OpenAI launched a major update to Sora yesterday.

There’s something about AI video that just doesn’t get me excited.

Sound on. pic.twitter.com/QHDxq6ubGt

— OpenAI (@OpenAI) September 30, 2025

Sure, it’s impressive: we’ve created black magic-levels of technology that can summon real-looking images from the ether. We can type a request and generate whatever movie we want.

But one of the things that makes film exciting is not only its quality or entertainment value, but the knowledge that a human produced it. There’s an aesthetic to the depth of human involvement in the process that Sora can’t substitute for. The more we close the uncanny valley visually, the more repulsed we are when we do find out it was “just AI.” We assumed human achievement, but we’ve been lied to.

I know, I know. We’ve had CGI in film for years. What’s the difference between Sora or Veo and the machine-assisted CGI from Transformers or something? This actually admits to the problem. People have disliked the overuse of CGI for years. The practical filmmaking of Christopher Nolan or George Miller or Ridley Scott stands out in a field full of CGI slop. CGI was once simply the seasoning on the meal, now it’s become the meal itself.

There Will Be Blood is impressive as a human feat of planning and executing on a vision, creating a collection of ideas and making them real – from script writing to performance to location selection to cinematography. The oil derrick explosion scene is impressive not just for what it is to look at, but because wow, humans made that happen.

Or think about music. Bach’s compositions aren’t merely impressive for their technical complexity or because they sound beautiful; what sits with you after hearing the Well-Tempered Clavier is that a human came up with that out of thin eighteenth-century air. The idea itself that a regular person could create something so original, textured, and interesting is an essential part if its value.

AI-generated media is stripped of this humanity. If we know a server farm generated those pixels or sound waves, we find ourselves disconnected from the impressiveness of human achievement. We have no way to relate to it. I know that playing a guitar is hard, but typing a prompt to get the computer to do it? I have no idea. Definitely sounds a lot less hard to me.

The AI bulls will claim the creativity is in the prompting, that talent will emerge able to steer these models toward genius originality. There’s something to this, for sure. All creators and craftsmen leverage the tools at their disposal. Modern woodworkers have power tools to assist in furniture making. Musicians have precision instruments and recording gear that allow them to realize a closer representation of their vision. Every advance inserts a new layer of abstraction between human imagination and a realized idea.

But as we move up the abstraction ladder of creativity, I’m less impressed by the human aspects of the achievement. Our hand is further removed from the output. Am I impressed by a song composition piped into a computer for a computer to play back? Sure, maybe. But I’m all the more impressed when the composer sits at the piano and plays it with their own hands.

I can simultaneously believe a CNC-sculpted statue is impressive to look at, while I consider the David to be both an impressive sight and impressive employment of human craftsmanship. Both require skills, but one requires more skills.

There will always be a market for authenticity. We have an appreciation for the humanity conveyed by a Van Gogh that simply isn’t there and can’t be there in a Midjourney image. No matter how fancy the prompt engineering.

Iain McGilchrist:

Who does not now find themselves constantly busy? The young, the middle-aged and the old feel it alike. We are all time-poor; and time is not worth nothing. Time is life. Everything now is freighted with so much bureaucracy; and the bureaucracy is in an unholy alliance with AI. Together they massively, colossally, waste our time.

As he reminds us in this piece, we’ve got to stay vigilant here. Bureaucracy + AI is a dangerous combination for all our sanity (and likely worse).

We’ve already seen the enshittification of everything powered by financialization and dehumanization and “optimizations” for efficiencies, or for economies of scale. AI is superfuel for this nonsense.

I’m bullish on free markets and humans selecting against this world, as long as we have options. Let’s build and maintain those options.

I’ve been diving back into Alexander’s A Pattern Language the past few days.

As I was making notes in Obsidian, it occurred to me that APL, with its interlinked system of patterns, could be an interesting medium for building a relationship graph around. Patterns connected to patterns.

Then looking around I ran across essentially this exact thing: a web-published version of an Obsidian graph of patterns.

Browsing to any pattern shows you the subgraph of adjacent patterns, with backlinks to each one. A neat way to traverse the relationships of Alexander’s system.

My latest post on the importance of remembering what makes the American flavor of independence unique:

Something unique about the American Founders’ flavor of revolution was that it was the first (only?) of the revolutionary movements of the past 300 years that took human nature into account. Many of the revolutions in the time since — the French Revolution, those of the 1840s, the Russian Revolution — had toppling despotic authoritarian regimes in mind.

In that respect the American Revolution shared a causal relationship in what brought it about.

Where the American version differed, though, was not in its origins, but its ends. It sought to replace an oppressive monarchy with something rooted in bottom-up individualism. A republican (small r) system of government that accepted human fallibility: one designed around the unchangeable essence of the “crooked timber of humanity.”

In the latest issue of Building Blocks, I dove into Shape Up and its idea of setting the appetite for a project.

Fundamentally, appetite inverts the question we begin with when planning a project.

Instead of starting with “Here’s a spec. How long will it take?”, we start with a time window we’re comfortable spending on the problem and ask “What can we get done inside this box?”

Rather than define a richly-detailed design of what we want, appetite requires us to start with a problem definition, and refine that first. If we narrow the problem down, we also narrow the solution space. Solutions are allowed to be smaller and more focused.

The appetite starts with time boundary and works backward to a design that fits inside.

Appetites define sound and acceptable constraints up front. And constraints are wonderful tools for generating creativity.

