Conclusion First

Giants are pouring $300 million in salaries just to capture your precious last minute of daily attention and clicks.
Generative AI pretends to release productivity while secretly creating sellable leisure time.
GPU prices have skyrocketed, becoming a new currency, and computing power futures allow bubbles and profits to dance together today.
Attention is now exhausted; even sleep, the final bastion, is openly priced beneath the sky by commercial algorithms.
If you don’t price your own time first, the giants will purchase your future and dreams at sky-high costs.

Overview

Over the past 15 years, the logic of Internet commerce has evolved from “killing time” to “creating time.” Mobile devices and short videos have squeezed every fragment of time, and now generative AI aims to fill the void left by productivity tools. The reason giants are willing to pay top researchers salaries reaching $300 million and frantically hoard computing power is simple—they want to squeeze an extra minute out of everyone’s constant 24 hours and monetize it.

This article will dissect this trillion-dollar time heist, covering the evolution of the attention war, the battle for top talent, the layout of personal superintelligence, the economic transformation of computing power, future trends, and specific action guides.

Before ChatGPT appeared, people realized in the heated competition of the mobile internet that what was truly being captured was user time. The full-screen experience of TikTok, which makes people forget time, is a remarkably successful design!

1 The Attention War 3.0: From “Killing Time” to “Creating Time”

When all waking hours have been divided, where is the next battlefield?

In 2017, Netflix CEO Reed Hastings made a shocking statement during an earnings call: “Our biggest competitor isn’t HBO or Disney, but sleep.” This remark was taken as a joke at the time but has now become a prediction for the entire tech industry. netflix-competitor-sleep-uber-facebook

Three Waves of Attention Harvesting

The first wave came in the PC era with portal websites monetizing through time spent on the homepage; the second wave was the mobile era’s information streams, where TikTok and Instagram exploited fragmented moments to the extreme; the third wave is the AI era’s “efficiency paradox”—when ChatGPT helps you save two hours of work time, who will fill in those two hours?

According to the latest report from App Annie (now Data.ai), the average daily mobile usage time in the world’s top ten markets has surpassed 5.2 hours, with a year-on-year growth of only 0.3%, marking a mature platform phase. What does this mean? Incremental time has been exhausted, making the remaining time the ultimate battlefield.

More importantly, that 5.2 hours is not the ceiling. According to stats from Stanford’s Digital Economy Lab, in first-tier cities in South Korea and China, the daily screen time for the 18-24 age group is nearing 7 hours, approaching the physical limits of human capability. Once the incremental time is depleted, the giants begin targeting “hidden time”: moments of daydreaming during commutes, waiting for meals, or even the last 15 minutes before falling asleep.

From “Time Killers” to “Time Makers”

However, the true disruption comes from generative AI. In the past, tech products merely redistributed existing time; now, AI tools are starting to “create” time—when Copilot boosts programming efficiency by 40% and when ChatGPT shrinks email writing time by 75%, these released time blocks are becoming the new battlefield for profit.

Dario Amodei, founder of Anthropic, candidly stated in an internal meeting in 2024: “We are not optimizing productivity but creating monetizable leisure.” This sentence captures the true motivation behind the AI race—not to make humans more efficient, but to give them more time to consume digital content.

The question then is: When the time that AI helps you “create” gets filled up again, are you still the one in control of your own time?

2 Jaw-Dropping Talent Poaching: The Financial Alchemy Behind $300 Million Salaries

Is a researcher worth $1.25 billion? Meta has an answer.

By the end of 2024, Silicon Valley was rocked by shocking news: Meta offered a top AI researcher a $300 million compensation package over four years, with over $100 million cash in the first year. More incredibly, according to The Information, an unnamed machine learning expert received an offer of $1.25 billion/4 years but ultimately chose to stay at their current company.

What business logic underlies such exorbitant talent poaching?

The Financial Magic of Algorithm Optimization

The answer lies in a simple mathematical formula: A 0.1% increase in recommendation algorithm efficiency = billions in increased ad revenue.

Take Meta as an example; with over 3 billion daily active users and an average daily usage time of around 2 hours, if an algorithm optimization allows users to stay an additional minute, that translates to an increase of 15 billion minutes/day in ad inventory. Based on Meta’s RPM of $2 per 1,000 impressions, that one minute would be worth approximately $30 million/day, leading to an annualized revenue of over $10 billion.

