More on Leadership
Al Anany
2 years ago
Because of this covert investment that Bezos made, Amazon became what it is today.
He kept it under wraps for years until he legally couldn’t.
His shirt is incomplete. I can’t stop thinking about this…
Actually, ignore the article. Look at it. JUST LOOK at it… It’s quite disturbing, isn’t it?
Ughh…
Me: “Hey, what up?” Friend: “All good, watching lord of the rings on amazon prime video.” Me: “Oh, do you know how Amazon grew and became famous?” Friend: “Geek alert…Can I just watch in peace?” Me: “But… Bezos?” Friend: “Let it go, just let it go…”
I can question you, the reader, and start answering instantly without his consent. This far.
Reader, how did Amazon succeed? You'll say, Of course, it was an internet bookstore, then it sold everything.
Mistaken. They moved from zero to one because of this. How did they get from one to thousand? AWS-some. Understand? It's geeky and lame. If not, I'll explain my geekiness.
Over an extended period of time, Amazon was not profitable.
Business basics. You want customers if you own a bakery, right?
Well, 100 clients per day order $5 cheesecakes (because cheesecakes are awesome.)
$5 x 100 consumers x 30 days Equals $15,000 monthly revenue. You proudly work here.
Now you have to pay the barista (unless ChatGPT is doing it haha? Nope..)
The barista is requesting $5000 a month.
Each cheesecake costs the cheesecake maker $2.5 ($2.5 × 100 x 30 = $7500).
The monthly cost of running your bakery, including power, is about $5000.
Assume no extra charges. Your operating costs are $17,500.
Just $15,000? You have income but no profit. You might make money selling coffee with your cheesecake next month.
Is losing money bad? You're broke. Losing money. It's bad for financial statements.
It's almost a business ultimatum. Most startups fail. Amazon took nine years.
I'm reading Amazon Unbound: Jeff Bezos and the Creation of a Global Empire to comprehend how a company has a $1 trillion market cap.
Many things made Amazon big. The book claims that Bezos and Amazon kept a specific product secret for a long period.
Clouds above the bald head.
In 2006, Bezos started a cloud computing initiative. They believed many firms like Snapchat would pay for reliable servers.
In 2006, cloud computing was not what it is today. I'll simplify. 2006 had no iPhone.
Bezos invested in Amazon Web Services (AWS) without disclosing its revenue. That's permitted till a certain degree.
Google and Microsoft would realize Amazon is heavily investing in this market and worry.
Bezos anticipated high demand for this product. Microsoft built its cloud in 2010, and Google in 2008.
If you managed Google or Microsoft, you wouldn't know how much Amazon makes from their cloud computing service. It's enough. Yet, Amazon is an internet store, so they'll focus on that.
All but Bezos were wrong.
Time to come clean now.
They revealed AWS revenue in 2015. Two things were apparent:
Bezos made the proper decision to bet on the cloud and keep it a secret.
In this race, Amazon is in the lead.
They continued. Let me list some AWS users today.
Netflix
Airbnb
Twitch
More. Amazon was unprofitable for nine years, remember? This article's main graph.
AWS accounted for 74% of Amazon's profit in 2021. This 74% might not exist if they hadn't invested in AWS.
Bring this with you home.
Amazon predated AWS. Yet, it helped the giant reach $1 trillion. Bezos' secrecy? Perhaps, until a time machine is invented (they might host the time machine software on AWS, though.)
Without AWS, Amazon would have been profitable but unimpressive. They may have invested in anything else that would have returned more (like crypto? No? Ok.)
Bezos has business flaws. His success. His failures include:
introducing the Fire Phone and suffering a $170 million loss.
Amazon's failure in China In 2011, Amazon had a about 15% market share in China. 2019 saw a decrease of about 1%.
not offering a higher price to persuade the creator of Netflix to sell the company to him. He offered a rather reasonable $15 million in his proposal. But what if he had offered $30 million instead (Amazon had over $100 million in revenue at the time)? He might have owned Netflix, which has a $156 billion market valuation (and saved billions rather than invest in Amazon Prime Video).
