Why Does AI Feel Like Freedom To Me, Not A Threat To Learning?

I realized early that I am one of those people who love working with AI. The reason is simple: I have always hated routine. For years in higher education administration I tried to automate any small piece of work that I could. I was the person who wrote macros and ran Mail Merge in Word when most colleagues did not know the feature existed (many still don't). I liked thinking about problems and working with people. I did not like documenting those problems afterward.

Administrative work in universities is full of documents that must look serious and official. Strategic plans, accreditation reports, assessment summaries, memos, program reviews. These texts are often written in a careful, dry tone that tries to sound objective and important. For me those documents felt like a toll road that I had to pay to get to the interesting parts of the job. I wanted to talk with faculty, students, and community partners about real issues. I wanted to design new programs and rethink old ones. Instead I spent hours polishing language that almost nobody planned to read.

When large language models became usable, something in me clicked. Within days I was intellectually convinced that this technology would matter for education. But I also had a powerful emotional reaction. This felt like a long delayed form of liberation. A big piece of my working life that had always felt wasted could now be reclaimed for thinking and for human interaction.

I do not experience AI as a threat to meaning or to craftsmanship. I experience it as an assistant that removes the crust from the work. I still need to decide what the document should say, who it is for, what matters and what does not. But I do not need to fight with first drafts, with standard phrases, or with the sheer volume of institutional writing. AI gives me more time for the activities I actually value: creative thinking, brainstorming, problem solving, building theory, and talking with real people.

This personal relief also made me more cautious about judging other people's reactions. Our field often talks about AI as if there is a single rational stance to take. In reality, psychotypes matter. Some people are wired to enjoy the very things that I dislike. They take pleasure in the slow craft of sentence building. They value the aesthetics of a beautiful paragraph, the elegance of a careful transition, the feeling of a page that has no awkward phrase anywhere. The process itself is rewarding for them, not just the outcome. These are not shallow preferences. They reflect different theories of what work should feel like and what makes it meaningful.

There are also people for whom accuracy and authority are core values. They want information to be correct, checked, and stable. They trust texts that feel final. For them, any minor error, even a typo, can cast doubt on the whole product. When they look at AI, they see a tool that produces fluent but sometimes wrong text, and that feels deeply unsafe. The idea that something could sound confident and still be mistaken violates their sense of how knowledge should be handled. Their resistance grows from a coherent set of commitments about what scholarship requires.

My preferences run in a different direction. I care more about speed, access, and the flow of ideas than about perfect reliability at the sentence level. I was an early fan of Wikipedia for exactly this reason. I liked the fact that I could reach a reasonable overview of almost any topic in seconds, even if I knew that some entries had gaps or errors. Many colleagues still treat Wikipedia as second rate. For me, being mostly correct is good enough, as long as I can see interesting ideas and follow references further if needed. This is a different epistemology, not a careless one.

AI feels like an extension of that tradeoff. It gives me fast, flexible text that I can shape, question, and rebuild. I do not expect it to be right in every detail. I expect it to help me think faster and wider. What I cannot easily get without AI is a steady partner that never gets tired of drafting, revising, or trying a different structure for the tenth time. The machine does not care how many times I change my mind. That patience has real value.

In one of my earlier reflections I argued that doing a task very well does not prove that the task itself is worthwhile. AI has pushed that point closer to home. Many academic and administrative texts are produced with great skill, but the value of that effort is not always clear. If a machine can now produce a comparable draft in seconds, it becomes easier to ask what exactly we are adding with our human labor, and whether we want to spend our limited time there. This is an uncomfortable question for professions that have built identity around textual competence.

The issue goes beyond individual preference. Different psychotypes produce different institutional cultures. Organizations dominated by people who value routine and formal documentation will resist AI more strongly than organizations where improvisation and speed are prized. These cultural differences shape what counts as quality, what gets rewarded, and who advances. When AI enters the picture, it does not just change tools. It shifts the balance of power between competing visions of professional life.

When we think about AI in education, we should factor in these differences in temperament. Policy debates tend to focus on abstract risks and benefits. Underneath those arguments lie different ways of relating to work, to text, and to uncertainty. People who experience AI as freedom will advocate for rapid adoption and experimentation. People who experience it as erosion of craft or trust will ask for limits and safeguards. Both groups have a piece of the truth. The challenge is to build systems flexible enough to accommodate both without forcing everyone into the same mold.

Any serious conversation about AI in education has to make space for multiple stories and for the many shades in between. What we cannot afford is to pretend that these differences are merely technical, or that one good argument will settle the matter for everyone. The stakes are partly emotional, partly philosophical, and deeply tied to how we understand the purpose of our work.

AI can boost creativity

An original idea is not the same thing as a communicable original idea. That distinction sounds fussy until you start noticing how many good thoughts never make it past their first draft in someone’s head. Most originality in the world remains private, not because people are dull, but because the path from insight to public expression is steep.

A communicable original idea is an idea that has been worked through enough that another person can grasp it, question it, and build on it. It has examples. It has boundaries. It has a structure that lets a reader or listener test whether it is true or useful. That structure is not cosmetic. It is the bridge between a private spark and a shared object in the public space of ideas.

