More on Entrepreneurship/Creators
Khoi Ho
3 years ago
After working at seven startups, here are the early-stage characteristics that contributed to profitability, unicorn status or successful acquisition.
I've worked in a People role at seven early-stage firms for over 15 years (I enjoy chasing a dream!). Few of the seven achieved profitability, including unicorn status or acquisition.
Did early-stage startups share anything? Was there a difference between winners and losers? YES.
I support founders and entrepreneurs building financially sustainable enterprises with a compelling cause. This isn't something everyone would do. A company's success demands more than guts. Founders drive startup success.
Six Qualities of Successful Startups
Successful startup founders either innately grasped the correlation between strong team engagement and a well-executed business model, or they knew how to ask and listen to others (executive coaches, other company leaders, the team itself) to learn about it.
Successful startups:
1. Co-founders agreed and got along personally.
Multi-founder startups are common. When co-founders agree on strategic decisions and are buddies, there's less friction and politics at work.
As a co-founder, ask your team if you're aligned. They'll explain.
I've seen C-level leaders harbor personal resentments over disagreements. A co-departure founder's caused volatile leadership and work disruptions that the team struggled to manage during and after.
2. Team stayed.
Successful startups have low turnover. Nobody is leaving. There may be a termination for performance, but other team members will have observed the issues and agreed with the decision.
You don't want organizational turnover of 30%+, with leaders citing performance issues but the team not believing them. This breeds suspicion.
Something is wrong if many employees leave voluntarily or involuntarily. You may hear about lack of empowerment, support, or toxic leadership in exit interviews and from the existing team. Intellectual capital loss and resource instability harm success.
3. Team momentum.
A successful startup's team is excited about its progress. Consistently achieving goals and having trackable performance metrics. Some describe this period of productivity as magical, with great talents joining the team and the right people in the right places. Increasing momentum.
I've also seen short-sighted decisions where only some departments, like sales and engineering, had goals. Lack of a unified goals system created silos and miscommunication. Some employees felt apathetic because they didn't know how they contributed to team goals.
4. Employees advanced in their careers.
Even if you haven't created career pathing or professional development programs, early-stage employees will grow and move into next-level roles. If you hire more experienced talent and leaders, expect them to mentor existing team members. Growing companies need good performers.
New talent shouldn't replace and discard existing talent. This creates animosity and makes existing employees feel unappreciated for their early contributions to the company.
5. The company lived its values.
Culture and identity are built on lived values. A company's values affect hiring, performance management, rewards, and other processes. Identify, practice, and believe in company values. Starting with team values instead of management or consultants helps achieve this. When a company's words and actions match, it builds trust.
When company values are beautifully displayed on a wall but few employees understand them, the opposite is true. If an employee can't name the company values, they're useless.
6. Communication was clear.
When necessary information is shared with the team, they feel included, trusted, and like owners. Transparency means employees have the needed information to do their jobs. Disclosure builds trust. The founders answer employees' questions honestly.
Information accessibility decreases office politics. Without transparency, even basic information is guarded and many decisions are made in secret. I've seen founders who don't share financial, board meeting, or compensation and equity information. The founders' lack of trust in the team wasn't surprising, so it was reciprocated.
The Choices
Finally. All six of the above traits (leadership alignment, minimal turnover, momentum, professional advancement, values, and transparency) were high in the profitable startups I've worked at, including unicorn status or acquisition.
I've seen these as the most common and constant signals of startup success or failure.
These characteristics are the product of founders' choices. These decisions lead to increased team engagement and business execution.
Here's something to consider for startup employees and want-to-bes. 90% of startups fail, despite the allure of building something new and gaining ownership. With the emotional and time investment in startup formation, look for startups with these traits to reduce your risk.
Both you and the startup will thrive in these workplaces.
Antonio Neto
3 years ago
What's up with tech?
Massive Layoffs, record low VC investment, debate over crash... why is it happening and what’s the endgame?
