Moonbirds NFT sells for $1 million in first week
On Saturday, Moonbird #2642, one of the collection's rarest NFTs, sold for a record 350 ETH (over $1 million) on OpenSea.
The Sandbox, a blockchain-based gaming company based in Hong Kong, bought the piece. The seller, "oscuranft" on OpenSea, made around $600,000 after buying the NFT for 100 ETH a week ago.
Owl avatars
Moonbirds is a 10,000 owl NFT collection. It is one of the quickest collections to achieve bluechip status. Proof, a media startup founded by renowned VC Kevin Rose, launched Moonbirds on April 16.
Rose is currently a partner at True Ventures, a technology-focused VC firm. He was a Google Ventures general partner and has 1.5 million Twitter followers.
Rose has an NFT podcast on Proof. It follows Proof Collective, a group of 1,000 NFT collectors and artists, including Beeple, who hold a Proof Collective NFT and receive special benefits.
These include early access to the Proof podcast and in-person events.
According to the Moonbirds website, they are "the official Proof PFP" (picture for proof).
Moonbirds NFTs sold nearly $360 million in just over a week, according to The Block Research and Dune Analytics. Its top ten sales range from $397,000 to $1 million.
In the current market, Moonbirds are worth 33.3 ETH. Each NFT is 2.5 ETH. Holders have gained over 12 times in just over a week.
Why was it so popular?
The Block Research's NFT analyst, Thomas Bialek, attributes Moonbirds' rapid rise to Rose's backing, the success of his previous Proof Collective project, and collectors' preference for proven NFT projects.
Proof Collective NFT holders have made huge gains. These NFTs were sold in a Dutch auction last December for 5 ETH each. According to OpenSea, the current floor price is 109 ETH.
According to The Block Research, citing Dune Analytics, Proof Collective NFTs have sold over $39 million to date.
Rose has bigger plans for Moonbirds. Moonbirds is introducing "nesting," a non-custodial way for holders to stake NFTs and earn rewards.
Holders of NFTs can earn different levels of status based on how long they keep their NFTs locked up.
"As you achieve different nest status levels, we can offer you different benefits," he said. "We'll have in-person meetups and events, as well as some crazy airdrops planned."
Rose went on to say that Proof is just the start of "a multi-decade journey to build a new media company."
More on NFTs & Art
Matt Nutsch
3 years ago
Most people are unaware of how artificial intelligence (A.I.) is changing the world.
Recently, I saw an interesting social media post. In an entrepreneurship forum. A blogger asked for help because he/she couldn't find customers. I now suspect that the writer’s occupation is being disrupted by A.I.
Introduction
Artificial Intelligence (A.I.) has been a hot topic since the 1950s. With recent advances in machine learning, A.I. will touch almost every aspect of our lives. This article will discuss A.I. technology and its social and economic implications.
What's AI?
A computer program or machine with A.I. can think and learn. In general, it's a way to make a computer smart. Able to understand and execute complex tasks. Machine learning, NLP, and robotics are common types of A.I.
AI's global impact
AI will change the world, but probably faster than you think. A.I. already affects our daily lives. It improves our decision-making, efficiency, and productivity.
A.I. is transforming our lives and the global economy. It will create new business and job opportunities but eliminate others. Affected workers may face financial hardship.
AI examples:
OpenAI's GPT-3 text-generation
Developers can train, deploy, and manage models on GPT-3. It handles data preparation, model training, deployment, and inference for machine learning workloads. GPT-3 is easy to use for both experienced and new data scientists.
My team conducted an experiment. We needed to generate some blog posts for a website. We hired a blogger on Upwork. OpenAI created a blog post. The A.I.-generated blog post was of higher quality and lower cost.
MidjourneyAI's Art Contests
AI already affects artists. Artists use A.I. to create realistic 3D images and videos for digital art. A.I. is also used to generate new art ideas and methods.
MidjourneyAI and GigapixelAI won a contest last month. It's AI. created a beautiful piece of art that captured the contest's spirit. AI triumphs. It could open future doors.
After the art contest win, I registered to try out these new image generating A.I.s. In the MidjourneyAI chat forum, I noticed an artist's plea. The artist begged others to stop flooding RedBubble with AI-generated art.
