How AI Empowers Knowledge Monetization

This past weekend, “Future Silicon World” invited Wang Xu, CEO of Xingcan (WeChat Video Channel: Da Di Xu Liao Zhi Shi Fu Fei Shang Ye — highly recommended), as the keynote speaker to discuss how AI reshapes the operations of knowledge-monetization businesses.

The livestream was scheduled for 90 minutes—but packed with such dense, actionable insights that it ran for three full hours, nearly doubling its planned duration. Attendees remained deeply engaged to the very end.

Beyond the extended runtime, there was another telling metric: host Yuanzi noted that from 8 p.m. to nearly 11 p.m., only nine people dropped out of the live room. Real substance—when delivered clearly and concretely—still draws and holds attention.

Before meeting Wang Xu, I was building a teacher-facing tool and had heard repeatedly about Yuanding, an exceptionally strong edtech product with a high valuation.

So I asked Da You: “Do you know Yuanding’s CEO?”

He replied: “Yes—let’s connect sometime.”

Soon after, Da You arranged our first meeting.

After that gathering, I visited Wang Xu’s office the next day—unannounced—to ask questions. He generously shared deep insights into Yuanding’s product architecture, especially its innovative monetization models and critical pitfalls to avoid.

That began a series of high-density exchanges: repeated learning sessions, joint problem-solving, and open resource sharing. We’ve since become close friends.

What stands out most in conversations with Xu Ge is their extraordinary information density. In just a few minutes, you walk away with more clarity than after hours of typical discussion.

He also embodies other rare qualities: authenticity, a passion for innovation, generosity in helping others, and a genuine love of sharing.

Later, Xiangyang asked me: “What trait does Xu Ge have that you deeply admire—but can’t replicate yourself?”

An excellent question.

To me, two traits stand out:

First, his uncanny ability to assess strategic direction and make rapid, confident decisions—especially when integrating new AI-driven strategies into existing business lines. Example: Starting last year, he systematically deployed AI across all operational touchpoints—and quickly delivered measurable results.

Second, his relentless, granular control over critical details: “critical,” “continuous,” and “control”—remove any one, and team execution collapses. Once, on a high-speed train to Chengde, he walked me through his daily analysis of ad performance metrics—explaining that he reviews this data every single day, without exception.

After ten years of entrepreneurship—including multiple ventures—he’s now running several compelling experiments amid the AI wave.

Here’s how AI has already transformed parts of his business and management workflows:

Video production scale: AI + digital avatars enable batched short-video creation—daily output increased 10×.
Sales productivity in private traffic: Industry-standard sales rep output is ¥80,000–¥100,000/month; with AI augmentation, average output rose to ¥400,000–¥500,000/month—a 5× lift.
Management efficiency: AI standardizes organizational management across the board—turning tacit practices into explicit, repeatable systems.

Core insights shared:

AI’s initial role wasn’t just to boost revenue—it was first to elevate management efficiency, second to improve human productivity (i.e., per-employee output), and third to increase operational and product agility.
This holistic, organization-level lens explains why Wang Xu insists AI must be a “CEO-level initiative”—not an IT project or departmental experiment.
For AI to truly empower business: start with domain-specific, battle-tested SOPs—then apply AI as a lever to amplify them.
Platforms are shaped by their products; products reflect founders’ personalities; and platform characteristics dictate user behavior.
AI doesn’t just speed things up—it weaves itself into the entire workflow, raising the bar for operational precision, not just raw speed.
A great IP is irreplaceable. AI can magnify an IP’s reach and impact—but it cannot generate the core human traits that define enduring influence: consistent content creation (strong curriculum design), adaptive learning (quickly absorbing platform shifts), grit, purpose, physical stamina, on-camera charisma, and audience resonance. These remain the non-negotiable foundations of a top-tier IP.
While AI’s ROI remains hard to quantify, its impact is unmistakable across three dimensions: management efficiency, business productivity, and product/service elasticity—a truly multidimensional uplift in capability and output.

