Month: February 2025

“The Net interprets censorship as damage and routes around it.” – John Gilmore

We are prisoners of narrative. This is because we struggle to make sense of a world that seems too complex for our imaginations.

I have always been at war with complexity. Sometimes it is necessary, but that only occurs at the fringes of science. For most of us, the most important thing we can achieve is a clarity of vision. It is possible to oversimplify stories, but overly complex ones are vulnerable to manipulation by those who claim to understand them.

We need technology tools to carry us out of this trap. The Knowledge Navigator project is based on the naïve thought that we might learn to see better and what that would mean to human societies.

We yearn for an escape from this hall of mirrors. Popular entertainment has really explored this in recent years, most notably on two shows from Apple TV+: Severance and Silo.

A society of 10,000 people in Silo lives physically confined within an underground silo for an unknown reason. It is a fascinating exploration of what it takes to keep this kind of constrained reality under control.

The leadership of the Silo does this by controlling what the population sees. If you can’t see, you can’t construct narratives that make sense of what’s going on.

The brilliance of Hugh Howey’s story (he’s both author of the books and a producer on the show), comes from his exploration of this layered seeing. The reason it is so chilling is that it reflects how our own society controls what we see and tries to dictate narrative.

We don’t live in physical holes in the ground (at least not yet), but we do live in informational silos. Elites shape them by manipulating what we can and cannot see.

There are no monolithic Illuminati here, but there don’t have to be. There just has to be blindness and more sight. In the kingdom of the blind, the one eyed man is king. The leader of the Silo in Howey’s book can control the Silo, not because he is omniscient, but because he knows more than everyone else.

Elites have controlled how we see the world since we organized into complex societies. I wrote about this almost 30 years ago when I was writing about nationalism and ethnonationalism. In my very first scholarly article, “What is Nationalism Really?” I discussed how elites shaped the character and direction of any national or belief-structured discourse.

Some ethnic communities are benign and some are aggressive and hostile. The stories we tell ourselves shape these realities. Our elites shape those stories by making sense of complexity through their manipulation of narrative.

Recently, political discourse in many countries has taken a dark turn. This reality is only possible because of the fictions that we allow ourselves to believe.

The Enlightenment and Scientific Method taught us to always question assumptions about the way the world worked. This questioning has always been difficult for humans.

It comes naturally to us to follow the dominant narratives we’ve encountered. When someone undermines those narratives, it can be profoundly disquieting.

Whoever has the power to define or manipulate those narratives has immense power over our societies. This makes them dangerous if left unchecked.

We can overcome irrational excesses if those narratives are characterized by questioning and rational inquiry. However, as a teacher, social scientist, and observer of politics, I know how poorly people understand the processes of rational inquiry introduced by Enlightenment thinking.

The power of the story concentrates power among those with the power to tell it. Money often accompanies the power to tell stories these days, but not invariably (it does offer an advantage, however).

Making sense of the information noise that confronts us every day has become an immense chore that sucks up far too much of our time. As a result, we are stuck in our individual silos, trying to make sense of it all.

I conceived of the Knowledge Navigator as a tool to put that power back into the hands of individuals. Perhaps I have too much faith in the power of individuals to discern rational patterns in information if they could but see them.

As a teacher, I’ve always seen that as our only hope. As an educator, I’ve long seen how the system itself is a narrative that works against this.

The technologist in me, however, is forever looking to technology as a way out of human limitations. We travel the world in our machines. We see it through our machines most of the time. I think we can apply the same to our thinking and our siloed narratives to feel our way out of our current dark narratives.

Severance takes siloed narratives to a whole new level. It posits a world where mind control allows us to be isolated from our consciousness. The company completely controls the narratives of severed workers while they are at work.

The show is an exploration of the power that emerges from this simple thought experiment. If you don’t know what’s going on for half your time, how do you know what’s going on for the rest of it?

This is Silo taken from a societal level to a personal one (although Silo does explore the personal level more thoroughly in books and perhaps in future seasons).

Severance takes Silo to its horrifying logical conclusion. It is a society of the blind. Unlike Silo, its Innies have no perception of the alternative world other than an awareness that it exists. Both shows are an active exploration of the power of controlling what people see and don’t see.

