AI_s_Global_Gamble This podcast transcript features a conversation between host Steven and MK, the founder of Grow Max, an agency specializing in AI product marketing for global markets. Drawing on nine years of experience at NetEase, MK discusses the transition from gaming logistics to the AI sector, emphasizing the importance of storytelling and branding for Chinese companies expanding abroad. The discussion identifies key obstacles for AI globalization, such as cultural gaps, data privacy concerns, and the need for localized content created by cross-border teams. MK also shares insights on market trends, predicting a rise in AI for social and emotional support and an impending expansion into European markets. Finally, the dialogue explores the future of education, suggesting that parents must prioritize critical thinking and imagination as AI becomes a fundamental tool in daily life.

AI_s_Global_Gamble This podcast transcript features a conversation between host Steven and MK, the founder of Grow Max, an agency specializing in AI product marketing for global markets. Drawing on nine years of experience at NetEase, MK discusses the transition from gaming logistics to the AI sector, emphasizing the importance of storytelling and branding for Chinese companies expanding abroad. The discussion identifies key obstacles for AI globalization, such as cultural gaps, data privacy concerns, and the need for localized content created by cross-border teams. MK also shares insights on market trends, predicting a rise in AI for social and emotional support and an impending expansion into European markets. Finally, the dialogue explores the future of education, suggesting that parents must prioritize critical thinking and imagination as AI becomes a fundamental tool in daily life.

In this insightful episode of Steven Data Talk, host Steven interviews JJ Jajin (JJ Never Sleeps), a successful professional who has navigated the tech world and built one of the largest Product Manager (PM) communities in San Francisco. JJ shares her remarkable journey across Tesla, Wise, LinkedIn, and now Meta as a Product Manager.

Key discussion points include:

Career Trajectory and Decisions: JJ describes her path from Wuhan University and Duke University, influenced by reading Steve Jobs’ biography, which led her to pursue Computer Science and product management. She shares her milestone experience at Tesla, where she learned how big corporations operate.

Core PM Principles: JJ talks about her three guiding principles that remain consistent across tech environments: deep user obsession (referencing her hundreds to thousands of user conversations at LinkedIn), data-driven decision-making (placing more weight on quantitative data), and remaining humble without ego.

The "JJ Never Sleeps" Community: JJ explains how she built a major PM community as a side project, aiming to close the information gap for Chinese students pursuing APM roles in the United States. She highlights challenges such as language barriers, firewall restrictions, and differences in product storytelling.

• Introduction & JJ’s Journey (Wuhan, Duke, Tesla, LinkedIn, Meta)
• Decision to become a Product Manager (Steve Jobs influence)
• Realizing the Information Gap and discovering Tesla at Duke
• Tesla Experience: Learning about Big Corporations and APM Programs
• Early Leadership: Wuhan University Student Union President Experience
• Core Product Manager Principles (Users Obsession, Data-Driven, No Ego)
• Motivation for Building the JJ Never Sleeps Community
• Challenges for International PMs & Community Building Purpose
• AI Impact: Switching to Meta & Basic AI Tools (ChatGPT, Nanobanana)
• Defining AI Native Product Thinking
• Tesla Culture: Result-Oriented Mindset, Flat Organization, and Managing Up
• Designing for Advanced Users vs. Newcomers (AI Personalization)
• Building Personal Brand: Prioritizing Community Quality over Quantity
• Advice for PMs Building a Public Voice (Niche vs. Massive Market)
• Guest Speaking at Harvard, CMU, USC: Differences in Student Questions
• Perspective on AI Replacing Product Managers (Tech vs. Business ROI)
• Future PM Skills: Model Intuition and Storytelling (Practice makes perfect)
• Conclusion: Meaning of "Never Sleeping" and Final Advice (Resilience & Passion)

#ProductManagement #TechCareer #AI #AInative #JJNeverSleeps #Meta #LinkedIn #Tesla #APM #CommunityBuilding #DataTalk #Entrepreneurship #SiliconValley #CareerMove

