Generative AI as a Traffic Factory: Recalibrating the Value of Technology

Two Phenomena, One Question

First, consider a phenomenon in the content industry. AI-driven assembly lines are rapidly expanding, particularly in short-form video platforms. Large language models (LLMs) automatically generate scripts based on fixed templates, while AI illustration and voice synthesis take over the production phase. This allows for the mass production of fast-food-style short dramas featuring extreme plot twists and strong hooks at incredibly low marginal costs. These contents intentionally simplify logic and compress depth, continuously harvesting users’ fragmented time with dense emotional stimuli. Many users describe them as “mind-numbing content.” More alarmingly, these short dramas are no longer confined to standalone apps; they have infiltrated news feeds. When a user clicks on a current affairs article to obtain useful information, exaggerated dramatic clips pop up everywhere, hijacking their attention midway.

Second, look at a phenomenon within the tech community. A professional technical interview aimed at developers was originally supposed to discuss the technical synergy between long-context LLMs and distributed databases, exploring the engineering bottlenecks of implementing long-context scenarios—a quintessential hardcore technical share. However, as the interview spread across the internet, the technical content became a mere backdrop. What actually went viral was a bizarre contrast: the participating engineer appeared in cross-dressing attire. The public’s attention was fixated on the spectacle itself, with almost no one caring about the technical breakthroughs discussed in the interview.

These two seemingly unrelated events point to the same question: What is the ultimate goal of the technological advancement we are driving with massive computing power, capital, and talent?

Deconstructing the Tech Pipeline of “Mind-Numbing Content”

To understand the severity of the problem, we must first understand how this content factory operates. Its terrifying aspect lies not in outdated technology, but precisely in its advanced nature. It organizes cutting-edge capabilities like generative AI, voice synthesis, and recommendation algorithms into a highly industrialized assembly line for harvesting attention.

1. Script Generation: Templated Narrative and Hook Density Control

LLMs are no longer used to tell a complete story. Instead, they batch-generate “emotional climax sequences” according to fixed templates. Typical tropes include identity reversal (a hidden heir to a conglomerate), revenge after humiliation (enduring abuse early on, sudden explosion in the middle), and instant gratification (throwing out suspense or conflict every few seconds). From an engineering perspective, this is an optimization process with a highly explicit objective function: the optimization target is not narrative quality, but the conversion rate for “watching the next episode.”

# Illustrative: Generation objectives of mind-numbing content
script = llm.generate(
    prompt=hook_chain_template,        # Reversal hook chain template
    constraints={
        "conflict_interval": "3~5 secs",   # Conflict and suspense density
        "logic_depth": "minimized",     # Logic depth minimized
        "cliffhanger": "mandatory at end",   # Forced suspense ending
    }
)
# The sole optimization metric: continuation rate, not story quality

2. Production: AIGC Pushing Costs to the Limit

The maturity of AI drawing, digital avatars, and voice synthesis means that visuals, actors, and voiceovers can all be mass-generated. Traditional film and television production cycles measured in months are compressed into days or even hours. Marginal costs approaching zero mean platforms can execute a “spray and pray” strategy, distributing massive amounts of content. As long as a tiny fraction hits the algorithm’s recommendation wave, the entire cost is recouped. Content supply has thus degraded from “carefully curated” to a “game of probability.”

3. Distribution: From Standalone Apps to News Feed Infiltration

The most critical change is on the distribution side. When this content existed only in standalone apps, users at least had a choice. Now, when embedded as clips within news feeds, attention hijacking shifts from “active consumption” to “passive ambush.” Users come with the intent to gather information but are intercepted by high-stimulation fragments. This is not need satisfaction; it is need mismatch.

Three Psychological Mechanisms Behind Attention Hijacking

These products work because they precisely exploit three natural cognitive weaknesses of humans, hardcoding them into the system design:

First, the curiosity gap. The “Zeigarnik effect” in psychology suggests that people’s memory and obsession with unfinished events are far stronger than with completed ones. The forced suspense at the end of each episode engineers and productizes this effect.

Second, instant emotional feedback. The gratification from strong plot twists is essentially high-frequency, short-cycle emotional stimulation. Compared to deep content that requires long-term investment to yield returns, instant stimulation naturally dominates the attention competition. Many people originally plan to spend just a few minutes browsing the news, only to unknowingly consume one or two hours.

