Sources of truth vs. a hallucination of consensus: Toward a common vocabulary of AI infrastructure
I spent the past month in New York, dividing my time between client work and hangouts with new friends and old. I talked myself hoarse and wandered about in four boroughs (sorry, Staten Island), and I'm feeling refreshed (with all the global distress caveats).
In Manhattan they're building the Bus Terminal of the Future, but all the pedestrians still need to cross the street. We can walk in the bike lane—a dangerous proposition this close to the Doordash throughways—or we can take the garbage bag sidewalk, where an unhoused man is having a very bad day.
One dude remarks to another, "Mad inconvenient infrastructure, man."
The next night New York understandably struggles to accept two inches of rain in one hour, and the subways flood. The following morning the tunnels are puddly and putrid, but the subways run fine. I arrive at work on time. Mad convenient infrastructure, man.
Infrastructure, no matter how solid or planned or continuously improved, gets messy. Observing humanity in July feels like the ultimate UX research experiment. People spill out the edges of the design, behaving all the ways, taking the environment for granted, accepting the strange fusion of industry and professionalism and construction and youth and exuberance and humanity like it's the most natural system in the world.
Terms and tools to master semantic optimization and contextual discovery
If you're reading, it's likely you value structure in some form. Your title or vocation probably involves some combination of the words "digital editorial content strategy information design storytelling." You enjoy correct, well-organized information that's entertaining to navigate. You'd prefer not to hang on the garbage bag sidewalk if you can help it. You'd prefer to build the flood-proof subway.
The preferred infrastructure of the AI era will be in flux for years. But some tools and terms have emerged to support the little information cities we're building (for whatever reason). If you're building content to be read by people, crawlers, or agents, the next few weeks of The Content Technologist will cover concepts to build better in our new weird, wild, digital epistemology.
This series will include digressions on:
- Language processing algorithms, formulas, and concepts
- Schema
- Machine learning and neural networks
- Knowledge graphs
- Retrieval augmented generation (RAG)
- Transformers
- Word2vec
- Model context protocol (MCP)
- LLMs vs other AI models
- Ontology and taxonomy
In each issue, I'll outline my understanding of each concept to the best of my abilities. Whether you're jaded or enthused by AI, I hope understanding how these systems work helps us all survive the hype, sift through what's good, and create better content.
Establishing structure—for clients, for users, for audiences, for stakeholders, for this newsletter's editorial schedule—keeps our information systems prepared for complexity and unpredictability. Perhaps it's because my entire worldview and education is downstream from Jane Jacobs, Sesame Street, and The More You Know, but building media and information systems is only going to get more confusing from here. We might as well have a common vocabulary and understanding of how different elements of AI systems work, practically.
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If you have questions, comments, expertise, doubts, concerns, terms you'd like to discuss, as always, feel free to reply or leave a comment.
The Content Technologist is a newsletter and consultancy based in Minneapolis, working with clients and collaborators around the world. The entire newsletter is written and edited by Deborah Carver, an independent content strategy consultant.
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Cultural recommendations / personal social: Spotify | Instagram | Letterboxd | PI.FYI
Did you read? is the assorted content at the very bottom of the email. Cultural recommendations, off-kilter thoughts, and quotes from foundational works of media theory we first read in college—all fair game for this section.
I'm in the middle of a Halt and Catch Fire rewatch, and it's fascinating how the rhetoric of tech marketing repeats itself in every era. If you don't get on board with the new technology now, you'll be left behind!
If haven't seen it, H+CF is to tech what Mad Men was to advertising: a savvy, researched industry and workplace drama, but with more women in leadership roles (we can all dream!). It's only streaming on AMC+, a service no one has, but you can purchase all four seasons for $30 on Apple TV.
The new season of Poker Face is similarly excellent. We're savoring the writing, style, and cameos very slowly, and I'm developing a parasocial relationship with the idea of Charlie Cale.