Sovereign PivotSovereign Pivot

/ SPEAKING

Speaking Catalog.

Choose from past topics that have been delivered around the world or request a new topic to be customized to any audience.

AI · Deeptech · Startups · Future of Work · SF / Tokyo / Taipei

/ On Stage

Selected keynotes and sessions from the 2026 tour and beyond.

Global Startup Acceleration Program · JETRO Innovation Garden, Tokyo
Global Startup Acceleration Program · JETRO Innovation Garden, Tokyo
INSEAD Career Fair Keynote · INSEAD San Francisco Hub for Business Innovation
INSEAD Career Fair Keynote · INSEAD San Francisco Hub for Business Innovation
Voice from the Industry · Orfalea College of Business, Cal Poly San Luis Obispo
Voice from the Industry · Orfalea College of Business, Cal Poly San Luis Obispo
Open Austria Executive Learning Journey · San Francisco
Open Austria Executive Learning Journey · San Francisco
AI Integration in the Workplace · Golden Gate University
AI Integration in the Workplace · Golden Gate University
Responsible AI Curriculum Workshop · Berkeley SkyDeck
Responsible AI Curriculum Workshop · Berkeley SkyDeck
See the full 2026 tour →
Filter by theme19 talks

  1. 01

    The Sovereign Pivot: Agency in the AI-Shaped Future of Work.

    AIFuture of WorkCareer

    Dr. Larry Chao shares a plain-spoken tour of how innovation and work is changing, with lessons from his own career to the trends we’re seeing across industry. The talk will start with a snapshot of today’s job market and look at the evolution of work as it migrated from artisans to corporations to today’s independent creators, gig economy, and startups. Finally, we’ll look ahead to the increasingly AI-shaped future of work and how GenAI tools and AI agents can help anyone upskill, stand out, or even run a “company of one,” covering practical tips and a picture of where your career can go in this new world.

  2. 02

    The AI-Native Startup Playbook.

    AIAI-NativeStartupsResponsible AI

    The AI-Native Startup Playbook introduces a new operating paradigm for founders building in the age of human-AI orchestration. Drawing on case studies and accelerator experience, the session contrasts AI-enhanced and AI-native models where trust, data, and agentic automation define competitive advantage. It explores two key innovation vectors: Vertical AI (deep domain solutions) and Agentic AI (horizontal, multi-function orchestration) and the infrastructure stack, ROI frameworks, and governance principles that make them scalable. The talk highlights responsible AI as both a compliance necessity and a growth enabler, showing how fairness, transparency, accountability, and privacy can become differentiators. Founders will learn how to design human-AI teams, price for the AI era, and use distribution and trust as moats. Ultimately, this playbook reframes leadership for the AI-native era: balancing speed with stewardship, embedding responsibility into innovation, and turning governance into strategic leverage.

  3. 03

    The Company of One.

    AIFuture of WorkCareerStartups

    For 150 years, the default container for ambition was the corporation. That's cracking. As AI collapses the cost of execution, the firm is shrinking toward its smallest viable form—one person. This talk examines the rise of the solopreneur and the idea of the "one-person unicorn": the solo founder running a tiny empire backed by AI tools and agents. What's different now is the enabling layer—work that once required a team can be delegated to agents that operate around the clock and compound each workflow into capacity for the next. For the first time, a single person can assemble the functional equivalent of a company. Solo founders have climbed from 17% to 36% of new company formations in seven years, and teams of a dozen are crossing tens of millions in revenue. OpenAI's Sam Altman has even described a betting pool among tech CEOs over the first one-person billion-dollar company—a milestone he calls unimaginable without AI. The claim isn't that AI abolishes work, but that it makes the individual a credible economic unit for the first time since the artisan. The real question is what to build in response: how to think in revenue-per-person instead of headcount, where human judgment still commands a premium, and what an AI-first operating discipline looks like in practice.

  4. 04

    AI-Native Outbound: Precision that compounds.