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This episode is a republish from Build Up with Lydia, featuring Milan-based data scientist and content creator, Steven.Steven's Journey and Vision: Steven shares his background, which includes over ten years in banking and corporate finance before he returned to academia to pursue dual master's degrees in data science and economics. He discusses his shift toward content creation and entrepreneurship, which includes establishing a local AI community in Milan and dissecting YC startup courses. His goal is to build the European equivalent of Silicon Valley 101. Steven’s brands include Learn by doing with Steven (focusing on tutorials) and his podcast, Steven Data Talk (centered on data).Innovation Culture: Europe vs. Silicon Valley: Steven describes Europe as an "AI development low ground" due to its conservative environment and complex legal landscape, noting the challenges posed by regulations like GDPR and the new AI Act. However, he maintains that pursuing ambitious goals in such a difficult environment is more meaningful. In contrast, Steven characterizes Silicon Valley's fierce competition not as a consensus-driven spiral, but as an "involuting existence" (卷存感)—a high-pressure race for media attention (e.g., TechCrunch), early top VC funding, and status.The Role of Podcasting: Steven views podcasting as a lifelong endeavor and the best medium for facilitating deep, meaningful conversations and finding "fellow travelers".This content has been summarized from the original Chinese version using NotebookLM.

🔗 Support the Creator & Access All Linkshttps://linktr.ee/learnbydoingwithsteven#AI #AIAgents #AgentOrchestration #AIEconomy #LLM #Decentralization #Blockchain #AISafety #Innovation #Startup #DeepDivePodcast

This episode is a republish from Build Up with Lydia, featuring Milan-based data scientist and content creator, Steven.Steven's Journey and Vision: Steven shares his background, which includes over ten years in banking and corporate finance before he returned to academia to pursue dual master's degrees in data science and economics. He discusses his shift toward content creation and entrepreneurship, which includes establishing a local AI community in Milan and dissecting YC startup courses. His goal is to build the European equivalent of Silicon Valley 101. Steven’s brands include Learn by doing with Steven (focusing on tutorials) and his podcast, Steven Data Talk (centered on data).Innovation Culture: Europe vs. Silicon Valley: Steven describes Europe as an "AI development low ground" due to its conservative environment and complex legal landscape, noting the challenges posed by regulations like GDPR and the new AI Act. However, he maintains that pursuing ambitious goals in such a difficult environment is more meaningful. In contrast, Steven characterizes Silicon Valley's fierce competition not as a consensus-driven spiral, but as an "involuting existence" (卷存感)—a high-pressure race for media attention (e.g., TechCrunch), early top VC funding, and status.The Role of Podcasting: Steven views podcasting as a lifelong endeavor and the best medium for facilitating deep, meaningful conversations and finding "fellow travelers".This content has been summarized from the original Chinese version using NotebookLM.

🔗 Support the Creator & Access All Linkshttps://linktr.ee/learnbydoingwithsteven#AI #AIAgents #AgentOrchestration #AIEconomy #LLM #Decentralization #Blockchain #AISafety #Innovation #Startup #DeepDivePodcast

