More on Personal Growth
Aparna Jain
3 years ago
Negative Effects of Working for a FAANG Company
Consider yourself lucky if your last FAANG interview was rejected.
FAANG—Facebook, Apple, Amazon, Netflix, Google
(I know its manga now, but watch me not care)
These big companies offer many benefits.
large salaries and benefits
Prestige
high expectations for both you and your coworkers.
However, these jobs may have major drawbacks that only become apparent when you're thrown to the wolves, so it's up to you whether you see them as drawbacks or opportunities.
I know most college graduates start working at big tech companies because of their perceived coolness.
I've worked in these companies for years and can tell you what to expect if you get a job here.
Little fish in a vast ocean
The most obvious. Most billion/trillion-dollar companies employ thousands.
You may work on a small, unnoticed product part.
Directors and higher will sometimes make you redo projects they didn't communicate well without respecting your time, talent, or will to work on trivial stuff that doesn't move company needles.
Peers will only say, "Someone has to take out the trash," even though you know company resources are being wasted.
The power imbalance is frustrating.
What you can do about it
Know your WHY. Consider long-term priorities. Though riskier, I stayed in customer-facing teams because I loved building user-facing products.
This increased my impact. However, if you enjoy helping coworkers build products, you may be better suited for an internal team.
I told the Directors and Vice Presidents that their actions could waste Engineering time, even though it was unpopular. Some were receptive, some not.
I kept having tough conversations because they were good for me and the company.
However, some of my coworkers praised my candor but said they'd rather follow the boss.
An outdated piece of technology can take years to update.
Apple introduced Swift for iOS development in 2014. Most large tech companies adopted the new language after five years.
This is frustrating if you want to learn new skills and increase your market value.
Knowing that my lack of Swift practice could hurt me if I changed jobs made writing verbose Objective C painful.
What you can do about it
Work on the new technology in side projects; one engineer rewrote the Lyft app in Swift over the course of a weekend and promoted its adoption throughout the entire organization.
To integrate new technologies and determine how to combine legacy and modern code, suggest minor changes to the existing codebase.
Most managers spend their entire day in consecutive meetings.
After their last meeting, the last thing they want is another meeting to discuss your career goals.
Sometimes a manager has 15-20 reports, making it hard to communicate your impact.
Misunderstandings and stress can result.
Especially when the manager should focus on selfish parts of the team. Success won't concern them.
What you can do about it
Tell your manager that you are a self-starter and that you will pro-actively update them on your progress, especially if they aren't present at the meetings you regularly attend.
Keep being proactive and look for mentorship elsewhere if you believe your boss doesn't have enough time to work on your career goals.
Alternately, look for a team where the manager has more authority to assist you in making career decisions.
After a certain point, company loyalty can become quite harmful.
Because big tech companies create brand loyalty, too many colleagues stayed in unhealthy environments.
When you work for a well-known company and strangers compliment you, it's fun to tell your friends.
Work defines you. This can make you stay too long even though your career isn't progressing and you're unhappy.
Google may become your surname.
Workplaces are not families.
If you're unhappy, don't stay just because they gave you the paycheck to buy your first home and make you feel like you owe your life to them.
Many employees stayed too long. Though depressed and suicidal.
What you can do about it
Your life is not worth a company.
Do you want your job title and workplace to be listed on your gravestone? If not, leave if conditions deteriorate.
Recognize that change can be challenging. It's difficult to leave a job you've held for a number of years.
Ask those who have experienced this change how they handled it.
You still have a bright future if you were rejected from FAANG interviews.
Rejections only lead to amazing opportunities. If you're young and childless, work for a startup.
Companies may pay more than FAANGs. Do your research.
Ask recruiters and hiring managers tough questions about how the company and teams prioritize respectful working hours and boundaries for workers.
I know many 15-year-olds who have a lifelong dream of working at Google, and it saddens me that they're chasing a name on their resume instead of excellence.
This article is not meant to discourage you from working at these companies, but to share my experience about what HR/managers will never mention in interviews.
Read both sides before signing the big offer letter.
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.
