Presented by Fivetran
Your pipeline works great for 3 data sources. But what happens when you hit 30? Or 300?
Building custom connectors is fine until you have to scale. Fivetran takes the headache out of the equation by handling the schema changes, API updates, and retries across hundreds of sources, delivering analytics-ready data in hours.
They handle the ingestion mechanics. You handle the actual data strategy, modeling, and AI.
Try Fivetran free for 14 days.
The Post-Literate Engineer
I’ve read my whole life, being jokingly called “a book with legs sticking out the bottom,” to borrow a phrase from Charlie Munger (also a big bookworm). If I’m not working, I most likely have a book glued to my face (reading is working too, I guess). Every week, I still get through a book or two, plus a pile of dozens of articles on my iPad. My favorite thing in the morning is to spin bike on my Kaiser M3i and read on my iPad for an hour or so. But reading feels different than it did in the past. I used to knock out a 300-page book in a day. I still can, but I’m time and attention-starved, and I notice reading just takes more deliberate effort now.
I’m sitting here holding the print edition of The Atlantic (yes, an actual physical magazine, because apparently I’m fancy like that) reading a piece called “The Age of Reading Is Over.“ It’s about whether civilization survives the post-literate era. It’s also about eleven pages long, which might be the funniest and most ironic part of the whole thing.
Marshall McLuhan called this in the 1960s. In The Gutenberg Galaxy, he argued that the printing press didn’t just give us books. It rewired how humans think: linear thought, individual authorship, sequential arguments, mass-producing information. The whole idea of the literate, rational person came out of that world. When you think of a “person of letters,” the image of a bookwork comes to mind. McLuhan figured electronic media like TV (new back then) would start reversing it: culture getting more immediate, visual, fragmented, oral, collective. He wasn’t saying reading or writing would disappear. He was saying literacy would stop being the operating system of society.
I think we’re watching that happen right now. The other day at the gym, I saw a woman with a copy of Atomic Habits (one of the best-selling books of all time) next to her. I was going to ask her what she thought of the book, but I noticed she never once picked it up, and instead spent her time between workouts scrolling TikTok. I joke the universal posture of humans is our heads down, staring at screens.
By nearly every account, deep reading of long-form is dying. Strangely, we’re not information-poor. If anything, we’re drowning in more information than ever - short videos, podcasts (like mine, hi), social feeds, AI convos and summaries. What’s different now is the sheer constancy of the competition for your attention.
So what does any of this have to do with engineering?
A lot, actually. We are entering the age of the post-literate engineer.
Generating Stuff Is Cheap. Judgment Is Scarce
For most of our industry’s history, you had to be literate in the thing you were making. Read code to write code. Understand SQL to write a real query, and ideally understand it well enough to read the query plan. Reason through a schema before you built one. Comprehend how various parts of architecture work together.
I see this breaking down, where our ability to produce stuff and comprehend it is diverging. Now, an engineer can generate code, pipelines, tests, docs, infrastructure, schemas, and whole architecture diagrams in minutes. Without being able to explain or evaluate a single piece of it. You’ve seen this. I’ve seen this. We do it all day. The assumption is “I can produce this, therefore I understand it.”
This is not an argument against AI-assisted engineering (I did warn about this last year in my talks on the Great Pacific Garbage Patch of AI Slopware). AI is real leverage for a strong engineer, in the way power tools are real leverage for a skilled carpenter.
There’s also false leverage in the hands of someone who’s unskilled, with AI tools creating a bigger blast radius with no corresponding increase in understanding. Last night I was talking with someone about people at his company using AI to create forecasts, but without understanding basic stuff like backtesting the results of the forecast. Not good. This plays out in various ways daily, where we have a mass Dunning-Kruger delusion of productivity, at the expense of understanding what the hell we’re doing.
Producing almost any technical artifact like code, a data model, etc now costs almost nothing. Maybe a few minutes and some tokens. Evaluating and understanding the output is the new bottleneck. I suspect this bottleneck will only explode as agents produce more artifacts.