    AIAI-NativeStartupsGTM

    Outbound has changed. Volume is cheap, inboxes are saturated, and most buyers aren’t in-market at all — which means the motion that worked before now runs into diminishing returns. This session covers what replaces it, in three parts. Signals, not lists: a list is who exists, a signal is who just changed — where buying triggers become public, which tiers of signal are actually defensible, and how to build one. Boundaries, not agents: anyone can run agents, few decide what they may do — the six agent roles in an outbound motion, what to buy versus build now that general agents exist, and the layer that decides what fires without you. Loops, not launches: a launch ends, a loop returns with what it learned — why reply rate is the wrong target, what to measure once execution stops being scarce, and why accumulated outcomes are the one advantage a competitor can’t purchase. Participants leave with three concrete moves they can start the same week, none of which requires budget approval.

  5. 05

    Business Strategies for Deeptech Startups.

    DeeptechStartups

    Deeptech startups follow a fundamentally different lifecycle than conventional startups: the innovation lies in developing and validating novel technology, the path to revenue is long, and the valley of death is deeper — but the end goal is the same: make money. This hands-on session covers the two pillars every deeptech business strategy needs: a commercialization strategy — turning a platform technology into prioritized product candidates, identifying real customers in the value chain, sizing markets realistically, and building a revenue model — and a financing strategy that bridges the valley of death by converting validation milestones into value inflection points, matched to the right funding sources at each stage. Founders leave with a working draft of their investment thesis: what the product is, who buys it and why, what it costs to reach market, and how big the return is — and the discipline to keep refining it as the ground shifts.

  6. 06

    The Deeptech Startup Lifecycle.

    DeeptechStartupsAI

    This talk provides a concise summary of the unique challenges and stages faced by companies built on deep technology—scientific discovery and meaningful engineering breakthroughs like AI, quantum computing, and biotech—as opposed to typical software or SaaS ventures. Deeptech is characterized by long time-to-market (7–15+ years), high capital intensity ($50M–$200M+), and a competitive moat built on defensible IP. The lifecycle begins with a Founding Insight from a lab or university, moves through multi-year stages of R&D and Proof of Concept (technical validation), followed by Go-to-Market Proof (pilots and clinical trials), and culminates in Scaling Commercialization, which requires navigating complex regulatory environments and securing large-scale funding for infrastructure. The talk emphasizes that exits are often later and frequently involve acquisition by industry giants, advising founders to plan for long horizons, anticipate regulation, and leverage strategic partnerships.

  7. 07

    A Practical Guide to Responsible AI.

    AIResponsible AIStartups

    In this session, we explore how companies and innovators can build and scale artificial intelligence responsibly—ensuring that rapid innovation aligns with human values and societal good. Drawing from global trends like accelerated AI advancement, emerging governance models, and rising ethical concerns, the talk introduces key distinctions between Ethical AI, Responsible AI, AI Safety, and Human-Centric AI. The heart of the discussion centers on four core principles of Responsible AI: fairness & bias mitigation, transparency, accountability, and privacy & security. The talk will also dive into practical steps startups can take to implement responsible AI including where to start, how to integrate it into your design process, and building the interdisciplinary teams and frameworks for continuous improvement and engagement. The session closes with a look at resources and how companies can treat Responsible AI not as an afterthought—but as a competitive advantage and moral imperative.

  8. 08

    How AI Is Reshaping Enterprise Innovation — and How to Become AI-First.

    AIAI-NativeResponsible AIEnterprise

    AI is no longer an experiment — it has become a strategic capability that is reshaping how organizations innovate, compete, and operate. Yet while adoption is nearly universal, transformation remains rare: most organizations are stuck in pilots, with almost 9 in 10 reporting regular AI use but only a fraction seeing measurable business impact. Drawing on the latest evidence from Silicon Valley and global research, this session examines what separates AI-first organizations from the rest — why rollouts stall, the implementation playbook that actually works, what leaders must do personally to become AI-fluent, and how to turn scattered experiments into a compounding advantage: an AI flywheel that makes the company smarter every day. Special attention is given to what these shifts mean for European companies, where trust and responsible AI are emerging as a genuine competitive edge rather than a constraint.

  9. 09

    How AI is Shaping Japan's Future: The Impact on Legal and IP Work.