Joseph Mavericks
3 years ago
The world's 36th richest man uses a 5-step system to get what he wants.
Ray Dalio's super-effective roadmap
Ray Dalio's $22 billion net worth ranks him 36th globally. From 1975 to 2011, he built the world's most successful hedge fund, never losing more than 4% from 1991 to 2020. (and only doing so during 3 calendar years).
Dalio describes a 5-step process in his best-selling book Principles. It's the playbook he's used to build his hedge fund, beat the markets, and face personal challenges.
This 5-step system is so valuable and well-explained that I didn't edit or change anything; I only added my own insights in the parts I found most relevant and/or relatable as a young entrepreneur. The system's overview:
Have clear goals
Identify and don’t tolerate problems
Diagnose problems to get at their root causes
Design plans that will get you around those problems
Do what is necessary to push through the plans to get results
If you follow these 5 steps in a virtuous loop, you'll almost always see results. Repeat the process for each goal you have.
1. Have clear goals
a) Prioritize: You can have almost anything, but not everything.
I started and never launched dozens of projects for 10 years because I was scattered. I opened a t-shirt store, traded algorithms, sold art on Instagram, painted skateboards, and tinkered with electronics. I decided to try blogging for 6 months to see where it took me. Still going after 3 years.
b) Don’t confuse goals with desires.
A goal inspires you to act. Unreasonable desires prevent you from achieving your goals.
c) Reconcile your goals and desires to decide what you want.
d) Don't confuse success with its trappings.
e) Never dismiss a goal as unattainable.
Always one path is best. Your perception of what's possible depends on what you know now. I never thought I'd make money writing online so quickly, and now I see a whole new horizon of business opportunities I didn't know about before.
f) Expectations create abilities.
Don't limit your abilities. More you strive, the more you'll achieve.
g) Flexibility and self-accountability can almost guarantee success.
Flexible people accept what reality or others teach them. Self-accountability is the ability to recognize your mistakes and be more creative, flexible, and determined.
h) Handling setbacks well is as important as moving forward.
Learn when to minimize losses and when to let go and move on.
2. Don't ignore problems
a) See painful problems as improvement opportunities.
Every problem, painful situation, and challenge is an opportunity. Read The Art of Happiness for more.
b) Don't avoid problems because of harsh realities.
Recognizing your weaknesses isn't the same as giving in. It's the first step in overcoming them.
c) Specify your issues.
There is no "one-size-fits-all" solution.
d) Don’t mistake a cause of a problem with the real problem.
"I can't sleep" is a cause, not a problem. "I'm underperforming" could be a problem.
e) Separate big from small problems.
You have limited time and energy, so focus on the biggest problems.
f) Don't ignore a problem.
Identifying a problem and tolerating it is like not identifying it.
3. Identify problems' root causes
a) Decide "what to do" after assessing "what is."
"A good diagnosis takes 15 to 60 minutes, depending on its accuracy and complexity. [...] Like principles, root causes recur in different situations.
b) Separate proximate and root causes.
"You can only solve problems by removing their root causes, and to do that, you must distinguish symptoms from disease."
c) Knowing someone's (or your own) personality can help you predict their behavior.
4. Design plans that will get you around the problems
a) Retrace your steps.
Analyze your past to determine your future.
b) Consider your problem a machine's output.
Consider how to improve your machine. It's a game then.
c) There are many ways to reach your goals.
Find a solution.
d) Visualize who will do what in your plan like a movie script.
Consider your movie's actors and script's turning points, then act accordingly. The game continues.
e) Document your plan so others can judge your progress.
Accountability boosts success.
f) Know that a good plan doesn't take much time.
The execution is usually the hardest part, but most people either don't have a plan or keep changing it. Don't drive while building the car. Build it first, because it'll be bumpy.
5. Do what is necessary to push through the plans to get results
a) Great planners without execution fail.
Life is won with more than just planning. Similarly, practice without talent beats talent without practice.
b) Work ethic is undervalued.
Hyper-productivity is praised in corporate America, even if it leads nowhere. To get things done, use checklists, fewer emails, and more desk time.
c) Set clear metrics to ensure plan adherence.
