More on Leadership

Nir Zicherman
3 years ago
The Great Organizational Conundrum
Only two of the following three options can be achieved: consistency, availability, and partition tolerance
Someone told me that growing from 30 to 60 is the biggest adjustment for a team or business.
I remember thinking, That's random. Each company is unique. I've seen teams of all types confront the same issues during development periods. With new enterprises starting every year, we should be better at navigating growing difficulties.
As a team grows, its processes and systems break down, requiring reorganization or declining results. Why always? Why isn't there a perfect scaling model? Why hasn't that been found?
The Three Things Productive Organizations Must Have
Any company should be efficient and productive. Three items are needed:
First, it must verify that no two team members have conflicting information about the roadmap, strategy, or any input that could affect execution. Teamwork is required.
Second, it must ensure that everyone can receive the information they need from everyone else quickly, especially as teams become more specialized (an inevitability in a developing organization). It requires everyone's accessibility.
Third, it must ensure that the organization can operate efficiently even if a piece is unavailable. It's partition-tolerant.
From my experience with the many teams I've been on, invested in, or advised, achieving all three is nearly impossible. Why a perfect organization model cannot exist is clear after analysis.
The CAP Theorem: What is it?
Eric Brewer of Berkeley discovered the CAP Theorem, which argues that a distributed data storage should have three benefits. One can only have two at once.
The three benefits are consistency, availability, and partition tolerance, which implies that even if part of the system is offline, the remainder continues to work.
This notion is usually applied to computer science, but I've realized it's also true for human organizations. In a post-COVID world, many organizations are hiring non-co-located staff as they grow. CAP Theorem is more important than ever. Growing teams sometimes think they can develop ways to bypass this law, dooming themselves to a less-than-optimal team dynamic. They should adopt CAP to maximize productivity.
Path 1: Consistency and availability equal no tolerance for partitions
Let's imagine you want your team to always be in sync (i.e., for someone to be the source of truth for the latest information) and to be able to share information with each other. Only division into domains will do.
Numerous developing organizations do this, especially after the early stage (say, 30 people) when everyone may wear many hats and be aware of all the moving elements. After a certain point, it's tougher to keep generalists aligned than to divide them into specialized tasks.
In a specialized, segmented team, leaders optimize consistency and availability (i.e. every function is up-to-speed on the latest strategy, no one is out of sync, and everyone is able to unblock and inform everyone else).
Partition tolerance suffers. If any component of the organization breaks down (someone goes on vacation, quits, underperforms, or Gmail or Slack goes down), productivity stops. There's no way to give the team stability, availability, and smooth operation during a hiccup.
Path 2: Partition Tolerance and Availability = No Consistency
Some businesses avoid relying too heavily on any one person or sub-team by maximizing availability and partition tolerance (the organization continues to function as a whole even if particular components fail). Only redundancy can do that. Instead of specializing each member, the team spreads expertise so people can work in parallel. I switched from Path 1 to Path 2 because I realized too much reliance on one person is risky.
What happens after redundancy? Unreliable. The more people may run independently and in parallel, the less anyone can be the truth. Lack of alignment or updated information can lead to people executing slightly different strategies. So, resources are squandered on the wrong work.
Path 3: Partition and Consistency "Tolerance" equates to "absence"
The third, least-used path stresses partition tolerance and consistency (meaning answers are always correct and up-to-date). In this organizational style, it's most critical to maintain the system operating and keep everyone aligned. No one is allowed to read anything without an assurance that it's up-to-date (i.e. there’s no availability).
Always short-lived. In my experience, a business that prioritizes quality and scalability over speedy information transmission can get bogged down in heavy processes that hinder production. Large-scale, this is unsustainable.
Accepting CAP
When two puzzle pieces fit, the third won't. I've watched developing teams try to tackle these difficulties, only to find, as their ancestors did, that they can never be entirely solved. Idealized solutions fail in reality, causing lost effort, confusion, and lower production.
As teams develop and change, they should embrace CAP, acknowledge there is a limit to productivity in a scaling business, and choose the best two-out-of-three path.

