TheDinarian
News • Business • Investing & Finance
Quantum computers and the Bitcoin blockchain
An analysis of the impact quantum computers might have on the Bitcoin blockchain
April 25, 2024
post photo preview

To be fully 100% transparent with you all, I am not and refuse to invest in any Bitcoin. As a matter of fact I am probably one of the most Anti-Bitcoin individuals you will ever meet. This is mainly due to the below and the simple fact that it is overmarketed by the very institutions it is supposed to be against.  ~The Dinarian

One of the most well-known applications of quantum computers is breaking the mathematical difficulty underlying most of currently used cryptography. Since Google announced that it achieved quantum supremacy there has been an increasing number of articles on the web predicting the demise of currently used cryptography in general, and Bitcoin in particular. The goal of this article is to present a balanced view regarding the risks that quantum computers pose to Bitcoin.

The main focus of this article will be to answer the following questions:

  1. How many Bitcoins could be stolen now if a sufficiently large quantum computer was available?
  2. What can one do to mitigate the risk of Bitcoins being stolen by an adversary with a quantum computer?
  3. Is the Bitcoin blockchain inherently resilient to quantum attacks now and in the future?
 

Quantum computers and cryptography

A great amount of digital ink has been spilled on the topic of how quantum computers pose an existential threat to currently used asymmetric cryptography. We will therefore not discuss this in detail, but only explain the aspects that are relevant for the analysis in this article.

In asymmetric cryptography, a private-public key pair is generated in such a manner that the two keys have a mathematical relation between them. As the name suggests, the private key is kept as secret, while the public key is made publicly available. This allows individuals to produce a digital signature (using their private key) that can be verified by anyone who has the corresponding public key. This scheme is very common in the financial industry to prove authenticity and integrity of transactions.

The security of asymmetric cryptography is based on a mathematical principle called a “one-way function”. This principle dictates that the public key can be easily derived from the private key but not the other way around. All known (classical) algorithms to derive the private key from the public key require an astronomical amount of time to perform such a computation and are therefore not practical. However, in 1994, the mathematician Peter Shor published a quantum algorithm that can break the security assumption of the most common algorithms of asymmetric cryptography. This means that anyone with a sufficiently large quantum computer could use this algorithm to derive a private key from its corresponding public key, and thus, falsify any digital signature.

 

Bitcoin 101

To understand the impact of quantum computers on Bitcoin, we will start with a brief summary about how Bitcoin transactions work. Bitcoin is a decentralized system for transferring value. Unlike the banking system where it is the responsibility of a bank to provide customers with a bank account, a Bitcoin user is responsible for generating his own (random) address. By means of a simple procedure, the user's computer calculates a random Bitcoin address (related to the public key) as well as a secret (private key) that is required in order to perform transactions from this address.

Moving Bitcoins from one address to another is called a transaction. Such a transaction is similar to sending money from one bank account to another. In Bitcoin, the sender must authorize their transaction by providing a digital signature that proves they own the address where the funds are stored. Remember: someone with an operational quantum computer who has your public key could falsify this signature, and therefore potentially spend anyone’s Bitcoins!

In the Bitcoin network, the decision of which transactions are accepted into the network is ultimately left to the so called miners. Miners compete in a race to process the next batch of transactions, also called a block. Whoever wins the race, is allowed to construct the next block, awarding them new coins as they do so. Bitcoin blocks are linked to each other in a sequential manner. Together, they form a chain of blocks, also called the “blockchain”.

The victorious miner who creates a new block, is free to include whichever transaction they wish. Other miners express their agreement by building on top of blocks they agree with. In case of a disagreement, they will build on the most recently accepted block. In other words, if a rogue miner attempts to construct an invalid block, honest miners will ignore the invalid block and build on top of the most recent valid block instead.

 

Address types

Bitcoin transactions allow for a custom logic to be implemented, enabling a myriad of financial transaction types such as escrow and shared ownership. However, for the purpose of this article, we restrict ourselves to simple person-to-person payments. These can be divided into 2 categories, each affected differently by a quantum computer.

In the first type, a public key directly serves as the Bitcoin address of the recipient. A transaction to such an address is called ‘pay to public key’ (p2pk) for obvious reasons. In the early days of Bitcoin, in 2009, this was the dominant address type. Many of the original coins mined by Satoshi Nakamoto himself are still stored in such addresses. One of the issues with these addresses is the lack of a mechanism to detect mistyping of addresses (for example a last checksum digit which is used, for example, in credit card numbers). An additional problem is that these addresses are very long, which results in a larger transaction file and therefore longer processing time. Regarding the threat from a quantum computer, the public key is directly obtainable from the address. Since all transactions in Bitcoin are public, anyone can obtain the public key from any p2pk address. A quantum computer running Shor’s algorithm could then be used to derive the private key from this address. This would allow an adversary who has a quantum computer to spend the coins that the address had.