I've written about the OKR strategy for organizations with multiple people here. If you're on your own, I recommend the Wheel of Life approach. Both systems start with goals and tasks to achieve them. Then start executing on a realistic timeline.
If you find solutions, weaknesses don't matter.
Everyone's weak. You, me, Gates, Dalio, even Musk. Nobody will be great at all 5 steps of the system because no one can think in all the ways required. Some are good at analyzing and diagnosing but bad at executing. Some are good planners but poor communicators. Others lack self-discipline.
Stay humble and ask for help when needed. Nobody has ever succeeded 100% on their own, without anyone else's help. That's the paradox of individual success: teamwork is the only way to get there.
Most people won't have the skills to execute even the best plan. You can get missing skills in two ways:
Self-taught (time-consuming)
Others' (requires humility) light
On knowing what to do with your life
“Some people have good mental maps and know what to do on their own. Maybe they learned them or were blessed with common sense. They have more answers than others. Others are more humble and open-minded. […] Open-mindedness and mental maps are most powerful.” — Ray Dalio
I've always known what I wanted to do, so I'm lucky. I'm almost 30 and have always had trouble executing. Good thing I never stopped experimenting, but I never committed to anything long-term. I jumped between projects. I decided 3 years ago to stick to one project for at least 6 months and haven't looked back.
Maybe you're good at staying focused and executing, but you don't know what to do. Maybe you have none of these because you haven't found your purpose. Always try new projects and talk to as many people as possible. It will give you inspiration and ideas and set you up for success.
There is almost always a way to achieve a crazy goal or idea.
Enjoy the journey, whichever path you take.
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Rita McGrath
3 years ago
Flywheels and Funnels
Traditional sales organizations used the concept of a sales “funnel” to describe the process through which potential customers move, ending up with sales at the end. Winners today have abandoned that way of thinking in favor of building flywheels — business models in which every element reinforces every other.
Ah, the marketing funnel…
Prospective clients go through a predictable set of experiences, students learn in business school marketing classes. It looks like this:
Understanding the funnel helps evaluate sales success indicators. Gail Goodwin, former CEO of small business direct mail provider Constant Contact, said managing the pipeline was key to escaping the sluggish SaaS ramp of death.
Like the funnel concept. To predict how well your business will do, measure how many potential clients are aware of it (awareness) and how many take the next step. If 1,000 people heard about your offering and 10% showed interest, you'd have 100 at that point. If 50% of these people made buyer-like noises, you'd know how many were, etc. It helped model buying trends.
TV, magazine, and radio advertising are pricey for B2C enterprises. Traditional B2B marketing involved armies of sales reps, which was expensive and a barrier to entry.
Cracks in the funnel model
Digital has exposed the funnel's limitations. Hubspot was born at a time when buyers and sellers had huge knowledge asymmetries, according to co-founder Brian Halligan. Those selling a product could use the buyer's lack of information to become a trusted partner.
As the world went digital, getting information and comparing offerings became faster, easier, and cheaper. Buyers didn't need a seller to move through a funnel. Interactions replaced transactions, and the relationship didn't end with a sale.
Instead, buyers and sellers interacted in a constant flow. In many modern models, the sale is midway through the process (particularly true with subscription and software-as-a-service models). Example:
You're creating a winding journey with many touch points, not a funnel (and lots of opportunities for customers to get lost).
From winding journey to flywheel
Beyond this revised view of an interactive customer journey, a company can create what Jim Collins famously called a flywheel. Imagine rolling a heavy disc on its axis. The first few times you roll it, you put in a lot of effort for a small response. The same effort yields faster turns as it gains speed. Over time, the flywheel gains momentum and turns without your help.
Modern digital organizations have created flywheel business models, in which any additional force multiplies throughout the business. The flywheel becomes a force multiplier, according to Collins.
Amazon is a famous flywheel example. Collins explained the concept to Amazon CEO Jeff Bezos at a corporate retreat in 2001. In The Everything Store, Brad Stone describes in his book The Everything Store how he immediately understood Amazon's levers.