    AIJapanFuture of WorkEnterprise

    Beneath the AI hype lies a quieter story about where value actually sits. Drawing on the legal-AI gold rush — where startups like Harvey found the model itself is becoming a commodity while judgment, workflow, and trust endure — this talk asks what AI is really doing to knowledge work. It turns to Japan’s distinctive path: a labor shortage rather than a layoff wave, adoption blocked by permission rather than technology, and an innovation-first stance apart from the US, EU, and China. For IP and tech professionals, it poses a sharper question — why the best moats are increasingly the ones IP can’t protect. As AI makes the work cheap, human judgment grows more valuable, not less.

  10. 10

    How AI is Shaping Japan's Future: What Legacy Industries Do Next.

    AIJapanFuture of WorkEnterprise

    The real constraint on AI in Japan's legacy industries isn't model capability — it's organizational permission and the willingness to redesign how work gets done. With the model layer now commoditized, durable value has shifted to data, workflow, and distribution, the very assets incumbents already hold. Add a shrinking labor pool that makes AI a way to hold output rather than cut headcount, and Japan's advantage becomes clear: operating knowledge, experienced judgment, and workflows competitors can't easily copy. This talk lays out the shift from renting a role defined by someone else to owning a workflow end to end — and argues you don't need the whole mandate to start, just one workflow.

  11. 11

    The AI Flywheel: Building the Recursive, Self-Improving Company.

    AIAI-NativeSDLCEnterprise

    This talk traces how AI builds companies that get smarter every day by turning software development into a compounding advantage rather than a one-time productivity gain. It opens with the mechanics of the flywheel itself—how recursive self-improvement loops let teams ship faster, learn from what they ship, and reinvest that learning into the next cycle—then works through what this means at every altitude: the strategy, org design, and governance enterprise leaders need to build an AI software factory; the spec-driven, loop-engineered practices that let engineering teams work rigorously with AI; and the honest limits that keep the whole thing credible. The throughline is that competitive advantage now accrues to organizations that treat improvement as a system, not an event—where signal precedes infrastructure, methodology compounds, and the company itself becomes the thing being recursively rebuilt. Rather than promising that AI makes everyone a generic builder, the talk argues for disciplined adoption: knowing which vocabulary is settled and which is still forming, verifying claims before betting on them, and designing loops deliberately so that each turn of the wheel makes the next one easier.

  12. 12

    What is the Software Factory?

    AIAI-NativeSDLC

    Every leap in software productivity has come from climbing one more rung on the ladder of abstraction — from machine code and assembly in the 1950s, to high-level languages in the 1970s, to the frameworks and reusable building blocks of the 2000s, and now to AI-assisted "vibe coding" in 2025. This talk traces that history to make sense of where we are today, then reframes the moment: AI isn't just a faster autocomplete, it's the point where software development starts to behave like a factory — a repeatable production system rather than bespoke craft. We define what that "software factory" actually is, show what it looks like in day-to-day practice, and close with a concrete plan for how teams can build one of their own.

  13. 13

    Design for Intent: What's Left to Spec-Driven Development.

    AIAI-NativeSDLCDesign

    In 1999, a $125 million Mars spacecraft burned up because two teams disagreed about units. The intent was correct — it just didn't survive the trip to the build. As AI makes software almost free to produce, that failure is becoming the defining problem in technology: when building is easy, the only thing left to get wrong is what you decided to build. Spec-driven development isn't new. It's the latest turn of a 40-year pattern hardware already lived through: as automation made building cheap, the leverage moved upstream to deciding. Drawing on Stanford doctoral research in design-process error-proofing and the lessons of Design for Manufacturing, this talk maps that discipline onto AI-native software — and names the gap spec-driven development hasn't noticed: a spec can be perfectly enforced and still be wrong. What's scarce now isn't building. It's knowing what's worth building — a teachable discipline, forty years in the making.

  14. 14

    Navigating Customer Value and Project Priority in Design.

    DesignStartupsGTM

    This presentation addresses the critical reality that most engineering failures stem from ecosystem or priority misalignment rather than technical incompetence. By integrating Customer Value Chain Analysis (CVCA) and the Project Priority Matrix (PPM), teams can transition from narrow technical focus to “Designing with Discipline.” These tools are essential because they force teams to define their “North Star” by mapping complex stakeholder value flows while simultaneously establishing a negotiated framework for internal trade-offs between scope, cost, and time. Ultimately, this strategic approach ensures that projects are not only technically sound but also viable within their external market ecosystems and sustainable under internal resource constraints.

