More on Leadership
Will Lockett
3 years ago
Tesla recently disclosed its greatest secret.
The VP has revealed a secret that should frighten the rest of the EV world.
Tesla led the EV revolution. Elon Musk's invention offers a viable alternative to gas-guzzlers. Tesla has lost ground in recent years. VW, BMW, Mercedes, and Ford offer EVs with similar ranges, charging speeds, performance, and cost. Tesla's next-generation 4680 battery pack, Roadster, Cybertruck, and Semi were all delayed. CATL offers superior batteries than the 4680. Martin Viecha, Tesla's Vice President, recently told Business Insider something that startled the EV world and will establish Tesla as the EV king.
Viecha mentioned that Tesla's production costs have dropped 57% since 2017. This isn't due to cheaper batteries or devices like Model 3. No, this is due to amazing factory efficiency gains.
Musk wasn't crazy to want a nearly 100% automated production line, and Tesla's strategy of sticking with one model and improving it has paid off. Others change models every several years. This implies they must spend on new R&D, set up factories, and modernize service and parts systems. All of this costs a ton of money and prevents them from refining production to cut expenses.
Meanwhile, Tesla updates its vehicles progressively. Everything from the backseats to the screen has been enhanced in a 2022 Model 3. Tesla can refine, standardize, and cheaply produce every part without changing the production line.
In 2017, Tesla's automobile production averaged $84,000. In 2022, it'll be $36,000.
Mr. Viecha also claimed that new factories in Shanghai and Berlin will be significantly cheaper to operate once fully operating.
Tesla's hand is visible. Tesla selling $36,000 cars for $60,000 This barely beats the competition. Model Y long-range costs just over $60,000. Tesla makes $24,000+ every sale, giving it a 40% profit margin, one of the best in the auto business.
VW I.D4 costs about the same but makes no profit. Tesla's rivals face similar challenges. Their EVs make little or no profit.
Tesla costs the same as other EVs, but they're in a different league.
But don't forget that the battery pack accounts for 40% of an EV's cost. Tesla may soon fully utilize its 4680 battery pack.
The 4680 battery pack has larger cells and a unique internal design. This means fewer cells are needed for a car, making it cheaper to assemble and produce (per kWh). Energy density and charge speeds increase slightly.
Tesla underestimated the difficulty of making this revolutionary new cell. Each time they try to scale up production, quality drops and rejected cells rise.
Tesla recently installed this battery pack in Model Ys and is scaling production. If they succeed, Tesla battery prices will plummet.
Tesla's Model Ys 2170 battery costs $11,000. The same size pack with 4680 cells costs $3,400 less. Once scaled, it could be $5,500 (50%) less. The 4680 battery pack could reduce Tesla production costs by 20%.
With these cost savings, Tesla could sell Model Ys for $40,000 while still making a profit. They could offer a $25,000 car.
Even with new battery technology, it seems like other manufacturers will struggle to make EVs profitable.
Teslas cost about the same as competitors, so don't be fooled. Behind the scenes, they're still years ahead, and the 4680 battery pack and new factories will only increase that lead. Musk faces a first. He could sell Teslas at current prices and make billions while other manufacturers struggle. Or, he could massively undercut everyone and crush the competition once and for all. Tesla and Elon win.
Bart Krawczyk
3 years ago
Understanding several Value Proposition kinds will help you create better goods.
Fixing problems isn't enough.
Numerous articles and how-to guides on value propositions focus on fixing consumer concerns.
Contrary to popular opinion, addressing customer pain rarely suffices. Win your market category too.
Core Value Statement
Value proposition usually means a product's main value.
Its how your product solves client problems. The product's core.
Answering these questions creates a relevant core value proposition:
What tasks is your customer trying to complete? (Jobs for clients)
How much discomfort do they feel while they perform this? (pains)
What would they like to see improved or changed? (gains)
After that, you create products and services that alleviate those pains and give value to clients.
Value Proposition by Category
Your product belongs to a market category and must follow its regulations, regardless of its value proposition.
Creating a new market category is challenging. Fitting into customers' product perceptions is usually better than trying to change them.
New product users simplify market categories. Products are labeled.
Your product will likely be associated with a collection of products people already use.
Example: IT experts will use your communication and management app.
If your target clients think it's an advanced mail software, they'll compare it to others and expect things like:
comprehensive calendar
spam detectors
adequate storage space
list of contacts
etc.
If your target users view your product as a task management app, things change. You can survive without a contact list, but not status management.
Find out what your customers compare your product to and if it fits your value offer. If so, adapt your product plan to dominate this market. If not, try different value propositions and messaging to put the product in the right context.
