Don't sleep with your own sister.

70% of lottery winners go broke within five years. You've heard the last one. People who got rich quickly without setbacks and hard work often lose it all. My father said, "Easy money is easily lost," and a wealthy friend who owns a family office said, "The first generation makes it, the second generation spends it, and the third generation blows it."

This is evident. Corrupt politicians in developing countries live lavishly, buying their third wives' fifth Hermès bag and celebrating New Year's at The Brando Resort. A successful businessperson from humble beginnings is more conservative with money. More so if they're atom-based, not bit-based. They value money.

Crypto can "feel" easy. I have nothing against capital market investing. The global financial system is shady, but that's another topic. The problem started when those who took advantage of easy money started affecting other businesses. VCs did minimal due diligence on FTX because they needed deal flow and returns for their LPs. Lenders did minimum diligence and underwrote ludicrous loans to 3AC because they needed revenue.

Alameda (hence FTX) and 3AC made "easy money" Genesis and DCG aren't. Their businesses are more conventional, but they underestimated how "easy money" can hurt them.

Genesis has been the victim of easy money hubris and insolvency, losing $1 billion+ to 3AC and $200M to FTX. We discuss the implications for the broader crypto market.

Here are the quick takeaways:

• Genesis is one of the largest and most notable crypto lenders and prime brokerage firms.

• DCG and Genesis have done related party transactions, which can be done right but is a bad practice.

• Genesis owes DCG $1.5 billion+.

• If DCG unwinds Grayscale's GBTC, $9-10 billion in BTC will hit the market.

• DCG will survive Genesis.

What happened?

Let's recap the FTX shenanigan from two weeks ago. Shenanigans! Delphi's tweet sums up the craziness. Genesis has $175M in FTX.

Cred's timeline: I hate bad crisis management. Yes, admitting their balance sheet hole right away might've sparked more panic, and there's no easy way to convey your trouble, but no one ever learns.

By November 23, rumors circulated online that the problem could affect Genesis' parent company, DCG. To address this, Barry Silbert, Founder, and CEO of DCG released a statement to shareholders.

• A few things are confirmed thanks to this statement.

• DCG owes $1.5 billion+ to Genesis.

• $500M is due in 6 months, and the rest is due in 2032 (yes, that’s not a typo).

• Unless Barry raises new cash, his last-ditch efforts to repay the money will likely push the crypto market lower.

• Half a year of GBTC fees is approximately $100M.

• They can pay $500M with GBTC.

• With profits, sell another port.

Genesis has hired a restructuring adviser, indicating it is in trouble.

Rehypothecation

Every crypto problem in the past year seems to be rehypothecation between related parties, excessive leverage, hubris, and the removal of the money printer. The Bankless guys provided a chart showing 2021 crypto yield.

In June 2022, @DataFinnovation published a great investigation about 3AC and DCG. Here's a summary.

• 3AC borrowed BTC from Genesis and pledged it to create Grayscale's GBTC shares.

• 3AC uses GBTC to borrow more money from Genesis.

• This lets 3AC leverage their capital.

• 3AC's strategy made sense because GBTC had a premium, creating "free money."

• GBTC's discount and LUNA's implosion caused problems.

• 3AC lost its loan money in LUNA.

• Margin called on 3ACs' GBTC collateral.

• DCG bought GBTC to avoid a systemic collapse and a larger discount.

• Genesis lost too much money because 3AC can't pay back its loan. DCG "saved" Genesis, but the FTX collapse hurt Genesis further, forcing DCG and Genesis to seek external funding.

bruh…

Learning Experience

Co-borrowing. Unnecessary rehypothecation. Extra space. Governance disaster. Greed, hubris. Crypto has repeatedly shown it can recreate traditional financial system disasters quickly. Working in crypto is one of the best ways to learn crazy financial tricks people will do for a quick buck much faster than if you dabble in traditional finance.

Moving Forward

I think the crypto industry needs to consider its future. This is especially true for professionals. I'm not trying to scare you. In 2018 and 2020, I had doubts. No doubts now. Detailing the crypto industry's potential outcomes helped me gain certainty and confidence in its future. This includes VCs' benefits and talking points during the bull market, as well as what would happen if government regulations became hostile, etc. Even if that happens, I'm certain. This is permanent. I may write a post about that soon.

Sincerely,

M.

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:

1. Fundamentals (gradient descent, training linear and logistic regressions in PyTorch)

2. Machine Learning (deeper models and activation functions, convolutions, transfer learning, initialization schemes)

3. Sequences (RNN, GRU, LSTM, seq2seq models, attention, self-attention, transformers)

4. 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.

Failures inspire young developers

Jason citron created many products.

These products flopped.

Microsoft offered $10 billion for one of these products.

He rejected the offer since he was so confident in his success.

Let’s find out how he built a product that is currently valued at $15 billion.

Early in his youth, Jason began learning to code.

Jason's father taught him programming and IT.

His father wanted to help him earn money when he needed it.

Jason created video games and websites in high school.

Jason realized early on that his IT and programming skills could make him money.

Jason's parents misjudged his aptitude for programming.

Jason frequented online programming communities.

He looked for web developers. He created websites for those people.

His parents suspected Jason sold drugs online. When he said he used programming to make money, they were shocked.

They helped him set up a PayPal account.

Florida higher education to study video game creation

Jason never attended an expensive university.

He studied game design in Florida.

“Higher Education is an interesting part of society… When I work with people, the school they went to never comes up… only thing that matters is what can you do…At the end of the day, the beauty of silicon valley is that if you have a great idea and you can bring it to the life, you can convince a total stranger to give you money and join your project… This notion that you have to go to a great school didn’t end up being a thing for me.”

Jason's life was altered by Steve Jobs' keynote address.

