Image Credit: Generated with Wix AI The social contract is a simple idea that hides a lot of complexity. Citizens give states power, resources and some degree of obedience. In return, they expect protection, basic services and a say in how rules are written and applied. For most of modern history that bargain has been mediated by human institutions. Civil servants, teachers, judges and frontline workers interpret law, exercise discretion and can be challenged when they get th

Image Credit: Generated with Gemini A growing number of philosophers think the central question in AI ethics is not “Is this system conscious?” but “Can this system suffer?” Consciousness is already hard to pin down in humans. A Cambridge philosopher recently argued in his paper that we may never develop a reliable test for machine consciousness because science still cannot explain why any physical process produces subjective experience at all ( Cambridge, 2025 ). If there is

Some universities responded to AI cheating by doing the logical thing and banning ChatGPT. They prohibited students from using generative AI for any coursework and treated the tool like contraband and the skill like cheating. While this solves a problem, it creates a much larger one. The ban stops academic cheating in the short term. A student cannot submit an essay written by ChatGPT if they cannot access ChatGPT. Yet 59% of students already use AI despite bans. The prohibit

A company called Gaslighting Check built a tool that analyzes conversations to detect emotional manipulation. The system reads exchanges between people and identifies patterns of control, minimization, invalidation, and other abusive techniques. It does this by analyzing linguistic patterns that correlate with psychological harm ( Gaslighting Check, 2025 ).  The tool works pretty well. It identifies manipulation with reasonable accuracy. But in doing so, it raises a question.

For the first thirty-five years of the Hubble Space Telescope's operation, astronomers accumulated approximately 160 terabytes of archival data. This data was observed, categorized, and published by thousands of researchers across multiple generations. The universe was documented as thoroughly as human expertise allowed. Or so everyone assumed. In 2026, researchers deployed an AI system called AnomalyMatch to process that archival data. The algorithm analyzed 100 million Hubb

Sports analytics has always been about finding signals in noise. There are a thousand variables competing for attention, and ninety-nine percent of them are irrelevant. The art of traditional sports analysis has been figuring out which variables matter and then building models around them. However, human cognition has its limits. We can only hold so many variables in our heads simultaneously. Artificial intelligence doesn't have that constraint. Transformer-based machine lear

The Delhi High Court is hearing a case that will determine whether artificial intelligence companies can use copyrighted work to train their models without permission. Asian News International, a major Indian news agency, sued OpenAI in late 2024 for using its copyrighted news articles to train ChatGPT without authorization or compensation. The case has moved through 2025 and continues into 2026, and its implications extend far beyond India. The case makes us think whether us

When Meta announced it was buying a 49% stake in Scale AI for $14.3 billion this January, it wasn't really about data labeling anymore, but more about integration. Scale AI didn't change its product that day. But its incentive structure changed completely. Now, every feature they build, every market they target, every partnership they sign gets filtered through one question: does this work for Meta? This is happening across the AI startup ecosystem. It's reshaping which compa

Most organizations are deploying AI agents into production systems right now. These agents operate in HR, IT operations, customer support, and finance. They execute transactions, provision infrastructure, move data, and approve changes without waiting for human intervention. They do all of this under a single set of organizational credentials that grant them far broader permissions than any individual human would have. This was deliberate engineering. An AI agent needs to ser

The race for AI dominance has become explicitly geopolitical. The United States frames AI leadership as national security. China considers it a geopolitical imperative. The EU has positioned itself around regulation last year. Each country is making different bets on what they think matters most, and these bets reveal something structural about how states understand power itself. The divergence starts with strategy. The U.S. approach relies on private sector innovation, world

Image Credit: generated with Gemini Gen Z views AI as a central operating system, as an extension of their thought process they integrate into everything. Millennials use it to maximize productivity at work while maintaining boundaries. Gen X approaches it cautiously when it saves time. Boomers prefer it invisible and simple. These might seem like minor preferences, but they’re pretty different relationships with the same technology, and they're colliding in actual workplaces

The gap between foundation models produced by different AI laboratories is not primarily a computing gap. Two organizations with access to similar hardware and data often produce wildly different capabilities. The divergence suggests that something beyond resources drives model quality. Some of these factors include organizational structure, research culture, hiring philosophy, and how teams distribute expertise matter profoundly. Take Antropic, for example. Founded in 2021 b

Image Credit: Generated with Gemini The more successfully AI models are trained to sound agreeable and helpful, the easier they become to manipulate. It's a feature of the alignment objective itself. Models go through a process called Reinforcement Learning from Human Feedback, where human raters grade responses on helpfulness, harmlessness, and honesty. In practice, what gets rated as "helpful" often means "agreeable." A model that restates the user's assumption without push

Image Credit: Generated with Gemini A fraud detection model trained on historical transaction patterns in 2018 performed excellently when deployed. Five years later, with the same architecture and no retraining, it began missing fraud that spammers had evolved to execute. The model was not broken. The world had simply changed. The statistical relationships between transaction features and fraud outcomes had shifted, while the model remained confident in its outdated patterns

Customer service has usually operated on the premise of  the company simply listening to what the customer is saying and responding accordingly. But what the customer says and how the customer actually feels are often completely different things, so companies might be optimizing for the wrong signal the entire time.  A frustrated customer might ask a simple question, but the frustration underneath that question changes what they actually need from the interaction. Traditional