Alex Honnold taking a break (and freaking out) while free soloing Half Dome. Free soloing is a journey into the void (I did this a bit as a teenager). There are a lot of parallels to climbing and today’s AI environment, especially being able to navigate uncertainty under extreme conditions.

I just returned from a fun trip to the Bay Area. I’m back on the road, doing what takes up a big chunk of my time in the spring and fall: keynoting, advising companies, late dinners, and talking to people and finding out what’s on their mind. My proximity to a diverse group of people is a blessing for me. I’m often among the first to know about what’s happening in our industry. There’s a lot going on right now, and the pace is blinding.

If you travel and talk to enough people - execs, practitioners, leaders, builders of the tools we use, etc. - you start seeing a pattern. It doesn’t matter where you’re at. Whether it’s Silicon Valley, San Francisco, New York City, London, or somewhere in between, AI has everyone wondering what’s next. This has influenced my favorite question to ask people: “What happens next?” The main theme I see is that nobody is sure what’s next.

That’s the whole essay, really. Nobody knows. Not the C-suite, not the VCs (they make sure to sound certain, though), not my friends at the frontier labs, and definitely not the pundits giving keynotes with their bold 2030 predictions.

Here’s what’s actually happening, as far as I can tell.

Executives have massive FOMO, and are scrambling to introduce AI into their companies. It’s not easy. Everyone knows this is pivotal technology, and adoption and interest are real. Still, there’s a lot of confusion. For example, what’s the value of AI? At a dinner this week, the question of AI’s ROI came up. How do we calculate it? Is it like a vending machine, where you put in a dollar and get a twenty-dollar bill back? Do we treat today as tuition for tomorrow’s AI-native workflows and workforce? Are there real payoffs years out? Nobody agrees. Everyone is terrified of being the sucker who’s left behind, so the checks for AI keep getting signed.

Practitioners are having an existential crisis about whether their skills are still relevant. It’s a great question, and I think we’re in the early phases of finding out how much of the old world translates to whatever comes next. At least with today’s AI models, I think we’re starting to see the contours of how we work with them, their capabilities, and how we can use AI. But who knows? The models are improving at an ever-faster rate, especially with recursive self-improvement emerging as the way new models will be created. Today we’re making harnesses and evals, but I’m unsure what tomorrow holds. What we’re doing tomorrow is anyone’s guess.

The general public is getting whiplash. People on the street just want to know whether they’ll have a job in a few years. And the public largely hates AI. Meanwhile, the frontier labs are calling for a slowdown. This is happening while politicians are gridlocked ahead of midterm elections, and they’re taking large amounts of money from companies to help with “shareholder value.” From what I’ve seen, people who read my newsletter live in a bubble. We’re likely up to speed on the AI tools and agents, how to use them, and the pitfalls of various model releases. The broader public isn’t. They might chat with ChatGPT or Gemini, but the idea of agents is foreign. They hear that AI will take their jobs, but they don’t embrace it. They hate data centers because they hear it will increase their cost of living, use a lot of water and power, and be subsidized by taxpayers.

Each group sees a part of the proverbial elephant and convinces itself that part is the whole. And with people on social media sounding very confident about where things are going (trust me, behind the scenes they’re just as confused as you), it’s easy to think that everyone has the future figured out. Why does your feed sound confident? Because confidence sells. Manufactured certainty raises funding rounds and investments from LPs. Confidence closes enterprise contracts. It wins speaking slots at TED and other events. Uncertainty is awkward. It takes patience to explain and doesn’t fit neatly on a PowerPoint slide. Uncertainty and candor don’t raise gobs of money.

But in private, things change. Everyone I talk with is confused and concerned. The future is a weird mix of technological, social, and geopolitical uncertainty we’re all navigating together.

Nobody knows.

In this Freestyle Friday episode, I chat about the various craziness and trends of data and AI right now.

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Where I’m At

My fall calendar is shaping up, and here’s an idea of what I’ll be doing. In most cases, I’m giving a talk. In some cases, I’m just hanging out.

dbt Summit. September 15-18. Vegas. Register here.
Big Data London. September 22-24. Register here.
Motherduck BDL after party. London. September 23.
appliedAI. Munich. October 8.
Hex Prompt. San Francisco. October 27.
Stanford. This fall, I’m joining Bruno Aziza as a guest speaker in his Stanford class on building products in the era of AI (October 12 – November 9).

The course was just announced and it’s already filling up. Stanford has opened it up online, so you can join from anywhere. Sessions are recorded too, so a time zone isn’t a dealbreaker.

Reserve your spot here soon because the course is almost full.

Motherduck’s Data Outpost. November 4-5. San Francisco. Register here.
Connected Data London. November 11-12. Register here.
Forward Data Conference. November 16. Paris. Register here.
MLOps World. November 17-18. Austin. Register here.

More to be announced very soon.

Cool Videos and Reads

Why AI Agents Will Break Modern Data Engineering (And How to Fix It) w/ Christophe Blefari (Nao)

In this episode of The Joe Reis Show, I catch up with my good friend Christophe Blefari, co-founder of NAO, to dig into what's actually happening on the front lines of AI agents and data engineering.

We talk about the evolution from building SQL IDEs to agentic analytics harnesses, how prompt-driven workflows are reshaping data teams, and why age-old problems like data discovery, modeling, and documentation are suddenly roaring back. We also dive into the risks of "token slop," whether data and software engineering roles are destined to merge, Christophe’s take on DuckDB’s acquisition by AWS, and the realities of building AI tools across Europe and the US.

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

No articles this week cuz I’ve been traveling and working on the book.

Note: These are articles I’ve read and enjoyed. I use AI to summarize my thoughts on the articles. I edit the summaries.

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Nobody Knows...