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title: "Unique Research Interview | Longyuanji AI: A Former Game Giant Executive Managing Tens of Billions in Revenue Builds an ROI-Driven AI Content Growth Platform"
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originalPublishedAt: "2026-06-27T12:01:44+00:00"
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---

# Unique Research Interview | Longyuanji AI: A Former Game Giant Executive Managing Tens of Billions in Revenue Builds an ROI-Driven AI Content Growth Platform

_Original · Unique Research · 2026-06-27_

_Editor's note: This is an in-depth interview with Yin Tianming (殷天明), founder of Longyuanji AI (龙渊纪AI). All statements, claims, and business figures are attributed to the interviewee and are not independently verified. Company names, product names, and historical employment details are preserved as stated in the source._

AI Industry Observation

Giving Up a Listed Company Executive Position to All in on AI

Longyuanji AI's Yin Tianming's "Disruptor" Journey

"

A person who served as product VP at a listed company, with experience operating tens of billions in game revenue—why would he, on a clear-headed morning, decide to give up everything and jump into the deep end of entrepreneurship?

When he met Yin Tianming, his answer came without hesitation: "It wasn't impulse; it was twenty years of accumulated grievances pouring out all at once."

This sentence almost condenses his long-simmering judgment of the entire user-acquisition (buying traffic) industry—a deep-seated sense of crisis, and also a certainty after seeing the endgame.

01\. Twenty Years of "Old Wounds"

With twenty years in the game industry, during his tenure as Group Product VP at 37 Interactive Entertainment (三七互娱), Yin Tianming personally experienced the complete journey of one globally distributed game after another going from zero to billions and then tens of billions in revenue. To outsiders, this was the pinnacle; but behind those numbers, he continuously saw the same crack.

"In user acquisition, the most stubborn pain point hasn't changed in twenty years: premium production capacity is extremely scarce, and ROI is completely a black box," he said bluntly. The essence of buying traffic is a博弈 between traffic and content, but under the traditional model, even if a human team works around the clock, the creative materials they produce are still "a drop in the bucket" against the algorithm engines of massive media platforms. Even more fatal is the dramatic shrinkage of material lifespan—"in the past, a good material could run for a month; now it might only last three days."

He experienced heavyweight materials like 3D CG trailers and live-action narrative shorts, with production cycles up to 14 days from script planning to post-production rendering, individual material costs often reaching tens of thousands or even hundreds of thousands of RMB. Yet after going live for just half a day, simply because initial CPA was high and algorithm tags weren't hit, the system directly "sentenced it to death." High sunk costs, extremely low fault tolerance—the profit margin of game publishing was being crushed alive.

He also described another kind of suffocation: bidding specialists stare at platform spend all day, knowing exactly which hero's display, which BGM segment is driving traffic, but this valuable data can't be fed back to the front-end content team in real time; at midnight a new material suddenly explodes in volume, the bidding specialist might be resting, missing the golden window for the system to automatically scale budget; when material conversion rates cliff-dive, human reaction is slow, and hundreds of thousands or even millions in budget quietly evaporate in "empty consumption."

"This sluggishness and high trial-and-error cost brought by human stacking made me realize the traditional user-acquisition model has hit an insurmountable physical ceiling."

This was an "old wound" that had festered for twenty years. And AI's emergence gave him the first glimpse of a real possibility for healing it.

02\. The Moment of Going All In

Founding Longyuanji AI, Yin Tianming defines it as "the most significant and also most naturally inevitable decision of my career."

When the R&D team verified that an AI system could automatically batch-generate user-acquisition videos based on front-end delivery tags, and could feed delivery data back in real time to guide the next batch of material variants, he said that at that moment he had already seen the shape of the endgame: the core competitiveness of game publishing will no longer be "human-wave tactic material stacking," but "low-cost, high-concurrency trial-and-error compute based on AI models."

He didn't jump in alone; he brought in two core partners with over ten years of deep industry experience: one a top operations expert proficient in business flow and user lifecycle management, the other a user-acquisition操盘手 extremely sensitive to ROI models with near-instinctive nose for platform traffic. The three have collectively managed over 100 billion RMB in game product revenue—"from content engineering and underlying models to commercialized delivery monetization, all the puzzle pieces were there from day one."

The sector this team chose is what they call "dual-engine"—AI video infrastructure and AI intelligent delivery engine.

Currently, the company is conducting a new round of fundraising, with Unique Capital (非凡资本) as exclusive financial advisor.

03\. The "Armory" and the "Radar"

Yin Tianming doesn't like defining AI as a "video-making tool." In his view, the vast majority of AI startups on the market are still in "workshop thinking," treating AI as a sharper pair of scissors—letting an editor go from cutting 10 clips a day to hundreds, but still not escaping the human-dependent heavy-asset trap.

What he wants to build is something else: infrastructure.

"Infrastructure isn't a weapon for individual soldiers; it's an automated armory supporting a thousand troops and ten thousand horses."

He used this metaphor to describe Longyuanji's AI video infrastructure—as long as existing materials or copy are distilled, the system can produce multimodal video ammunition containing different scripts, storyboards, BGM, special effects, and emotional inducement nodes, completely stripping away traditional human shooting and assembly-line editing, compressing production costs toward zero.

And the corresponding AI intelligent delivery engine, he calls the "omni-domain phased-array radar system"—it doesn't produce ammunition, but it aims the massive materials produced by the armory with the most precise compute and lowest CPA at the highest-conversion audience positions across the entire network.

