Writing

Early last year, I came across this tweet:

https://twitter.com/AdamRackis/status/1762321041899012307

The discussion was surprisingly contentious. Maybe I should stop being surprised given the state of Twitter/X and all social media, but still it’s sometimes shocking how dug-in people are in their beliefs about work. On the face of it, “people get twisted in their relationship with work” seems like a reasonable take. “Stop complaining—you are making 16x the median salary in this country. Just do the boring job with the toxic team.”

Let’s stop and think about this for a second. The original poster (OP) is making $800K annually due to the appreciation of Spotify stock. An obscene amount of money? Maybe, maybe not. I do not have the typical hang-ups about the wealthy or ultra-wealthy. I don’t see this world as a zero-sum game, and I think the richest people out there have usually done some amazing things, especially in countries like the US where most wealth is not old wealth.

Recently I read the AI 2027 paper. I was surprised to see Scott Alexander’s name on this paper, and I was doubly surprised to see him do his first face reveal podcast about it with Dwarkesh.

On its face, this is one of the most aggressive predictions for when we will have AGI (at least the new definition of AGI, which is something that is comparable to or better than humans at all non-bodily tasks) that I have read. Even as someone who has been a long believer in Ray Kurzweil’s Singularity predictions, 2027 strikes me as very early. I realize that Kurzweil’s AGI date was also late 2020s, which puts his prediction in line with AI 2027, while 2045 was his singularity prediction. But 2027 still feels early to me.

During my parental leave, which ends tomorrow, I played through quite a few video games - something I love and one of the easiest ways to spend time while rocking my baby daughter to sleep. And it doesn’t hurt that my amazing wife loves watching games about as much as TV or movies with me, as long as they are beautiful or cooperative in some way. All of these are.

They include, in order:

I have long been of the mind that LLMs and their evolutions are truly thinking, and that they are on their way to solving all of the intellectual tasks that humans can solve today.

To me, it is just too uncanny that the technology that seems to have made the final jump to some degree of competence in tasks that require what is commonly understood as “thinking” or “understanding”, after a long string of attempts and architectures that fail these tasks, is a type of neural network. It would be much easier to argue away transformer models as non-thinking stochastic parrots if we had happened to have had success with any other architecture than the one that was designed to mimic our own brains and the neurons firing off to one another within them. It’s just too weird. They are shaped like us, they sound like us in a lot of ways, and it’s obvious they are thinking something like us too.

I’m Brian Hockenmaier, dad, engineer, and fun haver.

I grew up in Ventura, California with two UCLA-engineer parents. Even though neither of them were engineers long-term, I was an engineering child. I was always building something, highlights including a functional submarine and weather balloon with Lego robotics.

I went to school for industrial engineering at Cal Poly SLO, nearly double majored in economics, and then promptly moved out of both fields just like my parents had moved out of their education fields of mechanical and aerospace. Since then, after reading Ray Kurzweil’s The Singularity is Near in 2011, feeling I needed to be closer to software and realizing I liked building systems more than anything in industrial engineering, I’ve built a career in software and then AI.

Lots of chatter right now about AI replacing software developers.

I agree - AI will take over software development. The question is: what work will be left when this happens?

I have been occasionally challenging GPT to create models using OpenSCAD, a “programming language for 3D models”

Both struggle, but GPT-4 has been a massive improvement. Here are both models’ outputs after asking for an acorn and 3 messages of me giving feedback:

For the record, it is impressive that these LLMs can get anything right with no visual input or training on shapes like these. Imagine looking at the programming reference for openSCAD and trying to do this blind. The fact that the 3.5 version has a bunch of strangely intersecting primitives and some union issues has been normal in my experience. It takes quite a bit of spatial logic to get a model not to look like that.

Editor’s note from 2025:

This article was written as part of the launch of Treekeepers VR and the sole proprietorship Together Again Studios, and represents some of my core beliefs of the value of VR and where it’s taking us socially. Though I’m no longer actively working on Treekeepers, I do hold that VR and AR are truly the “endgame” of interface and one that could save us from some of the social attitudes caused by social media of today. Enjoy!

With Together Again Studios and Treekeepers VR, we’re setting out to solve an insidious problem we see all around us: