Month: July 2025

“Tools provide a path, a context, and almost an excuse for developing enlightenment, but no tool ever contained it or can dispense it. Cesare Pavese observed: to know the world we must construct it. In other words, we make not just to have, but to know. But the having can happen without most of the knowing taking place.” – Alan Kay, 1993

Technology has always shaped how we think. I’ve written before about how technology turned me into a constructivist thinker. In short, that means I believe we create the world we live in. However, I am also a systems thinker, and what that means is that I understand that we are constrained by systems of technology, knowledge, culture, and practice even if the systems are constructions.

Over the last 50 years, we’ve watched the gradual democratization of technology. AI takes this to a new level. What was once a domain dominated by a technical elite, however, now risks becoming the domain of those who have the critical thinking skills to use AI tools to solve complex problems and drive new innovations.

Ted Nelson, in his manifesto Computer Lib/Thinking Machines, envisioned computers as “Thinkertoys” to liberate us from rigid, industrial-age systems. “We do not make important decisions, we should not make delicate decisions, serially and irreversibly. Rather, the power of the computer display (and its computing and filing support) must be so crafted that we may develop alternatives, spin out their complications and interrelationships, and visualize these upon a screen.” Using Thinkertoys effectively requires critical thinking skills.

However, Bill Gates’s letter to hobbyists in 1974 pointed us to an alternative future where someone else would code for us, because that was where the money was. We traded flexibility for convenience. Applications became our gatekeepers. For most of us, the ability to shape our digital environment vanished.

We accepted the software we were given because learning to code was too difficult and too costly in time. Most importantly, we adapted to their logic instead of thinking critically about alternative possibilities.

We allow ourselves to get into traps largely driven by applications and systems over which we have no control. Most of the pain I hear from computer users stems directly from technology that fences them in, doesn’t work right, or both. Whether we’re talking about web applications that frustrate us or corporate systems that undermine productivity, we are at the mercy of those who speak the language of machines.

In parallel to this world, there’s been a hacker world of coders that have developed their own software to do exactly what they wanted to do. However, this takes a level of expertise and commitment of time that most of us don’t have.

AI will change that calculus. For the last couple of months, I have been meme coding using an AI and it’s amazing to me how fast I have progressed while developing a relatively complex program.

A few weeks ago, a professor at another institution told me that he had created customizations to his learning management system using its API and meme coding. Learning management systems are one of those inflexible apps that we struggle with as we try to create the best online learning environments for our students.

This caused a lightbulb to go off in my head. Maybe we were finally entering the world that Ted Nelson had envisioned. This was a world where anyone could create whatever functionality they needed in a computer without the limitations of their coding skills or by relying on someone else’s coding skills.

However, even if this becomes simple, it’s still going to require sets of skills because you do have to know how to get what you want. This is a logical puzzle, not a technical one, and one that even many low-level programmers struggle with.

Master programmers understand that they are mediating between human and machine worlds and design software that serves the user. However, there are lot of programmers who simply must meet a deadline for a logic imposed by those who understand neither computers nor people.

Currently, I am debugging a portion of my application and, even though the AI is helpful, it does not explore all possible pathways and often gets stuck in a loop. I need to know the right questions to ask to get it out of that loop.

That process involves a combination of logical and lateral thinking. It also opens the door to more complex problems than simply getting the code to work right.

At the same time, I am dealing with students who are having trouble following rather straightforward directions and thinking critically about their own work. The real deficit in their learning is not computing or coding, it’s critical thinking.

This has always been the Achilles’ heel of education based on an industrial model. If the primary purpose is feeding students through a system, the easiest way to do that is down a straight path. The easiest way to go down a straight path is not to encourage them to think critically or creatively.

I am excited about the prospect of a maker world where I can customize my computing (and real world) existence around my needs. However, power should not be concentrated around even an elite characterized by critical thinking skills. If we can teach people to think like makers, to design rather than merely consume, we transform technology from a set of fences into a field of possibilities.

This is the real digital divide: not access to computing, but critical thinking abilities. Access to computing is a technical problem. Access to critical thinking is societal one. Without it, AI will not liberate us. It will deepen the gap between those who can shape technology and those who are shaped by it. This is the real threat of AI that no one is talking about.

It is time to get beyond being users of AI. We need to be architects of a maker world. There is an urgent need to integrate AI tools into our learning and work processes but do so in a critical manner that teaches thinking over replacement.