Finished Value Proposition
A comprehensive value proposition is when your solution addresses user problems and wins its market category.
Addressing simply the primary value proposition may produce a valuable and original product, but it may struggle to cross the chasm into the mainstream market. Meeting expectations is easier than changing views.
Without a unique value proposition, you will drown in the red sea of competition.
To conclude:
Find out who your target consumer is and what their demands and problems are.
To meet these needs, develop and test a primary value proposition.
Speak with your most devoted customers. Recognize the alternatives they use to compare you against and the market segment they place you in.
Recognize the requirements and expectations of the market category.
To meet or surpass category standards, modify your goods.
Great products solve client problems and win their category.
Aniket
3 years ago
Yahoo could have purchased Google for $1 billion
Let's see this once-dominant IT corporation crumble.
What's the capital of Kazakhstan? If you don't know the answer, you can probably find it by Googling. Google Search returned results for Nur-Sultan in 0.66 seconds.
Google is the best search engine I've ever used. Did you know another search engine ruled the Internet? I'm sure you guessed Yahoo!
Google's friendly UI and wide selection of services make it my top choice. Let's explore Yahoo's decline.
Yahoo!
YAHOO stands for Yet Another Hierarchically Organized Oracle. Jerry Yang and David Filo established Yahoo.
Yahoo is primarily a search engine and email provider. It offers News and an advertising platform. It was a popular website in 1995 that let people search the Internet directly. Yahoo began offering free email in 1997 by acquiring RocketMail.
According to a study, Yahoo used Google Search Engine technology until 2000 and then developed its own in 2004.
Yahoo! rejected buying Google for $1 billion
Larry Page and Sergey Brin, Google's founders, approached Yahoo in 1998 to sell Google for $1 billion so they could focus on their studies. Yahoo denied the offer, thinking it was overvalued at the time.
Yahoo realized its error and offered Google $3 billion in 2002, but Google demanded $5 billion since it was more valuable. Yahoo thought $5 billion was overpriced for the existing market.
In 2022, Google is worth $1.56 Trillion.
What happened to Yahoo!
Yahoo refused to buy Google, and Google's valuation rose, making a purchase unfeasible.
Yahoo started losing users when Google launched Gmail. Google's UI was far cleaner than Yahoo's.
Yahoo offered $1 billion to buy Facebook in July 2006, but Zuckerberg and the board sought $1.1 billion. Yahoo rejected, and Facebook's valuation rose, making it difficult to buy.
Yahoo was losing users daily while Google and Facebook gained many. Google and Facebook's popularity soared. Yahoo lost value daily.
Microsoft offered $45 billion to buy Yahoo in February 2008, but Yahoo declined. Microsoft increased its bid to $47 billion after Yahoo said it was too low, but Yahoo rejected it. Then Microsoft rejected Yahoo’s 10% bid increase in May 2008.
In 2015, Verizon bought Yahoo for $4.5 billion, and Apollo Global Management bought 90% of Yahoo's shares for $5 billion in May 2021. Verizon kept 10%.
Yahoo's opportunity to acquire Google and Facebook could have been a turning moment. It declined Microsoft's $45 billion deal in 2008 and was sold to Verizon for $4.5 billion in 2015. Poor decisions and lack of vision caused its downfall. Yahoo's aim wasn't obvious and it didn't stick to a single domain.
Hence, a corporation needs a clear vision and a leader who can see its future.
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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
Isaac Benson
3 years ago
What's the difference between Proof-of-Time and Proof-of-History?
Blockchain validates transactions with consensus algorithms. Bitcoin and Ethereum use Proof-of-Work, while Polkadot and Cardano use Proof-of-Stake.
Other consensus protocols are used to verify transactions besides these two. This post focuses on Proof-of-Time (PoT), used by Analog, and Proof-of-History (PoH), used by Solana as a hybrid consensus protocol.
PoT and PoH may seem similar to users, but they are actually very different protocols.
Proof-of-Time (PoT)
Analog developed Proof-of-Time (PoT) based on Delegated Proof-of-Stake (DPoS). Users select "delegates" to validate the next block in DPoS. PoT uses a ranking system, and validators stake an equal amount of tokens. Validators also "self-select" themselves via a verifiable random function."
The ranking system gives network validators a performance score, with trustworthy validators with a long history getting higher scores. System also considers validator's fixed stake. PoT's ledger is called "Timechain."
Voting on delegates borrows from DPoS, but there are changes. PoT's first voting stage has validators (or "time electors" putting forward a block to be included in the ledger).