Shutterstock and Getty Images have halted user uploads. AI-generated images flooded online marketplaces.
Imagining Videos with Meta
Meta released Make-a-Video this week. It's an A.I. app that creates videos from text. What you type creates a video.
This technology will impact TV, movies, and video games greatly. Imagine a movie or game that's personalized to your tastes. It's closer than you think.
Uses and Abuses of Deepfakes
Deepfake videos are computer-generated images of people. AI creates realistic images and videos of people.
Deepfakes are entertaining but have social implications. Porn introduced deepfakes in 2017. People put famous faces on porn actors and actresses without permission.
Soon, deepfakes were used to show dead actors/actresses or make them look younger. Carrie Fischer was included in films after her death using deepfake technology.
Deepfakes can be used to create fake news or manipulate public opinion, according to an AI.
Voices for Darth Vader and Iceman
James Earl Jones, who voiced Darth Vader, sold his voice rights this week. Aged actor won't be in those movies. Respeecher will use AI to mimic Jones's voice. This technology could change the entertainment industry. One actor can now voice many characters.
AI can generate realistic voice audio from text. Top Gun 2 actor Val Kilmer can't speak for medical reasons. Sonantic created Kilmer's voice from the movie script. This entertaining technology has social implications. It blurs authentic recordings and fake media.
Medical A.I. fights viruses
A team of Chinese scientists used machine learning to predict effective antiviral drugs last year. They started with a large dataset of virus-drug interactions. Researchers combined that with medication and virus information. Finally, they used machine learning to predict effective anti-virus medicines. This technology could solve medical problems.
AI ideas AI-generated Itself
OpenAI's GPT-3 predicted future A.I. uses. Here's what it told me:
AI will affect the economy. Businesses can operate more efficiently and reinvest resources with A.I.-enabled automation. AI can automate customer service tasks, reducing costs and improving satisfaction.
A.I. makes better pricing, inventory, and marketing decisions. AI automates tasks and makes decisions. A.I.-powered robots could help the elderly or disabled. Self-driving cars could reduce accidents.
A.I. predictive analytics can predict stock market or consumer behavior trends and patterns. A.I. also personalizes recommendations. sways. A.I. recommends products and movies. AI can generate new ideas based on data analysis.
Conclusion
A.I. will change business as it becomes more common. It will change how we live and work by creating growth and prosperity.
Exciting times, but also one which should give us all pause. Technology can be good or evil. We must use new technologies ethically, fairly, and honestly.
“The author generated some sentences in this text in part with GPT-3, OpenAI’s large-scale language-generation model. Upon generating draft language, the author reviewed, edited, and revised the language to their own liking and takes ultimate responsibility for the content of this publication. The text of this post was further edited using HemingWayApp. Many of the images used were generated using A.I. as described in the captions.”

Ezra Reguerra
3 years ago
Yuga Labs’ Otherdeeds NFT mint triggers backlash from community
Unhappy community members accuse Yuga Labs of fraud, manipulation, and favoritism over Otherdeeds NFT mint.
Following the Otherdeeds NFT mint, disgruntled community members took to Twitter to criticize Yuga Labs' handling of the event.
Otherdeeds NFTs were a huge hit with the community, selling out almost instantly. Due to high demand, the launch increased Ethereum gas fees from 2.6 ETH to 5 ETH.
But the event displeased many people. Several users speculated that the mint was “planned to fail” so the group could advertise launching its own blockchain, as the team mentioned a chain migration in one tweet.
Others like Mark Beylin tweeted that he had "sold out" on all Ape-related NFT investments after Yuga Labs "revealed their true colors." Beylin also advised others to assume Yuga Labs' owners are “bad actors.”
Some users who failed to complete transactions claim they lost ETH. However, Yuga Labs promised to refund lost gas fees.
CryptoFinally, a Twitter user, claimed Yuga Labs gave BAYC members better land than non-members. Others who wanted to participate paid for shittier land, while BAYCS got the only worthwhile land.
The Otherdeed NFT drop also increased Ethereum's burn rate. Glassnode and Data Always reported nearly 70,000 ETH burned on mint day.