We must overcome the idea that we are subject to the technologies that given to us, and that includes AI. I don’t teach my students to accept AI. I teach them to critique it and use it to critique themselves.

We stand on threshold of a maker world where you can call up digital tools to do anything you want and build a digital world customized around you. This is a truly constructed world. That’s the world I want to leave to my kids and students.

Part I: Democratizing Imagination

Part II: Making Opportunity with AI

“To extend our abilities to think and perceive in new ways, and to translate those into tools that enable others to think and perceive in new ways, is probably the essential part of the human story.” – Howard Rheingold and Petar Jandric, 2015

I can build things I only dreamt of even a few months ago. This is both liberating and a bit intimidating because it opens whole new questions about what is possible.

If you look at technology as an evolution towards simplicity, we may have crossed a milestone in recent months or years. It used to be that coding was confined to a relatively exclusive elite. You had to have a certain kind of mindset for it, and then acquire the skills.

We need to consider how AI fits into the broader technological and societal picture. I don’t tend to believe in revolutions, either as a political scientist or as a technologist. They don’t really fit the patterns of human behavior. People are naturally conservative unless they see no other way out and change.

The same thing is true with technology adoption. There are a few who delight in doing things no one else can do, but most are more interested in getting through their workload.

This doesn’t mean they don’t have ideas or needs that technology (or writing) can express for them. It is our responsibility to continuously lower barriers to access those technologies.

Most people fear technology because it wastes their time and takes tasks out of their control. Yet this fear has never been about a lack of imagination. It’s about the barriers between them and their ideas. AI, like the PC in the 1970s, represents a massive lowering of those barriers.

For decades, education has focused on technical skills rather than logical ones, because the technology itself was hard to master. But AI changes that. We don’t think about “technical skills” when we pick up an iPhone, and soon we won’t think about them when we build with AI either.

The true revolution with AI will come with making. There are so many projects that I have thought of over the years that I have discarded because I don’t have the technical skills to code them properly. No matter how much I wanted these tools, they required prohibitive opportunity costs to realize.

A few weeks ago, I was on the T is for Training podcast where I’m a regular panelist. We were discussing the use of escape rooms for teaching and training with Laura Gehringer and Madeline Shellgren.

It suddenly struck me that I could generate escape room scenarios for my classes using a generative AI Chatbot. This is not just about games. The escape room example showed me how AI doesn’t just speed up existing workflows. It makes previously impossible things practical. That shift in feasibility is the real revolution.

I have built games for learning before. The challenge in constructing games in an educational setting is that to do it well is incredibly time-consuming. Most teachers don’t have time for that.

You also want to tailor the game around your instructional goals and style as much as possible. Therefore, sharing games across multiple subjects or pedagogical styles doesn’t always scale well.

I have been writing over the last couple of months about how AI can be used to significantly speed up logistical tasks in my teaching and classroom management. I had not thought of using it to construct games until my conversation with Laura and Madeline.

The next day, I was speaking to a fellow professor at a startup meeting, and we started talking about escape rooms. He brought up the idea of coding a game that would have the same effect but would be interactive and repeatable. My first reaction to the suggestion was that I thought it would be too hard. My brain was still in the manual coding world.

However, I recognized the connection between that and what I was already doing with the Knowledge Navigator project, which is far more complex. Given what I’ve learned about meme coding over the last couple of months I realized that this might be technically and logistically feasible for me.

It is amazing to me how much progress I’ve made on a relatively sophisticated technology project. However, with this progress, I’ve unlocked new, much tougher, questions than the technical aspects of the project.

My priority has always been focused on the task, whatever that task may be. When the tasks don’t align with the software, I inject the human element (usually me) into the equation. I’m always considering the time investment into any task and, if I can use technology to save me time, I do it.

If technology wastes my time, I have no patience for it. I am here to help my students, not fight with technology, much of which was never designed to work in a learning environment in the first place.

Thinking about AI in this way will open whole new possibilities for human employment. I’m not talking about prompt engineering here. I’m talking about operationalizing dreams.

Augmenting ourselves with AI tools will unlock creative forces that will accelerate all economies forward at a rapid pace that need not come at the expense of killing the climate. That’s because this will be an acceleration of ideas, not products.

The revolution will come in the form of human potential. AI will make it easier for everyone to define their future. We just need to recognize and grasp that (and show others the way). But realizing that future will require a different kind of literacy: learning how to think like makers.

