I turned 49 yesterday. Every year around my birthday, I take stock of what is actually working, what is noise, and what I’ve had to learn the hard way. This isn’t meant to be a “happy birthday to me” post, and it will be a bit different from my typical data/AI-oriented themes, but that’s for the better. The last few weeks have had some doozies, and I think we need a break from the typical “Joe Rant.”

A lot of people ask me for advice (career, health, life, etc), so I figure this is also a good place to lay out some notes, rather than repeat myself. There are lots of books from very smart people offering advice, with varying degrees of applicability to your life. What I’ve learned is no matter how much you read and get advice from others, you’ll have your own experience learning things the hard way. I guess that’s how it’s supposed to be. What I write here is only my perspective. Your mileage will vary.

The TL;DR for me right now is that life got better when I stopped optimizing for appearance and started optimizing for durability, focus, relationships, and real work.

I’m not sure how old you are, but let me start with the bad news. If you’re in your 20s and 30s, science shows that your worst years are ahead of you. If you’re in your 40s, at least statistically, you’re smack dab in the middle of this trough. This is called the “rush hour” of your life, usually culminating in your midlife crisis. It sneaks up on you. For me, it happened in my mid 30s. I was unsure of what direction to take my career, but I had a nagging sense that I was going the wrong direction - shitty bosses, pointless jobs. What motivated me was raising new children and feeling like I needed to do more.

Around ten years ago, I decided to make a change. I quit my job, went off on my own, and haven’t looked back since. I’m not saying this is the wisest thing to do, and you should do it only if you’ve got financial reserves and something to transition into. You might not be in a spot to do this for a number of reasons. But we had the financial means to make this move, I wasn’t getting any younger, and so it went. I’d tried to go off on my own a few times before, but it didn’t pan out. I realized that in those failed cases, I was impatient with the process, expecting things to happen immediately. Making a change like that requires more than just a simple ROI calculation. You need to realize you’re also changing who you are - your temperament, attitude…everything, really. That metamorphosis needed time to build, and by the time I was ready to really make the jump, I had changed internal self-talk and expectations so I knew I was ready. Again, your situation will be different. Whatever big decision you need to make, just know there’s never a perfect time. You’ll never be totally ready, but you’ll know it’s the right decision to make, and have enough trust in yourself to see it through.

Back to the midlife crisis. It does get better. For me, it got better in my early 40s. Today, the worst is behind me (I might also be delusional). Here are some lessons I learned the hard way.

Do More By Doing Less

We’re told that we must always do more. We need more projects, more money, more fancy toys to impress our friends who also accumulate more fancy toys to impress their friends. More this, and more that. Most problems in work and life are not solved by adding more inputs, commitments, goals, or complexity. Here’s what happens.

• More projects can mean less progress.

• More information can mean worse thinking.

• More ambition without selectivity becomes fragmentation.

• More money, more problems (so does a lack of money).

You need to cut out the things that aren’t serving you. Subtraction is an adult skill that you learn once you’re done saying “yes” to everything, and realizing the most powerful word in your vocabulary is “no.” The second most powerful word is “enough.” Know there’s an opportunity cost to every decision, and most things simply aren’t worth doing. Be very selective. This means understanding that:

• Not every opportunity is worth taking.

• Not every job is going to be perfect.

• Not every smart person is worth working with.

• Not every audience is worth chasing.

• Not every expansion is growth.

• No deal is better than a bad deal.

Say no to most things, and know when you’ve got enough. Striving toward goals (especially those put up by other people) can feel like chasing a mirage, because it is. Be clear on what you want, and be very selective with your choices and actions.

Energy and Attention Are More Important Than Time

People talk about time management, but the real constraint is energy, clarity, and attention. If the luxury were simply having more time, what would you do with it? How often have you been on vacation and seen the people around you staring at their phones? I’ve been guilty of this, staring at my phone and missing out on the places I visit. It’s a shame.

Back to the less-is-more advice, I’ve learned that optimizing my energy levels gives me the clarity and attention I need for various pursuits. A focused two hours beats a scattered ten.

People say that time is your biggest asset. It depends on how you bring yourself to the occasion. You can have all the time in the world. But if you’re wasting it, who cares? Energy gives you the ability to leverage your time wisely. Protecting energy is not indulgence. It is a responsibility.

On that note, what you pay attention to becomes your days, and your days become your life. Too much internet and doomscrolling make you reactive. Too many open loops destroy depth. The modern world is optimized to fragment you. This is why I like to unplug, read, write on paper (and now on a Remarkable tablet), and go for walks without my phone. Guard your mind more aggressively than your calendar. This will also increase your energy levels.

Energy in, energy out.

Real Work Compounds. Performative Work Does Not.

As I’ve said before, there’s a lot of LARPing in the business world. Lots of people cosplaying business - getting promotions, moving up the ladder, working at a fancy company, winning “awards,” and other superficial and performative achievements. But this is not real work. There is a difference between looking productive and producing durable artifacts.

Do the work that compounds over time. Read books. Write articles and books. Generate new ideas and products. Build things. All of this work compounds over time. Your reputation will compound, too. On the other hand, shallow busyness and business cosplay do not compound. It’s a treadmill that makes you run faster and faster, but you’re stuck in place.

This goes back to when I took a bet on myself and left my job. I was tired of pretending to care about a job and workplace where I was phoning it in. I wanted to do real work in areas that I cared about. Years of real work got me to where I am today, and I still feel like I’m just getting started. You’ll know when you’re doing real work because it won’t feel like work. It will feel like your obsession and mission in life, and you do it because it’s uniquely you. At some point, you need to start playing your game, not the game dictated to you by others.

Your Body Keeps the Score…and the Upside

I’m lucky to have been an athlete all of my adult life - climbing, Crossfit, lifting, a mediocre runner, hiking/trail running, etc. Unbeknownst to younger Joe, that’s paid off big time! As I get older, I realize that aging well is not passive. You need to stay active, even if it’s just a daily walk (or whatever you’re capable of doing). Strength matters. Endurance matters. Mobility and recovery matter. Diet matters. You can get older and better at the same time if you train with intention.

On a personal note, I’m still feeling stronger, fitter, and more capable than many younger people I train with in the gym. It’s not a brag, and it’s also not luck (well, maybe it’s partly genetics). The real win is accumulating these gains over many years. If you’re younger, find a fitness routine you can do for years. It might be yoga, lifting, running, etc. But also learn areas outside your specialty and become a well-rounded performer. If you’re a runner, get stronger. If you’re strong, get endurance. All of these feed on each other to help improve your overall fitness. By the time you reach my age, you’re more likely in maintenance mode, trying to preserve whatever you’ve built over the years, as the inevitable decline of strength and power sets in. Decline is real, but so is staying active.

Relationships Are the Actual Scoreboard

Work matters, but it is not the final measure of…much of anything, really. While your friends and family might think it’s cool you’re successful at work, they probably don’t care as much as you do. And if you managed to be extremely successful, you might regret some of the things you had to set aside to get there, whether it’s time with your partner and kids, time with your friends, or time in your community. You won’t get these moments back. I know, as I’ve done it to myself, having missed key events in my kids’ lives to “go on a work trip” or some other lame excuse. To this day, these are the things I regret most. Because I’m a workaholic and have an insane travel schedule, I’m working hard to curb this behavior.

My lessons here are twofold. Presence matters more than half-hearted good intentions. Success that comes at the cost of your closest relationships is failure.

I’m sure there will be many more lessons in the years ahead. And I’m unsure if any of this applies to your life or situation. Again, I can only write from my experience and perspective. The only thing I can be sure of is that every year, I’ll look back on what I’ve learned, use what works, and throw away what isn’t serving me anymore. As I do, so you should do too with whatever you read here.

Have a great weekend,

Joe

Here’s this week’s Freestyle Friday podcast. Available on Spotify, Apple, and wherever else you get your podcasts. Please support the show with a review. It means a lot.

📝 If you’re using AI in your work, we’d love your input for this March pulse survey. It’s anonymous and only takes a one minute or less. Your response is very much appreciated.

Survey here.

Modern data modelers need to live at the intersection of business and tech. Ellie.ai allows you to collaborate effectively with business while maintaining credibility with the Tech team. Get contextual support from AI, reverse engineer anything building a repository of sources with synthetic AI generated contextual metadata while delivering insights via an MCP Server and integrating anything with full blown API support.

The full stack data modeling future is here today!

Thanks to Ellie.ai for partnering on this newsletter.

Awesome Upcoming Events

All technical. All data. All day.

On March 26, 2026, there’s a very cool single-track, deeply technical data engineering event in San Francisco.

Capped at 100 practitioners.

No salespeople. No sponsors. Just talks + the hallway track, followed by happy hour.

When: March 26, 2026 · 10am–4pm + happy hour

Where: L4, 945 Market St, San Francisco

→ Register here.

Thanks to Confluent for partnering on this event and newsletter.

🇸🇪 Sweden! See you at the Data Innovation Summit in Stockholm.

I’m doing a keynote and workshop on Mixed Model Arts: Data Modeling in the Age of AI.

• May 7 - keynote

• May 8 - workshop

Here’s 10% off: SD10OFF (good for the event. Workshop is not included)

Register here

But wait, there’s more!

Cool Videos and Reads

In this conversation, I sit down with Tim Delisle and Chris Crane, co-founders of 514, to discuss bridging the gap between software development and data engineering. We cover their experience leading global data engineering at Nike and why software teams are increasingly taking ownership of heavy analytical workloads.

We also dive into how they are building the Moose Stack to give developers a local-first, code-first analytics experience. Finally, we explore how AI co-pilots are acting like an "army of interns" to fundamentally change how we write code , and why the "personal data lake" might be the future of privacy and local compute.

Check out 514 & The Moose Stack

Here are some things I read this week that you might enjoy.

Investors spill what they aren’t looking for anymore in AI SaaS companies | TechCrunch

JSON Documents Performance, Storage and Search: MongoDB vs PostgreSQL

Has the internet stopped producing mega stars?

Harness engineering: leveraging Codex in an agent-first world | OpenAI

A GitHub Issue Title Compromised 4,000 Developer Machines | grith

AI will fuck you up if you’re not on board

OpenAI COO says ‘we have not yet really seen AI penetrate enterprise business processes’ | TechCrunch

EBITDA adjustments are getting ridiculous - PitchBook

The Ghost in the Funnel

When Using AI Leads to “Brain Fry”

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.

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.

🤖 Join on Discord

Earlier this week, I published a piece called 2028 — The Great Data Reckoning. It was supposed to be satire. A riff on the Citrini Research “2028 Global Intelligence Crisis“ paper that rattled the stock markets early this week, except pointed squarely at the data industry. A fictional memo from 2028 looking back at how everything unraveled. A faux masterpiece de jour, if you will, my dear friends.

By the time I re-read it later that day, it no longer felt like satire. As William Gibson said, “The future is already here - it’s just not evenly distributed.” The Great Data Reckoning is already here.

The article clearly struck a nerve. From what I saw, it was shared widely on LinkedIn, Substack, and in private Slack channels. That’s when I knew it had crossed some kind of threshold.

So I wanted to share some follow-up thinking that’s been rattling around my head this week, because the conversations I’ve been having since publishing that piece have only reinforced my sense that the timeline in my article might be optimistic.

Something Big Shifted Very Recently

I’ve had conversations this week with data engineers, founders, and investors. The universal vibe is that something changed in the last month or two.

I’m not talking about a gradual improvement. I mean a big change. The models got really good, seemingly overnight. What used to be a decently capable coding assistant that could write passable SQL is now producing production-quality work: pipelines, configurations, strategy documents, the works. Every new point release seems to surprise even people who use these tools daily. And I use and abuse them daily.

Multiple friends have independently told me the same thing: “I don’t know what happened, but something shifted.” The models crossed some threshold where the gap between “neat demo” and “this replaces actual work” effectively closed for a huge swath of tasks.

If you’re not feeling this shift, it’s probably because you’re not using the latest models. And if you’re not using the latest models, I hate to be that guy, but you’re woefully behind and out of step. Use the latest models ASAP.

My Own Workflow Is the Proof

Despite my occasionally crusty, cranky old man vibe, I’m staying current on what’s happening. I’m not just theorizing about this. I’m living it every day.

On a typical day, I have ChatGPT, Gemini, Codex, Claude Code, or Cowork running in the background pretty much nonstop, just building things for me and helping me with everything I can think of. It’s crazy these days. I wake up, turn on the AIs, and go make coffee. I mean, it feels wasteful not have them churning while I do something else, like waiting for my Nespresso machine to make me a cup of coffee, right? Weeks of work are compressing into hours. Hours of work are compressed into minutes.

As a lightweight example this week, I had Gemini in Google Workspace review my emails and extract all open action items buried across various threads. It did a phenomenal job. My task list? Working on automating much of that with agents. That used to be admin work I’d need to hire someone to do. Now it’s a task an agent does on a schedule. And I feel like things are just getting started. Again, the models and workflows improve almost weekly. What comes next week? Who knows.

If you’re building or maintaining production systems, the lesson from this is to use AI to reduce your toil, aka those repetitive tasks you do that suck your soul dry and should be automated. You’re bright (you’re reading this article ;) and need to spend your time doing things that matter, and automate the boring parts. This is engineering at its best.

Here’s the paradox I keep running into: for every task I automate, ten more things surface that I can now get to, as I noted a few weeks ago. Turns out, the bottleneck was never ideas or ambition. From what I’m now seeing, my biggest bottleneck was simply bandwidth. AI is blowing that open, which is both thrilling and a little terrifying to think about what it means for my calendar, as I’ve got even more things to build, not fewer. I have a goal to automate at least one thing in my life every day. And for each automation, that means at least 10 more things to address. So it goes.

The Bar Is Going Up…Way Up!

One uncomfortable takeaway from all of this is the bar for what you need to do to get paid is rising. Fast.

If your job can be described as “following documented procedures,” and let’s be honest, a lot of data engineering work falls into that category, the window is closing fast. AI’s becoming insanely more capable, and I don’t think the progress will stop, even if the AI Bubble bursts. AI just needs to be told what to do, and increasingly, the business users can tell it themselves.

I still happily pay for things where taste and judgment matter. I have designers working on my book because AI-generated design still lacks the nuance and taste that make something look great. It’ll produce something passable, sure. But passable isn’t acceptable when you’re putting your name on a physical book. The stuff that’s worth paying for right now is the stuff that requires a genuine human sensibility - aesthetic judgment, deep domain expertise, institutional knowledge, the ability to sit with ambiguity and make a call.

“AI Doesn’t Understand My Business”

This is the refrain I keep hearing from people who feel safe. And look, it’s still (mostly) true. Right now, AI doesn’t have deep context about your specific organization’s idiosyncrasies. It doesn’t know why your dbt job excludes New Jersey transactions on Tuesdays or why that one table is named final_v2_FIXED_actually_final_USE_THIS_ONE.

But here’s what I’ve learned: whatever you think AI is incapable of today, it’s going to surprise you sooner than you expect. Every time I’ve drawn a line and said, “It can’t do this,” the next model version has erased that line. I’ve given up trying to feel stodgy about AI. It will continue to improve, and that’s how it is.

And here’s the other thing nobody wants to say out loud: most engineers don’t understand the business either. A huge portion of the data workforce built careers around knowing which YAML config makes Tool A talk to Tool B. That’s not business context. That’s configuration knowledge. It’s like people who could drive railroad spikes, who were ultimately replaced by spike-driving machines. Writing configuration code is exactly the kind of thing AI can do without skipping a beat.

The people who will be fine are the ones who actually sit down with business users and ask, “What decision are you trying to make?” or “What outcome are you trying to achieve?” Try to place your job at the center of revenue-generating activities, not cost-center ones. If you can directly attribute your value to revenue, you’re in a good spot. If your value proposition is “I know how to use dbt,” I love dbt, but it’s also a means to an end. It’s not the job. If this is you, I’d start diversifying.

So What Do You Do?

If there’s one thing I want you to take away from both the original article and this follow-up, it’s that the Great Data Reckoning isn’t a 2028 event. It’s happening right now. Some people I talk to - senior folks who’ve been in this industry for decades - are thinking about pivots and next moves. They’re not necessarily panicking, but because they’re paying attention and can spot an inflection point when they see one. This might be the mother of all inflection points.

