Notes From the Field: AI, Energy Shocks & the End of the Old Playbook (Spring 2026 Edition)

• • Touching grass, as they say. On a stroll in Parliament Hill, London UK. One of my favorite things to do on the road, especially after giving a talk, is get outside and wander aimlessly for several hours. A fun way to decompress and see things off the beaten path.I’ve been mostly on the road for a couple of months, traveling pretty much nonstop since late March. San Francisco on repeat, all over Asia, through Europe, and a stop in Detroit last week. Spring is conference season, which means a lot of events, hallway conversations, dinners, and too many drinks afterward, where people eventually say what they actually think. I like to do these recaps when I finally come up for air, so here’s what I’ve been seeing this season. If I had to boil the whole trip down to one word, it’s uncertainty. And it’s everywhere. Sponsored by Revefi (https://www.revefi.com/): Speaking of uncertainty…Are your Snowflake costs going crazy? Most data teams spend months analyzing Snowflake costs. You can reclaim up to 50% of yours in just hours. Talk to a Revefi expert (https://www.revefi.com/demo)today. Lots of Physical UncertaintyWe tend to talk about uncertainty like it’s a vibe or a line on a chart. Lately, it’s hitting the physical world in the form of fuel shortages and sky-high prices. In parts of Southeast Asia, fuel (mainly diesel) was hard to come by because of the war (this is also happening globally). In my dad’s area in Thailand, diesel prices doubled; in some cases, it’s not available. This happened alongside an insanely hot, dry season, during which there were also water shortages (water thankfully replenishes during the monsoon season). The trucks that hauled water into dry regions were far more expensive, so water became expensive fast, assuming you could get it at all. Farmers were cutting back on water and fertilizer-intensive crops because natural gas became scarce after several pipelines were damaged. No gas means no fertilizer. Weird how that works. Europe’s gas prices are insane right now, with the US equivalent over $8/gallon. Food is also way more expensive, and this is already on top of food inflation since the COVID era. Same in the US. Gas prices in the US aren’t nearly as high as in Europe, but I know plenty of people who are cutting back on nonessential expenses because of insanely high fuel and food prices. My Uber driver earlier this week told me he runs a moving company, and that his fuel costs have doubled, forcing him to drive for Uber and DoorDash to help make ends meet for his family. I hear anecdotes from people in similar situations. It’s grim. And all of that ripples outward into everything, including the thing I spend much of my life doing, which is travel. In London last week, while sipping a pint in a pub, I got a text from KLM canceling my flight back to the US the night before, with no warning. That’s becoming normal. Fuel is scarce, so airlines are just cutting routes or rerouting traffic. Thankfully, I have high status on Delta (a KLM partner), so I got rebooked on a direct flight to Detroit. While we’re on it, here’s a travel tip: get direct flights to and from the US right now if you can. The short connections are where you might end up stranded. Avoid them if you can. I’m leading with all this because it’s the backdrop for everything else I want to talk about. The physical world was one of the big things people at conferences would talk about. It’s hard to focus when the world around you feels out of your control (fuel, food, geopolitical, crazy weather, etc.). And congrats! You’re paying far more for everything than you did earlier in the year. And this uncertainty doesn’t even include AI, which is the other elephant in the room. Let’s now talk about AI through the lens of vendors, practitioners, leaders, and the opportunity it brings. AI Scrambled the Vendor PlaybookThe pre-AI era was a far simpler time. It was the Modern Data Stack era, and the hallway conversation was about Data Mesh, giant funding rounds for the hottest tools (and features) that exemplified that era - observability, data pipelines and integration, Snowflake vs Databricks. That era is over. AI has flipped that world upside down. MDS vendors have had to scramble hard to pivot to becoming “AI companies.” A few years ago, it was about bolting on a chatbot to one’s product. That’s old hat. Even agents feel like table stakes. It feels like there’s much more work to do. Now it’s about fundamentally rethinking one’s company - and product - from the ground up. At a conference, everyone puts on their best face. Everyone seems to be killing it. But I know for a fact that quite a few companies are having genuinely existential conversations behind the scenes. If you’re trying to run the old playbook from the MDS era, good luck. The vendors who get it are tearing things down to the studs and rebuilding from the ground up to be “AI native” - the company’s operating system, employee reskilling, the company’s product, and whatever else needs to be refactored. The MDS was human-centric, and we need to build for a world where humans are not the primary consumers of data. This is a revolutionary shift.

The shift is jarring, especially against the backdrop where coding agents are improving every week, models are getting way more powerful (e.g., Claude Mythos and Dynamic Workflows), and vendors have Faustian bargains with the frontier model companies, who give you powerful AI in exchange for your company’s secret sauce. Either some nerd in her bedroom can create a version of your product in a day, or the frontier model company might release something that makes part of your offering obsolete. There is a heap of uncertainty right now in vendor-land, and I think the ones who succeed reassess or start something new from first principles. The ones who come out ahead are not playing it safe and resting on their laurels. They’re inverting their companies from the inside out. AI Also Scrambled the Corporate PlaybookLet’s now move to practitioners and leaders. Both groups operate against a backdrop very much in flux. Employers are struggling to figure out how to navigate and incorporate AI into the organization. Employees are on the receiving end of this. Often, the tension is between leading from behind and waiting for companies to decide on the direction they want to go. What do teams look like? Do we hire? Do we postpone hiring? Who/what do we hire - humans or AI? What does the ideal candidate look like? All these are questions that companies are sorting out. Overall, it’s a very precarious time for employers and employees. Let’s start with juniors. The job market for juniors is notoriously difficult. There’s real concern among junior-level practitioners about whether they’ll be able to enter the field, let alone get enough experience to progress. Just this morning, I got a message on Discord from someone asking whether data engineering has a future. Stay tuned for my thoughts on that, as I think that is worthy of its own article and podcast. For senior and mid-level practitioners who presumably know what they’re doing, AI can be a real superpower. You bring the judgment, AI brings the speed. But just as vendors are sharing their secrets with the very companies that might be their undoing, practitioners may face the same risks. In the back of their heads, engineers I speak with feel like they’re facing two risks - the AI model might eventually replace them, or their employer sees their gains with AI and makes them move faster and faster. Leaders are in a bind. In my discussion, there’s a sense that leaders will be impacted in a few ways. We keep hearing about how major companies are re-shifting their org charts to be more amenable to AI. There’s a debate over whether these shifts are due to overhiring from a few years ago or are real. But regardless, the narrative is out there, and companies will seize this moment to make the changes they see their peers making. Following the herd is a human condition that AI won’t replace. First, let’s assume org charts change. We’re already seeing this happen. Matthew Prince at Cloudflare wrote a letter in the WSJ (https://www.wsj.com/opinion/how-i-choose-which-cloudflare-employees-to-replace-with-ai-40a197e5?st=L2mg6f&reflink=desktopwebshare_permalink) last week about their recent layoffs. He wrote, “To understand the issue, I went back to a book published in 1954, 20 years before I was born: Peter Drucker’s “The Practice of Management.” Drucker explores the different roles inside every business, which I would categorize as builders, sellers and measurers. Builders create products. Sellers sell those products. Measurers do everything else: internal audit, revenue recognition, finance, legal, compliance, middle management, operations and on and on.” Prince splits his org into builders, sellers, and measurers. He intends to keep the first two, let AI handle the measuring as much as possible. I have mixed feelings about it. For one, 1954 and 2026 are pretty different worlds. Organizations aren’t clean machines you can just optimize. They’re a morass of tacit knowledge, politics, and friction, and some of that friction is actually doing work. Pull the wrong gear out, and yeah, you go faster, but sometimes that’s how you end up off the road in a ditch. That said, one piece of it rings true to me. Leading from PowerPoint and sitting in endless meetings seems like old hat. A common complaint I hear from leaders is that they miss building things. There was a certain joy in being an individual contributor who built and shipped rather than just spending time in alignment meetings and other managerial headaches. When I talk with leaders, many are back in builder-mode and having a ton of fun. One Chief Data Officer commented that a team quoted her six months for what seemed like a simple task. She went ahead and built a solution in an hour with AI. She then turned around and asked, pretty reasonably, why the team had quoted six months for something they knocked out in an hour. Apart from allowing her to discover for herself whether the work was doable, this opportunity enabled her to make quick iterations on many other things that would have seemingly taken months to complete. This calls into question the speed of delivery we took for granted and opens the door to what is possible. There is also a notion that leaders are expected to do a lot more with a lot less and much faster. This goes back to the podcast I recently did with Eric Weber, where we discussed how the nature of leadership is changing in amazing, but often violent ways. Leaders are burning