…next up: life in a Maker World (Part III)

Part I: Democratizing Imagination

Part III: The Maker World

“Our fine arts were developed, their types and uses were established, in times very different from the present, by men whose power of action upon things was insignificant in comparison with ours. But the amazing growth of our techniques, the adaptability and precision they have attained, the ideas and habits they are creating, make it a certainty that profound changes are impending in the ancient craft of the Beautiful.” – Walter Benjamin, 1935

Less than 200 years ago, a small group of people decided what counted as art. They could do this because only a few had both the talent and the patronage to create it. The vast majority of art that was created and survived to this day was therefore images of the rich and powerful.

Speaking as an artist for a change, I also know that artists are an ornery lot. They frequently stepped out of line and painted things that they were not paid for. However, even then they represented a very small slice of the population with the imaginative and physical talents necessary to create complex works of art.

With the invention of photography, however, the level of physical talent required declined. Suddenly people who were previously excluded from expressing themselves individually had the power to do so.

Initially, the costs and time necessary to do photography limited the group of people who could create images, but with the introduction of technologies like the Kodak Brownie in 1900, suddenly the masses had access to creation tools. Now, we take over 2 trillion pictures per year.

This literally changed what we can see. Visual media are far more powerful than textual media, especially when a large proportion of the population was illiterate. But even today a picture is far more dramatic than lines and lines of text like I’m producing now.

Technology democratizes access to creation. It empowers people to do things that were impossible before. This goes against the centralization theory of technology, such as that expressed by Tim Wu, which argues that technology will inevitably be gathered into a small number of hands.

In an era of tech giants, that is an easy theory to believe. AI carries the baggage of Web 2.0, where centralized platforms like Facebook and Google built empires by controlling data and attention. Companies like OpenAI and Anthropic are explicitly modeling themselves on the first generation of Web 2.0 giants.

These companies were, in turn, modeled on industrial models where massive machines produced everything from automobiles to washing machines. In the industrial age size meant power. It manifested in systems of production that dictated how we worked and what was available to us as consumers.

Over the last 40 years, this model has been undermined as production has become far more efficient. Technology has allowed relatively small startups to become successful. Of course, the founders of these startups were raised in the industrial age and very much see size as the ultimate arbiter of success.

However, we no longer need thousands of people to produce one product. It can be produced by a few dozen people or even one person with the right technology.

We have also democratized access to art. We went from glass plate photography to the iPhone in the span of a little more than a century.

When we democratize access to creation, we empower new voices. In societies where people are increasingly frustrated by the old voices who seem to only lead them down blind alleys, this is a powerful force to counterbalance the conservative elites.

I agree that sometimes in order to build stuff you have to go big. However, why does this have to be the end state? We used to need a massive factory to do injection molding. Now I can create custom versions of the same thing in my living room with a 3-D printer.

Until recently, the biggest barrier to makerspace innovation was the complexity of design software like AutoCAD, which was needed to operate CNC machines or 3D printers. Now, I can get an AI to generate models for me with a text prompt.

As I play with the relative capabilities of large and small AI models, I recognize the power of brute force when it comes to transformer technology. At the same time, I see a narrowing of the gap between the smaller models and those that require huge server farms.

Of course, those huge server farms come with immense cost, and only large corporations and governments can shoulder that level of investment. With that, comes control. They may control the smartest models in the world, but where does the true power lie? It lies in the creative inputs they use to train the models.

I have argued before that the power of the PC revolution was in its democratizing force. It spread technology over a much wider group of people and every one of those people had a brain and experience that were unique. Kevin Kelly called this the Hive Mind. Diversity is a bedrock of innovation.

Even with today’s biggest models locked behind corporate walls, I can now accomplish things that were once impossible for me. I can run powerful models locally on my Apple silicon Mac and use the big models for a limited set of problems. This is why small, open models are the future, not massive server farms.

This is tremendously empowering for humans. AI has made me a more efficient teacher. It has made me a more efficient creator. And the only real limits are in my imagination. And this is only the beginning.

… of a three part series on our transition into an AI-driven maker world.

Part II: Making Opportunity With AI

Part III: A Maker World

“Every time there is a new technology, there is a paranoid faction of less than brilliant artists who feel that that’s the end of their career; who don’t understand that art never dies; that technology will go on and serve art.” – Stan Winston, 1993

I’ve always been fascinated by creative journeys. When people create something amazing I want to know how they got there. Technology always plays a role. Creativity comes from understanding how to augment our vision with technology, from ink to notation to paint to film to pixels.