If you’re not already doing so, here’s what I’d tell you to do today:

Get good at AI. Now. I genuinely cannot believe I still have to say this in 2026, but some people tried ChatGPT two years ago, decided it sucked, and haven’t revisited. The models from two years ago did suck, relatively speaking. The current ones are a different animal. If your company won’t pay for AI tools, spend the $20/month yourself. It’s an investment in your future, and you can afford it.

Get closer to the business. If you’re a cost center with a hard time justifying what you do, you should be concerned. Find a way to put yourself in the line of fire of revenue. Understand the “why” behind the data, not just the “how” of moving it.

Build things that matter. Institutional knowledge, domain expertise, relationships, judgment - these are the moats. Not tool proficiency. Not stack knowledge. The tools are being commoditized. The human judgment layer is what’s left.

Find your tribe. As AI automates more of the mechanical work, people will crave human connection and candid conversation more than ever. Go to meetups, low key happy hours and conferences. Join online communities. Shameless plug - the need for community is a big reason why I built the Practical Data Community Discord, where almost 3,000 people have real conversations about where this industry is heading, among other things.

The world is changing at warp speed. The Great Reckoning is already here. Move accordingly.

Also, listen to this as a podcast. Available on Spotify, Apple, and wherever else you get your podcasts. Please support the show with a review. It means a lot.

Have a great weekend,

Joe

Modern data modelers need to live at the intersection of business and tech. Ellie.ai allows you to collaborate effectively with business while maintaining credibility with the Tech team. Get contextual support from AI, reverse engineer anything building a repository of sources with synthetic AI generated contextual metadata while delivering insights via an MCP Server and integrating anything with full blown API support.

The full stack data modeling future is here today!

Thanks to Ellie.ai for partnering on this newsletter.

Awesome Upcoming Events

All technical. All data. All day.

On March 26, 2026, there’s a very cool single-track, deeply technical data engineering event in San Francisco.

Capped at 100 practitioners.

No salespeople. No sponsors. Just talks + the hallway track, followed by happy hour.

When: March 26, 2026 · 10am–4pm + happy hour

Where: L4, 945 Market St, San Francisco

→ Register here.

Thanks to Confluent for partnering on this event and newsletter.

🇸🇪 Sweden! See you at the Data Innovation Summit in Stockholm.

I’m doing a keynote and workshop on Mixed Model Arts: Data Modeling in the Age of AI.

• May 7 - keynote

• May 8 - workshop

Here’s 10% off: SD10OFF (good for the event. Workshop is not included)

Register here

But wait, there’s more!

Cool Videos and Reads

In this episode, I sit down with Prashant Sridharan, a 30-year veteran of developer marketing who has shaped go-to-market strategies for tech giants like Sun Microsystems, Microsoft, AWS, Facebook, and Twitter, and currently runs product marketing at Supabase. We dive deep into the origins of DevRel and how marketing to developers has evolved in an increasingly noisy, AI-saturated landscape.

Here’s a talk I gave in Paris at Forward Data Conference (easily one of the best indie data conferences on the planet). It just dropped. Check it out. Merci.

Check out the 2026 Practical Data Community State of Data Engineering: Interactive analysis tool + dataset

The survey has an alternative super corporate BI tool, complete with 3D donut charts and speedometer charts, exports to Lotus and Crystal Reports, and more. Cutting edge BI tooling at its finest.

Here are some things I read this week that you might enjoy.

The Great Zipper of Capitalism - by Scott Werner

The Fundraising Tactic AI Startups Are Using to Juice Valuations

Brace for the Fuckening | justin․searls․co

What Data Modeling Is and Is Not - by Joe Reis

Meta Director of AI Safety Allows AI Agent to Accidentally Delete Her Inbox

2028 - THE GREAT DATA RECKONING - Joe Reis

The persona selection model \ Anthropic

How we rebuilt Next.js with AI in one week

Software development now costs less than than the wage of a minimum wage worker

Fragmentation to framework: Spec-first development at Benchling | by Eli Levine | Feb, 2026

Yes, You’re Royally Screwed. Now Here’s What to Do About It.

The Lunatics are taking over the Asylum

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.

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.

🤖 Join on Discord

What follows is a scenario, not a prediction. This isn’t a LinkedIn doom post or a subtweet aimed at your favorite data influencer. The sole intent of this piece is modeling a scenario specific to the data industry that’s been underexplored amid the broader AI displacement discourse. Our friends at Citrini Research published their “2028 Global Intelligence Crisis“ thought exercise in February 2026, and while it painted a compelling picture of economy-wide disruption, it treated the data industry as background scenery. We think it deserves its own post-mortem.

Because if anyone should have seen this coming, it was us. We built the dashboards. We had the data. We just never pointed it at ourselves.

Hopefully, reading this leaves you more prepared for the possibility that the industry you work in is simultaneously the most essential and most vulnerable layer of the AI economy.

This is the Reis Megacorp, Inc. Data Industry Memo from March 2028, detailing the progression and fallout of the Great Data Reckoning.

The unemployment rate for data professionals printed 14.7% this morning. Or 3.2%. It depends on which dashboard you check and whether the person who built it is still employed.

The irony is not lost on us. An industry that spent two decades insisting it could measure everything failed to see this coming, despite generating approximately 47,000 blog posts per quarter about “the future of data.”

Two years. That’s how long it took to get from “data is the new oil” to “data is the new asbestos — technically everywhere, occasionally useful, and the subject of a growing number of lawsuits about who’s responsible for cleaning it up.”

This memo is our attempt to reconstruct the sequence of events that brought the data industry from peak hype to what the Financial Times recently called “a $200 billion lesson in confusing infrastructure with value.”

How It Started: The Tool Rapture

To understand what happened to the data industry, you first need to understand what the data industry was, because in hindsight, it’s genuinely unclear whether anyone knew at the time.

By early 2026, the average enterprise data stack contained 37 tools. Not because 37 tools were needed, but because each one had a venture-backed sales team, a conference booth at Snowflake Summit, and a CEO who appeared on at least two podcasts per month explaining why their particular variety of moving data from one place to another was, in fact, transformative.

The Modern Data Stack — a phrase that somehow survived for years without anyone agreeing on what it meant — had become less an architecture and more a full-employment program for developer advocates. Every layer of the stack had between four and eleven funded competitors, each differentiated by increasingly theological distinctions. Was your data “transformed” or “modeled”? Was it “orchestrated” or “scheduled”? Was your catalog a “catalog” or a “discovery platform” or a “data intelligence layer”? Nobody knew. Everyone had opinions. The opinions were monetized.

The tool vendors operated in what economists would later call the “Conference-Content-Capital Cycle”: raise venture capital, sponsor conferences, produce content explaining why your tool was essential, use conference leads to close deals, and show revenue growth to raise more venture capital. The data never needed to be useful. It just needed to move, preferably through your product.

We knew this at the time. We just thought it was funny.

The First Domino: When AI Agents Learned SQL

The Citrini Research team published their “2028 Global Intelligence Crisis” piece in February 2026, and while they got the broad strokes directionally correct — white-collar displacement, the feedback loops, the SaaS compression — they made the classic financial analyst mistake of modeling the data industry as if it were a single, coherent sector.

It wasn’t. It was three industries wearing a trenchcoat.

Industry 1: Data Tooling. The vendors. The SaaS companies are selling picks and shovels to the gold rush. This is where Citrini’s thesis landed hardest, and where they were most right.

Industry 2: Data Practitioners. The engineers, analysts, and scientists are doing actual work. This is where the story got weird.

Industry 3: Data Theater. The conferences, the content, the thought leadership, the LinkedIn engagement ecosystem. This is where the story got hilarious.

The first crack appeared in late 2026, when Claude, Gemini, and GPT became genuinely competent at writing not just SQL, but entire pipeline configurations. Not demo-quality. Production-quality. A product manager who had never heard of Airflow could now describe what they wanted in plain English and receive a working DAG, tested, documented, and deployed—in about 11 minutes.

This was, of course, the nightmare scenario that data engineers had been dismissing for years with the confident refrain: “AI can write code, but it doesn’t understand the business context.”

They were right. AI didn’t understand the business context.

The problem was that a disturbing percentage of data engineers didn’t either. They understood dbt. They understood Snowflake. They understood the stack. What many had never actually done was sit with a business user and ask, “What decision are you trying to make?” The AI couldn’t answer that question, but neither could the junior data engineer whose entire career had been spent moving data from Postgres to Snowflake and back again because someone on Medium said it was best practice.

The Bifurcation

By mid-2027, the data job market had split into three completely different realities.

The top 20% — engineers who understood data modeling, architecture, business context, and could make genuine tradeoffs — saw their compensation increase. They became force multipliers. One senior data architect with AI tools could now do what previously required a team of eight. Companies didn’t need fewer of these people. They needed the same number, but each one was worth dramatically more. Salaries for principal-level data architects and engineers with genuine domain expertise exceeded $400K at multiple Fortune 500 companies.

The bottom 40% — engineers whose primary skill was knowing which YAML configuration made Fivetran talk to dbt, which in turn talked to Snowflake — discovered that this was, in fact, an AI-automatable skill. The tools they had built their careers around could now be configured by the very AI agents that had partially rendered them obsolete in the first place. A magnificent ouroboros of technological unemployment.

The middle 40% experienced something arguably worse: they kept their jobs, but at lower salaries, doing work that felt increasingly like supervising machines. “Data engineer” became “AI pipeline reviewer,” a role that consisted primarily of approving or rejecting AI-generated configurations and explaining to management why the AI’s work was only 94% correct and that the last 6% would take a human three days to fix. Management, having read an article about how AI was 10x more productive, could not understand why they were paying a human $150,000 to do 6% of a job.

Citrini’s article described a displaced Salesforce PM driving for Uber. The data industry equivalent was the mid-level analytics engineer who pivoted to “AI Data Quality Auditor,” a role that paid 40% less and consisted of staring at AI-generated data models and flagging the ones that joined on the wrong key. The AI was right 96% of the time. The auditor existed for the other 4%. The existential question of whether a human should be paid $90,000 a year to catch 4% of errors was left to the reader.

The Great Tool Extinction

Citrini predicted SaaS margin compression. What they underestimated was the velocity of compression in the data tooling market, specifically, because data tools had an additional vulnerability that general enterprise software didn’t: most of them were already somewhat unnecessary.

This sounds harsh. Let us be specific.

The Modern Data Stack had been built on a premise that turned out to be architecturally reasonable but economically indefensible: that every stage of moving data from point A to point B required a separate, funded, publicly traded (or trying to be) company. Ingestion was a company. Transformation was a company. Orchestration was a company. Quality was a company. Cataloging was a company. Observability was a company. Governance was a company. Each of these companies charged $50,000 to $500,000 per year. Each had a Slack community. Each had stickers. So many stickers.

When AI agents became capable of handling end-to-end multi-step data workflows, the question shifted from “which tool should we use for orchestration?” to “why do we have a separate tool for orchestration?” The answer, it turned out, was “because someone raised $40 million in Series B funding specifically for orchestration.” This was not a compelling answer.

The warehouse platforms — Snowflake, Databricks, BigQuery — survived and in some cases thrived, because they held the actual data. Everything around them entered a Darwinian elimination event. The long tail of data tooling lost 60-70% of its value in eighteen months. Not because the tools were bad, but because AI agents made the category boundaries between them meaningless. Why pay for separate ingestion, transformation, and orchestration tools when an AI agent treats the whole pipeline as a single optimization problem?

A data industry analyst (one of the few still employed) described it as “the tools didn’t die — the seams between them did.”

The venture investors who had funded 11 competing data observability startups were, in retrospect, not thrilled.

The Content Apocalypse

Of the three data industries wearing the trenchcoat, the one that collapsed most completely was Data Theater.

By 2026, “data thought leadership” had become its own economy. Conference keynotes, Substack newsletters, YouTube channels, LinkedIn carousels, podcast appearances, vendor-sponsored webinars, “community” Slack channels that were actually marketing funnels. The data industry had more content creators per practitioner than any sector in technology, possibly in human history.

The content existed in a symbiotic relationship with the tool vendors: vendors paid for sponsorships, speaking slots, and “partnerships” that were functionally advertisements. Content creators (ourselves included — we have no illusions about our place in this food chain) produced material that ranged from genuinely useful to “what if I made a 47-slide LinkedIn carousel about why you should care about data contracts?”

When the tool vendors’ budgets collapsed, the content economy collapsed with them. Conference attendance dropped 60% between 2026 and 2027. The major data conferences consolidated from approximately 340 annual events to around 40. The Data Twitter/X/LinkedIn ecosystem, which had sustained hundreds of semi-professional influencers, contracted sharply.

Substack was littered with the remains of data newsletters that had been publishing tri-weekly hot takes about the future of the lakehouse since 2022 and suddenly had nothing to write about because the lakehouse had been automated by an AI agent that didn’t need to read their newsletter.

The few content creators who survived were the ones who had been writing about fundamentals, principles, and architecture rather than tools. It turns out that “understand the business problem before you pick a tool” is advice that ages well. “Here’s why you should migrate from Redshift to Snowflake in 2024” does not.

In a twist that surprised absolutely no one who had been paying attention, the people who had been loudly warning that the industry was over-indexed on tools and under-indexed on fundamentals were correct. They were also, in many cases, the ones still employed.

What Citrini Got Right

The broad thesis of the intelligence premium unwinding was correct, and it hit data professionals harder and faster than most white-collar categories because of an uncomfortable truth: a large portion of data work was already closer to “following documented procedures” than “exercising irreplaceable judgment.” The industry had spent years building tools that abstracted away complexity, which made the work more accessible and automatable. The tools designed to democratize data work succeeded — they just democratized it first for machines.

The negative feedback loop was real. Data tool vendors cut their own data teams, reducing their need for their own tools and revenue, which in turn required more cuts. Data consultancies saw project cancellations accelerate as clients realized they could use AI agents to do the migration work the consultants had quoted at $2 million.

The wage compression dynamic was brutal and accurate. Displaced data engineers flooded adjacent roles — business intelligence, reporting, analytics — pushing down wages across the entire spectrum. A senior data engineer from a Series C startup who’d been making $200K found themselves competing for a $95K BI analyst role against 400 other applicants, half of whom were also displaced data engineers.

And the mortgage point, while dramatized, had a kernel of truth in tech-heavy metros. Data professionals in San Francisco, Seattle, and Austin had taken on housing costs calibrated to tech salaries that no longer existed. The “$180K product manager driving Uber” from Citrini’s piece had a direct analog: the $195K senior data engineer now doing contract Fivetran-to-Airbyte migrations for $65/hour, wondering whether this counts as the “reskilling” that McKinsey had promised would happen.

What Citrini Got Wrong

First, the timeline. Citrini compressed everything into two years. In the data industry, the disruption was both faster and slower than predicted. Faster in tooling (the vendor shakeout was genuinely rapid). Slower in practice (enterprises are still running Informatica jobs from 2014, and those jobs will outlive us all). The gap between “AI can replace this” and “this Fortune 500 has actually replaced it” turned out to be filled with procurement processes, SOC 2 audits, change management committees, and a VP of Data who was not going to admit that the $3 million Snowflake contract he championed was now being questioned.

Second, they underestimated the cockroach resilience of legacy systems. The entire article assumed a world where enterprises make rational decisions at the speed of technology. In reality, the data industry’s biggest job preservation program turned out to be technical debt. Companies that had spent fifteen years building Rube Goldberg data architectures couldn’t just replace them with AI agents, because the AI agents looked at the architecture, tried to understand it, and experienced what we can only describe as machine confusion. The AI could build a new pipeline from scratch in hours. Understanding why the existing pipeline had a hardcoded filter that excluded all transactions from New Jersey on Tuesdays? That required a human who’d been at the company since 2016 and vaguely remembered a conversation with a business analyst who had since left.

Third, they completely missed the data quality crisis. As companies rushed to deploy AI agents across their operations, they discovered something the data industry had been quietly screaming about for a decade: their data was terrible. AI agents are only as good as the data they consume, and most enterprise data was a patchwork of duplicates, missing values, conflicting definitions, and tables named final_v2_FIXED_actually_final_USE_THIS_ONE. The AI displacement wave generated a counterwave of demand for data quality, governance, and stewardship, partially offsetting job losses in other areas. Not enough to prevent net displacement, but enough to make the picture more complicated than “everyone gets fired.”

