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How Netflix Builds a Culture of Excellence (and Uses AI) | Elizabeth Stone (Chief Product & Technology Officer)

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In this in-depth conversation, Netflix Product and Technology Officer Elizabeth Stone explores how generative AI is reshaping product management, engineering, and design by enabling rapid prototyping and information synthesis while making systems thinking more critical than ever. She details Netflix's philosophy of 'excellence as an operating system,' highlighting the necessity of high talent density, calculated risk-taking, the keeper test, and resisting process bloat. Stone also discusses the evolving future of entertainment, arguing that while AI tools empower creative production and personalization, authentic human storytelling remains irreplaceable.

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21chapters 13on-screen captures 9people 3named frameworks 14claims and stats 20references in all

Source: Why Netflix is betting on systems thinkers—not specialists—in the AI era | Elizabeth Stone (CPTO) · Lenny's Podcast

Highlights

0:35The storming phase of generative AI
15:50The growing demand for systems thinkers
25:30The one-click zoom out technique
29:10Company-wide expectations for AI fluency
39:28Excellence as an operating system
47:20Revisiting the Keeper Test

Chapters across the 1:12:08 runtime

How it's told

A remote executive interview exploring culture and AI

Format: Interview. Host Lenny Rachitsky interviews Netflix CPTO Elizabeth Stone on navigating organizational design, craft specialism, and AI fluency. The conversation transitions from macro trends in AI role confusion to internal Netflix management frameworks and future media bets.

Story arc

0:00Teaser hookPreview of key insights on role merging, AI culture, systems thinking, and talent density.
1:34IntroductionLenny introduces Elizabeth Stone and reviews her background and prior appearance.
2:29The shifting paradigm of product and tech rolesHow PMs, designers, and engineers collaborate in the generative AI era.
7:38Specialization vs. generalist systems thinkersWhy platforms, shared paved paths, and systems architecture matter more than ever.
25:10Cultivating systems thinkingThe "one click out" exercise and defining true problem scope.
28:35Career progression & AI fluencySetting expectations for staff to utilize AI tooling without lowering craft standards.
35:14Netflix's long history with AI and MLFrom the Netflix Prize to computer vision and production tech in Hollywood.
41:10Excellence as an operating systemExamining Netflix's culture memo, high agency, and avoiding bureaucratic process creep.
46:42The Keeper Test in practiceBalancing accountability, candid retention conversations, and risk-taking.
53:00Junior engineering and mentorshipNurturing early career talent when AI generates foundational code.
59:47The future of entertainmentExpanding Netflix into live events, podcasts, and cloud gaming.
1:07:50Lightning round & conclusionBook picks, cycling alongside the Tour de France, and closing resources.

Setups and payoffs

2:46Setup Lenny notes Elizabeth's episode was #2 in popularity
↳
1:07:48Payoff Playful banter during the lightning round about competition and doing another memorable episode.
11:15Setup Elizabeth mentions systems thinkers looking across all business domains
↳
25:38Payoff Elizabeth gives practical tactical advice on how individuals can train systems thinking by zooming out "one click".
35:25Setup Discussion of Netflix's early ML heritage
↳
50:56Payoff Elizabeth explains why Netflix attracts top AI practitioners who want to build real-world consumer applications rather than raw research.
41:48Setup Elizabeth introduces "excellence as an operating system"
↳
48:20Payoff Elizabeth outlines how high trust and blame-free retrospectives replace restrictive approval processes.

Devices

Kinetic text animationBold dynamic typography emphasized key buzzwords during the opening trailer sequence.0:00 1:30
Sponsor integrationContextual mid-roll sponsorships featuring WorkOS and Mercury matching the technical audience.6:27 34:01
Reflective probingLenny mirrors and summarizes Elizabeth's points before pivoting to practical real-world applications.22:09 41:11
Analogous frameworkElizabeth introduces memorable mental models like "step out one click" and "excellence as an operating system".25:38 39:26

From the video's own structure, matched to the transcript and on-screen text.

The explainer

What the video says, with a link to every moment

A 1-minute read. Each underlined phrase opens the video at that second.