The linkage between the two systems relies on one mechanism: second-level adaptive feedback based on real consumption data. Every CPA fluctuation at the delivery end penetrates the system at millisecond level, directly directing the back-end AI model's generation direction. "When the radar catches that a certain game kill effect or short drama reversal plot is rapidly driving traffic, the infrastructure immediately knows what variants the next batch of materials should make and which audience pool to target."

Yin Tianming insists the two must be integrated, never split, for a simple reason: "content generation" and "traffic delivery" were never two isolated businesses; they are the heart and aorta on the same commercial lifeline. Only doing video generation is at best a high-end art outsourcing; only doing delivery optimization, in today's environment where material lifespans are only three to five days, even the smartest algorithm faces the "no rice to cook" dead end.

04\. Whose Money Hurts Most Where

Longyuanji's core target market is the three major user-acquisition sectors: games, commercial short dramas, and cross-border e-commerce.

"Clients are no longer buying a 'video-making' tool; the only standard they vote with real money on is 'can drive volume, high conversion.'" In his view, what enterprises truly lack now is not video production capacity or delivery matching capability, but a dynamic production capacity engine that "understands platform data and rapidly generates viral hits based on it."

For clients of different scales, they have differentiated entry strategies: for top-tier giants, the core is building a private "dynamic memory asset library," converting historical user-acquisition data into exclusive high-conversion memory assets; for the vast middle-and-tail enterprises and startup going-global teams, it's "out-of-the-box" and "low-cost trial and error"—just set CPA and ROI targets, and the closed-loop system completes material generation and precise delivery, "instantly flattening their generational gap with giants in production capacity."

The commercialization rhythm is structured as a three-stage rocket: short-term precise service for top-tier clients as benchmarks; mid-term upgrade to a scaled model of "product annual fee + CPS revenue share on delivery performance"; long-term, leveraging extremely low-cost automated content infrastructure, entering high-customization deep-interaction sectors like global AI emotional companionship, replicating the experience of their existing social cash-cow business to open a second growth curve.

05\. Ending the Old Stone Age

At the end of this conversation, Yin Tianming articulated his core vision with unusual clarity.

"What I most want to do is completely end this industry's long-standing Old Stone Age of 'high energy consumption, extreme involution, and blind volume spreading.'"

He listed the models that will be eliminated: heavy-asset material workshops relying on human-wave tactics to blindly spread volume, pure agency delivery services earning labor information asymmetry margins... "The old way of buying traffic was blind delivery, betting on short material lifespans with human effort; the future of buying traffic will inevitably be millisecond-level high-frequency博弈 between underlying compute and the algorithms of major media platforms."

He also described the future he wants to see: when the dirtiest, most tiring, and lowest-fault-tolerance mechanical trial-and-error work is entirely taken over by algorithms, whether a hundred-billion giant or a startup team can validate their business ideas at extremely low risk. "Let digital advertising practitioners return most of their brainpower to genuine creative refinement and the emotional resonance of user value."

He said this isn't just his reason for starting the company; it's also the endpoint he believes this industry should reach.

Giving up a big-company executive role, all in on AI—this jump wasn't about riding a trend; it was a concentrated counterattack on an old wound after twenty years of accumulation.

Longyuanji AI focuses on the three major user-acquisition sectors of games, short dramas, and e-commerce, driven by dual engines of AI video infrastructure and AI intelligent delivery engine, dedicated to becoming the underlying growth infrastructure in digital entertainment performance advertising.

More Interview Details

Part One: Character Foundation | Hundreds of Billions in Game Revenue, Understanding the Industry's Underlying Pain Points

1\. You worked at 37 Interactive Entertainment for over ten years as Group Product VP, managing multiple globally distributed game projects with tens of billions in revenue. In this executive experience, what is the most stubborn, never-cured core pain point in the user-acquisition industry?

Answer: In my 20 years in the game industry, especially through the experience of managing globally distributed projects with tens of billions in revenue, I deeply felt that the most stubborn and suffocating pain point in user acquisition is "the extreme scarcity of premium production capacity and the black-box uncontrollability of ROI." User acquisition is essentially a博弈 between traffic and content, but under the traditional model, we rely extremely heavily on humans to guess user preferences. Whether a material "blows up" and whether CPA can be lowered is often alchemy. Even if a human team works 24 hours, the material volume is a drop in the bucket against the massive media platform algorithm recommendation engines. Even more fatal, material lifecycles are being infinitely compressed—what was a good material that ran for a month might now only last three days. This misalignment of "production can't keep up with consumption, data can't guide production in real time" causes massive marketing budgets to be wasted on ineffective impressions, with extremely high trial-and-error costs that directly eat into enterprise net profit. This is a life-or-death劫 that no company dependent on traffic growth can escape.

2\. In past multi-billion RMB project operations, where did the traditional video material production and manual delivery model suffer the greatest efficiency loss and cost bottleneck? Was this also the core reason you decided to all in on AI entrepreneurship?

Answer: If I deeply analyze from my 20-year game industry perspective, this question touches the "life-or-death pain point" currently facing all game giants and publishers. In the process of managing multiple tens-of-billions-revenue game titles, I experienced the complete journey from the dividend era to the stock-era involution battle.

In the game sector, the biggest cost bottleneck is essentially the structural collapse between "heavy-asset traditional production capacity" and "extremely short material lifecycles." Previously, when launching a heavy MMO or SLG, a high-quality 3D CG trailer or live-action narrative short, from script planning and actor scheduling to location shooting and post-production rendering, could take up to 14 days, with individual material costs often tens of thousands or even hundreds of thousands of RMB. But with the extreme involution of recommendation algorithms on media platforms like Douyin, TikTok, and Facebook, the running lifecycle of such expensive, polished materials can be compressed to just 3 days. Often, a high-cost material goes live for just half a day, and because initial CPA from a few hundred clicks is high and algorithm tags aren't hit, it can't scale volume and is directly "sentenced to death" by the system. High sunk costs, extremely low fault tolerance, and severely lagging production capacity are crushing game publishing profit margins.