Humans Reading/Writing Less. Machines Reading/Writing More.
Here’s the paradox. Humans are reading and writing less right as machines are reading and writing more. Agents need documentation, metadata, lineage, policy, definitions, decisions, constraints, and context. Constantly, and at volume. They need to know what terms mean, which sources to trust, what they’re allowed to do, where information came from, and what other agents already did with it. The end state is a civilization producing more written material than any before it, most of it written by machines, for machines, read by almost nobody. This is a strange inversion where humans go post-literate right as the infrastructure underneath them goes hyper-literate.
I don’t think we’re headed back to an era where everyone hand-writes every line of code. Remember when every kid in the 2010s was supposed to learn to code? The times sure have changed. CS enrollment is reportedly dropping in places now, and I’m not sure what the future of technical education looks like in a few years. Most engineers (as shown in my surveys and elsewhere) use AI to write most, if not all, of their code. I hear arguments that nobody needs to learn to code or understand things like architecture, how infra works, data modeling, system design, etc., since AI will do it all. I think these arguments are superficially correct while also totally idiotic.
Less manual coding doesn’t mean technical understanding stops mattering. Understanding just moves up a layer of abstraction. You still need to know what the system does, who it serves, how it fails, and what trade-offs got baked in. Ignore this at your peril.
If anything, understanding things matters more now. AI lets you build bigger systems faster. It also lets you be confidently wrong at a much bigger scale and blast radius. The AI can fart out artifacts all day. It doesn’t own the consequences of those artifacts. You do. Perhaps this will be a moot point in a few years when AI is shipping things to production with no human intervention. That will truly be the post-literate engineering moment.
For now, the post-literate engineer isn’t just someone who uses AI. Soon that’ll be everyone. The post-literate engineer is someone who ships systems they can’t read, explain, challenge, or own.
The Post-Literate Future
McLuhan may turn out to be right that literacy stops running the show. I have no idea what comes after. Maybe some machine-assisted enlightenment, maybe we all end up strapped into headsets like WALL-E, maybe it’s closer to Idiocracy.
What does this mean for accountability and responsibility for the things AI builds (and destroys)? Someone still has to understand what the system is doing. Someone still has to decide whether they should be doing it. Someone still owns what happens next. Engineering is going post-literate, but can’t go post-responsibility/accountability. That might be the defining problem of software and data engineering for the next ten years.
In this Freestyle Friday episode, I chat about the various craziness and trends of AI right now.
Freestyle Fridays and my other podcasts are available on Spotify, Apple, and wherever else you get your podcasts. Please support the show with a review. It means a lot.
Presented by Revefi
Save serious money on your cloud costs with Revefi’s new autonomous AI DBA, a tool built to handle the gritty reality of cloud data management so you can stop babysitting your infrastructure.
With a five-minute, zero-touch setup, it deploys 18 specialized agents across your data estate to automatically manage FinOps, performance tuning, and data quality.
If you want to cut your cloud costs by 30% to 70% and get back to actual data architecture, engineering, and AI, check out Revefi and start saving money today.
Start saving money on your cloud costs with Revefi
Where I’m At
My fall calendar is shaping up, and here’s an idea of what I’ll be doing.
Big Data London. September 22-24. Register here.
Got a very busy fall travel schedule in the US and Europe. More to be announced very soon.
Cool Videos and Reads
I sat down with Sharon Goldman - AI journalist, formerly at VentureBeat and Fortune, now running her own platform, Ground Level AI on Substack - for a conversation that went a lot further than I expected.
We cover a lot of ground about the AI backlash, data centers, AI hype vs. reality, and the future of writing.
Check out Sharon Goldman’s Ground Level AI.
Matthew Skelton joined the Practical Data Community book club to discuss his book, Team Topologies. We also discuss what team topologies looks like for agents. Definitely a rare treat to hear from Matthew. Enjoy!
