2026 - General Thoughts on What's Ahead

The Weekend Windup #15 - Reflections, Cool Reads, Events, and More

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.

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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

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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.

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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?

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