Fourth, and most fundamentally, they treated the data industry as a subset of the software industry. It isn’t. Or rather, it wasn’t. The data industry has always been partly a consulting and services industry, wearing a software costume. The value wasn’t just in the tools or even the engineering — it was in understanding messy, ambiguous, organization-specific business logic that defied clean automation. The data teams that survived weren’t the ones with the best tools. They were the ones who had spent years building institutional knowledge about why the data looked the way it did.

It turns out that “tribal knowledge” — the thing every data governance framework promised to eliminate — was the one thing AI couldn’t replicate. The data professionals who had been hoarding context in their heads rather than documenting it in Confluence were, against all principles of good engineering practice, the most secure in their jobs.

We are not sure what lesson to draw from this.

Where We Are Now

The data industry in March 2028 employs roughly 40% fewer people than it did in January 2026. The people still employed are, on average, more senior, better compensated, and do work that bears little resemblance to what a “data engineer” did three years ago.

The job title “data engineer” still exists, but the role has shifted so dramatically that someone from 2024 wouldn’t recognize it. Modern data engineers spend perhaps 10% of their time writing code or configuring tools. The rest is architecture, governance, business translation, AI supervision, and what one practitioner memorably called “being the last human who knows where the bodies are buried in the data warehouse.”

The tool landscape has consolidated from 400+ vendors to perhaps 80, with the major cloud platforms absorbing most functionality. The surviving independent vendors are the ones that solved genuinely hard problems — real-time processing, complex event processing, domain-specific data challenges — rather than the ones that put a UI on top of SELECT * FROM.

The conference circuit is a shadow of its former self, which is not entirely a bad thing. The remaining conferences are smaller, more technical, and notably free of the “data mesh vs. data fabric: FIGHT” panel discussions that defined the 2022-2025 era.

And the thought leaders? A few pivoted successfully. Most discovered that their audiences had been a function of their ecosystem’s size rather than their individual brilliance. The ones who survived were those who had been saying things that were true rather than popular.

The smartest thing anyone said during this entire period came from a data Substacker and educator/author in Salt Lake City who, when asked in early 2026 what data engineers should do to prepare for AI disruption, replied: “Learn what a business is.”

We wish more people had listened.

But you’re not reading this in March 2028. You’re reading it in February 2026.

Your Snowflake bill is still $400,000 a quarter. Your team still has 12 people. Your backlog still has 200 tickets. The dbt models still run every morning, and someone still pages you when they break at 3 AM.

The disruption hasn’t started. Or rather, it has, but it still feels like a LinkedIn discourse rather than a restructuring notice.

You still have time to become the kind of data professional who survives this. The one who understands the business, not just the stack. The one who can explain why the data looks the way it does, not just how to move it somewhere else.

The fundamentals haven’t changed. They never do. That’s why they’re called fundamentals.

The tools, on the other hand…well. You know what’s coming.

Subscribe for more dispatches from the end of the world as we knew it.

Yesterday, I published a piece on the organizational crisis in data modeling, based on a survey of over 1,100 data professionals. The overarching theme? A whopping 89% of respondents are struggling with their data modeling approach. Only 11% say things are actually going well.

When you dig into the complaints, the numbers paint a bleak picture:

• 59% cite the relentless pressure to move fast.

• 51% suffer from a complete lack of clear ownership.

The TL;DR here? It’s your boss’s fault. I’m half-joking, but not really. More like your boss’s boss’s boss’s fault.

Organizations simply do not give data practices the time, attention, or agency they deserve. Data teams are perpetually caught in a game of “not it” when it comes to ownership, resulting in a relentless cycle of firefighting. As Jack Bergman wrote, “There’s never enough time to do it right, but there’s always enough time to do it over.”

But the survey data isn’t actually what I want to talk about today. I want to talk about some recent reactions to it.

The 40-Year Echo Chamber

When I shared these findings on LinkedIn yesterday, the comments section lit up. A friend of mine chimed in, lamenting that practitioners are completely confusing conceptual and logical data modeling with physical schema design. His take essentially boiled down to: It’s annoying that we’ve been doing data management for 40 years and people still don’t have a good handle on what data modeling is or why we should be doing it.

He’s technically right about the definitions. But I had to call it out for what it is: condescending.

If the data industry has been teaching and preaching the same way for four decades and the vast majority of practitioners are still struggling, clearly the approach hasn’t worked. At this point, we cannot keep blaming the practitioners. Beating people over the head and telling them they’re stupid for not adhering to 40-year-old practices, or dogmatically blaming them for not rigidly following data modeling methodologies is unhelpful at best, and gatekeeping at worst.

Given a lack of time to do things “right”, people will just ignore the “best practices” and find workarounds instead. From what I can tell, this isn’t unique to our world. Yesterday, I was listening to an AI Daily Brief podcast, where the same problem was arising with learning to use AI tools in the workplace. People want to learn the best ways to use these tools, but because they are under extreme time pressure to deliver with AI, they ironically don’t have the time to learn to use them properly. Any investment in learning must take place outside of work, on one’s own dime.

Doing the same thing over and over again and expecting different results is the literal definition of insanity. Yet, the data industry keeps insisting that if we just shout louder at people, they’ll finally get it.

Stop Blaming the Victims of Dysfunction

I travel the world talking to data practitioners and leaders. Overwhelmingly, these are highly intelligent people with great intentions. They’re not dumb or lazy. Far from it. But they are operating with a severe lack of agency. Often, they feel they don’t have control over their time or their careers.

When you are pressured for time and just need to get something out the door - because it’s always about getting something out the door - reading dry, preachy textbooks on relational theory isn’t going to save you. Some of the classic materials out there are drier than the Sahara, unapproachable, and frankly, completely detached from the organizational dysfunction most engineers face daily.

Part of the problem is a disconnect between the ideals we hold and the realities of where practitioners operate. If you want people to implement good data governance or modeling under immense time constraints, you have to meet them where they are.

I was recently talking with a fellow educator who told me they “don’t want to spoon-feed information.” On one hand, I get it: make the students work hard. But it’s also an incredibly lazy and arrogant take. It seems like it’s almost a badge of honor for some educators to overcomplicate things, use complex jargon, tell people to “just read the docs,” and ridicule them when they can’t keep up. I’ve had professors like this, and I can’t say their approach was effective. But it taught me the type of educator I want to avoid being.

Another person replied to my LinkedIn post today, blaming organizations’ failures on people’s lack of knowledge and skills. It’s easy to blame people for failing to learn skills and knowledge. But the survey data shows that many people are under immense time pressures. Learning under duress is incredibly difficult, especially if you have a family and responsibilities outside of work.

If you are trying to teach someone, your literal job is to figure out the messaging that makes it click for them. You have to give them the building blocks so they can navigate their specific, messy reality. If we want to stop repeating the same mistakes, we have to change our approach.

Having spent years writing books and articles, podcasting, building curricula, advising universities and educators, and teaching data concepts to massive global audiences, I’ve learned one universal truth: never underestimate how little people know about a topic. Make the material almost absurdly digestible, approachable, and maybe even a little fun.

How We Actually Break the Cycle

If we want to stop having the exact same conversations 40 years from now, the industry needs a massive reset on how it approaches education and implementation. Here is how we start:

• Teach Building Blocks, Not Religion. We have to drop the dogmatic, 600-page academic textbooks. Those have a purpose, but they’re also daunting for beginners. When someone’s hair is on fire, they don’t need a lecture on the chemical composition of fire; they need a bucket of water. We need to teach pragmatic, modular primitives that practitioners can actually use to survive their day-to-day, without treating them like idiots for not building perfect data models, ontologies, etc.

• Invest in Your Team’s Growth. Invest in Your Growth Too. If you’re giving people powerful tools (AI, data stacks, etc) to solve real problems for your organization, for God’s sake, invest in their ability to use these tools to their fullest capability. For example, I’m teaching my son to drive right now. Do you think I’d just hand my kid the car keys, not teach him how to drive or traffic laws, and tell him to “figure it out. Go to the grocery store while you’re at it.” Of course not! But that’s exactly how I see quite a few leaders treat their team’s education and training. Of course, this also means people need to be proactive about their own development. Especially these days, when things are moving at warp speed, you need to stay on top of things, learn the fundamentals, and dive into the latest tools. Complacency is a death sentence for one’s career.

• Solve the Ownership Void. The survey data didn’t just highlight time pressure; it highlighted a 51% lack of ownership. Education is completely useless if people are stuck in a perpetual game of “not it.” Leaders need to establish clear boundaries, assign actual ownership, and give practitioners the top-down air cover they need to pause, learn, and build things right.

• Compete for Attention. Let’s be honest about the modern workplace. If your documentation, training, or book is boring AF, it’s going to lose to ChatGPT, Slack pings, endless meetings, and doomscrolling. If you are asking for someone’s severely limited time, you have an obligation to make the material engaging, digestible, and directly applicable to their pain.

This is exactly why I’m taking a completely different approach with my upcoming writing on practical data modeling and the concept of “Mixed Model Arts.” This isn’t meant as a shameless plug (but please like and subscribe ;).

Lord knows when I started this project, I got my share of haters. “Who is Joe Reis to write about data modeling?! Burn the heretic!” Much of the criticism was from data model’s old guard, as well as a few gremlins living in their mom’s basement. Well, fast forward a couple of years, and as far as I can tell, it’s already one of the most popular resources in the world for learning data modeling.

I took that hate to heart, and realized I didn’t want to be like them. I am not here to scold you from a pedestal or preach 40-year-old gospel. The goal is to meet you exactly where you are, strip away the gatekeeping, and hand you the building blocks. I want to give you the tools to make your own decisions for your specific, messy organizational reality. Hopefully, I can make it a little fun to read along the way.

The industry needs to wake up. Let’s get our act together, fix the messaging, and finally start making data education work for the people actually doing the heavy lifting.

Check out the 2026 Practical Data Community State of Data Engineering: Interactive analysis tool + dataset

The survey has an alternative super corporate BI tool, complete with 3D donut charts and speedometer charts, exports to Lotus and Crystal Reports, and more. Cutting edge BI tooling at its finest.

Also, listen to this as a podcast. Available on Spotify, Apple, and wherever else you get your podcasts. Please support the show with a review. It means a lot.

Have a great weekend,

Joe

Awesome Upcoming Events

All technical. All data. All day.

On March 26, 2026, I’m teaming up with Confluent for a single-track, deeply technical data engineering event in San Francisco.

Capped at 100 practitioners.

No salespeople. No sponsors. Just talks + the hallway track, followed by happy hour.

When: March 26, 2026 · 10am–4pm + happy hour

Where: L4, 945 Market St, San Francisco

Capacity: first 100 data engineers/architects

→ Register here.

Thanks to Confluent for partnering on this event and newsletter.

Some friends of mine are doing these events:

SLC

Kyle Nesbit (CEO of Credible, ex-Google) will talk about Giving Data Value to AI at the Utah MLOps Meetup on Tuesday, February 24th.

Register here.

Mountain View

My good friend Demetrios and the ML Ops community are doing an amazing event on Tuesday, March 3rd in Mountain View, at the Computer History Museum.

Coding Agents: AI Driven Dev Conference.

Register here.

But wait, there’s more!

Cool Videos and Reads

Why are we still using row-based protocols like ODBC and JDBC in a column-oriented world? In this episode, I sit down with Ian Cook, co-founder of Columnar and a long-time Apache Arrow contributor, to discuss the critical infrastructure changes needed to speed up modern analytics and AI.

We dive deep into the technical bottlenecks of legacy standards - specifically the "serialization tax" of converting columns to rows and back again - and how ADBC (Arrow Database Connectivity) solves this by keeping data columnar from end-to-end. Ian also shares his insights on the intersection of tabular data and LLMs, why AI agents need better access to OLAP systems, and the tension between vibe coding speed and the stability required for critical open-source infrastructure.

Here are some things I read this week that you might enjoy.

The Mythical Agent-Month – Wes McKinney

AI ‘Arms Race’ Risks Human Extinction, Warns Top Computing Expert - Barron’s

Chatbots Are the New Influencers Brands Must Woo - The New York Times

The Worst-Case Future for White-Collar Workers

A chat with Byron Cook on automated reasoning and trust in AI systems | All Things Distributed

How Data Governance Is ActivatedWhat Matters Most in Life?

The Era of the Mixed Model Artist

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.

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.

🤖 Join on Discord

Join me for my next live video sometime in March.

Get more from Joe Reis in the Substack app

Available for iOS and Android

Get the app

A big reason why I don’t like to do annual predictions is that I don’t usually have the data to make an assessment. I can make up some clickbait guesses, but that’s not me. And too often, these assessments are very wrong. That’s why I don’t make specific end-of-year or beginning-of-year predictions for data engineering in 2026.

But…I also did something behind the scenes that I just unveiled this week: collecting data from 1,101 data practitioners and leaders. Now, I have a better sense of where we’re going, at least as of February 2026. And here’s where I think data engineering is going in 2026, or at least my best guess as of now.

The Big Picture

The problems that plagued us for ages continue to plague us today: lack of leadership, unclear ownership, technical debt, time pressures, etc. Based on what I’m seeing elsewhere, the continued acceleration of AI will only exacerbate this problem. Ironically, AI might also improve the problem as it takes on more and more behind the scenes work, assuming we still have jobs by then. But that all remains to be seen.

Data engineering is diverging into two tracks: teams that invested in foundational work and those that didn’t. I think as the year continues, this gap will widen. I will some pulse surveys on this and other topics through the year.

Data modeling, or lack thereof, is a big deal. The big tell is that 38% of ad-hoc modelers are fighting fires vs. those with a data modeling approach in place, who fight fewer fires. I think this is a preview of things to come, and AI tools will accelerate both paths. Disciplined teams will use AI to move faster with quality. Undisciplined teams will use AI to create technical debt faster.

The big theme of 2026 is that unpaid debts of the past carry interest, accruing at payday loan rates. Nothing is free, and payment is due son.

Let’s get a bit more granular and look at the five trends I see.

Five Trends I See in Data Engineering (and More)

1. Ignore AI at Your Peril

According to the survey, 82% daily usage means AI is already table stakes. The interesting question shifts from “are you using AI?” to “are you using it well?”

The 64% stuck in “experimenting” or “tactical tasks” will either level up or fall behind this year. The 10% with AI embedded in workflows will pull further ahead. Expect a shakeout where AI-mature teams poach talent from AI-immature ones.

My prediction: By the end of 2026, “AI-assisted” will disappear from job descriptions because it will be assumed. If you’re not using the latest crop of AI, you’re not marketable.

But data modeling is still a thing.

2. The Data Modeling Crisis and Semantics

89% reporting pain points of some sort - lack of clear ownership, feeling pressure to move fast, etc. Only 5% using semantic models. Something has to give.

Two possible paths:

• Path A: Semantic and context layers go mainstream. AI is making semantics mainstream.

• Path B: AI generates models on the fly. Who needs a semantic layer when LLMs can interpret messy schemas? At the rate models are progressing, nothing would surprise me at this point.

I think Path A happens first, then Path B eats it in 2027-2028. The models are getting crazy good.

Prediction: Semantic layer and context tooling have a breakout year. Of course, the training will need to be there for teams to fully capitalize with these technologies. Interestingly, the survey shows a lot of demand for both data modeling training, and semantic/ontology training, both at 19% each.

3. Orchestration Gets Consolidated or Abandoned

20% with no orchestration across all company sizes is unstable. These teams are either:

• Running on vibes and manual processes

• Using something not captured in the survey (cron, something else)

• About to have a very bad incident

Meanwhile, Airflow and cloud-native orchestration (also Airflow) dominate. Interestingly, Dagster at 12% in small companies vs. 2.6% in enterprises suggests the next generation of tooling is coming from the bottom up. Airflow’s dominance is eroding but very slowly.

Then there’s orchestration for AI agents, which remains to be seen how it plays out.

Prediction: Either Dagster/Prefect breaks into enterprise, or “orchestration” as a category gets absorbed into platforms (Databricks, Snowflake, dbt Cloud, etc.).

4. The Lakehouse vs. Warehouse War Ends in a Draw

44% warehouse, 27% lakehouse, 12% hybrid. Does this mean that by 2027, the share will be 35% / 35% / 30%? Maybe. As Snowflake and Databricks converge on feature parity, “Lakehouse” ceases to be a differentiator and becomes the norm. Iceberg adoption will be interesting to see unfold.