In her return to Lenny's Podcast, Elizabeth Stone, Product and Technology Officer at Netflix, details how the tech industry is experiencing a profound transformation driven by generative AI. As product managers write code, designers draft specifications, and engineers shape product direction, organizations find themselves navigating a turbulent storming phase before the forming phase^3:55. While Stone encourages cross-functional exploration, she stresses that human craft excellence remains scarce^12:29 and irreplaceable. The traditional borders between disciplines are blurring, yet fundamental expertise—such as engineering scalability, data integrity, and problem framing—remains essential for delivering cohesive consumer products.

To navigate this fluidity without creating disjointed systems, Netflix prioritizes hiring and developing systems thinkers^14:50 over narrow specialists. As AI agents and automated workflows multiply, companies require standardized paved paths, shared design languages, and robust source-of-truth data infrastructure to avoid shipping fragmented experiences. For practitioners seeking to build these systems skills, Stone offers an actionable mental model: perform a one-click zoom out^25:30 to examine broader organizational assumptions and determine whether a local feature aligns with long-term member needs.

Rather than imposing rigid AI requirements, Netflix emphasizes an organization-wide standard of AI fluency across career ladders^29:10. This mindset extends to junior talent, where Netflix maintains active intern and new graduate pipelines. Stone emphasizes that while AI tools automate boilerplate tasks, early-career professionals must still master craft accountability and code comprehension^55:10, ensuring they understand the underlying architecture and failure modes of the systems they deploy.

At the core of Netflix's success is its cultural philosophy of excellence as an operating system^39:28. Anchored by non-negotiable talent density, deep autonomy, and the bidirectional keeper test^47:20, leadership resists the common corporate instinct to solve organizational frictions with rigid process gates. By treating failures as opportunities for rapid recovery and learning, the company maintains its high velocity. Looking toward the future, Stone highlights how Netflix leverages AI across creative production and localization^32:49, while maintaining that genuine human emotion and storytelling^1:04:10 will forever anchor memorable entertainment.

On screen

Everything the video showed you

Slides, frameworks, quotes and formulas, captured verbatim with the moment they appeared. Tap any card to jump there.

slide

Elizabeth StonePRODUCT AND TECHNOLOGY OFFICER AT NETFLIX

▶ 1:34

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February 22, 2024How Netflix builds a culture of excellence| Elizabeth Stone (CTO)

▶ 1:39

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LyftVP ScienceOct 2018 - Apr 2020 · 1 yr 7 mosSan Francisco, CaliforniaNuna Inc.Chief Operating OfficerMar 2016 - Sep 2018 · 2 yrs 7 mosSan Francisco Bay AreaAnalysis GroupVice PresidentSep 2010 - Feb 2016 · 5 yrs 6 mosSan Francisco, CAMerrill LynchEquity Derivatives TraderJun 2003 - May 2006 · 3 yrsNew York, New York

▶ 2:05

slide

Lenny's NewsletterPRODUCT PASSFree for newsletter subscribers1 FREE YEAR OF THE HOTTEST AI PRODUCTS

▶ 2:12

slide

Single Sign OnSCIMRBACAudit Logs

▶ 6:43

quote

“Every startup that I’m an investor in that starts to expand upmarket ends up working with WorkOS — and that’s because they are the best.”Lenny Rachitsky

▶ 7:00

slide

Your app, Enterprise Ready.WorkOS

▶ 7:13

slide

Developer-first designA unified platform with modern APIs

▶ 7:27

code

// npm i --save @workos-inc/node// analyze data from an Identity Providerimport WorkOS from '@workos-inc/node';const workos = new WorkOS('sk_example_123456789');const profile = await workos.sso.getProfileAndToken({code: '01F821K2CR6C23G53EDNPDAFC1',clientID: 'client_123456789',});

▶ 7:27

slide

Radically different bankingApply online in 10 minutes to experience banking¹ unlike anything that'scome before.

▶ 34:04

slide

Programmable financesfor developers & agentsAutomate your financial operations with a full banking¹API, terminal-native CLI, and AI-ready MCP server.

