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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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🤖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.
 
 

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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.

Source

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