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September 01, 2022
🌐 A NEW ERA FOR MONEY 🌐

As bytes replace dollars, euros, and renminbi, some changes will be welcome; others may not

Money has transformed human society, enabling commerce and trade even between widely dispersed geographic locations. It allows the transfer of wealth and resources across space and over time. But for much of human history, it has also been the object of rapacity and depredation.

Money is now on the cusp of a transformation that could reshape banking, finance, and even the structure of society. Most notably, the era of physical currency, or cash, is drawing to an end, even in low- and middle-income countries; the age of digital currencies has begun. A new round of competition between official and private currencies is also looming in both the domestic and international arenas. The proliferation of digital technologies that is powering this transformation could foster useful innovations and broaden access to basic financial services. But there is a risk that the technologies could intensify the concentration of economic power and allow big corporations and governments to intrude even more into our financial and private lives.

Traditional financial institutions, especially commercial banks, face challenges to their business models as new technologies give rise to online banks that can reach more customers and to web-based platforms, such as Prosper, capable of directly connecting savers and borrowers. These new institutions and platforms are intensifying competition, promoting innovation, and reducing costs. Savers are gaining access to a broader array of saving, credit, and insurance products, while small-scale entrepreneurs are able to secure financing from sources other than banks, which tend to have stringent loan-underwriting and collateral requirements. Domestic and international payments are becoming cheaper and quicker, benefiting consumers and businesses.

Stability concerns
The emergence of cryptocurrencies such as Bitcoin initially seemed likely to revolutionize payments. Cryptocurrencies do not rely on central bank money or trusted intermediaries such as commercial banks and credit card companies to conduct transactions, which cuts out the inefficiencies and added costs of these intermediaries. However, their volatile prices, and constraints to transaction volumes and processing times, have rendered cryptocurrencies ineffective as mediums of exchange. New forms of cryptocurrencies called stablecoins, most of which ironically get their stable value by being backed by stores of central bank money and government securities, have gained more traction as means of payment. The blockchain technology underpinning them is catalyzing far-reaching changes to money and finance that will affect households, corporations, investors, central banks, and governments in profound ways. This technology, by allowing secure ownership of purely digital objects, is even fostering the rise of new digital assets, such as non-fungible tokens.

At the same time, central banks are concerned about the implications for both financial and economic stability if decentralized payment systems (offshoots of Bitcoin) or private stablecoins were to displace both cash and traditional payment systems managed by regulated financial institutions. A payment infrastructure that is entirely in the hands of the private sector might be efficient and cheap, but some parts of it could freeze up in the event of a loss of confidence during a period of financial turmoil. Without a functioning payment system, a modern economy would grind to a halt.

In response to such concerns, central banks are contemplating issuing digital forms of central bank money for retail payments—central bank digital currencies (CBDCs). The motives range from broadening financial inclusion (giving even those without a bank account easy access to a free digital payment system) to increasing the efficiency and stability of payment systems by creating a public payment option as a backstop (the role now played by cash).

A CBDC has other potential benefits. It would hinder illegal activities such as drug deals, money laundering, and terrorism financing that rely on anonymous cash transactions. It would bring more economic activity out of the shadows and into the formal economy, making it harder to evade taxes. Small businesses would benefit from lower transaction costs and avoid the hassles and risks of handling cash.

Risk of runs
But a CBDC also has disadvantages. For one, it poses risks to the banking system. Commercial banks are crucial to creating and distributing credit that keeps economies functioning smoothly. What if households moved their money out of regular bank accounts into central bank digital wallets, perceiving them as safer even if they pay no interest? If commercial banks were starved of deposits, a central bank could find itself in the undesirable position of having to take over the allocation of credit, deciding which sectors and firms deserve loans. In addition, a central bank retail payment system could even squelch private sector innovation aimed at making digital payments cheaper and quicker.

Of equal concern is the potential loss of privacy. Even with protections in place to ensure confidentiality, any central bank would want to keep a verifiable record of transactions to ensure that its digital currency is used only for legitimate purposes. A CBDC thus poses the risk of eventually destroying any vestige of anonymity and privacy in commercial transactions. A carefully designed CBDC, taking advantage of fast-developing technical innovations, can mitigate many of these risks. Still, for all its benefits, the prospect of eventually displacing cash with a CBDC ought not to be taken lightly.

The new technologies could make it harder for a central bank to carry out its key functions—namely, to keep unemployment and inflation low by manipulating interest rates. When a central bank such as the Federal Reserve changes its key interest rate, it affects interest rates on commercial bank deposits and loans in a way that is reasonably well understood. But if the proliferation of digital lending platforms diminishes the role of commercial banks in mediating between savers and borrowers, it’s unclear how or whether this monetary policy transmission mechanism will continue to function.

Currency competition
The basic functions of central-bank-issued money are on the threshold of change. As recently as a century ago, private currencies competed with each other and with government-issued currencies, also known as fiat money. The emergence of central banks decisively shifted the balance in favor of fiat currency, which serves as a unit of account, medium of exchange, and store of value. The advent of various forms of digital currencies, and the technology behind them, has now made it possible to separate these functions of money and has created direct competition for fiat currencies in some dimensions.

Central bank currencies are likely to retain their importance as stores of value and, for countries that issue them in digital form, also as mediums of exchange. Still, privately intermediated payment systems are likely to gain in importance, intensifying competition between various forms of private money and central bank money in their roles as mediums of exchange. If market forces are left to themselves, some issuers of money and providers of payment technologies could become dominant. Some of these changes could affect the very nature of money—how it is created, what forms it takes, and what roles it plays in the economy.

