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J.P. Morgan Payments’ Georgakopoulos: Internet of Things and Embedded Finance Forge New Commerce Ecosystems
May 10, 2023
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No one wakes up, grabs their phone and declares, I’m going to go omnichannel shopping today.

But here we are, three years after the pandemic took root, navigating online and in-person interactions every day.

As Takis Georgakopoulos, global head of J.P. Morgan Payments, told Karen Webster, even our in-person experiences are changing, based on how we’ve become habituated during the pandemic.

“We’re social animals,” he said. “We like to go into stores. We like to order food. We like to go to restaurants.” But nowadays, even if we go into a store, we want the brick-and-mortar experience to be as easy as it would be online. We want to be recognized, we want to skip the lines — whether buying in-store to have items shipped to the doorstep or ordering online to pick up in-store.

Major brands are shifting innovation spending from a pure online and digital experience to place much more emphasis on the integration between the digital and in-store experience. Many brands underinvested in the in-store space for years, and the experience has simply fallen behind, he noted. These brands are also finding that a digital-only approach is short-sighted, especially in the fast-moving consumer and retail sector.

This is why a seamless omnichannel set up is critical. It helps ensure a consumer can be recognized and receives customized attention whether they go to the store or switch between online, mobile and in-store. In at least some cases, leading brands replace their chief digital officer role with a chief omnichannel officer to ensure focus on the customer journey, regardless if it’s online or in-store.

Forging that seamless cross-channel navigation, Georgakopoulos explained, comes with activating commerce within every connected endpoint — the mobile devices, voice assistants, handheld POS terminals and even “smart” cars that are becoming more commonplace. Social media platforms play a critical role, too, as new commerce ecosystems evolve; consumers now use TikTok and Instagram to search and shop just as much as to connect with friends.

The Next Big Thing Is the Internet of Things — With Payments

The Internet of Things is evolving to have embedded payments in the mix, and smart devices all around us are now being required to include the ability to make payments, and make them instantly.

The secure, interconnected commerce experience, noted Georgakopoulos, needs to be underpinned by data that travels with consumers across each and every touchpoint. We’re not all that far away from the day when, as he illustrated, a vacationer in Las Vegas will travel through the casino, play the slots, go to the swimming pool, head to the restaurant, have the meal, and up to the room … navigating it all without juggling keys or wallets.

“Why do you need a wallet?” asked Georgakopoulos. “Your face is with you, your palm is with you, your wearable is with you. You are used to using all of those things. Why can’t you use them in person as well?” Biometrics can play a big role in eliminating the checkout line, voice commands have the promise of becoming among the most natural conduits of transacting.

Right now?

The continuum — of the customer journey from the store to their phone, back to the store buying, returning — well, the seamlessness, is not there.

But the roadmap is there.

As payments systems get faster — instant, and on 24/7 — and we get ever more accustomed to using smart devices in the home to keep daily life running smoothly and to pay utilities, for example, the more adept we’ll become at using biometrics and other advanced technologies.

“The technologies are maturing,” he said.

Payments tie it all together no matter where commerce is taking place. Marketplaces and platforms, for example, have to make it easy for customers to interact, to have a simple checkout experience — ideally without leaving the page they are browsing. Payment choice is essential to attracting sellers (or, say, drivers, if it’s a platform catering to gig economy workers).

Embedded finance, he mentioned, can offer merchants loans against their sales, and can help give them information about who buys from them, thus helping them adapt their own product offerings. As they reach new audiences and markets, preferred and local payments need to be top of mind.

“If you are an ecosystem or a platform player, you need to be able to do all of this,” he told Webster. “You need to do it securely, and you need to be able to handle [commerce] peaks — because the last thing you want is for your platform to go down.”

No Need to Rewire the Business

The model’s working, at least for some of the larger eCommerce players, Georgakopoulos said. But many other companies have not been able to get to this level of intuitive commerce. They don’t have the capability or infrastructure, or simply the expertise, to get there. The good news is that these firms need not “rewire” themselves.

“That’s why companies such as ours,” he said of J.P. Morgan Payments, “have stepped in, to help these companies to develop these capabilities more simply.” J.P. Morgan Payments has helped, with partners such as the FinTech Sightline, to foster the interaction layer between the consumer’s accounts and credit cards and the provider’s (take a hotel, for example) own systems so that closed-loop ecosystems begin to take shape.

The conventional wisdom may be that the FinTech landscape has been decimated beyond repair. And, indeed, investors are pulling in a bit, valuations have plummeted, and some FinTechs are scrambling for cash in the wake of the Silicon Valley Bank collapse. The companies that relied on relatively cheap capital and low interest rates — but with no focus on profitability — will have a tough time of it.

