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.
Florian Wahl
3 years ago
An Approach to Product Strategy
I've been pondering product strategy and how to articulate it. Frameworks helped guide our thinking.
If your teams aren't working together or there's no clear path to victory, your product strategy may not be well-articulated or communicated (if you have one).
Before diving into a product strategy's details, it's important to understand its role in the bigger picture — the pieces that move your organization forward.
the overall picture
A product strategy is crucial, in my opinion. It's part of a successful product or business. It's the showpiece.
To simplify, we'll discuss four main components:
Vision
Product Management
Goals
Roadmap
Vision
Your company's mission? Your company/product in 35 years? Which headlines?
The vision defines everything your organization will do in the long term. It shows how your company impacted the world. It's your organization's rallying cry.
An ambitious but realistic vision is needed.
Without a clear vision, your product strategy may be inconsistent.
Product Management
Our main subject. Product strategy connects everything. It fulfills the vision.
In Part 2, we'll discuss product strategy.
Goals
This component can be goals, objectives, key results, targets, milestones, or whatever goal-tracking framework works best for your organization.
These product strategy metrics will help your team prioritize strategies and roadmaps.
Your company's goals should be unified. This fuels success.
Roadmap
The roadmap is your product strategy's timeline. It provides a prioritized view of your team's upcoming deliverables.
A roadmap is time-bound and includes measurable goals for your company. Your team's steps and capabilities for executing product strategy.
If your team has trouble prioritizing or defining a roadmap, your product strategy or vision is likely unclear.
Formulation of a Product Strategy
Now that we've discussed where your product strategy fits in the big picture, let's look at a framework.
A product strategy should include challenges, an approach, and actions.
Challenges
First, analyze the problems/situations you're solving. It can be customer- or company-focused.
The analysis should explain the problems and why they're important. Try to simplify the situation and identify critical aspects.
Some questions:
What issues are we attempting to resolve?
What obstacles—internal or otherwise—are we attempting to overcome?
What is the opportunity, and why should we pursue it, in your opinion?
Decided Method
Second, describe your approach. This can be a set of company policies for handling the challenge. It's the overall approach to the first part's analysis.
The approach can be your company's bets, the solutions you've found, or how you'll solve the problems you've identified.
Again, these questions can help:
What is the value that we hope to offer to our clients?
Which market are we focusing on first?
What makes us stand out? Our benefit over rivals?
Actions
Third, identify actions that result from your approach. Second-part actions should be these.
Coordinate these actions. You may need to add products or features to your roadmap, acquire new capabilities through partnerships, or launch new marketing campaigns. Whatever fits your challenges and strategy.
Final questions:
What skills do we need to develop or obtain?
What is the chosen remedy? What are the main outputs?
What else ought to be added to our road map?
Put everything together
… and iterate!
Strategy isn't one-and-done. Changes occur. Economies change. Competitors emerge. Customer expectations change.
One unexpected event can make strategies obsolete quickly. Muscle it. Review, evaluate, and course-correct your strategies with your teams. Quarterly works. In a new or unstable industry, more often.
Al Anany
2 years ago
Because of this covert investment that Bezos made, Amazon became what it is today.
He kept it under wraps for years until he legally couldn’t.
His shirt is incomplete. I can’t stop thinking about this…
Actually, ignore the article. Look at it. JUST LOOK at it… It’s quite disturbing, isn’t it?
Ughh…
Me: “Hey, what up?” Friend: “All good, watching lord of the rings on amazon prime video.” Me: “Oh, do you know how Amazon grew and became famous?” Friend: “Geek alert…Can I just watch in peace?” Me: “But… Bezos?” Friend: “Let it go, just let it go…”
I can question you, the reader, and start answering instantly without his consent. This far.
Reader, how did Amazon succeed? You'll say, Of course, it was an internet bookstore, then it sold everything.
Mistaken. They moved from zero to one because of this. How did they get from one to thousand? AWS-some. Understand? It's geeky and lame. If not, I'll explain my geekiness.
Over an extended period of time, Amazon was not profitable.
Business basics. You want customers if you own a bakery, right?
Well, 100 clients per day order $5 cheesecakes (because cheesecakes are awesome.)
$5 x 100 consumers x 30 days Equals $15,000 monthly revenue. You proudly work here.
Now you have to pay the barista (unless ChatGPT is doing it haha? Nope..)
The barista is requesting $5000 a month.