This article generalizes a diverse industry. For objectivity, specific tech company challenges like growing competition within named segments won't be considered. Please comment on the posts.
According to Layoffs.fyi, nearly 120.000 people have been fired from startups since March 2020. More than 700 startups have fired 1% to 100% of their workforce. "The tech market is crashing"
Venture capital investment dropped 19% QoQ in the first four months of 2022, a 2018 low. Since January 2022, Nasdaq has dropped 27%. Some believe the tech market is collapsing.
It's bad, but nothing has crashed yet. We're about to get super technical, so buckle up!
I've written a follow-up article about what's next. For a more optimistic view of the crisis' aftermath, see: Tech Diaspora and Silicon Valley crisis
What happened?
Insanity reigned. Last decade, everyone became a unicorn. Seed investments can be made without a product or team. While the "real world" economy suffered from the pandemic for three years, tech companies enjoyed the "new normal."
COVID sped up technology adoption on several fronts, but this "new normal" wasn't so new after many restrictions were lifted. Worse, it lived with disrupted logistics chains, high oil prices, and WW3. The consumer market has felt the industry's boom for almost 3 years. Inflation, unemployment, mental distress...what looked like a fast economic recovery now looks like unfulfilled promises.
People rethink everything they eat. Paying a Netflix subscription instead of buying beef is moronic if you can watch it for free on your cousin’s account. No matter how great your real estate app's UI is, buying a house can wait until mortgage rates drop. PLGProduct Led Growth (PLG) isn't the go-to strategy when consumers have more basic expense priorities.
Exponential growth and investment
Until recently, tech companies believed that non-exponential revenue growth was fatal. Exponential growth entails doing more with less. From Salim Ismail words:
An Exponential Organization (ExO) has 10x the impact of its peers.
Many tech companies' theories are far from reality.
Investors have funded (sometimes non-exponential) growth. Scale-driven companies throw people at problems until they're solved. Need an entire closing team because you’ve just bought a TV prime time add? Sure. Want gold-weight engineers to colorize buttons? Why not?
Tech companies don't need cash flow to do it; they can just show revenue growth and get funding. Even though it's hard to get funding, this was the market's momentum until recently.
The graph at the beginning of this section shows how industry heavyweights burned money until 2020, despite being far from their market-share seed stage. Being big and being sturdy are different things, and a lot of the tech startups out there are paper tigers. Without investor money, they have no foundation.
A little bit about interest rates
Inflation-driven high interest rates are said to be causing tough times. Investors would rather leave money in the bank than spend it (I myself said it some days ago). It’s not wrong, but it’s also not that simple.
The USA central bank (FED) is a good proxy of global economics. Dollar treasury bonds are the safest investment in the world. Buying U.S. debt, the only country that can print dollars, guarantees payment.
The graph above shows that FED interest rates are low and 10+ year bond yields are near 2018 levels. Nobody was firing at 2018. What’s with that then?
Full explanation is too technical for this article, so I'll just summarize: Bond yields rise due to lack of demand or market expectations of longer-lasting inflation. Safe assets aren't a "easy money" tactic for investors. If that were true, we'd have seen the current scenario before.
Long-term investors are protecting their capital from inflation.
Not a crash, a landing
I bombarded you with info... Let's review:
Consumption is down, hurting revenue.
Tech companies of all ages have been hiring to grow revenue at the expense of profit.
Investors expect inflation to last longer, reducing future investment gains.
Inflation puts pressure on a wheel that was rolling full speed not long ago. Investment spurs hiring, growth, and more investment. Worried investors and consumers reduce the cycle, and hiring follows.
Long-term investors back startups. When the invested company goes public or is sold, it's ok to burn money. What happens when the payoff gets further away? What if all that money sinks? Investors want immediate returns.
Why isn't the market crashing? Technology is not losing capital. It’s expecting change. The market realizes it threw moderation out the window and is reversing course. Profitability is back on the menu.