out very quickly. In my conversations with leaders, I get a sense that the old playbook taught in MBA schools will become increasingly irrelevant in an age of less management, leaders becoming practitioners and shipping product, and hyperspeed agility. Against this backdrop, as companies try to find their footing in this new world, leaders must adapt continually. It’s OK. Nobody Else Has Things Figured Out EitherBack to my podcast with Eric. The most reassuring thing I heard all spring came from him, who spends his days talking to leaders at a lot of the big AI companies. Even the top 0.1% of AI (the people whose names you’d recognize) don’t really know what’s going on either. Nobody has the future figured out. We’re all collectively staring off into the void, wondering what’s on the other side So go easy on yourself. You’re not behind, because everybody’s behind. Take stock of what you’re genuinely good at, figure out where those skills transfer, and maybe build one small thing for yourself. Rinse and repeat. A lot of people I talk to are quietly working on a plan B. This might mean solopreneurship, freelancing, or going into a new field. I think this is awesome, and increasingly, people will take bets on themselves as the employer-employee contract becomes more nebulous. I’m fielding a lot of calls from people asking about how to go off on their own. This will likely be another podcast and article I do at some point. For all the doom, this is a genuinely great time to be making things. There’s no gatekeeping on the future right now. The old world and playbook are disappearing. The old guard is freaking out and will desperately try to hold on. Phase-changes don’t care. There are no guardrails left over from the old playbook telling you what you’re allowed to build. If you know what you want to make, go make it. Be wild and be unhinged. Start the business. Build the product. Create the future. Anyway, those are my notes from the field. I’d love to hear what you’re seeing out there. A few quick updates. The new book is now targeting late July for publication. Travel pushed it from June, but the manuscript is done, so it’s just final edits, artwork, and praise quotes left. A companion course is coming around the same time, so expect a lot more video from me. I’m going to mix audio-only Freestyle Fridays back in alongside the video ones. The Practical Data Community newsletter is live. The first article’s up, more in the queue, and you can pitch your own. I’m off the road for the summer (not at Snowflake or Databricks this year, sorry), back on the road in the fall. Also, stay tuned for an announcement on a super cool event I’m launching in January 2027. One last thing. In our surveys this year, “lack of leadership direction” and “poor requirements” combined for nearly twice the share of “legacy systems” as the top bottleneck. Yet this is relatively unexplored territory, and to my knowledge, nobody has actually mapped what that organizational dysfunction for data engineers looks like in practice.

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Anonymous, 8 to 9 questions (1 optional), takes about a minute or two to fill out. Take the survey here (https://docs.google.com/forms/d/e/1FAIpQLSdnMmRjCyLs4dRL6wGb3CMHWisVnl6r4VV1VaqiZr1AarEmFg/viewform?usp=dialog). 🙏 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. Cool Videos and ReadsHere are some things I read this week that you might enjoy.US Law Enforcement Warns of ‘Anti-Tech Extremism’ as AI Hatred Grows | WIRED (https://www.wired.com/story/us-law-enforcement-warns-of-anti-tech-extremism) SaaS outfit ClickUp promises seven-figure salaries for survivors of 22 percent staff purge (https://www.theregister.com/saas/2026/05/26/saas-outfit-clickup-promises-seven-figure-salaries-for-survivors-of-22-percent-staff-purge/5245929) A reality check on the AI jobs hysteria | MIT Technology Review (https://www.technologyreview.com/2026/05/26/1137855/a-reality-check-on-the-ai-jobs-hysteria/) State of the software engineering job market in 2026 (https://newsletter.pragmaticengineer.com/p/state-of-the-job-market-2026) Selling Abstraction (https://asteriskmag.com/issues/14/selling-abstraction) Tech CEOs are apparently suffering from AI psychosis | TechCrunch (https://techcrunch.com/2026/05/27/tech-ceos-are-apparently-suffering-from-ai-psychosis/) The Costco theory of the internet (https://www.joanwestenberg.com/the-costco-theory-of-the-internet/) Most Enterprise Agentic Projects Are Doomed, Here’s Why — Jess Grogan-Avignon & Jack Wang, Accenture (https://youtu.be/AGkzpxMdPn8?si=EejY4k_7b6C8YqNX) Interesting Links in the Data & AI World: May 2026 (Robin Moffat) (https://interestinglinks.substack.com/p/2026-05) Find My Other Content Here📺 YouTube (https://www.youtube.com/@joereisdata) - Interviews, tutorials, product reviews, rants, and more. 🎙️ Podcasts (https://open.spotify.com/show/3mcKitYGS4VMG2eHd2PfDN?si=7bd8ffc4d5b840ce) - Listen on Spotify or wherever you get your podcasts 📝 Practical Data Modeling (https://practicaldatamodeling.substack.com/) - This is where I’m writing my upcoming book, Mixed Model Arts, mostly in public. Free and paid content. If you’re interested in sponsoring my newsletter and podcast, H2 2026 is opening up. Please message me for details. The Practical Data CommunityThe 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 (https://discord.gg/gNfw5AKWSK)

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