The Intricate Salary Structure

These sky-high salaries are not simply cash payments but carefully designed financial engineering:

Core Components:

Base Salary: $2-5 million/year
Performance Stock Units (PSU): 70-80% of total package, tied to model performance metrics
Unlimited GPU Quotas: Valued at $20-50 million, providing exclusive computing resources
Research Freedom: 20% of time dedicated to personal projects, with results owned by the individual

Risk Control Mechanism: The conditions for exercising PSUs are stringent: not only must the researcher maintain full attendance for four years, but the model they oversee must achieve a 15% annual improvement in key metrics. If they leave before meeting these standards, hundreds of millions in options go to zero.

The Reality of the Talent Arms Race

The essence of this poaching war is not technology but the time window. Sam Altman from OpenAI disclosed in an internal email: “We must secure the top 200 researchers globally within 18 months, or we will be entirely split between Meta and Google.”

Why 18 months? Because that is the shortest cycle for bringing model development to user experience. Missing this window could mean elimination from the next generation of AI product competition.

When a researcher’s salary can buy an entire mansion in Silicon Valley, and stock options can elevate one to the Forbes billionaire list overnight, what does this “money for time” game fundamentally scramble to acquire?

3 | “Personal Super-Intelligence”: How Meta Fills New Leisure Time

Mark Zuckerberg’s ultimate goal isn’t to improve your work efficiency but to claim every minute of your leisure time.

In September 2024, Meta CEO Mark Zuckerberg released an internal letter proposing the concept of Personal Super-Intelligence. Unlike OpenAI’s focus on work efficiency, Meta’s AI strategy targets entertainment, social connection, and lifestyle.

Differentiation Strategy: Prioritizing Entertainment

Meta CPO Chris Cox made it clear at the developer conference: “We won’t compete with Microsoft on office suites; we’re looking to build a moat around entertainment, friend connections, and lifestyle.”

The logic behind this strategy is clear and ruthless: Efficiency gains are one-time, but entertainment consumption is infinite. When AI helps you finish a report in 10 minutes, what will you do with the remaining 50 minutes? Meta’s answer is: watch Reels, chat with AI characters, or experience social interaction through AR glasses.

Three Core Technologies

1. AI Character Store: The Virtual Companion Economy

Meta has launched over 100 AI characters, covering scenarios such as fitness coaches, psychological counselors, and gaming companions. The most popular “virtual girlfriend,” Billie, boasts over 5 million daily active users, averaging 45 minutes of conversation time daily, even exceeding human social interaction in user stickiness.

2. Full-Process Reels Generation: Bringing Creation Barriers Down to Zero

The new Meta AI can generate a complete short video based on a simple sentence: from script to visuals to soundtrack. Test data shows that AI-generated content has a completion rate 23% higher than that produced manually, as the algorithm knows naturally what is most addictive.

3. Ray-Ban Smart Glasses: Seizing the “Last Screen”

The Ray-Ban Meta smart glasses, launched in collaboration with EssilorLuxottica, saw sales double in Q4 2024, becoming the highlight of Meta’s hardware business. Zuckerberg’s ambition is evident: when users wear these glasses, every gap in the real world can be filled with AI content.

Commercializing the Efficiency Paradox

A profound paradox exists here: AI tools make you more efficient, yet the beneficiaries are other companies. When you use Claude to finish a proposal and leave work early, the extra two hours are most likely spent on Instagram; when you quickly create a graphic using Midjourney, the saved time is likely consumed by YouTube’s recommendation algorithm.

Meta’s “Personal Super-Intelligence” is fundamentally an efficiency-entertainment converter: the front end helps you boost work efficiency, while the back end fills the released time with entertainment content, forming a perfect commercial loop.

But this model requires vast amounts of computing power, leading us to the next question: Why is computing power so expensive?

4 GPU and the Pricing of Time: Bubble or Gold?

When GPUs become the new “oil,” time itself acquires an accurate market price.

If talent is the brain of the AI war, then GPUs are the muscle. The market for computing power in 2024 displayed an unprecedented polarization: on one side, the monopolistic high prices from cloud firms, and on the other, price wars in the secondary market, with arbitrage opportunities giving rise to new business models.