Some he could control. Some were uncontrollable. Nonetheless, every action he made in the foregoing circumstances led him to invest in AWS.
Sam Hickmann
3 years ago
Improving collaboration with the Six Thinking Hats
Six Thinking Hats was written by Dr. Edward de Bono. "Six Thinking Hats" and parallel thinking allow groups to plan thinking processes in a detailed and cohesive way, improving collaboration.
Fundamental ideas
In order to develop strategies for thinking about specific issues, the method assumes that the human brain thinks in a variety of ways that can be intentionally challenged. De Bono identifies six brain-challenging directions. In each direction, the brain brings certain issues into conscious thought (e.g. gut instinct, pessimistic judgement, neutral facts). Some may find wearing hats unnatural, uncomfortable, or counterproductive.
The example of "mismatch" sensitivity is compelling. In the natural world, something out of the ordinary may be dangerous. This mode causes negative judgment and critical thinking.
Colored hats represent each direction. Putting on a colored hat symbolizes changing direction, either literally or metaphorically. De Bono first used this metaphor in his 1971 book "Lateral Thinking for Management" to describe a brainstorming framework. These metaphors allow more complete and elaborate thought separation. Six thinking hats indicate ideas' problems and solutions.
Similarly, his CoRT Thinking Programme introduced "The Five Stages of Thinking" method in 1973.
| HAT | OVERVIEW | TECHNIQUE |
|---|---|---|
| BLUE | "The Big Picture" & Managing | CAF (Consider All Factors); FIP (First Important Priorities) |
| WHITE | "Facts & Information" | Information |
| RED | "Feelings & Emotions" | Emotions and Ego |
| BLACK | "Negative" | PMI (Plus, Minus, Interesting); Evaluation |
| YELLOW | "Positive" | PMI |
| GREEN | "New Ideas" | Concept Challenge; Yes, No, Po |
Strategies and programs
After identifying the six thinking modes, programs can be created. These are groups of hats that encompass and structure the thinking process. Several of these are included in the materials for franchised six hats training, but they must often be adapted. Programs are often "emergent," meaning the group plans the first few hats and the facilitator decides what to do next.
The group agrees on how to think, then thinks, then evaluates the results and decides what to do next. Individuals or groups can use sequences (and indeed hats). Each hat is typically used for 2 minutes at a time, although an extended white hat session is common at the start of a process to get everyone on the same page. The red hat is recommended to be used for a very short period to get a visceral gut reaction – about 30 seconds, and in practice often takes the form of dot-voting.
| ACTIVITY | HAT SEQUENCE |
|---|---|
| Initial Ideas | Blue, White, Green, Blue |
| Choosing between alternatives | Blue, White, (Green), Yellow, Black, Red, Blue |
| Identifying Solutions | Blue, White, Black, Green, Blue |
| Quick Feedback | Blue, Black, Green, Blue |
| Strategic Planning | Blue, Yellow, Black, White, Blue, Green, Blue |
| Process Improvement | Blue, White, White (Other People's Views), Yellow, Black, Green, Red, Blue |
| Solving Problems | Blue, White, Green, Red, Yellow, Black, Green, Blue |
| Performance Review | Blue, Red, White, Yellow, Black, Green, Blue |
Use
Speedo's swimsuit designers reportedly used the six thinking hats. "They used the "Six Thinking Hats" method to brainstorm, with a green hat for creative ideas and a black one for feasibility.
Typically, a project begins with extensive white hat research. Each hat is used for a few minutes at a time, except the red hat, which is limited to 30 seconds to ensure an instinctive gut reaction, not judgement. According to Malcolm Gladwell's "blink" theory, this pace improves thinking.
De Bono believed that the key to a successful Six Thinking Hats session was focusing the discussion on a particular approach. A meeting may be called to review and solve a problem. The Six Thinking Hats method can be used in sequence to explore the problem, develop a set of solutions, and choose a solution through critical examination.
Everyone may don the Blue hat to discuss the meeting's goals and objectives. The discussion may then shift to Red hat thinking to gather opinions and reactions. This phase may also be used to determine who will be affected by the problem and/or solutions. The discussion may then shift to the (Yellow then) Green hat to generate solutions and ideas. The discussion may move from White hat thinking to Black hat thinking to develop solution set criticisms.