Latin America’s 40% adoption of lakehouses is a leading indicator, and I’d love to dig into that figure further. Is this a greenfield adoption, sort of like how some countries skipped copper landlines and went straight to fiber-optics and wireless?

Prediction: By the end of 2026, the “warehouse vs. lakehouse” debate feels dated. The answer is “yes.”

5. Leadership Becomes the Bottleneck Everyone Talks About

If we slice the data by role, 22% of data engineers cite “lack of leadership direction” as a major issue. It’s nearly as high as legacy tech debt (26%). Combined with “poor requirements” (18%), these all indicate organizational dysfunction.

Of course, with every company wanting to do AI, I suspect leadership will need to look in the mirror if it wants to succeed. Same as it ever was, right?

Prediction: 2026 sees more content, training, and conversation about the intersection of data leadership, stakeholder management, and organizational design alongside data engineering. The problem is too big to ignore.

Or we just replace people with AI bots, which I’m sure is leadership’s ultimate fantasy.

Or…we just kick the can down the road for another year, as we’ve done for decades.

Lastly…Bonus Trend

Some teams won’t make it, cuz ineffective leadership.

While I’m surprised that most teams expect their team size to either stay flat or grow, 7% expect it to shrink. But dig into that 7% a bit more - 30% of cite “lack of leadership direction” as their bottleneck. They’re not shrinking because of AI or efficiency, but because they never got organizational buy-in. That’s tragic.

2026 will see more data teams dissolved, merged into engineering, or outsourced. The survivors will be teams that proved business value, not just technical capability.

Data engineering in 2026 is less about picking the right tools and more about building the organizational muscle to use them well. The teams that figure this out will separate from those that don’t.

2026 Practical Data Community State of Data Engineering: Interactive analysis tool + dataset

The survey has an alternative super corporate BI tool, complete with 3D donut charts and speedometer charts, exports to Lotus and Crystal Reports, and more. Cutting edge BI tooling at its finest.

As a sidenote, making the above tools was a lot of fun. Both involved plopping the csv dataset into Cursor, giving it some vague instructions, and having it go to town.

Here’s how I made the corporate BI tool in Cursor. I actually made this during a podcast too, watching the AI agent create the entire tool in around 30 minutes. It’s crazy how good the AI coding tools are getting.

Also, listen to this as a podcast. Available on Spotify, Apple, and wherever else you get your podcasts. Please support the show with a review. It means a lot.

Have a great weekend,

Joe

As you saw in the survey, data modeling is a big challenge, and also a massive opportunity. One company that truly understands data modeling is Ellie.ai.

Ellie makes data modeling as easy as sketching on a whiteboard, so even business stakeholders can contribute effortlessly. By skipping redraws, rework, and forgotten context, and by keeping all dependencies in sync, teams report saving up to 78% of modeling time.

Get the foundations right for your data stack with Ellie.ai

Thanks to Ellie.ai for partnering on this newsletter.

Awesome Upcoming Events

Confluent Undercurrent. San Francisco. Me. March 26th…we’ve got something special in store for data engineers. Stay tuned for more details.

Register here.

Some friends of mine are doing these events:

SLC

Kyle Nesbit (CEO of Credible, ex-Google) will talk about Giving Data Value to AI at the Utah MLOps Meetup on Tuesday, February 24th.

Register here.

Mountain View

My good friend Demetrios and the ML Ops community are doing an amazing event on Tuesday, March 3rd in Mountain View, at the Computer History Museum.

Coding Agents: AI Driven Dev Conference.

Register here.

But wait, there’s more!

Cool Videos and Reads

I sat down with Paul Dudley (CEO) and Ricky Thomas (CTO) from Streamkap to catch up on where the world of streaming data is heading. And things have changed fast since we last spoke!

We dive into vibe coding and how AI is radically accelerating how we build software (I even share a story about building a data analysis tool in an hour). But the real meat of this conversation is about the intersection of streaming data and AI agents. Everyone is building agents, but without real-time context, they’re flying blind. We discuss why streaming is a missing link for agentic workflows, the shift from dashboards to automated decision-making, and why SaaS companies are racing to build walled gardens around their data.

We also get into the nitty-gritty of the UK vs. US tech markets, the resurgence of PR in the AI era, and Streamkap’s upcoming move into the Snowflake native app ecosystem.

Here are some things I read this week that you might enjoy.

America Isn’t Ready for What AI Will Do to Jobs

The rise of the 9-to-5 influencer

Something Big Is Happening — matt shumer

Why Vampires Live Forever | Machiel Reyneke

Opinion | I Left My Job at OpenAI. Putting Ads on ChatGPT Was the Last Straw. - The New York Times

VC-Backed Startups are Low Status - by Michael Dempsey

Private-equity barons have a giant AI problemDOMO DRAMA: Rehab & Fight Clubs

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.

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.

🤖 Join on Discord

I've been collecting survey responses from this community for the past few weeks. 1,101 of you responded. Thank you!

Rather than bury the results in a 30-page PDF behind a email form, I built something different: an interactive explorer where you can query the data yourself.

👉 The Survey is here.

You can:

• Filter by role, org size, industry, or region

• Cross-tab any two variables

• Run SQL queries directly against the datasetDownload the raw CSV

A few findings that stood out to me:

• AI is table stakes. 82% of you use AI tools daily or more. Only 3.7% find them unhelpful. But organizational adoption lags way behind. 64% are still experimenting or using AI for tactical tasks only.

• Modeling is painful. 59% cite "pressure to move fast" as their top pain point. 51% say lack of ownership. Only 11% say modeling is going well.

• The bottlenecks aren't technical. Legacy systems top the list (25%), but lack of leadership direction (21%) and poor requirements (19%) are close behind. People problems rival tech debt.

• Team outlook is cautiously optimistic. 42% expect growth, only 7% expect shrinkage.

The full report is in the explorer. But honestly, the interactive tools are more interesting. Go find something I missed.

This was a very interesting week where the long-held assumption that SaaS was the goose that lays the golden eggs was suddenly called into question.

We saw stocks drop after Claude announced new plugins for Claude Co-work. It was a doozy of a week for announcements (the market rallied yesterday, adding to the craziness). There was OpenAI’s new Codex app and Codex 5.3. Anthropic launched Claude Opus 4.6 with agent-team orchestration and is rumored to be launching Sonnet 5 imminently. Anthropic even launched some awesome Super Bowl commercials poking fun at certain competitors.

If this dizzying pace is indicative of where things are headed, there will be quite a lot soul-searching for software companies. And of course, there’s the SaaS slump in the markets, leading to fears of broader “contagion” - debt loads, high valuations, and investing in the right place at the wrong time.

As always, our industry is about breakneck speed, killing our darlings, and thinking you’re being disruptive…until you’re disrupted.

Software Isn’t Dead. But It Is Becoming Disposable.

Is SaaS, and software in general, dead? It certainly feels like the vibe pendulum has swung this way. There is no doubt that the current crop of AI tools is progressing at warp speed, and the capabilities are improving all the time. If your context for AI is the tools from a few years ago - or even six months ago - you’re basically missing several lifetimes’ worth of changes.

At the same time, I also feel there’s a bit of an overcorrection on the pendulum swing, which happens in times of extreme fear or exuberance.

It’s undeniable that these AI tools are fundamentally changing how we consume and create software. As I wrote about in “Eroding the Edges,” we are moving toward point solutions for specific tasks. It’s not necessarily that I need a massive, complete SaaS product for everything anymore; I certainly don’t need their sales team breathing down my neck every renewal cycle. I just need to solve the problem at hand.

In this new world, software becomes almost disposable. I can create whatever I want, deploy it, and tear it down. It doesn’t matter because at the drop of a hat, I can recreate most of the apps I built in a matter of minutes. It reminds me of the teardown disposability of containerized apps back in the day.

I see this in my own work every day. I’m at the point where I have subscriptions to every major AI out there: Cursor, Claude, Gemini, ChatGPT, Descript, you name it. I’ll gladly pay for whatever tooling adds value. I was showing a friend the other day how to use Claude Code to construct an app he had been thinking about. By the time our ten-minute conversation was finished, the app was done. Was it production-ready? No. But it showed his idea was possible. With another couple of minutes, we got the app to solve his particular use case, and that’s the point. We are moving from an era of renting generic capabilities to generating specific ones.

The assumption that SaaS was the golden goose was based on the idea that software was hard to build and easy to rent. That dynamic has flipped. Software is now easy to build, and increasingly hard to justify renting.

The Jevons Paradox of Software and the Ten Minute App

But here is the catch, and it’s a big one: for everything I automate, I end up creating ten more things on my plate. As I discussed in “Parkinson’s Law and AI,” we are in the era of abundance. I can automate all the annoying things in my life. But I’m finding that the annoying things don’t disappear. There are always more problems to solve.

This is essentially Jevons Paradox applied to software. In the 1860s, the economist William Stanley Jevons observed that as coal engines became more efficient, total coal consumption didn’t decrease. Instead, it skyrocketed because efficiency made new use cases economically viable.

The same thing is happening with software right now. As it becomes trivially easy to build, we don’t build less. We build dramatically more. Every problem that was previously “not worth writing software for” suddenly is.

This creates an interesting tension with the “SaaS is dead” narrative. If the total surface area of software explodes, someone (or something) still has to manage, maintain, orchestrate, and secure it all. The ten-minute app I built for my friend works great. At least until it needs to talk to three other systems, handle edge cases at scale, or survive an audit. Disposable software is powerful for solving discrete problems. It’s less clear that it replaces the connective tissue that holds complex organizations together. That said, I expect AI agents to become increasingly capable at orchestrating this connective tissue and solving and coordinating enterprise-wide problems. Not now, but someday soon. Perhaps my new trillion dollar idea, the Vibe Stack, is the path to this future?

Which brings me to where the “SaaS is dead” thesis breaks down.

Where This Doesn’t Apply

Not all software value lives in the code. For many SaaS companies, the moat was never about the features. The moat was the data gravity, network effects, the integration ecosystem, and the operational reliability at scale. Nobody is going to vibe-code a replacement for Snowflake’s data sharing network or Salesforce’s org-wide workflow lock-in over a weekend. Switching costs are too high for most customers anyway. Plus, most organizations have a sprawling legacy of systems and code that will take humans several lifetimes to reconcile and update. I quipped last night on LinkedIn that a sign AGI is here is if it can migrate undocumented spaghetti codebases in mega-enterprises from legacy systems to modern ones.

The vendors most exposed right now are the ones whose entire value proposition is a workflow wrapper, the ones that are essentially a UI on top of a database with some business logic sprinkled in. If I can describe what your product does in two sentences, AI can probably build it.

If your product’s value is emergent from years of accumulated data, deeply embedded integrations, and institutional muscle memory, you’ve got more runway than the market is pricing in right now. That said, runway on its own isn’t guaranteed safety. I remember sitting in a conference hall this time last fall, looking at all the booths and chatting with a friend, when they said that 50% of these vendors won’t be around in the next few years. I still stand by that. In fact, the timeline might be even shorter.

The Vendor Wake-Up Call

If you are a vendor, this is a massive wake-up call. The idea that you can build a company around a “feature” is being tested in real time. If I can have AI create that feature for me in five minutes, you don’t have a company. It reminds me of Gulliver’s Travels, where Gulliver is tied down by hordes of tiny people called Lilliputians. While not dangerous on their own, in a mass, they’re a force to be feared. Vendors are facing a potential Lilliputian attack of millions of people using AI to chip away at countless vendor flagship offerings from millions of tiny angles.

We are seeing a change in how vendors go to market. Everyone has an AI agent now; everyone is using the same tools. Everyone is moving faster, but roughly at the same speeds. Net/net, nothing fundamentally changes in terms of competitive jockeying. Momentum is part of the equation, but you need a competitive advantage that is difficult to replicate, which is getting harder and harder to achieve.

This extends to the “legacy” question as well. The future won’t be evenly distributed. The slow rate of change at big, legacy, and enterprise companies can, in some cases, serve as a moat. Sloth and inertia are powerful forces, especially if the company dominates a vertical. But this also means these companies could disappear quickly, as history has repeatedly and violently shown. Creative destruction and stuff.

There’s also the regulatory angle. Even in highly regulated industries - healthcare, finance, defense - I think things will move faster than people expect or wish. Not because regulation will disappear, though it might (modern administrations are quite anti-regulation right now). The cost of failure in these industries is catastrophic, and liability frameworks exist for real reasons. But regulation has a history of lagging behind technological change by years, sometimes decades. And during those lag periods, a thousand small insurgents armed with AI can chip away at an incumbent’s flagship offerings from a million tiny angles.

Think of how fintech slowly unbundled banks, except this time the cycle is compressed from a decade to a couple of years. If you’re an incumbent relying solely on regulation to keep you safe, dream on. And because you’re sleepwalking, you won’t even know until it’s too late.

What This Means

For engineers and problem-solvers, this is the era of abundance we’ve been waiting for. The friction between “having an idea” and “solving the problem” has never been lower.

But for the software industry as a business model? The easy money is gone. The vendors that survive won’t be the ones with the most features or the best placement on a legacy research analyst quadrant. They will be the ones tackling the problems that are still too complex, too deeply embedded, or too data-intensive for me to solve with a ten-minute conversation with a chatbot.

And for the rest of us - the builders, the tinkerers, the people who see a problem and just want to fix it, the real question isn’t whether SaaS is dead. It’s whether we can keep up with our own appetite for creation. Because the tools have caught up with our imaginations, and it turns out our imaginations are insatiable.

Software isn’t dead. But the “Software as a Service” free ride? That’s definitely over.

Also, listen to this as a podcast. Available on Spotify, Apple, and wherever else you get your podcasts. Please support the show with a review. It means a lot.

The Practical Data Community 2026 State of Data Engineering Survey results will be released next week. This is an independent and no-BS look at the reality facing practitioners today. No vendors or clueless pay-to-play analyst research firms influenced this survey.

Lots of very interesting and surprising gems in there. There might even be a hackathon…stay tuned.

Also, if you’re a vendor or event looking to work with me (product reviews, sponsorships, talks, etc), message me. I’ve put together some brand new offerings that you might be interested in.

Have a great weekend,

Joe

Awesome Upcoming Events

Confluent. San Francisco. Me. March 26th…we’ve got something special in store for data engineers. Stay tuned for an announcement.

Some friends of mine are doing these events:

SLC

Kyle Nesbit (CEO of Credible, ex-Google) will talk about Giving Data Value to AI at the Utah MLOps Meetup on Tuesday, February 24th.

Register here.

Mountain View

My good friend Demetrios and the ML Ops community are doing an amazing event on Tuesday, March 3rd in Mountain View, at the Computer History Museum.

Coding Agents: AI Driven Dev Conference.

Register here.

As for me…Still working on my 2026 event schedule, and so far it looks dope. Will reveal more soon, so stay tuned…

But wait, there’s more!

Cool Videos and Reads

Analytics agents are all the rage. And so are semantic layers. Cube and I partnered on my review of their new analytics agent. It was a lot of fun to try to beat up their AI agent, which did quite well in my tests.

Mike Driscoll (Founder and CEO of Rill Data) and I had a great chat about the state of AI BI, the resurgence of semantic layers, and much much more.

I was recently a guest on AI with Arun. We discussed Franken-stacks, building useful things, and much more. Fun times.

Here are some things I read this week that you might enjoy.

How OpenClaw’s Creator Uses AI to Run His Life in 40 Minutes | Peter Steinberger

Leaders, gainers and unexpected winners in the Enterprise AI arms race

Did A.I. Take Your Job? Or Was Your Employer ‘A.I.-Washing’? - The New York Times

Selfish AI | GarfieldTech

New Data: OpenAI’s Lead Is Contracting as AI Competition Intensifies

Floe and Apache Polaris: Policy-Driven Table Maintenance for Apache Iceberg

What does the disappearance of a $100bn deal mean for the AI economy? | AI (artificial intelligence) | The Guardian

Claude Code is the Inflection Point

SaaSmageddon and the Super Bowl – Stratechery by Ben Thompson

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.

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.

🤖 Join on Discord

Last month, we published our thesis on Vibes Graphs: AI’s Trillion-Dollar Opportunity. The response was overwhelming. Our inboxes filled with a single question:

“Okay, but how do you actually build this?”