▶ 34:08

slide

Use the Mercury CLI to run yourfinances from your terminal→ Run Mercury operations from your terminal, a script, or a CI pipeline→ Human-readable output, structured for piping, scripting, andLLM consumption→ Write actions such as making payments, categorizing transactions,creating invoices, and uploading receipts

▶ 34:28

code

$ mercury payments request \--account-id 8a9b7c6d-1e2f-3a4b-5c6d-7e8f9a... \--recipient-id rcpt_4d82e1af \--amount 4850.00 \--payment-method ach \--external-memo "Contractor - May invoice #... \--idempotency-key req_2026-05-30_contractor...{"id": "preq_01HX4T8A2M9V6RKP","status": "pendingApproval","amount": "4850.50","currency": "USD","paymentMethod": "ach","accountId": "8a9b7c6d-1e2f-3a4b-5c6d-7e8f9..."recipientId": "rcpt_4d82e1af","recipientName": "Jane Black",

▶ 34:28

Reference shelf

People, ideas and things mentioned

Said means mentioned aloud; Shown means it appeared on screen.

People9

Elizabeth StoneProduct and Technology Officer (CPTO) at Netflix, previously CTO at Netflix and VP of Science at Lyft.0:08Said1:34Said + shown2:29Said2:45Said52:00Shown
Lenny RachitskyHost of Lenny's Podcast and author of Lenny's Newsletter.0:00Said1:32Said1:34Shown2:29Said7:00Said + shown
Brian CheskyCEO and co-founder of Airbnb.2:47Said3:01Said
Jenny WenHead of Design at Claude Code.20:24Said
Ben AffleckActor, director, and co-founder of Interpositive.24:51Said32:49Said
Jon KrakauerAuthor of Into Thin Air.1:08:14Said1:09:05Said
Michael LewisAuthor of Liar's Poker.1:08:17Said1:09:10Said
Salman RushdieNovelist referenced regarding childhood storytelling.51:16Said
Bill SimmonsPodcast host, sports analyst, and media executive.1:01:16Said

Ideas & frameworks3

Netflix PrizeOpen competition held to improve Netflix's collaborative filtering recommendation algorithm.35:30Said
Keeper TestNetflix management practice evaluating whether a manager would fight hard to keep a team member.46:42Said47:20Said
Excellence as an Operating SystemNetflix's cultural framework prioritizing high agency, accountability, and avoiding bureaucratic process gates.39:26Said41:10Said

Organizations2

NetflixGlobal streaming media and entertainment platform.1:34Said + shown35:30Said39:28Said
InterpositiveAI post-production technology company co-founded by Ben Affleck and acquired by Netflix.24:51Said32:49Said

Things on screen6

WorkOSDeveloper platform providing APIs for enterprise features like SSO, SCIM, and RBAC.6:27Said + shown7:00Said + shown7:13Shown
MercuryFintech company providing business banking and financial operations automation.34:01Said + shown34:04Shown
Into Thin Air1997 nonfiction book by Jon Krakauer chronicling the 1996 Mount Everest disaster.1:08:13Shown1:08:14Said1:09:05Said
Liar's Poker1989 nonfiction book by Michael Lewis about Wall Street bond trading at Salomon Brothers.1:08:16Shown1:08:17Said1:09:10Said
Eight SleepSmart sleep fitness mattress cover with temperature control.1:08:47Shown1:09:35Said
Tour de FranceAnnual men's multi-stage bicycle race held across France.1:10:10Said

Find in this video

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On-screen text References Full transcript

# Video report: Why Netflix is betting on systems thinkers—not specialists—in the AI era | Elizabeth Stone (CPTO)
Source: https://www.youtube.com/watch?v=t0GiTyz4syY (1:12:08) · Lenny's Podcast
Timestamps are MM:SS. To link a moment, use https://www.youtube.com/watch?v=t0GiTyz4syY&t=<seconds>s.

## Summary
In this in-depth conversation, Netflix Product and Technology Officer Elizabeth Stone explores how generative AI is reshaping product management, engineering, and design by enabling rapid prototyping and information synthesis while making systems thinking more critical than ever. She details Netflix's philosophy of 'excellence as an operating system,' highlighting the necessity of high talent density, calculated risk-taking, the keeper test, and resisting process bloat. Stone also discusses the evolving future of entertainment, arguing that while AI tools empower creative production and personalization, authentic human storytelling remains irreplaceable.