But, as he stated, J.P. Morgan Payments, moving nearly $10 trillion daily, has found a number of FinTech partners that have built out strong capabilities to help client firms (and, by extension, J.P. Morgan’s client firms) manage know your customer (KYC), anti-money laundering (AML) and other back-end functions. The partnerships enable software as a service and fraud defense as a service.

“If you do this well, and you do it at scale — and globally – this is what gives us the license to do everything else,” Georgakopoulos explained.

That “everything else” includes payments, of course. Payments acceptance is the most basic building block. But then once the money starts coming in, firms have to be able to manage payout, and to make the customer experience as frictionless as possible. No matter the payment method, whether debit, credit or buy now, pay later (BNPL), or whether face, palm or thumbprints are part of the equation, it’s imperative to make sure all the methods work, and that they’re safe and tokenized.

“Payments become an invisible kind of back-end to that whole infrastructure,” he said, “and for the consumers, they can think about what payment methods they want to use in the casino, how much money they want to spend, how long they want to spend there. And you can preset those options.”

Changing B2B — for the Better

The same trends and technologies, he said, have the potential to reshape business-to-business (B2B) transactions, where 40% of hundreds of trillions of dollars are still done by paper check. But just as the pandemic pushed consumers and companies to rethink how and where they find one another, how price discovery is done, and of course, how they transact, the industrial economy is moving online too.

We’re seeing the emergence of embedded finance in commercial settings, helping bring trade credit, net terms and other fund flows into supply chains that improve the very nature of business itself.

“We’re in a world of higher interest rates, and higher inflation and a market environment that focuses much more on profitability,” Georgakopoulos said.

 

“Larger companies desire to become more efficient and small companies that serve those larger companies want to be able to work with as many of those platforms as possible so that they themselves can also grow … everyone from the CFO to the CEO to the head of product, head of technology, are all there at the table,” when it comes to discussions about digitization.

APIs are gaining ground, he mentioned, but there’s a long way to go before B2B finally enters the modern age.

Looking ahead, he told Webster, there will be more room for partnerships between J.P. Morgan Payments and FinTechs. “They’re very good clients, and they also raise the bar in terms of what we need to do.” And the key word for the months and years ahead boils down to one thing: resiliency.

As commerce — retail and commercial commerce alike — moves between the digital and physical realms, “you need to offer value,” Georgakopoulos noted. “And, increasingly, the value is coming through embedded finance.”

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Revolut Leak Shows the Cost of Constant ID Collection
Revolut’s mistake is the news, but the bigger problem is the growing number of companies being encouraged or required to keep copies of our most sensitive identity documents.

Online bank Revolut has revealed that it gave out sensitive personal and financial information of an undisclosed number of its customers in response to a fake government request.

The information that was handed over to an “unauthorized third party” reportedly includes names, dates of birth, occupations, addresses, phone numbers, account numbers, transaction histories (including Bitcoin), and even copies of government-issued IDs and onboarding verification selfies.

Revolut claims that derived biometric face data was not.

The company said that the data was handed over in response to an email that came from a real government agency’s domain, but was not actually sent or authorized by that agency.

The email passed several authentication checks (SPF, DKIM, and DMARC) that are designed to establish the authenticity of a message’s origin and integrity, but do not verify the legitimacy of the legal request itself.

Revolut said that it complied with the request “under the reasonable belief that it was an authentic government agency request” – and only later found out that it was not.

Revolut said it later realized its mistake, blocked the email address, and reported the incident to the relevant authorities.

Revolut said that only a “limited” number of its customers were affected by the data leak, and that the company’s systems were not hacked, nor was any money stolen.

The story broke on September 11 when Revolut customers started receiving an email notice about a data leak, and the news was picked up by media outlets the following day.

Revolut notice explaining customer identity and financial data was shared after an unauthorized government email request.

The reason this is a recurring problem is that companies are keeping highly sensitive information about their customers’ identities, and sometimes even financial transactions, for a long time, and this data is then available to be disclosed to third parties – either in response to valid legal requests, or, as in the case of Revolut, fake ones.

One reason for this is know your customer (KYC) and anti-money laundering (AML) rules. Revolut’s current UK customer privacy notice spells it out: the company generally keeps personal data of UK customers for no more than seven years after the relationship ends, and sometimes longer – for legal reasons.