The biggest efficiency loss is the "blind-men-feeling-an-elephant fragmentation and physical limits of manual delivery." Game user acquisition has extremely strict assessments of day-one ROI and long-term LTV; this is a war fought with real money. In traditional workflows, game video copywriters, editors, and back-end delivery specialists are completely disconnected. The delivery specialist stares at platform spend all day, knowing which hero display, which BGM segment, even which special effect is driving traffic, but this valuable data can't feed back to the front-end content team for iteration at millisecond level.

Meanwhile, manual monitoring has physiological limits that can't be broken: when a new material suddenly explodes at midnight, the user-acquisition specialist might be resting, missing the golden window for the system to automatically scale budget and grab traffic share; when material conversion rates cliff-dive, humans are slow to react, causing hundreds of thousands or millions in budget to be wasted—this is what the industry calls "empty consumption."

This sluggishness and high trial-and-error cost from human stacking made me deeply feel the traditional game user-acquisition model has hit an insurmountable physical ceiling. Earlier this year, when our R&D team verified that an AI system could automatically and batch-generate game user-acquisition video materials based on front-end delivery tags, and feed delivery data back in real time to guide the next batch of material variants, it strengthened my conviction in the future endgame: the core competitiveness of game publishing will no longer be human-wave material stacking, but "low-cost, high-concurrency trial-and-error compute based on AI models." Future digital content user acquisition for games, short dramas, and e-commerce will inevitably move toward complete light-asset and AI-ification, using machine millisecond-level reactions to replace human sluggishness, using AI's endless generation to replace expensive shooting. This is the leap of game digital marketing from the "cold weapons era" to "AI warfare," and it's also the core driver that pushed me out of the big-company comfort zone to found Longyuanji AI and firmly All in on "AI video infrastructure + intelligent delivery engine." I firmly believe we are on the path to pulling out the most painful nail in the game and digital entertainment user-acquisition industry, and this is our confidence that we can create inestimable commercial value in this sector.

3\. The industry mostly defines AI as an "editing and video-making tool," but you broke out of single-point content creation early. How do you understand the essential difference between AI video infrastructure and ordinary AI video tools?

Answer: The vast majority of AI startups on the market are still stuck in traditional "workshop thinking." They treat AI as a sharper "scissors" or faster "renderer"—defining it as an ordinary "video-making tool." This type of tool only solves tactical efficiency at a single point, like letting an editor who used to cut 10 short videos a day now make hundreds with AI. But it still doesn't escape the "heavy-asset" trap of relying heavily on human prompt writing and human assembly. It only locally improves traditional hand labor, fundamentally unable to satisfy the massive user-acquisition market's demand for high-volume, high-frequency, precise material throughput.

What I define as "AI video infrastructure" is true "product engineering" and "AI platformization" underlying reconstruction. Infrastructure isn't a weapon for individual soldiers; it's an automated armory supporting a thousand troops. Its essential difference from ordinary tools集中 in three core dimensions:

First, is full automation and industrialized mass production of underlying flow. We no longer rely on individual subjective creativity. Earlier this year, our R&D department already successfully ran an internal system that can automatically generate AI short dramas and game user-acquisition materials. This system eloquently proved that infrastructure can in the future completely escape deep human intervention. In the future, as long as underlying data and core tags are connected, the system can automatically complete the industrialized pipeline from script variant generation to multimodal audiovisual generation to final finished film.

Second, is the extreme release of "low-cost trial-and-error" capability. Without market feedback, video is still a pile of ineffective production capacity. And our AI infrastructure is born to connect with the back-end intelligent delivery engine. Our AI intelligent delivery growth engine aims to allow enterprises to generate hundreds or thousands of video materials with subtle variable differences every day at extremely low marginal cost, feeding them into real traffic pools for testing. In today's era of extreme algorithm involution, this "low-cost trial-and-error" capability relying on massive machine concurrency and rapid iteration is the only antidote for enterprises fighting rising traffic costs and turning ROI positive.

Third, is accumulating irreplaceable enterprise-level core assets. Tools are use-and-leave; infrastructure is an AI middle platform deeply rooted in the enterprise. It continuously learns the customer product's unique conversion style, audience preference, and high-priority genes through the closed loop of "generation-delivery-feedback." The longer it runs, the smarter it gets, eventually evolving into the enterprise's unique private growth model.

In short, making tools is just earning modest "wage money" for the industry,随时可能被下一个大模型颠覆；做基建，是为整个数字娱乐和效果广告行业修筑一条不可或缺的"水电煤"与"高速公路"。在流量红利消退的今天，只有掌握了这套轻资产、快迭代的底层基建，才能让企业的每一次买量投入都转化为确定性的商业增长！这也是我们最坚不可摧的商业护城河。

Part Two: Entrepreneurship Decision | Giving Up Big-Company Executive, Anchoring Dual-Engine Sector

4\. As a listed company's product VP, giving up a stable executive identity to start a company, firmly choosing the "AI video infrastructure + AI intelligent delivery engine" sector—what was the inner journey of this entrepreneurship decision?

Answer: This is actually the most significant but also most naturally inevitable decision of my career. Leaving the stable halo and generous compensation of a listed company's product VP role, I want to truly build products that change people's困局. This comes from the deep sense of crisis accumulated over 20 years deep in the game industry; AI's development has given me immense confidence in future digital content productivity improvements.