People solve problems and make money, but they also cost money. Huge cost for the tech industry. Engineers, Product Managers, and Designers earn up to 100% more than similar roles. Businesses must be careful about who they keep and in what positions to avoid wasting money.
What the future holds
From here on, it's all speculation. I found many great articles while researching this piece. Some are cited, others aren't (like this and this). We're in an adjustment period that may or may not last long.
Big companies aren't laying off many workers. Netflix firing 100 people makes headlines, but it's only 1% of their workforce. The biggest seem to prefer not hiring over firing.
Smaller startups beyond the seeding stage may be hardest hit. Without structure or product maturity, many will die.
I expect layoffs to continue for some time, even at Meta or Amazon. I don't see any industry names falling like they did during the .com crisis, but the market will shrink.
If you are currently employed, think twice before moving out and where to.
If you've been fired, hurry, there are still many opportunities.
If you're considering a tech career, wait.
If you're starting a business, I respect you. Good luck.
Nik Nicholas
3 years ago
A simple go-to-market formula
“Poor distribution, not poor goods, is the main reason for failure” — Peter Thiel.
Here's an easy way to conceptualize "go-to-market" for your distribution plan.
One equation captures the concept:
Distribution = Ecosystem Participants + Incentives
Draw your customers' ecosystem. Set aside your goods and consider your consumer's environment. Who do they deal with daily?
First, list each participant. You want an exhaustive list, but here are some broad categories.
In-person media services
Websites
Events\Networks
Financial education and banking
Shops
Staff
Advertisers
Twitter influencers
Draw influence arrows. Who's affected? I'm not just talking about Instagram selfie-posters. Who has access to your consumer and could promote your product if motivated?
The thicker the arrow, the stronger the relationship. Include more "influencers" if needed. Customer ecosystems are complex.
3. Incentivize ecosystem players. “Show me the incentive and I will show you the result.“, says Warren Buffet's business partner Charlie Munger.
Strong distribution strategies encourage others to promote your product to your target market by incentivizing the most prominent players. Incentives can be financial or non-financial.
Financial rewards
Usually, there's money. If you pay Facebook, they'll run your ad. Salespeople close deals for commission. Giving customers bonus credits will encourage referrals.
Most businesses underuse non-financial incentives.
Non-cash incentives
Motivate key influencers without spending money to expand quickly and cheaply. What can you give a client-connector for free?
Here are some ideas:
Are there any other features or services available?
Titles or status? Tinder paid college "ambassadors" for parties to promote its dating service.
Can I get early/free access? Facebook gave a select group of developers "exclusive" early access to their AR platform.
Are you a good host? Pharell performed at YPlan's New York launch party.
Distribution? Apple's iPod earphones are white so others can see them.
Have an interesting story? PR rewards journalists by giving them a compelling story to boost page views.
Prioritize distribution.
More time spent on distribution means more room in your product design and business plan. Once you've identified the key players in your customer's ecosystem, talk to them.
Money isn't your only resource. Creative non-monetary incentives may be more effective and scalable. Give people something useful and easy to deliver.
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Katherine Kornei
3 years ago
The InSight lander from NASA has recorded the greatest tremor ever felt on Mars.
The magnitude 5 earthquake was responsible for the discharge of energy that was 10 times greater than the previous record holder.
Any Martians who happen to be reading this should quickly learn how to duck and cover.
NASA's Jet Propulsion Laboratory in Pasadena, California, reported that on May 4, the planet Mars was shaken by an earthquake of around magnitude 5, making it the greatest Marsquake ever detected to this point. The shaking persisted for more than six hours and unleashed more than ten times as much energy as the earthquake that had previously held the record for strongest.
The event was captured on record by the InSight lander, which is operated by the United States Space Agency and has been researching the innards of Mars ever since it touched down on the planet in 2018 (SN: 11/26/18). The epicenter of the earthquake was probably located in the vicinity of Cerberus Fossae, which is located more than 1,000 kilometers away from the lander.
