I'm curious, how many folks are using the OpenAI agents SDK today? Okay, about a quarter of you. um any other um a agentic frameworks? Okay, about the same set of you. So, it's it looks like there's ...

Core Insights - Retailers experience revenue loss whenever an online store goes down, a system fails, or frontline employees lack necessary tools [1] - Generative AI platforms and AI agents are creating new opportunities to enhance online retail experiences and improve conversion rates [1] - Despite the potential of AI to help retailers achieve better margins and competitive advantages, many still struggle to translate experimentation into consistent operational and revenue improvements [1]

[music] Hey everyone, gonna get started here. Thanks so much for joining us today. Um, I'm Sally. I'm the director of RISE.I'm going to be walking you through some of crowd prompt learning. Uh we're actually going to be building a driven optimization loop for the part of the workshop. Um I come from a technical background and started off in data science before I made my way over to product.Uh I do like to still be touching code today. I think one of my favorite projects that I work on is building our own ag ...

Thank you all for coming. Hello. Hello.>> Got you. Uh, I don't know about you, but um, my ride agents, I like focusing on the capabilities and the features, and I like not thinking about um, all of the extra effort that goes into getting something that works locally into production. And something that's very useful for that is uh, a workflow pattern. And that's why we developed the workflow def kit which is what we're talking about today.Presumably if you're here you've had similar issues. Um and today uh w ...

Core Insights - Venture capital firm Andreessen Horowitz (a16z) identifies stablecoins, real-world asset tokenization, and privacy infrastructure as key forces shaping the crypto industry by 2026 [1] Group 1: Stablecoins - Stablecoins have reached mainstream scale with an estimated $46 trillion in transaction volume last year, rivaling major payment networks like PayPal and approaching U.S. ACH volumes [2] - The next challenge for stablecoins is to build better on-ramps and off-ramps that connect digital dollars to everyday financial systems [2][3] - Startups are emerging to link stablecoins to local payment rails, QR-based networks, and card-issuing platforms, enabling users to spend stablecoins at traditional merchants [3] Group 2: Tokenization - There is growing interest from banks, fintech firms, and asset managers in bringing equities, commodities, and other assets on-chain [4] - Much of today's real-world asset tokenization remains "skeuomorphic," reflecting traditional financial structures rather than leveraging crypto-native capabilities [4] - Momentum is building around crypto-native derivatives, particularly perpetual futures, which offer deeper liquidity and simpler implementation [5] Group 3: Privacy Infrastructure - Privacy is emerging as a competitive moat for blockchain networks, becoming a central theme for 2026 [6] - As public blockchains become more interoperable, privacy-preserving systems could enhance network effects by making user migration more difficult and protecting against transaction-level surveillance [6] Group 4: AI and Payments - The intersection of AI agents and crypto infrastructure is growing, particularly as autonomous systems begin transacting without human intervention [7]

Core Insights - 2026 is anticipated to be a pivotal year for AI, particularly in transforming infrastructure, manufacturing, and climate monitoring, moving from reactive to predictive systems [1] - Voice AI is seen as a natural and efficient mode of interaction, with potential for reimagining products and experiences [2] - Many enterprise AI companies are expected to transition from product-focused businesses to AI consulting, leveraging existing customer workflows to create additional use cases [3] Industry Trends - Enterprises are beginning to recognize that large language models (LLMs) are not a one-size-fits-all solution, leading to a focus on custom models and data sovereignty [4] - A significant 95% of enterprises reported not receiving meaningful returns on their AI investments, indicating a struggle in realizing the benefits of AI adoption [5] - The AI landscape has evolved significantly since the launch of ChatGPT, with a surge in enterprise AI startups driven by substantial investments [6] Investment Opportunities - There is a growing interest in how frontier labs are approaching application layers, with expectations for more turnkey applications in sectors like finance and healthcare [7] - Investment focus is shifting towards future data center technologies, emphasizing efficiency and sustainability [10] - Vertical enterprise software is gaining traction, particularly in regulated industries where proprietary workflows create defensibility [11] AI Adoption and Budgeting - Enterprises are expected to increase their AI budgets in 2026, but with a nuanced approach, reallocating labor spend towards AI technologies that demonstrate strong ROI [21] - A bifurcation in spending is anticipated, where a small number of AI vendors will capture a disproportionate share of budgets while others may see revenue decline [22] - CIOs are likely to push back against AI vendor sprawl, rationalizing overlapping tools and focusing on proven AI technologies [24] Startup Growth and Retention - Successful AI startups are those that identify workflow gaps created by generative AI and execute on product-market fit [33] - Companies that provide foundational infrastructure rather than point solutions tend to have higher retention rates, as they become integral to enterprise operations [39] - Retention is strongest in companies that solve problems that grow with AI adoption, making their solutions mission-critical [36]