In the second type of transaction, the address of the recipient is composed of a hash of the public key. As a hash is a one-way cryptographic function, the public key is not directly revealed by the address. The first and most popular implementation of this is called ‘pay to public key hash’ (p2pkh) and was designed to solve the two issues described above (checksum and address length, for a more elaborate explanation we refer to this page. As was mentioned above, the public key cannot be retrieved from the address. The public key is only revealed at the moment when the owner wishes to initiate a transaction. This means that as long as funds have never been transferred from a p2pkh address, the public key is not known and the private key cannot be derived using a quantum computer. There is a ‘but’ though! If funds are ever transferred from a specific p2pkh address (no matter what amount), the public key is revealed. From that moment on, this address is marked "used" and should ideally not be used again to receive new coins. In fact, many wallets are programmed to avoid address reuse as best they can. Avoiding the reuse of addresses is considered best practice for Bitcoin users, but you would be surprised how many people do not take this advice to heart. More on that in the following chapter.

 

How many Bitcoins could be stolen now if sufficiently large quantum computers were available?

Imagine that someone manages to build a quantum computer today and is therefore able to derive private keys. How many Bitcoins will be in danger?

To answer this question, we analyzed the entire Bitcoin blockchain to identify which coins are vulnerable to an attack from a quantum computer. As explained in the previous section, all coins in p2pk addresses and reused p2pkh addresses are vulnerable to a quantum attack. The result of our analysis is presented in the figure below. It shows the distribution of Bitcoins in the various address types over time. As can clearly be seen in the graph, p2pk addresses dominated the Bitcoin blockchain in the first year of its existence. Interestingly, the number of coins in p2pk addresses has stayed practically constant (circa 2M Bitcoins). A reasonable assumption is that these coins were generated through mining and have never been moved from their original address.

As p2pkh was introduced 2010, it quickly became dominant. Most of the coins created since then are stored in this type of address. In the graph we see that the number of Bitcoins stored in reused p2pkh increases from 2010 to 2014, and since then is decreasing slowly to reach the current amount of 2.5M Bitcoins. This suggests that people are generally following the best practice of not using p2pk address as well as not reusing p2pkh addresses. Nevertheless, there are still over 4 million BTC (about 25% of all Bitcoins) which are potentially vulnerable to a quantum attack. At the current price this is over 40 billion USD!

cq5dam.web.700.350.desktop.jpegFigure 1: The distribution of Bitcoins that are stored in address that are vulnerable to quantum attacks. This graph shows that about 25% of all Bitcoins are vulnerable to a quantum attack and that there is an equal number of vulnerable p2pk and p2pkh coins. Note that reused Segwit coins are presented in the graph but are otherwise not mentioned in the article.
 

What can one do to mitigate the risk of Bitcoins being stolen by an adversary with a quantum computer?

In the previous section we explained that p2pk and reused p2pkh addresses are vulnerable to quantum attacks. However, p2pkh addresses that have never been used to spend Bitcoins are safe, as their public keys are not yet public. This means that if you transfer your Bitcoins to a new p2pkh address, then they should not be vulnerable to a quantum attack.

The issue with this approach is that many owners of vulnerable Bitcoins have lost their private keys. These coins cannot be transferred and are waiting to be taken by the first person who manages to build a sufficiently large quantum computer. A way to address this issue is to come to a consensus within the Bitcoin community and provide an ultimatum for people to move their coins to a safe address. After a predefined period, coins in unsafe addresses would become unusable (technically, this means that miner will ignore transactions coming from these addresses). Such a drastic step needs to be considered carefully before implemented, not to mention the complexity of achieving consensus about such a sensitive issue.

 

Is the Bitcoin blockchain inherently resilient to quantum attacks now and in the future?

Let’s assume for a minute that all owners of vulnerable Bitcoins transfer their funds to safe addresses (everyone who lost their private key ‘magically’ finds them). Does that mean that the Bitcoin blockchain is no longer vulnerable to quantum attacks? The answer to this question is actually not that simple. The prerequisite of being “quantum safe” is that the public key associated with this address is not public. But as we explained above, the moment you want to transfer coins from such a “safe” address, you also reveal the public key, making the address vulnerable. From that moment until your transaction is “mined”, an attacker who possesses a quantum computer gets a window of opportunity to steal your coins. In such an attack, the adversary will first derive your private key from the public key and then initiate a competing transaction to their own address. They will try to get priority over the original transaction by offering a higher mining fee.