Technology provides us with an escape from our blindness. In the past, we had little choice but to believe what our elites told us. They controlled all but the most personal levers of information.

The internet changed this. In a 1993 article, John Gilmore, one of the founders of the Electronic Frontier Foundation, recognized this power over narrative when he said, “the Net interprets censorship as damage and routes around it.”

There are other early internet documents such as the ClueTrain Manifesto that proclaim a vision of seeing incorporated into a decentralized thought network. This vision is still there, even if it is clouded by corporate social media and other narrative platforms.

The internet brought with it the potential of transparency. However, it did not solve the problem of elite manipulation of information. Information is not the problem now. Connected knowledge is the deficit we’re struggling with now. As long as we allow elites to make those connections for us, they will still control the narrative.

AI will provide us with a new way to map these connections and make connections as transparent as the internet made information. This is the vision behind the Knowledge Navigator.

The Knowledge Navigator would be a tool to harness the connective power of generative AI to see. I know we can build it. The question is whether people are willing to climb out of their silos and face a world where reality is not dictated to them. The question is not whether an AI connection tool can be built, but whether we are ready to see.

Empowering human agency is key to our future in a confusing and complex world. Without sight, agency is impossible. And in that world, we’re always going to be prisoners to circumstances dictated by those with power over our narratives.

We shape our tools and thereafter our tools shape us. – Marshall McLuhan

The spreadsheet is a tool, and it is also a world view — reality by the numbers. If the perceptions of those who play a large part in shaping our world are shaped by spreadsheets, it is important that all of us understand what this tool can and cannot do. – Steven Levy

Picture the office environment of 1980. Telephones, stacks of paper, the occasional calculator, and reams of data stored in impenetrable filing cabinets dominate it.

The logic of this office environment was essentially unchanged for the better part of a century. Calculators and typewriters transitioned from mechanical to electronic, but their basic functionality remained the same. Neither transition altered the daily functions of most businesses or their employees.

We are still struggling with this transition today. The typical office of 2025 still operates in a paper metaphor, and most operate according to industrial scheduling.

While communication and sharing have changed considerably, it has done nothing more than speed up the speed at which we receive, store, and process data. The world has shifted slightly, but we are still in the midst of an unfinished evolution.

For most people, this transitory period started around 1980. Computers existed at a distance in the early 80s for most companies. Perhaps you sent out data requests to one of the big computing firms, but the idea of doing this yourself at speed was something completely alien to your thought process or workflow.

In the consumer space, some people started playing with personal computers, like the Apple ][, or one of the Radio Shack computers. People viewed these as toys.

However, one of those computers, the Apple ][, had a new application on it. This application allowed you to do advanced calculations on grids of numbers. It grew out of a Harvard business school practice of creating these grids manually and the insight of Dan Bricklin and Bob Frankston that this was an ideal task for personalized computing.

Today, we take spreadsheets for granted, but in 1980 they were an alien and revolutionary idea. Business was operating at full speed just to keep up with the day-to-day requirements of running itself.

Technology limited their ability to predict the future. Paper is an artifact of the past. Once you put it on paper, it has already happened.

If you can’t predict the future, you can’t plan. You can’t lay in for unexpected crises and emergencies. You are hostage to the whims of fate.

Most importantly, you couldn’t model alternative futures and build scenarios to prepare your company for the likelihood that the unexpected will happen. This all changed with VisiCalc and its instantaneous calculation of rows of figures.

This idea was so powerful and revolutionary that people started buying Apple ][‘s with their own money and bringing them into the office. Businesses that looked to the future installed these strange devices and explored ways to use them more efficiently.

AI’s integration into business routines mirrored VisiCalc’s. Within weeks of ChatGPT’s release in late 2022, people were already using it to increase their personal efficiency.

They did this with little understanding of its strengths and limitations. This led to some hilarious missteps and professional embarrassment when people assumed the computing infallibility they expected from existing systems only to discover ChatGPT didn’t work like that.

For two years, considerable debate surrounded AI’s influence on our lives, both personal and professional. I am still awaiting the VisiCalc moment when our ability to perceive the world shifts.

I have written many times before about our struggles to put tasks in front of tools. Generative AI is no exception. It’s the latest and greatest thing. Everyone wants to use it. No one knows what to do with it.