Each cheesecake costs the cheesecake maker $2.5 ($2.5 × 100 x 30 = $7500).
The monthly cost of running your bakery, including power, is about $5000.
Assume no extra charges. Your operating costs are $17,500.
Just $15,000? You have income but no profit. You might make money selling coffee with your cheesecake next month.
Is losing money bad? You're broke. Losing money. It's bad for financial statements.
It's almost a business ultimatum. Most startups fail. Amazon took nine years.
I'm reading Amazon Unbound: Jeff Bezos and the Creation of a Global Empire to comprehend how a company has a $1 trillion market cap.
Many things made Amazon big. The book claims that Bezos and Amazon kept a specific product secret for a long period.
Clouds above the bald head.
In 2006, Bezos started a cloud computing initiative. They believed many firms like Snapchat would pay for reliable servers.
In 2006, cloud computing was not what it is today. I'll simplify. 2006 had no iPhone.
Bezos invested in Amazon Web Services (AWS) without disclosing its revenue. That's permitted till a certain degree.
Google and Microsoft would realize Amazon is heavily investing in this market and worry.
Bezos anticipated high demand for this product. Microsoft built its cloud in 2010, and Google in 2008.
If you managed Google or Microsoft, you wouldn't know how much Amazon makes from their cloud computing service. It's enough. Yet, Amazon is an internet store, so they'll focus on that.
All but Bezos were wrong.
Time to come clean now.
They revealed AWS revenue in 2015. Two things were apparent:
Bezos made the proper decision to bet on the cloud and keep it a secret.
In this race, Amazon is in the lead.
They continued. Let me list some AWS users today.
Netflix
Airbnb
Twitch
More. Amazon was unprofitable for nine years, remember? This article's main graph.
AWS accounted for 74% of Amazon's profit in 2021. This 74% might not exist if they hadn't invested in AWS.
Bring this with you home.
Amazon predated AWS. Yet, it helped the giant reach $1 trillion. Bezos' secrecy? Perhaps, until a time machine is invented (they might host the time machine software on AWS, though.)
Without AWS, Amazon would have been profitable but unimpressive. They may have invested in anything else that would have returned more (like crypto? No? Ok.)
Bezos has business flaws. His success. His failures include:
introducing the Fire Phone and suffering a $170 million loss.
Amazon's failure in China In 2011, Amazon had a about 15% market share in China. 2019 saw a decrease of about 1%.
not offering a higher price to persuade the creator of Netflix to sell the company to him. He offered a rather reasonable $15 million in his proposal. But what if he had offered $30 million instead (Amazon had over $100 million in revenue at the time)? He might have owned Netflix, which has a $156 billion market valuation (and saved billions rather than invest in Amazon Prime Video).
Some he could control. Some were uncontrollable. Nonetheless, every action he made in the foregoing circumstances led him to invest in AWS.
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Thomas Smith
3 years ago
ChatGPT Is Experiencing a Lightbulb Moment
Why breakthrough technologies must be accessible
ChatGPT has exploded. Over 1 million people have used the app, and coding sites like Stack Overflow have banned its answers. It's huge.
I wouldn't have called that as an AI researcher. ChatGPT uses the same GPT-3 technology that's been around for over two years.
More than impressive technology, ChatGPT 3 shows how access makes breakthroughs usable. OpenAI has finally made people realize the power of AI by packaging GPT-3 for normal users.
We think of Thomas Edison as the inventor of the lightbulb, not because he invented it, but because he popularized it.
Going forward, AI companies that make using AI easy will thrive.
Use-case importance
Most modern AI systems use massive language models. These language models are trained on 6,000+ years of human text.
GPT-3 ate 8 billion pages, almost every book, and Wikipedia. It created an AI that can write sea shanties and solve coding problems.
Nothing new. I began beta testing GPT-3 in 2020, but the system's basics date back further.
Tools like GPT-3 are hidden in many apps. Many of the AI writing assistants on this platform are just wrappers around GPT-3.
Lots of online utilitarian text, like restaurant menu summaries or city guides, is written by AI systems like GPT-3. You've probably read GPT-3 without knowing it.
Accessibility
Why is ChatGPT so popular if the technology is old?
ChatGPT makes the technology accessible. Free to use, people can sign up and text with the chatbot daily. ChatGPT isn't revolutionary. It does it in a way normal people can access and be amazed by.