Also, it looks like the 2nd edition Kindle version is on sale for $2.99 right now. Get it!
Laura Hutchins gives a practical tour of the most physical layer of the modern data stack: the data center. She covers the power systems, cooling infrastructure, and real-world constraints behind the cloud. This is from the Practical Data Community Lunch and Learn.
Here are some things I read this week that you might enjoy
So, is data modeling dead?
Daniel's spot on, and his job posting test proves what practitioners in the trenches already know. Cheap compute let an entire generation of engineers trick themselves into believing they could skip business logic and just dump raw Parquet into cloud storage. That works great right up until your AI agents and analysts hit a wall because nobody defined what a 'customer' actually is. As I wrote in the comments, if you think data modeling is overrated or dead, FAFO. (Data Engineering Central)
Becoming Frontier
Atlan is showing their work on internal AI adoption, and I respect the open lab approach over standard marketing fluff. Most tech vendors just hand you a polished press release, but sharing raw agent commits and operational experiments is actually useful for practitioners. The real test is whether these autonomous workflows survive contact with chaotic real-world data without breaking down customer trust. But I trust that the Atlan team has some ideas on how to sort this out. (Atlan)
A year of RDF in DuckDB
Embedding RDF inside DuckDB tackles the main reason graph tech never took off: miserable tooling and isolated infrastructure. What catches my eye isn't just the SPARQL support, but how an experienced architect used AI assistants to jump back into low-level C++ and ship real software. System design and fundamental engineering still do the heavy lifting here, not the prompt. If you actually understand how the plumbing works, LLMs let you build niche data infrastructure at a pace that was unthinkable five years ago. (NoNodeName)
Larry Ellison Bet It All on the A.I. Boom. Will He Be the Face of the A.I. Bubble?
Leveraging mountains of debt to build data centers before anyone proves the real unit economics of generative AI is classic tech mania. Larry Ellison is running his old aggressive playbook at a ridiculous scale, assuming infinite compute demand will bail out the balance sheet. But compute isn't magic. If actual enterprise utility doesn't show up to pay for hundreds of billions in capex, Oracle is going to hit a wall. (NYTimes)
Note: These are articles I’ve read and enjoyed. I use AI to summarize my thoughts on the articles. I edit the summaries.
Find My Other Content Here
📺 YouTube - Interviews, tutorials, product reviews, rants, and more.
🎙️ Podcasts - Listen on Spotify or wherever you get your podcasts
📝 Practical Data Modeling - This is where I’m writing my upcoming book, Mixed Model Arts, mostly in public. Free and paid content.
If you’re interested in sponsoring my newsletter and podcast, Q3 and Q4 2026 are opening up. Space is very limited. Please fill out this form if you’re interested.
The Practical Data Community
The Practical Data Community is a place for candid, vendor-free conversations about all things tech, data, and AI. We host regular events such as book clubs, lunch-and-learns, Data Therapy, and more.
I remember DE approaching me asking for recommendation to learn data modelling. I listed them some books for which they replied - "yeah but that's more than 400 pages and it's for sure outdated now. do you have something simpler and faster?"
I already work with DEs for whom "code review" is asking AI to do the review and copy/pasting results back.
Now we can complain about all of these but important question remains - what to do with this? I, similarly to you, don't have the answer.
This post-literate approach seems completely broken for me. I wonder if world around will verify it and correct over time. OR... if it will adapt, build workarounds and normalize it.
Time will show.
btw, this lady with the book on gym is such a telling example... book as a symbol / appearance. Not as a source for knowledge.
For me, there is a tension in deciding to go through the slow rigors of learning organically through books, study, and hands on application knowing that it is costing me time spent "productively" through heavy AI utilization. However, I push forward with the belief that becoming learned and literate will lead to more beneficial AI use in the future than if I were to depend on it now without foundational knowledge, as if it were some mystical power.
In short, I'm trying to touch on the nagging feeling that if you are spending time learning you aren't moving fast enough and will be left behind.