Aakash Gupta Profile picture
May 2, 2023 14 tweets 7 min read Read on X
The fastest moving part of AI?

Generative video.

How RunwayML ushered in the era of generative video (genius):
Chapter 1: Beginnings in NYC

The story begins at NYU, where 3 grad students in a tech art program met up.

They were each building tools for designers - and surprised how enthusiastic the response was.

@agermanidis left, @matamalaortiz middle, and @c_valenzuelab right. Image
Chapter 2: Challenger

But Runway almost never even began.

Each of the the trio received fancy job offers. Cristobal, eg, had an offer on Adobe's AI team.

To test the market for Runway, the team sent a tweet looking for beta testers. The response was huge: 570 likes 👇
Chapter 3: The NYC Shoebox

The response gave them conviction to say no to the big companies.

The trio accepted jobs making $2k/month as researchers at NYU.

In NYC, that's pennies. So, they built fast. Cool things like this plugin for Photoshop:

Chapter 4: Rave Reviews

And, of course, they continued work on their own software. It was in private beta.

Designers loved it - calling it "a new Photoshop."

VCs agreed. Runway raised a $2M seed round just a few months after the trio went full-time.
Chapter 5: Tools for Creative Workflows

This allowed the team to move out of their shoeboxes.

They then built Runway entirely on the web & built easy-to-use apps on top.

A particularly epic one is Green Screen - which can generate a mask & propagate through.
Chapter 6: Going All In On Models

After raising a $8.5M series A at the end of 2020, the team went all in on models:

Latent Diffusion in 2021
Stable Diffusion in 2022

This helped the team raise series B and C rounds, setting them up for their next masterful launch... Image
Chapter 7: Gen-1

The company dropped Gen-1 on February 6 - and got massive reception on Twitter.

User uptake was enormous.

Because the company opened it up for human reinforcement learning, Week 1 to Week 6 Gen-1 improved crazily fast.
Chapter 8: #Gen2

This set them up to launch Gen-2 a mere 6 weeks later!

It has been producing absolutely stunning videos every single day.

It's not even public yet. Runway has masterfully rolled out access to active Twitter artists like @icreatelife

As it stands, Runway is the clear leader in the text-to-video AI landscape.

It's been genius execution of a genius strategy. Image
How Has @runwayml Had Such Success?

1. Master audience targeting:
Runway identified a key persona of techie creatives

2. Leverage a powerful go-to-market:
Runway cleverly designed Twitter-friendly features

3. Build a self-sustaining flywheel
Runway's growth feeds on itself Image
Want the full deep-dive of Runway's genius?

Check out my 3,300 word deep dive.

I read every Tweet, watched every YouTube video, & applied my 15 years in tech to bring you the best breakdown on the web:

🔗 tinyurl.com/3uw9tx3e Image
If you have any doubts about the company's just watch this video:

The multi-billion dollar advertising and trailer industries are being disrupted in front of our eyes:

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More from @aakashg0

Jul 29
If I had to learn AI PM again, I would start here:

($2,000 course totally free) Image
1️⃣ AI PM Foundations

@marilynika: How to become an AI PM - youtube.com/watch?v=GQd54g…

Sam Kawsarani: 15+ Years PM in Era of AI - tinyurl.com/2txs9mab

@_amankhan: Prototyping → Observability → Evals - youtube.com/watch?v=Ej4pBD…
2️⃣ AI Strategy

@tomocchino: Behind v0 (Top AI Prototyping Tool) - open.spotify.com/episode/2wAfwJ…

Scott Jones: 21 Harsh Truths about PM in AI - tinyurl.com/8zcw26rs

@PawelHuryn: AI basics to expert - youtube.com/watch?v=IfW1FM…
Read 10 tweets
Jul 23
You want to code with AI.

But you don't know what tool to use.

Here's your guide on when to use which AI coding tools:

(+ free resources to learn each) Image
1: Cursor

They surpassed $500M ARR for a reason. Your developer probably uses it. It's great for pros - but it's also great for non-technical folks now, as its agent can build things just via natural language.

Free guide:
2: Windsurf

Despite losing most of its technical staff to Google, Windsurf still exists. Now a part of Cognition (makers of Devin), it's great for non-technical people to deal with large code bases.

Free tutorial:
Read 10 tweets
Jul 17
There's a new PM interview in town.

But most PMs don't know it yet.

Last week, a PM messaged me in panic:

"I just got destroyed in a Google interview. They wanted me to prototype something with AI tools. I'd never even heard of 'vibe coding' before."

Here's what happening:
This isn't isolated. I'm coaching more PMs who are blindsided by this new interview format every week.

Here's what's happening behind the scenes:

The nature of the PM role is undergoing its biggest shift in years.

We're now moving to the era of AI PMs.
Companies are looking for PMs who can:

1. Build AI features (understanding evals & tech details)
2. Use AI to focus on high-leverage tasks
3. Prototype with AI tools like pros

And their interviews are changing to match.

At Google, this is only in India (for now).
Read 7 tweets
Jul 16
>50% of PM interviews now ask metrics questions.

But not all metrics questions are the same.

5 categories + 16 sub-categories to know: Image
𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 (𝟮𝟭% 𝗼𝗳 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀)

Three major flavors here:

Product launch success: Measuring launching GPT-5.

Feature success: Measuring a mobile payment feature.

Business model success: What would you look at if we launched a marketplace?
𝗗𝗶𝗮𝗴𝗻𝗼𝘀𝘁𝗶𝗰𝘀 (𝟮𝟮% 𝗼𝗳 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀)

Investigating issues when metrics drop.

Sudden drops: IG Stories dropped 5% overnight. Approach?

Gradual declines: Monthly retention declining 6 mos. Why?

Unexpected changes: Feature 30% → 60% engagement. WDYD?
Read 7 tweets
Jul 14
Context engineering is the new prompt engineering.

Here's everything you need to know: Image
1. Etymology

It's unclear who coined the term, but two folks have been particularly important in its rise:

Andrej Karpathy and Dex Horthy.
2. What It Is

It's all about thinking beyond the prompt - thinking about ALL of the tools you have to drive success.

5 are the most important to understand:

a. RAG
b. Memory
c. State/ History
d. Prompt Engineering
e. Structured Outputs
Read 8 tweets
Jul 11
Which is it: use LLMs to improve the prompt, or is that over-engineering?

By now, we've all seen a 1000 conflicting prompt guides.

So, I wanted to understand:

• What do actual studies say?
• What do experts at OpenAI, Anthropic, & Google say?

Here are the answers: Image
I spent the past month in Google Scholar, figuring it out.

I firmed up the learnings with Miqdad Jaffer at OpenAI.

Some of my favorite takeaways from the research:
1. It's not just revenue, but cost

You have to realize that APIs charge by number of input and output tokens.

An engineered prompt can deliver the same quality with 76% cost reduction.

We're talking $3,000 daily vs $706 daily for 100k calls.
Read 10 tweets

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