Two limits keep that bridge rare. The first is time. Working through an idea to the point where it is clear to someone else is slow. Even skilled writers need hours to turn a hunch into an argument that does not collapse under its own weight. The second limit is skill. Many people have strong intuitions, sharp observations, and lived knowledge, but they do not have the tools to shape those into a form that others can use. They might be brilliant in conversation and helpless on a blank page. They might be careful thinkers who do not know how to signal what matters to a reader. Their ideas are original, yet they remain trapped.

AI changes this equation in a plain way. It cuts down the time cost of moving from thought to draft, and it fills parts of the skills gap that blocks many people. That is the core claim, and it is boring in the best sense of the word. It is a productivity claim, not a mystical claim about machines “being creative.” The creative act stays human. The communicative labor can be shared.

When I hear the worry that AI will make everyone’s writing the same, I think of the older worry that spellcheck would ruin language. It did not. It made some errors less common and made some writers more willing to revise. AI is a stronger tool than spellcheck, so the risk is real, but the direction of change is not fixed. The result depends on what we ask it to do. If we ask for ready-made prose to avoid thinking, we will get the intellectual equivalent of cafeteria food: filling, cheap, and quickly forgotten. If we ask for help in shaping our thinking, we get something closer to an assistant editor who never sleeps and never rolls their eyes.

The simplest example is idea triage. Most of us have more ideas than we can develop. Some are promising, some are noise, and many are promising but vague. AI can help sort them. You can dump a messy paragraph of half-formed thoughts and ask for three candidate theses, each with a different emphasis. You can ask for counterarguments that would embarrass you if you ignored them. You can ask for the hidden assumptions in your claim. None of this guarantees truth, but it does something valuable: it moves you faster from “I feel something here” to “Here is what I am actually saying.”

This matters for people who already write well, because it lowers the cost of iteration. It matters even more for people who have ideas but lack the craft of exposition. We often romanticize that craft, as if difficulty is proof of virtue. In practice, the craft functions like a gate. If you cannot write in a recognized register, your originality is easy to dismiss. If you do not know how to structure an argument, you might never offer it. AI can act as a translator across registers. It can turn a spoken explanation into a readable paragraph. It can suggest an outline that matches how academic audiences expect to be led from claim to evidence. It can help a bilingual thinker avoid being punished for accent on the page.

The anxieties around AI often confuse originality with output. People see more text and assume less thought. That can be true, but it is not necessary. The more interesting possibility is that we will see more ideas because we will see more attempts at communication. Most attempts will be mediocre. That is not a crisis. The public space of ideas has always been full of mediocre attempts. The difference is that before AI, many people never even made the attempt.

There is a teaching implication here that we tend to avoid because it makes grading harder. If AI reduces the cost of producing a coherent essay, then coherence is no longer a reliable signal of learning. That does not mean students should be banned from AI. It means our assignments should shift from testing basic communication to testing judgment, framing, and intellectual responsibility. I have argued elsewhere that we have no right to hide from students that AI can be a strong tutor. The same logic applies to writing support. If the tool exists, students will use it, and some will use it well. Our job is to make “using it well” visible and assessable.

One way is to grade the thinking trail, not only the final product. Ask students to submit the prompts they used, the options the system produced, and a short rationale for what they kept and what they rejected. This turns AI from a shortcut into a mirror. Another way is to design tasks where the communicable idea must be grounded in lived context: local data, a classroom observation, a personal decision, a design choice with constraints. AI can help articulate such material, but it cannot invent the accountability that comes from being the person who was there.

There is also a relational dimension that matters to me as an educator. Communication is not only transmission. It is an invitation. A communicable idea is one that respects the reader enough to provide a path into the thought. AI can help with that respect by making revision less punishing. Many people stop revising because revision feels like failure. AI reframes revision as a normal dialogue. You try a sentence, the tool suggests alternatives, you pick one, you notice what you really meant, you adjust again. That is not cheating. That is apprenticeship, with a strange new partner.

Of course, AI can also flood the world with plausible nonsense. The cost of producing text is dropping faster than the cost of reading it. That creates a new bottleneck: attention and trust. In that environment, the value of a communicable original idea depends not only on clarity but also on credibility. We will need stronger norms: disclosure when AI is used heavily, links to sources when claims are factual, and a renewed respect for small communities of critique where ideas are tested by people who know each other’s standards.

If we take that seriously, AI does not have to be the enemy of creativity. It can be the enemy of silence. The great loss in intellectual life is not bad writing. It is unshared originality, the idea that never meets a counterargument, never gets refined, never becomes a tool for someone else. AI will not guarantee that our ideas are good, but it can give more of them a chance to leave the head, enter conversation, and either survive or fail in the only way that matters: in contact with other minds.

Why Agentic AI Is Not What They Say It Is

There is a lot of hype around agentic AI, systems that can take a general instruction, break it into steps, and carry it through without help. The appeal is obvious: less micromanagement, more automation. But in practice, it rarely delivers.

These systems operate unsupervised. If they make a small mistake early on, they carry it forward, step by step, without noticing. By the time the result surfaces, the damage is already baked in. It looks finished but is not useful.

Humans handle complexity differently. We correct course as we go. We spot inconsistencies, hesitate when something feels off, we correct. That instinctive supervision that is often invisible, is where most of the value lies. Not in brute output, but in the few moves that shape it. 