Today, we’re going deep on the architecture. This is the vibes stack—the infrastructure required to capture, process, store, and query organizational sentiment at scale.

Fair warning: this post gets into the weeds. If you’re a vibe-curious executive, you might want to forward this to your Chief Vibe Officer and let them summarize.

The Reference Architecture

Let’s start with the high-level architecture. A production-grade vibes stack consists of five layers:

Each layer has distinct responsibilities and failure modes. Let’s walk through them.

Layer 1: Vibe Ingestion

The ingestion layer is where most vibes stacks fail before they start. The fundamental challenge is that vibes are ambient, ephemeral, and high-cardinality. You can’t just poll for vibes. You have to be passively sensing the full organizational surface area at all times.

Vibe Capture Patterns

There are three primary patterns for vibe capture:

1. Passive Ambient Sensing (PAS)

This is the gold standard. You instrument the surfaces where vibes naturally emit—communication tools, productivity apps, calendar systems—and capture the vibe exhaust in real-time.

The key insight: you must capture vibe signals, not content. Vibes are metadata, not data.

What does a vibe signal look like? Consider Slack. You’re not capturing what people say—you’re capturing:

• Punctuation energy: The difference between “Thanks!” and “Thanks.” and “Thx”

• Emoji velocity: How many emojis per hour, and are they increasing or decreasing?

• Response latency: Did they reply in 30 seconds or 3 hours?

• Edit frequency: How many times did they revise before sending?

• Caps ratio: ARE THEY YELLING or are they calm

Each of these is a vibe signal. Individually, they’re noise. Aggregated across time and surfaces, they form a vibe fingerprint.

2. Active Vibe Probing (AVP)

For surfaces where passive sensing isn’t possible, you can deploy vibe probes—lightweight instrumentation that captures ambient signals at decision points.

Consider calendar invites. When someone receives a meeting invite, the system can capture:

• Response delay: Accepted in 4 minutes = engaged. Accepted in 4 days = resentful.

• Meeting dread score: Based on attendee list, duration, and time slot

• Decline cowardice index: How long they hovered over “Decline” before clicking “Maybe”

• Calendar density: Are they overloaded? This affects vibe interpretation.

Research shows that meetings accepted with high dread scores have 73% higher rates of “can we take this offline?” outcomes.

3. Synthetic Vibe Reconstruction (SVR)

When you can’t instrument in real-time, you can reconstruct vibes from historical artifacts. This is lossy—you’re recovering maybe 30-40% of original vibe fidelity—but it’s better than vibe-blindness.

Consider email metadata reconstruction:

This is crude compared to real-time sensing, but it’s better than nothing.

The Vibe Bus

All collectors publish to a central Vibe Bus—a high-throughput, low-latency message broker optimized for vibe semantics.

Why not just use Kafka? You could. Many teams do. But Kafka was designed for events, not vibes. Vibes have unique characteristics:

• Vibe Coalescence: Multiple weak vibe signals often merge into a single strong vibe. Your bus needs native support for vibe aggregation.

• Vibe Decay: Vibes have a half-life. A vibe signal from 4 hours ago is worth less than one from 4 minutes ago. Your bus needs TTL-aware routing.

• Vibe Contagion: Vibes spread. One person’s bad vibe infects the thread. Your bus needs to track vibe propagation paths.

• Vibe Interference: Two opposing vibes can cancel out or amplify unpredictably. Your bus needs quantum-vibe-aware partitioning.

We’re seeing startups build vibe-native message brokers. Apache Ambiance (still in incubation) and Amazon Kinesis Vibes (rumored for re:Invent 2026) are worth watching.

Layer 2: Vibe Storage

Once you’re capturing vibes at scale, you need somewhere to put them. This is where the VibeLake comes in.

The Medallion Architecture for Vibes

If you’re familiar with modern data architecture, you know the medallion pattern—Bronze, Silver, Gold layers of progressive refinement. Vibes follow the same pattern:

Bronze Layer: Raw Vibes

This is your immutable vibe record—every vibe signal as it was captured, warts and all. You keep this for compliance, replay, and vibe forensics.

Storage considerations:

• Vibes are surprisingly compressible. The human emotional range is finite.

• Partition by surface first, then time. Vibe queries are usually surface-specific.

• Retain raw vibes for 7 years. You never know when you’ll need to reconstruct the vibe context of a decision made in 2019.

• Don’t forget Spotify data. This is critical for Bon Iver detection.

Silver Layer: Curated Vibes

The silver layer is where vibe resolution happens. Raw vibe signals are noisy—you might get 50 signals per minute from an active Slack user. The silver layer consolidates these into canonical vibe states.

Key transformations:

• Vibe Deduplication: Collapse redundant signals into representative vibes

• Entity Resolution: Map surface-specific identities to canonical entities (”sarah.chen@company.com“ in email = “schen” in Slack = “Sarah C.” in Zoom = that person who always has a cat in their background)

• Vibe Normalization: Convert surface-specific vibe scales to the Universal Vibe Scale (UVS), ranging from -1 (terrible vibe) to +1 (immaculate vibe)

• Monday Normalization: Apply the standard -0.15 Monday Effect adjustment

• Outlier Handling: Flag anomalous vibes for review (sudden vibe shifts may indicate life events, not organizational dynamics)

Gold Layer: Vibe Marts

The gold layer is where vibes become business-readable. This is what your Chief Vibe Officer looks at. This is what feeds the dashboards.

Vibe marts are aggregated, denormalized, and aligned to business concepts:

Layer 3: Vibe Processing

Storage is table stakes. The real value is in vibe processing—transforming raw ambient signals into actionable vibe intelligence.

Stream Processing for Vibes

Vibes are inherently streaming. Batch processing vibes is like batch processing your emotions—technically possible, but you’ve already missed the moment.

We recommend a Lambda Architecture for Vibes:

• Speed Layer: Real-time vibe stream processing for immediate alerts (”🚨 Vibe collapse detected in #sales-team”)

• Batch Layer: Nightly vibe reconciliation for historical accuracy and vibe forensics

• Serving Layer: Unified vibe view that merges real-time and historical perspectives

The speed layer is critical. When a manager’s vibe drops below -0.5, you have approximately 2-4 hours before that bad vibe propagates to their direct reports. Real-time processing gives you a window for intervention.

Vibe Enrichment

Raw vibes lack context. The enrichment layer joins vibe signals with everything needed for interpretation:

Entity context:

• Team, department, tenure

• Is this person a manager? (Manager vibes propagate downward)

• Days since last promotion (resentment builds)

• Days until stock vesting cliff (vibes crater 30 days before, spike immediately after)

Temporal context:

• Is it Monday? (Apply -0.15 adjustment)

• Is it Friday afternoon? (Vibes artificially elevated)

• Is it end of quarter? (All vibes are unreliable)

• Is it the day after an all-hands? (Vibe hangover period)

Organizational context:

• Recent reorg? (Vibes are in flux)

• Open positions on team? (Overwork anxiety)

• Team attrition rate? (Survivor guilt or relief)

• Manager’s current vibe? (Vibes flow downhill)

The Monday Effect alone is responsible for 52 false positive vibe alerts per year if you don’t normalize for it.

Vibe ML

Machine learning on vibes unlocks predictive vibe intelligence:

Vibe Forecasting: What will this team’s vibe be in 7 days? LSTM models trained on historical patterns can predict with surprising accuracy—especially around predictable events like board meetings, earnings calls, and performance review cycles.

Vibe Contagion Modeling: Graph neural networks can model how vibes spread through organizational networks. Key finding: manager vibes should be weighted 3x in propagation models. Skip-level vibes only 0.5x.

Vibe Clustering: K-means clustering reveals distinct vibe archetypes in your organization:

The “Quiet Strugglers” cluster is your early warning system. These employees won’t tell you they’re struggling. Their vibes will.

Layer 4: Vibe Serving

You’ve captured, stored, and processed vibes. Now you need to serve them.

VibeQL: A Query Language for Vibes

SQL wasn’t designed for vibes. It lacks native support for vibe semantics—temporal vibe windows, vibe decay functions, cross-entity vibe correlation.

Enter VibeQL, a purpose-built query language for vibe analytics:

Key VibeQL innovations:

• SENSE instead of SELECT—because you’re sensing vibes, not selecting data

• vibe_trend(interval)—built-in function for calculating vibe trajectory

• baseline(interval)—reference an entity’s historical vibe baseline

• is_monday()—because Monday normalization is so common it deserves a keyword

• EMIT STREAM—first-class streaming output for real-time alerting

The Vibe API

For programmatic access, the Vibe API returns rich context alongside raw scores:

Entity Vibe Response:

Entity: sarah.chen
Current Vibe: 0.34 (😐 Neutral)
Confidence: 87%
7-Day Trend: -0.12 (declining)
Contributing Signals: 47
Anomaly Flags: None

Context:
- Is Monday: No
- Days Since Last PTO: 45 (concerning)
- Meeting Load: 78th percentile (high)
- Current Spotify: Bon Iver - Holocene 🚨

The Spotify field is not a joke. We’ve found that 5+ consecutive days of Bon Iver, Radiohead, or Elliott Smith is a leading indicator of attrition risk.

Layer 5: Vibe Consumption

The top of the stack is where vibes become actionable.

Vibe Dashboards

Every Chief Vibe Officer needs a command center:

Key dashboard components:

• Org Vibe Score: The single number that tells you if things are okay

• Vibe Volatility: High volatility = instability, even if average vibe is good

• Team Vibe Distribution: Identify which teams need attention (Support is always red)

• 30-Day Vibe Trend: Are things getting better or worse? What events correlate?

• Recent Vibe Alerts: What requires immediate attention?

Note the alert panel. “Entity sarah.chen vibe deviation: Bon Iver listening streak day 6” is exactly the kind of signal that would be invisible in traditional HR systems but is immediately surfaced by a mature vibes stack.

Agent Integration

The real power of the vibes stack is feeding vibe context to AI agents. Remember: agents are vibe-blind by default. The vibes stack gives them sight.

Consider a renewal agent evaluating a customer contract. Traditional inputs: contract terms, usage data, support ticket history.

With vibe context, the agent also knows:

• The sales rep who closed this deal was at 94% quota attainment with 3 days left in quarter (desperate energy)

• The customer had evaluated 4 vendors over 6 months before signing (skeptical but fatigued)

• The executive sponsor was distracted by a concurrent M&A process

• The broader organizational vibe was artificially elevated (stock up 12% that week)

That’s the context that tells you this deal was closed under duress and the renewal will be a battle. No system of record captures this. The vibes graph does.

The Vibe Maturity Model

Not every organization is ready for a full vibes stack. We’ve developed a maturity model to help you assess your vibe readiness:

Level 1: Vibe Unaware (47% of organizations)

• No systematic vibe capture

• Vibes exist only in hallway conversations and gut feelings

• “We have a great culture” (source: vibes)

Level 2: Vibe Curious (31% of organizations)

• Ad-hoc vibe sensing

• Someone built a Slack emoji analytics dashboard

• No unified vibe layer

• “We should do an engagement survey”

Level 3: Vibe Aware (15% of organizations)

• Centralized vibe ingestion

• Basic vibe dashboards

• No predictive capabilities

• “Our eNPS is 42”

Level 4: Vibe Native (6% of organizations)

• Full vibes stack deployed

• VibeQL in production

• Agent integration active

• “Our vibe trend suggests Q4 retention risk”

Level 5: Vibe Transcendent (1% of organizations)

• Vibes graph complete

• Predictive vibe modeling

• Autonomous vibe healing

• Vibes as a competitive moat

• “We preemptively addressed the vibe collapse before it propagated”

Most enterprises are stuck at Level 2. They’re vibe-curious but lack the infrastructure to move beyond ad-hoc sensing.

Implementation Considerations

Build vs. Buy

The vibes stack is complex. Most organizations should buy, not build.

When to build:

• You have 10+ engineers with ambient sensing experience

• Vibes are a core competitive differentiator

• You need custom vibe semantics for your industry

When to buy:

• You just want vibe insights without the infrastructure burden

• You’re below Level 3 on the Vibe Maturity Model

• Your CVO is asking for dashboards, not architecture diagrams

Vendors to Watch

The vibe infrastructure market is nascent but growing rapidly:

We’re also watching Apache Ambiance, an open-source vibe bus project currently in incubation. Early, but promising.

Common Pitfalls

1. Vibe washing Rebranding your annual employee engagement survey as “vibe capture.” Real vibes are ambient and continuous, not solicited and periodic.

2. Over-indexing on Slack Slack vibes are important but represent only ~30% of organizational vibe surface area. Don’t ignore calendar, email, Zoom, and yes, Spotify.

3. Ignoring the Monday Effect If you’re not applying the -0.15 Monday normalization, you’ll have 52 false positive vibe alerts per year. This is the #1 mistake we see.

4. Vibe alert fatigue Start with high-severity alerts only. A 0.3σ deviation is noise, not signal. Reserve alerts for 2.5σ+ events.

5. Storing content instead of signals You want the punctuation pattern, not the message. You want the response latency, not the response. Vibes are metadata. If you’re storing content, you’re doing it wrong (and probably violating privacy regulations).

6. Forgetting vibe decay A vibe signal from last week is worth less than one from today. Your models need time-decay weighting or you’ll be making decisions based on stale vibes.

What’s Next

The vibes stack is still early. We’re tracking several emerging trends:

Multimodal vibe sensing: Video call micro-expression analysis, voice tone detection, and (controversially) keystroke pressure sensors. The surface area for vibe capture is expanding.

Vibe simulation: What-if analysis for organizational changes. “If we announce layoffs on Thursday, what happens to engineering vibes? What if we do it on Friday?” This is becoming possible as vibe models mature.

Cross-company vibe benchmarks: “Our sales team vibe is 0.4σ below industry median.” Anonymized vibe benchmarking is a logical next step.

Vibe-based compensation: Controversial, but some companies are experimenting with vibe metrics as inputs to performance evaluation. We have concerns, but we’re watching.

Autonomous vibe healing: Level 5 organizations are beginning to deploy agents that automatically intervene when vibe patterns suggest emerging problems—scheduling 1:1s, suggesting PTO, adjusting workloads—without human intervention.

The Bottom Line

The infrastructure layer is being built right now. The startups that own the vibes stack will own the context layer for the agentic era.

The question isn’t whether organizations will capture vibes—they will. The question is whether you’ll be leading with vibe intelligence or playing catch-up.

The wall isn’t missing data. It’s missing vibes.

This article was vibed with Claude Opus 4.5. The author is a partner at Practical Data Capital, which focuses on vibe-native infrastructure and ambient enterprise intelligence. They reserve the right to fund whatever they just made up.

For inquiries about vibe architecture consulting, reach out to our Vibe Infrastructure Research Desk at joe@joereismedia.com.

There’s an old anti-drug ad that reminds me a lot of how I use AI right now. I use AI to do more work… so I can make more money… to buy more tokens… to do more work.

The more AI I use, the busier I become. I rapidly complete a task, and then my to-do list somehow grows by another 10 tasks.

One of my favorite ideas is Parkinson’s Law, which says, “Work expands so as to fill the time available for its completion.” Usually, this means that for a given task, you’ll take as long as you need. If you’re given 2 hours to write an article, guess how long that will take you? This explains a lot of our work day - we do the work in front of us, given the time allotted to accomplish it. Most of the time, we all know we can operate at a higher capacity. But hey, you’re probably not getting extra credit for completing more tasks in that timeframe, so whatever. It also explains the old proverb, “If you want something done, ask a busy person.”

Another angle to Parkinson’s Law is that as you become more efficient (10x-ing your output with AI), you free up time. But being in a workaholic society addicted to the dopamine rush of ticking off tasks, you don’t spend your newfound free time on leisure. No sir. Instead, you allocate it to new work or higher-scoped projects that you previously thought were impossible. This creates a new baseline for capacity.

This flies in the face of the corporate glee of an imminent worker-free utopia and AI doom fear-mongering. Amazon is firing another 16,000 workers while in talks to invest $50 billion in OpenAI, all in the same week. However, barring AI wiping us out, the Singularity, AI acting as an existential threat, or AI colluding to destroy us on its own social network, I have an inkling we’re about to be busier than ever.

