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Invisible Work: The Labor AI Systems Don’t Eliminate
In many organizations, AI systems now sit directly inside everyday workflows. Drafts are generated before meetings begin. Reports appear before requests are fully specified. Decisions arrive…
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The Rise of Human Fallback Labor in AI-Driven Work
In deployment reviews, exception dashboards, and post-incident reconciliations, teams often encounter a quiet discrepancy. Systems described as automated depend on steady streams of human…
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What Audiences Are Actually Trusting When They Follow a Virtual Influencer
Virtual influencers are evaluated in concrete settings: brand reviews, platform moderation decisions, sponsorship approvals, and audience engagement dashboards. In these environments, they…
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Why Platforms Quietly Govern Virtual Influencers Differently
Virtual influencers appear in the same feeds as human creators, are managed through the same creator tools, and are subject to the same published platform policies. In reviews, policy…
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The Human Work Required to Run a “Synthetic” Influencer
In brand reviews, content calendars, and platform risk discussions, virtual influencers are often treated as low-friction assets: digital entities that can post continuously without fatigue,…
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When Virtual Influencers Stop Being Cheaper Than Humans
In marketing budget reviews and brand planning discussions, virtual influencers are often justified on economic grounds. Compared to human creators, they appear more predictable, easier to…
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Technology & Platforms
AI’s Frontier Battle: The Breaking Point of the Model Arms Race
In late 2025, a product team running an AI-powered operations agent encountered an unexpected failure in production. The system had worked reliably for months. Then, overnight, without any change to their own code, task execution slowed, tool calls became inconsistent, and the agent began…
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How Roblox Uses AI Inside Studio to Shorten Iteration and Reduce Setup Work
For many players, Roblox looks like a gaming platform. For creators, it functions more like a live production environment. Experiences are shipped, updated, and monetized continuously, often by teams small enough that every inefficiency becomes immediately visible. In that context, the…
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Why Frontier AI Competition Is Pushing Models Toward Riskier Behavior
Frontier AI development rarely happens in isolation. Teams building and evaluating large-scale models are responding to competitors, benchmarks, public announcements, and release cycles that reward visible progress. When one organization announces a capability jump, others feel pressure…
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Business & Markets
When Virtual Influencers Stop Being Cheaper Than Humans
In marketing budget reviews and brand planning discussions, virtual influencers are often justified on economic grounds. Compared to human creators, they appear more predictable, easier to reuse, and less exposed to the risks that come with independent talent. Early planning commonly treats…
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Why Global Investors Are Looking to Chinese AI as U.S. Tech Valuations Stretch
By late 2024, during routine year-end portfolio reviews, many global portfolios showed an unusual feature: a large share of their growth exposure was effectively tied to a small group of U.S. AI stocks. Names associated with cloud infrastructure, advanced chips, and foundation models…
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The Hidden Cost of AI Infrastructure: What Capex Numbers Don’t Show
AI infrastructure spending is often described through headline capital expenditures: billions committed to GPUs, data centers, and networking gear. Those numbers feel concrete and reassuring to investors, executives, and planners making long-term bets. They also create a false sense of…
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Organizations & Operations
The Human Work Required to Run a “Synthetic” Influencer
In brand reviews, content calendars, and platform risk discussions, virtual influencers are often treated as low-friction assets: digital entities that can post continuously without fatigue, inconsistency, or personal risk. The underlying assumption is that once a synthetic persona is built,…
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What Hyundai’s CES 2026 AI Robotics Reveal Signals About the Shift From Robot Demos to Real Deployment
Robots are often described as “almost ready.” They move fluidly in videos, perform complex motions on controlled stages, and generate excitement at technology events. The assumption is that the remaining gap between demonstration and deployment is small. In practice, that gap is…
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When AI Adoption Stops Making Sense: Where the ROI Breaks Down
AI adoption is often framed as a one-way curve: more capability leads to more value, and falling behind is not an option. This framing shows up frequently in boardrooms, product roadmaps, and internal strategy discussions. It assumes that additional AI adoption is always economically…
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Policy & Society
What “Responsible AI” Actually Means Inside Big Tech Companies
An AI product team prepares to launch a new model feature. Before release, the system is routed through an internal review: risk documentation is updated, legal weighs in on potential misuse, policy teams flag edge cases, and a cross-functional committee debates whether safeguards are…
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Why AI Regulation Struggles With Models That Change After Release
Most regulatory systems are built around stable products. A device is approved, a drug is certified, or a financial instrument is licensed based on a fixed design. AI models do not fit this pattern. Once deployed, they continue to change, complicating how AI outputs are trusted…
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The AI “Misuse Gap”: Why Safety Tools Lag Behind Real-World Abuse
AI systems are increasingly deployed with safety tools, usage policies, and guardrails intended to prevent harm. On paper, these measures suggest a mature and cautious ecosystem. In practice, misuse continues to surface faster than safeguards can realistically respond. The resulting gap…
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Media & Informations
What Audiences Are Actually Trusting When They Follow a Virtual Influencer
Virtual influencers are evaluated in concrete settings: brand reviews, platform moderation decisions, sponsorship approvals, and audience engagement dashboards. In these environments, they often perform well despite lacking lived experience, personal identity, or human accountability. This…
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Why Platforms Quietly Govern Virtual Influencers Differently
Virtual influencers appear in the same feeds as human creators, are managed through the same creator tools, and are subject to the same published platform policies. In reviews, policy briefings, and moderation workflows, they are often treated as just another account type. In practice, that…
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What Virtual Influencers Actually Are — And Why They Exist
Brand and marketing teams assessing creator partnerships increasingly encounter accounts that operate without a human individual behind them. These accounts appear in campaign reviews, content calendars, and platform dashboards alongside conventional creators. The prevailing assumption is…
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Future of Work
Invisible Work: The Labor AI Systems Don’t Eliminate
In many organizations, AI systems now sit directly inside everyday workflows. Drafts are generated before meetings begin. Reports appear before requests are fully specified. Decisions arrive with supporting analysis already attached. The common assumption is that this speed reduces the…
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The Rise of Human Fallback Labor in AI-Driven Work
In deployment reviews, exception dashboards, and post-incident reconciliations, teams often encounter a quiet discrepancy. Systems described as automated depend on steady streams of human intervention: outputs flagged for review, decisions paused for override, results routed to support…
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The Real Impact of AI on Jobs: What’s Actually Changing
Discussions about AI and work often jump to a single conclusion: machines will replace people. This assumption shows up everywhere, from headlines to workplace conversations. It is also a poor description of what most organizations are experiencing. AI rarely arrives as mass…
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