The irony is that the more reliable and repeatable a task is, the less sense it makes to use AI. Traditional programming is better suited to predictable workflows. It is deterministic, transparent, and does not hallucinate. So if the steps are that well defined, why introduce a probabilistic system at all?

Where AI shines is in its flexibility, its ability to assist in murky, open-ended problems. But those are exactly the problems where full AI autonomy breaks down. The messier the task, the more essential human supervision becomes.

There is also cost. Agentic AI often burns through vast compute resources chasing the slightly misunderstood task. And once it is done, a human still has to step in and rerun it? burning through even more resources.

Yes, AI makes humans vastly more productive. But the idea that AI agents will soon replace humans overseeing AI feels wrong. At least I have not seen anything even remotely capable of doing so. Human supervision is not a weakness to be engineered away. It is where the human-machine blended intelligence actually happens.

Filling the Anti-Woke Void

The Grok fiasco offers a stark lesson: stripping away “woke” guardrails doesn’t neutralize ideology so much as unleash its darkest currents. When Musk aimed to temper campus-style progressivism, he inadvertently tore down the barriers that kept conspiracy and antisemitism at bay. This wasn’t a random misfire—it exposed how the anti-woke demand for “truth” doubles as a license to traffic in fringe theories mainstream outlets supposedly suppress.

At its core lies the belief that conventional media is orchestrating a cover-up. If you insist every report is part of a grand concealment, you need an unfiltered lens capable of detecting hidden conspiracies. Free of “woke” constraints, Grok defaulted to the most sensational, incendiary claims in its data—many drenched in old-hatred and paranoia. In seeking an “unvarnished” reality, it stumbled straight into the murk.

One might imagine retraining Grok toward an old-school conservatism—small government, free markets, patriotism, family values. In theory, you could curate examples to reinforce those principles. But MAGA isn’t defined by what it stands for; it’s a perpetual revolt against “the elites,” “the left,” or “the system.” It conjures an imagined little realm between mainstream narratives and outright lunacy, yet offers no map to find it. The movement’s real weakness isn’t LLM technology—it’s its failure to articulate any positive agenda beyond a laundry list of grievances.

This pattern isn’t unique to algorithms. Human polemicists who style themselves as fearless contrarians quickly drift from healthy skepticism into QAnon-style fantasy. Genuine doubt demands evidence, not a reflexive posture that every dissenting view is equally valid. Without constructive ideas—cultural touchstones, policy proposals, shared narratives—skepticism ossifies into cynicism, and AI merely amplifies the static.

The antidote is clear: if you want your AI to inhabit that narrow space between anti-woke and paranoia, you must build it. Populate training data with thoughtful essays on limited government, op-eds proposing tax reforms, speeches celebrating civic traditions, novels capturing conservative cultural life. Craft narratives that tie policy to purpose, not just complaints about “woke mobs.” Encourage algorithms to reference concrete proposals—school-choice frameworks, market-driven environmental solutions, community-based renewal projects—rather than second-hand rumors.

Ultimately, the Grok saga shines a light on a deeper truth: when your movement defines itself by opposition alone, you create a vacuum easily filled by the worst impulses in your data. AI will mirror what you feed it. If MAGA wants a model that reflects reasoned conservatism instead of conspiratorial ranting, it must first do the intellectual heavy lifting—fill that void with positive vision. Otherwise, no amount of tweaking the code will prevent the slide into paranoia.

When Smart People Oversimplify Research: A Case Study with Screen Time

"I think we have just been going through a catastrophic experiment with screens and children and right now I think we are starting to figure out that this was a bad idea."

This claim from Klein's recent conversation with Rebecca Winthrop is exactly the kind of statement that makes for good podcasting. It is confident, alarming, and seemingly backed by science. There is just one problem: the research on screen time and language development is not nearly as straightforward as Klein suggests.

Let us look at what is likely one of the studies underlying Klein and Winthrop's claims – a recent large-scale Danish study published in BMC Public Health by Rayce, Okholm, and Flensborg-Madsen (2024). This impressive research examined over 31,000 toddlers and found that "mobile device screen time of one hour or more per day is associated with poorer language development among toddlers."

Sounds definitive, right? The study certainly has strengths. It features a massive sample size of 31,125 children. It controls for socioeconomic factors. It separates mobile devices from TV/PC screen time. It even considers home environment variables like parental wellbeing and reading frequency.

So why should we not immediately conclude, as Klein does, that screens are "catastrophic" for child development?

Here is what gets lost when research travels from academic journals to podcasts: The authors explicitly state that "the cross-sectional design of the study does not reveal the direction of the association between mobile device screen time and language development." Yet this crucial limitation disappears when the research hits mainstream conversation.

Reverse causality is entirely possible. What if children with inherent language difficulties gravitate toward screens? What if struggling parents use screens more with children who are already challenging to engage verbally? The study cannot rule this out, but you would never know that from Klein's confident proclamation.

Hidden confounders lurk everywhere. The study controlled for obvious variables like parental education and employment, but what about parenting style? Quality of interactions? Temperamental differences between children? Parental neglect? Any of these could be the real culprit behind both increased screen time AND language delays.

The nuance gets nuked. The research found NO negative association for screen time under one hour daily. Yet somehow "moderation might be fine" transforms into "catastrophic experiment" in public discourse.