Because everyone is focused on one approach at a time, the group is more collaborative than if one person is reacting emotionally (Red hat), another is trying to be objective (White hat), and another is critical of the points which emerge from the discussion (Black hat). The hats help people approach problems from different angles and highlight problem-solving flaws.
Will Lockett
3 years ago
Tesla recently disclosed its greatest secret.
The VP has revealed a secret that should frighten the rest of the EV world.
Tesla led the EV revolution. Elon Musk's invention offers a viable alternative to gas-guzzlers. Tesla has lost ground in recent years. VW, BMW, Mercedes, and Ford offer EVs with similar ranges, charging speeds, performance, and cost. Tesla's next-generation 4680 battery pack, Roadster, Cybertruck, and Semi were all delayed. CATL offers superior batteries than the 4680. Martin Viecha, Tesla's Vice President, recently told Business Insider something that startled the EV world and will establish Tesla as the EV king.
Viecha mentioned that Tesla's production costs have dropped 57% since 2017. This isn't due to cheaper batteries or devices like Model 3. No, this is due to amazing factory efficiency gains.
Musk wasn't crazy to want a nearly 100% automated production line, and Tesla's strategy of sticking with one model and improving it has paid off. Others change models every several years. This implies they must spend on new R&D, set up factories, and modernize service and parts systems. All of this costs a ton of money and prevents them from refining production to cut expenses.
Meanwhile, Tesla updates its vehicles progressively. Everything from the backseats to the screen has been enhanced in a 2022 Model 3. Tesla can refine, standardize, and cheaply produce every part without changing the production line.
In 2017, Tesla's automobile production averaged $84,000. In 2022, it'll be $36,000.
Mr. Viecha also claimed that new factories in Shanghai and Berlin will be significantly cheaper to operate once fully operating.
Tesla's hand is visible. Tesla selling $36,000 cars for $60,000 This barely beats the competition. Model Y long-range costs just over $60,000. Tesla makes $24,000+ every sale, giving it a 40% profit margin, one of the best in the auto business.
VW I.D4 costs about the same but makes no profit. Tesla's rivals face similar challenges. Their EVs make little or no profit.
Tesla costs the same as other EVs, but they're in a different league.
But don't forget that the battery pack accounts for 40% of an EV's cost. Tesla may soon fully utilize its 4680 battery pack.
The 4680 battery pack has larger cells and a unique internal design. This means fewer cells are needed for a car, making it cheaper to assemble and produce (per kWh). Energy density and charge speeds increase slightly.
Tesla underestimated the difficulty of making this revolutionary new cell. Each time they try to scale up production, quality drops and rejected cells rise.
Tesla recently installed this battery pack in Model Ys and is scaling production. If they succeed, Tesla battery prices will plummet.
Tesla's Model Ys 2170 battery costs $11,000. The same size pack with 4680 cells costs $3,400 less. Once scaled, it could be $5,500 (50%) less. The 4680 battery pack could reduce Tesla production costs by 20%.
With these cost savings, Tesla could sell Model Ys for $40,000 while still making a profit. They could offer a $25,000 car.
Even with new battery technology, it seems like other manufacturers will struggle to make EVs profitable.
Teslas cost about the same as competitors, so don't be fooled. Behind the scenes, they're still years ahead, and the 4680 battery pack and new factories will only increase that lead. Musk faces a first. He could sell Teslas at current prices and make billions while other manufacturers struggle. Or, he could massively undercut everyone and crush the competition once and for all. Tesla and Elon win.
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Nick Nolan
3 years ago
In five years, starting a business won't be hip.
People are slowly recognizing entrepreneurship's downside.
Growing up, entrepreneurship wasn't common. High school class of 2012 had no entrepreneurs.
Businesses were different.
They had staff and a lengthy history of achievement.
I never wanted a business. It felt unattainable. My friends didn't care.
Weird.
People desired degrees to attain good jobs at big companies.
When graduated high school:
9 out of 10 people attend college
Earn minimum wage (7%) working in a restaurant or retail establishment
Or join the military (3%)
Later, entrepreneurship became a thing.
