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Power of Payments Ep. 24: Talking FedNow and real-time payments with Bottomline’s Jessica Cheney
March 01, 2023
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  • Jessica Cheney, VP of Product – Digital Banking Solutions at Bottomline Technologies, joins host Ismail Umar on this week’s podcast.
  • She discusses the current state of adoption of real-time payments in the US, and how the launch of FedNow is going to impact the banking industry.

Welcome back to the Power of Payments podcast. I’m your host Ismail Umar, and today I’m joined by Jessica Cheney, VP of Product for the Digital Banking Solutions group at Bottomline Technologies.

Jessica has been with Bottomline for over a decade. Prior to that, she held similar roles at a number of other fintechs, and was also part of the commercial product management group at US Bank. She has been involved with real-time payments for many years now, and says she has a comprehensive outlook on how payments impact financial services from a commercial, fintech, and retail perspective.

In our conversation today, Jessica discusses the current state of adoption of real-time payments in the US, and how the launch of FedNow – the Federal Reserve's instant payment service – is going to impact the banking industry. She also talks about how SMBs can use real-time payments to improve their day-to-day operations, and the overall impact that RTP adoption will likely have on banks, businesses, and consumers in the coming years.

The following excerpts were edited for clarity.

I lead the product management function for the banking segment of Bottomline Technologies. I've been there for about 11 years now. Prior to that, I was in similar roles at other fintech companies – S1 and Clear2Pay, most notably. I’ve also worked directly in the financial services industry in several areas. I was part of the commercial product management group at US Bank, and led the retail group at Skowhegan Savings Bank. So I sort of have a very comprehensive perspective on how payments impact the financial services world from a commercial perspective, a fintech perspective, and a retail perspective. I've also been involved with real-time payments since its conceptual launch with the Federal Reserve, for several years now. That really sparked my interest with the Fed Task Force, and I've been really involved in the industry ever since.

Given your expertise, what would you say is the current state of adoption of real-time payments in the US compared to other parts of the world?

I think that, to answer that question, it really depends on how clinical we’re going to be in using the term ‘real-time payments.’ And that is a concept that's applicable in the US and throughout the world. The term is really an umbrella that covers many payment options, especially in the US: P2P payments from Zelle, Cash App, Venmo, Same Day ACH supported by NACHA, the Fed, and The Clearing House, RTP launched by The Clearing House in November 2017, and now FedNow launching the instant payment network that's coming live this summer. In general, to answer your question, I would describe this as an industry that’s continuing to grow, though a bit more slowly lately. The P2P space continues to drive most volume and growth. Zelle reported over 550 million transactions, representing the movement of over $155 billion in June. That’s a 27% growth from 2021. Venmo is reporting more than $63 billion moved in Q3, a 6% growth over their record year in 2021.

Now, Same Day ACH, and ACH in general in the US, is continuing to grow. It saw 6% growth in Q3, with Same Day seeing the most increase in use. There were 176 million Same Day ACH payments made. And that's a huge, 102% increase since Q3 2021. The RTP network has also seen huge growth, reporting 49 million transactions in Q4, moving about $22 billion, another 9% growth over Q3.

When I really dig into this a little bit deeper, though, I think that there are some things driving this. There's a recent American banking article that noted disbursements and rent payments are among the fastest-growing Zelle use cases. And that kind of indicates that more users are relying on Zelle’s speed to make last-minute billing deadlines. The number of companies including insurance providers, education and government agencies, using Zelle to transfer funds also dramatically increased, 87% in the second quarter of 2022, compared to the year before. So while all this growth seems impressive, I think the industry is actually on the brink of truly having breakout adoption. There’s a saying that goes, ‘A rising tide lifts all boats.’ This tide is growing in the RTP industry, aided by the FedNow launch, as well as more B2C and B2B adoption. The current economic condition is also ripe for assisting growth in real-time payments as personal and corporate liquidity management becomes more and more important.

Can you share your thoughts on the kind of impact FedNow is going to have on the financial industry?

The biggest thing is that the Fed has for a very long time been seen as the preferred payment network provider. And that's probably based on their perceived stability and competitive neutral reach to all financial institutions. The Clearing House, for example, has roughly 280 participating banks. The Fed has a built-in customer base of over 9000 financial institutions that FedNow will now be offered to. That sheer jump in volume of banks reached that will have access to real-time or instant payments will lead to a really game-changing adoption in the future. The launch of the FedNow service also removes the “let's wait and see” excuse that some banks have used when it comes to real-time payments. Many until this point have seen RTP as only the purview of the largest banks in the US. Just as an aside, The Clearing House members that were initial drivers of RTP, and those member banks, are among the largest in the US. What the launch of FedNow does is make RTP mainstream in America. The Fed and NACHA launched ACH and direct deposit in the mid-70s, and that helped make ACH mainstream. Today, 94% of Americans get paid that way. FedNow has the potential to do the same thing with real-time or instant payments.

