Information Architecture in the Age of AI, Part 1: The Many Faces of AI

Image by DALL·E - An abstract and surrealistic representation of 'the many faces of AI' in a busy city environment. Bright colors dominate the scene. Human and robotic.

(Image by DALL-E)


I’m not sure where I am in the race to be the last person to write about IAC24, but I have to be pretty close to leading the pack of procrastinators. The annual information architecture conference was held in my home city of Seattle this year, and despite not having to travel for it, it’s taken me a while to consolidate my thoughts.

No matter. It’s not about being first or last, just about crossing the finish line. So here I am, like a pokey yet determined walker in a marathon, presenting the first of a four-part series of posts on IA and AI, mainly inspired by presentations and conversations at IAC24 and things I’ve noticed since then as a result of better understanding the field.

In this first part, like a good information school graduate, I argue for better definitions of AI to support better discussions of AI issues. The second part covers what Generative AI seems to be useful for, and the third part is about the dangers and downsides of Generative AI. Finally, I’ll look at the opportunities for the information architecture practice in this new AI-dominated world, and I’ll suggest the proper role for AI in general (spoiler: the AI serves us, not the other way around).

Note: I used ChatGPT to brainstorm the title of the series and to create a cover photo for each post. Other than that, I wrote and edited all the content. I leave it as an exercise to the reader to decide whether an AI editor would have made any of this better or not.

The many faces of AI

Let’s start with some definitions.

AI is not a single thing. Artificial Intelligence is a catchall term that describes a range of technologies, sub-disciplines, and applications. We tend to use the term AI to apply to cutting-edge or future technologies, making the definition something of a moving target. In fact, “AI Effect” is the term used for a phenomenon where products that were once considered AI are redefined as something else, and the term AI is then applied to “whatever hasn’t been done yet”[1].

So, let’s talk a bit about what we mean when we say “AI”. As I learned from Austin Govella and Michelle Caldwell’s workshop “Information Architecture for Enterprise AI,” there are at least three different types of AI:

  1. Generative AI creates content in response to prompts.
  2. Content AI processes and analyzes content, automating business processes, monitoring and interpreting data, and building reports.
  3. Knowledge AI extracts meaning from content, building knowledge bases, recommendations engines, and expert systems.

Most of us are probably barely aware of the second two types of AI tools, but we know all about the first one. In fact, when most of us say “AI” these days, we’re really thinking about Generative AI, and probably ChatGPT specifically. The paradigm of a chat interface to large language models (LLMs) has taken up all the oxygen in AI discussion spaces, and therefore chat-based LLMs have become synonymous with AI. But it’s important to remember that’s just one version of machine-enhanced knowledge representation systems.

There are other ways to slice AI. AI can be narrow (focused on a specific task or range of tasks), general (capable of applying knowledge like humans do), or super (better than humans).[2] It can include machine learning, deep learning, natural language processing, robotics, and expert systems. There are subcategories of each, and more specific types and applications than I could list here.

The different types of AI can also take different forms. Like the ever-expanding list of pumpkin-spiced foods in the fall, AI is included in more and more of the digital products we use, from photo editors to writing tools to note-taking apps to social media… you name it. In some cases, AI is a mild flavoring that gently seasons the UI or works in the background to enhance the software. In other cases, it’s an overwhelming, in-your-face, dominant flavor of an app or service.

As IAs, we need to do a better job helping to define the discrete versions of AI so that we can have better discussions about what it is, what it can do, and how we can live with it. Because, as Kat King pointed out in her talk “Probabilities, Possibilities, and Purpose,” AI can be used for good or ill. Borrowing a metaphor from Luciano Floridi, Kat argued that AI is like a knife. It can be like a butter knife – something that is used as a tool – or like a rusty bayonet – something used as a weapon.

In containing this inherent dichotomy, AI is like any other technology, any other tool that humans have used to augment our abilities. It’s up to us to determine how to use AI tools, and to use them responsibly. To do this, we need to stop using the term “AI” indiscriminately and start naming the many faces of AI so that we can deal with each individually.

There are some attempts at this here, here, and here, and the EU has a great white paper on AI in the EU ecosystem. But we need better, more accessible taxonomies of AI and associated technologies. And, as IAs, we need to set an example by using specific language to describe what we mean.

So, in that spirit, I’m going to focus the next two parts of this series specifically on Generative AI, the kind of AI that includes ChatGPT and similar LLM-powered interfaces. In Part 2, I’ll talk about the benefits of GenAI, and in Part 3, I’ll look at GenAI’s dark side.

  1. Per Tesler’s Theorem, apparently misquoted.  ↩

  2. General AI and Super AI are still theoretical. ChatGPT, Siri, and all the other AI agents we interact with today are considered Narrow AI.  ↩

Are Generative AIs just really expensive Ouija boards?