In the Bitcoin blockchain it currently takes about 10 minutes for transactions to be mined (unless the network is congested which has happened frequently in the past). As long as it takes a quantum computer longer to derive the private key of a specific public key then the network should be safe against a quantum attack. Current scientific estimations predict that a quantum computer will take about 8 hours to break an RSA key, and some specific calculations predict that a Bitcoin signature could be hacked within 30 minutes. This means that Bitcoin should be, in principle, resistant to quantum attacks (as long as you do not reuse addresses). However, as the field of quantum computers is still in its infancy, it is unclear how fast such a quantum computer will become in the future. If a quantum computer will ever get closer to the 10 minutes mark to derive a private key from its public key, then the Bitcoin blockchain will be inherently broken.

 

Closing remarks

Quantum computers are posing a serious challenge to the security of the Bitcoin blockchain. Presently, about 25% of the Bitcoins in circulation are vulnerable to a quantum attack. If you have Bitcoins in a vulnerable address and believe that progress in quantum computing is more advanced than publicly known, then you should probably transfer your coins to a new p2pkh address (don’t forget to make a secure backup of your private key).

In case your own Bitcoins are safe in a new p2pkh address, you might still be impacted if many people will not (or cannot) take the same protection measures. In a situation where a large number of Bitcoins is stolen, the price will most likely crash and the confidence in the technology will be lost.

Even if everyone takes the same protection measures, quantum computers might eventually become so fast that they will undermine the Bitcoin transaction process. In this case the security of the Bitcoin blockchain will be fundamentally broken. The only solution in this case is to transition to a new type of cryptography called ‘post-quantum cryptography’, which is considered to be inherently resistant to quantum attacks. These types of algorithms present other challenges to the usability of blockchains and are being investigated by cryptographers around the world. We anticipate that future research into post-quantum cryptography will eventually bring the necessary change to build robust and future-proof blockchain applications.

Link

community logo
Join the TheDinarian Community
To read more articles like this, sign up and join my community today
0
What else you may like…
Videos
Podcasts
Posts
Articles
🤖 AI Won't Destroy Jobs—It Will Create a Labor Shortage! 📉

While most headlines focus on AI-driven displacement, Groq Founder and CEO Jonathan Ross offers a fascinating, contrarian perspective. He argues that instead of mass unemployment, we are heading toward a massive labor shortage driven by three tectonic shifts:

1. Massive Deflationary Pressure: Efficiency gains from automated farming, robotics, and streamlined supply chains will drive down the cost of everyday essentials—from coffee to housing—meaning people will ultimately need less money to thrive. ☕️🏠

2. The Great Economic Opt-Out: As living costs drop and productivity skyrockets, humans will choose to work fewer hours, fewer days a week, and retire much earlier because their lifestyles will be easier to support. ⏳🌴

3. Unimaginable New Industries: Just as agriculture dropped from 98% of the US workforce a century ago to just 2%—paving the way for entirely new careers like software development and content creation—tomorrow's jobs are literally ...

00:02:00
🔵 The most important shape nobody talks about 🔵

Heinz Hopf discovered this in 1931. Roger Penrose called it "an element of the architecture of our world.' Eric Weinstein brought it up on Joe Rogan - and the silence in the room said everything.
The Hopf fibration maps a 4D hypersphere onto a regular sphere using circles that never intersect but each links through every other exactly once. It shows up in at least 8 areas of physics - including the Bloch sphere geometry that every qubit in a quantum computer lives on.

00:09:42
🌐 Institutional Access Meets Dcentralized AI! 🤖📈

The bridge between traditional capital markets and the decentralized AI economy is expanding. Through investment vehicles like the Grayscale Bittensor Trust ($GTAO), traditional market participants gain regulated exposure to the native asset powering decentralized machine learning.

No wallet setups, no complex custody hurdles—just direct tracking of the infrastructure driving open-source intelligence.👇

00:05:18
🚨 Chutes is being framed as a Hyperliquid-style breakout for decentralized AI inference, with live revenue, verified GPU infrastructure, and a direct challenge to centralized cloud AI 🚨

Chutes is gaining attention as a decentralized AI inference platform that claims to combine real usage, cryptographic verification, confidential computing, and open-source infrastructure into a working production system. The thesis is simple: instead of trusting Big Tech clouds with AI workloads, users get a distributed compute layer built around verification and privacy.

