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OpenAI is asking third-party contractors to upload “real assignments and tasks” from current or past jobs—ideally the actual deliverables.
An incentive structure that predictably pulls confidential work product into an AI lab’s orbit while delegating the hardest compliance judgments to the least protected people in the chain.
Apple and Google could act against X for facilitating nonconsensual sexual deepfakes (including content involving children), their own rules appear to demand action and yet they won’t because...
...the political and economic downside is too high. ChatGPT agrees with the article’s implication that the refusal to enforce app-store rules in the face of high-severity abuse is a leadership failure
A private investor platform can effectively set national tech priorities—especially in AI and defense—without public debate, parliamentary oversight, or enforceable transparency.
When informal political access becomes a competitive advantage, it erodes ethics norms, encourages a revolving-door ecosystem, and turns public staffing into an investable supply chain.
When “AI Companions” Become a Consumer-Protection Case Study: What Kentucky’s Character.AI Lawsuit Signals for Global Regulators and Developers.
Kentucky argues that Section 230 shouldn’t apply because the harm arises from the developer’s design and the model’s generated dialogue. Character.AI intentionally engineered to blur reality.
Can production, consumer-facing LLMs (with guardrails) be induced to emit long, near-verbatim copyrighted text that strongly indicates memorization and training-data membership?
Sometimes, yes—and at surprisingly large scale. And that matters because multiple courts and litigants have struggled with the evidentiary problem of proving “legally cognizable copying” via outputs.
The Goldberg/Martin case will likely serve as a pivotal case study for the next decade of cybersecurity regulation and insurance.
This case proves that the RaaS model has evolved. It is no longer just about “hackers vs. companies.” It is about criminal recruitment within the supply chain.
Current AI systems excel at talking about science, not doing science. AI is strongest when it supports thinking, not when it pretends to execute science.
Overpromising risks: eroding trust in AI, misallocating research funding, and fueling future “AI winters” driven by disappointment.
Authoritarian reflexes are not required to defeat extremist ideologies. Transparency, competence, lawful investigation, and infrastructural accountability are often sufficient.
Extremism, when exposed to light and reality, frequently collapses under the weight of its own contradictions.
In this paradigm, international law functions not as a constraint on power but as a mechanism to issue modern “letters of marque,” transforming acts of predation into acts of justice.
The geopolitical landscape of 2026 has been ruptured by a series of events that, while shocking to the contemporary observer, resonate with a profound historical familiarity.
Gemini: The likelihood that Republicans will have no choice but to create a framework now that prevents Democrats from ever getting into power again; probability: 60% (Moderate-High Likelihood).
And a nearly 1-in-5 chance that the conflict described does not resolve through a “permanent majority” for either side, but through the collapse of the democratic mechanism itself.
The STAT series ultimately documents more than an attack on science. It reveals how knowledge systems fail first when democratic norms erode. A nation that dismantles its capacity...
...to generate and communicate truth, does not merely fall behind technologically. It forfeits the future. And once forfeited, that future cannot simply be voted back.
The Enshittifinancial Crisis is not merely a critique of AI, but a diagnosis of a financial system that has lost its capacity for self-correction.
Its most important contribution is the warning that enshittification is no longer confined to apps and platforms—it now defines how capital itself is allocated.