Jayden Levitt
3 years ago
Starbucks' NFT Project recently defeated its rivals.
The same way Amazon killed bookstores. You just can’t see it yet.
Shultz globalized coffee. Before Starbucks, coffee sucked.
All accounts say 1970s coffee was awful.
Starbucks had three stores selling ground Indonesian coffee in the 1980s.
What a show!
A year after joining the company at 29, Shultz traveled to Italy for R&D.
He noticed the coffee shops' sense of theater and community and realized Starbucks was in the wrong business.
Integrating coffee and destination created a sense of community in the store.
Brilliant!
He told Starbucks' founders about his experience.
They disapproved.
For two years.
Shultz left and opened an Italian coffee shop chain like any good entrepreneur.
Starbucks ran into financial trouble, so the founders offered to sell to Shultz.
Shultz bought Starbucks in 1987 for $3.8 million, including six stores and a payment plan.
Starbucks is worth $100.79Billion, per Google Finance.
26,500 times Shultz's initial investment
Starbucks is releasing its own NFT Platform under Shultz and his early Vision.
This year, Starbucks Odyssey launches. The new digital experience combines a Loyalty Rewards program with NFT.
The side chain Polygon-based platform doesn't require a Crypto Wallet. Customers can earn and buy digital assets to unlock incentives and experiences.
They've removed all friction, making it more immersive and convenient than a coffee shop.
Brilliant!
NFTs are the access coupon to their digital community, but they don't highlight the technology.
They prioritize consumer experience by adding non-technical users to Web3. Their collectables are called journey stamps, not NFTs.
No mention of bundled gas fees.
Brady Brewer, Starbucks' CMO, said;
“It happens to be built on blockchain and web3 technologies, but the customer — to be honest — may very well not even know that what they’re doing is interacting with blockchain technology. It’s just the enabler,”
Rewards members will log into a web app using their loyalty program credentials to access Starbucks Odyssey. They won't know about blockchain transactions.
Starbucks has just dealt its rivals a devastating blow.
It generates more than ten times the revenue of its closest competitor Costa Coffee.
The coffee giant is booming.
Starbucks is ahead of its competitors. No wonder.
They have an innovative, adaptable leadership team.
Starbucks' DNA challenges the narrative, especially when others reject their ideas.
I’m off for a cappuccino.
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Sanjay Priyadarshi
2 years ago
Using Ruby code, a programmer created a $48,000,000,000 product that Elon Musk admired.
Unexpected Success
Shopify CEO and co-founder Tobias Lutke. Shopify is worth $48 billion.
World-renowned entrepreneur Tobi
Tobi never expected his first online snowboard business to become a multimillion-dollar software corporation.
Tobi founded Shopify to establish a 20-person company.
The publicly traded corporation employs over 10,000 people.
Here's Tobi Lutke's incredible story.
Elon Musk tweeted his admiration for the Shopify creator.
30-October-2019.
Musk praised Shopify founder Tobi Lutke on Twitter.
Happened:
Explore this programmer's journey.
What difficulties did Tobi experience as a young child?
Germany raised Tobi.
Tobi's parents realized he was smart but had trouble learning as a toddler.
Tobi was learning disabled.
Tobi struggled with school tests.
Tobi's learning impairments were undiagnosed.
Tobi struggled to read as a dyslexic.
Tobi also found school boring.
Germany's curriculum didn't inspire Tobi's curiosity.
“The curriculum in Germany was taught like here are all the solutions you might find useful later in life, spending very little time talking about the problem…If I don’t understand the problem I’m trying to solve, it’s very hard for me to learn about a solution to a problem.”
Studying computer programming
After tenth grade, Tobi decided school wasn't for him and joined a German apprenticeship program.
This curriculum taught Tobi software engineering.
He was an apprentice in a small Siemens subsidiary team.
Tobi worked with rebellious Siemens employees.
Team members impressed Tobi.
Tobi joined the team for this reason.
Tobi was pleased to get paid to write programming all day.
His life could not have been better.
Devoted to snowboarding
Tobi loved snowboarding.
He drove 5 hours to ski at his folks' house.
His friends traveled to the US to snowboard when he was older.
However, the cheap dollar conversion rate led them to Canada.
2000.