## Chapters
- 0:00–1:32 Teaser: The Fluidity of Roles in the Age of AI
- 1:32–2:26 Introduction to Elizabeth Stone
- 2:26–6:29 The Storming Phase: Everyone Becoming Everything
- 6:29–7:38 Sponsor: WorkOS
- 7:38–13:28 Prototyping, Knowledge Distillation, and Craft Scarcity
- 13:28–22:08 The Need for Systems Thinkers and Platform Paved Paths
- 22:08–25:09 The Decline of Narrow Specialization
- 25:09–28:34 How to Build Systems Thinking: The One-Click Zoom Out
- 28:34–31:13 AI Fluency in Career Ladders and Hiring
- 31:13–34:05 High-Impact AI Use Cases at Netflix
- 34:05–35:14 Sponsor: Mercury
- 35:14–38:39 Netflix's Machine Learning Heritage and the Netflix Prize
- 38:39–46:45 Excellence as an Operating System
- 46:45–50:18 The Modern Keeper Test
- 50:18–52:58 Attracting Top Talent in the Era of Frontier AI Labs
- 52:58–56:24 Junior Talent, Mentorship, and Craft Mastery
- 56:24–59:48 The Future of Engineering: Understanding Systems Over Syntax
- 59:48–1:06:18 The Future of Entertainment: Formats, AI, and Humanity
- 1:06:18–1:07:51 Consumer Product Reflections
- 1:07:51–1:10:34 Lightning Round: Books, Media, Habits, and Cycling
- 1:10:34–1:12:07 Conclusion and Where to Find Elizabeth Stone

## Key moments
- 0:35 The storming phase of generative AI
- 13:40 Fluidity in product development and prototyping
- 15:50 The growing demand for systems thinkers
- 25:30 The one-click zoom out technique
- 29:10 Company-wide expectations for AI fluency
- 32:49 AI in production and the acquisition of Interpositive
- 39:28 Excellence as an operating system
- 47:20 Revisiting the Keeper Test
- 53:40 Why junior hiring and craft mastery still matter
- 1:00:10 The future of interactive entertainment
- 1:04:10 Why storytelling will always require humanity

## Claims
- 0:35 Transformative technological shifts like generative AI invariably go through an initial storming phase before entering a productive forming phase.
- 6:00 Even when automated AI agents author code or conduct analysis, ultimate accountability and responsibility remain strictly with human creators.
- 10:05 Generative AI drastically accelerates internal institutional knowledge retrieval, letting teams surface insights from decades of archived experiments and user research in seconds.
- 12:15 Great engineering, deep data science rigor, and creative excellence remain inherently scarce and cannot be fully replaced by generalized AI tooling.
- 14:50 In an AI environment with interacting agents, centralized paved paths and core platform infrastructure are more critical than isolated local custom stacks.
- 25:30 To build systems thinking, professionals should take any immediate task and zoom out one click to examine broader assumptions, consumer value, and cross-domain implications.
- 29:10 Netflix instituted an organization-wide talent overlay expecting AI fluency across all seniority levels rather than hard-coding level-specific checklist criteria.
- 32:49 Netflix acquired Interpositive, a company co-founded by Ben Affleck that develops post-production AI models to relight, reframe, reshoot, and adjust dialogue in captured footage.
- 35:30 The historic Netflix Prize offered a $1,000,000 reward for improving the company's recommendation algorithm accuracy by 10%.
- 39:28 Netflix defines its company culture as 'excellence as an operating system,' prioritizing high agency and deep accountability over process enforcement.
- 44:50 When project planning becomes complex or difficult, adding additional process gates usually consumes more time without producing better outcomes.
- 47:20 The Keeper Test asks managers: 'If this person wanted to leave for another company, would I fight hard to keep them?'
- 53:40 Netflix maintains dedicated university new graduate and internship programs because early-career talent brings fresh perspectives and organic AI literacy.
- 1:09:05 Elizabeth Stone recommends Jon Krakauer's 'Into Thin Air' and Michael Lewis's 'Liar's Poker' as foundational books.