Of course, supporting my decisive "All in" resolve was also my complementary and formidable core team. I brought in two partners with extremely strong commercial combat experience in the industry: one a top operations expert with over ten years in games, proficient in business flow and user lifecycle management; the other a user-acquisition and traffic injection operator with over ten years of experience, deeply familiar with ROI models and with excellent platform traffic intuition. The game products the three of us have each managed total over 100 billion RMB in revenue. This triangle combination meant that from day one of our entrepreneurship, all the puzzle pieces from content engineering and underlying models to commercialized delivery monetization were complete.

5\. Currently AI products polarize: some only do video generation, some only do delivery optimization. Why do you insist on integrating "underlying video infrastructure" and "omni-domain intelligent delivery engine" into a closed loop rather than splitting into single-point products?

Answer: This is a sharp question that hits the essence of commercial monetization. Why do most teams on the market choose single-point products? Because single-point is easy—it only requires understanding local technology; but closed loop is extremely hard—it requires extremely deep intuition and hands-on accumulation of real "commercial flesh." From day one of wanting to start this company, we reached an absolute consensus: we will never touch single-point tools; we must dead-setly pursue the dual-engine closed loop, empowering the AI content growth platform with each of our nearly 20 years of operations experience, data model accumulation behind tens of billions in revenue, and business expertise!

Because in the real game and short drama user-acquisition battlefield, "content generation" and "traffic delivery" were never two isolated businesses; they are the heart and aorta on the same commercial lifeline.

If we only do video generation, we're at best a high-end "art outsourcing." Without real delivery data, CPA performance, and ROI model feedback, even the most visually beautiful AI-generated videos are just digital garbage with no "volume-driving genes." Losing real feedback and building behind closed doors makes clients spend money and get results they can't deliver. Conversely, if we only do omni-domain intelligent delivery optimization, in the current extreme involution environment where material lifecycles are compressed to just three to five days, without the front-end infrastructure's continuous, high-frequency "ammunition" supply, even the smartest algorithm faces the "no rice to cook" dead end.

We insist on fully integrating the two to build a complete content and growth platform ecosystem, to achieve a highly disruptive commercial goal: deep combination of AI product engineering and Know-How, pushing the client's "low-cost trial-and-error" and "rapid iteration" capabilities to the extreme.

When our intelligent delivery engine injects traffic at the front end, it monitors every material's CPA and ROI performance at millisecond level. Once it catches a tiny viral element (like a specific narrative beat, game effect, or emotional node), this signal instantly penetrates the platform's underlying layer, and data feeds back to the AI video tool about what new materials to make.

This death cycle from traffic testing and data verification to production capacity reconstruction is the "light-asset" growth flywheel we firmly believe in. It turns performance advertising delivery from alchemy guesswork relying on human power into a precise, self-evolving arithmetic problem. Splitting into single points will always circle at the industry's edges, earning modest software usage fees; only by打通 the "infrastructure + delivery" ren-and-du meridian can we truly grasp the throat of digital marketing and become the rule-setter for future digital entertainment growth! This is our most irreplaceable capital value and industry barrier.

6\. In the early stage of entrepreneurship, how did you confirm the long-term value of this sector?

Answer: My confidence comes not only from my nose for underlying AI technology explosion, but more from our core team's deep insight into the essence of commercial growth and user-acquisition underlying logic, as veterans who have scraped and fought nearly 20 years in the game industry.

In the years of our team's experience and operation of hundreds of billions-level product revenue, I witnessed the game industry go from the grass-roots traffic dividend era, step by step, to today's extremely残酷 stock-era battle. I deeply realized an irreversible commercial iron law: as long as digital entertainment and e-commerce still need online traffic for commercial monetization, the extreme pursuit of "lower CPA" and "higher ROI" is eternal essential demand.

Part Three: Team Barrier | Content + Delivery + AI, A Hundred-Billion Team's Composite Entrepreneurship Foundation

7\. What capability profiles do the founding team's core members have? What is the unique core barrier of your team compared to pure-tech AI startups and pure delivery service providers?

Answer: Our team's capability底牌 I think is not complex at all, but it's something many AI application-layer teams don't have. In this extremely involuted sector that fights both technology and commercial monetization, I always firmly believe one thing: the sturdiest moat always comes from a "composite iron triangle" team that both understands AI product architecture, understands frontline platform needs, and knows how to convert technology into real money.

Our team's three core partners can be said to be tailor-made for the "AI infrastructure + intelligent delivery engine" dual-engine sector. First is me—20 years scraping and fighting in the game industry, previously Group Product VP at 37 Interactive Entertainment, having completely experienced globally distributed products from several billion to tens of billions in revenue. This experience gives me bone-deep understanding of the underlying architecture of digital entertainment products, commercial monetization logic, and the industry's most stubborn pain points. I know where clients' money hurts most, and I know what weapons we need to build to solve that pain.

Second, my two core partners are also top-tier实战派 in the industry. One is an extremely senior business system and operations大牛. He deeply understands product platform flow logic and operations user lifecycle management; his presence ensures our AI infrastructure isn't a hanging geek toy, but an "enterprise product engineering" that knows how to seamlessly embed into client workflows with extremely high usability and retention.

The other partner is a top-tier "traffic acquisition and marketing" operator deeply immersed in user-acquisition platforms, extremely sensitive to ROI. He has personally experienced countless real-money traffic battles, with near-instinctive intuition about media's underlying distribution algorithms, CPA control, and the scaling logic of large-scale buying.