VisiCalc reshaped the paradigm of the possible. It changed our sense of time in very subtle but profound ways. Suddenly, an application linked the past, present, and future.

Most applications of AI we see today are merely substitutions for existing tasks. All too often, it doesn’t do these tasks well. Early computers seemed obtuse and without a clear, practical function until spreadsheets and word processors came around.

I still use AI in “legacy” ways. For instance, this blog will get run through ChatGPT, which will offer suggestions for structure and content. I used to have human partners read through drafts, but they are usually much too busy to read my drafts.

This is not transformative. I am substituting ChatGPT for my busy friends. This has changed the speed and efficiency of my writing, but it has not significantly altered my workflow (yet).

Everyone seems to think that AI has changed everything, but no one seems to be able to identify what has changed. It probably took several years in the early 80s for the impact of spreadsheets to sink in. We had to play with them for a while to figure out how they changed our thinking.

I heard a speaker refer recently to AI as either a “toy, tool, or teammate.“ I don’t like the implication of separation between the three. We should play with our tools and our teammates in order to create innovative ideas. Therefore, everything should be a toy.

VisiCalc let us play with numbers in ways that were impossible before. AI lets us play with ideas in ways that were impossible before.

The dominant Generative AI tools today don’t make that very obvious. They reflect existing metaphors. It’s not obvious to most people how they synthesize information and so they look like more sophisticated versions of legacy information retrieval systems, like Google search.

We are still operating like we’ve been operating for the last quarter century. Sure, ChatGPT and other large language models give us more useful results than a Google search has done, but that’s not because they represent a new modality for thinking.

VisiCalc changed how we think. It grew out of the computational capabilities provided by access to PC computing power.

AI is not VisiCalc in this metaphor. AI is the PC. Generative Large Language Models are engines. We are still building cars around them to attach them to transformative action in our business and personal lives, much like VisiCalc transformed the utility of the Personal Computer.

Scott McCloud refers to these kinds of transformative changes as “durable mutations.” He describes durable mutations as lasting shifts in how we think and create, not just passing trends. Spreadsheets did that for the PC. We are still looking for AI’s durable mutations of how we work and live.

The thing that makes Generative AI unique is its ability to synthesize information at scale. This enables play on a vastly larger scale than the labor-intensive tools of the last 40 years have let us do.

The revolution that VisiCalc ushered in was systems that automated calculation. AI automates creativity.

Durable mutation will come from our willingness to play with boundaries in what we think is possible in the ideas space, just as VisiCalc encouraged us to play with boundaries in the business space. AI allows for unstructured combinations of ideas. That is where its power lies.

Instead of employing technical people to create macros for data, we can skip that step and let AI augment our vision in ways that show us the possibilities rather than constraints. We must allow ourselves to play in order to complete our unfinished digital evolution.

There is a world market for maybe five computers. – Thomas Watson, CEO of IBM in 1943

Knowledge is power and so it tends to be hoarded. Experts in any field rarely want people to understand what they do, and generally enjoy putting people down. – Ted Nelson

Do you remember when we would say “power to the people?” Thatʼs what I’m doing, I’m giving power to the people. Iʼm building a computer that every person can put on their desktop, and Iʼm going to get rid of the high price of the mainframes. – Steve Jobs

Bigger is not better in computing technology. Small tech is where the actual power has been over the last 50 years.

When we democratize computing power (or power in general), we create the seeds for actual change. In doing so, we have harnessed the power of millions of imaginations. That is where the actual power lies.

Every technology goes through a phase where people can’t wrap their heads around what the new technology is for, how it works, or how it changes our social and economic paradigms. This is where we are now with Generative AI.

In the 90s, the problem was getting people to think of the Internet as anything beyond an “online billboard.” This line of thinking only extended the existing models of the roadside billboard or the display ad in a newspaper or magazine.

In another example, the 1950s saw a palpable excitement over the possibilities of computing technology. You saw this in science fiction story after science fiction story as they expanded on the power of postwar computing. However, it also reflected in the dominance of IBM’s thinking through its CEO Thomas Watson, who famously predicted in 1943 that there wouldn’t ever be a market for over five computers in the world..