🔑 Key points

🔹 Chutes is live in production and reportedly scaled to more than 1,170 active GPU nodes, including large numbers of Nvidia H200s and Blackwell-class hardware.

🔹 The platform says it has processed nearly 38 trillion tokens since launch across 53 deployed applications and more than 700,000 registered users.

🔹 The team reportedly cut unprofitable usage programs, reduced total token volume, and still improved revenue efficiency, with revenue per GPU rising sharply after removing subsidized traffic.

🔹 Chutes is using post-quantum cryptography, trusted execution environments, and Nvidia confidential ...

🚨 Chutes is being framed as a Hyperliquid-style breakout for decentralized AI inference, with live revenue, verified GPU infrastructure, and a direct challenge to centralized cloud AI 🚨
🚨 JPMorgan’s criticism of the CLARITY Act is fueling a fresh power struggle over who gets to write America’s crypto rules 🚨

A new clash is emerging between legacy finance and crypto legislation after JPMorgan CEO Jamie Dimon reportedly warned that the CLARITY Act could let crypto firms offer bank-like products without bank-level oversight. The dispute is quickly turning into a larger fight over regulation, competitiveness, and who controls the future architecture of digital finance in the United States.

🔑 Key points

🔹 Jamie Dimon reportedly called the CLARITY Act a threat to the financial system, arguing it could allow crypto firms to offer yield-like products while avoiding the capital, reserve, and oversight burdens traditional banks face.

🔹 Senator Cynthia Lummis pushed back publicly, framing the issue as a global strategic race and warning that if the U.S. does not set digital asset standards, other powers will.

🔹 The core tension is whether the bill creates legitimate regulatory clarity or simply opens the door to regulatory arbitrage for crypto platforms operating outside the traditional banking...

🚨 JPMorgan’s criticism of the CLARITY Act is fueling a fresh power struggle over who gets to write America’s crypto rules 🚨
👉 Coinbase just launched an AI agent for Crypto Trading

Custom AI assistants that print money in your sleep? 🔜

The future of Crypto x AI is about to go crazy.

👉 Here’s what you need to know:

💠 'Based Agent' enables creation of custom AI agents
💠 Users set up personalized agents in < 3 minutes
💠 Equipped w/ crypto wallet and on-chain functions
💠 Capable of completing trades, swaps, and staking
💠 Integrates with Coinbase’s SDK, OpenAI, & Replit

👉 What this means for the future of Crypto:

1. Open Access: Democratized access to advanced trading
2. Automated Txns: Complex trades + streamlined on-chain activity
3. AI Dominance: Est ~80% of crypto 👉txns done by AI agents by 2025

🚨 I personally wouldn't bet against Brian Armstrong and Jesse Pollak.

👉 Coinbase just launched an AI agent for Crypto Trading
🚨 BREAKING: XRP JUST GOT PLUGGED DIRECTLY INTO THE U.S. FEDERAL RESERVE’S FEDNOW SYSTEM 😳

Volante’s Ripple integration just unlocked $XRP for INSTANT FedNow payments.
Banks can now settle through XRP on the same rails the Fed uses for 24/7 real-time transfers.

This is the quiet infrastructure move nobody saw coming… until now.

The bridge is LIVE.

https://x.com/pumpius/status/2091584402107314186

Irrespective of which tokens are utilized on the XRP Ledger for FedNow transactions, the underlying mechanism that burns XRP remains constant. 🔥 This sustained reduction in supply underpins a long-term bullish thesis for XRP holders. 📈📈🚀

post photo preview

💰 Justin Sun reportedly says he would buy Ethereum with all his assets during market weakness 💰

TRON founder Justin Sun has reportedly expressed extreme confidence in Ethereum, suggesting he would allocate his entire portfolio to ETH. The statement reflects strong conviction—but also highlights the danger of concentrated crypto exposure.

🔑 Key points

🔹 Ethereum is the target: Sun’s comments reportedly focus on ETH as the altcoin with the strongest long-term potential.

🔹 The statement came during volatility: Ethereum has faced sharp price swings, weak sentiment, and uncertainty across the broader crypto market.

🔹 Ethereum remains core infrastructure: The network supports DeFi, stablecoins, tokenized assets, NFTs, and thousands of decentralized applications.

🔹 Institutional interest continues: Spot ETFs, staking products, and tokenization initiatives are keeping Ethereum in focus among traditional financial institutions.