Once FedNow is launched, do you expect to see rapid adoption of real-time payments in the US, or do you think it will slowly build up over time?

Unfortunately, I think at least the next couple of years, we will continue to see a little bit of a slow adoption curve. And then we will reach a major launching point where we will have critical mass in both receivers and senders of real-time transactions. Too many banks have waded into this pool as receivers only, and not enough have jumped into the deep end to be senders as well. And you really can't have a network that is full of receivers but not senders and be successful.

Another unknown factor in how much interoperability will occur between these two networks will really impact adoption. Once we know that, and though the interoperability between the two networks is established, that's the linchpin of growth going forward. The networks have both been set up for interoperability, and they’re using similar message sets, similar operating guidelines and value propositions. But actual interoperability remains to be improved between the two.

A key point here is that eventually, the demand that we're seeing in the P2P space will also push into the business payments space. That, along with FedNow's reach, will really push adoption rates along. When payments become mainstream, their value is more widely understood, and that obviously drives demand as well.

Do you think there is a sufficient level of awareness among American businesses about what adopting real-time payments would mean for them? And what do you think is most important for FIs and businesses to understand about adopting real-time payments?

Unfortunately, I think the comprehension level of the value of RTP remains low. I’ll share a story with you. I was talking to a CFO of a midsize fintech about RTP about 18 months ago. And his initial question to me was, ‘Why on earth would I want to pay invoice faster? I want to hold on to my cash.’ So I went on to explain that RTP is actually the liquidity and cash management tool that helps him do that better than any other payment type out there. RTP lets you wait till the absolute last minute to pay an invoice, and either take advantage of payment terms offered or to get shipments released and delivered when needed.

I think lots of energy is now going into the education and benefits for businesses to use RTP to make payments, and RFP, request for payments, to get paid. First and foremost, I think that RTP and RFP are key business operating tools for small businesses. They help with liquidity management, financial planning, customer service, and efficiencies in both the accounts receivable and accounts payable processes.

First of all, liquidity management. As I mentioned before, RTP allows small businesses who are managing their cash really tightly to make payments at the absolute last minute. Sure, they can be scheduled in advance, but when cash is tight and you need to pay a vendor just in time, RTP provides that. RFP, or request for payment, is the ability to send an electronic invoice and request a real-time payment in response. This can really streamline the invoice to collection process for any small business and aid in reducing collection time. It is also a very cost-efficient way to send electronic invoices. Many merchant services providers are now offering instant settlement, providing access to funds immediately, which helps small businesses meet their immediate cash flow needs. What these merchant services are doing is, at the end of any particular sales period, where the small business will close out their credit card sales for the day, the merchant service company is providing settlement to these small businesses via RTP. On the flip side of this, RTP can also be used for instant payroll, and it can help many small businesses attract employees in this highly competitive labor market.

The other part of this that I haven't really talked about before is the fact that real-time payments can also have an accompanying real-time acknowledgment, meaning a payment that has been made by RTP can be acknowledged by the receiver. And that can also aid in reducing some of the financial anxiety that many small businesses are facing when they make just-in-time payments.

How do you think the current macroeconomic conditions and market volatility will impact the adoption and effectiveness of real-time payments?

I think that it’s absolutely going to have an impact. We don't have to look too far to see proof of that. During the COVID-19 pandemic, the use of cashless, contactless, and real-time payments grew like crazy. You just have to look at the volumes from Zelle, Venmo, and Cash App to see that impact. But today's economic conditions are driving consumers and businesses away from wanting to use credit or credit cards as means of payment – the interest rate’s just way too high. Companies and consumers alike do need to wait till the last minute to make key payments for things like rent and utilities, but they also need the financial certainty that these last-minute payments have been acknowledged. And RTP can do that.

Looking into the future, what kind of impact do you think the adoption of real-time payments is going to have on banks, businesses and consumers in the coming years?

I really think that RTP is the next revolution in payments. I think it’ll be a soft change. We've evolved into real-time payments in the P2P space being mainstream. And that will continue to flow into the B2B and B2C aspects of this industry. Financial institutions are already making investments to take advantage of this. It’s just going to be the next expectation, just like the expectation we have that the phones we carry in our pockets are mini-computers and basically can do everything that we want to be done instantaneously. That's the natural evolution and the next wave of payments in the industry.

There's a couple of things that I did want to mention, though. I think that people get hung up on the speed of these payments. But there are other aspects of RTP that also add value. The added value to this also takes advantage of some of the other things that we've grown very accustomed to. And that’s the instant communication that goes along with these payment types. There’s the ability to have these real-time payments instantly acknowledged. There are communication vehicles built into the payment rails that allow the sender and receiver to communicate with each other about questions that they have, either about the amounts that have been received or the amounts that have been requested to be paid. Again, it kind of takes what’s become very mainstream in our personal lives, with the use of instant messaging and texting, and goes along with the natural change in payments that’s occurring. I think that's the key to why RTP is the next revolution in payments.

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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.
 
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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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