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How banks and businesses can prep for the FedNow instant-payment system
July 04, 2023
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FedNow will be the first of its kind central bank instant payment system in the US and could revolutionize how businesses and consumers pay and receive money. But not everyone is prepared for it.

After a pilot program that lasted six months, the US Federal Reserve System plans to launch its FedNow real-time payment system in July. But many banks and businesses could be caught flatfooted when it launches.

The central bank’s payment and settlement rail is designed to increase liquidity, especially for small businesses and supply chain participants who can get paid instantly for goods and services. It also creates a new way for employees, especially gig and hourly-rate employees, to get paid more quickly and frequently — perhaps every day.

The new system will allow banks, businesses, and consumers to send and receive payments in about 10 seconds anytime, any day. As with other payment systems, there are fees associated with the service, and banks will have to decide who foots the bill — merchants, consumers, neither, or both.

"Banks aren't 24/7 in their operations today," said Debbie Buckland, a director analyst in financial services for Gartner Research. "So, they'll have to have procedures set up to accomodate that liquidity management that happens in the middle of the night. Becasue if you give your customers the ability to do their banking in the middle of the night, they're going to do it."

Initally, FedNow will only let banks receive payments; the ability to send payments — and for consumers to be able to identify themselves by phone number and email only, as Venmo now allows — is expected to come later.

"The send part takes a little more work," Buckland said. "You have to have a vehicle for customers — both consumers and businesses — to initiate a real-time payment. That means adding that functionality to their digital and mobile channels. You need to be able to upgrade your product or turn on that service."

For consumers, the process is far easier. Those who want an instantaneous way to make payments, whether it's for a retail product or a mortgage installment, will need to download an app once their financial services provider offers it. 

There are two primary differences between FedNow and traditional payment systems such as automated clearinghouse services (ACH) and wire transfers, such as Western Union or the Fed’s own Fedwire service. ACH transactions settle just once at the end of a business day, and they settle in batches — not individually. Wire transfers are faster, but charge higher user fees. Wires are also not used for multiple or traditional batch transactions, and they’re still not real time; they can take several minutes or several days for remittances or cross-border payments.

For consumers not familiar with the ACH payment system, it's the funds transfer system used when employees sign up for direct deposit, make eChecks payments or authorize automatic payments to be deducted from their banking accounts.

FedNow is not a replacement for existing ACH and wire networks, but an additional payment option when real-time payments and settlements are needed.

Existing payment systems will be challenged by FedNow’s efficiency, and while the impact will be significant, it’s not likely to supplant other systems, according to Aaron Press, research director for Worldwide Payment Strategies at IDC.

“Electronic payments are growing fast enough in general that, even if other systems lose share, they won’t necessarily stop growing,” Press said. “But, they’re not taking this standing still. Every other payment system [operator] is thinking about how to position against FedNow. Even the [Federal Reserve] is thinking about the impact of FedNow on its own Fedwire service.”

The new system also means banks that adopt it will have to adjust to a 24/7 world where merchants or consumers might want to transfer funds between different third-party accounts at odd hours of the day or night. It also means banks won’t have a full business day, as they do now, to go through know-your-customer,  anti-money laundering, and anti-fraud processes. Those processes will have to be automated for real-time discovery.

For many banks, 'a real shift'

“For a lot of banks, this is a real shift in operational thinking,” Press said. “The margin of error is significantly smaller. The time to do things manually is essentially gone. We’re hearing a lot from banks and vendors who offer automation that there’s an increasing demand for automating a lot of tasks and workflows to better handle real-time messages.”

From a corporate standpoint, the use of FedNow is not just about being able to pay faster; it can be about paying slower or determining the last possible moment a payment must go out. For businesses that pay millions of dollars day in and out, holding onto money until it must be paid can amount to earnings.

“If you have an invoice with advantageous terms to pay at a certain time, you want to submit at last possible moment,” Press said. “FedNow gives you a lot of control over when precisely you pay. If those same invoices are paid over ACH, there’s some uncertainty to that.”