Dual Standards for Computing Power Pricing

Primary Market (Cloud Providers):

AWS p5.48xlarge (8×H100): $9.98/hour
Google Cloud A3-highgpu-8g: $10.32/hour
Azure ND96isr_H100_v5: $9.55/hour

Secondary Market (Computing Power Platforms):

Vast.ai H100 Cluster: $1.87/hour
RunPod H100 Spot: $2.45/hour
Lambda Labs H100: $3.20/hour

The price difference exceeds 5 times! This polarization stems from an extremely asymmetric supply-demand structure: cloud providers control quality data centers and networks, while abundant idle GPUs are scattered across mining farms, laboratories, and individuals.

Blackwell: A Turning Point for Computing Power Democratization

NVIDIA’s release of the Blackwell architecture at the end of 2024 brought revolutionary changes: supporting 1/7 hardware slicing, meaning one B100 can simultaneously serve seven independent tasks, effectively halving inference costs.

More importantly, Blackwell’s slicing technology allows small teams to access top-tier computing power. Previously, training a mid-scale multimodal model required 256 H100s running continuously for 72 hours at a cost of $180,000. Now, through slicing and mixed precision optimization, the same task only requires $45,000.

Computing Power as a New Recruitment Bargaining Chip

An intriguing phenomenon is that computing power itself has become a weapon for talent competition. Meta not only promises exorbitant salaries to top researchers but also “unlimited GPU quotas.” This promise is valued at up to $50 million annually because it allows researchers to freely explore the wildest ideas without worrying about computing budgets.

In contrast, OpenAI researchers often queue for GPU access due to insufficient quotas, and this difference in “computing power freedom” is becoming a decisive factor in talent mobility.

The downward price pressure on computing power directly influences the competitive landscape: when inference costs are low enough, independent developers can challenge the monopoly of giants. But will this democratization of computing power truly make competition fairer?

5 Nine Major Changes: The Time Allocation Revolution in the Next Three Years

The ownership of time is being reshuffled, with the following nine trends determining whether you are a beneficiary or a victim.

Based on a thorough analysis of current technological trajectories and business models, we predict the emergence of the following nine major changes over the next three years:

Explosion of End-Side Multimodal Hardware

Apple Vision Pro’s sales are expected to exceed 8 million units in 2025, and Meta’s Orion AR glasses will enter mass production in 2026. These devices will become the “second phone,” occupying the remaining visual channel time of users. Key milestones: battery life exceeding 8 hours, weight dropping below 80 grams.

Daily Active Users of AI Characters Surpassing 500 Million

Platforms like Character.AI, Meta AI Studio, and ByteDance’s Kouzi will experience a user scale explosion. Virtual characters will no longer be mere entertainment tools but intelligent agents for emotional companionship, knowledge acquisition, and even business negotiations. Commercialization paths include virtual streamers, personalized advertising, and emotional value-added services.

GPU Rental Prices Plummeting to ≤ $1/hour

With the mass production of Blackwell and the maturation of China’s GPU alternatives, computing power costs will experience a cliff-like decline. It’s expected that by 2026, the rental price for H100-level computing power will drop below $0.8/hour, completely breaking the pricing monopoly of cloud providers.

FTC Introducing Regulatory Guidelines on Algorithm Addiction

The U.S. Federal Trade Commission is drafting restrictive regulations on algorithm recommendations, focusing on protecting users under 18. A teen mode will transition from an optional feature to a compliance standard, enforcing limits on daily usage and nighttime alerts.

Figma Locking Up “Design Taste” Moat Through Acquisitions

In the face of pressure from AI-generated tools, Figma will establish a “taste barrier” by acquiring top design studios. Technology can be copied, but unique aesthetic styles cannot be covered by training data.

Sleep Technology Emerging as a New Gold Rush

As waking time is being completely consumed, sleep has become the last untapped market. Innovations like Apple Watch’s sleep monitoring, Oura Ring’s deep sleep optimization, and even Neuralink’s dream recording are paving the way for “sleep commercialization.”

Birth of the Attention Futures Market

“Attention futures,” based on user behavior data, will become a new financial product. Advertisers can pre-lock specific demographics’ attention during specific time slots, forming a secondary market for time trading.