Tobi originally decided to snowboard instead than ski.
Snowboarding captivated him in Canada.
On the trip to Canada, Tobi encounters his wife.
Tobi meets his wife Fiona McKean on his first Canadian ski trip.
They maintained in touch after the trip.
Fiona moved to Germany after graduating.
Tobi was a startup coder.
Fiona found work in Germany.
Her work included editing, writing, and academics.
“We lived together for 10 months and then she told me that she need to go back for the master's program.”
With Fiona, Tobi immigrated to Canada.
Fiona invites Tobi.
Tobi agreed to move to Canada.
Programming helped Tobi move in with his girlfriend.
Tobi was an excellent programmer, therefore what he did in Germany could be done anywhere.
He worked remotely for his German employer in Canada.
Tobi struggled with remote work.
Due to poor communication.
No slack, so he used email.
Programmers had trouble emailing.
Tobi's startup was developing a browser.
After the dot-com crash, individuals left that startup.
It ended.
Tobi didn't intend to work for any major corporations.
Tobi left his startup.
He believed he had important skills for any huge corporation.
He refused to join a huge corporation.
Because of Siemens.
Tobi learned to write professional code and about himself while working at Siemens in Germany.
Siemens culture was odd.
Employees were distrustful.
Siemens' rigorous dress code implies that the corporation doesn't trust employees' attire.
It wasn't Tobi's place.
“There was so much bad with it that it just felt wrong…20-year-old Tobi would not have a career there.”
Focused only on snowboarding
Tobi lived in Ottawa with his girlfriend.
Canada is frigid in winter.
Ottawa's winters last.
Almost half a year.
Tobi wanted to do something worthwhile now.
So he snowboarded.
Tobi began snowboarding seriously.
He sought every snowboarding knowledge.
He researched the greatest snowboarding gear first.
He created big spreadsheets for snowboard-making technologies.
Tobi grew interested in selling snowboards while researching.
He intended to sell snowboards online.
He had no choice but to start his own company.
A small local company offered Tobi a job.
Interested.
He must sign papers to join the local company.
He needed a work permit when he signed the documents.
Tobi had no work permit.
He was allowed to stay in Canada while applying for permanent residency.
“I wasn’t illegal in the country, but my state didn’t give me a work permit. I talked to a lawyer and he told me it’s going to take a while until I get a permanent residency.”
Tobi's lawyer told him he cannot get a work visa without permanent residence.
His lawyer said something else intriguing.
Tobis lawyer advised him to start a business.
Tobi declined this local company's job offer because of this.
Tobi considered opening an internet store with his technical skills.
He sold snowboards online.
“I was thinking of setting up an online store software because I figured that would exist and use it as a way to sell snowboards…make money while snowboarding and hopefully have a good life.”
What brought Tobi and his co-founder together, and how did he support Tobi?
Tobi lived with his girlfriend's parents.
In Ottawa, Tobi encounters Scott Lake.
Scott was Tobis girlfriend's family friend and worked for Tobi's future employer.
Scott and Tobi snowboarded.
Tobi pitched Scott his snowboard sales software idea.
Scott liked the idea.
They planned a business together.
“I was looking after the technology and Scott was dealing with the business side…It was Scott who ended up developing relationships with vendors and doing all the business set-up.”
Issues they ran into when attempting to launch their business online
Neither could afford a long-term lease.
That prompted their online business idea.
They would open a store.
Tobi anticipated opening an internet store in a week.
Tobi seeks open-source software.
Most existing software was pricey.
Tobi and Scott couldn't afford pricey software.
“In 2004, I was sitting in front of my computer absolutely stunned realising that we hadn’t figured out how to create software for online stores.”
They required software to:
to upload snowboard images to the website.
people to look up the types of snowboards that were offered on the website. There must be a search feature in the software.
Online users transmit payments, and the merchant must receive them.
notifying vendors of the recently received order.
No online selling software existed at the time.
Online credit card payments were difficult.
How did they advance the software while keeping expenses down?
Tobi and Scott needed money to start selling snowboards.
Tobi and Scott funded their firm with savings.
“We both put money into the company…I think the capital we had was around CAD 20,000(Canadian Dollars).”