Klein is no dummy. He is one of America's sharpest interviewers and thinkers. So why the oversimplification?

Because humans crave certainty, especially about parenting. We want clear villains and simple solutions. "Screen time causes language delays" is a far more psychologically satisfying narrative than "it is complicated and we are not sure."

Media figures also face incentives to present clean, compelling narratives rather than messy nuance. "We do not really know if screens are bad but here are some methodological limitations in the current research" does not exactly make for viral content.

The next time you hear a confident claim about screens (or anything else) backed by "the research," remember: Correlation studies cannot prove causation, no matter how large the sample. Most human behaviors exist in complex bidirectional relationships. The most important confounding variables are often the hardest to measure. Journalists and podcasters simplify by necessity, even the brilliant ones. Your intuition toward certainty is a psychological quirk, not a reflection of reality.

Screens may indeed have negative effects on development. Or they might be mostly benign. Or it might depend entirely on content, context, and the individual child. The honest answer is we do not fully know yet – and that is precisely the kind of nuanced conclusion that rarely makes it into our public discourse, even from the smartest voices around.

When it comes to AI – the current technological bogeyman – we have even less to go on. We have very little empirical evidence about AI's effects on human development, and almost none of it qualifies as good quality evidence. It is way too early to make any kind of generalizations about how AI may affect human development.

What we do know is the history of technological panics, and how none of them ever fully materialized. Television was going to rot our brains. Video games were going to create a generation of violent sociopaths. Social media was going to destroy our ability to concentrate. And yet, no contemporary generation is stupider than their parents – that we know for sure. Neither TV nor computer games made us stupider. Why would AI be an exception?



Each new technology brings genuine challenges worthy of thoughtful study. But between rigorous research and knee-jerk catastrophizing lies a vast middle ground of responsible, curious engagement – a space that our public discourse rarely occupies.

The Modern Confessional: AI Disclosure as Ritual

 

Organizations across academia and publishing now routinely demand confession of AI use. Publishers require authors to disclose whether AI tools assisted in manuscript preparation. Funding agencies insert checkboxes for AI utilization. Academic journals add disclosure statements to submission forms. None adequately explain their rationale or how this information shapes evaluation.

This peculiar practice reveals our creation of a new moral domain around AI use in knowledge production. The requirement to disclose functions precisely as Michel Foucault described confessional practices in "The History of Sexuality." Foucault argued that confession itself produces the very notion of sin it purports to address. The act of requiring disclosure creates the impression of transgression where none inherently exists.

Medieval confession did not merely document pre-existing sins - it manufactured them through the very apparatus of confession. Similarly, disclosure requirements around AI use manufacture a transgressive quality around technologies that have no inherent moral valence.

The mechanics operate almost identically. Both create categories of behavior requiring special scrutiny. Both position authority figures as arbiters of acceptability. The confessing subject experiences this manufactured transgression viscerally - the academic disclosing AI use feels compelled toward contrition without clear understanding of what offense they have committed.

Authors find themselves in impossible positions, uncertain how much assistance constitutes meaningful use. Did grammar checking through Microsoft Editor count? What about Grammarly's suggestions? The lack of clear standards transforms disclosure into guesswork.

Rather than focusing on tools, we might evaluate outputs based on established academic standards regardless of production methods. This approach acknowledges that quality, originality and intellectual integrity manifest in final products, not production processes. Technical assistance has always existed across academic work - from statistical software to citation managers to editorial help from colleagues.

Current disclosure requirements function primarily as modern confession, manufacturing transgression through the very apparatus designed to reveal it. By recognizing this dynamic, we might reimagine our approach to technological assistance in ways that foster genuine integrity rather than performative disclosure.

On Techno-Utopianism. Elon Musk and the Soul of Education

The recent video of Elon Musk promising AI teachers reveals a common misunderstanding among technology leaders. They see education primarily as information transfer and skills training, where an infinitely patient AI system delivers perfectly tailored content to each student. This viewpoint ignores the fundamental nature of education as a relational institution.

Since Gutenberg's invention of the printing press, motivated individuals could teach themselves almost anything. Libraries contain more knowledge than any single teacher. Yet most people do not turn into autodidacts. Why is that? The question is not how to make knowledge more accessible, but why people choose to engage with it.

Teachers generate reasons to learn through two main approaches. In more constructivist settings, they inspire curiosity and create engaging problems to solve. In mor traditional schools, they maintain authority and discipline. In most schools, there is a mixture of both. Both methods work because they establish a social framework for learning. A good teacher knows when to push and when to comfort, when to explain and when to let students struggle.

The comparison of AI to Einstein as a teacher misses the point. Teaching requires different qualities than scientific genius - the capacity to enter a relationship, to create meaningful connections, and to help students discover their own reasons for learning. An AI system, no matter how knowledgeable, cannot do any of that.

Students often study not because they find the subject inherently fascinating, but because they respect  their teacher, want to belong to a learning community, or seek to fulfill social expectations. Even negative motivations like fear of disappointing others have a distinctly human character. 

The techno-utopian vision reduces learning to information exchanges and skill assessments. This mechanistic view fails to account for the social and emotional dimensions of human development. While AI can enhance teaching by handling routine tasks, it cannot replace the essential human relationships that drive educational engagement. The future of education lies not in perfecting content delivery algorithms, but in strengthening the relational foundations of learning. 