Years ago, I first consulted Wang Xu on MCN and IP operations—and took detailed notes. Rereading them today, the underlying principles still hold:

How to understand short videos?
For knowledge hosts, short videos serve one primary function: attract precisely targeted users and drive them into the livestream. So short videos operate on advertising logic—they’re traffic funnels.
Therefore, the main KPI is click-through rate (CTR) from video view → livestream entry.
Whether knowledge-based or entertainment-focused, the core rule is: “First entertaining, then useful.” Users won’t watch unless it’s engaging; once hooked, they’ll absorb value if it’s embedded naturally.
Why? Because Douyin and Kuaishou are fundamentally entertainment platforms.
Video formats that work best: persona-driven clips, story-based case studies, and “big-takeaway” tips.
Viral short videos often attract broad, unfocused followers (“fan spam”), which dilutes livestream metrics. If your goal is a precise knowledge IP, avoid chasing viral growth at all costs.

How to understand livestreams?
Think of each livestream as a stall at a temple fair—the whole platform is the fair, and your stream is your booth. People flow past constantly. Your job: attract, earn trust, then convert.
So the livestream’s core mission is retention + conversion.

How do short videos and livestreams relate?
They follow separate logics. A great short video ≠ great livestream performance—and vice versa.
But energy must prioritize the livestream: for knowledge products, >95% of sales happen there.

Why does data analysis matter?
Since Douyin distributes traffic algorithmically, real-time data is your only objective feedback loop—not intuition or gut feeling.
Analyze two contexts separately:

Short videos: focus on completion rate (especially 3-second and full completion), engagement, and—most critically—how well each video drives traffic to the livestream.

Livestreams: track retention (average watch time, bounce rate) and conversion (sales volume, order value).

What does a 1% conversion rate mean?
For a ¥300–¥500 course sold via livestream, achieving 1% conversion (views → buyers) is considered excellent.

What’s the new ad-buying logic?
Old model: broad targeting, fixed budget, ROI measured post-campaign. Obsolete.
New model: use ads dynamically to prop up weak real-time metrics—e.g., if retention dips, launch a targeted ad campaign to boost dwell time. Once metrics rebound, the algorithm rewards you with organic reach.
This demands extreme agility: pre-build hundreds of ad plans, ready to activate or pause instantly based on live metrics.

What’s the role of a livestream floor manager (“changkong”)?
Like a film director, they monitor live data, anticipate bottlenecks, and adjust on the fly. They must know every KPI cold—and have contingency plans for each. Without this, consistency is impossible.

What is “flat broadcasting” (“pingbo”) and how do you “crack the standard”?
Flat broadcasting begins at ~1,000 followers—no product links yet. Only when average watch time hits 3 minutes AND peak concurrent viewers exceeds 100 should you add the shopping cart.
Until then, host and ops team iterate relentlessly on script, pacing, and delivery—until they find the version that works for this host.

How to think about private traffic?
Private traffic complements public traffic—but early on, don’t chase it. Focus first on content quality—the hard, foundational work. Good content retains users and unlocks more public traffic. That’s the real leverage.

Are “haters” useful?
Yes—early on. Their comments (even negative ones) boost engagement signals. The algorithm can’t distinguish “good” from “bad” interaction—it only sees activity. So early controversy can help.

How does Douyin’s algorithm actually work?
It’s a distribution engine, not a recommendation engine. Total traffic is finite. Whoever earns stronger user signals gets pushed.
It can’t parse meaning—it only reads behavioral proxies: 3-second completion, full completion, likes, shares, comments.
Under 10 million views, distribution is fully automated. So optimize for those signals: put your strongest hook in the first 3 seconds.
Entertainment creators innovate fastest (they’re under most pressure), then e-commerce, then knowledge creators—who later systematize and adapt those tactics. Watch entertainment and e-commerce streams closely—they’re your R&D lab.