Despite investing their savings.
They minimized costs.
They tried to conserve.
No office rental.
They worked in several coffee shops.
Tobi lived rent-free at his girlfriend's parents.
He installed software in coffee cafes.
How were the software issues handled?
Tobi found no online snowboard sales software.
Two choices remained:
Change your mind and try something else.
Use his programming expertise to produce something that will aid in the expansion of this company.
Tobi knew he was the sole programmer working on such a project from the start.
“I had this realisation that I’m going to be the only programmer who has ever worked on this, so I don’t have to choose something that lots of people know. I can choose just the best tool for the job…There is been this programming language called Ruby which I just absolutely loved ”
Ruby was open-source and only had Japanese documentation.
Latin is the source code.
Tobi used Ruby twice.
He assumed he could pick the tool this time.
Why not build with Ruby?
How did they find their first time operating a business?
Tobi writes applications in Ruby.
He wrote the initial software version in 2.5 months.
Tobi and Scott founded Snowdevil to sell snowboards.
Tobi coded for 16 hours a day.
His lifestyle was unhealthy.
He enjoyed pizza and coke.
“I would never recommend this to anyone, but at the time there was nothing more interesting to me in the world.”
Their initial purchase and encounter with it
Tobi worked in cafes then.
“I was working in a coffee shop at this time and I remember everything about that day…At some time, while I was writing the software, I had to type the email that the software would send to tell me about the order.”
Tobi recalls everything.
He checked the order on his laptop at the coffee shop.
Pennsylvanian ordered snowboard.
Tobi walked home and called Scott. Tobi told Scott their first order.
They loved the order.
How were people made aware about Snowdevil?
2004 was very different.
Tobi and Scott attempted simple website advertising.
Google AdWords was new.
Ad clicks cost 20 cents.
Online snowboard stores were scarce at the time.
Google ads propelled the snowdevil brand.
Snowdevil prospered.
They swiftly recouped their original investment in the snowboard business because to its high profit margin.
Tobi and Scott struggled with inventories.
“Snowboards had really good profit margins…Our biggest problem was keeping inventory and getting it back…We were out of stock all the time.”
Selling snowboards returned their investment and saved them money.
They did not appoint a business manager.
They accomplished everything alone.
Sales dipped in the spring, but something magical happened.
Spring sales plummeted.
They considered stocking different boards.
They naturally wanted to add boards and grow the business.
However, magic occurred.
Tobi coded and improved software while running Snowdevil.
He modified software constantly. He wanted speedier software.
He experimented to make the software more resilient.
Tobi received emails requesting the Snowdevil license.
They intended to create something similar.
“I didn’t stop programming, I was just like Ok now let me try things, let me make it faster and try different approaches…Increasingly I got people sending me emails and asking me If I would like to licence snowdevil to them. People wanted to start something similar.”
Software or skateboards, your choice
Scott and Tobi had to choose a hobby in 2005.
They might sell alternative boards or use software.
The software was a no-brainer from demand.
Daniel Weinand is invited to join Tobi's business.
Tobis German best friend is Daniel.
Tobi and Scott chose to use the software.
Tobi and Scott kept the software service.
Tobi called Daniel to invite him to Canada to collaborate.
Scott and Tobi had quit snowboarding until then.
How was Shopify launched, and whence did the name come from?
The three chose Shopify.
Named from two words.
First:
Shop
Final part:
Simplify
Shopify
Shopify's crew has always had one goal:
creating software that would make it simple and easy for people to launch online storefronts.
Launched Shopify after raising money for the first time.
Shopify began fundraising in 2005.
First, they borrowed from family and friends.
They needed roughly $200k to run the company efficiently.
$200k was a lot then.
When questioned why they require so much money. Tobi told them to trust him with their goals. The team raised seed money from family and friends.
Shopify.com has a landing page. A demo of their goal was on the landing page.
In 2006, Shopify had about 4,000 emails.
Shopify rented an Ottawa office.
“We sent a blast of emails…Some people signed up just to try it out, which was exciting.”
How things developed after Scott left the company
Shopify co-founder Scott Lake left in 2008.
Scott was CEO.
“He(Scott) realized at some point that where the software industry was going, most of the people who were the CEOs were actually the highly technical person on the founding team.”