This episode of Steven Data Talk is a republication of a discussion originally aired on Abby's podcast, 风平浪静. (Content summarized from the original Chinese version using NotebookLM.)Steven, an experienced AI content creator and entrepreneur, joins Abby to discuss his career pivot and his work in the AI community.Key Topics Covered:• Steven's Background and Transition: Steven shares his 14 years of professional experience, primarily in commercial banking and financing, and his current pursuit of a second Master's degree in Data Science and Economics at the University of Milan. This academic focus includes deep learning, reinforcement learning, and NLP.• The Podcast's Mission: Steven's podcast, Steven Data Talk (English version) and Steven 數 據 慢 談 (Chinese version), is inspired by major hosts like Lex Fridman and Andrew Huberman. The show targets entrepreneurs, AI engineers, AI researchers, and investors (VC/PE) in the AI industry. Its primary goal is to provide a platform for in-depth communication to reduce information asymmetry and help various stakeholders find collaborators or opportunities.• Networking and Content Strategy: Steven uses the podcast format as a highly focused tool for deep communication, contrasting it with typical surface-level networking. He finds most of his high-caliber guests, including professors and engineers, via the Xiao Hong Shu platform.• New Project: Steven introduced a parallel project called Stephen MOE (Mixture of Experts), a live-streamed roundtable discussion inspired by an IBM podcast, which focuses on debating major AI events and topics.• AI Entrepreneurship in Europe: Steven, based in Milan, observes a limited number of AI startups in continental Europe compared to the US and China. European projects often focus on vertical/niche areas, such as industrial process automation, Excel plugins, or humanitarian projects combining 3D printing and AI for custom footwear.• Challenges of the European Market: Major obstacles for AI entrepreneurs include severe regulatory pressure (such as the AI Act and GDPR), high taxes, and a lack of compute resources (GPUs), which makes the environment challenging for startups. Italy was noted as the first country to mandate the take-down of the Deepfake App (DeepFace).• Opportunities for Chinese Firms: Steven suggests that the European market is likely waiting for US and Chinese AI companies to take market share. Chinese AI companies, particularly those focused on open-source models, have a comparative advantage and are starting to explore the market by building developer communities.

🎧 Follow and explore all content:👉 ⁠linktr.ee/learnbydoingwithsteven⁠

#AIEntrepreneurship #FutureOfWork #TeamBuilding #AIAgents #StartupMindset #Leadership #HRTech #AICommunity #DeepDivePodcast #LearnByDoing

This episode of Steven Data Talk is a republication of a discussion originally aired on Abby's podcast, 风平浪静. (Content summarized from the original Chinese version using NotebookLM.)Steven, an experienced AI content creator and entrepreneur, joins Abby to discuss his career pivot and his work in the AI community.Key Topics Covered:• Steven's Background and Transition: Steven shares his 14 years of professional experience, primarily in commercial banking and financing, and his current pursuit of a second Master's degree in Data Science and Economics at the University of Milan. This academic focus includes deep learning, reinforcement learning, and NLP.• The Podcast's Mission: Steven's podcast, Steven Data Talk (English version) and Steven 數 據 慢 談 (Chinese version), is inspired by major hosts like Lex Fridman and Andrew Huberman. The show targets entrepreneurs, AI engineers, AI researchers, and investors (VC/PE) in the AI industry. Its primary goal is to provide a platform for in-depth communication to reduce information asymmetry and help various stakeholders find collaborators or opportunities.• Networking and Content Strategy: Steven uses the podcast format as a highly focused tool for deep communication, contrasting it with typical surface-level networking. He finds most of his high-caliber guests, including professors and engineers, via the Xiao Hong Shu platform.• New Project: Steven introduced a parallel project called Stephen MOE (Mixture of Experts), a live-streamed roundtable discussion inspired by an IBM podcast, which focuses on debating major AI events and topics.• AI Entrepreneurship in Europe: Steven, based in Milan, observes a limited number of AI startups in continental Europe compared to the US and China. European projects often focus on vertical/niche areas, such as industrial process automation, Excel plugins, or humanitarian projects combining 3D printing and AI for custom footwear.• Challenges of the European Market: Major obstacles for AI entrepreneurs include severe regulatory pressure (such as the AI Act and GDPR), high taxes, and a lack of compute resources (GPUs), which makes the environment challenging for startups. Italy was noted as the first country to mandate the take-down of the Deepfake App (DeepFace).• Opportunities for Chinese Firms: Steven suggests that the European market is likely waiting for US and Chinese AI companies to take market share. Chinese AI companies, particularly those focused on open-source models, have a comparative advantage and are starting to explore the market by building developer communities.

🎧 Follow and explore all content:👉 ⁠linktr.ee/learnbydoingwithsteven⁠

#AIEntrepreneurship #FutureOfWork #TeamBuilding #AIAgents #StartupMindset #Leadership #HRTech #AICommunity #DeepDivePodcast #LearnByDoing

This is notebooklm summary version.