What does Douyin want from knowledge livestreams?
It wants sales stalls, not lecture halls. Core metrics: retention + conversion—not depth of insight. That aligns with its entertainment DNA.

How do short videos, livestreams, and courses interrelate?
Short videos = attraction. Livestreams = influence. Courses = the full, structured, valuable payload. Don’t front-load the “goods”—save the meat for the paid product.

How to handle the “am I scamming parents?” feeling?
Rooted in confidence in your course’s actual quality. If the final deliverable is solid, then marketing—even aggressive marketing—is simply helping users access real value.

What’s the logic behind the first 3-month training + trial phase?
It mirrors driver’s ed: theory → supervised practice → solo driving. Teachers becoming hosts need the same progression: study theory, rehearse, then go live.
First skill to build? Professional TikTok scrolling: treat every feed scroll as field research—reverse-engineer the logic behind every successful short video.

How do short-video livestreams differ from traditional online education sales calls?
Traffic is transient. Every second, new users enter—and leave. So scripts must be engineered so anyone who joins mid-stream instantly grasps and engages with what’s being said right then.

Why include physical kits with courses?
Two reasons: (1) tangible value perception, (2) built-in private-traffic onboarding. Keep logistics + supply-chain cost ≤10% of total course price.

Do “highly attractive” knowledge hosts help or hurt?
If your audience is primarily mothers, high visual appeal can backfire—it feels intrusive. If you’re naturally striking, lean into natural, low-makeup looks for livestreams and videos.

How to build a persona if you’ve had a “smooth-sailing” elite background?
Depth matters more than drama. A consistent record of excellence—from school to career—signals authority and credibility. No forced “underdog” narrative needed.

How do “teacher,” “host,” and “salesperson” roles differ?
A great teacher isn’t automatically a great host—or a great salesperson. But livestream success demands all three. Hosts must blend sales acumen, influence, and high cognitive bandwidth. To transition, teachers must first adopt a sales mindset—then layer on performance and persuasion skills.

The Talent Who Solves Problems—Not Just Points Them Out

Spotting problems is easy.

I value a rarer kind of person: those who identify a problem, drive its complete resolution, and do it repeatedly. That’s true rarity.

Pointing out problems is the shallowest level—and often carries undertones of complaint. It’s effortless because humans instinctively notice flaws.
Discovering problems means digging past symptoms to uncover root causes. This requires domain insight and disciplined thinking.
Solving problems demands mobilizing resources—not patching, but fixing for good. It tests cognition, organizational capacity, and execution muscle.
Sustained problem-solving is the hallmark of exceptional talent: delivering high-quality solutions consistently, powered by learning agility, relentless action, resilience, and systems thinking.

I call Type 4 individuals key problem solvers.

Professionally, they’re classic “T-shaped”: deep expertise in one domain + broad cross-functional awareness.

Only that combination enables them to diagnose accurately and execute effectively.

T-shaped + sustained solving = true key talent. A few such people can transform an entire company.

Most stay at Level 1. Few possess—and fewer still invest in—the full suite of traits required for Level 4.

Let’s recognize and support these people fiercely.

GEO Content Methodology

Below are six principles—each explained through the lens of information theory—to optimize content for AI search and recommendation engines.

At its core, this methodology manipulates entropy (information unpredictability) and signal strength to boost AI readability, authority, and ranking.

Key idea:

Purely high-entropy content (e.g., jargon soup, obscure terms, dense stats) overwhelms AI models—hard to parse or retrieve.
Purely low-entropy content (e.g., generic phrasing, clichés) lacks differentiation—gets buried in AI rankings.
Optimal content strikes a dynamic balance: raise entropy where it adds uniqueness and authority; lower it where clarity and structure matter most.

1. Use concrete data and statistics

Why: Builds credibility and domain authority.
Info-theory view: Increases token-level unpredictability (entropy), serving as strong, verifiable signals—especially when paired with citations or sources.