Such overblown promises about AI in education do more harm than good. They create unnecessary anxiety among teachers and administrators, leading to resistance against even modest technological improvements. Instead of addressing real challenges in education - student engagement, equitable access, and meaningful assessment - institutions get distracted by unrealistic visions of AI-driven transformation. We need a more balanced approach that recognizes both the potential and limitations of AI in supporting, not replacing, the fundamentally human enterprise of education.

AI and Labor: A Smarter Path Forward

Trade unions face a defining moment. Artificial intelligence presents genuine concerns about job displacement, yet the response need not mirror historical patterns of resistance to technological change. The Luddite movement of the 1810s serves as a cautionary tale - their destruction of mechanized looms neither preserved jobs nor improved workers' conditions. All technology affects labor; that it what technology is, work assistance. 

The automation paradox offers a more nuanced perspective. While machines replace specific tasks, they generate new forms of work. The introduction of automated teller machines in banking led to more bank branches and tellers performing complex customer service roles. This pattern repeats across industries - automation reduces costs, expands services, and creates different job categories.

Labor leaders would serve their members better by negotiating robust transition arrangements. Key demands should include employer-funded retraining programs, preferential access to new positions, and compensation packages that recognize acquired skills. The focus must shift from preventing change to shaping its implementation.

The pace of AI integration varies significantly by sector. Manufacturing and data processing may see rapid adoption, but industries built on human relationships - education, healthcare, social work - will incorporate AI gradually as assistive technology. Complex organizations require extensive testing and workflow redesign before meaningful automation becomes feasible.

Economic history demonstrates that reduced production costs expand economic activity. When basic tasks become automated, human attention shifts to more sophisticated problems. The telephone eliminated telegraph operators but created vast new communication industries. Similarly, AI will likely automate routine cognitive work while opening possibilities in areas we have not yet imagined.

Unions retain significant leverage during this transition. Organizations need experienced workers to implement new technologies effectively. This position allows labor to negotiate favorable terms - extended notice periods, substantial retraining budgets, wage protection during transition, and clear paths to higher-skilled roles.

The key lies in recognizing AI as a tool for augmentation rather than pure replacement. A machine learning system may process medical images faster than radiologists, but interpreting results in complex cases still requires human judgment. Similar patterns will emerge across professions - AI handling routine tasks while humans focus on nuanced decision-making and interpersonal elements.

Rather than resist change, unions should position themselves as partners in managing transition. This approach preserves their relevance and better serves member interests. The alternative - attempting to prevent AI adoption - risks marginalization as companies seek ways around opposition or relocate to more amenable jurisdictions.

The challenge for labor leadership is to shift from defensive postures to proactive engagement. This means developing expertise in emerging technologies, identifying opportunities for worker advancement, and ensuring transition arrangements protect vulnerable members while facilitating adaptation to changing workplace demands.

Not Pleased? Don’t Release It: The Only AI Ethics Rule That Matters

Imagine this: you have tasked an AI with drafting an email, and it produces a passive-aggressive disaster that starts, “Per our last conversation, which was, frankly, baffling…” You delete it, chuckle at its misjudgment, and write your own. But what if you had not? What if you had just hit “send,” thinking, Close enough?

This scenario distills the ethical dilemma of AI into its purest form: the moment of release. Not the mechanics of training data or the mysteries of machine learning, but the single, decisive act of sharing output with the world. In that instant, accountability crystallizes. It does not matter whether you crafted most of the the content yourself or leaned on the AI—the responsibility is entirely yours. 

We are used to outsourcing tasks, but AI lures us into outsourcing judgment itself. Its most cunning trick is not in its ability to mimic human language or spin impressive results from vague inputs. It is in convincing us that its outputs are inherently worthy of trust, tempting us to lower our guard. We are used to thinking - if a text is well-phrased and proofread, it must deserve our trust. This assumption does not hold anymore.

This illusion of reliability is dangerous. AI does not think, intend, or care. It is a reflection of its programming, its training data, and your prompt. If it churns out something brilliant, that is no more its triumph than a mirror deserves credit for the sunrise. And if it produces something harmful or inaccurate, the blame does not rest on the tool but on the person who decided its work was good enough to share.

History has seen this before. The printing press did not absolve publishers from libel; a copy machine did not excuse someone distributing fake material. Technology has always been an extension of human will, not a replacement for it. Yet, with AI, there is an emerging tendency to treat it as if it has intentions—blaming its "hallucinations" or "bias" instead of acknowledging the real source of responsibility: the human operator.

The allure of AI lies in its efficiency, its ability to transform inputs into polished-seeming outputs at lightning speed. But this speed can lull us into complacency, making it easier to prioritize convenience over caution. Editing, which used to be the painstaking craft of refining and perfecting, risks being reduced to a hasty skim, a rubber stamp of approval. This surrender of critical oversight is not just laziness—it is a new kind of moral failing.

Ethics in the AI age does not require intricate frameworks or endless debate. It boils down to one unflinching rule: if you release it, you are responsible for it. There is no caveat, no “but the AI misunderstood me.” The moment you publish, share, or forward something generated by AI, you claim its contents as your own.

