While engineers in San Francisco press Tab in Cursor to complete code, editing rooms in Zhengzhou and Hangzhou are running hundreds or thousands of machines on Seedance to batch-generate short-drama storyboards.
The compute consumption patterns of large models on the two sides have split into two completely different tracks:
In the U.S., roughly 90% of leading large-model Token go into writing code, finding bugs, automated testing, and engineering orchestration. In China, more than 55% of large-model Token flow directly into short-drama production.
One side uses compute to replace senior software engineers earning $200,000 to $300,000 a year, saving money through enterprise software subscriptions; the other uses compute to mass-produce feed slices from hundreds of daily short-drama episodes, cashing out through same-day paid traffic and top-up revenue.
Most people’s first reaction to this comparison is mockery, often with a little self-deprecation: they are rebuilding the software substrate, while we are chasing quick money in down-market entertainment. It feels less respectable.
But after spending so long inside content production and technical workflows, after doing the math and watching render jobs run, I’ve reached the opposite conclusion: compute has never cared what looks respectable. Behind the roar of the machines, there is only the coldest business ROI.
The starkly different Token consumption paths in China and the U.S. have nothing to do with which side has better technical taste. They are simply the smartest money in each market voting for the fastest monetization channel available there.
but each side is balancing a completely different ledger.
01Silicon Valley’s Ledger: Paying for Expensive Talent
Why was the U.S. able to produce Cursor?
The logic is actually very clean: an experienced Silicon Valley engineer often costs $200,000 to $400,000 a year.
For Silicon Valley tech companies, labor is one of the most expensive items on the balance sheet. If a tool can save an engineer two hours of boilerplate code a day or one low-level syntax hunt, companies will pay $20 or $200 a month for it without blinking.
This logic works smoothly because it is rooted in the mature U.S. enterprise-service market: companies are very willing to buy software, decision chains are short, and the savings from better R&D efficiency can be absorbed directly by high-ACV enterprise services. Translate “30% higher productivity per person” into a financial statement, and it becomes a very clear cost-saving calculation.
The Token burned by Cursor are, in essence, converting relatively cheap electricity and chip depreciation into the most expensive engineering hours in the world.
02China’s Ledger: Only Businesses That Pay Back the Same Day Dare to Burn Token Openly
Shift the scene to China, and the logic of selling productivity software becomes much heavier.
In China, enterprise services, collaboration tools, and enterprise-grade Agents often collect payment on quarterly or even yearly cycles. Sales has to crawl through long bidding processes, engineering has to handle customized delivery, and then everyone waits through a long receivables cycle. That process simply cannot offset the millions in daily GPU depreciation sitting in the data center.
Short dramas are a completely different species. Many people see them as video versions of melodramatic wish-fulfillment stories, but look deeper and the industry is fundamentally a high-frequency paid-traffic business with extremely fast turnover.
Its operating cycle has been compressed to an extreme: scripts are broken into dozens or hundreds of standardized shots in the morning; AI tools run unattended to batch-generate visuals and voiceovers; in the afternoon, editors assemble dozens of feed ads whose first three seconds are packed with conflict; at night, the backend shows top-up conversion rates and same-day ad ROI; winners get extra paid-traffic budget before dawn, and losers are killed that same night.
In a traditional live-action short drama, once the crew starts shooting, locations, costumes, props, makeup, and actors’ fees mean that even an extremely compressed production still costs tens of thousands to over a hundred thousand yuan, with production cycles measured in weeks.
Move into an AI workflow, and virtual characters are locked down with reference images, while scenes and camera movement are handed to the model for batch processing. Production costs are cut by more than half; more importantly, the frequency of testing new plotlines rises to the level of days or even hours.
In a business where input and output can close the loop within 24 hours, producers’ attitude toward Token changes completely: compute is no longer an R&D expense; it becomes a raw material on the assembly line. Burn an extra 10,000 yuan of Token, and if it finds a shot combination with a higher completion rate, the ad account may earn back 30,000 yuan that same day.