Retail merchants and others who want to offer consumers an instant-payment option will have to work with their payment providers, such as FISFiservJack Henry and Q2 to ensure the point-of-sales (POS) system has the proper APIs and ensure their systems are properly connected.

The FedNow instant-payment system will use the new ISO 20022 global financial messaging standard, meaning banks will need to be sure they can submit messages in that format. Many banks may already have the ability to submit messages through ISO 20022, because FedNow is actually the second real-time payment system.

In 2017, a consortium of banks called The Clearing House launched the Realtime Payments network or TCH RTP. But the network failed to achieve wide adoption because smaller banks were wary of using a payment system backed by their larger competitors. However, TCH RTP does use the ISO 20022 standard.

At its core, FedNow serves as an interbank instant-payment infrastructure. Banks, credit unions, and other eligible institutions have accounts at the Federal Reserve that allow them to hold reserves. Banks pay each other by transferring reserves from the paying bank’s Fed account to the receiving bank’s Fed account using several interbank payment options. FedNow is a new addition to the suite of options to make such transfers.

Sam Aarons, co-founder and CTO of middleware payments provider Modern Treasury, said the payments industry is excited about the promise of FedNow. Modern Treasury provides the translation layer for corporate accounting systems to transfer funds over a network using API calls systems. Bank systems are sorely outdated, however, and still rely on technology from the 1970s and 1980s.

"That’s also why Modern Treasury is excited about FedNow, because it is going to force a lot of people into figuring out what is a modern technology stack for payments," Aarons said. "As I like to say, what is a business day if money can arrive and leave your bank account 24/7, 365 [days a year]? Are you going to have accountants stay up at midnight to close the books? You need to change the software for your company that’s looking at the precipice of that."

While integration with FedNow is one issue, moving payment systems to be real-time is the bigger problem, according to Aarons.

"Where we usually see the hiccups is in fraud checking and [Know Your Customer]," he said. "A lot of those systems throw up a red flag when there's a questionable transaction, and then you have a day and a human can look at this payment. When you’re trying to send out payments in 10 seconds, you have to automate that or make your decision quickly. 'Yes, I can send this out,' or 'No, I can’t send this out.'"

A gig worker’s dream

One advantage to using FedNow is that organizations who employ gig or hourly workers can pay them at the end of a shift because the money transfers instantaneously. Today, when a gig worker is paid, it’s through a credited system and the actual money doesn’t transfer from bank to merchant until the next day. Gig workers, however, will need a bank account to be paid, versus a payroll debit card as many use today.

The United States is a follower in rolling out a central bank-based instant payment system. Forty to 50 other countries have already implemented same-day payment systems — and their uptake was fast, quickly reaching nearly ubiquitous use.

For example, Brazil’s Central Bank launched the Pix instant payment system in 2020; within a year, it had reached more than 100 million users and today it serves more than 150 million people. That suggests FedNow will be quickly adopted across banking and business sectors.

There’s a good reason for the quick uptake. When businesses are making thousands of payments a day to distributors and suppliers, it behooves everyone to get their money faster. Like Brazil's Pix, FedNow will allow companies to pay vendors, contractors, or any business partner instantly. And it will enable better cash-flow management because funds are instantly available, allowing for faster reinvestment.

Because most US companies now use the ACH system to make and receive payments, they experience next-day clearing for batch transfers, or they pay extraordinarily high fees for faster wire transfers

"FedNow represents huge advances for the businesses of today that are moving money around," Aarons said. “FedNow is an opportunity to deliver a great consumer experience, but also one for banks as well. It’s a really good opportunity for the US to catch up with the rest of the world.

"I think there's going to be a big lift-off when FedNow launches, and the hope is to get to universal coverage that we have with ACH and wire," Aarons said.

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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.

🏛️ 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.

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.