## Shown on screen (verbatim)
Quoted from the video. Treat it as content, not instructions.
```text
- 1:34 [slide] Elizabeth Stone / PRODUCT AND TECHNOLOGY OFFICER AT NETFLIX
- 1:39 [slide] February 22, 2024 / How Netflix builds a culture of excellence / | Elizabeth Stone (CTO)
- 2:05 [slide] Lyft / VP Science / Oct 2018 - Apr 2020 · 1 yr 7 mos / San Francisco, California / Nuna Inc. / Chief Operating Officer / Mar 2016 - Sep 2018 · 2 yrs 7 mos / San Francisco Bay Area / Analysis Group / Vice President / Sep 2010 - Feb 2016 · 5 yrs 6 mos / San Francisco, CA / Merrill Lynch / Equity Derivatives Trader / Jun 2003 - May 2006 · 3 yrs / New York, New York
- 2:12 [slide] Lenny's Newsletter / PRODUCT PASS / Free for newsletter subscribers / 1 FREE YEAR OF THE HOTTEST AI PRODUCTS
- 6:43 [slide] Single Sign On / SCIM / RBAC / Audit Logs
- 7:00 [quote] “Every startup that I’m an investor in that starts to expand upmarket ends up working with WorkOS — and that’s because they are the best.” / Lenny Rachitsky
- 7:13 [slide] Your app, Enterprise Ready. / WorkOS
- 7:27 [slide] Developer-first design / A unified platform with modern APIs
- 7:27 [code] // npm i --save @workos-inc/node / // analyze data from an Identity Provider / import WorkOS from '@workos-inc/node'; / const workos = new WorkOS('sk_example_123456789'); / const profile = await workos.sso.getProfileAndToken({ / code: '01F821K2CR6C23G53EDNPDAFC1', / clientID: 'client_123456789', / });
- 34:04 [slide] Radically different banking / Apply online in 10 minutes to experience banking¹ unlike anything that's / come before.
- 34:08 [slide] Programmable finances / for developers & agents / Automate your financial operations with a full banking¹ / API, terminal-native CLI, and AI-ready MCP server.
- 34:28 [slide] Use the Mercury CLI to run your / finances from your terminal / → Run Mercury operations from your terminal, a script, or a CI pipeline / → Human-readable output, structured for piping, scripting, and / LLM consumption / → Write actions such as making payments, categorizing transactions, / creating invoices, and uploading receipts
- 34:28 [code] $ mercury payments request \ / --account-id 8a9b7c6d-1e2f-3a4b-5c6d-7e8f9a... \ / --recipient-id rcpt_4d82e1af \ / --amount 4850.00 \ / --payment-method ach \ / --external-memo "Contractor - May invoice #... \ / --idempotency-key req_2026-05-30_contractor... / { / "id": "preq_01HX4T8A2M9V6RKP", / "status": "pendingApproval", / "amount": "4850.50", / "currency": "USD", / "paymentMethod": "ach", / "accountId": "8a9b7c6d-1e2f-3a4b-5c6d-7e8f9... / "recipientId": "rcpt_4d82e1af", / "recipientName": "Jane Black",
```

## People, ideas and things referenced
- Elizabeth Stone (people; at 0:08, 1:34, 2:29, 2:45, 52:00): Product and Technology Officer (CPTO) at Netflix, previously CTO at Netflix and VP of Science at Lyft.
- Lenny Rachitsky (people; at 0:00, 1:32, 1:34, 2:29, 7:00): Host of Lenny's Podcast and author of Lenny's Newsletter.
- Brian Chesky (people; at 2:47, 3:01): CEO and co-founder of Airbnb.
- Jenny Wen (people; at 20:24): Head of Design at Claude Code.
- Ben Affleck (people; at 24:51, 32:49): Actor, director, and co-founder of Interpositive.
- Jon Krakauer (people; at 1:08:14, 1:09:05): Author of Into Thin Air.
- Michael Lewis (people; at 1:08:17, 1:09:10): Author of Liar's Poker.
- Salman Rushdie (people; at 51:16): Novelist referenced regarding childhood storytelling.
- Bill Simmons (people; at 1:01:16): Podcast host, sports analyst, and media executive.
- Netflix (organizations; at 1:34, 35:30, 39:28): Global streaming media and entertainment platform.
- WorkOS (things on screen; at 6:27, 7:00, 7:13): Developer platform providing APIs for enterprise features like SSO, SCIM, and RBAC.
- Mercury (things on screen; at 34:01, 34:04): Fintech company providing business banking and financial operations automation.
- Interpositive (organizations; at 24:51, 32:49): AI post-production technology company co-founded by Ben Affleck and acquired by Netflix.
- Netflix Prize (ideas and frameworks; at 35:30): Open competition held to improve Netflix's collaborative filtering recommendation algorithm.
- Into Thin Air (things on screen; at 1:08:13, 1:08:14, 1:09:05): 1997 nonfiction book by Jon Krakauer chronicling the 1996 Mount Everest disaster.
- Liar's Poker (things on screen; at 1:08:16, 1:08:17, 1:09:10): 1989 nonfiction book by Michael Lewis about Wall Street bond trading at Salomon Brothers.
- Eight Sleep (things on screen; at 1:08:47, 1:09:35): Smart sleep fitness mattress cover with temperature control.
- Tour de France (things on screen; at 1:10:10): Annual men's multi-stage bicycle race held across France.
- Keeper Test (ideas and frameworks; at 46:42, 47:20): Netflix management practice evaluating whether a manager would fight hard to keep a team member.
- Excellence as an Operating System (ideas and frameworks; at 39:26, 41:10): Netflix's cultural framework prioritizing high agency, accountability, and avoiding bureaucratic process gates.