The result (drawn on a napkin):
Low prices and a large selection of products attracted customers, while a focus on customer service kept them coming back, increasing traffic. Third-party sellers then increased selection. Low-cost structure supports low-price commitment. It's brilliant! Every wheel turn creates acceleration.
Where from here?
Flywheel over sales funnel! Consider these business terms.
Ray Dalio
3 years ago
The latest “bubble indicator” readings.
As you know, I like to turn my intuition into decision rules (principles) that can be back-tested and automated to create a portfolio of alpha bets. I use one for bubbles. Having seen many bubbles in my 50+ years of investing, I described what makes a bubble and how to identify them in markets—not just stocks.
A bubble market has a high degree of the following:
- High prices compared to traditional values (e.g., by taking the present value of their cash flows for the duration of the asset and comparing it with their interest rates).
- Conditons incompatible with long-term growth (e.g., extrapolating past revenue and earnings growth rates late in the cycle).
- Many new and inexperienced buyers were drawn in by the perceived hot market.
- Broad bullish sentiment.
- Debt financing a large portion of purchases.
- Lots of forward and speculative purchases to profit from price rises (e.g., inventories that are more than needed, contracted forward purchases, etc.).
I use these criteria to assess all markets for bubbles. I have periodically shown you these for stocks and the stock market.
What Was Shown in January Versus Now
I will first describe the picture in words, then show it in charts, and compare it to the last update in January.
As of January, the bubble indicator showed that a) the US equity market was in a moderate bubble, but not an extreme one (ie., 70 percent of way toward the highest bubble, which occurred in the late 1990s and late 1920s), and b) the emerging tech companies (ie. As well, the unprecedented flood of liquidity post-COVID financed other bubbly behavior (e.g. SPACs, IPO boom, big pickup in options activity), making things bubbly. I showed which stocks were in bubbles and created an index of those stocks, which I call “bubble stocks.”
Those bubble stocks have popped. They fell by a third last year, while the S&P 500 remained flat. In light of these and other market developments, it is not necessarily true that now is a good time to buy emerging tech stocks.
The fact that they aren't at a bubble extreme doesn't mean they are safe or that it's a good time to get long. Our metrics still show that US stocks are overvalued. Once popped, bubbles tend to overcorrect to the downside rather than settle at “normal” prices.
The following charts paint the picture. The first shows the US equity market bubble gauge/indicator going back to 1900, currently at the 40% percentile. The charts also zoom in on the gauge in recent years, as well as the late 1920s and late 1990s bubbles (during both of these cases the gauge reached 100 percent ).
The chart below depicts the average bubble gauge for the most bubbly companies in 2020. Those readings are down significantly.
The charts below compare the performance of a basket of emerging tech bubble stocks to the S&P 500. Prices have fallen noticeably, giving up most of their post-COVID gains.
The following charts show the price action of the bubble slice today and in the 1920s and 1990s. These charts show the same market dynamics and two key indicators. These are just two examples of how a lot of debt financing stock ownership coupled with a tightening typically leads to a bubble popping.
Everything driving the bubbles in this market segment is classic—the same drivers that drove the 1920s bubble and the 1990s bubble. For instance, in the last couple months, it was how tightening can act to prick the bubble. Review this case study of the 1920s stock bubble (starting on page 49) from my book Principles for Navigating Big Debt Crises to grasp these dynamics.
The following charts show the components of the US stock market bubble gauge. Since this is a proprietary indicator, I will only show you some of the sub-aggregate readings and some indicators.
Each of these six influences is measured using a number of stats. This is how I approach the stock market. These gauges are combined into aggregate indices by security and then for the market as a whole. The table below shows the current readings of these US equity market indicators. It compares current conditions for US equities to historical conditions. These readings suggest that we’re out of a bubble.
1. How High Are Prices Relatively?
This price gauge for US equities is currently around the 50th percentile.
2. Is price reduction unsustainable?
This measure calculates the earnings growth rate required to outperform bonds. This is calculated by adding up the readings of individual securities. This indicator is currently near the 60th percentile for the overall market, higher than some of our other readings. Profit growth discounted in stocks remains high.