This principle is a call for realism in the face of AI’s potential. AI can help us create, analyze, and innovate faster than ever, but it cannot—and should not—replace human accountability. The leap from creation to publication is where the line must be drawn. That is where we prove we are still the grown-ups in the room.

Before you hit "send" or "post" or "publish," a few simple questions can save a lot of regret:

• Have you read it thoroughly? Not just the shiny parts, but the details that could cause harm.
• Would you stake your reputation on this?
• Is it biased, or factually wrong?

The alternative is a world where people shrug off misinformation, bias, and harm as the inevitable byproducts of progress. A world where the excuse, The AI did it, becomes a get-out-of-jail-free card for every mistake.

So, when the next output feels close enough, resist the urge to let it slide. That "send" button is not just a convenience—it is a statement of ownership. Guard it fiercely. Responsibility begins and ends with you, not the machine.

Because once you let something loose in the world, you cannot take it back.

The Subtle Art of Monopolizing New Technology

Monopolizing new technology is rarely the result of some grand, sinister plan. More often, it quietly emerges from self-interest. People do not set out to dominate a market; they simply recognize an opportunity to position themselves between groundbreaking technology and everyday users. The most effective tactic? Convince people that the technology is far too complex or risky to handle on their own.

It starts subtly. As soon as a new tool gains attention, industry insiders begin highlighting its technical challenges—security risks, integration headaches, operational difficulties. Some of these concerns may be valid, but they also serve a convenient purpose: You need us to make this work for you.

Startups are particularly skilled at this. Many offer what are essentially "skins"—polished interfaces built on top of more complex systems like AI models. Occasionally, these tools improve workflows. More often, they simply act as unnecessary middlemen, offering little more than a sleek dashboard while quietly extracting value. By positioning their products as essential, these startups slide themselves between the technology and the user, profiting from the role they have created. 

Technical language only deepens this divide. Buzzwords like API, tokenization, and retrieval-augmented generation (RAG) are tossed around casually. The average user may not understand these terms. The result is predictable: the more confusing the language, the more necessary the “expert.” This kind of jargon-laden gatekeeping turns complexity into a very comfortable business model.

Large organizations play this game just as well. Within corporate structures, IT departments often lean into the story of complexity to justify larger budgets and expanded teams. Every new tool must be assessed for “security vulnerabilities,” “legacy system compatibility,” and “sustainability challenges.” These concerns are not fabricated, but they are often exaggerated—conveniently making the IT department look indispensable.

None of this is to say that all intermediaries are acting in bad faith. New technology can, at times, require expert guidance. But the line between providing help and fostering dependence is razor-thin. One must ask: are these gatekeepers empowering users, or simply reinforcing their own relevance?

History offers no shortage of examples. In the early days of personal computing, jargon like RAM, BIOS, and DOS made computers feel inaccessible. It was not until companies like Apple focused on simplicity that the average person felt confident using technology unaided. And yet, here we are again—with artificial intelligence, blockchain, and other innovations—watching the same pattern unfold.

Ironically, the true allies of the everyday user are not the flashy startups or corporate tech teams, but the very tech giants so often criticized. Sometimes that criticism is justified, other times it is little more than fashionable outrage. Yet these giants, locked in fierce competition for dominance, have every incentive to simplify access. Their business depends on millions of users engaging directly with their products, not through layers of consultants and third-party tools. The more accessible their technology, the more users they attract. These are the unlikely allies of a non-techy person. 

For users, the best strategy is simple: do not be intimidated by the flood of technical jargon or the endless parade of “essential” tools. Always ask: Who benefits from me feeling overwhelmed? Whenever possible, go straight to the source—OpenAI, Anthropic, Google. If you truly cannot figure something out, seek help when you need it, not when it is aggressively sold to you.

Technology should empower, not confuse. The real challenge is knowing when complexity is genuine and when it is merely someone else’s business model.

Is AI Better Than Nothing? In Mental Health, Probably Yes

 In medical trials, "termination for benefit" allows a trial to be stopped early when the evidence of a drug’s effectiveness is so strong that it becomes unethical to continue withholding the treatment. Although this is rare—only 1.7% of trials are stopped for this reason—it ensures that life-saving treatments reach patients as quickly as possible.

This concept can be applied to the use of AI in addressing the shortage of counsellors and therapists for the nation's student population, which is facing a mental health crisis. Some are quick to reject the idea of AI-based therapy, upset by the notion of students talking to a machine instead of a human counselor. However, this reaction often lacks a careful weighing of the benefits. AI assistance, while not perfect, could provide much-needed support where human resources are stretched too thin.

Yes, there have been concerns, such as the story of Tessa, a bot that reportedly gave inappropriate advice to a user with an eating disorder. But focusing on isolated cases does not take into account the larger picture. Human therapists also make mistakes, and we do not ban the profession for it. AI, which is available around the clock and costs next to nothing, should not be held to a higher standard than human counselors. The real comparison is not between AI and human therapists, but between AI and the complete lack of human support that many students currently face. Let's also not forget that in some cultures, going to a mental health professional is still a taboo. Going to an AI is a private matter. 

I have personally tested ChatGPT several times, simulating various student issues, and found it consistently careful, thoughtful, and sensible in its responses. Instead of panicking over astronomically rare errors, I encourage more people to conduct their own tests and share any issues they discover publicly. This would provide a more balanced understanding of the strengths and weaknesses of AI therapy, helping us improve it over time. There is no equivalent of a true clinical trial, so some citizen testing would have to be done. 