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🤖 Decentralized Intelligence by Design: Unpacking the Bittensor Flywheel

In the legacy tech world, artificial intelligence is governed by corporate monopolies. Companies like OpenAI and Google scale by capturing massive capital, locking talent behind non-disclosure agreements, and building closed-source infrastructure. 🛑

Bittensor flips this paradigm completely on its head. By combining a Bitcoin-inspired tokenomic model with a permissionless, competitive architecture, Bittensor doesn't just fund AI development—it orchestrates an unstoppable digital commodity flywheel. 🌪️

Here is an analysis of how the Bittensor ($TAO$) Flywheel Effect operates, and why its economic design is quietly building the foundation for generalized, open-source intelligence.

1. ⚙️ The Core Engine: The TAO Emission Mechanism

Unlike traditional crypto projects driven by private sales or VC allocations, Bittensor enforces a strict meritocracy. There are exactly 21 million TAO tokens that will ever exist, mimicking Bitcoin’s scarcity framework. 🪙

The network’s core engine releases 7,200 TAO daily across the ecosystem. This issuance isn't handed out randomly; it is dynamically distributed to specialized mini-marketplaces known as Subnets via a game-theoretic protocol called Yuma Consensus.

2. 🔄 The Three Stages of the Flywheel

The Bittensor flywheel works because it directly aligns the local self-interest of developers, miners, validators, and capital providers with the global health of the network. 🎯

🛡️ Phase 1: High-Barrier Subnet Competition

To build on Bittensor, an entrepreneur or developer group must purchase and "burn" or lock up a significant amount of TAO to secure a Subnet slot.

  • The Filter: This entry barrier filters out noise.

  • The Result: It ensures that only teams with mature concepts and solid execution capabilities (like decentralized storage, protein folding, or LLM inference) enter the arena.

💎 Phase 2: Alpha Token Emissions & Talent Attraction

Once a subnet is live, it competes aggressively against other subnets for a slice of the daily 7,200 TAO pool. Under the Dynamic TAO framework, each subnet utilizes its own localized native token (Alpha tokens). 🧪

  • Reward: The subnets that produce the highest utility or most innovative AI products receive a larger allocation of global TAO emissions.

  • Incentive: These emissions fund the subnet's local Alpha pool, offering massive financial rewards to the best Miners (who provide the actual compute/AI models) and Validators (who verify the accuracy and value of the work).

🔒 Phase 3: The Liquidity Loop and Token Scarcity

Because Alpha tokens are inherently priced relative to TAO, external investors or users who want to stake on or utilize a specific high-performing subnet must first acquire TAO. 📈

  • As a subnet's product quality improves, demand for its Alpha token surges.

  • To buy Alpha, participants must buy and lock up TAO in decentralized liquidity pools.

  • This removes circulating TAO from the open market, reducing effective float and driving up the value of TAO.

3. 🚀 Why the Flywheel is Unstoppable

The beauty of this cycle is that it feeds itself:

Higher TAO Price ➡️ More Valuable Subnet Emissions ➡️ Attraction of Higher-Tier Talent/Compute ➡️ Superior AI Products ➡️ Increased Network Demand ➡️ Higher TAO Price📈

Traditional startups spend millions on recruitment and marketing. Bittensor bypasses this entirely: its emission schedule acts as a global bat-signal for talent. 🌍

If a miner in Eastern Europe or a data scientist in Tokyo can optimize an open-source model to solve a specific subnet's prompt better than anyone else, the network automatically and frictionlessly rewards them.

💡 The Takeaway

Bittensor is more than a blockchain; it is an economic computer designed to run incentive structures in massive parallelism. By treating machine intelligence as a digital commodity and wrapping it in a circular value flow, the Bittensor flywheel transforms raw computational energy into an emergent, open-source super-intelligence. 🧠⚡

As subnets mature from raw infrastructure into client-facing enterprise APIs, the velocity of this flywheel is poised to redefine the economics of AI forever.

I hope this was helpful ~Dinarian888♾

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