Justine Moore Profile picture
Dec 11, 2022 β€’ 10 tweets β€’ 5 min read β€’ Read on X
ChatGPT just killed homework, essays, and take-home exams.

Or did it?

How written work might survive in the age of AI πŸ‘‡
ChatGPT was a shock to the educational system.

Overnight, students could input a prompt or problem and get a solid result across subjects - English, history, CS, even science.

And unsurprisingly, they're using it to do their assignments and exams.

Apps to answer math questions have been around for a while.

But ChatGPT is different - it can do things that previously required human judgment and analysis, like writing full essays or solving complex problem sets.

The result? An "existential crisis" for educators.
What's next? I see three paths forward:

1⃣ Schools adjust assignments to prevent the use of AI.

Take-home work largely disappears. Class time is used for proctored essays, problem sets, and exams.

Homework time is spent learning asynch via video - a "flipped classroom" model.
2⃣ Schools embrace AI.

Students will use AI in real life. Why make them do things the "old fashioned way" at school?

Instead, lessons will incorporate AI - teaching students how to write prompts, analyze outputs, and edit as needed (CC: @emollick).

3⃣ Schools learn to audit AI.

In this case, AI assistance is viewed like plagiarism. Educators learn how to detect it, and have policies in place to downgrade or disqualify assignments.

A "GPT watermark" may already be in the works at OpenAI πŸ‘€

Alternatively, a Turnitin.com style tool could be used to predict the likelihood that an assignment was AI-generated.

It could flag:
- Discrepancies between in-class and take-home work
- Heavy use of words/phrases popular with AI tools
- "Unnatural" sentence structure
Today, we're in a state of "AI panic" as parents and educators scramble to address ChatGPT.

FWIW - I think it's not entirely a bad thing. Technological progress is, by nature, disruptive.

Even calculators sparked heavy debate in the 1980s!

onlinelibrary.wiley.com/doi/abs/10.111…
To me, the rise of AI tools presents a question to us all:

Do we maintain the status quo, or use AI as an opportunity to rethink the way we deliver education?

If you're building at the intersection of AI and learning, our team @a16z would love to hear from you πŸ‘€
@a16z Quick addition: @omooretweets and I asked our Accelerated πŸš€ audience (~30K millennials + Gen Zers) about the most likely outcome here.

The results, and a few more thoughts on the subject ⬇️

readaccelerated.com/p/is-this-the-…

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More from @venturetwins

Jun 10
New market map: AI assistants in iMessage πŸ“±

The next major interface for consumer AI? Text messages.

People don’t want to open an app every time they need help - they want a contact they can text like a friend.

My roundup of the assistants + infra providers below πŸ‘‡ Image
Why iMessage and not plain SMS?

1) Deliverability. SMS from unknown numbers gets carrier-filtered or marked as spam.

2) Capabilities. iMessage gives typing indicators, read receipts, link previews, and more.

3) Trust. Blue bubble = a person, green bubble = a marketing blast.
Historically, it's been hard for AI companies to build on iMessage. Apple has no public API.

Infra providers solved this by running fleets of real Apple devices on real phone numbers.

Now, Apple is opening a front door. Last week, Poke became the first AI agent approved on Apple Messages for Business - more to come here πŸ‘€
Read 5 tweets
May 20
Short dramas have quietly become a massive entertainment format.

Think next-gen soap operas: serialized, mobile-first, and monetized like games.

In China, they already generate more revenue than the domestic box office.

And now AI is going to blow this format wide open πŸ‘‡ Image
Short dramas are almost tailor-made for AI.

They typically feature small casts with limited sets, constraining the "world" in which you have to generate videos.

And audiences care about the hook, pacing, and cliffhanger - they don't expect HBO-level production quality. Image
There are a few layers of the stack:

1) Foundation models that generate videos (e.g. Seedance, Kling)

2) Creation tools & agents that guide generations + organize production (e.g. Martini, Preview)

3) Distribution platforms for reaching users (e.g. ReelShort, DramaBox)
Read 8 tweets
Feb 26
Nano Banana 2 is out.

I had early access for the past few days, and tested it across a ton of prompts.

It's leveled up for a bunch of use cases - infographics, ads, action shots, even cartoons. And it's crazy fast!

Some styles + prompts you should try πŸ‘‡ Image
Image
1) Infographics

NB Pro was a big upgrade for this - but sometimes the text would get strange or the information wasn't accurate.

NB 2 can generate long, detailed infographics incredibly fast by leveraging Google search.

E.g. "make a guide for Americans riding public transit in Japan" or "explain how McDonald's ice cream machines work and why they break"Image
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2) Action shots

Sports photography has historically been difficult for AI image models - the little details of muscle and physics of movement are challenging.

NB 2 is surprisingly good at this. Some of the shots even looked like ads, so I asked the model to write overlays.Image
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Read 11 tweets
Jan 14
Fun experiment: you can now automate content production with Claude Cowork.

I gave Claude a browser and asked it to:
1) Find a NeurIPS best paper
2) Write a thread on it
3) Generate graphics on Krea
4) Validate its work with ChatGPT.

Shockingly, it worked! How to do it πŸ‘‡
You start in the "Cowork" tab in the desktop app.

Claude will open a browser and search, so you can give a vague or very specific prompt (like I did) and it will find context.

I mentioned I wanted image gen on Krea because I got sick of watching Claude search random sites πŸ˜‚ Image
In terms of the actual written content - answer Claude's Qs about who your audience is + how long it should be.

This will heavily influence results!

It will then browse the relevant sources and open up an internal scratchpad to save down insights and start drafting the thread.
Read 5 tweets
Dec 28, 2025
My favorite paper this year: "Video models are zero-shot learners and reasoners"

It illustrates that video models show emergent visual reasoning at scale - they can solve vision tasks they weren't trained for.

This may be the "GPT moment" for vision. Let's break it down πŸ‘‡
To start - why believe that video models might develop visual reasoning?

A similar thing happened in text. We used to train specific models for each task - but now, LLMs have general language understanding and can tackle lots of tasks that they weren't explicitly trained for.

It's feasible that video models may do the same at scale.Image
This paper measured 18k+ videos generated by Veo 3 across both qualitative and quantitative tasks.

It found that Veo can perceive, modify, and manipulate the visual world (starting from image + text prompts) - showcasing early reasoning skills that it wasn't explicitly trained for.

We'll tackle each category one-by-one.Image
Read 10 tweets
Dec 14, 2025
There's an insane new hack for prompting Nano Banana Pro.

You can now save image references as "Elements" and tag them in prompts to get consistent characters, styles, and environments.

I used it to put myself into Ghibli sets. How to do this on @krea_ai πŸ‘‡
@krea_ai First, define your Elements. I used:

- Me (tagged @ justine)
- An image of each scene I wanted (tagged @ ghibli)
- The type of photo (tagged @ selfie)

Then prompt things like - "@ justine taking an @ selfie style image on set of @ ghibli Spirited Away"
A few things to note:

1) Elements PERSIST across chat sessions. So I can start a new chat and tag "@ justine" and it will work!

2) You can also give context to the model. I usually provide a description of the goal.

3) The model can reason within the Elements. So I can tag "@ Ghibli" + the movie name and it will find the right frame.Image
Read 5 tweets

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