2014-ish
I was in the military and most of my high school friends were in college, so I didn't hear anything.
Entrepreneurship soared in 2015, according to Google Trends.
Then more individuals were interested. Entrepreneurship went from unusual to cool.
In 2015, it was easier than ever to build a website, run Facebook advertisements, and achieve organic social media reach.
There were several online business tools.
You didn't need to spend years or money figuring it out. Most entry barriers were gone.
Everyone wanted a side gig to escape the 95.
Small company applications have increased during the previous 10 years.
2011-2014 trend continues.
2015 adds 150,000 applications. 2016 adds 200,000. Plus 300,000 in 2017.
The graph makes it look little, but that's a considerable annual spike with no indications of stopping.
By 2021, new business apps had doubled.
Entrepreneurship will return to its early 2010s level.
I think we'll go backward in 5 years.
Entrepreneurship is half as popular as it was in 2015.
In the late 2020s and 30s, entrepreneurship will again be obscure.
Entrepreneurship's decade-long splendor is fading. People will cease escaping 9-5 and launch fewer companies.
That’s not a bad thing.
I think people have a rose-colored vision of entrepreneurship. It's fashionable. People feel that they're missing out if they're not entrepreneurial.
Reality is showing up.
People say on social media, "I knew starting a business would be hard, but not this hard."
More negative posts on entrepreneurship:
Luke adds:
Is being an entrepreneur ‘healthy’? I don’t really think so. Many like Gary V, are not role models for a well-balanced life. Despite what feel-good LinkedIn tells you the odds are against you as an entrepreneur. You have to work your face off. It’s a tough but rewarding lifestyle. So maybe let’s stop glorifying it because it takes a lot of (bleepin) work to survive a pandemic, mental health battles, and a competitive market.
Entrepreneurship is no longer a pipe dream.
It’s hard.
I went full-time in March 2020. I was done by April 2021. I had a good-paying job with perks.
When that fell through (on my start date), I had to continue my entrepreneurial path. I needed money by May 1 to pay rent.
Entrepreneurship isn't as great as many think.
Entrepreneurship is a serious business.
If you have a 9-5, the grass isn't greener here. Most people aren't telling the whole story when they post on social media or quote successful entrepreneurs.
People prefer to communicate their victories than their defeats.
Is this a bad thing?
I don’t think so.
Over the previous decade, entrepreneurship went from impossible to the finest thing ever.
It peaked in 2020-21 and is returning to reality.
Startups aren't for everyone.
If you like your job, don't quit.
Entrepreneurship won't amaze people if you quit your job.
It's irrelevant.
You're doomed.
And you'll probably make less money.
If you hate your job, quit. Change jobs and bosses. Changing jobs could net you a greater pay or better perks.
When you go solo, your paycheck and perks vanish. Did I mention you'll fail, sleep less, and stress more?
Nobody will stop you from pursuing entrepreneurship. You'll face several challenges.
Possibly.
Entrepreneurship may be romanticized for years.
Based on what I see from entrepreneurs on social media and trends, entrepreneurship is challenging and few will succeed.
Zuzanna Sieja
3 years ago
In 2022, each data scientist needs to read these 11 books.
Non-technical talents can benefit data scientists in addition to statistics and programming.
As our article 5 Most In-Demand Skills for Data Scientists shows, being business-minded is useful. How can you get such a diverse skill set? We've compiled a list of helpful resources.
Data science, data analysis, programming, and business are covered. Even a few of these books will make you a better data scientist.
Ready? Let’s dive in.
Best books for data scientists
1. The Black Swan
Author: Nassim Taleb
First, a less obvious title. Nassim Nicholas Taleb's seminal series examines uncertainty, probability, risk, and decision-making.
Three characteristics define a black swan event:
It is erratic.
It has a significant impact.
Many times, people try to come up with an explanation that makes it seem more predictable than it actually was.
People formerly believed all swans were white because they'd never seen otherwise. A black swan in Australia shattered their belief.
Taleb uses this incident to illustrate how human thinking mistakes affect decision-making. The book teaches readers to be aware of unpredictability in the ever-changing IT business.
