Stanford MBA, Investor & Founder's Take on What AI Cannot Commoditize: The Future of Human Advantage

The U Lab Brief 23 | The AI Deployment Layer

Enterprise AI may not be won by the company with the strongest model. It may be won by the company that can make AI work inside real organizations. According to TechCrunch, Microsoft’s new Frontier Company, backed by a $2.5 billion commitment and 6,000 industry and engineering experts, signals an important shift. The AI race is moving from capability to deployment. The question is no longer just: who can build intelligence? It is: who can translate intelligence into operating results? That is where Microsoft has a structural advantage: enterprise relationships, Fortune 500 presence, and the ability to bring AI into large, complex institutions. In enterprise AI, the next moat may not be the model alone. It may be the deployment layer. #AI #EnterpriseAI #Microsoft #VentureCapital #Technology #Innovation #TheULab

The U Lab Brief 22 | Engram: The Learned Memory Layer For AI

A startup called Engram has emerged from stealth with $98 million in funding from some of Silicon Valley's leading venture capital firms. Founded by researchers from Stanford, Berkeley, and Cornell, the company is already partnering with Microsoft, Notion, and Harvey. But the funding isn't the real story. The real story is that Engram is building what it calls a learned memory layer for AI. Today's AI is incredibly intelligent, but inside an enterprise it often behaves like a brilliant stranger. Every time it answers a question, it largely reconstructs an organization's context. It rereads documents, relearns processes, and rediscovers institutional knowledge again and again. As enterprises deploy AI agents across more functions, those repeated computations consume vast numbers of tokens, increase inference costs, and limit the efficiency of AI at scale. Engram takes a different approach. Instead of repeatedly retrieving information, its models study an organization's knowledge in advance and compress it into a compact, reusable memory. The longer the AI is used, the more it learns about the organization. According to the company, this allows its models to match or outperform frontier models while using up to 100 times fewer tokens, enabling faster responses, lower inference costs, stronger personalization, and more efficient long-running AI agents. One distinction is worth understanding. Conversation memory helps AI remember your interactions. Organizational memory helps AI understand your organization. The next competitive layer in enterprise AI may not be intelligence itself. It may be memory. Because intelligence answers questions. Memory compounds organizational knowledge. I'm Hurratul and this is The U Lab Daily Brief on venture capital, technology, and the future of innovation. Source: PR Newswire, StrictlyVC

Stanford MBA, Investor & Founder's Take on What AI Cannot Commoditize: The Future of Human Advantage