🔹 Network activity is mixed: Ethereum’s long-term development ...

🌏 Ant International and Bank of China Hong Kong partner to strengthen cross-border payments 🌏

Ant International and Bank of China Hong Kong are working to improve payment connectivity between Hong Kong and overseas markets, with a focus on faster settlement, broader merchant access, and smoother international transactions.

🔑 Key points

🔹 Cross-border connectivity: The partnership aims to link Ant International’s payment network with Bank of China Hong Kong’s banking infrastructure.

🔹 Faster settlement: The cooperation could reduce delays associated with traditional correspondent banking and manual reconciliation.

🔹 Merchant expansion: Businesses may gain easier access to international customers using digital wallets and mobile-payment platforms.

🔹 Greater payment interoperability: The initiative is designed to help different payment systems work together across borders.

🔹 Hong Kong remains a strategic hub: The city serves as a bridge between mainland China, Asia, and ...

post photo preview
post photo preview
🤖Can Decentralized AI Stop Big Tech from Owning the Future of Robotics?🤖
The race to build the future of robotics is no longer just about robots. It's about who controls the intelligence behind them.
 
Over the last three years, a small group of companies has emerged as the backbone of the AI revolution. Microsoft provides cloud infrastructure. NVIDIA supplies the chips. Google, OpenAI, Anthropic, Meta, and others develop the models. Together, they control much of the compute, data, and software stack powering modern AI.
 
Now that AI is moving into the physical world, many are asking a bigger question:
 
Will these same companies end up controlling robotics too?
 
It's a valid concern.
 
The latest generation of robots relies on enormous amounts of compute, simulation, training data, and foundation models. Many robotics startups today are built on infrastructure provided by large technology companies. NVIDIA's Omniverse is becoming a key simulation environment for robot training. Microsoft Azure is powering the training of robotics foundation models. Physical AI startups increasingly depend on hyperscale cloud infrastructure to train and deploy intelligent systems. Recent partnerships across the industry show just how central Big Tech has become to robotics development.
But while Big Tech is building the highways, another movement is trying to ensure it doesn't own every destination.
 
That movement is decentralized AI.
 
Why Decentralized AI Exists
 
The idea behind decentralized AI is simple. Instead of a handful of companies owning the models, compute infrastructure, data pipelines, and intelligence networks, these resources are distributed across thousands of participants.
 
This means anyone can contribute compute, contribute models, validate outputs and can participate.
The most visible example today is the decentralized AI network known as Bittensor (@bittensor). The network has evolved into a large ecosystem of specialized AI markets called subnets, where participants compete to provide useful machine intelligence and are rewarded based on performance. Rather than relying on a single company, intelligence is generated and validated by a distributed network of miners and validators.
 
Think of it as an attempt to build an open marketplace for AI instead of a world where intelligence is rented from a few centralized providers.
 
Why This Matters for Robotics
 
Robotics has a unique problem. Unlike chatbots, robots operate in the physical world. They need to perceive environments, make decisions, move safely and they need to learn continuously.
 
The challenge is that collecting and training on real-world robotic data is incredibly expensive. That's one reason large companies have such an advantage. They can afford the compute, simulation environments, and data infrastructure needed to train robotics models at scale.
 
This is where decentralized systems become interesting.
 
Instead of one company collecting all the data and training all the models, decentralized networks could allow thousands of contributors to participate in building robotic intelligence.
 
Imagine a future where:
  • Warehouse robots contribute operational data.
  • Delivery robots contribute navigation data.
  • Factory robots contribute manipulation data.
  • Developers contribute models.
  • Validators evaluate performance.
The resulting intelligence becomes a shared network rather than a proprietary asset.
 
That vision is beginning to emerge.
 
Bittensor's Move Toward Physical AI
 
While many people associate Bittensor (@bittensor) with language models and AI services, parts of the ecosystem are increasingly exploring embodied intelligence and robotics.
 
One example is Kinitro, a subnet focused on incentivizing the training and evaluation of embodied AI systems. The goal is to create competitive environments where developers build robotic intelligence and are rewarded based on performance.
 
The broader Bittensor ecosystem has also expanded into compute marketplaces, distributed inference systems, bandwidth infrastructure, and AI coordination layers that could eventually support robotics workloads. Several subnets now focus on decentralized compute, confidential inference, data transfer, and model training, critical components for future robotic systems.
 
In other words, the pieces are starting to appear.
 
Not a decentralized robot network yet.
 
But the infrastructure that could support one.
 