After graduating, Jason joined an incubator.

Jason created a video-dating site first.

Bad idea.

Nobody wanted to use it when it was released, so they shut it down.

He made a multiplayer game.

It was released on Bebo. 10,000 people played it.

When Steve Jobs unveiled the Apple app store, he stopped playing.

The introduction of the app store resembled that of a new gaming console.

Jason's life altered after Steve Jobs' 2008 address.

“Whenever a new video game console is launched, that’s the opportunity for a new video game studio to get started, it’s because there aren’t too many games available…When a new PlayStation comes out, since it’s a new system, there’s only a handful of titles available… If you can be a launch title you can get a lot of distribution.”

Apple's app store provided a chance to start a video game company.

They released an app after 5 months of work.

Aurora Feint is the game.

Jason believed 1000 players in a week would be wonderful. A thousand players joined in the first hour.

Over time, Aurora Feints' game didn't gain traction. They don't make enough money to keep playing.

They could only make enough for one month.

Instead of buying video games, buy technology

Jason saw that they established a leaderboard, chat rooms, and multiplayer capabilities and believed other developers would want to use these.

They opted to sell the prior game's technology.

OpenFeint.

Assisting other game developers

They had no money in the bank to create everything needed to make the technology user-friendly.

Jason and Daniel designed a website saying:

“If you’re making a video game and want to have a drop in multiplayer support, you can use our system”

TechCrunch covered their website launch, and they gained a few hundred mailing list subscribers.

They raised seed funding with the mailing list.

Nearly all iPhone game developers started adopting the Open Feint logo.

“It was pretty wild… It was really like a whole social platform for people to play with their friends.”

What kind of a business model was it?

OpenFeint originally planned to make the software free for all games. As the game gained popularity, they demanded payment.

They later concluded it wasn't a good business concept.

It became free eventually.

Acquired for $104 million

Open Feint's users and employees grew tremendously.

GREE bought OpenFeint for $104 million in April 2011.

GREE initially committed to helping Jason and his team build a fantastic company.

Three or four months after the acquisition, Jason recognized they had a different vision.

He quit.

Jason's Original Vision for the iPad

Jason focused on distribution in 2012 to help businesses stand out.

The iPad market and user base were growing tremendously.

Jason said the iPad may replace mobile gadgets.

iPad gamers behaved differently than mobile gamers.

People sat longer and experienced more using an iPad.

“The idea I had was what if we built a gaming business that was more like traditional video games but played on tablets as opposed to some kind of mobile game that I’ve been doing before.”

Unexpected insight after researching the video game industry

Jason learned from studying the gaming industry that long-standing companies had advantages beyond a single release.

Previously, long-standing video game firms had their own distribution system. This distribution strategy could buffer time between successful titles.

Sony, Microsoft, and Valve all have gaming consoles and online stores.

So he built a distribution system.

He created a group chat app for gamers.

He envisioned a team-based multiplayer game with text and voice interaction.

His objective was to develop a communication network, release more games, and start a game distribution business.

Remaking the video game League of Legends

Jason and his crew reimagined a League of Legends game mode for 12-inch glass.

They adapted the game for tablets.

League of Legends was PC-only.

So they rebuilt it.

They overhauled the game and included native mobile experiences to stand out.

Hammer and Chisel was the company's name.

18 people worked on the game.

The game was funded. The game took 2.5 years to make.

Was the game a success?

July 2014 marked the game's release. The team's hopes were dashed.

Critics initially praised the game.

Initial installation was widespread.

The game failed.

As time passed, the team realized iPad gaming wouldn't increase much and mobile would win.

Jason was given a fresh idea by Stan Vishnevskiy.

Stan Vishnevskiy was a corporate engineer.

He told Jason about his plan to design a communication app without a game.

This concept seeded modern strife.

“The insight that he really had was to put a couple of dots together… we’re seeing our customers communicating around our own game with all these different apps and also ourselves when we’re playing on PC… We should solve that problem directly rather than needing to build a new game…we should start making it on PC.”

So began Discord.

Online socializing with pals was the newest trend.

Jason grew up playing video games with his friends.

He never played outside.

Jason had many great moments playing video games with his closest buddy, wife, and brother.

Discord was about providing a location for you and your group to speak and hang out.

Like a private cafe, bedroom, or living room.

Discord was developed for you and your friends on computers and phones.

You can quickly call your buddies during a game to conduct a conference call. Put the call on speaker and talk while playing.

Discord wanted to give every player a unique experience. Because coordinating across apps was a headache.

The entire team started concentrating on Discord.

Jason decided Hammer and Chisel would focus on their chat app.

Jason didn't want to make a video game.

How Discord attracted the appropriate attention

During the first five months, the entire team worked on the game and got feedback from friends.

This ensures product improvement. As a result, some teammates' buddies started utilizing Discord.

The team knew it would become something, but the result was buggy. App occasionally crashed.

Jason persuaded a gamer friend to write on Reddit about the software.

New people would find Discord. Why not?

Reddit users discovered Discord and 50 started using it frequently.

Discord was launched.

Rejecting the $10 billion acquisition proposal

Discord has increased in recent years.

It sends billions of messages.

Discord's users aren't tracked. They're privacy-focused.

Purchase offer

Covid boosted Discord's user base.

Weekly, billions of messages were transmitted.

Microsoft offered $10 billion for Discord in 2021.

Jason sold Open Feint for $104m in 2011.

This time, he believed in the product so much that he rejected Microsoft's offer.

“I was talking to some people in the team about which way we could go… The good thing was that most of the team wanted to continue building.”

Last time, Discord was valued at $15 billion.

Discord raised money on March 12, 2022.

The $15 billion corporation raised $500 million in 2021.