This reflected industrial thinking. In that paradigm, the bigger you made it and the more it was consolidated, the more powerful it would be.

The Industrial Age dehumanized people by making them part of vast machines and systems of production. Computing reversed this logic as Douglas Engelbart, Ted Nelson, and other visionaries in the 1960s foresaw. Their revolutionary insight was to humanize computing by making it smaller, more accessible, and more intuitive.

However, if you had asked a journalist, businessperson, or ordinary consumer in 1970 how they envisioned the future of computing, it wouldn’t have been that different from Thomas Watson’s 1943 prediction. They did not see what people like Wozniak and Jobs were building in their garages.

Our vision of AI seems based on the big models of social media and search engines of yesterday instead of imagining how the technological world might become more humanized. National competition and a focus on “big” AI further divorces ordinary people from technology. Lack of access to this infrastructure will exclude those who can’t afford it.

This isn’t necessarily how it will play out. AI will get smaller and more efficient.

Right now it doesn’t seem like this kind of world, but that is a vision of technology that we are being sold like IBM sold mainframes in the 1970s. Companies want to consolidate power because it gives them control over market share and profit.

We’ve seen massive consolidation occur through the tech giants that emerged from Silicon Valley over the last two decades. Open AI is mimicking the business model established by Facebook and Google.

This phenomenon could be transitory. However, it is a narrative that the media, itself until recently monolithic, can wrap its head around and sell.

The story of big is easier to tell than the quiet revolutions that are taking place on desktops and phones across the country. Our storytellers don’t understand the technology, much less the story. The panic of Monday, January 27th, was driven by ill-informed media reporting.

We feel compelled to follow narratives when we do not understand what’s going on. This is particularly true with a fast-moving technology like AI. We almost need AI to understand AI.

I talk with a broad range of people, some of whom are very technical. Even those technical people who understand large language models developed in an environment dominated by giants like Google, Apple, Amazon, and Microsoft. Similarly, IBM, Honeywell, and UNIVAC dominated these narratives in the 1960s.

AI is software. It doesn’t suffer from the same sort of physical limitations that developing microchips, hard drives, and displays involves. Therefore, it has even fewer physical constraints about how it might evolve.

We look at most AI models today and associate them with the brute force models developed by companies like OpenAI, Google, and Perplexity. We think they have all the answers because they are dominating the narrative like IBM did in the 60s.

This is only one version of reality, however. For instance, if you combine the computing power of all of our PCs with our mobile devices in the US, it comes out to around 130 Exaflops. Aggregating all the commercial data centers in the country only gets you around 20 Exaflops or 1/6 as much computing as is in the hands of individuals. (I had ChatGPT crunch these numbers for me.)

More importantly, you vastly increase the number of minds at play with a disaggregated model. PCs produced most of the billionaires of today. AI will produce the billionaires of tomorrow. We don’t want those to be the same people. The ideas and products they produce will be far less transformative than otherwise.

Small footprint information models are already beginning to prove their utility. I’ve already begun experimenting with a Retrieval-Augmented Generation (RAG) system, Google’s Notebook LM. Other lightweight models are proliferating, particularly in the Open Source world, like Mistral and Llama 3, that don’t require massive computing power to operate.

We are reaching the limits of data collection for AI models as well. We have scraped the internet for our existing AI models. Doing it repeatedly is only going to yield marginal results.

Eventually, someone is going to develop a comprehensive open data set any AI model can access. If you don’t have to reinvent the wheel with your model every time, this also reduces size and power requirements.

Just like with the PC, as the overhead gets smaller, it becomes more accessible to everyone. Sure there will still be some need for large complex server farms to crack the most complicated problems we’re looking at, so Nvidia-style chips will still have a home at the bleeding edge.

However, most of us won’t need that kind of horsepower. We already have so much power in our hands (120 Exaflops, to be exact) that this power will migrate from the large corporations and their black boxes.

AI disrupts information silos. If the source of any company’s power is its database, AI will erode that power. AI will either find the information elsewhere or triangulate on what’s missing and develop information that way. That is a losing corporate strategy.

The Information Age is over. We are now entering the Connection Age. To see that, it pays to look at what humans are doing, not corporations. Humanized technology will always win out in the end.