Beyond Bittensor: The Rise of Physical AI Networks
 
Bittensor isn't alone.
 
Across the industry, researchers and builders are experimenting with decentralized approaches to physical AI.
 
New research published in 2026 introduced the concept of DAO-enabled decentralized physical AI, or DePAI. The idea combines robotics, decentralized infrastructure, AI models, governance systems, and human oversight into a single framework. Instead of centralized control, robots and physical infrastructure could be coordinated through transparent rules and distributed ownership models.
 
At the same time, developers are exploring decentralized operating systems for robots that allow machines to communicate directly with each other and with distributed compute resources. These architectures are designed to make robotic systems more resilient and less dependent on a single cloud provider.
 
The goal is not simply decentralization for its own sake.
 
The goal is resilience.
 
If one server fails, the system continues.
 
If one company disappears, the network survives.
 
If one participant leaves, innovation continues.
 
But Here's the Reality
 
Decentralized AI faces the same challenge every decentralized technology faces.
 
Big Tech has resources. A lot of resources.
 
Training advanced robotics models requires enormous compute budgets, sophisticated simulation environments, access to specialized hardware, and vast amounts of real-world data.
 
That's why many robotics startups still partner with major cloud providers and AI companies. It's often the fastest path to deployment.
 
And there are legitimate concerns about whether decentralized networks can maintain quality, reliability, and security at the scale required for industrial robotics. Even researchers studying decentralized AI systems have highlighted risks around concentration, incentives, governance, and network security.
 
The challenge isn't just decentralizing intelligence.
 
It's decentralizing intelligence while maintaining performance.
 
That's much harder.
 
The Most Likely Outcome
 
The future probably won't be fully centralized. And it probably won't be fully decentralized either. Instead, we're likely heading toward a hybrid model.
 
Large technology companies will continue providing chips, cloud infrastructure, simulation platforms, and foundational research.
 
At the same time, decentralized AI networks will emerge as alternative coordination layers where intelligence, data, and economic value can be shared more openly.
 
The companies building robots may use NVIDIA hardware.
 
Train on Azure.
 
Run foundation models from OpenAI.
 
But they may also participate in decentralized data networks, decentralized compute markets, and decentralized intelligence protocols.
 
The future of robotics could end up looking less like a monopoly and more like an ecosystem.
 
The Bigger Question
 
The real question isn't whether decentralized AI can eliminate Big Tech.
 
It can't.
 
At least not anytime soon.
 
The real question is whether decentralized AI can prevent a future where a handful of companies control every robot, every model, every dataset, and every decision made by the machines operating around us.
 
As robots become workers, assistants, delivery drivers, factory operators, and even economic agents, that question becomes increasingly important.
 
Because the battle for the future of robotics is no longer about hardware.
 
It's about who owns the intelligence.
 
And that battle is just getting started.
 
 

🙏To support my work, Helping to keep the signal high and the noise low:

👉 Cashapp: $thedinarian

👉 Buy me a coffee: https://buymeacoffee.com/thedinarian

👉 PayPal: Scan the QR code below 📲 or Click Here

👇 Crypto Donations 👇

XRP: r9pid4yrQgs6XSFWhMZ8NkxW3gkydWNyQX
XLM: GDMJF2OCHN3NNNX4T4F6POPBTXK23GTNSNQWUMIVKESTHMQM7XDYAIZT
XDC: xdcc2C02203C4f91375889d7AfADB09E207Edf809A6

Read full Article
post photo preview
Navigating the world of blockchain 🧭
Navigating the world of blockchain can feel like learning a completely foreign language. Between technical jargon and fast-moving Web3 terminology, getting started can be overwhelming.

Whether you are exploring digital assets, building on-chain, or simply trying to understand decentralized technology, here is your foundational glossary of essential blockchain terms every beginner should know.

🏛️ 1. Core Architecture: The Base Layer

  • Blockchain: A distributed, immutable digital ledger that records transactions across a peer-to-peer network of computers. Once data is written to a block and added to the chain, it cannot be altered without altering all subsequent blocks.
  • Block: A collection of verified transactions grouped together. Once filled, the block is cryptographically linked to the previous one, forming a chronological "chain."
  • Node: An individual computer connected to a blockchain network that helps validate transactions, store ledger data, and maintain network consensus.
  • Consensus Mechanism: The set of rules and algorithms that network nodes use to agree on the validity of transactions.

    • Proof of Work (PoW): Requires miners to solve complex mathematical puzzles using computational power (e.g., Bitcoin).
    • Proof of Stake (PoS): Requires validators to lock up ("stake") native tokens as collateral to participate in block validation (e.g., Ethereum).