Based on this extremely hardcore composite foundation, compared to pure-tech AI teams and pure delivery service providers, we have formed a "dimensional reduction strike" unique core barrier:

First, compared to pure-tech AI teams, we don't blindly worship parameters; we only believe in ROI closed loops. Many pure-tech teams are "holding a hammer looking for nails"—they understand AI but completely don't understand the extremely残酷 consumption and conversion logic of platform user acquisition. But from the first line of code we write, it's all about lowering single-user acquisition cost to the minimum. We clearly know what kind of multimodal video can drive volume, so our model training direction is extremely precise, with no compute wasted on ineffective "self-indulgence."

Second, compared to pure delivery service providers, we have true underlying "light-asset" blood-making capability. Traditional agency or delivery service providers are still stuck in the "heavy-asset" stage relying on large numbers of specialists and editors. They have no technology DNA and can only passively use general tools on the market. But we are building underlying "infrastructure and engines"; what we will do in the future is achieve high-frequency system iteration and low-cost trial-and-error by打通 data and generation.

In summary, we not only understand how to use AI to produce ammunition, but more importantly understand how to aim this ammunition precisely at the bullseye of traffic. This is the composite实战 ability that perfectly meshes "content engineering," "platform operations," and "traffic injection."

8\. The industry generally has fragmentation: "technology doesn't understand delivery, delivery doesn't understand AI, content doesn't understand data." In team building, how do you打通 the three parties' understanding of content, algorithm, and delivery to fit the "infrastructure + delivery" dual-product R&D?

Answer: This question strikes at the "Achilles' hammer" of current AI application-layer startups. In my nearly 20 years of experience, I've seen too many teams where the fragmentation of "technology doesn't understand delivery, delivery doesn't understand AI, content doesn't understand data" caused tens of millions in R&D and user-acquisition budgets to go down the drain. Because of this fragmentation, what many teams build is often "lab toys," not heavy weapons that can fight in the real battlefield.

In our team building and organizational architecture, we eliminated this fragmentation from the root. This is thanks to our extremely complementary and hardcore founding team DNA already being the iron triangle fusion of "product engineering, platform operations, and omni-domain delivery."

In the specific "infrastructure + delivery" dual-product R&D adaptation, we thoroughly打通 the three parties' cognitive barriers through two core mechanisms:

First, reshape everyone's belief with a unified "North Star metric" and "first principles."

In our team, there are no geeks only building behind closed doors. We take "ROI-driven" and "CPA (customer acquisition cost) control" as the only underlying logic everyone aligns to. Whether it's an algorithm engineer or a delivery specialist monitoring the front line, their goals are directly deeply tied to final delivery conversion results. We require algorithm engineers to understand every data fluctuation in the user-acquisition platform, and require the delivery team to understand AI model boundaries and logic. Through this strong-consensus cross-boundary fusion, we make these three groups truly fight in the same trench with the same map.

Second, break department walls with "AI product engineering" to achieve low-cost, rapid-iteration data flow.

We advocate "light-asset" and "low-cost trial-and-error" combat philosophy. This is itself the result of three-party cognition deeply meshing. We built a highly transparent data middle platform, letting every traffic feedback and every viral tag the delivery end runs in the real battlefield penetrate the system at millisecond level, directly directing the back-end AI model's generation direction. In this closed loop, data speaks for itself, thoroughly cutting away the lengthy human communication and mutual blame of the traditional model.

I always firmly believe that technology purely for showing off has no commercial value. This collaborative organizational architecture that打通 content variation, algorithm compute, and intelligent delivery ensures that every line of code we敲, every frame of multimodal画面 we generate, serves breaking the industry's "production capacity black box" and achieving "rapid volume scaling."

Part Four: Market and Users | Understanding the Real Essential Needs of Omni-Domain User-Acquisition Clients

9\. Currently the three core user-acquisition markets of games, commercial short dramas, and cross-border e-commerce—clients no longer need "video making" but "volume-driving, high-conversion." Combining frontline client reality, do enterprises currently lack video production capacity or delivery matching capability?

Answer: This question hits the most underlying logic of the digital marketing business. Currently in the three core user-acquisition markets of games, commercial short dramas, and e-commerce, clients' real essential needs have long passed the initial "video making" stage; the only standard they vote with real money on is "volume-driving, high-conversion." Combining the reality of frontline clients' daily millions in platform consumption, I believe what enterprises face is definitely not a simple either/or of "lack capacity" or "lack matching," but an extreme lack of "adaptive closed-loop engine that highly meshes rapid production capacity with precise matching."

If we only talk about "video production capacity," the market is actually already severely oversupplied. Traditional workshop-style content teams and cheap outsourcing produce massive videos daily through human-machine collaboration, but in front of the残酷 media platform recommendation algorithms, all of this is called "ineffective production capacity." Blind production without real conversion data as the North Star guide often means the more you produce, the higher the enterprise's trial-and-error and sunk costs.

On the other hand, purely talking about "delivery matching capability" is also a rootless tree today when material lifecycles are compressed to just three to five days. No matter how sharp a specialist's gut feel, no matter how precise a media platform's auto-bidding algorithm, if the front end can't provide massive, high-frequency "content ammunition" supply, even the smartest algorithm faces no-rice-to-cook dead end, and all traffic dividends will be snatched away by competitors in an instant.

Our breakthrough approach is to thoroughly打通 the two through product engineering thinking. In our self-developed AI content growth platform, the partner responsible for system flow and the partner responsible for omni-domain traffic injection work side by side, continuously optimizing CPA reduction and ROI improvement. We make every frame of multimodal画面 generated by the front-end infrastructure tightly follow the conversion tags sent back by the back-end delivery engine.

