Bezos understood the importance of moving quickly.
Especially as Amazon grew.
To survive as a large company, you have to move at the pace of a startup.
If not, you'll die.
I read every annual letter Jeff Bezos published at Amazon.
It's a masterclass on strategy & leadership.
11 critical insights from a legendary leader:
Bezos understood the importance of moving quickly.
Especially as Amazon grew.
To survive as a large company, you have to move at the pace of a startup.
If not, you'll die.
Some problems require long thought and deliberation.
But many don't.
If a decision is easily reversible, Bezos thought they should be made quickly by "high judgement individuals or small groups."
In the 1998 letter, Bezos remarked that it was Amazon's customers and not competition that should be feared.
He understood that Amazon was providing a service.
Amazon to this day has unmatched service to their customers.
Amazon constantly focused on things that they had input on.
-# of products available
-# of items available for Prime shipping
The focus on the inputs (what Amazon could control) led to the outputs Wall Street looked for.
In the 1st letter on the 1st page, long term mindset is mentioned.
It would remain a theme throughout and something the company stuck to.
Even forgoing profitability to invest back into the company for long term results.
Many companies have shiny new object syndrome.
Amazon isn't different.
But they knew for seeds to grow, it took patience.
Amazon fulfillment center and AWS were both started as seed projects at Amazon.
The 3 questions before making a hiring decision for an employee at Amazon:
-Will you admire this person?
-Will this person raise the average level of effectiveness of the group they’re entering?
-Along what dimension might this person be a superstar?
In the early days, many vendors wondered why Amazon would allow customers to publish negative reviews.
Simply, Amazon knew it would improve the customer experience.
Better products would ultimately end up in the customers hands.
Bezos knew that if he wanted the company to focus on long-term results, ownership mentality was key.
Autonomy is given at Amazon to ensure that employees feel some sense of ownership.
It is the only way to remove the bias of short term results.
May be relevant very soon as fears raise around the risk of recession.
Amazon was able to weather the dot com bubble by building cash reserves.
A tight cash conversion cycle was critical in 2000 & 2001.
Many businesses are great at executing set plans.
But that is not where innovation is sparked.
Ideas are sparked by wanderers.
Amazon aimed to be a see-saw with wandering on one end and efficiency on the other.
You can find them on Google and total there are only about 80 pages.
I'm currently reading Berkshire and that's around 900 pages.
1/ Move Fast
2/ Don't Sweat Reversible Decisions
3/ Stay Terrified of Customers
4/ Focus On Inputs
5/ Long-Term Mindset
6/ Plant Seeds & Wait
7/ Talent Is King
8/ Be Customer Centric
9/ Owner's Mentality
10/ Build A Cash Moat
11/ Wander but Be Efficient
And give me a follow @chrisxmunn if you deem me worthy of blabbering my insights onto your timeline! https://t.co/fVfhozHnBC
I read every annual letter Jeff Bezos published at Amazon.
— Chris Munn (@chrisxmunn) April 19, 2022
It's a masterclass on strategy & leadership.
11 critical insights from a legendary leader: pic.twitter.com/d5Tj1lEuuA
More from All
APIs in general are so powerful.
Best 5 public APIs you can use to build your next project:
1. Number Verification API
A RESTful JSON API for national and international phone number validation.
🔗 https://t.co/fzBmCMFdIj
2. OpenAI API
ChatGPT is an outstanding tool. Build your own API applications with OpenAI API.
🔗 https://t.co/TVnTciMpML
3. Currency Data API
Currency Data API provides a simple REST API with real-time and historical exchange rates for 168 world currencies
🔗 https://t.co/TRj35IUUec
4. Weather API
Real-Time & historical world weather data API.
Retrieve instant, accurate weather information for
any location in the world in lightweight JSON format.
🔗 https://t.co/DCY8kXqVIK
Best 5 public APIs you can use to build your next project:
1. Number Verification API
A RESTful JSON API for national and international phone number validation.
🔗 https://t.co/fzBmCMFdIj
2. OpenAI API
ChatGPT is an outstanding tool. Build your own API applications with OpenAI API.
🔗 https://t.co/TVnTciMpML
3. Currency Data API
Currency Data API provides a simple REST API with real-time and historical exchange rates for 168 world currencies
🔗 https://t.co/TRj35IUUec
4. Weather API
Real-Time & historical world weather data API.
Retrieve instant, accurate weather information for
any location in the world in lightweight JSON format.
🔗 https://t.co/DCY8kXqVIK
How can we use language supervision to learn better visual representations for robotics?
Introducing Voltron: Language-Driven Representation Learning for Robotics!
Paper: https://t.co/gIsRPtSjKz
Models: https://t.co/NOB3cpATYG
Evaluation: https://t.co/aOzQu95J8z
🧵👇(1 / 12)
Videos of humans performing everyday tasks (Something-Something-v2, Ego4D) offer a rich and diverse resource for learning representations for robotic manipulation.
Yet, an underused part of these datasets are the rich, natural language annotations accompanying each video. (2/12)
The Voltron framework offers a simple way to use language supervision to shape representation learning, building off of prior work in representations for robotics like MVP (https://t.co/Pb0mk9hb4i) and R3M (https://t.co/o2Fkc3fP0e).
The secret is *balance* (3/12)
Starting with a masked autoencoder over frames from these video clips, make a choice:
1) Condition on language and improve our ability to reconstruct the scene.
2) Generate language given the visual representation and improve our ability to describe what's happening. (4/12)
By trading off *conditioning* and *generation* we show that we can learn 1) better representations than prior methods, and 2) explicitly shape the balance of low and high-level features captured.
Why is the ability to shape this balance important? (5/12)
Introducing Voltron: Language-Driven Representation Learning for Robotics!
Paper: https://t.co/gIsRPtSjKz
Models: https://t.co/NOB3cpATYG
Evaluation: https://t.co/aOzQu95J8z
🧵👇(1 / 12)
Videos of humans performing everyday tasks (Something-Something-v2, Ego4D) offer a rich and diverse resource for learning representations for robotic manipulation.
Yet, an underused part of these datasets are the rich, natural language annotations accompanying each video. (2/12)
The Voltron framework offers a simple way to use language supervision to shape representation learning, building off of prior work in representations for robotics like MVP (https://t.co/Pb0mk9hb4i) and R3M (https://t.co/o2Fkc3fP0e).
The secret is *balance* (3/12)
Starting with a masked autoencoder over frames from these video clips, make a choice:
1) Condition on language and improve our ability to reconstruct the scene.
2) Generate language given the visual representation and improve our ability to describe what's happening. (4/12)
By trading off *conditioning* and *generation* we show that we can learn 1) better representations than prior methods, and 2) explicitly shape the balance of low and high-level features captured.
Why is the ability to shape this balance important? (5/12)