The surface of Cerberus Fossae is notorious for being broken up and experiencing periodic rockfalls. According to geophysicist Philippe Lognonné, who is the lead investigator of the Seismic Experiment for Interior Structure, the seismometer that is onboard the InSight lander, it is reasonable to assume that the ground is moving in that area. "This is an old crater from a volcanic eruption."
Marsquakes, which are similar to earthquakes in that they give information about the interior structure of our planet, can be utilized to investigate what lies beneath the surface of Mars (SN: 7/22/21). And according to Lognonné, who works at the Institut de Physique du Globe in Paris, there is a great deal that can be gleaned from analyzing this massive earthquake. Because the quality of the signal is so high, we will be able to focus on the specifics.
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
Vishal Chawla
3 years ago
5 Bored Apes borrowed to claim $1.1 million in APE tokens
Takeaway
Unknown user took advantage of the ApeCoin airdrop to earn $1.1 million.
He used a flash loan to borrow five BAYC NFTs, claim the airdrop, and repay the NFTs.
Yuga Labs, the creators of BAYC, airdropped ApeCoin (APE) to anyone who owns one of their NFTs yesterday.
For the Bored Ape Yacht Club and Mutant Ape Yacht Club collections, the team allocated 150 million tokens, or 15% of the total ApeCoin supply, worth over $800 million. Each BAYC holder received 10,094 tokens worth $80,000 to $200,000.
But someone managed to claim the airdrop using NFTs they didn't own. They used the airdrop's specific features to carry it out. And it worked, earning them $1.1 million in ApeCoin.
The trick was that the ApeCoin airdrop wasn't based on who owned which Bored Ape at a given time. Instead, anyone with a Bored Ape at the time of the airdrop could claim it. So if you gave someone your Bored Ape and you hadn't claimed your tokens, they could claim them.
The person only needed to get hold of some Bored Apes that hadn't had their tokens claimed to claim the airdrop. They could be returned immediately.
So, what happened?
The person found a vault with five Bored Ape NFTs that hadn't been used to claim the airdrop.
A vault tokenizes an NFT or a group of NFTs. You put a bunch of NFTs in a vault and make a token. This token can then be staked for rewards or sold (representing part of the value of the collection of NFTs). Anyone with enough tokens can exchange them for NFTs.
This vault uses the NFTX protocol. In total, it contained five Bored Apes: #7594, #8214, #9915, #8167, and #4755. Nobody had claimed the airdrop because the NFTs were locked up in the vault and not controlled by anyone.
The person wanted to unlock the NFTs to claim the airdrop but didn't want to buy them outright s o they used a flash loan, a common tool for large DeFi hacks. Flash loans are a low-cost way to borrow large amounts of crypto that are repaid in the same transaction and block (meaning that the funds are never at risk of not being repaid).
With a flash loan of under $300,000 they bought a Bored Ape on NFT marketplace OpenSea. A large amount of the vault's token was then purchased, allowing them to redeem the five NFTs. The NFTs were used to claim the airdrop, before being returned, the tokens sold back, and the loan repaid.
During this process, they claimed 60,564 ApeCoin airdrops. They then sold them on Uniswap for 399 ETH ($1.1 million). Then they returned the Bored Ape NFT used as collateral to the same NFTX vault.
Attack or arbitrage?
However, security firm BlockSecTeam disagreed with many social media commentators. A flaw in the airdrop-claiming mechanism was exploited, it said.
According to BlockSecTeam's analysis, the user took advantage of a "vulnerability" in the airdrop.
"We suspect a hack due to a flaw in the airdrop mechanism. The attacker exploited this vulnerability to profit from the airdrop claim" said BlockSecTeam.
For example, the airdrop could have taken into account how long a person owned the NFT before claiming the reward.
Because Yuga Labs didn't take a snapshot, anyone could buy the NFT in real time and claim it. This is probably why BAYC sales exploded so soon after the airdrop announcement.