So what clients truly lack is dynamic production capacity that "understands platform data and rapidly generates viral hits based on it." When the delivery radar catches a specific short-drama reversal plot or game kill effect rapidly driving traffic, we know what variant materials the AI video creation tool should make and precisely which high-conversion audience pool to target. Only through this dual-engine driven seamless loop, letting machines replace humans in completing massive low-cost trial and error, can we truly help enterprises cut a bloody path in this red-ocean market.

10\. Who are your main product target customers? What are the differences in core needs for AI video infrastructure and intelligent delivery engine across different tiers of users?

Answer: Regarding our target customer profile and need differences across tiers, this is actually the most underlying logic in our product engineering design. We know very well that doing To B commercialization absolutely cannot use one standard answer for everyone; we must provide different AI video creation and intelligent delivery needs for different sectors, and must precisely hit clients' "bleeding" pain points in real platform user acquisition.

Currently, our core target market is locked on the three major high-traffic-consumption, ROI-extremely-sensitive core user-acquisition sectors: "games, short dramas, and e-commerce." But in specific commercial deployment and service strategy, we split functionality by client business scale, because different tiers of clients have vastly different core needs for "AI video infrastructure" and "intelligent delivery engine."

First tier: top-tier giants and top content distributors. These clients (like tens-of-billions-revenue game giants or top short drama platforms) usually have massive self-built data middle platforms and massive historical delivery consumption data. For them, the core need is "private accumulation of core data assets and deep customization of the underlying engine." They pay extreme attention to commercial data security and absolutely don't want their viral material genes to benefit general large models.

Therefore, our entry point is using AI infrastructure as pure underlying "water, electricity, and fuel," seamlessly integrating into their existing business flow. My core partner, deeply familiar with business operations and system flow logic, works with the technical team to build exclusive private dynamic model libraries for KA clients. This not only helps large clients convert massive historical user-acquisition data into exclusive high-conversion "memory assets," but also thoroughly restructures their originally heavy-asset production relationships.

Second tier: the vast middle-and-tail enterprises and startup going-global teams. This is our core base for scaled commercial monetization and high-speed growth through the product platform. These clients often lack strong technical R&D teams, and may not even have complete content creation and user-acquisition specialist headcounts. Their biggest life-or-death劫 is "extremely high trial-and-error cost."

Their core need for the dual-engine system is: "out-of-the-box" and "low-cost trial-and-error." We precisely package this battle-proven capability into standardized light-asset solutions. Leveraging the platform scaling strategies accumulated by my other market partner specializing in user acquisition and traffic injection, these middle-tier clients only need to simply set CPA and ROI targets, and our closed-loop system completes material generation and precise delivery for them, instantly flattening their production-capability gap with giants.

In summary, we have both customized underlying foundations for large clients to build deep moats, and AI video creation platforms and AI intelligent delivery growth platforms as weapons to help SMEs low-cost trial and scale.

11\. From a long-term user development perspective, will integrated AI infrastructure + delivery restructure the job division and work models of advertising material screenwriters, editors, and delivery specialists? What industry employment changes will it bring?

Answer: My answer is very affirmative: this is absolutely not a simple "position optimization," but an "industrial revolution" that thoroughly reshapes the production relationships of digital entertainment and performance advertising.

In our team's past 20 years of game title publishing and user-acquisition operations, I deeply felt the heaviness and inefficiency of traditional "labor-intensive" work.

Future positions will comprehensively abandon mechanical labor, showing extremely significant "brain-intensive" characteristics:

First, basic content creators, editors, and video packaging personnel will evolve into "product engineers" and "AI directors." They no longer need to抠 picture frame by frame on the timeline of editing software, but through the AI platform, establish underlying rules for material variation and input high-conversion prompt parameters (Prompt). One person, together with our underlying infrastructure and accumulated material resources, can command AI to achieve the production capacity that previously took dozens of people, achieving truly low-cost trial-and-error and high-frequency iteration.

Second, delivery specialists will elevate to "omni-domain traffic strategists." Their core value will focus on top-level ROI model construction, platform traffic direction assessment, and overall commercial budget macro management, completely handing over the boring execution of massive material A/B testing and auto-scaling during viral phases to this closed-loop system that can adapt and adjust in real time.

Third, when underlying, formulaic user-acquisition short dramas and advertising materials are fully taken over by AI infrastructure, human creators can truly dig into the deep human emotional resonance that AI currently cannot easily replace.

In summary, the普及 of "AI infrastructure + delivery integration" will ruthlessly eliminate low-end execution positions that purely拼 physical labor, but will amplify the per-person efficiency of top strategy and creative talents by hundreds and thousands of times. Enterprises will go from the残酷 "拼 human waves, material volume, overtime" to "拼 compute, models, system flow efficiency."

Part Five: Business Depth | Product Architecture, Technical Logic, Growth Commercialization

12\. Please break down your core products in plain terms: what functions do the AI video infrastructure and AI intelligent delivery engine each perform, and how do the two systems link to achieve the full-chain loop from content production to data feedback to optimized delivery?

Answer: This is our core weapon to break the "impossible triangle" (high production capacity, high quality, low cost) in traditional user acquisition. To help you understand more intuitively, we can describe this system as a modern, automated "high-tech precision-guided warfare system."

First, the "AI video infrastructure" is our "super digital armory."