Try multiple tactics and models because you may find the answer.
2. High Output Management
Author: Andrew Grove
Intel's former chairman and CEO provides his insights on developing a global firm in this business book. We think Grove would choose “management” to describe the talent needed to start and run a business.
That's a skill for CEOs, techies, and data scientists. Grove writes on developing productive teams, motivation, real-life business scenarios, and revolutionizing work.
Five lessons:
Every action is a procedure.
Meetings are a medium of work
Manage short-term goals in accordance with long-term strategies.
Mission-oriented teams accelerate while functional teams increase leverage.
Utilize performance evaluations to enhance output.
So — if the above captures your imagination, it’s well worth getting stuck in.
3. The Hard Thing About Hard Things: Building a Business When There Are No Easy Answers
Author: Ben Horowitz
Few realize how difficult it is to run a business, even though many see it as a tremendous opportunity.
Business schools don't teach managers how to handle the toughest difficulties; they're usually on their own. So Ben Horowitz wrote this book.
It gives tips on creating and maintaining a new firm and analyzes the hurdles CEOs face.
Find suggestions on:
create software
Run a business.
Promote a product
Obtain resources
Smart investment
oversee daily operations
This book will help you cope with tough times.
4. Obviously Awesome: How to Nail Product Positioning
Author: April Dunford
Your job as a data scientist is a product. You should be able to sell what you do to clients. Even if your product is great, you must convince them.
How to? April Dunford's advice: Her book explains how to connect with customers by making your offering seem like a secret sauce.
You'll learn:
Select the ideal market for your products.
Connect an audience to the value of your goods right away.
Take use of three positioning philosophies.
Utilize market trends to aid purchasers
5. The Mom test
Author: Rob Fitzpatrick
The Mom Test improves communication. Client conversations are rarely predictable. The book emphasizes one of the most important communication rules: enquire about specific prior behaviors.
Both ways work. If a client has suggestions or demands, listen carefully and ensure everyone understands. The book is packed with client-speaking tips.
6. Introduction to Machine Learning with Python: A Guide for Data Scientists
Authors: Andreas C. Müller, Sarah Guido
Now, technical documents.
This book is for Python-savvy data scientists who wish to learn machine learning. Authors explain how to use algorithms instead of math theory.
Their technique is ideal for developers who wish to study machine learning basics and use cases. Sci-kit-learn, NumPy, SciPy, pandas, and Jupyter Notebook are covered beyond Python.
If you know machine learning or artificial neural networks, skip this.
7. Python Data Science Handbook: Essential Tools for Working with Data
Author: Jake VanderPlas
Data work isn't easy. Data manipulation, transformation, cleansing, and visualization must be exact.
Python is a popular tool. The Python Data Science Handbook explains everything. The book describes how to utilize Pandas, Numpy, Matplotlib, Scikit-Learn, and Jupyter for beginners.
The only thing missing is a way to apply your learnings.
8. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
Author: Wes McKinney
The author leads you through manipulating, processing, cleaning, and analyzing Python datasets using NumPy, Pandas, and IPython.
The book's realistic case studies make it a great resource for Python or scientific computing beginners. Once accomplished, you'll uncover online analytics, finance, social science, and economics solutions.
9. Data Science from Scratch
Author: Joel Grus
Here's a title for data scientists with Python, stats, maths, and algebra skills (alongside a grasp of algorithms and machine learning). You'll learn data science's essential libraries, frameworks, modules, and toolkits.
The author works through all the key principles, providing you with the practical abilities to develop simple code. The book is appropriate for intermediate programmers interested in data science and machine learning.
Not that prior knowledge is required. The writing style matches all experience levels, but understanding will help you absorb more.
10. Machine Learning Yearning
Author: Andrew Ng
Andrew Ng is a machine learning expert. Co-founded and teaches at Stanford. This free book shows you how to structure an ML project, including recognizing mistakes and building in complex contexts.
The book delivers knowledge and teaches how to apply it, so you'll know how to:
Determine the optimal course of action for your ML project.
Create software that is more effective than people.
Recognize when to use end-to-end, transfer, and multi-task learning, and how to do so.
