A practical Conjugo experiment: ask AI to question your reasoning, offer one useful hint, and help you test what you understand. The goal is to leave the conversation more capable.

For years, one of the most seductive promises surrounding artificial intelligence has been democratization. AI, we are told, will put capabilities once reserved for corporations, universities, governments and wealthy institutions into the hands of everyone. A person with a laptop will have access to a programmer, researcher, designer, analyst, tutor and scientist. Small businesses will compete with corporations. Individuals will build things that once required entire departments. Knowledge will become abundant. And there is considerable truth in that vision. Artificial intelligence is already allowing individuals and small organizations to accomplish things that would have required far great

A live Conjugo experiment in human–AI collaboration: one idea, two kinds of intelligence, and a finished artifact neither participant would have made alone.

For years, the technological singularity has been presented as an event somewhere over the horizon. One day, the story goes, artificial intelligence becomes smarter than humanity. Perhaps it becomes capable of improving itself, technological progress accelerates beyond our ability to follow it, and somewhere along that curve sits an invisible boundary beyond which prediction becomes increasingly meaningless. It is an idea that has lived comfortably in science fiction, futurist books, academic arguments and, increasingly, serious discussions inside the laboratories actually building advanced artificial intelligence.But perhaps we have been looking for too dramatic a moment. Perhaps there will

Artificial intelligence may be creating a strange new problem for education.It is getting too good at giving students the right answer.That sounds ridiculous. For centuries, education has been organized around helping people arrive at correct answers. Teachers explain concepts. Students practice them. Exams measure whether they understood them. Wrong answers are corrected and right answers are rewarded.Then artificial intelligence arrived.A student can now encounter a difficult mathematics problem, ask an AI system for help and receive a polished explanation within seconds. The equation is solved. The steps are displayed. The reasoning appears clear. The answer is correct.Mission accomplishe

For years, artificial general intelligence has existed somewhere over the horizon. Researchers have argued about how to define it, technology companies have predicted when it might arrive, skeptics have questioned whether it is even possible, optimists have promised abundance, and pessimists have warned of catastrophe. Through all of it, AGI remained comfortably distant. Artificial intelligence could write an essay, generate an image, pass professional examinations, write sophisticated software, interpret medical images or defeat increasingly difficult benchmarks, and we could continue arguing that none of it constituted general intelligence. There was always another limitation to point towa

For most of modern history, expertise has carried two kinds of authority at once.An expert was someone who understood a domain better than most people, but also someone whose judgment deserved greater weight because of that understanding. We trusted the physician because the physician knew medicine. We trusted the engineer because the engineer understood structures. We trusted the lawyer because the lawyer understood the law. Expertise and authority were not identical, but they usually traveled together.Artificial intelligence may be pulling them apart.Professional Go offers an early glimpse of what that could look like.Researchers recently published a nine-year study examining what happened

The debate over artificial intelligence is beginning to look less like a disagreement about technology and more like an emerging identity divide. On LinkedIn, increasingly distinct pro-AI and anti-AI camps are developing their own language, assumptions and cultural signals, including the anti-AI shorthand “AI slop.” Beneath those arguments are deeper questions about professional status, economic security, creativity, power and what remains uniquely valuable about human work. The challenge ahead may be resisting both reflexive AI boosterism and reflexive rejection long enough to build a more thoughtful relationship with increasingly capable machines.

What Happens When AI Can Reconstruct a Philosophy From the Digital Breadcrumbs We Leave Behind

For most of the artificial intelligence era, the public conversation about work has revolved around a deceptively simple question: Will AI replace human workers? It is an understandable question, because automation has historically been discussed in terms of substitution. A machine performs a task that a person once performed, productivity rises, and some jobs disappear while others emerge. But the newest generation of AI systems is beginning to make that framework feel inadequate. Artificial intelligence is no longer confined to answering questions, generating text, or performing isolated tasks. AI agents are increasingly able to research information, analyze files, work across connected ap

For most of human history, making culture was difficult. A musician had to learn an instrument. A painter had to acquire technique. A writer needed time, language, persistence, and usually a very tolerant relationship with revision. Film required actors, cameras, editors, sets, money, and an army of people whose names appeared in tiny letters at the end. Even relatively simple creative work carried friction. Creation was constrained by skill, labor, cost, access, and time.Artificial intelligence is rapidly dismantling that scarcity.Today, a person can describe a song and hear something resembling a finished recording minutes later. An image can move from idea to polished illustration almost

Companies are beginning to convert workers’ conversations, judgment, relationships, and experience into permanent AI assets. What happens when the knowledge stays, but fewer workers do?

Companies are beginning to convert workers’ conversations, judgment, relationships, and experience into permanent AI assets. What happens when the knowledge stays, but fewer workers do?

Generative and agentic AI failures dominate headlines, while millions of successful everyday uses quietly become part of normal work. The real lesson is not that AI has failed, but that we are still in the messy early stages of learning how to combine machine capability with human judgment, oversight, and experience.

How AI chips in phones, handhelds, consoles, and PCs could reshape game design, production, storytelling, and business models