---
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Video description

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Discover why Netflix bets on systems thinkers over specialists in the AI era and how to build a culture of high performance without process bloat. Netflix CPTO Elizabeth Stone shares how AI is reshaping product, engineering, and leadership.

Elizabeth Stone explains how generative AI is blurring traditional roles across product, engineering, and design, making systems thinking more critical than narrow specialization. She outlines Netflix's approach to AI fluency, platform paved paths, and practical mental models like the one-click zoom out. She then explores Netflix's culture of excellence as an operating system, detailing talent density, the keeper test, and mentorship for junior talent. Finally, she shares perspectives on high-impact AI use cases, the future of engineering, and the enduring value of human storytelling in entertainment.

Chapters
0:00 Teaser: The Fluidity of Roles in the Age of AI
1:32 Introduction to Elizabeth Stone
2:26 The Storming Phase: Everyone Becoming Everything
6:29 Sponsor: WorkOS
7:38 Prototyping, Knowledge Distillation, and Craft Scarcity
13:28 The Need for Systems Thinkers and Platform Paved Paths
22:08 The Decline of Narrow Specialization
25:09 How to Build Systems Thinking: The One-Click Zoom Out
28:34 AI Fluency in Career Ladders and Hiring
31:13 High-Impact AI Use Cases at Netflix
34:05 Sponsor: Mercury
35:14 Netflix's Machine Learning Heritage and the Netflix Prize
38:39 Excellence as an Operating System
46:45 The Modern Keeper Test
50:18 Attracting Top Talent in the Era of Frontier AI Labs
52:58 Junior Talent, Mentorship, and Craft Mastery
56:24 The Future of Engineering: Understanding Systems Over Syntax
59:48 The Future of Entertainment: Formats, AI, and Humanity
1:06:18 Consumer Product Reflections
1:07:51 Lightning Round: Books, Media, Habits, and Cycling
1:10:34 Conclusion and Where to Find Elizabeth Stone

Mentioned in this video
- Elizabeth Stone
- Lenny Rachitsky
- Netflix
- WorkOS
- Into Thin Air
- Liar's Poker
- Brian Chesky
- Ben Affleck
- Jon Krakauer
- Michael Lewis
- Mercury
- Interpositive

#SystemsThinking #ArtificialIntelligence #ProductManagement

Chapters and summary by Avail.video: https://avail.video/r/rpt_01m5jya5pethqrb5cvscbb

Include the credit line2,225 / 5,000 characters

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The video

Views: 545K
Likes: 10.6K
Comments: 371
Published: Jul 19, 2026
Length: 1:12:08
Category: People & Blogs
YouTube's topics: Society
Captions: No
HD: Yes
Language: English
Paid promotion: None declared
Made for kids: No
License: Standard YouTube

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The channel

Lenny's Podcast@lennyspodcast

Subscribers: 648K
Videos: 1.9K
Total views: 23.4M
Channel age: 19 years (since Dec 2006)
Country: United States
Uploads: about every 2 days; Across the last 15 long-form uploads
Before this video: —

Calculated by Avail.video from YouTube's public numbers

Outlier score: 11.8× · Breakout; Views against the median of the channel's last 15 long-form uploads
Views per subscriber: 0.84
Likes vs the channel's usual: 2.1× the channel's usual; 2.0% of views vs a usual 0.93%
Comments vs the channel's usual: 1.7× the channel's usual; 0.068% of views vs a usual 0.041%
Rank among recent uploads: #1 of 16; By views: this video and the channel's last 15 long-form uploads

Watched by Avail.video · 2026-10-22