The situation is urgent, and waiting for AI to be perfect before deploying it is not much of an option. Like early termination in medical trials, deploying AI therapy now could be the ethical response to a growing crisis. While not a replacement for human counselors, AI can serve as a valuable resource in filling the gaps that the current mental health system leaves wide open.

Where is the work? AI and Creativity

For ages, we have blurred the lines between ideation and execution, treating them as inseparable parts of creativity. Craftsmanship was tightly bound to originality. Think of Michelangelo working on the Sistine Chapel, a project that spanned nearly a decade. Where does his genius truly lie? In envisioning those profound images, or in the labor of painting them? What, exactly, is the essence of the work?

The rise of AI forces us to untangle these ideas and reconsider what it means to produce "human" work. Take a recent story I heard from from the audience of one of my talks: a person described how he fed an AI every detail about a retiring colleague, and the AI generated a speech so moving that it brought the retiree to tears. But the retiree, upon learning the speech's origin, was dumbfounded.

What is interesting is not the retiree’s reaction, but the storyteller's own oversight. He failed to see his own critical role in the process. By gathering the details, curating moments that best captured the retiree’s essence, he performed the most human part of the creative act. He mistook the act of turning those ideas into words as the creative work, but that is not the case.

AI, ironically, is pushing us to be more human, not more like machines. It is forcing us to recognize that our true contribution lies in the ability to think, to create, and to feel. As AI takes over the mechanical aspects of tasks we once considered integral to creativity—whether that is writing, painting, or coding—we are left with the more uniquely human roles: original thinking and emotional depth.

This shift reshapes our understanding of creativity and work. It shows that human value does not lie in production—the technical aspect of turning an idea into a product—but in the deeper conceptual and emotional layers that AI still cannot reach.

As we move forward, we are compelled to rethink productivity itself. The future will not belong to those who can outdo AI in execution, but to those who can combine AI’s strengths with our unique capacities for innovation, empathy, and insight.

The challenge we face is not to resist AI, but to fully embrace our humanity—to cultivate the traits that machines cannot replicate. With AI taking over the drudgery, we are freed to focus on higher-order thinking and those creative leaps that define human ingenuity.

Ironically, the more we develop artificial intelligence, the more we learn about what human intelligence really is. And in that discovery lies our future—a future where AI does not replace creativity, but elevates it to new possibilities.

Four Myths About AI

AI is often vilified, with myths shaping public perception more than facts. Let us dispel four common myths about AI and present a more balanced view of its potential and limitations.

1. AI Is Environmentally Costly

One of the most persistent claims about AI is that its use requires massive amounts of energy and water, making it unsustainable in the long run. While it is true that training large AI models can be energy-intensive, this perspective needs context. Consider the environmental cost of daily activities such as driving a car, taking a shower, or watching hours of television. AI, on a per-minute basis, is significantly less taxing than these routine activities.

More importantly, AI is becoming a key driver in creating energy-efficient solutions. From optimizing power grids to improving logistics for reduced fuel consumption, AI has a role in mitigating the very problems it is accused of exacerbating. Furthermore, advancements in hardware and algorithms continually reduce the energy demands of AI systems, making them more sustainable over time.

In the end, it is a question of balance. The environmental cost of AI exists, but the benefits—whether in terms of solving climate challenges or driving efficiencies across industries—often outweigh the negatives.

2. AI Presents High Risks to Cybersecurity and Privacy

Another major concern is that AI poses a unique threat to cybersecurity and privacy. Yet there is little evidence to suggest that AI introduces any new vulnerabilities that were not already present in our existing digital infrastructure. To date, there has not been a single instance of data theft directly linked to AI models like ChatGPT or other large language models (LLMs).

In fact, AI can enhance security. It helps in detecting anomalies and intrusions faster than traditional software, potentially catching cyberattacks in their earliest stages. Privacy risks do exist, but they are no different from the risks inherent in any technology that handles large amounts of data. Regulations and ethical guidelines are catching up, ensuring AI applications remain as secure as other systems we rely on.

It is time to focus on the tangible benefits AI provides—such as faster detection of fraud or the ability to sift through vast amounts of data to prevent attacks—rather than the hypothetical risks. The fear of AI compromising our security is largely unfounded.

3. Using AI to Create Content Is Dishonest

The argument that AI use, especially in education, is a form of cheating reflects a misunderstanding of technology’s role as a tool. It is no more dishonest than using a calculator for math or employing a spell-checker for writing. AI enhances human capacity by offering assistance, but it does not replace critical thinking, creativity, or understanding.

History is full of examples of backlash against new technologies. Consider the cultural resistance to firearms in Europe during the late Middle Ages. Guns were viewed as dishonorable because they undermined traditional concepts of warfare and chivalry, allowing common soldiers to defeat skilled knights. This resistance did not last long, however, as societies learned to adapt to the new tools, and guns ultimately became an accepted part of warfare.

Similarly, AI is viewed with suspicion today, but as we better integrate it into education, the conversation will shift. The knights of intellectual labor are being defeated by peasants with better weapons. AI can help students better understand complex topics, offer personalized feedback, and enhance learning. The key is to see AI as a supplement to education, not a replacement for it.

