The Compression

I've been tinkering my whole life, not to take things apart, to discover. Growing up, I wanted to be an architect. I loved sketching and building things in AutoCAD: the designing, the visualizing, the making of something that didn't exist yet. But everything around the making (the permits, the regulation, the red tape) was demoralizing. I kept the making. Jazz improv showed me what it could feel like: you learn the song and the theory until they become invisible, and what's left is flow. I've been chasing that flow in everything I've made since.

The distance between an idea and the thing itself has never been shorter.

That distance used to be the work. You'd have an idea for a website, and the path from concept to running code ran through a designer who mocked it up, a frontend developer who built the interface, a photographer who shot the assets, a backend engineer who wired the data. Each handoff was a translation, and each translation lost something. The idea dimmed as it moved through specialists.

Now I sit with the same idea and the path is mostly mine to walk. The tools haven't replaced the specialists so much as they've absorbed their skills into a single workflow. I can design, build, photograph, fabricate, and deploy without leaving my desk. The distance hasn't disappeared, but it has compressed, and the compression is accelerating.

Whether that terrifies you or liberates you depends on a question I've been sitting with: do you identify with the specialization, or with the making?

The Seams

The chase took me to the University of Nebraska-Lincoln on a trombone scholarship. Music was the first domain. All State four years, the whole identity wrapped up in it. Then freshman year I pivoted to physics and philosophy. And then, through a physics class, I discovered computer science and fell in love hard enough to complete the entire CS degree as a senior-year pivot.

I graduated in 2012 with a degree in computer science. Also physics. Also philosophy. Also music. The world I entered rewarded depth, and I could have specialized. I chose range.

The contract years were deliberate. Design and art in Omaha's Old Market, building fancy frontends and startup proof-of-concepts, shooting photography on the side. Fitbit. Google. Samsung. Boutique firms. Silicon Valley software companies. 3D scanning tools. Drone photography in Iceland, Peru, Colorado, Canada. 3D printing from the early days when the technology barely worked to now, when I fabricate tools I can't buy off a shelf. Each role lasted long enough to learn the domain, and then I'd move, because no single role could hold all the things I wanted to do. I got promoted. I landed work at companies most people would kill for. I just didn't want a full-time job until I found one worth staying for.

I was always at the seams between fields. The labels didn't fit, but the work was good and the work was mine.

The seams are where the translation happens. Philosophy taught me to look for the structure under an argument. Physics taught me to model systems. Music taught me that form and feeling aren't separate, they're the same thing at different resolutions. Computer science gave me the tools to build. None of these were hobbies. They were different angles on the same impulse: understand something deeply enough to make something with it.

I was on the ground floor of agent engineering, shooting from the hip, trying to find what was possible and what was worth exploring more. I started at Flatfile as a contractor, worked up to leading Developer Experience, then started building agents and finding the limits of what AI is good at. Flatfile was a data transformation tool with deep engineering roots. I had a three-month mission to optimize the company at large as much as possible. Then another mission: make a powerful agent loop that could replace Flatfile with a prompt. We did it. I led a small team to a week-long hackathon in NYC to match that engine with agents for a big customer. Six months later, the whole company pivoted to that mission. We were born again.

The years before that were a blur of hats: engineering, design, leadership, fundraising, surviving layoffs. A few promotions along the way, but the title was never the point. The point was holding the whole system in my head.

That breadth was a choice. Now it's the thing that makes the tools useful.

The Meaning Barrier

The technical barrier is collapsing. You can generate code, design layouts, write copy, edit photos, build prototypes, and fabricate physical objects with tools that cost less than the software licenses I used to need for a single discipline. The question "can you make it?" is becoming trivial.

The question that's getting harder is "should you?" What does this thing mean. Who is it for. What is it doing in the world. These used to be questions you could defer, because the technical work of making something was hard enough that the meaning could ride along quietly. When the technical work takes an afternoon instead of a quarter, the meaning can't hide. It becomes the thing you're actually doing.

This is where philosophy becomes load-bearing. Not as a credential. Nobody cares about my degree. As a way of seeing. The habit of asking what something is for before asking how to build it. The discipline of noticing when a solution is solving a problem nobody has. The willingness to sit with a question longer than is comfortable, because the first answer is usually the wrong one.

When the technical barrier was high, you could build a career on execution. When it drops, execution becomes table stakes. What's left is judgment, and judgment is what a philosophical education trains, not the answers, but the quality of the questions.

The same is true of aesthetic sensibility. AI can generate images. It can compose a frame, balance exposure, match a style. What it can't do is decide which image matters, which one holds weight, which one stops you. That judgment comes from years of looking. From drone footage over glaciers in Iceland and salt flats in Peru. From studio product photography, portraits, weddings. From learning to see light the way a camera sees it. The technical skill is compressible. The sensibility isn't.

The Same Patterns

People hear "interdisciplinary" and think I'm making analogies. That I see a connection between coffee and machine learning and I'm reaching for a clever comparison. I'm not.

When I dial in a new coffee, I'm running the same optimization loop I use in retrieval-augmented generation: adjust a parameter, evaluate the output, narrow the search space. The coffee is an extraction problem. The RAG system is an extraction problem. They're not like each other. They're the same problem at different scales.

When I'm pacing a long climb on the bike (2,040 rides have taught me something about pacing), I'm doing resource allocation across a duration I can't fully predict. That's the same multi-agent coordination problem I solve at work. When I'm designing a 3D-printed tool, I'm doing the same constraint-satisfaction work that agent design requires: what can I remove without losing function?

The patterns aren't metaphors. They're the actual structure. Different domains, same math. The generalist-maker isn't someone who knows a little about everything. They're someone who has seen the same pattern enough times to recognize it when it shows up wearing different clothes.

AI tools are useful to me not because they know things I don't (they know more than I do, in every domain) but because they can execute across domains fast enough to keep up with how I think. For the first time, the tools match the mind.

The Maker's Question

When machines can make anything, what do you choose to make?

I sit with this question the way I sit with a philosophical problem, not because I expect to solve it, but because the quality of the asking matters. The technical barrier to making something is collapsing. The question of what to make, and whether to make it at all, is what remains.

I don't have a clean answer. I have a practice: make things across domains, pay attention to what the making teaches, and let the patterns accumulate. The bike, the camera, the code, the coffee, each one is a way of asking the question through a different material. None of them answers it. All of them refine it.

This is the maker's question that compression raises: when machines can make anything, what do you choose to make? Not more. More deliberate. The response to abundance isn't acceleration. It's selection.

I work at Obvious, an applied AI research lab. I'm there because it's the natural home for someone whose career has been about translation between domains. The lab is exploring what AI means for how people work, make, and think. Applied, not theoretical. Research, not just product. I spent years looking for a place where breadth was an asset. I found it in a lab that's trying to understand the same compression I've been living in.

This is a living document, and I'd rather mark the uncertainty than paper over it.

I don't know where the compression stops. The tools are getting better faster than I'm getting better at using them, and I don't know if that gap closes or widens. I don't know if the meaning barrier holds, if judgment stays a human skill or if the tools learn that too. I don't know if the generalist-maker's advantage is permanent or a transitional moment while the tools catch up to every specialization.

What I know is this: I've been making things across domains, and the tools have never been better matched to how I think. The distance between idea and artifact is shorter than it's ever been, and the question of what to make with that distance is the most interesting question I know how to ask.

This is version one. The thinking will develop. I'll update this when it does.