Identifying machine learning system flaws
Ng writes easy-to-read books. No rigorous math theory; just a terrific approach to understanding how to make technical machine learning decisions.
11. Deep Learning with PyTorch Step-by-Step
Author: Daniel Voigt Godoy
The last title is also the most recent. The book was revised on 23 January 2022 to discuss Deep Learning and PyTorch, a Python coding tool.
It comprises four parts:
Fundamentals (gradient descent, training linear and logistic regressions in PyTorch)
Machine Learning (deeper models and activation functions, convolutions, transfer learning, initialization schemes)
Sequences (RNN, GRU, LSTM, seq2seq models, attention, self-attention, transformers)
Automatic Language Recognition (tokenization, embeddings, contextual word embeddings, ELMo, BERT, GPT-2)
We admire the book's readability. The author avoids difficult mathematical concepts, making the material feel like a conversation.
Is every data scientist a humanist?
Even as a technological professional, you can't escape human interaction, especially with clients.
We hope these books will help you develop interpersonal skills.
SAHIL SAPRU
3 years ago
How I grew my business to a $5 million annual recurring revenue
Scaling your startup requires answering customer demands, not growth tricks.
I cofounded Freedo Rentals in 2019. I reached 50 lakh+ ARR in 6 months before quitting owing to the epidemic.
Freedo aimed to solve 2 customer pain points:
Users lacked a reliable last-mile transportation option.
The amount that Auto walas charge for unmetered services
Solution?
Effectively simple.
Build ports at high-demand spots (colleges, residential societies, metros). Electric ride-sharing can meet demand.
We had many problems scaling. I'll explain using the AARRR model.
Brand unfamiliarity or a novel product offering were the problems with awareness. Nobody knew what Freedo was or what it did.
Problem with awareness: Content and advertisements did a poor job of communicating the task at hand. The advertisements clashed with the white-collar part because they were too cheesy.
Retention Issue: We encountered issues, indicating that the product was insufficient. Problems with keyless entry, creating bills, stealing helmets, etc.
Retention/Revenue Issue: Costly compared to established rivals. Shared cars were 1/3 of our cost.
Referral Issue: Missing the opportunity to seize the AHA moment. After the ride, nobody remembered us.
Once you know where you're struggling with AARRR, iterative solutions are usually best.
Once you have nailed the AARRR model, most startups use paid channels to scale. This dependence, on paid channels, increases with scale unless you crack your organic/inbound game.
Over-index growth loops. Growth loops increase inflow and customers as you scale.
When considering growth, ask yourself:
Who is the solution's ICP (Ideal Customer Profile)? (To whom are you selling)
What are the most important messages I should convey to customers? (This is an A/B test.)
Which marketing channels ought I prioritize? (Conduct analysis based on the startup's maturity/stage.)
Choose the important metrics to monitor for your AARRR funnel (not all metrics are equal)
Identify the Flywheel effect's growth loops (inertia matters)
My biggest mistakes:
not paying attention to consumer comments or satisfaction. It is the main cause of problems with referrals, retention, and acquisition for startups. Beyond your NPS, you should consider second-order consequences.
The tasks at hand should be quite clear.
Here's my scaling equation:
Growth = A x B x C
A = Funnel top (Traffic)
B = Product Valuation (Solving a real pain point)
C = Aha! (Emotional response)
Freedo's A, B, and C created a unique offering.
Freedo’s ABC:
A — Working or Studying population in NCR
B — Electric Vehicles provide last-mile mobility as a clean and affordable solution
C — One click booking with a no-noise scooter
Final outcome:
FWe scaled Freedo to Rs. 50 lakh MRR and were growing 60% month on month till the pandemic ceased our growth story.
How we did it?
We tried ambassadors and coupons. WhatsApp was our most successful A/B test.
We grew widespread adoption through college and society WhatsApp groups. We requested users for referrals in community groups.
What worked for us won't work for others. This scale underwent many revisions.
Every firm is different, thus you must know your customers. Needs to determine which channel to prioritize and when.
Users desired a safe, time-bound means to get there.
This (not mine) growth framework helped me a lot. You should follow suit.
