AI Literacy · Workshop №1
Prediction, ambiguity, and the limits of machines.
Where AI is powerful. Where it falls apart. And the difference your judgment makes.
AI Literacy Salon №1 · Led by Nitzan Hermon · Index Greenpoint · CBS, 2026
Welcome
You're here because you're curious about AI — not because you need to become a technologist, but because this stuff is reshaping the terrain you walk on.
Tonight we'll think together about what AI actually does, where it's powerful, and where it falls apart.
AI is a prediction engine.
Almost everything modern AI does comes back to one word: prediction.
- Given this image, predict: what's in it?
- Given these words, predict: what word comes next?
- Given this data, predict: what will the customer do?
AI finds patterns in past data and uses them to make guesses about future or unseen data. That is the whole game.
When prediction works, it is transformative.
Catching tumors earlier than human eyes. Translating text across a hundred languages. Surfacing the song you didn't know you needed. Turning a description into a working prototype.
Real, tangible value. But prediction has edges.
It works when the future resembles the past, when there is enough quality data, when the problem has a clear right answer, and when the context is stable and well-defined.
When those conditions break down, so does the prediction. For creative work, those conditions break down constantly.
The ambiguity problem.
Many of the things that matter most in creative work are not prediction problems. They are ambiguity problems.
- What should this brand feel like?
- Is this essay saying something true?
- What does this community actually need?
- Should we take this project in a new direction?
These questions don't have answers hiding in past data. They require judgment, interpretation, and taste.
Prediction vs. ambiguity reduction.
| Prediction | Ambiguity reduction | |
|---|---|---|
| Input | Data from the past | Uncertainty about the present or future |
| Method | Pattern matching at scale | Framing, interpretation, conversation |
| Output | A guess, with a confidence score | A clearer understanding of what matters |
| Who does it well | Machines | Humans |
| Risk | Wrong answer | Wrong question |
A key distinction.
Prediction asks: given what has happened, what will happen next?
Ambiguity reduction asks: what is actually going on, and what should we do about it?
AI is exceptional at the first. It is, at best, a thinking partner for the second. Knowing which one you're facing is the skill.
Bring this workshop to your team.
Ninety minutes, on-site or remote. Each workshop stands alone; together they build a literacy your team shares.
- Where AI is powerful. Where it falls apart. And the difference your judgment makes.
- AI is fluent. It sounds like it understands. Fluency and understanding are not the same thing.
- A 90-minute masterclass on the kinds of slowness that pay off.
Bring it in-house
Run this workshop
for your team.
The full session goes deeper than this page — the Fermi estimation exercise, objective vs. subjective goals, why confidence is not competence, and a framework for telling prediction problems from ambiguity problems that participants take into their own work the next morning.
Most teams using AI have learned to move faster. Fewer have learned to tell when they're solving the wrong kind of problem at speed. This 90-minute workshop gives your team shared vocabulary for both.
What your team leaves with
- A working definition of AI as a prediction engine — and the conditions under which prediction breaks down
- The Fermi estimation exercise, repeatable on any 'unknowable' question
- The prediction-vs-ambiguity table as a daily framework
- Language for telling objective goals from subjective ones, and which one your AI tool can actually help with
- A printed artifact each participant takes home, plus a follow-up reading list
Formats
In-person · NYC
90 minutes
Up to 20 people. Your office or Index Greenpoint. Includes materials.
Remote
90 minutes
Zoom, up to 30 people. Same exercise, asynchronous follow-up.
Quarterly retainer
Series of four
Themed sessions across a quarter. Best for teams adopting AI in production.
Nitzan replies with a proposal within two business days.
Your instructor
Nitzan Hermon
Coach, writer, and educator. Founder of Critical Business School, a meta-disciplinary design school in Greenpoint, New York City.
Nitzan's work sits at the intersection of creative practice and business — helping individuals and organizations articulate what they actually do, what they're for, and what to build next. He has spent years translating between technologists and the people who have to live with what they build, and has been paying close attention to what AI is changing about how creative people work.
He writes Being in Space, a newsletter on creative surplus.
Produced by Critical Business SchoolInquiries info@criticalbusinessschool.com