Third, sunk cost. After watching three episodes, users feel that “the previous time was wasted” if they quit, so they continue watching. Algorithms further reduce the probability of users leaving through mechanisms like auto-play and lowering the friction of exit paths.

It must be emphasized: these three points are not product flaws, but deliberate designs. Writing human weaknesses into the objective function is the real ethical issue of “mind-numbing content.”

Three Fallacies of the Traffic Logic

The spread of such phenomena is backed by a set of traffic logic widely accepted by the industry but unjustifiable upon closer inspection. It can be deconstructed into three fallacies:

• Fallacy 1: User time spent = User value. Time spent is the platform’s value, not the user’s. A user spends two hours watching short dramas, their attention is consumed, their information gain is near zero, and their cognitive patience is further weakened. Measuring value by time spent equates “being consumed” with “being satisfied.”
• Fallacy 2: Popularity = Advancement. The widespread use of a technology only proves it effectively hits human weaknesses or market demands; it does not mean it pushes the boundaries of cognition. Historically, products with the largest user bases are not necessarily the ones with the highest technological gold content.
• Fallacy 3: Commercial viability = Right direction. A closed commercial loop can prove a model is sustainable, but not that it is worth sustaining. A commercial loop designed around addiction can also work, but no one would argue it represents advanced productive forces.

What Should Be the Baseline for Technological Advancement?

Returning to the initial question. Measuring technological advancement has always had a clear baseline: Does it solve real-world pain points? Does it foster a healthier mental state? Does it cultivate higher-level aesthetics and cognition? Does it convey positive value?

Technology can innovate, and expression can be diverse, but treating sensationalism, manufacturing addictive stimuli, and deliberately pandering to human weaknesses as the main line of development deviates from the mission of technology. Generative AI can be used to compress creative costs and amplify individual expression, or it can be used to mass-produce mental consumables. The technology itself is neutral, but the objective function is not. When an increasing number of tech-garbed products fail to solve real pain points and instead manufacture mental consumption, such a trend must never become the mainstream of our era.

Course Correction: Four Actionable Engineering Steps

Beyond critique, it is more worthwhile to discuss what the engineering side can do. Here are four practical directions:

1. Introduce Quality Scores and Addiction Risk Constraints in Recommendation Ranking

# Illustrative: Recommendation ranking with value constraints
final_score = (
    w_q * quality_model.predict(item)        # Content quality score
    + w_e * engagement_model.predict(item)     # Engagement score
    - w_a * addiction_risk(item, user)        # Addiction risk penalty
    + w_d * diversity_bonus(user_history)     # Content diversity bonus
)

The quality score model can be trained on signals like information density, factual accuracy, and originality, ensuring that “content worth reflecting on” receives its rightful weight in ranking, rather than letting instant stimulation monopolize top-tier traffic.

2. Attention Protection Design

Referencing the “Privacy by Design” philosophy, products should build attention protection mechanisms into their architecture: actively reminding users and lowering recommendation intensity after continuous consumption reaches a threshold; strictly segregating news feeds from entertainment short dramas to prevent high-stimulation clips from mixing into information streams; keeping exit paths visible, accessible, and low-friction.

3. Model Value Alignment

Governance cannot stop at patching the application layer. Content value evaluations should be introduced during the model training phase, applying constraints to generation objectives that “deliberately weaken logic and mass-produce addictive hooks.” This should cover the entire pipeline from data filtering and reward model design to post-launch monitoring.

4. Industry Standards and External Regulation

Self-regulation by a single platform is insufficient. The industry needs to establish evaluation standards for content quality and addictiveness. On the regulatory front, existing practices like minor protection and gaming anti-addiction systems can be referenced to constrain product forms designed around addiction, turning course correction from a moral appeal into enforceable rules.

Conclusion

The history of technology repeatedly proves that what truly drives eras forward is never the products most adept at harvesting attention, but the technologies that solve real problems and expand the boundaries of human capability. For tech professionals, the criteria have always been there, and they are simple enough: Does every line of code you write, every model you train, and every objective function you design expand the human mental space, or does it compress it? The answer determines whether we are driving technological progress or a cleverly packaged regression.