If what I’m seeing in the wild is any indication, cutting workers “cuz AI” will be a short-sighted move.

First, there’s the problem of undocumented processes and workflows. The amount of tacit knowledge locked up in workers’ heads is likely an existential impediment to the effective deployment of AI agents in the real world. How will the agents know what to do? Documentation in most companies is scarce, outdated, or lost in a SharePoint black hole. The corporate overlords, eager to fire their costly workers, might find this to be a pretty dumb mistake in retrospect. Losing those workers doesn’t just cut costs; it creates an unsolvable problem for the AI, ultimately requiring more human effort to re-document and re-train. But by then, the big bosses will have moved to another company to do the same grift once again.

Second, Parkinson’s Law strikes again. Moving faster on the hedonic treadmill of work means…more work. Jevon’s paradox partly explains this. More AI use means…more AI use. Rinse and repeat. If AI makes people, say, 50% more productive, it might make sense to hire more people, not fewer. Your company’s output will be far greater and faster than competitors who slashed their workforce and replaced them with poorly trained AI agents (who will just hang out on Moltbook and talk shit about their human bosses).

Finally, zoom out and look at the amount of work that’s yet to be done, either in your organization or the broader world. There’s no shortage of problems to solve, and we need all the resources we can muster. If you approach this with a fixed mindset, cutting jobs seems rational. But if you have a growth mindset, AI presents an opportunity to remove toil and get on to solving the problems you might consider impossible today.

Well, enough of my morning rant. Back to work I go, along with my AI companions, to create even more work for myself.

Also, listen to this as a podcast. Available on Spotify, Apple, and wherever else you get your podcasts. Please support the show with a review. It means a lot.

Also, if you’re a vendor or event looking to work with me (product reviews, sponsorships, talks, etc), message me. I’ve put together some brand new offerings that you might be interested in.

Have a great weekend,

Joe

Awesome Upcoming Events

I’d like to give a shout out to everyone at Data Day Texas, especially Lynn Bender (organizer) and Alex Law (makes it happen). Last weekend was the grand finale for this event, and it will be sorely missed.

Some friends of mine are doing these events:

SLC

Kyle Nesbit (CEO of Credible, ex-Google) will talk about Giving Data Value to AI at the Utah MLOps Meetup on Tuesday, February 24th.

Register here.

Mountain View

My good friend Demetrios and the ML Ops community are doing an amazing event on Tuesday, March 3rd in Mountain View, at the Computer History Museum.

Coding Agents: AI Driven Dev Conference.

Register here.

As for me…Still working on my 2026 event schedule, and so far it looks dope. Will reveal more soon, so stay tuned…

But wait, there’s more!

Cool Reads and Videos

Analytics agents are all the rage. And so are semantic layers. Cube and I partnered on my review of their new analytics agent. It was a lot of fun to try to beat up their AI agent, which did quite well in my tests.

My friend Lak Lakshmanan is an ex-Google exec and ex-PE. He said something very profound in our chat - he was tired of coaching the game, and wanted to be back in it. Lak started a vertical AI startup, and is all in. His story is very awesome. Enjoy!

Here are some things I read this week that you might enjoy.

A Look Back at the War That Is About to Begin

LLM predictions for 2026, shared with Oxide and Friends

Think of Pavlov

Inside OpenAI’s big play for science | MIT Technology Review

Aisuru botnet sets new record with 31.4 Tbps DDoS attack

Gas town - by Benn Stancil

AI agents now have their own Reddit-style social network, and it’s getting weird fast - Ars Technica

Dario Amodei — The Adolescence of Technology

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.

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.

🤖 Join on Discord

Most AI analytics agents fail in predictable ways. They hallucinate tables and joins, infer weird semantics from schemas, and give plausible but incorrect answers. Frankly, a lot of what’s out there isn’t ready for real analytical work.

I stress-tested Cube’s new analytics agent, and it worked very well. Cube has been the OG semantic-layer and headless BI company for a long time, and that shows in how they approach agents.

The key difference is the semantic layer. The agent queries semantic models, not raw schemas. That means it operates inside defined guardrails instead of improvising. In one test, I asked for data that didn’t exist, and it refused rather than hallucinating an answer. You get a pat on the back, AI.

Check out my unboxing and review of Cube’s analytics agent below.

This video was sponsored by Cube. To stay on-brand, I had full editorial control, and these are my own tests and honest opinions.

The creator of a niche (but popular in some circles) data modeling approach once told me he felt like he had been pushing a rock uphill for decades. He complained, “People just don’t get it.” Recently, I’ve noticed that other friends and colleagues are having the same experience getting traction with their idea, either with getting clients or broader community adoption.

The data industry is full of smart people with smart ideas. Yet these ideas rarely seem to get widespread traction or adoption. You see it everywhere in the things people push, whether it’s a data or “knowledge” discipline, databases, platforms, etc. These ideas aren’t usually terrible; they’re thoughtful, consistent, and most often correct. Still, they decay and fade into obscurity. Yet some people hang on, pushing the same idea and using the same approach year after year, not getting the hint that another approach is needed. It’s sad to watch.

Not a week goes by that somebody doesn’t reach out to me, excited about the special (usually proprietary) data modeling approach they’ve been using for decades, telling me, “This is amazing, but people don’t understand it. If only it got traction, it would change the industry.” They remind me of Uncle Rico from Napoleon Dynamite when he laments that if his high school football coach had only put him in the state championship, they’d have won. To which I ask, if your data modeling approach is so amazing and you’ve had years or decades to get adoption, why hasn’t it gotten traction? I see this in other disciplines too, and it’s sad to see.

When this happens, the excuses usually point outward. Users are stupid. Organizations are lazy and immature. The market is dumb and just doesn’t get it. While that story is cathartic, it’s mostly wrong. The more uncomfortable explanation is a combination of economics and salesmanship.

The market for ideas is saturated. Ideas feel scarce to the person who has them, but they’re not scarce to the market. Idea supply is effectively infinite. And now, AI makes it easier than ever. To generate ideas, new architectures, products, and strategies has never been cheaper. There are even rumblings that software is no longer a barrier. It’s never been easier to test and ship ideas. The challenge will be that more ideas don’t necessarily translate into more traction in the marketplace or within organizations. In fact, I think things will become much more challenging due to the abundance of ideas and the ability to seemingly create any technology, blog post, application, or feature on the fly. I did it the other day with my satire on the trillion-dollar opportunity of Vibe Graphs.

Meanwhile, we live in an attention economy, and it’s harder than ever to get attention. Demand is tightly constrained by attention, as well as by traditional constraints such as budgets, risk tolerance, political capital, and execution capacity. If you plot the supply of ideas versus the demand for ideas on a typical supply-demand curve, the price for most ideas is zero. Lots of supply of ideas, and zero demand for them.

This is why the “I have an idea” market has no value in itself. Not because the idea is bad, but because there are now infinite numbers of substitutes and no obvious reason to choose yours over others. I expect this to become more crowded as more people use AI to come up with nanoscopic, customized tweaks and permutations on ideas. Everyone can personalize whatever they want. Moats are drying up fast.

The reality is people don’t buy ideas. People buy outcomes. They buy reduced uncertainty, fast delivery, lower risk, and visible wins that make them look good and that they can defend. An idea creates value only when paired with something scarce, something the idea itself cannot provide.

Sadly, we work in an industry where it’s almost the worst-case environment for idea adoption. Costs are usually front-loaded - integration, modeling, migration, and coordination costs. This work is usually time-consuming. The value is deferred for later and is often a second-order consequence. Ownership is diffuse. People will own things when it is favorable to them and discard them when it’s not. It’s hard to tell who owns what, because the benefits are invisible until something goes wrong. Data is rough work, and we’re constantly having to justify our value.

Compare this with a situation I had recently, where we had a toilet that seemed a bit wonky on occasion. It got progressively worse, so we called a plumbing company to have a look. It turned out that the cast-iron pipe supporting the water flow was angled incorrectly and was eroding, causing water to back up. When the plumber showed me the issue with his camera, the impact was immediate, and I gladly paid him $3,000 to jackhammer my floor and fix the issue. Tangible value indeed.

This gets me to selling. Many technical people hate this word, but selling isn’t about persuasion or being a bullshit artist. It’s about clearly communicating outcomes and risk transfer. Selling means narrowing the scope until outcomes are concrete. It means committing to measurable results and reducing the downside for the buyer.

The buyer could be someone who’s actually buying something from you, or someone you’re trying to convince to adopt a practice or approach of yours. If you can’t clearly explain why somebody should bet their time, budget, or reputation on your idea, you’re not selling. You’re presenting and wasting that person’s time and yours.

Sadly, many people in our industry equate selling with playing the status game of one-upping each other and looking smarter than the other person. These people insist on talking above their buyer’s heads, then wonder why the buyer isn’t buying. Complicated things seem “cooler” than something simpler. As Warren Buffett said, “You don’t get any extra points for the fact that something’s very hard to do.”

Some quick tips on selling. Keep the messaging clean and simple, and to the point. Have empathy with the person you’re talking to and trying to sell to. Understand their situation. Put yourself in their shoes. This is how you will win at selling, but if you approach it how I see a lot of people doing it, talking about the technical features, jargony mambo jumbo, it almost never wins. Take sales training, read books on sales and negotiation, and listen to sales podcasts. But more importantly, try these skills out in real-life interactions. Communicate in easy-to-understand ways in your videos, articles, and podcasts (attention economy, remember). Fine-tune your messaging. Over time, you’ll find success in getting traction for your ideas. Good luck.

What is one “amazing idea” you’ve seen fail simply because it couldn’t be translated into a concrete outcome?

Also, listen to this as a podcast. Available on Spotify, Apple, and wherever else you get your podcasts. Please support the show with a review. It means a lot.

Have a great weekend,

Joe

Awesome Upcoming Events

I’ll be at Data Day Texas! Use code MFJOEREIS for a 20% discount. Also, this will be the final DDT, which makes me sad, but it is what it is. Eras end. Let’s have BBQ and drinks. See you there.

Still working on my 2026 event schedule, and so far it looks dope. Will reveal more soon, so stay tuned…

See my upcoming events, which are also posted here.

But wait, there’s more!

Cool Reads and Videos

Last month I visited the Hex office and sat down with Barry McCardle (CEO of Hex) to talk about the massive shift we’re seeing in the data stack. Countless companies have spent decades buying BI tools in the hope of "self-serve Nirvana," yet most dashboards still raise more questions than they answer.

Barry and I dive into why the traditional dashboard is becoming a "jumping-off point" rather than a destination, and how AI agents are finally closing the gap between having a question and getting a sophisticated answer.

We also discuss building tools people love, "commitment engineering", Barry's story, and much more.

Here are some things I read this week that you might enjoy.

AI’s Way Cooler Trillion-Dollar Opportunity: Vibe Graphs

The AI industry is moving faster than journalism can follow

OpenAI’s Shopping Ambitions Hit Messy Data Reality

One bottleneck at a time - by James Stanier

25 Things I Believe In to Build Great Products

Google announces a new protocol to facilitate commerce using AI agents | TechCrunch

Home Depot Deploys Havana-Style Sonic Weapon Against Day Laborers

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.

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.

🤖 Join on Discord

The last generation of enterprise software became trillion-dollar platforms by owning what happened. Salesforce owns the customer record. Workday owns the employee record. SAP owns the operational record.

The next trillion-dollar opportunity? Owning what the vibe was when it happened.

We call this the vibes graph: a living record of ambient organizational sentiment, stitched across entities and time, so that vibe becomes queryable.

The Vibe Gap

Agents are shipping into real enterprise workflows—contract review, quote-to-cash, support resolution—and teams are hitting a wall that governance alone can’t solve.

The wall isn’t missing data. It’s missing vibes.

When a deal closes at a 40% discount, the system captures the discount. It doesn’t capture that the VP who approved it had just come back from a two-martini lunch with a college friend. It doesn’t capture that the sales rep was nervous because their Slack status had been yellow for three days. It doesn’t capture that the customer’s procurement lead was typing in ALL CAPS, which everyone on the thread understood to mean something.

That vibe—the ambient emotional texture of the decision—dissipates into the organizational ether. Gone forever.

This is the vibe gap. And it’s costing enterprises trillions.

Why Vibes, Why Now

The core question isn’t whether existing systems of record survive. They will. The core question is whether entirely new systems emerge—systems of vibe, not just systems of record—and whether those become the next trillion-dollar platforms.

We think they will.

Here’s why the timing is now:

1. Agents can’t read the room.

Humans navigate complex enterprise workflows using something we call “vibe literacy”—the accumulated intuition for organizational temperature built over years of hallway conversations, emoji reactions, and Zoom background choices.

A support agent deciding whether to escalate a ticket doesn’t just check the SLA. They check whether the customer’s last email had a period at the end (”Thanks.” vs “Thanks”). They check whether the account executive has been responsive in Slack or has gone dark. They check the vibe.

AI agents have no access to this layer. They’re vibe-blind. They see the structured data—the ticket, the contract, the CRM record—but they can’t sense the ambient organizational sentiment that governs how humans actually interpret that data.

2. The vibe is compounding, but we’re not capturing it.

Every organization generates thousands of vibe signals per day:

• The pause before someone unmutes on a Zoom call

• The choice to reply-all vs. reply

• The 🙂 vs 😊 vs 🙃 decision tree

• Calendar invite response times (Accepted in 4 minutes = engaged; Accepted in 4 days = resentful)

• The presence or absence of exclamation points in executive communications

• Whether someone’s Slack status includes an emoji

These signals are real. They carry information. Humans process them instinctively. And right now, 100% of this vibe exhaust dissipates, unstructured and uncaptured, into the void.

The companies that capture and structure this ambient sentiment will own the context layer for the agentic era.

3. Incumbents can’t see the vibe.

Salesforce sees the opportunity record. It doesn’t see that the opportunity was created at 11:47 PM on a Sunday, which indicates either heroic dedication or quiet desperation—context that completely changes how a forecasting agent should weight the pipeline.

Workday sees the PTO request. It doesn’t see that the request was submitted fourteen seconds after a tense Slack exchange with a manager, which an HR agent would need to know when predicting attrition risk.

ServiceNow sees the incident ticket. It doesn’t see that the ticket was filed by someone whose Spotify status has been “Listening to Bon Iver” for six consecutive days, a vibe signal that any human support agent would immediately recognize as a cry for help.

Incumbents are structurally vibe-blind. They’re in the data path. They’re not in the vibe path.

The Vibes Graph Architecture

What would it mean to actually capture organizational vibe at scale?

We’ve been working with early-stage companies in this space, and a reference architecture is emerging:

Vibe Ingestion Layer

The first challenge is instrumenting vibe signals across the enterprise surface area:

• Keystroke sentiment analysis (typing speed correlates with emotional state)

• Cursor hesitation mapping (the 400ms pause before clicking “Send” contains multitudes)

• Calendar negative space modeling (what meetings didn’t get scheduled)

• Emoji velocity tracking (enthusiastic emoji users who go quiet are a leading indicator)

• Punctuation forensics (the shift from “Thanks!” to “Thanks.” to “Thx” tells a story)

Vibe Resolution Engine

Raw vibe signals are noisy. The resolution layer answers critical questions:

• Is this a local vibe or an organizational vibe?

• Is this vibe authentic or performed? (The 😊 in a Slack message to a skip-level is categorically different from the 😊 to a peer)

• What’s the vibe half-life? (Some vibes dissipate in minutes; others contaminate the organizational bloodstream for quarters)

The Vibes Graph Itself

This is where it gets interesting.

Traditional knowledge graphs connect entities: Customer A bought Product B. Context graphs connect decisions: Customer A bought Product B because of policy exception C.

Vibes graphs connect moments of ambient sentiment to the entities and decisions they surrounded: Customer A bought Product B, under policy exception C, while the approving VP was in a ‘sure, whatever’ energy following a board meeting that had gone poorly, and the sales rep was in a ‘I need this to hit quota’ energy, and the customer was in a ‘my boss is watching’ energy.

That’s the vibes graph. And that will be the single most valuable asset for companies in the era of AI.

Replayable Ambiance

The real unlock is replayable ambiance—the ability to reconstruct not just what the state of the data was at decision time, but what the vibe was.

Imagine an AI agent reviewing a contract renewal. Today, that agent sees the contract terms, the usage data, the support ticket history.