Group 1: Company Performance - Bumble Inc. reported total revenue of $246 million for Q3 2025, reflecting a 10% decline year-over-year [2] - The Bumble App generated $199 million, also down 10%, while Badoo and other revenue decreased by 11% to $47 million [2] - Despite revenue contraction, Bumble achieved a net income of $52 million, recovering from a net loss of $849 million in the previous year, which was impacted by approximately $900 million in non-cash impairment charges [2] Group 2: User Metrics and Strategy - The company has prioritized quality over quantity, resulting in a 16% decline in total paying users to 3.6 million [3] - This focus on higher-value members led to a 6.9% increase in Average Revenue per Paying User, which rose to $22.64 [3] - The relaunch of BFF (formerly Bumble For Friends) has improved user retention, although the new app has not yet generated significant revenue as of September 30 [3] Group 3: Future Outlook - Bumble provided a cautious outlook for Q4, projecting revenue between $216 million and $224 million, indicating a 14% to 17% year-over-year decline [4] - Adjusted EBITDA is expected to be between $61 million and $65 million, with margins of 28% to 29% [4] Group 4: Analyst Insights - Jefferies lowered Bumble's price target to $4 from $5 while maintaining a Hold rating, recommending a cautious stock-picking approach for the Internet sector in 2026 [1] - Key risks identified include limited margin growth due to rising investment costs and the potential impact of AI agents on traditional platforms, which may cap stock multiples [1]

Welcome to Big Ideas for 2026. We'll hear from Mark Andrew on the evolution of AI user interfaces and how the way we interact with intelligent systems is fundamentally changing. Stephanie Zayn discusses what it means to design for agents rather than humans, a shift that's reshaping product development.And Olivia Moore will share her thoughts on the rise of AI voice agents and their growing role in our daily lives. These aren't just predictions. They're insights from the people working directly with the foun ...

Core Problem with AI Agents - AI Agents relying on scraping websites and inferring meaning from HTML can lead to unpredictable behavior due to DOM shifts and pop-ups [1][2] - Prompt-engineering alone is insufficient for ensuring the reliable operation of AI Agents [2] - Agents require predictable contracts, which APIs provide through clear rules for requests, responses, and failure expression [2] Solution: Standardized API Access and Management - Standardizing API access, exposing APIs to Agents, and maintaining inspectability are crucial for debugging and monitoring [3] - Unified management, testing, and observability of raw API access are necessary to prevent chaos in the backend [3] - Teams building production-grade AI agents are converging on setting up infrastructure for API management [3] Postman's Solution for AI Agents - Postman offers a centralized hub for documenting, versioning, and accessing APIs [4] - Postman's MCP server exposes APIs directly to Agents, enabling them to call APIs without manual wiring [5] - Postman logs every request made by Agents with full history for debugging purposes [5] Key Takeaway - Agents understand interfaces, not intent; explicit interfaces lead to reliable behavior, while implicit interfaces lead to unpredictable behavior [5] - APIs, when built with the right infrastructure, provide the necessary context for AI [6]