When confidence disappears, even the biggest crypto platforms can unravel faster than most people expect.

One of the biggest lessons from the past few years in crypto is that an exchange doesn’t always fail because it has run out of money. Sometimes, it fails because everyone believes it will.

Imagine waking up to news that your preferred platform may be facing financial difficulties. Within minutes, social media is flooded with rumours. Thousands of users begin withdrawing their funds. Others follow – not because they know the platform is insolvent, but because they fear being the last person left if it is.

This chain reaction is known as a depositor run, or more commonly, a crypto bank run.

We’ve seen it happen with platforms like Celsius, Voyager Digital, and most famously, FTX. These events demonstrated that confidence is one of the most valuable – and fragile – assets in the entire cryptocurrency industry.

So why do depositor runs happen, and why are crypto platforms particularly vulnerable?

What Is a Depositor Run?

A depositor run occurs when a large number of customers attempt to withdraw their funds from a financial institution at the same time because they fear their assets may no longer be safe.

Traditional banks have faced depositor runs throughout history. Cryptocurrency platforms face the same challenge, but the risks are often amplified.

Unlike most banks, centralized crypto exchanges generally do not benefit from government-backed deposit insurance. Once confidence begins to erode, customers can often withdraw their assets instantly, placing enormous pressure on the platform’s available liquidity.

The painful irony is that a platform that might have survived under normal conditions can become insolvent simply because too many people tried to leave at once.

Why Crypto Platforms Are Especially Vulnerable?

Most centralized cryptocurrency exchanges and lending platforms act as custodians, holding digital assets on behalf of millions of users.

While customers often assume their assets remain untouched, some platforms use part of those deposits to support lending, provide liquidity, or facilitate leveraged trading.

This can improve capital efficiency, but it also means that not every deposited asset is immediately available for withdrawal at the same time. The model resembles fractional reserve banking, where institutions do not hold every customer’s deposit in liquid form.

As long as withdrawals happen gradually, the system generally functions smoothly. Problems arise however when everyone wants their money back at once.

What Triggers a Crypto Depositor Run?

Several factors can quickly undermine confidence in a cryptocurrency platform.

• Lack of Transparency

Trust depends heavily on transparency. If users cannot verify whether an exchange actually holds sufficient reserves, rumours can spread rapidly.

The collapse of FTX in 2022 illustrated this risk dramatically. What initially appeared to be a liquidity problem ultimately exposed an estimated US$8 billion shortfall in customer assets, triggering one of the largest withdrawal waves in crypto history.

• Market Volatility

Sharp declines in cryptocurrency prices can reduce the value of assets held by exchanges and lending platforms. During the 2022 crypto market downturn, platforms like Celsius Network and Voyager Digital faced intense withdrawal pressure as falling prices weakened their financial positions and eroded user confidence.

• Leverage and Counterparty Risk

Many crypto businesses are deeply interconnected. When one major firm experiences financial distress, the effects tend to spread.

The collapse of Three Arrows Capital exposed this vulnerability. Several lenders and exchanges with significant exposure to the hedge fund suffered substantial losses, forcing some to suspend withdrawals and intensifying fears across the broader market.

• Operational Failures

Confidence can disappear overnight if users believe a platform is no longer secure. Exchange hacks, smart contract vulnerabilities, cybersecurity breaches, or governance failures can all trigger sudden withdrawal requests – even when customer assets have not actually been compromised.

• Regulatory Uncertainty

Legal uncertainty can also fuel panic. Where regulations are weak or customer protections are unclear, users often have little assurance about what happens if an exchange becomes insolvent. Without clear rules governing custody, reserve management, or asset segregation, rumours can quickly become self-fulfilling.

What Has Changed Since the 2022 Crypto Crisis?

The failures of several major platforms forced the industry to rethink transparency.

One notable development is the introduction of Proof of Reserves – a system that allows exchanges to demonstrate they hold certain customer assets on-chain. Many platforms now use cryptographic techniques such as Merkle Trees to improve reserve verification.

However, Proof of Reserves has limitations. Showing assets alone does not reveal a platform’s liabilities. An exchange may demonstrate substantial reserves while still owing customers more than it actually holds. For this reason, many experts argue that Proof of Reserves should be complemented by independent audits, clear financial disclosures, and stronger governance.

Regulators have also begun introducing more comprehensive rules covering customer asset segregation, custody standards, reserve management, and capital requirements to reduce the likelihood of future depositor runs.

Can Depositor Runs Be Prevented?

No financial system can eliminate the risk entirely but several measures can significantly reduce the likelihood and severity of a depositor run: maintaining adequate liquid reserves, publishing transparent reserve and liability disclosures, segregating customer assets from company funds, strengthening corporate governance and risk management, and complying with prudential and regulatory standards.