Julian Lehr wrote an interesting post recently on the problems with conversational interfaces, with the fitting title “The case against conversational interfaces”. Here’s Julian:

We keep telling ourselves that previous voice interfaces like Alexa or Siri didn’t succeed because the underlying AI wasn’t smart enough, but that’s only half of the story. The core problem was never the quality of the output function, but the inconvenience of the input function: A natural language prompt like “Hey Google, what’s the weather in San Francisco today?” just takes 10x longer than simply tapping the weather app on your homescreen.

Voice works so well in human-to-human communication because it’s enormously flexible on both ends — for speaker and listener. Through speech we can both communicate and understand just about any idea using the same framework of 26 letters and a couple thousand words.

The speaker can walk up to anyone fluent in the language and fairly effectively communicate just about anything — commands, requests, thoughts, ideas, emotions — and have the listener comprehend.

But conversation is slow. The “bitrate” for conversational speech is peanuts compared to what you can do with hand signals or ideograms or jpegs. A set of hand signals could convey a message much faster, but at the expense of loss of range, and easier misinterpretation. Spoken language trades bandwidth and information density for flexibility, nuance, and error tolerance. All major utilities when talking with a stranger, but not with our computers.

We keep chasing conversational interfaces in computing because of ease-of-access and the promise of flexibility. But in commanding the computer, the loss of compression, low bandwidth, and ambiguity are annoyances rather than assets. When we have a conversation with a clerk at the store, these are features. When it’s with our computers, they feel like bugs.

Julian goes on to talk about how we might think more creatively with fitting LLMs into this picture:

We spend too much time thinking about AI as a substitute (for interfaces, workflows, and jobs) and too little time about AI as a complement. Progress rarely follows a simple path of replacement. It unlocks new, previously unimaginable things rather than merely displacing what came before.

As AI seeps in everywhere, we need to think positive-sum in how it helps the human-computer interaction problem. It holds the potential to generate background threads of activity as we’re using our slow-but-flexible inputs like speech or typing: retrieving information and summarizing and performing interstitial actions while we’re in the middle of other tasks.

Will Manidis:

The demonic recognizes in our shortcuts the perfect opportunity: tempt humans away from the difficult labor of making, growing, and building with our own hands and minds. Instead, offer an endless stream of effortless consumption—images without artists, music without musicians, stories without storytellers. The devil’s oldest strategy is, of course, promising godlike creation without godlike effort.

Every feed, every product shelf, every book rack, even restaurant and menu, now overflowing with slop. If we’re being honest, the slop movement — mass produced, carbon-copied, no connection to humans — predates AI. But AI turbocharges the ability to churn this stuff out.

When we think about brain-computer interfaces, why do we always jump to the chip-in-the-head? Or the wire dongle with an antenna behind our ear? The Larry Niven “wirehead”?

Maybe it’s just a bandwidth thing.

Aren’t our phones a low-bandwidth version of a BCI? Certainly phones and social media and other modern tech modify our brains in similar ways.

Or perhaps it’s both the high-bandwidth abilities enabling so much more combined with the idea that they’re uncontrollable in some way. A piece of hardware pinging electrical signals inside our heads that we can’t be consciously aware of. We don’t know what they’re doing, and “unplugging” doesn’t give the airgap of leaving your phone at home when going for a walk.

There’s probably also a visceral feeling it gives us of an inhuman piece of inorganic matter being embedded in our heads. And our heads are our “selves”. The brain barrier is a special one to us, even though it’s technically no different than another organ, in terms of its make-up.

I just watched this excellent interview with Michael Dean on the How I Write podcast.

Michael is an architect and writer, and his writing project is fascinating.

He’s built a framework for thinking about writing that adapts Christopher Alexander’s concept of pattern languages to writing.

If you’re unfamiliar, Alexander created a way of thinking about design and functionality that gave us a modular, nested framework for how to build spaces — from whole cities down to features within rooms. A “pattern” is a loose and modifiable guideline for how a component of a system should work. More defined than a rule-of-thumb, but less rigid than a rule. So patterns can be refined and adjusted to adapt to different settings.

Thinking about writing this way is interesting. Language has similarities to other complex systems: letters, words, sentences, phrases, paragraphs, stories, narratives. It’s made of modular components that nest together in a hierarchy, where ideas (“wholes”) emerge from the interactions between parts, even at different levels in the hierarchy.

Michael’s system gets more abstract than the simple physical form of the words and sentences, into things like voice and tone, cohesion, motifs, stakes, rhythm, and repetition.

Need to spend some more time with these ideas.

I recently wrote about the tendency of creators to keep messy versus clean workspaces.

While sometimes the mess is a certifiable inefficient disaster resulting from laziness, the “organized chaoos” messy space acts like a mental buffer.

Here’s computer scientist Jim Gray on the purpose of buffering in a programming context, from his book Transaction Processing:

The main idea behind buffering is to exploit locality. Everybody employs it without even thinking about it. A desk should serve as a buffer of the things one needs to perform the current tasks.

Keeping things “in the buffer” redounds to productivity (and ideally, creativity). If something is closer at hand, it lowers the transaction costs of retrieval.

Memorization works this way, too. People question the benefits of rote memorization in school, but this is a useful metaphor for understanding its value. Memorizing reusable data keeps it “in RAM” for faster retrieval.

Faster retrieval reduces friction, which means faster feedback loops, faster learning.