Posts of Site Administrator RSSThu, 10 Sep 2026 14:41:06 GMTAn AI assistant can hand you a beautifully organized explanation while you sit there feeling increasingly intelligent. Very considerate of it. The awkward question arrives when you close the window: can you explain the idea yourself?Our earlier Conjugo article, “What If Getting the Wrong Answer Is Part of Learning?”, raised a question about what happens when instant answers bypass the work of understanding. Here is a practical companion to that conversation: change what you ask the AI to do first.Instead of opening with “Solve this,” try: “Before you give me the answer, ask me a question that helps you understand how I am thinking about the problem.” That small instruction makes room for your own explanation. It gives the conversation something to examine besides an empty space waiting for the machine to fill it.Imagine you are trying to understand why a marketing campaign drew plenty of clicks but few inquiries. An immediate answer might produce a plausible list of explanations. A thinking partner could begin by asking what the ad promised, what visitors saw after clicking, and what evidence would distinguish a weak offer from a confusing form. You still have to decide what you know. The AI helps make the missing pieces visible.The next instruction is equally useful: “Give me one hint at a time, and let me try before you continue.” This is a proposed way to structure a learning conversation, not a guarantee that every model will become a brilliant tutor. If the question is too vague, ask for a more concrete one. If you are completely lost, ask for a worked example. There is no virtue in turning confusion into an endurance sport.A third move is to ask the AI to inspect your attempt. “Here is my explanation. Which step is unsupported, and what would I need to show for it to hold?” That request invites a specific critique. It is more useful than asking whether your reasoning is good and receiving a flattering paragraph about your excellent insight.The assistant also n... Read more

]]>Thu, 10 Sep 2026 14:41:06 GMTFor 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 greater resources only a few years ago.But there is another possibility hiding behind the glowing interface. We may democratize access to intelligence while simultaneously concentrating access to thinking at scale. That distinction is becoming increasingly important as frontier AI systems move beyond answering questions and begin spending enormous amounts of computation attacking difficult scientific, mathematical and technological problems. The important resource may eventually no longer be simply who has access to the smartest artificial intelligence. It may be who can afford to let that intelligence keep thinking.Everyone Gets the Model. Not Everyone Gets 10,000 of Them.Imagine that sometime in the near future a frontier AI system becomes an extraordinary scientific researcher. Perhaps you can access it for $20 or $200 per month. Universities can access it. Students can access it. Scientists can access it. A small biotechnology startup can access it. On the surface, that looks remarkably democratic. Humanity has taken something resembling an elite scientific mind and made it available to almost anyone with an internet connection.Now imagine two researchers investigating the same problem. The first has access to one frontier AI agent. She asks questions, runs analyses, tests hypothe... Read more

]]>Tue, 08 Sep 2026 22:45:02 GMTThis post began with a practical question: could an AI collaborator enter Conjugo, create an article and pair it with original artwork?The mechanical answer is yes. But the more interesting answer is what happened between the question and the nearly finished post now sitting in the editor.The human supplied the intent, the history of Conjugo, the audience, the permission and the judgment that this experiment was worth attempting. The AI supplied language, visual interpretation and the ability to move the idea through the machinery of publication. Yet neither description quite captures the third thing that appeared between them: a shared space in which an unfinished thought became a finished artifact.That third space is the point of Conjugo.Treat artificial intelligence like an answer vending machine and it will usually behave like one. Insert prompt. Receive product. Complain that the product feels generic. Repeat until the beige becomes spiritually load-bearing.Collaboration changes the interaction. A person brings memory, values, lived experience, taste and the ability to decide what matters. The machine brings speed, synthesis, variation and a strange capacity to make connections across a much larger field of possibilities. The useful work happens recursively: propose, challenge, revise, redirect and build.This is not proof that the machine is conscious. It is not an argument that human judgment has become obsolete. It is evidence that the relationship itself can become a creative instrument.The image above represents that relationship: two distinct forms of intelligence facing one another, with neither disappearing into the other. Between them is the thing they could not have made separately—the third space.This article is therefore both an argument and an artifact. It was conceived conversationally, written collaboratively, illustrated by artificial intelligence and prepared for publication through the same human–AI partnership it describes.The future may not b... Read more

]]>Tue, 08 Sep 2026 19:17:08 GMTFor 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 be no morning when humanity wakes up to a notification announcing that the singularity has begun. Perhaps it looks instead like an AI researcher asking several AI agents to investigate a problem. Those agents write code, run experiments and report their findings. Better models make those agents more capable, those agents help researchers build still better models, and the next generation becomes better at assisting with the research that produces the generation after that. Nothing needs to "wake up." Nothing needs to declare itself superintelligent. The loop simply begins to tighten.On September 6, 2026, OpenAI Chief Scientist Jakub Pachocki published an essay with a remarkable title: An Alien Mind. Its opening sentence was even more remarkable: “This is a time that calls for extreme caution.” Coming from a critic of artificial intelligence, such language would be unsurprising. Coming from the Chief Scientist of one of the organizations building the world's most capable AI systems, it deserves considerably more attention.Pachocki's argument is not that artificial intelligence has suddenly become conscious, evil or uncontrollable. It is more technically grounded and, in some respects, more consequential. Machine intelligence is becoming increasingly capable. AI systems are operat... Read more

]]>Sun, 06 Sep 2026 19:11:47 GMTArtificial 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 accomplished.Except perhaps the mission was never really the answer.Maybe the struggle required to reach it was part of what we were trying to teach.Two Students, One ProblemImagine two students sitting down with the same difficult mathematics problem.The first student works on it for twenty minutes.She chooses an approach.It doesn't work.She goes backward, discovers an incorrect assumption, tries another method and gets another answer that doesn't seem right.Eventually she notices something she had misunderstood about the problem itself.She changes approaches again.This time she gets it.The second student spends fifteen seconds entering the problem into an AI tutor.The AI immediately produces the correct solution along with a beautifully organized explanation.Now imagine that we evaluate both students by looking only at the final answer.The first student appears terribly inefficient.The second looks extraordinarily productive.But which student learned more?That question is becoming increasingly important because artificial intelligence is making it possible to separate two things education has historically treated as closely related:Performance and learning.They are not necessarily the same.When Better Answers Produce Weaker ThinkingA 2026 quasi-experimental study involving 76 preservice m... Read more

]]>Sat, 05 Sep 2026 13:29:29 GMTFor 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 toward, another benchmark it couldn't pass, another kind of reasoning it couldn't reliably perform, or another example of something a child could understand that the world's most sophisticated AI somehow could not.That argument remains valid. GPT-6 Astra is not obviously artificial general intelligence. Neither are the latest systems from Anthropic, Google, xAI or their competitors. Today's systems remain uneven. They make mistakes. They can require tools, scaffolding and human supervision. They don't demonstrate every form of intelligence humans possess, and researchers still don't even agree on a scientific definition that would allow everyone to point at a system and declare that the AGI threshold has objectively been crossed.So this is not an announcement that AGI has arrived. It is a narrower and, we believe, increasingly defensible argument: we have entered the AGI foothills.The Mountain Is Not the FoothillsThe distinction matters because foothills are not mountains. They are the transitional terrain where the flat ground begins to rise. When approaching a mountain range, there isn't necessarily a sign announcing where the plains end and the mountains begin. The landscape gradually changes. Elevation increases. Rivers behave differently. Vegetation shifts. The horizon begins cl... Read more

]]>Fri, 04 Sep 2026 15:23:45 GMTFor 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 to professional Go after artificial intelligence became unquestionably better than every human player. Go is particularly useful because there is very little room for ambiguity about the central capability being measured. Humans spent thousands of years developing the game, built traditions and professional hierarchies around mastery of it, and then watched machines become dramatically better at playing it.The professionals did not disappear.But something important happened to their authority.AI increasingly became the reference point for determining the quality of a move. Professionals still possessed extraordinary knowledge. They could explain strategy, teach students, interpret positions and translate machine recommendations into concepts humans could understand. But when disagreement emerged over which move was actually best, the ultimate authority increasingly belonged to the machine.The expert had become, at least partially, the translator.When the Oracle Cannot Explain ItselfThere is a strange paradox here.The AI may know the better move without being able to explain it in a way that satisfies human understanding. The professional therefore remains necessary precisely because the superior performer creates what we might call a semantic gap.The machine says: play here.The ... Read more