The woman
3 years ago
Why Google's Hiring Process is Brilliant for Top Tech Talent
Without a degree and experience, you can get a high-paying tech job.
Most organizations follow this hiring rule: you chat with HR, interview with your future boss and other senior managers, and they make the final hiring choice.
If you've ever applied for a job, you know how arduous it can be. A newly snapped photo and a glossy resume template can wear you out. Applying to Google can change this experience.
According to an Universum report, Google is one of the world's most coveted employers. It's not simply the search giant's name and reputation that attract candidates, but its role requirements or lack thereof.
Candidates no longer need a beautiful resume, cover letter, Ivy League laurels, or years of direct experience. The company requires no degree or experience.
Elon Musk started it. He employed the two-hands test to uncover talented non-graduates. The billionaire eliminated the requirement for experience.
Google is deconstructing traditional employment with programs like the Google Project Management Degree, a free online and self-paced professional credential course.
Google's hiring is interesting. After its certification course, applicants can work in project management. Instead of academic degrees and experience, the company analyzes coursework.
Google finds the best project managers and technical staff in exchange. Google uses three strategies to find top talent.
Chase down the innovators
Google eliminates restrictions like education, experience, and others to find the polar bear amid the snowfall. Google's free project management education makes project manager responsibilities accessible to everyone.
Many jobs don't require a degree. Overlooking individuals without a degree can make it difficult to locate a candidate who can provide value to a firm.
Firsthand knowledge follows the same rule. A lack of past information might be an employer's benefit. This is true for creative teams or businesses that prefer to innovate.
Or when corporations conduct differently from the competition. No-experience candidates can offer fresh perspectives. Fast Company reports that people with no sales experience beat those with 10 to 15 years of experience.
Give the aptitude test first priority.
Google wants the best candidates. Google wouldn't be able to receive more applications if it couldn't screen them for fit. Its well-organized online training program can be utilized as a portfolio.
Google learns a lot about an applicant through completed assignments. It reveals their ability, leadership style, communication capability, etc. The course mimics the job to assess candidates' suitability.
Basic screening questions might provide information to compare candidates. Any size small business can use screening questions and test projects to evaluate prospective employees.
Effective training for employees
Businesses must train employees regardless of their hiring purpose. Formal education and prior experience don't guarantee success. Maintaining your employees' professional knowledge gaps is key to their productivity and happiness. Top-notch training can do that. Learning and development are key to employee engagement, says Bob Nelson, author of 1,001 Ways to Engage Employees.
Google's online certification program isn't available everywhere. Improving the recruiting process means emphasizing aptitude over experience and a degree. Instead of employing new personnel and having them work the way their former firm trained them, train them how you want them to function.
If you want to know more about Google’s recruiting process, we recommend you watch the movie “Internship.”

Sean Bloomfield
3 years ago
How Jeff Bezos wins meetings over
We've all been there: You propose a suggestion to your team at a meeting, and most people appear on board, but a handful or small minority aren't. How can we achieve collective buy-in when we need to go forward but don't know how to deal with some team members' perceived intransigence?
Steps:
Investigate the divergent opinions: Begin by sincerely attempting to comprehend the viewpoint of your disagreeing coworkers. Maybe it makes sense to switch horses in the middle of the race. Have you completely overlooked a blind spot, such as a political concern that could arise as an unexpected result of proceeding? This is crucial to ensure that the person or people feel heard as well as to advance the goals of the team. Sometimes all individuals need is a little affirmation before they fully accept your point of view.
It says a lot about you as a leader to be someone who always lets the perceived greatest idea win, regardless of the originating channel, if after studying and evaluating you see the necessity to align with the divergent position.
If, after investigation and assessment, you determine that you must adhere to the original strategy, we go to Step 2.
2. Disagree and Commit: Jeff Bezos, CEO of Amazon, has had this experience, and Julie Zhuo describes how he handles it in her book The Making of a Manager.
It's OK to disagree when the team is moving in the right direction, but it's not OK to accidentally or purposefully damage the team's efforts because you disagree. Let the team know your opinion, but then help them achieve company goals even if they disagree. Unknown. You could be wrong in today's ever-changing environment.
So next time you have a team member who seems to be dissenting and you've tried the previous tactics, you may ask the individual in the meeting I understand you but I don't want us to leave without you on board I need your permission to commit to this approach would you give us your commitment?
You might also like

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.

Yuga Labs
3 years ago
Yuga Labs (BAYC and MAYC) buys CryptoPunks and Meebits and gives them commercial rights
Yuga has acquired the CryptoPunks and Meebits NFT IP from Larva Labs. These include 423 CryptoPunks and 1711 Meebits.
We set out to create in the NFT space because we admired CryptoPunks and the founders' visionary work. A lot of their work influenced how we built BAYC and NFTs. We're proud to lead CryptoPunks and Meebits into the future as part of our broader ecosystem.
"Yuga Labs invented the modern profile picture project and are the best in the world at operating these projects. They are ideal CrytoPunk and Meebit stewards. We are confident that in their hands, these projects will thrive in the emerging decentralized web.”
–The founders of Larva Labs, CryptoPunks, and Meebits
This deal grew out of discussions between our partner Guy Oseary and the Larva Labs founders. One call led to another, and now we're here. This does not mean Matt and John will join Yuga. They'll keep running Larva Labs and creating awesome projects that help shape the future of web3.
Next steps
Here's what we plan to do with CryptoPunks and Meebits now that we own the IP. Owners of CryptoPunks and Meebits will soon receive commercial rights equal to those of BAYC and MAYC holders. Our legal teams are working on new terms and conditions for both collections, which we hope to share with the community soon. We expect a wide range of third-party developers and community creators to incorporate CryptoPunks and Meebits into their web3 projects. We'll build the brand alongside them.
We don't intend to cram these NFT collections into the BAYC club model. We see BAYC as the hub of the Yuga universe, and CryptoPunks as a historical collection. We will work to improve the CryptoPunks and Meebits collections as good stewards. We're not in a hurry. We'll consult the community before deciding what to do next.
For us, NFTs are about culture. We're deeply invested in the BAYC community, and it's inspiring to see them grow, collaborate, and innovate. We're excited to see what CryptoPunks and Meebits do with IP rights. Our goal has always been to create a community-owned brand that goes beyond NFTs, and now we can include CryptoPunks and Meebits.

Vitalik
4 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