  15. 15

    What to Do When Nobody Knows What’s Coming: Navigating an Unpredictable, AI-Shaped Future of Work.

    AIFuture of WorkCareer

    The ground beneath careers is already shifting — jobs are being unbundled, career arcs are shortening, and the people building AI are the ones warning us about what’s next. But predictions about the future of work have a lousy track record in both directions: the doomers and the cheerleaders are both reliably wrong. So how do you make real decisions inside genuine uncertainty? This talk cuts through the noise with a practical framework — six moves for building a career (and a life) that holds up across multiple possible futures, from investing in complements to AI rather than competing with it, to building the things AI can’t fake, to diversifying what defines you beyond your job title. For students, early-career professionals, and anyone advising them who wants honest guidance instead of false reassurance.

  16. 16

    Building Your AI-Native Career.

    AIAI-NativeCareer

    In a world where excellence kills and capabilities save, the divide between success and obsolescence is no longer about hard work—it is about agency and orchestration. This session provides a strategic roadmap for those early in the career to upskill and stand out, moving from an AI luddite to a high-output architect. Whether your goal is to thrive within a global enterprise, join a startup, or launch a “company of one,” you will learn to transition from using AI as a microtasker for simple emails to a copilot for collaborative coding or to a delegate that handles complex goals independently. This talk helps you show the agency and invest in your skills to build your career with confidence.

  17. 17

    The AI-Native Job Search.

    AIAI-NativeCareer

    The traditional “post and pray” approach to job seeking has become obsolete in a hyper-competitive, AI-driven market. This presentation proposes a strategic shift toward an AI-native search, where candidates operate with the precision of a modern Go-to-Market campaign. By leveraging AI to synthesize a North Star professional identity and transform static resumes into optimized, industry-aligned assets, applicants can bypass automated hurdles. The framework emphasizes predictive targeting and signal-based opportunism to identify high-value roles before they are widely publicized. Ultimately, this session demonstrates how to move from passive searching to active career execution, treating the job search as a sophisticated acquisition process defined by data-driven excellence and personalized strategy.

  18. 18

    The Impact of AI on Campus Talent Ecosystem and the Future of Work.

    AIFuture of WorkCareer

    This talk explores the transformative impact of artificial intelligence on the transition from campus to career, detailing how the traditional “job ladder” is being reshaped. Dr. Larry Chao provides a comprehensive Job Market Overview, highlighting the “job-pocalypse” where entry-level roles are vanishing at an alarming rate as companies freeze hiring to assess AI’s capabilities. From an HR and Recruiting perspective, the presentation examines the “hiring arms race,” where AI tools automate everything from resume screening to live technical interviewing, yet sometimes prioritize speed over quality. For the Student Perspective, the discussion addresses the “unraveling of credentialism,” warning against AI-dependency that can lead to a lack of foundational reasoning, while encouraging students to embrace “solopreneurship” and the gig economy. Finally, the talk outlines a roadmap for Building AI Fluency, distinguishing it from mere literacy by emphasizing the strategic application of AI as a “microtasker,” “copilot,” and “teammate” to drive organizational effectiveness.

  19. 19

    Building Bridges Beyond the PhD: Empowering Your Next Chapter.

    ResearchDeeptechCareer

    The commitment of PhD students affirms their role as essential “bridge builders.” The talk outlines how the PhD mindset—rigor, patience, and deep thinking—can guide their future through three essential bridges: From Foundation to Frontier, the PhD is a “masterclass in uncertainty” and a system for failing forward. The skill learned is resilience over perfection. From Isolation to Connection, research must be translated and ideas amplified beyond journals. PhDs must be bridge builders between theory and practice to earn public trust. From Experiment to Impact, the goal is turning knowledge into innovation (e.g., deeptech startups) and building things that endure. This requires focusing on responsibility and ethics, especially when building human-centric AI. The talk concludes that these bridges form a worldview valuing curiosity, collaboration, and integrity, encouraging students to keep building with courage.