I wonder if Generative AIs are like Ouija boards. Every time I hear someone get freaked out about something that an AI wrote, they ascribe human characteristics to it. They anthropomorphize the computer program and act as if there’s a thinking being expressing human-like desires and feelings. But what the AI produces exists in dialog with the humans who read it. It both comes from human thought (because it has access to a vast quantity of human writing) and it gets filtered through human brains.

We interpret works of art the same way, as if if each piece has a specific, inherent, immutable meaning. But each of us brings something to a work of art. We bring our experiences and biases and we project our own identities on the work in front of us. We create meaning in dialog with the art. The art doesn’t mean anything without an observer. A blob of AI text doesn’t mean anything until we invest it with meaning.

The current crop of AIs are really good at what they do, but they don’t think any more than a Ouija board thinks. A Ouija board isn’t controlled by a supernatural being, but we can convince ourselves that it is if we want to. The AIs are a reflection of ourselves, and that reflection can often fool us into thinking there’s something in there. Like a parakeet with a mirror. It’s a really cool and useful trick, but let’s not give the really clever software more credit than it deserves.

Just bought my first carbon removal offsets on It was super easy, and I wish I had done it sooner.

Well, I’ve done it: I finished my Sustainability Certificate from UCLA Extension. I enrolled in it because the climate crisis felt so huge that I couldn’t begin to think about it. I’m far from an expert now, but I at least I can understand the big picture. Now to figure out how to apply what I know…

Ecological tipping points could occur much sooner than expected, study finds

“More than a fifth of ecosystems worldwide, including the Amazon rainforest, are at risk of a catastrophic breakdown within a human lifetime.”

♻️ A promising development in plastic recycling: Scientists convert everyday plastics into fully recyclable and potentially biodegradable materials If it’s scalable, plastic waste could become raw material for plastic with the same qualities as that created from virgin petroleum. No new oil needed.

Just getting the word out there: A lot of us support climate change policies… “Research published in 2022 in Nature Communications showed that although 66 to 80 percent of Americans support climate change policies, they think only 37 to 43 percent of the population does.” –Scientific American

Boy… losing Mrs. Maisel, Barry, and Ted Lasso in the same week has me a bit emotional. Three great shows with crackerjack writing and exceptional ensembles. I’ll miss them all.

A factoid I read tonight and can’t get out of my head: In 1978, there were 4 billion people on earth. Today there are over 8 billion. By 2100, there will be 10.1 billion.

Congratulations to the Toronto Maple Leafs for getting their first Round One playoff win in nearly 20 years. And big cheers to my Tampa Bay Lightning, who have had a hell of a few years of playoff hockey. Three Cup finals in three years, winning two… that’s a hell of a run.

And that’s it for the climate quiz. Hope you enjoyed it. Remember: climate change is real and scary, but it’s not hopeless. Lots of people are working on ways to keep the worst of global warming at bay, so educate yourself, and do what you can to help. 4/4

Microscopic fossil shells can reveal climatic conditions by the amount and type of calcium carbonate in their shells. has a great primer on “How ‘proxy’ data reveals the climate of the Earth’s distant past” 3/4

You’re familiar with paleoproxies if you’ve ever heard of studying tree rings to understand historic periods of drought, pests, or fire. Evidence of chemical changes in air and water can also be found trapped in layers of ice drilled out of ancient glaciers. 2/4

Today’s answer: By studying “paleoproxies” such as ocean sediments and sedimentary rocks, we can study ocean and atmospheric temperature from as many as tens of millions of years ago. 1/4

Sooo… How far back can we measure ocean and atmospheric temperatures? a) Hundreds of years b) Thousands of years c) Hundreds of thousands of years d) Millions of years has a great dashboard of climate indicators so that you can check out some of this evidence for yourself. Just take a look at some of the trend snapshots. The trends are… not great.


… more things you can measure to look for evidence of climate change: 6. Strengthening mid-latitude westerly winds 7. Fewer extreme cold events / more extreme heat events 8. Increased precipitation events 9. Biological and ecological changes


Things you can measure to look for evidence of climate change include:

  1. Surface, oceanic, and atmospheric temperatures
  2. Ice melt
  3. Sea level rise
  4. Storm frequency and intensity
  5. Reduced snow cover … 1/3

Next question: What types of physical evidence could you look at to understand whether climate is changing? What could you measure?

Okay, let’s check your answers: Aside from methane and carbon dioxide, natural greenhouse gases include: water vapor, nitrous oxide, ozone

Synthetic greenhouse gases are: chlorofluorocarbons (CFCs), hydrochlorofluorocarbons (HCFCs)