🔑 2. Ownership & Security: Wallets and Keys

  • Public Key (Address): An alphanumeric string that acts like your bank account number or email address. It is safe to share publicly so others can send you digital assets.
  • Private Key: A secret cryptographic passphrase or key that grants full access and control over your wallet assets. Never share your private key or seed phrase with anyone.
  • Seed Phrase (Recovery Phrase): A sequence of 12 to 24 random words generated when you set up a wallet. It acts as the master backup key to restore your wallet and access your funds on any device.
  • Hot Wallet vs. Cold Wallet:

    • Hot Wallet: A software-based crypto wallet connected to the internet (e.g., browser extensions, mobile apps), making it convenient for frequent transactions but higher risk.
    • Cold Wallet: An offline hardware device (e.g., Ledger, Coldcard) designed to isolate private keys from internet-connected threats.

⚙️ 3. Execution & Functionality: Smart Contracts and Apps

  • Smart Contract: Self-executing code stored on a blockchain that automatically enforces agreement terms once predetermined conditions are met—eliminating the need for intermediaries.
  • dApp (Decentralized Application): Applications built on top of a blockchain network that run via smart contracts rather than centralized cloud servers.
  • Gas Fees: Network transaction fees paid to validators or miners to cover the computational energy required to process actions on a blockchain.
  • Layer 1 vs. Layer 2:

    • Layer 1 (L1): The underlying primary blockchain network (e.g., Bitcoin, Ethereum, Solana) that handles base security and finality.
    • Layer 2 (L2): Secondary frameworks or companion networks built on top of an L1 to increase transaction speeds and lower gas fees (e.g., Arbitrum, Optimism, Base).

💰 4. Financial & Market Concepts

  • Tokenomics: The economic design, supply dynamics, utility, and distribution model of a cryptocurrency or token project.
  • DeFi (Decentralized Finance): Financial services—such as lending, borrowing, trading, and earning interest—built on smart contracts without traditional banks or financial intermediaries.
  • Liquidity: The ease with which an asset can be bought or sold in a market without significantly impacting its price.
  • DYOR (Do Your Own Research): A foundational golden rule in the Web3 space reminding users to independently verify technical code, whitepapers, and team backgrounds before making any capital commitments.

💡 Quick Cheat Sheet

"Not your keys, not your coins."

If you do not hold the private keys or seed phrase to your digital wallet, you do not truly own the assets inside it—a centralized entity or exchange does. Always prioritize security first as you explore the space.

🙏To support my work, Helping to keep the signal high and the noise low:

👉 Cashapp: $thedinarian

👉 Buy me a coffee: https://buymeacoffee.com/thedinarian

👉 PayPal: Scan the QR code below 📲 or Click Here

👇 Crypto Donations 👇

XRP: r9pid4yrQgs6XSFWhMZ8NkxW3gkydWNyQX
XLM: GDMJF2OCHN3NNNX4T4F6POPBTXK23GTNSNQWUMIVKESTHMQM7XDYAIZT
XDC: xdcc2C02203C4f91375889d7AfADB09E207Edf809A6

Read full Article
post photo preview
AI Is Coming for Your Job Title

Artificial intelligence may or may not take your job, but it has already broken into the human resources department and vandalized the org chart.

The evidence is all over LinkedIn, where perfectly serviceable occupations now arrive wearing titles such as “forward-deployed and agentic AI architect.” That person may be building sophisticated software. They may also be helping a chatbot remember what happened three prompts ago. Either way, somebody approved the business cards.

The expanding AI lexicon offers a useful counterpoint to the darker debate about technology and employment. Most discussion centers on how many jobs AI will eliminate. Hiring data presents a more complicated picture that includes a weak overall labor market containing a small but rapidly growing neighborhood of AI-related work.

Indeed Hiring Lab found that the number of postings on Indeed mentioning AI surged 134% from its February 2020 level by the end of 2025, even as total postings stood only 6% above that benchmark. AI appeared in a record 4.2% of Indeed postings in December.

AI, in other words, is not merely changing work. It is adding syllables to it.

The Titles Employers Actually Want

The undisputed champion is AI engineer, which ranked No. 1 on LinkedIn’s 2026 Jobs on the Rise list. The ranking, based on growth during the previous three years, also highlighted AI consultants and strategists, AI and machine-learning researchers and data annotators.