It is absolutely not a single-point auxiliary tool for people to manually type prompts, but a deeply AI-engineered, productized light-asset content foundation. This means that as long as existing mature or validated materials or copy content are distilled, this armory can produce multimodal video ammunition containing different scripts, storyboards, BGM, special effects, and different emotional inducement nodes. Each node can be freely adjusted or replaced according to your needs, and becomes your own digital capital for continuous reuse. It completely strips away traditional heavy-asset human shooting and assembly-line editing, gradually compressing production cost to infinitely approach zero, bringing us unlimited firepower authority.

Second, the "AI intelligent delivery engine" is our extremely sensitive "omni-domain phased-array radar system."

In this engine, my market partner proficient in platform traffic delivery turned the ROI scaling strategies he accumulated through countless hundreds-of-millions-level game and short drama consumption battles entirely into underlying automated algorithms. This radar system doesn't produce ammunition, but it aims the massive materials made by the armory with the most precise compute and lowest CPA at high-conversion audience positions across the entire network.

The soul of these two systems' full-chain closed-loop cycle is "second-level adaptive feedback based on real consumption data."

13\. AI videos on the market generally have material homogenization, low delivery adaptability, disconnected platform data, and poor volume-driving performance. What optimizations have you made in technical architecture, models, data, memory assets, and infrastructure underlying R&D?

Answer: The fundamental reason why most AI video content on the market is mired in "homogenization" and "poor volume-driving performance" is that they're all eating from the public-domain data "big pot." They use one general model to应付 the ever-changing user-acquisition platform, neither打通 media platform data feedback interfaces nor accumulating their own clients' high-conversion genes. This "blind man shooting arrows" generation naturally has extremely low delivery adaptability. To thoroughly smash these industry pain points, we have conducted revolutionary reconstruction and optimization in the following underlying dimensions:

First, we created the "enterprise-level dynamic memory asset library," feeding private models with real money data.

In our data and memory asset architecture, every client connecting to our AI infrastructure forms a highly confidential independent asset library. All materials produced by the client become reusable assets in their own library, extracting those feature tags that truly have "volume-driving genes" (like specific game kill rhythm, short drama high-reversal emotional points). This high-quality data with battle experience feeds back to the model as private assets. Data from AI intelligent delivery then provides the basis for subsequent new material production.

Second, we firmly implement "product engineering" to achieve API-level data direct connection.

In infrastructure underlying R&D and technical architecture, from day one we rejected the "technology and delivery fragmentation" island model. In this system's iteration, the operations partner responsible for platform operation and the market partner proficient in traffic deeply participated in underlying rule setting and product design. We achieved underlying direct connection between every CPA fluctuation captured by the back-end intelligent delivery engine and the front-end video rendering layer. As long as the delivery end reports that an element is going viral, the viral material data feeds back to the AI video creation platform, and the AI video creation infrastructure knows through feedback what materials to make next.

Third, we reject being broad and comprehensive, focusing on "extreme ROI-oriented" model fine-tuning.

At the model level, we don't blindly pursue how encyclopedic AI's common sense is; we only require it to be extremely sensitive to one thing: "conversion rate." What we will do in the future is refine vertical model performance through performance advertising platform data, specializing in achieving high-concurrency generation at extremely low compute cost, pushing the "low-cost trial-and-error" and "rapid iteration" light-asset commercial logic to the extreme.

This technical barrier that perfectly fuses "private memory asset accumulation," "underlying engineering closed-loop integration," and "extreme conversion-rate-oriented model fine-tuning" cannot be easily copied by any pure-tech team or traditional delivery company.

14\. AI iteration is extremely fast now, and products easily homogenize. How do you balance short-term commercial deployment and continuous product R&D investment?

Answer: This is not only about technology evolution, but also about a startup company's survival and strategic resolve. Facing the rapidly iterating and easily homogenized AI sector, our core strategy is very firm, highly浓缩 into four words: "fighting war with war."

We firmly don't do the bottomless pit of脱离 real commercial scenarios and pure capital-burning to compete on large models; we firmly choose to do "closest to money," directly creating real profits for clients in the AI application infrastructure and engine layer. In terms of "short-term commercial deployment," we are already operating a market-validated, continuously revenue-generating social cash-cow business to feed the technical R&D team.

Regarding "continuous product R&D investment," our understanding is: on the real digital marketing battlefield, "commercial deployment" and "R&D investment" are never opposing消耗, but a deeply meshed data flywheel.

Why do AI tools on the market quickly homogenize? Because they all call identical open-source interfaces and public-domain data. But our AI infrastructure, in the process of massive commercial consumption for clients, is constantly absorbing the most valuable "conversion feedback" in the real platform. This practical data with real-money battle experience directly accumulates as our private "dynamic memory assets," becoming exclusive nourishment for continuously fine-tuning models and opening a generational gap with competitors. When we use this AI commercialization plus continuously growing social business cash flow to build an impregnable technical moat, our R&D investment has even broader strategic depth.

In summary, for us, sharp commercial deployment is our blade piercing the industry's homogenization bubble, while underlying infrastructure R&D built on real user-acquisition data is our confidence to continuously cross boundaries and reshape the entire digital entertainment ecosystem. Use validated social business light assets to earn profits, use battle data to feed large models, use profits to raise technical barriers.

15\. What is the current user growth playbook and commercialization model of the project? Is it deeply cultivating vertical clients based on industry resources, or standardized product scaled acquisition? What are the short-, medium-, and long-term commercialization plans?