If market forces are left to themselves, some issuers of money and providers of payment technologies could become dominant.
International money flows
Novel forms of money and new channels for moving funds within and between economies will reshape international capital flows, exchange rates, and the structure of the international monetary system. Some of these changes will have big benefits; others will pose new challenges.

International financial transactions will become faster, cheaper, and more transparent. These changes will be a boon for investors seeking to diversify their portfolios, firms looking to raise money in global capital markets, and economic migrants sending money back to their home countries. Faster and cheaper cross-border payments will also boost trade, which will be particularly beneficial for emerging market and developing economies that rely on export revenues for a significant portion of their GDP.

Yet the emergence of new conduits for cross-border flows will facilitate not just international commerce but also illicit financial flows, raising new challenges for regulators and governments. It will also make it harder for governments to control the flows of legitimate investment capital across borders. This poses particular challenges for emerging market economies, which have suffered periodic economic crises as a result of large, sudden outflows of foreign capital. These economies will be even more vulnerable to the monetary policy actions of the world’s major central banks, which can trigger those capital outflows.

Digital central bank money is only as strong and credible as the institution that issues it.
Neither the advent of CBDCs nor the lowering of barriers to international financial flows will alone do much to reorder the international monetary system or the balance of power among major currencies. The cost of direct transactions between pairs of emerging market currencies is falling, reducing the need for “vehicle currencies” such as the dollar and the euro. But the major reserve currencies, especially the dollar, are likely to retain their dominance as stores of value because that dominance rests not just on the issuing country’s economic size and financial market depth but also on a strong institutional foundation that is essential for maintaining investors’ trust. Technology cannot substitute for an independent central bank and the rule of law.

Similarly, CBDCs will not solve underlying weaknesses in central bank credibility or other issues, such as a government’s undisciplined fiscal policies, that affect the value of a national currency. When a government runs large budget deficits, the presumption that the central bank might be directed to create more money to finance those deficits tends to raise inflation and reduce the purchasing power of central bank money, whether physical or digital. In other words, digital central bank money is only as strong and credible as the institution that issues it.

Government’s role
Central banks and governments worldwide face important decisions in coming years about whether to resist new financial technologies, passively accept private-sector-led innovations, or embrace the potential efficiency gains the new technologies offer. The emergence of cryptocurrencies and the prospect of CBDCs raise important questions about the role the government ought to play in financial markets, whether it is impinging on areas that are preferably left to the private sector, and whether it can compensate for market failures, particularly the large number of unbanked and underbanked households in developing economies and even in advanced economies such as the United States.

As the recent cryptocurrency boom and bust have shown, regulation of this sector will be essential to maintain the integrity of payment systems and financial markets, ensure adequate investor protection, and promote financial stability. Still, given the extensive demand for more efficient payment services at the retail, wholesale, and cross-border levels, private-sector-led financial innovations could generate significant benefits for households and corporations. In this respect, the key challenge for central banks and financial regulators lies in balancing financial innovation with the need to mitigate risks to uninformed investors and to overall financial stability.

New financial technologies hold the promise of making it easier even for indigent households to gain access to an array of financial products and services, and of thereby democratizing finance. However, technological innovations in finance, even those that might allow for more efficient financial intermediation, could have double-edged implications for income and wealth inequality.

The benefits of innovations in financial technologies could be captured largely by the wealthy, who could use them to increase financial returns and diversify risks, and existing financial institutions could co-opt these changes for their own benefit. Moreover, because those who are economically marginalized have limited digital access and lack financial literacy, some of the changes could draw them into investment opportunities whose risks they do not fully appreciate or have the ability to tolerate. Thus, the implications for income and wealth inequality—which has risen sharply in many countries and is fomenting political and social tensions—are far from obvious.

Another key change will be greater stratification at both the national and international levels. Smaller economies and those with weak institutions could see their central banks and currencies swept away, concentrating even more economic and financial power in the hands of the large economies. Meanwhile, major corporations such as Amazon and Meta could accrete more power by controlling both commerce and finance.

Even in a world with decentralized finance built around Bitcoin’s innovative blockchain technology (which is likely to be its true legacy), governments have important roles to play in enforcing contractual and property rights, protecting investors, and ensuring financial stability. After all, it appears that cryptocurrencies and innovative financial products, too, work better when they are built on the foundation of trust that comes from government oversight and supervision. Governments have the responsibility to ensure that their laws and actions promote fair competition rather than favoring incumbents and allowing large players to stifle smaller rivals.

Continue Reading: https://www.imf.org/en/Publications/fandd/issues/2022/09/A-new-era-for-money-Prasad

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đŸ€– 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. â˜•ïžđŸ 

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
đŸ”” The most important shape nobody talks about đŸ””

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! đŸ€–đŸ“ˆ

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

đŸ”č 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.

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

đŸ”č 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.

đŸ”č 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

đŸ”č 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.

đŸ”č 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.

đŸ”č 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
The Government Is Tracking Your Silver...

Hippius (hippius.com) offers storage at a lower price per terabyte than Google Drive, iCloud, and Dropbox.

1/100th the cost (!)

Only possible on Bittensor $TAO

Now with a Dropbox-like desktop storage app as well as an S3-compatible API.

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🚹 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. đŸ”„ This sustained reduction in supply underpins a long-term bullish thesis for XRP holders. 📈📈🚀

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đŸ€–Can Decentralized AI Stop Big Tech from Owning the Future of Robotics?đŸ€–
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.
 
 

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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.

đŸ›ïž 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.

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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.

A 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.

A 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

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