Dan Luu nails one aspect of our current moment in the software industry:

people are out there shoveling out huge piles of crap and calling it gold

Bill Gates:

We may need to develop the taste to actually not use technology to its maximum.

Everyone’s talking about how important “taste” is in making software nowadays.

What about the taste to not make software?

We have had groups — not large groups, but like the Amish — who don’t live at the technology frontier. A lot of humanity may choose not to live at the technological frontier because of things we get from helping each other

Now those are two ideas I can get behind:

1. Maybe we shouldn’t saturate every aspect of our lives with tech.
2. Maybe we could learn a thing or two from the Amish.

Bryan Cantrill talking about a video he recorded at Google Tech Talks in 2007

If you think I am candid now, you should’ve heard me when the video cameras weren’t rolling, it was something else. When no one could live-tweet what I said, it was more candid.

It is interesting to think about how much technology shapes our conversations with each other.

When everybody can consume everybody else’s conversations, what does that mean for what we say to each other?

(For example, what a speaker says at a conference might be very different if that talk is not recorded, vs. if they know that talk will be recorded, broadcast, and archived on the web for the rest of their lives.)

Oh, and I liked this part too where Bryan talks about why companies should write down what they do:

Record things you don’t otherwise record. In comments (where appropriate), in videos (where appropriate), record these things, not just for future generations but also because it allows a sourcespace to project its values. “Here’s what we’re about.” When people want to come work at Joyent, I have them go watch a couple of videos so they know what they’re getting in to. I tell people, “I have a couple videos […] if you watch them, you will want to work here more or less but you will not want to work here the same.” By doing that, you allow people to opt-in to your traditions and your values. It leads to more success in hiring, you get a more diverse team […] because the values you have transcend the traditional ways you hire people.

You can totally see this ethos being lived at Bryan’s company Oxide, like in their RFDs.

Elizabeth Dias:

The company argued that how people treated Claude could affect how Claude treated people.

I think that’s the golden rule, isn’t it?

“Do unto your tools as you would have them do unto you.”

It pains me to link to LinkedIn, but for a Rich Hickey take I’ll do it.

He calls out referring to AI as a “tool”:

As a lifelong tool builder and user, I know what tools are. LLMs are not tools [...] LLMs are automation.

Shots fired!

You don't direct the action of an LLM - you make requests and it acts on its own, without your energy or continuous direction

You have traded working with the world to working for a capricious robot.

What have we lost?

What do you miss by abdicating doing the reading? Pretty much everything. First and foremost, you miss learning how to write from those who can. You miss learning how to formulate an argument. You miss noticing things. You miss loading your background mind with fodder for novel ideas. You miss building trust in some authors and suspicion of others. You miss all the jokes. You miss all of the edges. You miss learning how to think.

(I love his point about how AI is not a “tool” for reading the same way a Kindle is a “tool” for reading or “glasses” are tools for reading. Just read the whole thing.)

Lastly:

Tools don't get "misaligned", they don't "go rogue", they don't "escape containment", "conspire" etc.

It just reeks of the offloading of responsibility. “I was mindlessly cutting an apple and the knife in my hand escaped containment and cut my finger. Not my fault!”

Anthropic sheds light on how they view the role of engineers in the AI future:

Very often, we found that the role of the engineers was to tell Claude “no.”

Sean Voisen (emphasis mine):

This means specifically telling the agent to actually use the intended design system, use these components in such and such a way, use these colors for these purposes and not other purposes, always lay out elements in this fashion using these pre-defined spacing tokens, avoid inventing new components, and so forth and so on. Constraining generation shrinks the disturbance variety of the whole system by reducing what’s possible.

This vibes with my experience (pun intended).

Being an editor and “constraining generation” for the LLM seems like the primary function.

Alex Wellerstein has a good post detailing the runaway improvements in speed and efficiency in war:

The first atomic bombs did with a single plane, in a single instant, what would have taken hundreds of bombers loaded with conventional munitions the work of many hours […]

In less than ten years, the bomber forces of the Strategic Air Command had so swollen that it was possible to imagine the complete destruction of the Soviet bloc in a matter of hours. By the 1960s, the deployment of intercontinental ballistic missiles (ICBMs) meant that thermonuclear payloads could be delivered from one side of the globe to the other in around half an hour.

The way nuclear weapons accelerate the process of human-led global destruction is craaaaaazy:

• Thousands of planes loaded with millions of pounds of traditional munitions can destroy a city in a few days.
• One plane loaded with one atom bomb can destroy a city in a few hours (time it takes plane to fly to location).
• Multiple planes with multiple atom bombs can destroy multiple cities in a few hours.
• Multiple planes with multiple thermonuclear bombs can destroy multiple nations in a few hours.
• Thousands of missiles with multiple thermonuclear bombs can destroy the world in minutes.

Fast! Efficient! Automated! I guess you call that — progress?

[we made systems to be] able to order up the deaths of half a billion people in less time than people typically get for a lunch break.

A glimpse at how war planners saw it all working:

An SLBM launches. About a minute later and a half later (95 seconds), NORAD gets the alert. If the missile was on a “depressed” trajectory, you now have between 135 seconds […] and 645 seconds […] before the detonation, depending on the SAC target. If it is on a “nominal” trajectory, you get between 245 seconds […] and 755 seconds

We’re talking about (seemingly) rationale human beings designing systems capable of hundreds of millions of deaths and thinking, “It’s probably best to communicate this in seconds rather than minutes because of how fast we’ve made it all.”

Paul Ford gives a metaphor for how people are amazed at what AI can do when they use it:

They’re giving birth to a baby in two hours and they’re like, “That’s a great baby. That’s a two-hour baby.”

We used to live in a world where people equivocated a large artifact, like a 250-page PDF, with hard work (consulting companies made a living off this equivocation).

But generative AI destroyed this illusion. Now we’re all readjusting and reevaluating how to judge good, thoughtful work.

Adam Silver:

It’s harder to include something and prove it’s unnecessary, than it is to leave something out and prove that it is.

Truth.

Let’s praise more people and organizations who say “No”.