Scott leaving the company worried Tobi.
Tobis worried about finding a new CEO.
To Tobi:
A great VC will have the network to identify the perfect CEO for your firm.
Tobi started visiting Silicon Valley to meet with venture capitalists to recruit a CEO.
Initially visiting Silicon Valley
Tobi came to Silicon Valley to start a 20-person company.
This company creates eCommerce store software.
Tobi never wanted a big corporation. He desired a fulfilling existence.
“I stayed in a hostel in the Bay Area. I had one roommate who was also a computer programmer. I bought a bicycle on Craiglist. I was there for a week, but ended up staying two and a half weeks.”
Tobi arrived unprepared.
When venture capitalists asked him business questions.
He answered few queries.
Tobi didn't comprehend VC meetings' terminology.
He wrote the terms down and looked them up.
Some were fascinated after he couldn't answer all these queries.
“I ended up getting the kind of term sheets people dream about…All the offers were conditional on moving our company to Silicon Valley.”
Canada received Tobi.
He wanted to consult his team before deciding. Shopify had five employees at the time.
2008.
A global recession greeted Tobi in Canada. The recession hurt the market.
His term sheets were useless.
The economic downturn in the world provided Shopify with a fantastic opportunity.
The global recession caused significant job losses.
Fired employees had several ideas.
They wanted online stores.
Entrepreneurship was desired. They wanted to quit work.
People took risks and tried new things during the global slump.
Shopify subscribers skyrocketed during the recession.
“In 2009, the company reached neutral cash flow for the first time…We were in a position to think about long-term investments, such as infrastructure projects.”
Then, Tobi Lutke became CEO.
How did Tobi perform as the company's CEO?
“I wasn’t good. My team was very patient with me, but I had a lot to learn…It’s a very subtle job.”
2009–2010.
Tobi limited the company's potential.
He deliberately restrained company growth.
Tobi had one costly problem:
Whether Shopify is a venture or a lifestyle business.
The company's annual revenue approached $1 million.
Tobi battled with the firm and himself despite good revenue.
His wife was supportive, but the responsibility was crushing him.
“It’s a crushing responsibility…People had families and kids…I just couldn’t believe what was going on…My father-in-law gave me money to cover the payroll and it was his life-saving.”
Throughout this trip, everyone supported Tobi.
They believed it.
$7 million in donations received
Tobi couldn't decide if this was a lifestyle or a business.
Shopify struggled with marketing then.
Later, Tobi tried 5 marketing methods.
He told himself that if any marketing method greatly increased their growth, he would call it a venture, otherwise a lifestyle.
The Shopify crew brainstormed and voted on marketing concepts.
Tested.
“Every single idea worked…We did Adwords, published a book on the concept, sponsored a podcast and all the ones we tracked worked.”
To Silicon Valley once more
Shopify marketing concepts worked once.
Tobi returned to Silicon Valley to pitch investors.
He raised $7 million, valuing Shopify at $25 million.
All investors had board seats.
“I find it very helpful…I always had a fantastic relationship with everyone who’s invested in my company…I told them straight that I am not going to pretend I know things, I want you to help me.”
Tobi developed skills via running Shopify.
Shopify had 20 employees.
Leaving his wife's parents' home
Tobi left his wife's parents in 2014.
Tobi had a child.
Shopify has 80,000 customers and 300 staff in 2013.
Public offering in 2015
Shopify investors went public in 2015.
Shopify powers 4.1 million e-Commerce sites.
Shopify stores are 65% US-based.
It is currently valued at $48 billion.

Bastian Hasslinger
3 years ago
Before 2021, most startups had excessive valuations. It is currently causing issues.
Higher startup valuations are often favorable for all parties. High valuations show a business's potential. New customers and talent are attracted. They earn respect.
Everyone benefits if a company's valuation rises.
Founders and investors have always been incentivized to overestimate a company's value.
Post-money valuations were inflated by 2021 market expectations and the valuation model's mechanisms.
Founders must understand both levers to handle a normalizing market.
2021, the year of miracles
2021 must've seemed miraculous to entrepreneurs, employees, and VCs. Valuations rose, and funding resumed after the first Covid-19 epidemic caution.