Even more so in the US software sector. Analysts' earnings growth expectations for this sector have slowed, but remain high historically. P/Es have reversed COVID gains but remain high historical.
3. How many new buyers (i.e., non-existing buyers) entered the market?
Expansion of new entrants is often indicative of a bubble. According to historical accounts, this was true in the 1990s equity bubble and the 1929 bubble (though our data for this and other gauges doesn't go back that far). A flood of new retail investors into popular stocks, which by other measures appeared to be in a bubble, pushed this gauge above the 90% mark in 2020. The pace of retail activity in the markets has recently slowed to pre-COVID levels.
4. How Broadly Bullish Is Sentiment?
The more people who have invested, the less resources they have to keep investing, and the more likely they are to sell. Market sentiment is now significantly negative.
5. Are Purchases Being Financed by High Leverage?
Leveraged purchases weaken the buying foundation and expose it to forced selling in a downturn. The leverage gauge, which considers option positions as a form of leverage, is now around the 50% mark.
6. To What Extent Have Buyers Made Exceptionally Extended Forward Purchases?
Looking at future purchases can help assess whether expectations have become overly optimistic. This indicator is particularly useful in commodity and real estate markets, where forward purchases are most obvious. In the equity markets, I look at indicators like capital expenditure, or how much businesses (and governments) invest in infrastructure, factories, etc. It reflects whether businesses are projecting future demand growth. Like other gauges, this one is at the 40th percentile.
What one does with it is a tactical choice. While the reversal has been significant, future earnings discounting remains high historically. In either case, bubbles tend to overcorrect (sell off more than the fundamentals suggest) rather than simply deflate. But I wanted to share these updated readings with you in light of recent market activity.
Percy Bolmér
3 years ago
Ethereum No Longer Consumes A Medium-Sized Country's Electricity To Run
The Merge cut Ethereum's energy use by 99.5%.
The Crypto community celebrated on September 15, 2022. This day, Ethereum Merged. The entire blockchain successfully merged with the Beacon chain, and it was so smooth you barely noticed.
Many have waited, dreaded, and longed for this day.
Some investors feared the network would break down, while others envisioned a seamless merging.
Speculators predict a successful Merge will lead investors to Ethereum. This could boost Ethereum's popularity.
What Has Changed Since The Merge
The merging transitions Ethereum mainnet from PoW to PoS.
PoW sends a mathematical riddle to computers worldwide (miners). First miner to solve puzzle updates blockchain and is rewarded.
The puzzles sent are power-intensive to solve, so mining requires a lot of electricity. It's sent to every miner competing to solve it, requiring duplicate computation.
PoS allows investors to stake their coins to validate a new transaction. Instead of validating a whole block, you validate a transaction and get the fees.
You can validate instead of mine. A validator stakes 32 Ethereum. After staking, the validator can validate future blocks.
Once a validator validates a block, it's sent to a randomly selected group of other validators. This group verifies that a validator is not malicious and doesn't validate fake blocks.
This way, only one computer needs to solve or validate the transaction, instead of all miners. The validated block must be approved by a small group of validators, causing duplicate computation.
PoS is more secure because validating fake blocks results in slashing. You lose your bet tokens. If a validator signs a bad block or double-signs conflicting blocks, their ETH is burned.
Theoretically, Ethereum has one block every 12 seconds, so a validator forging a block risks burning 1 Ethereum for 12 seconds of transactions. This makes mistakes expensive and risky.
What Impact Does This Have On Energy Use?
Cryptocurrency is a natural calamity, sucking electricity and eating away at the earth one transaction at a time.
Many don't know the environmental impact of cryptocurrencies, yet it's tremendous.
A single Ethereum transaction used to use 200 kWh and leave a large carbon imprint. This update reduces global energy use by 0.2%.
Ethereum will submit a challenge to one validator, and that validator will forward it to randomly selected other validators who accept it.
This reduces the needed computing power.
They expect a 99.5% reduction, therefore a single transaction should cost 1 kWh.
Carbon footprint is 0.58 kgCO2, or 1,235 VISA transactions.
This is a big Ethereum blockchain update.
I love cryptocurrency and Mother Earth.