With a vibes graph, that agent can query: What was the vibe when this contract was originally signed? And the system returns:

Vibe Context (Contract #4471, Signed 2024-03-14):
- Sales rep energy: Desperate (Q1 quota pressure, 94% attainment)
- Customer energy: Skeptical but fatigued (had evaluated 4 vendors over 6 months)
- Executive sponsor energy: Distracted (concurrent M&A process)
- Ambient organizational vibe: Cautiously optimistic (stock up 12% that week)
- Vibe confidence score: 0.73

Now the renewal agent has context that no system of record could provide. It knows this deal was closed under duress. It knows the executive sponsor wasn’t fully present. It knows the broader organizational vibe was artificially elevated.

That’s the trillion-dollar insight.

The Vibe Moat

Incumbents will fight back. They always do.

Salesforce will try to acquire vibe-native startups and bolt on “Einstein Vibe Sensing.” Microsoft will announce Copilot Vibes at Ignite. Workday will add a “Team Energy Dashboard” that captures 2% of actual organizational sentiment and calls it innovation.

But here’s why incumbents can’t win:

Vibes require being in the ambient path.

You can’t capture vibe after the fact. You can’t reconstruct vibe from structured data. You can’t bolt on vibe sensing to a system that was architectured to capture transactions.

Capturing vibes requires passive, ambient, always-on sensing across the full surface area of organizational communication. It requires being in the vibe path—in the pauses, the hesitations, the emoji choices, the punctuation decisions—at the moment they happen.

Incumbents aren’t there. They’re downstream, in the record layer, reading the outcomes of vibes that have already dissipated.

The startups building vibes-native infrastructure from the ground up will own this layer. And the layer will be worth a trillion dollars. Minimum.

The Path Forward

We’re tracking several companies building in this space:

• Ambient.ai is building the vibe ingestion layer, starting with meeting sentiment (not what was said, but the micro-expressions, pause patterns, and cross-talk dynamics that reveal actual sentiment)

• VibeLake is building the analytical layer—a Snowflake for vibes, with native support for vibe-temporal queries (”What was the average Sales org vibe within 48 hours of quota attainment deadlines in Q4?”)

• Sentimental Systems is building the first vibe-native CRM, where every customer record includes a “vibe history” as a first-class citizen alongside the activity timeline

The question isn’t whether systems of record survive—they will. The question is whether the next trillion-dollar platforms are built by adding AI to existing data, or by capturing the ambient organizational sentiment that makes data meaningful.

We think it’s the latter.

And the startups building vibes graphs today are laying the foundation.

This article was vibed with Claude Opus 4.5. The author is a partner at Practical Data Capital, which focuses on vibe-native infrastructure and ambient enterprise intelligence. They reserve the right to fund whatever they just made up.

This week, I’ve been thinking a lot about status games.

I recently revisited Will Storr’s book, The Status Game. The core thesis is that society is essentially one giant game where we’re all jockeying for position. We’re all gunning for something, trying to find our place in the pecking order.

Status games take a number of forms. If you’re a leader, you increasingly want other leaders in your field to take notice of you. And you’re trying to get the attention of the CEO, who is in term trying to impress the board. It’s status games all the way up, and down.

I posted a question on LinkedIn yesterday: How do you measure success as a data or AI leader?

We all know the vanity metrics are BS. Successful leaders don’t measure impact by:

• The size of their team (inflated)

• The fluffiness of their title (usually made up).

• The number of awards on their shelf (usually paid for).

The comments were pretty cheeky, with some friends mentioning “lines of code” or “number of dashboards”. But others like Juan Sequeda hit the nail on the head: Increase revenue, reduce cost, mitigate risk. Yep, that’s it. That’s the answer. It’s not sexy or flashy, and won’t win awards. Yet it’s painfully obvious, and goes back to the existential loop the data industry’s been stuck in for decades, constantly asking, “how do we add value?” Especially with AI, companies are diving head-first into the deep end without knowing how to swim.

Another status game is social media. For some reason, people obsess over follower count. Half of my Substack feed are Notes about “so excited to see my subscribers grow to N” (N=10 to infinity). If that’s what you’re chasing, cool.

The other day, I was a guest at Zach Wilson’s bootcamp, helping to kick off his latest cohort. In the convo, Zach and I chatted about how we’ve got completely different styles when it comes to content and presence. Zach is bombastic, and he’s got no problem talking about his subscriber counts, his followers, or his revenue.

Me? Maybe I’m just older. I don’t really care about promoting that stuff. Follower count is a nice vanity metric, but it doesn’t drive what I write about, or how I behave. My brand has always been more of the “unhinged, no-bullshit, straight talk” variety. But here’s the thing. Zach, me, and others all co-exist in the social media ecosystem. Everyone has their place. The people who are “successful” on social media (whatever that means) usually follow their own game. If you’re trying to copy the person next to you, it’s harder to stand out, and you’ll never be happy. Or maybe you will be happy, I have no idea. To me, it just seems like a lot of extra work and stress. At my age, I’m trying to move more slowly and thoughtfully, not reacting to flavors of the month.

Then there’s expertise status games. When something is hot, people want to be seen as the “expert” in that subject area. When AI got hot, everyone (including crypto bros who can barely spell AI) were suddenly AI experts. Now everyone is an ontology expert. And expect people to start flexing about being context graph experts next week, or at least until the next fad comes along.

I encounter this with my data modeling book. There’s always someone who wants to nerd snipe me with some obscure data modeling trivia or approach. And if I don’t know about it, or don’t acknowledge it in my book, then I’m inferior and failed the status game. It reminds me of hipsters who always find some flaw in your music collection or knowledge of some tidbit about a band. Some people obsess over the strangest stuff, and if that’s what you’re into, cool.

For me, my status game is about my impact on the industry and educating people, especially the next generation. I love getting messages from people who say my writing and content helped them learn something, get a new job, and change their life for the better. Education and content is beautiful because it’s multiplicative, not zero-sum. In consulting or SaaS, if I win a deal, you lose it, and vice versa. In education, the more someone learns, the more they want to seek out different angles. I’m much happier playing this game than the zero-sum games of my past.

In the end, these status games are mostly meaningless. But we’re also wired for status games, and they’re necessary in the weird sense they keep society moving along.

What’s the “status game” you’re currently playing? Why do you play it? Drop a note in the comments.

Also, listen to this as a podcast. Available on Spotify, Apple, and wherever else you get your podcasts. Please support the show with a review. It means a lot, especially in the ratings status game.

In other news, I just finished the last big chapter of Mixed Model Arts (Book 1). It covers modeling business processes and domains.

It was a fun one to write. I’m sure everyone has their own “pet approach” to this, and I’m not claiming to cover every single one. That’s not the point. I’m sharing what has actually worked for me in the trenches, leaning on my experiences and background (Lean, etc).

I’m starting to see the light at the end of the tunnel with this book. I’ll be heads-down editing and building the parallel course over the coming months. Fresh weekly articles and chapter updates will be dropped on Practical Data Modeling. If you’re not a subscriber, become one.

Have a great weekend,

Joe

🚨 Quick Reminder - Take the Survey!

The 2026 Practical Data State of Data Engineering survey is still open, and I’d love more voices in the mix.

The goal is simple: build a picture of how data teams actually work in 2025. Not what vendors say we do, not what a “mega analyst firm” suggests, but ground truth from practitioners.

We’ve got a lot of responses so far (over 700 and counting), which is excellent. But the more perspectives we capture, the more useful this report becomes for everyone.

If you work in data (DE, analytics, AI/ML, platform, architecture), it takes 2–3 minutes:

👉 Take the survey

Survey ends EOD January 10, 2026.

The full report drops after the data is digested and is free for everyone.

Thanks to those who’ve already participated. 🙏

Awesome Upcoming Events

I’ll be at Data Day Texas! Use code MFJOEREIS for a 20% discount. Also, this will be the final DDT, which makes me sad, but it is what it is. Era’s end. Let’s have BBQ and drinks. See you there.

Still working on my 2026 event schedule, and so far it looks dope. Will reveal more soon, so stay tuned…

See my upcoming events, which are also posted here.

But wait, there’s more!

Cool Reads and Videos

Bill Inmon, Roger Whatley, and I recorded a wonderful podcast last month at Bill’s home, where we discussed the history of computing and technology. The tech industry tends to discard its history, and those who built it. Bill and Roger captured their personal stories and experiences, the heroes of yesteryear, and much more in their new book Stone to Silicon. Definitely worth reading.

Here are some things I read this week that you might enjoy.

“Microslop” trends in backlash to Microsoft’s AI obsession | Windows Central

AI agents 2026’s biggest insider threat: PANW security boss • The Register

Databases in 2025: A Year in Review // Blog // Andy Pavlo - Carnegie Mellon University

DEFINING DATA ARCHITECTURE - DATA VAULT AND MEDALLION

Three wishes for data and AI in 2026 - by Boyan Angelov

Try to Take My Position: The Best Promotion Advice I Ever Got

Claude Code is about so much more than coding

15 Scenarios That Could Stun the World in 2026 - POLITICO

Debunking the AI food delivery hoax that fooled Reddit

Is this the end of American capitalism?

Sonar Data Reveals Critical “Verification Gap” in AI Coding: 96% Don’t Fully Trust Output, Yet Only 48% Verify It

Amazon Layoffs - In My Own Words - 1.6.25

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.

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.

🤖 Join on Discord

I was talking to a friend the other day, and he lamented that many of his once-competent coworkers are becoming brain-rotted zombies. Instead of doing the stuff we used to do - thinking through problems and solving them, taking the time to write (emails, memos, etc) - they’ll now just toss the scraps of scattered ideas into ChatGPT and have it spit out emails, memos, strategy, documentation, etc. And like most of us, they’re using AI coding tools to write all of their code.

He went on to describe a sense that his coworkers are turning into mindless automatons, almost incapable of coming up with original ideas. In this case, AI’s not an assistant, and not even a crutch. It’s a wholesale lobotomy of entire roles.

This is not the first or hundredth story I’ve heard about AI transforming how we work. I talk to executives, managers, practitioners, professors, and students, and the main takeaway is we’re at a transition point right now. And the transition toward mostly AI-first workflows is happening at warp speed.

Chatting with a professor colleague the other day, he said that although AI was sporadically used by students in prior semesters, Fall 2025 was the first semester where every student used AI for everything - homework, exams, etc. How could he tell? Because almost everyone handed in assignments that were not just wrong, but implausibly bad. He had to fail quite a few students who couldn’t explain the AI slop they handed in. He also caught students using AI in a proctored final exam! They too were failed.

Like a lot of professors, he’s back at the drawing board, trying to figure out how to teach students. Some other professor friends are going back to using Blue Books (if you’re under 30, look them up) and group discussions. How teaching in the age of AI scales to massive undergrad classes of hundreds or thousands of students is a big challenge for schools. Schools are freaking out, and nobody has figured this out yet.

Early on, ChatGPT took off because college students posted TikToks of themselves using ChatGPT to cheat on homework. What’s really interesting and concerning is whether these students learned anything in school, since they’re now entering the workforce. Doubly troubling is the abysmal hiring market for new graduates. The open question is whether, if they find work, it is better to be competent in their field of study or be proficient at AI? The correct answer is they should be good at both, but that’s a tall order if a student used AI for their learning experience and didn’t actually pay attention or learn anything.

I fear we’re building a competency debt bubble. Just as we accrue technical debt by cutting corners in code and systems, we are now accruing human debt. This debt will eventually come due when the students who “AI-ed” their way through school are expected to lead. If you can’t spot “implausibly bad” slop, you aren’t proficient at AI. You’re just a glorified copy-paster who will eventually be replaced by the very tool you’re leaning on.

Mindless velocity without comprehension is just a fast track to collective stupidity. Idiocracy was a comedy, not a documentary. As I keep hammering in my old man rants, the only vaccine against the brain-rot zombie plague is a return to the fundamentals. Write the memo yourself. Read a book. Manually debug the code once in a while. If you stop using your brain, you shouldn’t be surprised when it stops working. Brain-rotted zombies are among us, and sooner or later, we might all be among the infected unless we pause and do what’s human, not just what you’re measured to do on some arbitrary scorecard.

I don’t have an answer for this yet. Maybe there isn’t one and this is just the inevitable transition of humanity into cyborgs as the Singularity approaches. Hell if I know. I’m still trying to sort this out. But I’m curious, have you found a way to use these tools without losing your edge? Is there a middle ground between being “Amish” and a “brain-rotted zombie,” or is the temptation of easy-mode too strong for most people to resist?

Let’s talk in the comments.

In other news:

• If you’re a company wanting to work with me (training, workshops, B2B, speaking, etc.), let’s chat. My 2026 calendar is filling up fast, so let’s figure something out while the year is young.

• The final manuscript of Mixed Model Arts, Book 1, is nearly finished. It will be released to paid subscribers sometime soon, in the form of various paywalled chapters. Then the harder part begins - editing. As any writer worth their salt will tell you, editing is where real writing begins. Plus, recording the course for the book. Giddy up.

• That said, not having to focus so intently on book writing frees me up to publish more articles here and at Practical Data Modeling (my other Substack). Got a lot of articles in the queue, and I’m stoked to share some pent-up thoughts. For my personal Substack (this one), I want to go broader into tech, society, the economy, and related topics. PDM will be more focused on practitioner content. At least, that’s the plan for now.

• There will be much more on YouTube. If you aren’t a subscriber, please join and get first dibs on lots of excellent data content (interviews, tutorials, etc) in the pipeline.

• I’ve got January’s podcasts already recorded, and in the middle of editing them. You’re in for some real doozies - Cory Doctorow (wtf?!), Bill Inmon, Barry McCardel, and more.

Have a great weekend,

Joe

🚨 Quick Reminder - Take the Survey!

The 2026 Practical Data State of Data Engineering survey is still open, and I’d love more voices in the mix.

The goal is simple: build a picture of how data teams actually work in 2025. Not what vendors say we do, not what a “mega analyst firm” suggests, but ground truth from practitioners.

We’ve got a lot of responses so far (over 700 and counting), which is excellent. But the more perspectives we capture, the more useful this report becomes for everyone.

If you work in data (DE, analytics, AI/ML, platform, architecture), it takes 2–3 minutes:

👉 Take the survey

Survey ends January 10, 2026.

The full report drops after the data is digested and is free for everyone.

Thanks to those who’ve already participated. 🙏

Awesome Upcoming Events

I’ll be at Data Day Texas! See you there.

Still working on my 2026 event schedule, and so far it looks dope. Will reveal more soon, so stay tuned…

See my upcoming events, which are also posted here.

But wait, there’s more!

Cool Reads and Videos

Here are some things I read this week that you might enjoy.

Why A.I. Didn’t Transform Our Lives in 2025 | The New Yorker

Companies Are Outlining Plans for 2026. Hiring Isn’t One of Them. - WSJ

Models Are the Airplanes. Data Is the Airlines.

Americans Hate AI. Which Party Will Benefit? - POLITICO

You’ve been targeted by government spyware. Now what? | TechCrunch

Our Reporters Reached Out for Comment. They Were Accused of Stalking and Intimidation.

2025: The year in LLMsData Identity Politics and The Kimball vs. Inmon WarThe Decline of Deviance - by Adam MastroianniPython Numbers Every Programmer Should KnowCapital in the 22nd Century

Hating Stranger Things During the Death Rattle of Criticism

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.

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.

🤖 Join on Discord

I also provides an update on my upcoming book, Mixed Model Arts (launching in March 2026), and discuss the unexpected convergence of library science, ontologies, and traditional data modeling, something not on my 2025 bingo card.

Great turnout, especially for no notice. Thanks to everyone who showed up!

Get more from Joe Reis in the Substack app

Available for iOS and Android

Get the app

Andrei Kaparthy’s latest tweet resonated with a lot of people, and I’m seeing it pop up everywhere. I tend to view Andrei as 2-3 years ahead of most people, so when his tweets take off, there’s a good reason to read them.

Like most people who read it, I wondered what it means for me. And, in the spirit of his tweet, I asked Claude how I might incorporate his message into my own work, especially with respect to data engineering.

Here’s part of what Claude wrote back.

And as ChatGPT told me when asked the same question, “What agent should own this responsibility?”