In 2021, VC investments increased from $335B to $643B. 518 new worldwide unicorns vs. 134 in 2020; 951 US IPOs vs. 431.
Things can change quickly, as 2020-21 showed.
Rising interest rates, geopolitical developments, and normalizing technology conditions drive down share prices and tech company market caps in 2022. Zoom, the poster-child of early lockdown success, is down 37% since 1st Jan.
Once-inflated valuations can become a problem in a normalizing market, especially for founders, employees, and early investors.
the reason why startups are always overvalued
To see why inflated valuations are a problem, consider one of its causes.
Private company values only fluctuate following a new investment round, unlike publicly-traded corporations. The startup's new value is calculated simply:
(Latest round share price) x (total number of company shares)
This is the industry standard Post-Money Valuation model.
Let’s illustrate how it works with an example. If a VC invests $10M for 1M shares (at $10/share), and the company has 10M shares after the round, its Post-Money Valuation is $100M (10/share x 10M shares).
This approach might seem like the most natural way to assess a business, but the model often unintentionally overstates the underlying value of the company even if the share price paid by the investor is fair. All shares aren't equal.
New investors in a corporation will always try to minimize their downside risk, or the amount they lose if things go wrong. New investors will try to negotiate better terms and pay a premium.
How the value of a struggling SpaceX increased
SpaceX's 2008 Series D is an example. Despite the financial crisis and unsuccessful rocket launches, the company's Post-Money Valuation was 36% higher after the investment round. Why?
Series D SpaceX shares were protected. In case of liquidation, Series D investors were guaranteed a 2x return before other shareholders.
Due to downside protection, investors were willing to pay a higher price for this new share class.
The Post-Money Valuation model overpriced SpaceX because it viewed all the shares as equal (they weren't).
Why entrepreneurs, workers, and early investors stand to lose the most
Post-Money Valuation is an effective and sufficient method for assessing a startup's valuation, despite not taking share class disparities into consideration.
In a robust market, where the firm valuation will certainly expand with the next fundraising round or exit, the inflated value is of little significance.
Fairness endures. If a corporation leaves at a greater valuation, each stakeholder will receive a proportional distribution. (i.e., 5% of a $100M corporation yields $5M).
SpaceX's inherent overvaluation was never a problem. Had it been sold for less than its Post-Money Valuation, some shareholders, including founders, staff, and early investors, would have seen their ownership drop.
The unforgiving world of 2022
In 2022, founders, employees, and investors who benefited from inflated values will face below-valuation exits and down-rounds.
For them, 2021 will be a curse, not a blessing.
Some tech giants are worried. Klarna's valuation fell from $45B (Oct 21) to $30B (Jun 22), Canvas from $40B to $27B, and GoPuffs from $17B to $8.3B.
Shazam and Blue Apron have to exit or IPO at a cheaper price. Premium share classes are protected, while others receive less. The same goes for bankrupts.
Those who continue at lower valuations will lose reputation and talent. When their value declines by half, generous employee stock options become less enticing, and their ability to return anything is questioned.
What can we infer about the present situation?
Such techniques to enhance your company's value or stop a normalizing market are fiction.
The current situation is a painful reminder for entrepreneurs and a crucial lesson for future firms.
The devastating market fall of the previous six months has taught us one thing:
Keep in mind that any valuation is speculative. Money Post A startup's valuation is a highly simplified approximation of its true value, particularly in the early phases when it lacks significant income or a cutting-edge product. It is merely a projection of the future and a hypothetical meter. Until it is achieved by an exit, a valuation is nothing more than a number on paper.
Assume the value of your company is lower than it was in the past. Your previous valuation might not be accurate now due to substantial changes in the startup financing markets. There is little reason to think that your company's value will remain the same given the 50%+ decline in many newly listed IT companies. Recognize how the market situation is changing and use caution.
Recognize the importance of the stake you hold. Each share class has a unique value that varies. Know the sort of share class you own and how additional contractual provisions affect the market value of your security. Frameworks have been provided by Metrick and Yasuda (Yale & UC) and Gornall and Strebulaev (Stanford) for comprehending the terms that affect investors' cash-flow rights upon withdrawal. As a result, you will be able to more accurately evaluate your firm and determine the worth of each share class.