Accessibility isn't easy. OpenAI's Sam Altman tweeted that opening ChatGPT to the public increased computing costs.
Each chat costs "low-digit cents" to process. OpenAI probably spends several hundred thousand dollars a day to keep ChatGPT running, with no immediate business case.
Academic researchers and others who developed GPT-3 couldn't afford it. Without resources to make technology accessible, it can't be used.
Retrospective
This dynamic is old. In the history of science, a researcher with a breakthrough idea was often overshadowed by an entrepreneur or visionary who made it accessible to the public.
We think of Thomas Edison as the inventor of the lightbulb. But really, Vasilij Petrov, Thomas Wright, and Joseph Swan invented the lightbulb. Edison made technology visible and accessible by electrifying public buildings, building power plants, and wiring.
Edison probably lost a ton of money on stunts like building a power plant to light JP Morgan's home, the NYSE, and several newspaper headquarters.
People wanted electric lights once they saw their benefits. By making the technology accessible and visible, Edison unlocked a hugely profitable market.
Similar things are happening in AI. ChatGPT shows that developing breakthrough technology in the lab or on B2B servers won't change the culture.
AI must engage people's imaginations to become mainstream. Before the tech impacts the world, people must play with it and see its revolutionary power.
As the field evolves, companies that make the technology widely available, even at great cost, will succeed.
OpenAI's compute fees are eye-watering. Revolutions are costly.
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
The Verge
3 years ago
Bored Ape Yacht Club creator raises $450 million at a $4 billion valuation.
Yuga Labs, owner of three of the biggest NFT brands on the market, announced today a $450 million funding round. The money will be used to create a media empire based on NFTs, starting with games and a metaverse project.
The team's Otherside metaverse project is an MMORPG meant to connect the larger NFT universe. They want to create “an interoperable world” that is “gamified” and “completely decentralized,” says Wylie Aronow, aka Gordon Goner, co-founder of Bored Ape Yacht Club. “We think the real Ready Player One experience will be player run.”
Just a few weeks ago, Yuga Labs announced the acquisition of CryptoPunks and Meebits from Larva Labs. The deal brought together three of the most valuable NFT collections, giving Yuga Labs more IP to work with when developing games and metaverses. Last week, ApeCoin was launched as a cryptocurrency that will be governed independently and used in Yuga Labs properties.
Otherside will be developed by “a few different game studios,” says Yuga Labs CEO Nicole Muniz. The company plans to create development tools that allow NFTs from other projects to work inside their world. “We're welcoming everyone into a walled garden.”
However, Yuga Labs believes that other companies are approaching metaverse projects incorrectly, allowing the startup to stand out. People won't bond spending time in a virtual space with nothing going on, says Yuga Labs co-founder Greg Solano, aka Gargamel. Instead, he says, people bond when forced to work together.
In order to avoid getting smacked, Solano advises making friends. “We don't think a Zoom chat and walking around saying ‘hi' creates a deep social experience.” Yuga Labs refused to provide a release date for Otherside. Later this year, a play-to-win game is planned.
The funding round was led by Andreessen Horowitz, a major investor in the Web3 space. It previously backed OpenSea and Coinbase. Animoca Brands, Coinbase, and MoonPay are among those who have invested. Andreessen Horowitz general partner Chris Lyons will join Yuga Labs' board. The Financial Times broke the story last month.
"META IS A DOMINANT DIGITAL EXPERIENCE PROVIDER IN A DYSTOPIAN FUTURE."
This emerging [Web3] ecosystem is important to me, as it is to companies like Meta,” Chris Dixon, head of Andreessen Horowitz's crypto arm, tells The Verge. “In a dystopian future, Meta is the dominant digital experience provider, and it controls all the money and power.” (Andreessen Horowitz co-founder Marc Andreessen sits on Meta's board and invested early in Facebook.)
Yuga Labs has been profitable so far. According to a leaked pitch deck, the company made $137 million last year, primarily from its NFT brands, with a 95% profit margin. (Yuga Labs declined to comment on deck figures.)
But the company has built little so far. According to OpenSea data, it has only released one game for a limited time. That means Yuga Labs gets hundreds of millions of dollars to build a gaming company from scratch, based on a hugely lucrative art project.
Investors fund Yuga Labs based on its success. That's what they did, says Dixon, “they created a culture phenomenon”. But ultimately, the company is betting on the same thing that so many others are: that a metaverse project will be the next big thing. Now they must construct it.