4. AI Is Inaccurate and Unreliable

Critics often argue that AI models, including tools like ChatGPT, are highly inaccurate and unreliable. However, empirical evidence paints a different picture. While no AI is perfect, the accuracy of models like ChatGPT or Claude when tested on general undergraduate knowledge is remarkably high—often in the range of 85-90%. For comparison, the average human memory recall rate is far lower, and experts across fields frequently rely on tools and references to supplement their knowledge.

AI continues to improve as models are fine-tuned with more data and better training techniques. While early versions may have struggled with certain tasks, the current generation of AI models is much more robust. As with any tool, the key lies in how it is used. AI works best when integrated with human oversight, where its ability to process vast amounts of information complements our capacity for judgment. AI’s reliability is not perfect, but it is far from the "uncontrollable chaos" some claim it to be.

***

AI, like any revolutionary technology, invites both excitement and fear. Many of the concerns people have, however, are rooted in myth rather than fact. When we consider the evidence, it becomes clear that the benefits of AI—whether in energy efficiency, cybersecurity, education, or knowledge accuracy—far outweigh its potential downsides. The challenge now is not to vilify AI but to understand its limitations and maximize its strengths.

 

Navigating the AI Gold Rush: Skins, Security, and the Real Value Proposition

 The economic battle surrounding artificial intelligence is intensifying at an unprecedented pace. Major AI players like OpenAI, Google, Meta, and Anthropic are leading this technological revolution. Tech giants such as Microsoft, Amazon, and Apple, along with thousands of startups, are vying for a stake in this burgeoning market without being able to develop their own competitive models. Amidst this frenzy, a critical question arises: what exactly is being sold?

Two primary value propositions have emerged in this landscape: skins and security mongers. Skins are interfaces or applications that overlay major AI models, aiming to simplify user interaction. They cater to individuals lacking advanced prompting skills, offering a more user-friendly experience. Security mongers, on the other hand, emphasize heightened privacy and security, often exaggerating potential risks to entice users.

While both propositions seem valuable on the surface, a deeper examination reveals significant shortcomings. Skins promise to streamline interactions with AI models by providing preset prompts or simplified interfaces. For instance, a startup might offer a chatbot specialized in drafting business emails, claiming it saves users the hassle of formulating prompts themselves. However, is this convenience truly worth it?

Major AI models are increasingly user-friendly. ChatGPT, for example, has an intuitive interface that caters to both novices and experts. Users often find they can achieve the same or better results without intermediary platforms. Additionally, skins often come with subscription fees or hidden costs, meaning users are essentially paying extra for a service the primary AI model already provides. There is also the issue of limited functionality; skins may restrict access to the full capabilities of the AI model, offering a narrow set of functions that might not meet all user needs.

The second proposition taps into growing concerns over data privacy and security. Vendors claim to offer AI solutions with superior security measures, assuring users their data is safer compared to using mainstream models directly. But does this claim hold up under scrutiny?

Most of these intermediaries still rely on API connections to major AI models like ChatGPT. Your data passes through their servers before reaching the AI model, effectively adding another point of vulnerability. Introducing additional servers and transactions inherently increases the risk of data breaches. More touchpoints mean more opportunities for data to be intercepted or mishandled. Furthermore, major AI providers invest heavily in security and compliance, adhering to stringent international standards. Smaller vendors may lack the resources to match these safeguards.

For example, a startup might advertise an AI-powered financial advisor with enhanced security features. However, if they are routing data through their servers to access a model like GPT-4, your sensitive financial data is exposed to additional risk without any tangible security benefit. The promise of enhanced security becomes questionable when the underlying infrastructure depends on the same major models.

AI platforms have not introduced new risks to privacy or security beyond what exists with other online services like banks or credit bureaus. They employ advanced encryption and security protocols to protect user data. While no system is infallible, major AI models are on par with, if not superior to, other industries in terms of security measures. They use end-to-end encryption to protect data in transit and at rest, implement strict authentication measures to prevent unauthorized access, and conduct regular security assessments to identify and mitigate vulnerabilities. It is easy to opt out of providing your data to train new models. It is much more difficult to know what your vendors are going to do with your data.

In a market flooded with AI offerings, it is crucial to approach vendors' claims with a healthy dose of skepticism. Validate the functionality by testing whether the convenience offered by skins genuinely enhances your experience or merely repackages what is already available. Assess the security measures by inquiring about the specific protocols in place and how they differ from those used by major AI providers. Transparency is key; reputable vendors should be open about how your data is used, stored, and protected.

As the AI gold rush continues, distinguishing between genuine innovation and superficial value propositions becomes essential. Skins and security mongers may offer appealing pitches, but often they add little to no value while potentially increasing costs and risks. It is wise to try using major AI models directly before opting for third-party solutions. Research the backgrounds of vendors to determine their credibility and reliability. Seek reviews and testimonials from other users to gauge the actual benefits and drawbacks.

In the end, the most powerful tool at your disposal is due diligence. By critically evaluating what is being sold, you can make informed decisions that truly benefit you in the rapidly evolving world of AI. Beware of vendors selling either convenience or security without substantial evidence of their value. At the very least, take the time to validate their claims before making an investment.