Be wary of approving excessively protective share terms.
The trade-offs should be considered while negotiating subsequent rounds. Accepting punitive contractual terms could first seem like a smart option in order to uphold your inflated worth, but you should proceed with caution. Such provisions ALWAYS result in misaligned shareholders, with common shareholders (such as you and your staff) at the bottom of the list.

Vitalik
3 years ago
An approximate introduction to how zk-SNARKs are possible (part 1)
You can make a proof for the statement "I know a secret number such that if you take the word ‘cow', add the number to the end, and SHA256 hash it 100 million times, the output starts with 0x57d00485aa". The verifier can verify the proof far more quickly than it would take for them to run 100 million hashes themselves, and the proof would also not reveal what the secret number is.
In the context of blockchains, this has 2 very powerful applications: Perhaps the most powerful cryptographic technology to come out of the last decade is general-purpose succinct zero knowledge proofs, usually called zk-SNARKs ("zero knowledge succinct arguments of knowledge"). A zk-SNARK allows you to generate a proof that some computation has some particular output, in such a way that the proof can be verified extremely quickly even if the underlying computation takes a very long time to run. The "ZK" part adds an additional feature: the proof can keep some of the inputs to the computation hidden.
You can make a proof for the statement "I know a secret number such that if you take the word ‘cow', add the number to the end, and SHA256 hash it 100 million times, the output starts with 0x57d00485aa". The verifier can verify the proof far more quickly than it would take for them to run 100 million hashes themselves, and the proof would also not reveal what the secret number is.
In the context of blockchains, this has two very powerful applications:
- Scalability: if a block takes a long time to verify, one person can verify it and generate a proof, and everyone else can just quickly verify the proof instead
- Privacy: you can prove that you have the right to transfer some asset (you received it, and you didn't already transfer it) without revealing the link to which asset you received. This ensures security without unduly leaking information about who is transacting with whom to the public.
But zk-SNARKs are quite complex; indeed, as recently as in 2014-17 they were still frequently called "moon math". The good news is that since then, the protocols have become simpler and our understanding of them has become much better. This post will try to explain how ZK-SNARKs work, in a way that should be understandable to someone with a medium level of understanding of mathematics.
Why ZK-SNARKs "should" be hard
Let us take the example that we started with: we have a number (we can encode "cow" followed by the secret input as an integer), we take the SHA256 hash of that number, then we do that again another 99,999,999 times, we get the output, and we check what its starting digits are. This is a huge computation.
A "succinct" proof is one where both the size of the proof and the time required to verify it grow much more slowly than the computation to be verified. If we want a "succinct" proof, we cannot require the verifier to do some work per round of hashing (because then the verification time would be proportional to the computation). Instead, the verifier must somehow check the whole computation without peeking into each individual piece of the computation.
One natural technique is random sampling: how about we just have the verifier peek into the computation in 500 different places, check that those parts are correct, and if all 500 checks pass then assume that the rest of the computation must with high probability be fine, too?
Such a procedure could even be turned into a non-interactive proof using the Fiat-Shamir heuristic: the prover computes a Merkle root of the computation, uses the Merkle root to pseudorandomly choose 500 indices, and provides the 500 corresponding Merkle branches of the data. The key idea is that the prover does not know which branches they will need to reveal until they have already "committed to" the data. If a malicious prover tries to fudge the data after learning which indices are going to be checked, that would change the Merkle root, which would result in a new set of random indices, which would require fudging the data again... trapping the malicious prover in an endless cycle.
But unfortunately there is a fatal flaw in naively applying random sampling to spot-check a computation in this way: computation is inherently fragile. If a malicious prover flips one bit somewhere in the middle of a computation, they can make it give a completely different result, and a random sampling verifier would almost never find out.
It only takes one deliberately inserted error, that a random check would almost never catch, to make a computation give a completely incorrect result.
If tasked with the problem of coming up with a zk-SNARK protocol, many people would make their way to this point and then get stuck and give up. How can a verifier possibly check every single piece of the computation, without looking at each piece of the computation individually? There is a clever solution.
see part 2