There is not much romantic baggage behind short dramas eating 55% of the compute. The reason is very practical: right now, this is one of the very few industrial arteries in China that can turn compute into same-day cash at high frequency.
03Why Seedance? The Real Wall for Latecomers Is Post-Training Data
Once you understand the business loop, it becomes clear why Seedance can own this scene so tightly.
From the outside, people looking at AI video often like to compare base-model parameter counts, physical simulation, or whether the image reaches 4K. But if you actually try to make dramas with open-source or general public video models, you hit a wall very quickly.
Short dramas ask for something very specific from the image: they do not demand the complex lighting artistry of theatrical cinema, but a character’s face absolutely cannot change across dozens of consecutive shots; viewers can ignore occasional background flaws, but story-driving actions such as pushing open a door, turning the head, or hardening the eyes must land with precise rhythm inside two seconds.
Generic open-source models can often produce a beautiful empty shot with nice light and shadow, but once specific character drama enters the frame, limbs deform or the model fails to deliver the right emotional response.
Seedance’s moat is built exactly here. Anyone can rent GPUs and compute clusters; the real gap lies in the “post-training data” produced every day by tens of thousands of professionals on the production front line.
Put plainly, post-training data is humans on the front line telling the model “what looks good, what passes review, and what keeps viewers watching.”
On a real short-drama editing desk, to get a three-second slap-in-the-face or turn-around shot, an editor will usually generate several versions in a row:
If the movement looks natural and the eyes feel right, the editor clicks Download/export, drags it into Jianying, and cuts it into the finished episode; if the face warps or the walk clips through the geometry, the editor takes one glance and, without hesitation, clicks “Regenerate” or deletes it outright; if the lip shape is wrong, the editor circles the mouth area on the frame and runs one targeted inpainting pass.
These actions may look like routine editorial picking and choosing, but in the platform backend, every “final download and use” is an extremely valuable positive sample, and every “drop it immediately and reroll” is a precise negative sample.
This kind of data simply cannot be crawled from the open web.
The open-source community can scrape every HD film and short video on the internet to train a base model, but those videos contain only finished cuts, not rejected takes. Public data can never tell you which tiny bodily twitch humans will kill on sight, or which shift in the eyes keeps viewers through the third second.
Every day, hundreds or thousands of short-drama teams spend real money burning Token on the platform, working overnight and nitpicking every shot. It is equivalent to countless professional editors paying out of pocket to perform high-intensity targeted tuning for the model.
As massive amounts of hands-on feedback flow back into the model, Seedance’s feel for the niche question of “how short dramas should be shot” starts to resemble that of a seasoned assembly-line assistant director.
Later competitors may buy the same GPUs and train base models with similar parameter counts, but without this group of professional users who find faults on the production line every day and calibrate the model’s feel with real business metrics, their generated images will always carry a layer of awkward guesswork.
04Two Realities, Two Species
America grew Cursor; China produced Seedance.
This is not about which track is technically superior. It is simply the inevitable result of two different sets of economic factor endowments:
The U.S. uses compute to replace extremely expensive high-end knowledge work, turning programmers into super-individuals who can operate like one-person companies; China uses compute to reconstruct a down-market content industry obsessed with turnover, breaking traditional film and television workflows into a digital assembly line priced by the second.
Many people still treat short dramas as fast food. But Hollywood’s studio system also first crawled out of nickelodeons and low-cost cheap genre films.
In the early stages of an industry, roughness often means stronger vitality.
When 55% of China’s large-model compute is doing brute-force calculations every day for action consistency, shot language, and audiovisual rhythm, the post-training barrier fed by real money is already much thicker than outsiders imagine.
The business world never lacks respectable blueprints. More often, the real way to nail technology into reality is to turn the roar of machines into cash that genuinely moves across the books every morning.