Enterprise AI Assistants Reshaping Office Time Allocation

Tools like Microsoft Copilot and Google Workspace AI will reduce knowledge workers’ working hours by 30-40%, but this “released” time is likely to be filled with more meetings and communication tasks, creating new time-wasting traps.

Establishment of a Personal Time Value Assessment System

AI-based personal time value assessment will become a standard service. How much is your attention worth per hour? Which apps are “losing” your time? These questions will have precise numerical answers.

6 Counterattack Strategies: How to Maintain Initiative in the Time Heist

When giants divide your time with algorithms and money, what are your counterattack weapons?

In this time heist, most people are in a passive position. However, those who understand the rules of the game can turn passivity into initiative and even benefit from it.

Personal Defense Section: Establishing a Time Firewall

Strategy 1: Quantify Time Value Price your own time and evaluate the ROI of each app based on hourly rates. If your hourly wage is 100 yuan, then spending an hour on TikTok has an opportunity cost of 100 yuan. This quantitative mindset will naturally filter out low-value time consumption.

Strategy 2: Reverse Utilization of AI Tools Don’t let the time saved by AI be consumed by other apps. After using ChatGPT to write a proposal, immediately turn off the computer and engage in offline activities. Invest the time saved with Claude into deep learning or exercise. The value of AI isn’t in making you consume more content, but in creating space for a high-quality life.

Strategy 3: Diversified Attention Investment Don’t concentrate your attention on a single platform. Use multiple AI tools (ChatGPT, Claude, Gemini) simultaneously to avoid being shackled by a single algorithm. Regularly clean out algorithmic recommendation records and reset personalized tags.

Entrepreneurial Opportunities Section: Arbitraging Computing Power and Flow Dividend

Opportunity 1: Arbitraging Computing Power Price Differences The secondary market GPU prices are five times cheaper than cloud providers, offering significant cost advantages for small to medium teams. Locking in low-cost coupons for 500-1000 GPU hours is advisable; the arbitrage window is expected to close within 3-6 months.

Opportunity 2: First-Mover Advantage in AI Characters Character.AI and Meta AI Studio are still in a period of traffic distribution dividends, making it easy for quality AI characters to secure recommendation positions. Seizing first-mover advantages in niche fields and building user stickiness before rising platform traffic costs can allow for a successful cold start.

Opportunity 3: Enterprise AI Training Services Most companies’ usage of AI tools remains superficial, with huge demands for deep integration and customization. Focusing on “prompt engineering + design collaboration” in corporate training can enhance client efficiency while solidifying taste barriers.

Corporate Layout Section: From Passive Adaptation to Proactive Strikes

Large Corporations: Establish CAO (Chief AI Officer) Integrate strategic planning across the three dimensions of talent, computing power, and compliance. The core responsibility of the CAO is not technical implementation, but ensuring that the time released by AI is effectively utilized within the company rather than flowing to competitors.

Brand Owners: Experiment with Conversational Advertising Traditional banner ads will fail in the AI era because user attention is occupied by AI characters and personalized content. Conversational advertising—where AI characters subtly embed brand information—will become a new monetization model.

Content Entrepreneurs: Claiming Authority over AI Toolchains Don’t just be consumers of AI content; become early promoters and opinion leaders of AI tools. During this period of declining computing costs, establish your own AI content production pipeline to seize pricing power in the personal brand landscape of the AI era.

Conclusion | Action List: Defending the Sovereignty of Time

Time is the only truly scarce resource and the only wealth that cannot be stored. At this critical juncture where AI is reshaping the allocation of time, everyone faces a choice: will you let algorithms determine your time value, or will you actively maintain pricing power?

Final Thoughts

Reed Hastings stated that Netflix’s competitor is sleep, but what he didn’t say is that when sleep too is quantified, optimized, and monetized, what remains that truly belongs to humanity?

Perhaps the answer lies in this very moment as you read this article—maintaining independent thinking amidst the flood of information, insisting on subjective choices within algorithmic recommendations, and safeguarding depth of experience amidst efficiency tools. The war for time sovereignty has already begun, and you are the ultimate decision-maker in this battle.

This isn’t just an article about technology; it’s a declaration of freedom.

Data sources for this article: Data.ai, The Information, NVIDIA, Meta financial reports, Stanford Digital Economy Lab.