Hey Everyone - I hope you had a great weekend. I’m coming back from a little trip, and I’m ready to really dive in this week. This is what we have on the agenda:

Let’s dive in.

In last week’s video I said something that got people fired up. I said that even if we are in an AI bubble… so what.

Some took this to mean I think bubbles are harmless or that the consequences don’t matter. That is not what I am saying. A bubble bursting can absolutely hurt people, slow innovation, and cause real financial pain. I am not making light of that.

What I am saying is that a bubble should not change how we prepare.

If AI continues accelerating, then preparing early puts you in a far stronger position. If AI slows down or the hype temporarily collapses, the exact same preparation still helps. The skills we build, the communities we invest in, the income streams we create, and the resilience we develop are useful across every scenario. None of that effort is wasted.

This is the part people miss. Preparing for AI disruption is not a bet on the hype cycle. It is a hedge against uncertainty. It is a strategy that works whether the market keeps climbing or falls apart.

Think back to 2019. If you knew a global shutdown was coming, would you have regretted preparing a little early? Would you have regretted having a business that did not rely on foot traffic? Extra savings? A little physical space away from chaos?

Preparation is not prediction. It is insurance.

Even if AI is in a bubble now, the underlying direction is unchanged. The smartest people in tech, the largest companies on earth, and trillions of dollars in infrastructure all point to the same long-term trend. Maybe the timing is off. Maybe the market overheats. But the trajectory remains.

So yes, the bubble might pop. It might even be healthy for it to pop. But the actions that protect you are the same actions that position you to thrive.

Learn durable skills. Build things. Strengthen your network. Create real value. Invest in local community. Reduce dependency on institutions that may not adapt fast enough.

A bubble can change prices. It does not change the future.

The cost of preparation is near zero. The cost of being unprepared is enormous.

That is the point I want to make clear.

In the spirit of Ken’s advice to bridge the gap separating the early and mid-career engineer, I’d like to offer a glimpse “behind the wall” to demonstrate how finally succumbing to internal encouragement to use AI actually made me better at programming; this in turn allowed me more time to become a true subject matter expert (SME) in my domain, digital subscriptions (which, incidentally, includes the backend for my org’s portfolio of newsletters). As someone who began their career before AI was widely accepted and adopted, I cut my teeth through brute force and hours of frantic StackOverflow “research.” 

To give you an idea of how transformative AI is, I’ll recap a typical day, highlighting AI usage. At the beginning of the week, I have my weekly check in with my boss. We’re discussing the capabilities of a new vendor’s API; I plug the URL into Gemini and get a high-level recap ready to reference. After the meeting I review notes on my GitHub pull request (PR); my boss hasn’t yet seen the code because Copilot just completed its review. Copilot reminds me I forgot to define a schema for a metadata table. In my IDE, I access the data frame’s columns and use the field names and types as an input in the LLM to yield a schema; it is 98% correct. After adding the schema, my code passes my boss’ review and is deployed to production where it causes an error in the Kubernetes pod I’ve spun up in Airflow. 

The error message is long so I copy and paste it into my chat with the LLM. The suggestion is to increase resources, which I do in my VS Code environment. But with a meeting approaching, I don’t have time to remember the exact config, so I hit tab and CoPilot autofills my DAG task based on previous work. After the call I need to answer a stakeholder’s question about missing data; I go way down the rabbit hole with a technical explanation so before I hit send in Slack I let the LLM rewrite my message for a non-technical audience. 

You’ll notice none of the tasks I’m asking the LLM to conduct is revolutionary. I’m not seeking complete pipeline builds or model deployments. The value I get is in compounding time saved. Schemas take 10-15 minutes to write and review, documentation review and synthesis can take 1-2 hours and troubleshooting infrastructure failures can take 2-4 hours for a pesky bug. And this is why it feels difficult for entry-level devs right now. If a senior engineer can free up hours of work time, how can a junior be expected to work as quickly and efficiently? I sympathize with that frustration. So as I peer over the wall, my advice to you isn’t to sink time into shiny tools and abstract projects; instead, find and implement small-scale automations that “win back” time and 10x your bandwidth. Because the only thing more valuable than a busy dev is one with more time to build. 