Answer: This not only tests product monetization ability, but also interrogates a startup's strategic resolve and commercial rhythm. Our commercialization model is by no means single "software selling," but AI product ecosystem monetization based on "traffic operations closed loop" plus existing social business's continuously stable revenue profit source. In growth playbook, we will never make a blind either/or between "deep cultivation of vertical large clients" and "standardized scaled acquisition," but extremely precisely weave them into our short-, medium-, and long-term three-stage rocket strategy.

Short-term plan (first stage rocket): high-profile, large-client benchmarking and private deep cultivation

Precisely enter these top-tier giants suffering from production capacity and ROI black holes, providing them deep AI infrastructure and intelligent delivery engines, helping them build private "dynamic memory asset libraries." This model is already fully validated in our own social business and game/short drama business.

Mid-term plan (second stage rocket): AI product platformization and standardized scaled acquisition

At this stage, our commercial model will upgrade to a scaled money printer of "product annual fee + CPS revenue share on delivery performance," achieving exponential business growth.

Long-term plan (third stage rocket): cross-border dimensional reduction strike, opening a "second growth curve"

In the long run, when our AI infrastructure thoroughly becomes the underlying "water, electricity, and fuel" of the digital entertainment user-acquisition industry, our commercial ambition will no longer be limited to performance advertising and short drama delivery. Leveraging this extremely low marginal cost automated content generation system, half a year later we will open a highly explosive "second growth curve"—entering the overseas globalized "AI emotional companionship" and "confidant tree" and other highly customized deep-interaction sectors, replicating domestic social cash-cow business. This is a hidden blue ocean with extremely high AOV, extremely strong user stickiness, and completely impossible to scale under traditional high labor cost models.

Part Six: Industry Judgment | Landscape Prediction and Long-Term Vision

16\. In 2026, AI advertising and AI short drama sectors are crowded with entrants—giants, tool vendors, and delivery service providers all entering. What do you think is the core competitiveness that the "AI video infrastructure + intelligent delivery engine" sector ultimately competes on?

Answer: This question points to the current extremely残酷 yet most attractive competitive landscape. In 2026, AI advertising and AI short drama sectors are indeed crowded with entrants—giants, tool vendors, and delivery service providers all entering, presenting a "hundred regiments battle" noisy态势. But in this seemingly crowded sector, I firmly believe the "AI video infrastructure + intelligent delivery engine" sector's ultimate core competitiveness is by no means simply compute or model benchmark scores, but "the commercial closed-loop engineering capability that deeply meshes underlying technical compute with real user-acquisition platform ROI."

In this sector, whoever can fastest help clients turn ROI positive, who can first build the "generation-delivery-feedback-variation" closed-loop flywheel without deep human intervention, will take all in this battle. We understand better than AI companies how to spend money on user acquisition, and better than user-acquisition companies how to use AI to reshape productivity. Who can fastest and at lowest cost help clients turn ROI positive is the ultimate winner.

17\. Standing from the perspective of a 20-year user-acquisition growth veteran, how will AI reconstruct the entire industry chain from production to delivery for advertising videos and commercial short dramas in the next 2-3 years? Which traditional user-acquisition models will be completely eliminated?

Answer: This is actually a soul interrogation of the entire digital marketing and user-acquisition industry's endgame. As a veteran who has scraped and fought nearly 20 years in the game industry, I believe in the next 2-3 years, AI's reconstruction of the entire advertising video and commercial short drama industry chain is absolutely not warm-boiling-the-frog reform, but a摧枯拉朽 productivity revolution.

Which traditional user-acquisition models will be completely eliminated? First to go is the "heavy-asset material workshop" that purely relies on human waves, blindly spreads volume, and gambles luck by "piling editors and screenwriters"; second, traditional delivery service providers with no underlying technical infrastructure capability, only doing fragmented "pure agency delivery" and earning labor information asymmetry margins, will also completely lose living space. The old way of buying traffic was blind delivery, betting on extremely short material lifespans with human effort; the future of buying traffic will inevitably be millisecond-level high-frequency博弈 between our underlying compute and major media platform recommendation algorithms.

18\. In this all-in AI entrepreneurship, what medium-to-long-term goals have you set for yourself and the team? What change do you hope this AI infrastructure + delivery engine product will ultimately bring to the entire performance advertising and user-acquisition industry?

Answer: In this all-in AI entrepreneurship, the medium-to-long-term goals I've set for myself and the team are absolutely not just being an ordinary company earning software price differences. We want to become the underlying "water, electricity, fuel" and "highway" for global digital entertainment and performance advertising marketing using AI technology.

We not only want to eat the largest share in the current game and short drama user-acquisition red ocean with ROI and compute, but also leverage this extremely low trial-and-error cost automated content infrastructure to continuously create revenue for our global AI interactive social business, using AI to fill the massive spiritual demand blue ocean.

As for what change this "AI video infrastructure + intelligent delivery engine" will bring to the entire user-acquisition industry? My core vision is: completely end this industry's long-standing Old Stone Age of "high energy consumption, extreme involution, and blind volume spreading!"

Over the past decade-plus, material sunk costs and high CPA, like black holes, have stripped user-acquisition enterprises of profit and stifled the living space of countless quality content. We hope to use algorithms and compute to completely take over the dirtiest, most tiring, and lowest-fault-tolerance mechanical trial-and-error work, providing the entire industry with "low-cost trial-and-error democratization," so whether a hundred-billion giant or a startup team can validate their business ideas at extremely low risk. Let digital advertising and content practitioners return most of their brainpower and energy to product R&D初心, to high-dimensional creative refinement and the emotional resonance of user value.

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Original publication: https://uniqueresearch.substack.com/p/src-20260627-01html
On-site reading page: https://ffcap.cn/en/research/src-20260627-01html