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It’s hard to do anything about a copy of your old passport, or a photo of your face, or a record of your past transactions. These can be used to identify and profile you, and can be used to carry out targeted fraud. And this can happen even if the initial disclosure didn’t result in financial loss.

The more companies are forced to collect and store such information, and the more of it they have, the more opportunities there are for this data to be leaked, either by the company itself or a third party it works with. That's what makes governments' push for more ID checks just to access ordinary parts of life so reckless.

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This Is The Income A Family Needs To Live Comfortably In Every US State

Here’s the short version of what it takes for a family of four to live comfortably in 2026 by state:

In Massachusetts, you’d need nearly $330,000 a year - the highest figure in the entire country. Only three states clear the $300,000 mark: Massachusetts, Hawaii, and California. At the other end of the spectrum, Mississippi is the most affordable at about $188,000. That’s a full $142,000 less than what you’d need in Massachusetts.

So… how much does a family of four need in your state?

This map shows the pre-tax income a household with two working adults and two kids needs to live comfortably in every U.S. state.

The numbers come from SmartAsset (as of February 2026). They’re based on the familiar 50/30/20 budget: 50% for necessities, 30% for discretionary spending, and 20% for savings or other goals. These aren’t bare-minimum survival numbers—they’re what it takes to live pretty well while still putting money aside.

And as Visual Capitalist notesMassachusetts sits at the very top of that list. Massachusetts tops the ranking, with a family of four needing $329,555 per year to meet the 50/30/20 benchmark.

Hawaii follows at $313,165, while California ranks third at $302,682.

Rank State Income needed for family of four (2026)

  • 1 - Massachusetts - $329,555
  • 2 - Hawaii - $313,165
  • 3 - California - $302,682
  • 4 - Connecticut - $298,189
  • 5 - New Jersey - $295,110
  • 6 - New York - $291,533
  • 7 - Colorado - $283,213
  • 8 - Washington - $281,798
  • 9 - Oregon - $280,966
  • 10 - Vermont - $280,384
  • 11 - Alaska - $272,064
  • 12 - New Hampshire - $267,904
  • 13 - Rhode Island - $264,659
  • 14 - Minnesota - $263,078
  • 15 - Maryland - $257,837
  • 16 - Maine - $250,931
  • 17 - Montana - $249,434
  • 18 - Pennsylvania - $247,936
  • 19 - Illinois - $244,109
  • 20 - Virginia - $242,944
  • 21 - Nevada - $242,278
  • 22 - Indiana - $241,696
  • 23 - Wisconsin - $238,451
  • 24 - Arizona - $236,870
  • 25 - Utah - $235,789
  • 26 - Delaware - $228,134
  • 27 - Ohio - $226,221
  • 28 - Idaho - $226,054
  • 29 - Florida - $223,392
  • 30 - New Mexico - $223,142
  • 31 - Nebraska - $223,059
  • 32 - Missouri - $217,734
  • 33 - Georgia - $214,573
  • 34 - Michigan - $214,323
  • 35 - South Carolina - $212,909
  • 36 - North Carolina - $212,410
  • 37 - Wyoming - $212,410
  • 38 - Oklahoma - $211,910
  • 39 - North Dakota - $210,496
  • 40 - Kansas - $207,917
  • 41 - Iowa - $204,422
  • 42 - Texas - $203,424
  • 43 - West Virginia - $202,592
  • 44 - South Dakota - $201,760
  • 45 - Alabama - $198,931
  • 46 - Louisiana - $197,933
  • 47 - Tennessee - $197,267
  • 48 - Arkansas - $195,437
  • 49 - Kentucky - $194,854
  • 50 - Mississippi - $187,533

Connecticut, New Jersey, and New York aren't far behind, bringing the number of states with comfortable-income thresholds above $290,000 to six.

Colorado and Vermont Make the Top 10

As expected, many of the highest income thresholds are concentrated in the Northeast and along the West Coast.

However, Colorado has the seventh-highest threshold in the country at $283,213, ranking above Washington and Oregon.

Vermont rounds out the top 10 at $280,384, despite having the second-smallest population of any U.S. state. Meanwhile, nearby states like New Hampshire, Maine, and Rhode Island all fall outside the top 10.

Just Six States Come in Below $200,000

Despite the wide range in living costs across the country, only six states have a comfortable-income threshold below $200,000 for a family of four.

Mississippi ranks lowest at $187,533, followed by Kentucky. The states of Arkansas, Tennessee, Louisiana, and Alabama also fall below the $200,000 mark.

The gap between Massachusetts and Mississippi exceeds $142,000 per year, meaning the Massachusetts benchmark is about 76% higher.

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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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