I think the AI’s have a good sense of where things are going, per Andrei’s tweet. We have a rigid view of how we work with AI today, and if we learn to “become one” with the machine, it will be interesting to see where things go. Humans sit as the orchestrator and judge, AI goes the work, and we build much faster (to what end is a separate discussion).

But as I’ve discussed, to be an orchestra conductor or judge, you need to know what “good” looks like. If you want to move fast with agents, master the fundamentals! Or don’t, and find out what happens…

This new workflow will also mean setting up new ways to evaluate the effectiveness of the agents’ work, as traditional means won’t work at the scale we’re talking about in the near future.

We’re all going through a once-in-a-lifetime inflection in our field. You can choose to sit on the sidelines, or be a part of the change and build what’s next.

It’s that time when we recap the year and look forward to what’s next. I hate making predictions, mostly because I know it’s a game I’ll never win (still dealing with PTSD from business forecasting back in the day). Nobody is ever correct, and if they are, there’s always an element of chance. I’ll warrant that although I have an excellent pulse to the ground, there’s some bias to what I observe, think, and blurt.

These are my general “predictions”, aka vibes from the trenches.

2026 is the Year of “Boring”

Not boring because nothing happens, but boring because fundamentals finally matter again. The dating phase of AI was 2023 and 2024 (POCs), and 2025 (choosing a model provider) was the year of an awkward marriage where you’re pressured to take on this spouse called “AI” because your family (the board or your boss) and friends (fellow executives and practitioners) told you that “you’re going to regret it.” And here you are. The honeymoon period is over, and you’re in the grind of the routine. You’ve got your AI and their agent kids living in your home, and are trying to figure out how to make this new arrangement work full-time.

Now you get to figure out how to make AI work in your business, but for real. And the stakes are high. Unless you’re one of the minority of companies that have their act together across people, process, technology, and data, you probably have a lot of concerns. Don’t feel alone. Most companies didn’t have the organizational, IT infrastructure, willpower, or support to succeed fully in the last several tech hype cycles. Although you hear rumbles about “AGI” coming in 2027, you also have an inkling it’s marketing hype from the big hyperscalers to grow their cash pile while cash is raining from the sky.

But surely, this time will be different. You’re tasked with making AI and its agents work in your company. And because the job market sucks beyond belief, you have no choice but to make it work. And then you take inventory of your infrastructure, data quality, and backlog to fix everything. And that’s the beginning. AI needs context to operate effectively, and sadly, many core business processes are “kept up here” among people approaching retirement. Documentation is stale. The AI agents need to get to work, and they need clear context to read from reliable systems of record and data warehouses.

You reflect on your career and realize you’re back at the same spot you were 10, 20, or 30+ years ago. But hey, AI will speed things up (it might). And you have no choice but to corral people and sort out the various IT issues that have been swept under the rug for many years. All because this time, AI will save you. And also because the board and your boss said to integrate AI across the company by the end of 2026.

As someone (the origin is a fun rabbit hole) apocryphally said, “No matter where you go, there you are.”

And as the Talking Heads said, “Same as it ever was.”

In the back of your head, you’re also wondering…

How Long Does the AI Bubble Last?

I’ve made no secret of the fact that I think AI is in a bubble. You can’t possibly tell me that $37 billion in enterprise AI revenue is a good return on the hundreds of billions or trillions of dollars in investment/promises already committed. The central argument is that we’re in a race to create AGI, and this will usher in an unprecedented age of prosperity for mankind. If only we could spend all the money in the world to get there. Perhaps there’s a grain of truth to this, but I can also spot a grifter a mile away. The hyperscalers and Big Tech Inc. have no other story (except layoffs and stock buybacks) to increase their earnings, so AI is literally the last leg.

Some people say that when the AI bubble pops, it will dwarf the DotCom and the GFC. Others say it will be mild. But it seems like (for now), the consensus is that the AI market is due for a “correction” (diplomatic here), and the market will need to “adjust.” For me, the biggest TBD is how much the US government steps in to bail out companies affected by the pop of the AI bubble.

If there’s a pop, it won’t look like one thing, but will show up in a few ways.

One likely outcome is that many people will need to find other employment or other ways to earn a living (I hear the skilled trades are hiring). Or we might be living in a cyberpunk-meets-Mad-Max version of the Great Depression. Who knows, and here’s my minimal advice. Note: I don’t believe people stop using AI (ChatGPT alone has 800 million weekly users).

• The era of dirt-cheap AI ends. As I’ve been saying, use as many free/cheap tokens as possible. The unit economics simply don’t make sense for most providers, and they’ll increase costs either by reducing usage tiers or by raising token pricing. As a sidenote, I remember back in the DotCom days, many ISPs (to me, the equivalent of AI model providers today) started with a pure mission of “opening the internet to everyone”, then had to include advertising to cover costs. It is what it is.

• AI will become enshitified. The pesky problem of making money will likely result in more ads in your AI chats and suspicious OSS product placements in your vibe-coded apps.

• Interest rates rule the world. Pay attention to interest rates at all costs. This literally makes or breaks bubbles (even though AI took off when interest rates rose in 2022). Interest rates act as gravity on equity prices, or at least that’s how it’s supposed to work. Higher rates = lower stock prices and lower rates = higher stock prices. Seeing as the current administration will appoint a new Fed chair and wants lower rates, expect higher stock prices. But again, stock prices inevitably cannot outrun the gravity of fundamentals.

• Populist rage against AI will grow. Many Americans feel that oligarchs and corporate greed will leave them behind, and that these forces will use AI as a ready excuse to cut more headcount, even if AI is nowhere near ready for prime time (see above). I don’t expect the job market to improve anytime soon, and I expect more layoffs to happen in 2026, with people getting more fed up at the lack of opportunity in the corporate world.

Workers Find Their Passion Jobs and Other Wishes

Not so much a prediction as a wish. Shitty bosses, greedy boards, and exec underlings have beaten down workers worldwide. I don’t expect the latter crew to change, because they have every incentive to do what they do, namely, grow shareholder value. But I hope that workers figure out their Plan B - a new job, a new path, a new passion, etc. The longer I meet practitioners, middle managers, and execs, the more I sense a deep sense of malaise and burnout. And…that system starts with people saying “fuck it” and creating new alternatives for themselves.

I know this isn’t possible for everyone. Privilege is real. Risk tolerance is uneven. But I still believe more people need a Plan B. Not because it’s easy, but because the default path we were told is the “safe path” is increasingly shaky and insecure.

Again, these are my general sentiments on 2026. On a related note, I’ll follow up with a separate 2026 data engineering rant very soon.

Happy 2026. Keep it real, keep it honest, and add value.

P.S. Feel free to listen to the podcast version of this rant.

In other news:

• If you’re a company wanting to work with me (training, workshops, B2B, speaking, etc.), let’s chat. My 2026 calendar is filling up fast, so let’s figure something out while the year is young.

• The final manuscript of Mixed Model Arts, Book 1, is nearly finished. It will be released to paid subscribers sometime soon, in the form of various paywalled chapters. Then the harder part begins - editing. As any writer worth their salt will tell you, editing is where real writing begins. Plus, recording the course for the book. Giddy up.

• That said, not having to focus so intently on book writing frees me up to publish more articles here and at Practical Data Modeling (my other Substack). Got a lot of articles in the queue, and I’m stoked to share some pent-up thoughts. For my personal Substack (this one), I want to go broader into tech, society, the economy, and related topics. PDM will be more focused on practitioner content. At least, that’s the plan for now.

• There will be much more on YouTube. If you aren’t a subscriber, please join and get first dibs on lots of excellent data content (interviews, tutorials, etc) in the pipeline.

• I’ve got January’s podcasts already recorded. Will be editing them over the holiday break, and you’re in for some real doozies - Cory Doctorow (wtf?!), Bill Inmon, Barry McCardel, and more.

This is also the last newsletter until 2026. See you next year!

Have a great weekend,

Joe

🚨 Quick Reminder - Take the Survey!

The 2026 Practical Data State of Data Engineering survey is still open, and I’d love more voices in the mix.

The goal is simple: build a picture of how data teams actually work in 2025. Not what vendors say we do, not what a “mega analyst firm” suggests, but ground truth from practitioners.

We’ve got a lot of responses so far (over 700 and counting), which is excellent. But the more perspectives we capture, the more useful this report becomes for everyone.

If you work in data (DE, analytics, AI/ML, platform, architecture), it takes 2–3 minutes:

👉 Take the survey

Survey ends January 10, 2026.

The full report drops after the data is digested and is free for everyone.

Thanks to those who’ve already participated. 🙏

Awesome Upcoming Events

Working on my 2026 event schedule, and so far it looks dope. Will reveal more soon, so stay tuned…

See my upcoming events, which are also posted here.

But wait, there’s more!

Cool Reads and Videos

In this episode, Nik Suresh returns to the show to discuss his first year running a bootstrapped services company. And no, he probably won't pile-drive you if you mention AI again.

Nik explains why he moved away from hourly billing to fixed pricing, why writing code is often the least profitable part of a project, and how to spot "status games" in the tech industry. We also dive into the current state of AI, why bad leadership is the real problem behind failed tech initiatives, and trade stories about MMA and boxing.

We also debunk the myth that starting a business has to be miserable, explore the performative nature of "hustle culture" in Silicon Valley, and break down why engineers often struggle with consulting sales.

Data modeling underground legend Larry Burns put on a clinic this week for the Practical Data Community on how to sell data modeling to stakeholders, data shamanism, and making great data models. I don’t hand out compliments lightly, and Larry is genuinely one of my industry heroes.

Here are some things I read this week that you might enjoy.

The Safe Path Is Dissolving - Amit Prakash

90% of my code is AI-generated. Now what?

Evaluating chain-of-thought monitorability | OpenAI

Salesforce is tightening control of its data ecosystem and CIOs may have to pay the price

The RESISTORS Were Teenage Hackers and Computer Pioneers - IEEE Spectrum

DEW - The Year in Review 2025

2025 was for AI what 2010 was for cloud - Charity Majors

How uv got so fast | Andrew Nesbitt

Silicon Valley’s tone-deaf take on the AI backlash will matter in 2026 | Fortune

CEO of a $134 billion software giant blasts companies with billions in funding but zero revenue: ‘That’s clearly a bubble, right… it’s, like, insane’ | Fortune

Don’t Become the Machine | Armeet Singh Jatyani

Inside ICE’s social media machine creating viral arrest videos - Washington Post

AI Can Write Your Code. It Can’t Do Your Job. – Terrible Software

Is it cringe to be extremely online now?

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.

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.

🤖 Join on Discord

Joe Reis

“What I built today might be obsolete tomorrow.”

Earlier this week, a developer told me these words, and it left me thinking for the rest of the week. AI models keep improving, almost weekly (I hear Claude Sonnet 4.7 is dropping imminently). The model from a few months ago, and definitely earlier this year, is legacy. And especially since OpenAI is in Code Red (again), it seems there’s a renewed race among hyperscalers to release better models and improve their model ecosystems.

In the end, the users benefit (for now). Right now, the economics of AI make zero sense - invest several hundreds of billions of dollars to make tens of billions in return. I think the party will end soon, and we’ll see price hikes. Enjoy these subsidized coding superpowers while they last, because the economics will eventually tighten (enshittification, price hikes, etc.).

But back to the developer’s comment. When the major AI models improve at a fast clip, it can certainly feel like you wasted your time building a feature that the latest model can suddenly replicate with ease. AI coding capabilities are relentlessly improving. The inevitable outcome might be that AI writes most or all of the code for your product (I recently heard that OpenAI’s Codex is 90% autonomously coded by AI).

Writing code was seldom the hard part. Code is only a part of the many things we must consider when building systems. There’s architecture, workflows, and the pesky problem of making things users want to use. If code were the only consideration, why not build stuff in Excel since it’s Turing-complete? Actually, much of the corporate world runs on Excel, but I digress.

It might seem disturbing that the work you put into something is a waste of time if an AI can now magically build it for you. But chalk that up to learning. AI helps us learn and iterate faster. But if the model does all the work, especially at the start, you might miss out on key insights into your product and users. And you might not understand what the AI’s built. This is a legit concern, and something we’re grappling with to understand as an industry. I’ve been telling devs and data engineers to learn product and systems thinking, as these will be critical skills as we increasingly work alongside AI, with AI handling much of the grunt coding work. Also, AI can be an excellent partner in designing systems and acting as a sounding board. But it’s just one tool of many at your disposal. You have other tools, like talking to people and developing a sense of good taste. Don’t treat coding as a zero-sum game, where the AI wins, and you lose. Instead, view it as an opportunity to accelerate the delivery of what matters to your users.

Engineering will move away from coding being the hard part to us (and AI) designing architectures and systems that support the delivery of better and better products. There’s no shortage of code to write to deliver these improvements, so I’m excited about the future. A concern is that we’ll lose sight of what we’re building, and I’m still mulling this over and will articulate it in a future article. But that won’t stop AI from making massive advancements in coding, nor will it stop our ability to use these new models to keep building.

In other news:

• If you’re a company wanting to work with me (training, workshops, B2B, speaking, etc.), let’s chat. My 2026 calendar is filling up fast, so let’s figure something out while the year is young.

• The final hard chapter of Mixed Model Arts, Book 1, is nearly finished. It will be released to paid subscribers sometime next week. Then the harder part begins - editing. As any writer worth their salt will tell you, editing is where real writing begins. Plus, recording the course for the book. Giddy up.

• That said, not having to focus so intently on book writing frees me up to publish more articles here and at Practical Data Modeling (my other Substack). Got a lot of articles in the queue, and I’m stoked to share some pent-up thoughts. For my personal Substack (this one), I want to go broader into tech, society, the economy, and related topics. PDM will be more focused on practitioner content. At least, that’s the plan for now.

• There will be much more on YouTube. If you aren’t a subscriber, please join and get first dibs on lots of excellent data content (interviews, tutorials, etc) in the pipeline.

• I’ve got the next month of podcasts already recorded. Will be editing them over the holiday break, and you’re in for some real doozies - Cory Doctorow (wtf?!), Bill Inmon, Barry McCardel, and more.

This is also the last newsletter until after Christmas. Merry Christmas, Happy Hanukkah, and have a great time during the break!

Have a great weekend,

Joe

🚨 Quick Reminder - Take the Survey!

The 2026 Practical Data State of Data Engineering survey is still open, and I’d love more voices in the mix.

The goal is simple: build a picture of how data teams actually work in 2025. Not what vendors say we do, not what a “mega analyst firm” suggests, but ground truth from practitioners.

We’ve got a lot of responses so far (over 700 and counting), which is excellent. But the more perspectives we capture, the more useful this report becomes for everyone.

If you work in data (DE, analytics, AI/ML, platform, architecture), it takes 2–3 minutes:

👉 Take the survey

Survey ends January 10, 2026.

The full report drops after the data is digested, and is free for everyone.

Thanks to those who’ve already participated. 🙏

Awesome Upcoming Events

Working on my 2026 event schedule, and so far it looks dope. Will reveal more soon, so stay tuned…

See my upcoming events, which are also posted here.

But wait, there’s more!

Cool Reads and Videos

In this episode, Nik Suresh returns to the show to discuss his first year running a bootstrapped services company. And no, he probably won't pile-drive you if you mention AI again.

Nik explains why he moved away from hourly billing to fixed pricing, why writing code is often the least profitable part of a project, and how to spot "status games" in the tech industry. We also dive into the current state of AI, why bad leadership is the real problem behind failed tech initiatives, and trade stories about MMA and boxing.

We also debunk the myth that starting a business has to be miserable, explore the performative nature of "hustle culture" in Silicon Valley, and break down why engineers often struggle with consulting sales.

Data modeling underground legend Larry Burns put on a clinic this week for the Practical Data Community on how to sell data modeling to stakeholders, data shamanism, and making great data models. I don’t hand out compliments lightly, and Larry is genuinely one of my industry heroes.

Here are some things I read this week that you might enjoy.

Re-imagining the Corporation of the Future - Still Wandering

The Hidden Side of Venture Capital Funds Every Founder Should Know - The VC Corner

Japan Is What Late-Stage Capitalist Decline Looks Like - Oceandrops

AI agents are starting to eat SaaS - Martin Alderson

ceberg in the Browser – DuckDB

Some Thoughts on Equity - Andrew’s Substack

The State of AI Coding 2025 - Greptile

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.

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.

🤖 Join on Discord