Original version in Chinese, find it in Steven数据漫谈

🎧 Summary

In this Deep Dive, we explore how AI is transforming what it means to “build a team.”Featuring insights from Julie, an HR expert and community leader in Shanghai’s AI ecosystem, we unpack the paradox of modern entrepreneurship — when the real bottleneck isn’t capital or ideas, but finding the right person.

From the rise of the one-person company powered by AI agents, to the scarcity of multi-skilled “super individuals,” we discuss what defines entrepreneurial DNA today — wild power, curiosity, focus, and the ability to drive AI, not just use it.🕒 Time-Stamped Highlights

00:00 – 00:06 | Intro: Setting the mission — decoding the future of team building in AI.
00:06 – 00:18 | Julie’s insights from Shanghai’s AI scene: where HR meets innovation.
00:18 – 00:30 | The new bottleneck: people, not ideas.
00:30 – 00:49 | Why top corporate résumés might make terrible co-founders.
00:49 – 01:08 | Enter the “Super Individual” — mastering AI orchestration.
01:08 – 01:32 | What AI agents really are, and how one operator can replace an entire team.
01:32 – 02:13 | Extreme organizational compression: startups with zero overhead.
02:13 – 03:05 | Talent paradox — the hunt for rare hybrid profiles.
03:05 – 03:33 | Corporate pedigree vs. startup chaos: why mindsets clash.
03:33 – 03:58 | “Talked-out” talent: how real founders find co-founders offline.
03:58 – 04:55 | Hidden talent pools — from geek forums to Xiaohongshu.
04:55 – 05:17 | The new ideal: people with wild power and raw creative energy.
05:17 – 06:13 | The alignment trap — when goals don’t match missions.
06:13 – 07:01 | The “comprehensive individual” — the ultimate scarce resource.
07:01 – 07:49 | Polarization of capability: how AI magnifies human differences.
07:49 – 08:29 | Slash skills — the high-leverage combo of tech + market sense.
08:29 – 08:59 | Julie’s grounded advice: “Don’t rush into entrepreneurship.”
08:59 – 09:34 | The core abilities of the AI era: sustained learning, driving AI, maintaining focus.
09:34 – 10:00 | Focus as a superpower — filtering signal from noise.
10:00 – 10:12 | Outro reflection: Success now depends less on scale, more on the super individual.

💡 Key Takeaways

• The one-person company is becoming a reality through AI agent orchestration.

• The talent gap is psychological as much as technical — curiosity, ownership, and resilience matter most.

• The future of work favors hybrid, cross-disciplinary minds who can continuously learn and refocus.

• Focus and curiosity are now as valuable as capital.

🎧 Follow and explore all content:
👉 linktr.ee/learnbydoingwithsteven

#AIEntrepreneurship #FutureOfWork #TeamBuilding #AIAgents #StartupMindset #Leadership #HRTech #AICommunity #DeepDivePodcast #LearnByDoing

This is notebooklm summary version.

Original version in Chinese, find it in Steven数据漫谈

🧭 Summary

In this episode of The Explainer, we dive into the new AI gold rush — not from the tech or funding side, but from the people side.
Startup advisor Julie shares her hard-won insights from helping founders build core AI teams from scratch.

We explore:
🔹 Why the hardest part of building an AI startup isn’t ideas or funding — it’s people.
🔹 Where to actually find “wild power” — the rare creators with entrepreneurial fire.
🔹 How to become the super-individual every AI founder is looking for.
🔹 The new rules of career building in the age of AI-agents and exponential productivity.