Validators are chosen randomly based on their ranking score and fixed stake. One validator is chosen at a time using a Verifiable Delay Function (VDF).
Validators use a verifiable delay function to determine if they'll propose a Timechain block. If chosen, they validate the transaction and generate a VDF proof before submitting both to other Timechain nodes.
This leads to the second process, where the transaction is passed through 1,000 validators selected using the same method. Each validator checks the transaction to ensure it's valid.
If the transaction passes, validators accept the block, and if over 2/3 accept it, it's added to the Timechain.
Proof-of-History (PoH)
Proof-of-History is a consensus algorithm that proves when a transaction occurred. PoH uses a VDF to verify transactions, like Proof-of-Time. Similar to Proof-of-Work, VDFs use a lot of computing power to calculate but little to verify transactions, similar to (PoW).
This shows users and validators how long a transaction took to verify.
PoH uses VDFs to verify event intervals. This process uses cryptography to prevent determining output from input.
The outputs of one transaction are used as inputs for the next. Timestamps record the inputs' order. This checks if data was created before an event.
PoT vs. PoH
PoT and PoH differ in that:
PoT uses VDFs to select validators (or time electors), while PoH measures time between events.
PoH uses a VDF to validate transactions, while PoT uses a ranking system.
PoT's VDF-elected validators verify transactions proposed by a previous validator. PoH uses a VDF to validate transactions and data.
Conclusion
Both Proof-of-Time (PoT) and Proof-of-History (PoH) validate blockchain transactions differently. PoT uses a ranking system to randomly select validators to verify transactions.
PoH uses a Verifiable Delay Function to validate transactions, verify how much time has passed between two events, and allow validators to quickly verify a transaction without malicious actors knowing the input.
Victoria Kurichenko
3 years ago
What Happened After I Posted an AI-Generated Post on My Website
This could cost you.
Content creators may have heard about Google's "Helpful content upgrade."
This change is another Google effort to remove low-quality, repetitive, and AI-generated content.
Why should content creators care?
Because too much content manipulates search results.
My experience includes the following.
Website admins seek high-quality guest posts from me. They send me AI-generated text after I say "yes." My readers are irrelevant. Backlinks are needed.
Companies copy high-ranking content to boost their Google rankings. Unfortunately, it's common.
What does this content offer?
Nothing.
Despite Google's updates and efforts to clean search results, webmasters create manipulative content.
As a marketer, I knew about AI-powered content generation tools. However, I've never tried them.
I use old-fashioned content creation methods to grow my website from 0 to 3,000 monthly views in one year.
Last year, I launched a niche website.
I do keyword research, analyze search intent and competitors' content, write an article, proofread it, and then optimize it.
This strategy is time-consuming.
But it yields results!
Here's proof from Google Analytics:
Proven strategies yield promising results.
To validate my assumptions and find new strategies, I run many experiments.
I tested an AI-powered content generator.
I used a tool to write this Google-optimized article about SEO for startups.
I wanted to analyze AI-generated content's Google performance.
Here are the outcomes of my test.
First, quality.
I dislike "meh" content. I expect articles to answer my questions. If not, I've wasted my time.
My essays usually include research, personal anecdotes, and what I accomplished and achieved.
AI-generated articles aren't as good because they lack individuality.
Read my AI-generated article about startup SEO to see what I mean.
It's dry and shallow, IMO.
It seems robotic.
I'd use quotes and personal experience to show how SEO for startups is different.
My article paraphrases top-ranked articles on a certain topic.
It's readable but useless. Similar articles abound online. Why read it?
AI-generated content is low-quality.
Let me show you how this content ranks on Google.
The Google Search Console report shows impressions, clicks, and average position.
Low numbers.
No one opens the 5th Google search result page to read the article. Too far!
You may say the new article will improve.
Marketing-wise, I doubt it.
This article is shorter and less comprehensive than top-ranking pages. It's unlikely to win because of this.
AI-generated content's terrible reality.
I'll compare how this content I wrote for readers and SEO performs.
Both the AI and my article are fresh, but trends are emerging.
My article's CTR and average position are higher.
I spent a week researching and producing that piece, unlike AI-generated content. My expert perspective and unique consequences make it interesting to read.
Human-made.
In summary
No content generator can duplicate a human's tone, writing style, or creativity. Artificial content is always inferior.
Not "bad," but inferior.
Demand for content production tools will rise despite Google's efforts to eradicate thin content.
Most won't spend hours producing link-building articles. Costly.
As guest and sponsored posts, artificial content will thrive.
Before accepting a new arrangement, content creators and website owners should consider this.