Creative work is about being able to express your vision in a way that the rest of humanity can appreciate. Technology is how we get there as humans, whether it’s through the mechanisms of writing, music, painting, photographing, coding, and a multitude of other pursuits. These are just the ways that we tell our stories, not the vision itself.

We chase our dreams through technology. We are always pushing it to see where it can take us. AI is just the latest chapter of this journey.

No matter what I’m doing I’m always frustrated with the limits of the technology to express my creative vision. I’ve probably come closest to my ideals with my photography and my writing, but in both cases, I still struggle to tell stories that I cannot capture or express.

The limiting factor is me messing things up with the technology of words or the photographic process. In both cases, digital has closed the gap between my vision and reality. However, even there, I still run up into blind alleyways, where I just can’t seem to make it work.

AI presents a unique opportunity and challenge. It extends our creative capacities even further by taking mundane tasks off the table. I can use it to help me write. I’m using it right now to dictate this into my phone. I will use it to edit and polish this blog before I publish it.

The latest iteration of this struggle is using AI to code a prototype of the Knowledge Navigator. Ironically, my creative vision is for a technology that helps others clarify theirs. However, until AI, I lacked the tools to execute this vision.

I have never been a very good coder. I understand how it works, but I get lost and frustrated with the details of passing my thoughts into something the machine can understand. Computers are far more particular about details than I am.

I am trying to use AI to overcome those mundane detail-oriented tasks, which limit the execution of my vision. As a result, I have made more progress in a month than I likely would have in a year of reading coding manuals.

Using AI, I have learned more about coding in the last month doing it this way than I have in the last 30 years. Now I will admit that I already have a fair foundation in how code works, but it’s different to have a theoretical knowledge of something than to have a practical knowledge in using it to create something new.

This is not “cheating” or “taking the easy way out,” as some have argued. You are cheating if you’re not learning. You are cheating if you’re not growing. You are cheating if you’re not innovating. Learning, growing, and innovating all stem from the basic human impulse to create and explore. AI can supplant us, but not if we use it to learn and grow.

Almost all criticisms of how AI is disrupting society come from contexts where creativity isn’t the main goal, like standardized testing or corporate output. That’s a different conversation than what we’re having here. These are disruptions to systems, not humans.

Systems drive us with extrinsic motivators like money or grades. AI challenges extrinsic motivators, but that gives us a chance to realign our systems around intrinsic learning and creativity.

The intrinsic motivation of most students is usually clouded if not eliminated by the extrinsic motivators of grades. The purely extrinsic motivation of a quick buck creates fake art. There is no intrinsic creative motivator there. There is no creative growth there, only mindless replication.

The industrial mindset is based on mindless replication. Over the last two centuries, we’ve learned to mass produce all sorts of things. I argued in Discovering Digital Humanity that we have learned to serve machines. This is not a good role for humans.

Those of us particularly sensitive to creative thought struggle to exist in this kind of world. We are naturally non-conformist and resist becoming part of mindless machines. We recognize that only humans can truly create.

Humans are naturally curious, but extrinsic motivators dull that curiosity because it is considered a luxury not a necessity in a paradigm where we are taught to be cogs in machines.

In a world where AI is omnipresent, creativity rooted in curiosity is no longer optional, it’s essential. We must inject that creative drive into our learning and working. This is the antithesis of industrial thinking because if you are just part of the machine, creativity is a liability. You don’t want a cog spinning at its own speed

I am not an AI accelerationist. I don’t tend to get excited by any technology. I get excited about what technologies can do for me to help me grow and to make the world a more interesting place.

AI offers vast potential to augment our creativity. But like any technology, we must use it with that end in mind. If we do not, the machine will discard us. We must teach ourselves and our students to create with the machines.

What we’re doing with the technology is at least as important as what the technology does. The more powerful the technology, the more powerful the human element needs to be. AI is a powerful technology. Humans must be mindful in how they use it.

Mindfulness and creativity are deeply intertwingled. They infuse our lives with meaning and purpose. I can create photographs, writing, music, tools, and so much more to help other people through mindfulness. Without mindful creativity, we are little more than machines. With it, we can create with any technology to reach heights limited only by our own imaginations.