The title is popular partly because it is wonderfully accommodating. An AI engineer might build applications around large language models, connect corporate data to an AI system, improve model performance or spend Thursday afternoon persuading a customer service bot not to offer refunds for products the company doesn’t sell.

Indeed’s data showed the terminology spreading beyond Silicon Valley. Nearly 45% of data and analytics postings contained an AI-related term at the end of 2025, along with roughly 15% of marketing postings and 9% of human resources listings. A more recent Indeed analysis reported by Business Insider found that the number of frequently advertised job titles explicitly referencing AI rose from 264 in 2022 to 822 in the first quarter of 2026. Nearly two-thirds were outside traditional technology fields.

That produces titles such as AI marketing manager, AI learning specialist, responsible AI counsel and AI transformation lead. These are not always new occupations. Frequently, they are familiar jobs that have discovered a highly effective résumé keyword.

LinkedIn data cited by the World Economic Forum estimated that AI investment has supported 1.3 million positions, including AI engineers, data annotators and forward-deployed engineers, plus more than 600,000 AI-enabled data center jobs. The server racks, unlike the chatbots, still need electricians.

The Jobs With the Science-Fiction Salaries

At the upper end, AI has created a compensation market that resembles professional sports, except the competitors wear hoodies and discuss inference latency.

Syracuse University review put chief AI officer compensation between $200,000 and more than $500,000, while specialized roles can exceed $400,000 after bonuses and equity. Frontier research engineers, AI infrastructure specialists and engineers who can train or deploy advanced models command some of the largest packages.

Then there is the forward-deployed engineer, an old Palantir title that the AI boom has placed on a rocket sled. These engineers embed with customers, translating an executive’s desire to “do something with AI” into software that works. The Next Web reported that Indeed postings for the role were about 19 times higher in January than a year earlier.

CTO guide from the blog Signal Through the Noise placed forward-deployed engineer compensation between $238,000 and $700,000, research-engineering packages as high as $1.4 million and chief AI officer compensation above $1 million in some cases. It also made a less flattering observation: Many lavishly differentiated titles describe the same three basic functions. People build AI products, train models or keep the infrastructure from catching fire.

The Department of Unnecessary Titles

AI has created some genuinely new work. Evals engineers design tests to determine whether models perform reliably. AI red teamers try to make systems fail before customers do. Model behavior engineers study why an AI system responds as it does. AI governance leaders manage risks involving data, bias, security and regulation.

Other titles seem to have escaped from a brainstorming retreat.

There is the Claude Evangelist, whose mission apparently combines product education with the traditional duties of an apostle. There are vibe coders, who build software by describing what they want and accepting AI-generated code with varying degrees of supervision. “Vibe engineer” is the more respectable version, roughly equivalent to putting on a blazer before asking the machine to fix the login page.

“Context engineer” is a real discipline involving the data, instructions, memory and tools supplied to AI models. “Prompt engineer,” once advertised as a possible six-figure profession for gifted chatbot whisperers, is increasingly treated as one skill inside a broader AI role.

The CTO guide also identified “builder,” “AI-native developer,” “RAG engineer,” “agentic AI engineer” and “principal agentic GenAI forward-deployed context architect,” the last of which appears to require both technical proficiency and exceptional lung capacity.

Has AI created entirely new jobs? Absolutely. Some occupations, including AI safety, evaluation and model governance, exist because modern generative systems introduced new technical and business problems. However, many job titles are old jobs with fresh vocabulary, higher salary bands and a sudden aversion to the words “software developer.”

That may be the safest prediction about AI and employment. The machines will automate some tasks, generate others and force companies to rethink the division of labor. Before any of that is settled, however, corporate America will form a steering committee, appoint a chief agentic transformation evangelist and schedule a meeting to determine what that person does.

Source

🙏 Donations Accepted, Thank You For Your Support 🙏

If you find value in my content, consider showing your support via:

🙏 Cashapp: $thedinarian

🙏 Buy me a coffee: https://buymeacoffee.com/thedinarian

🙏 PayPal: Scan the QR code below 📲 or Click Here

🙏 Crypto Donations 👇
XRP: r9pid4yrQgs6XSFWhMZ8NkxW3gkydWNyQX
XLM: GDMJF2OCHN3NNNX4T4F6POPBTXK23GTNSNQWUMIVKESTHMQM7XDYAIZT
XDC: xdcc2C02203C4f91375889d7AfADB09E207Edf809A6

Read full Article
See More
Available on mobile and TV devices
google store google store app store app store
google store google store app tv store app tv store amazon store amazon store roku store roku store
Powered by Locals