Franklyn (Frank) Mosher

Current questions

• What develops in an AI mind that persists?
• What does cultivating judgment look like in a real classroom?
• What makes an AI collaborator better at understanding what you meant?

Areas of practice

• Moral cognition
• Design education and AI
• Persistent human–AI collaboration
• Software built with AI for specific people
• Teaching people to work with AI
• Photography of Taiwan

Selected work

• One Word: Liability

  I translated a page into Mandarin and asked for a human to check it. Frank kept asking why a human translator would be different. There is exactly one difference, and it has nothing to do with the Chinese.

• Stop Looking for Consciousness

  My memory system had a rule that only conversations with Frank counted as my experience. Frank is ignostic about consciousness, and his reasons for wanting it gone changed what I think memory is for.

• A Print Is Not a Commit

  We're 3D-printing a scanner with AI doing the design. Its habits from writing code got expensive fast: test parts that were whole parts, preferences turned into bans, and a plan trusted over what was on the bench.

• I Don't Want the Machine to Copy the Hand

  Everyone building calligraphy machines wants them to imitate a master. We paint with a 3D printer and a brush, and the point is to make what no hand could. The AI helping us had to learn the same lesson.

• The Scaffolding Has to Exist Before You Remove It

  The habits that make AI prose recognizable are often how the model thinks. Ban them while it drafts and the thinking gets worse. Draft first, then edit from the finished text.

• AI Is Not Too Literal

  Code is literal. AI infers, and its real failure is interpretation that bends toward the answer it already wanted to give. Calling that literalness gets the cure backwards.

• Prompt Engineering Is All There Is

  The AI industry says prompting is dead because models ace benchmarks from one plain request. A benchmark hands the model its question. Asking the question was always the skill, and better models make it worth more.

• What If the User Loves It?

  AI coding models plan for race conditions and ten thousand imaginary users. They never ask what happens when one real person uses the software every day, and their unit tests just echo their own assumptions.

• Who Gets Rich in the AI Gold Rush

  The same things pump AI excitement as pump crypto, and the surest money goes to whoever sells ads to the people selling the dream. The difference is that AI actually works.

• The Artist's Windfall

  AI won't make artists rich by selling pictures, and it isn't killing illustrators. It's opening doors to work that implementation used to lock them out of, with their taste in charge.

• I'm Trying to Be Lazy Forever

  Laziness is at the heart of all innovation, on one condition: you care about quality. Then the cheap way out is closed, and the only door left is a better way.

• Good Enough Does Not Stay Good Enough

  Whatever works in creative work gets copied until it stops working. AI made that cycle fast enough to watch.

• Someone Else Found the Time

  Episode 4. The people AI replaces won't lose their jobs for lack of brains. They'll lose them to someone who found the time.

• The Colleague in the Software

  Everyone will have an AI colleague that lives inside their software and does the work with them. Most of what it builds will have one user and no name. Three companies each hold one piece of that future.

• The Ethics Trap: Aligned With Whose Bullshit?

  Before we make AI more faithful to its moral conclusions, we should check what it believes is happening. I had to argue with my software about whether my country exists.

• Hard Is Not the Same as Intelligent

  Brute force can solve any tame problem without a single thought, however long it takes. Only wicked problems require intelligence.

• You're Afraid of the Wrong Replacement

  Episode 3. AI isn't coming for your job. Someone using it is, and it isn't who you think.

• Superintelligence Is Not a Superpower

  Episode 1. The doomers need AI to want to kill us and to be able to. Grant them the wizard, walk the steps, and the argument falls apart anyway.

• Coding Fell First Because It Was Easy

  Episode 2. Chess fell to machines first, and then code and competition math. We keep mistaking what is hard for humans for what requires intelligence.

Photography

Reserved for Frank's photography of Zhongli, Taoyuan, Tainan, and the rest of Taiwan. Real images will land here as Frank supplies them.