Zach Quinn writes Extract. Transform. Read., a weekly data engineering newsletter delivering industry analysis and actionable career advice in 500 words or less. Read it here. 

I’m working on quite a few things now. I might as well fill you in on all of them. Better yet, I’d love it if you told me which you would like to hear more about (poll following).

I’ve come to the difficult realization that I just like working on a lot of stuff. Because of this, I have historically spread my focus and not seen many projects through. More recently (last few years), I’ve discovered the power of AI, working with other people, and delegating, and I’ve been able to do most of the things I like without drowning.

These are my current projects:

1) Newsletters - I’m most excited about this right now. I think I have the opportunity to become a true subject matter expert here. I’ve been writing 3 newsletters for the past few months and just started a 4th. These are the newsletters I have:

2) Newsletterhero.ai - This is a group project with a few friends. It is a tool for turning existing content into newsletters. It also helps you cross post that content to many platforms including linkedin and x.

I’ve been working on this for the last 4-5 months. I think the product is good, but we are really struggling to find the right place to spread awareness to engaged customers.

Planning to do a product hunt launch as well as some paid marketing to see if we can get this to take off.

3) YouTube - As you’ve probably seen, I’ve been posting on YouTube again. Honestly there isn’t a real motive behind this one. I’ve always liked making videos, and right now it feels like I can make them with relatively little pressure. It is also a good place to spread awareness about the other things I’m building.

4) The Exponential Podcast - I’m actually still working on this, but it has transitioned to more of a hobby. I feel like I could do this for the rest of my life, but I think it makes way more sense to strike while the iron is hot with my projects related to AI.

5) As a spinoff of the Exponential Athlete Podcast, I wrote a book on visualization for elite athletes (not the data kind…the kind in your brain) . This is one of my proudest achievements to date. The book has been out for a few months, so most of the work is done. I’m still experimenting with some advertising and awareness for the book now.

6) My Job - Yes, I still work in sports analytics. I’ve moved into a little different role where I do more consulting and oversight of our projects. Kinda crazy, but AI isn’t impacting the sports that we work in a ton yet. I expect this will change very soon.

Things I Learned

Content I Made

While there are plenty of digital skills worth learning, this week’s survival skill is building something tangible. Something physical. Something the real world can’t ignore.

For the past few years I’ve been experimenting with my fiber laser. I’ve engraved rocks, coins, markers, and all kinds of random objects. What started as curiosity turned into something surprisingly useful. People actually pay for these things. I’ve sold coins. I’ve sold rocks. I have even made bespoke gifts that mean far more than anything I could buy online.

It reminded me that physical skills create a different kind of value. A value that AI cannot fully replace. When you make something with your hands, you are producing an object with weight, texture, imperfections, and intention. Those qualities matter more than we realize in a digital world.

The same thing happened when I built the backdrop wall for my YouTube videos. I am not a skilled builder, but putting something together and seeing it stand on its own felt like a small superpower. The confidence you get from fixing, assembling, or creating something physical does not fade. It stays with you because it is earned through doing, not thinking.

You do not need specialized equipment to start. Learn how to repair something small in your home. Build a shelf. Plant herbs. Cook from scratch. Sew a button. Make something heavy. The only requirement is that it exists outside a screen.

These skills are not just hobbies. They are resilience. They are tradeable. They are valuable in every possible future. If AI shapes the world in unpredictable ways, the ability to create tangible things gives you optionality. It gives you usefulness. It gives you pride.

Digital tools will only get better. Physical skills will only get rarer.

Get your hands dirty. Make something real. It might open doors you did not expect.