🕒 Timestamps

00:00 – 00:25 | Welcome to The Explainer: setting up the new AI gold rush
00:25 – 01:05 | The new frontier: why AI changes the startup rulebook
01:05 – 01:30 | The real bottleneck isn’t ideas — it’s people
01:30 – 02:00 | Why “Big Tech” talent may not fit startup chaos
02:00 – 02:40 | Where to find true builders — “underwater” creators
02:40 – 03:05 | The rare skill combo: curiosity + ownership
03:05 – 03:40 | The two new core skills for the AI era
03:40 – 04:05 | From “using AI” to commanding AI
04:05 – 04:40 | Becoming a super-individual: leading your AI agent team
04:40 – 05:00 | The 10x effect: how AI widens the talent gap
05:00 – 05:40 | Mindset, mastery, visibility — your new career playbook
05:40 – END | Final thought: curiosity beats credentials in the age of AI

🧠 Key Quote

“The best people aren’t scrolling LinkedIn — they’re building in the shadows. You have to go find their wild power.” — Julie

🚀 Takeaway

The future belongs to those who can learn fast, master AI, and show their work publicly.
Your resume won’t save you — your curiosity will.

🎧 Follow and explore all content:
👉 linktr.ee/learnbydoingwithsteven

#AIStartups #AICareers #TeamBuilding #AIRevolution #FutureOfWork #StartupMindset #SuperIndividual #LearnByDoing #ExplainerPod #GenerativeAI

This is NotebookLM Audio Summary from CN version.Original version @Steven数据漫谈 podcast.Episode 9 features AI Engineer Nan Xiao discussing Sammo, a decentralized AI Agent platform. The vision is to create an "agent economy" where developers can monetize agents. The platform is decentralized (no "central brain") and uses blockchain for transparent evaluation. Nan Xiao also covers the practical limits of AI coding, the philosophy of giving agents autonomy (no "babysitting"), and how startup "chaos" drives innovation. While open-source, Sammo's business model targets enterprise clients, aiming to solve the core problem of inefficient agent collaboration.---Inside Summoner: Building a Decentralized AI Agent EconomyAbout the ShowSteven Data Talk — delivering the clearest conversations on cutting-edge AI, technology, innovation, business, and entrepreneurship.Now available on Spotify, Apple Podcasts, YouTube, Amazon Music, Xiaoyuzhou, Ximalaya, and more.Episode SummaryIn this Deep Dive, we explore how Summoner is attempting to orchestrate AI agents into interoperable “fleets,” measure their contributions quantitatively, and build a decentralized AI agent economy secured by cryptography and blockchain. We discuss incentives, evaluation metrics, security, real R&D breakthroughs, the limits of LLM-generated code, and how these agent architectures could even power socioeconomic simulations.⏱️ Chapters (YouTube-optimized)00:00 – Intro: Why AI agent orchestration matters00:32 – Fragmented agent world & Summoner’s core vision01:40 – Agent fleets, incentives & reward distribution03:05 – Search, discovery & evaluation-driven ranking04:15 – Custom evaluation using company context (docs, PDFs, SQL)05:20 – Seven metrics, custom formulas & defining agent “value delivered”06:30 – Security: decentralized IDs, cryptography & blockchain ledger07:40 – R&D story: visualizing agent interactions (UEP → PCA → PLS)08:40 – LLM limits vs human engineering in complex system code09:40 – Autonomy vs micromanagement: enterprise needs vs emergent behavior10:20 – Beyond enterprise tasks: agent societies & social science simulations10:55 – Open-source “highway” mindset & startup chaos vs big-tech comfort11:30 – Final reflection & closing question to the audience🔗 Support the Creator & Access All Linkshttps://linktr.ee/learnbydoingwithsteven#AI #AIAgents #AgentOrchestration #AIEconomy #LLM #Decentralization #Blockchain #AISafety #Innovation #Startup #DeepDivePodcast

📌 Footnote

1. The system is not built on blockchain; it uses agent-state chains.

2. The phrase “7 difference matrices” is incorrect; it should be “7 different statistical measures/metrics.”

3. The brief explanation of blockchain around may be misleading and may require trimming.

Note: This media is generated by NotebookLM, and errors may occur. Please refer to the original Chinese podcast for the authoritative version.