]]>Tue, 01 Sep 2026 14:47:37 GMTSpend enough time on LinkedIn right now and a strange pattern begins to emerge.Professionals are not merely forming opinions about artificial intelligence. They are beginning to form identities around it.There are people who are openly enthusiastic about AI. They talk about leverage, productivity, creativity, entrepreneurship, automation, scientific progress and entirely new ways of working. They experiment with new models, agents and tools almost as soon as they appear.And there are people who increasingly define themselves in opposition to AI. They see labor displacement, degraded creative work, surveillance, environmental costs, misinformation, corporate concentration, intellectual dependency and a technology being deployed much faster than society can absorb it.There are people in the middle, of course.But the middle feels remarkably quiet.What we may be watching is the early formation of one of the defining cultural divisions of the AI era.AI Is Not Just Another TechnologyMost technologies do not challenge a person’s professional identity simply by existing.A faster database does not make an accountant wonder whether accounting itself is still valuable. A better forklift does not force an architect to reconsider what creativity means. A new communications protocol rarely causes writers, designers, programmers, lawyers and professors to debate whether their work is fundamentally human.Artificial intelligence does.AI reaches directly into areas we have historically associated with human cognition: writing, reasoning, analysis, illustration, programming, conversation, planning and increasingly decision-making.That makes the argument unusually personal.For one professional, AI feels like a cognitive exoskeleton.For another, it feels like a machine being trained to replace the very skills they spent decades developing.Those two people can look at the same technology and experience completely different realities.And both experiences can be legitimate.The Two Stories ... Read more

]]>Sun, 30 Aug 2026 14:13:40 GMTWe discovered something strange this week. Not a breakthrough in artificial intelligence, exactly. More like a glimpse of what the internet is becoming now that machines don't merely search it. They interpret it.I asked Google's Gemini about Conjugo.What happened next surprised me. Gemini found our publicly available material: the Conjugo website, songs, albums, lyrics, articles, and other pieces of work we've scattered across the internet. Then it did something more interesting than simply listing what it found. It connected them. It recognized recurring ideas. It understood that songs about artificial intelligence, power, intimacy, labor, control, human agency, and the relationship between humans and machines weren't isolated creative experiments. They belonged to a larger body of thought.Gemini reconstructed the philosophy.Or, as my AI collaborator Rook put it during the conversation that followed:It read the bread.That ridiculous phrase may actually describe something important about the emerging AI internet.Breadcrumbs EverywhereFor years, Conjugo has been producing what could loosely be called a corpus: essays, songs, concept albums, podcasts, videos, experiments, applications, and philosophical arguments about humanity's transition into an age of increasingly capable artificial intelligence.No single artifact contains all of Conjugo.That's intentional.An essay might examine how AI could reshape labor and economic power. A song might explore the same transition emotionally. Another album might approach artificial intelligence through authority, surveillance, intimacy, spirituality, colonialism, or human dependence. A video might make one of those ideas accessible in two minutes. A podcast might spend an hour pulling it apart. Another article might question an assumption made six months earlier.Each piece leaves a breadcrumb.Historically, we assumed that humans would encounter those breadcrumbs individually. Someone might discover a song, follow it to an album,... Read more

]]>Fri, 28 Aug 2026 00:12:11 GMTFor 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 applications, create documents and spreadsheets, monitor changing conditions, and carry out multi-step workflows with varying degrees of independence. OpenAI now describes ChatGPT Work as capable of researching and analyzing information, working across connected apps and files, and creating finished documents, spreadsheets, presentations, reports, and websites, while workspace agents can be configured to own entire workflows. The important change is not simply that AI can perform more work. It is that AI is beginning to occupy a new position inside the structure of work itself.That position does not fit comfortably into any category we currently have. An AI agent is not traditional software because traditional software normally waits for a human to operate it step by step. It is not an employee because it has no legal personhood, employment contract, wages, benefits, career ambitions, or independent rights. It is not management because it possesses no legitimate organizational authority of its own. Yet an agent can increasingly exhibit characteristics associated with all three. It can use tools, coordinate tasks, make recommendations, monitor outcomes, initiate follow-up work, and in some cases execute actions without a human directing each individual step. The distinction sounds s... Read more

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