Artificial intelligence is rewriting how companies are built and how they get funded. The next generation of founders and investors will look nothing like the last. So what stays uniquely valuable when AI makes knowledge, execution, and coordination almost free? In this episode of The U Lab Podcast, I sit down with Jing Kuang, Founding Partner of Y+ Ventures and Co-Founder of Cresca, for a rigorous and deeply human conversation on the future of venture capital, consumer AI, and what stays scarce when knowledge and intelligence become abundant. Jing's path runs from Peking University to Procter & Gamble, from a Stanford GSB MBA to leading large-scale cross-border mergers and acquisitions, to building an AI-native venture firm and now a startup building relationship intelligence and memory infrastructure for the AI era. Across that arc she has developed a distinct thesis: as AI commoditizes what we know and what we can do, advantage shifts toward what it cannot replicate. Judgment. Context. Trust. Relationships. Agency. Interdisciplinary thinking. Character. This is a conversation for founders, investors, operators, and anyone trying to understand where human value compounds in the age of AI.   WHAT WE EXPLORE IN THIS EPISODE - Agency and leverage: how for Jing excellence became leverage, leverage became agency, and what fuels her unstoppable spirit - Meritocracy versus network effects: if meritocracy is the baseline, then what other factors decide how far you go - RootedIn VC Fellowship: how is it redesigning access into venture capital and solving for the chicken-and-egg problem of getting into VC - Build+ and "engineer-scouts": why the next generation of founders must become interdisciplinary and how Build+ is solving for it - AI-native venture capital: what structurally changes in a venture firm when AI becomes the operating system, not just another tool - Coase theory and the economics of AI: how falling coordination costs are reshaping the optimal size of firms and funds and the future of entrepreneurship - Pattern recognition versus human judgment: when AI can analyze massive amounts of market and behavioral data faster than humans, where investing instinct still wins, and why the founder is the constant variable - Consumer AI: what the market is still underestimating, and how people don’t just buy a product or service but they buy a projection of their future self - Behavioral moats: why the changing user behavior is becoming a stronger moat than the technology itself - Trust capital: why trust grows scarcer and more valuable as AI generates infinite content - Cresca: why they are building relationship infrastructure and a memory layer for the AI era - Building venture with a life partner: trust, complementary strengths, and the lowest-friction co-founder relationship - Venture and entrepreneurship as non-binary: why investors and founders sit on the same side of the table - Female founders and access: the structural barriers behind the 6% of US female only founder venture funding, and how to widen the door without lowering the bar - The first-mile handshake check: what signals and founder traits create conviction before metrics exist - Advice for founders and aspiring investors: why you should taste the freedom of being unemployable early   ABOUT JING KUANG Jing Kuang is the Founding Partner of Y+ Ventures, a human-centered, AI-native venture firm focused on consumer AI, and the Co-Founder of Cresca, a company building relationship intelligence and memory infrastructure for the AI era. A Stanford GSB alumna and graduate of Peking University, she began her career at Procter & Gamble before leading large-scale cross-border mergers and acquisitions. She founded the RootedIn VC Fellowship and Build+, programs reimagining access into venture capital and founder formation within the Stanford ecosystem, and writes widely on agency, AI-native investing, and the future of human systems.   ABOUT THE U LAB PODCAST The U Lab Podcast is a research-informed series at the intersection of venture capital, capital allocation, founder psychology, and the technologies reshaping how value is created. Hosted by Hurratul Maleka Taj, three-time founder, independent researcher, and author of Power Before Purpose, The U Lab brings rigor and depth to conversations with the investors, founders, and thinkers defining the next era of entrepreneurship. Follow The U Lab Podcast so you never miss an episode. New conversations on venture capital, startups, artificial intelligence, and the future of innovation.   Guest and partnership inquiries: via LinkedIn 🌍 Follow Us for More Inspiring Content: 💡LinkedIn: https://www.linkedin.com/in/hurratul [https://www.linkedin.com/in/hurratul] 📸 Instagram: https://www.instagram.com/hurratul [https://www.instagram.com/hurratul]  📘 Facebook: https://www.facebook.com/hurratul [https://www.facebook.com/hurratul]  🐦 X: https://X.com/Hurratul [https://X.com/Hurratul]  🎙️ Podcast on Spotify: https://open.spotify.com/show/66D0qTRStkBBrYPAMBdraL [https://open.spotify.com/show/66D0qTRStkBBrYPAMBdraL] 🎙️ Apple Podcast: https://podcasts.apple.com/us/podcast/the-u-lab-podcast/id1859801184 [https://podcasts.apple.com/us/podcast/the-u-lab-podcast/id1859801184]

The U Lab Brief 21 | FAANG to MANGOS

FAANG to MANGOS For nearly two decades, one acronym defined the technology industry: FAANG. Today, a new acronym is going viral on X: MANGOS. It stands for Meta, Anthropic, Nvidia, Google, OpenAI, and SpaceX, a group that could soon dominate the next era of technology as several companies prepare to go public. But here's what caught my attention. FAANG was built around the internet. MANGOS is being built around intelligence. FAANG was defined by search, social media, e-commerce, smartphones, and streaming. MANGOS is being defined by AI models, compute infrastructure, autonomous systems, and space infrastructure. This isn't just a new acronym. It may represent a shift in the technology economy - from companies that connected people to companies that are building intelligence itself. The question is no longer, Who owns the biggest platform? The question is, Who will own the infrastructure of the AI era? I'm Hurratul, and this is The U Lab Daily Brief on venture capital, technology, and the future of innovation. #FAANG #MANGOS #AI #AIInfrastructure #TheULab #technology

The U Lab Brief 20 | If AI Isn't Taking Your Job, What Is?

Most people assume AI is making it harder for new college graduates to find jobs. A recent analysis highlighted by a16z points to a more surprising possibility. What if remote work is a bigger factor? Researchers found that unemployment among younger college graduates has deteriorated more than for older workers, with the gap appearing particularly pronounced in occupations that can be performed remotely. Even more interesting, one study found that once remote work is taken into account, much of the observed relationship between AI exposure and declining early-career hiring largely disappears. By 2025, occupations with higher work-from-home exposure showed nearly a 2 percentage point decline in the share of new hires, while the AI-controlled estimate remained close to zero. This does not mean AI has no impact. But it suggests we may be asking the wrong question. Perhaps the challenge is not simply whether AI is replacing entry-level workers. Perhaps the challenge is whether remote work is weakening the apprenticeship systems that historically helped develop them. If firms increasingly hire experienced talent who can contribute immediately, who will train the next generation of managers, operators, and founders? I'm Hurratul and this is The U Lab Daily Brief on venture capital, technology, and the future of innovation.