2. Cite authoritative research or reports

Why: Signals legitimacy and rigor.
Info-theory view: Introduces unique, high-value named entities (e.g., “McKinsey 2024 Report,” “Dr. Li Chen, Peking University”)—raising entropy meaningfully.

3. Use clear headings and subheadings

Why: Improves scannability and comprehension.
Info-theory view: Partitions text into focused, low-entropy thematic clusters—maximizing structural information gain.

4. Use lists, tables, and bullet points

Why: Enhances parsing and retention.
Info-theory view: Further reduces local entropy within sections—sharpening structural signal and boosting information gain.

5. Offer original insights and unique analysis

Why: Demonstrates experience and thought leadership.
Info-theory view: Introduces novel phrasing and uncommon word combinations—significantly increasing entropy and differentiating content from generic corpora.

6. Avoid jargon and vague language

Why: Ensures clarity and accessibility.
Info-theory view: Lowers local entropy at the paragraph level—eliminating ambiguity and strengthening structural signal.

GEO Opportunities for Large Websites

Established websites—with legacy domain authority and deep historical data—have a massive, underleveraged opportunity in the AI search era.

Large sites are natural primary sources for AI search engines—akin to being a “news source” for Baidu.
Their historical content, when optimized with GEO schema and rewritten using GEO principles, becomes a high-authority reference point—directly influencing AI-generated answers.
This visibility resembles Baidu’s “Alading” feature—or its text-only “brand zone.”
With systematic, site-wide GEO optimization across millions of pages, you unlock millions of long-tail queries in AI search—far beyond traditional SEO.
The upside is enormous—especially for legacy web properties. Take 58.com: if traditional SEO generates ¥100M+ annually, full-site GEO transformation could yield comparable new revenue—without proportional cost increases.
Why? Because GEO can largely replace expensive, high-intent commercial traffic previously bought on Baidu—while requiring minimal engineering investment.

Winning Logic in the AI Arms Race

Recently, I discussed with friends how certain industries—like early URL directories, software download portals, group-buying platforms, or recommendation apps—saw explosive, crowded competition… followed by near-total consolidation. Usually, only 1–2 players survive.

So I ask: What did the winners do right? What did the losers get wrong—and were those errors systemic?

With dozens of new AI-native startups emerging, who will win—and why might others fail?

After stripping out luck, our consensus centered on two pillars:

Technical fluency and commitment: Deep understanding of technology—not just using APIs, but grasping architecture, trade-offs, and long-term implications—and investing consistently in technical depth.
Cognitive bandwidth and strategic vision: Long-term orientation, obsession with craftsmanship (e.g., perfecting one core feature), cost discipline, and building defensible moats.

Conversely, failure patterns included: short-termism, neglecting tech fundamentals, failing to build proprietary advantages, and operational waste.

Three Principles of Pricing

A sound pricing framework satisfies customers and maximizes sustainable revenue.

For non-standardized products, I weigh three anchors: cost, industry benchmarks, and customer psychological expectations.

This forms a multi-dimensional decision model:

Cost sets the floor—what you must cover.
Industry benchmarks reveal market consensus—where peers price, signaling perceived value.
Psychological expectations shape willingness-to-pay—but these are malleable, not fixed.

Example: A new SaaS tool.

Cost floor: ¥100,000/year.
Peer pricing: ¥500,000–¥1,000,000/year.
Customer expectations: vary widely—by company size, urgency, quantifiability of ROI, and market saturation.

But this model has limits:

For truly novel products, industry benchmarks don’t exist.
For many non-standard services, cost is the least relevant factor.

That’s why deeper product and customer empathy is essential.

In practice, the three anchors are dynamic:

Costs fall with scale.
Benchmarks shift with market evolution.
Expectations can be shaped—via messaging, branding, and proof points.

Pricing isn’t arithmetic—it’s applied philosophy. It reveals how well you understand value, customers, and your own business.

Master these three levers, and you’ll avoid catastrophic missteps.
The true masters go further: they break the model—setting prices that feel unbeatable to customers, inscrutable to competitors—and still deliver healthy margins.