For users, many in the crypto community embrace the principle: not your keys, not your coins.

This reflects the idea that assets held in a personal wallet remain under the user’s direct control rather than depending on a centralized custodian. That said, self-custody comes with its own responsibilities – including securely managing private keys and protecting against theft or accidental loss.

Why Depositor Runs Matter Beyond a Single Exchange

A depositor run affects far more than the platform at its centre.

When one major exchange suspends withdrawals or collapses, fear often spreads across the wider market. Investors rush to exit other platforms, stablecoins come under pressure, lending slows, and prices can decline sharply.

This contagion effect reveals how deeply interconnected the cryptocurrency ecosystem has become.

As the industry matures, maintaining trust is no longer simply a matter of technology. It increasingly depends on sound governance, effective risk management, and transparent operations.

Bottom Line

Cryptocurrency was created to reduce reliance on traditional financial intermediaries. Yet as centralized exchanges became the primary gateway to digital assets, they also reintroduced one of finance’s oldest risks: the loss of confidence.

The collapses of Celsius, Voyager, and FTX showed that even in a blockchain-based financial system, trust remains indispensable.

Today, the focus is now on whether crypto platforms can build and maintain the trust needed to endure uncertain times, rather than just attracting users.

Depositor runs are not just about liquidity. They are about trust. And in both traditional finance and digital finance alike, confidence remains the foundation on which every financial system is built.​​​​​​​​​​​​​​​​

If you enjoy analytical commentary on digital asset regulation, crypto markets, and emerging financial technologies, consider subscribing to my newsletter where I share additional research, commentary, and industry insights.

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Why Crypto Exchanges Collapse: Understanding Depositor Runs in Cryptocurrency Platforms was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story.

Most people still associate crypto with price charts: when $BTC moves 10% in a day, it becomes the headline. When nothing dramatic happens, the industry tends to disappear from mainstream conversations.

The funny thing is that some of crypto’s biggest developments happen when nobody is paying attention.

Crypto Is Quietly Becoming Infrastructure

Ten years ago, crypto products existed almost entirely within the crypto industry. Today, millions of people interact with blockchain technology without necessarily knowing it.

Stablecoins are being used for international payments, financial institutions are experimenting with tokenized assets, and fintech companies are integrating crypto services directly into their products. For many businesses, blockchain is slowly becoming infrastructure rather than a standalone industry.

The companies benefiting the most from this shift may not even describe themselves as crypto companies in the future.

User Experience Is Finally Winning

For years, crypto products were built primarily for crypto-native users. Setting up wallets, understanding seed phrases, and moving assets across networks became almost a rite of passage.

That approach is changing. The conversation has shifted from “How decentralized is this?” to “Can someone use this without reading a 20-minute tutorial?”

The products that simplify complexity are often the ones that achieve mainstream adoption. Most users don’t care which blockchain powers an application. They care whether it solves a problem quickly and safely.

The Next Wave of Adoption Will Look Different

The next stage of crypto adoption probably won’t look like the previous one. It won’t necessarily be driven by retail investors opening exchange accounts for the first time.

Instead, adoption is increasingly coming from businesses, financial institutions, and consumer applications quietly integrating crypto functionality into products people already use.

The most interesting question in crypto today isn’t whether blockchain technology will survive. It’s how invisible it will become once it succeeds.

Ironically, crypto may finally become mainstream when people stop talking about crypto altogether.

Why the Most Interesting Thing About Crypto in 2026 Isn’t the Price was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story.

Intelligence does not have to be artificial.

The goal of this field was always to reproduce what a brain does. Somewhere along the way “artificial” stopped meaning inspired by the real thing and started meaning nothing like it — enormous, power-hungry, and opaque.

You’re tired of AI launches and IPOs? So am I. Every week there’s a bigger model, a longer context window, another benchmark nobody outside the lab can reproduce — and underneath it, the same machine doing the same thing a little faster and a lot more expensively. I mean, just looking at my emails these days is making me nauseous. I do not even check my social media anymore, and even less the stock market.

But, instead of complaining and be satisfied with the status quo, I decided to look at the problem from a different angle.

The main problems everybody knows without knowing it…

AI is expensive (yet, it does not have to be)

The cost problem isn’t separate from the design. It falls out of four choices that the field made early and never really revisited.

1- It reasons in the dark. Which makes hallucination or fake generation very hard to catch, yet to fix. Hidden states are well, hidden.

2- Scale is not intelligence. The reflex has been to make the model bigger and hope understanding shows up (it never will, the bigger the model, the more “links” it can do between concept and give the illusion of understanding). Scale = $$$$$$$$$$$$$$$.

3- Biology as the last of their concern. The brain runs on about twenty watts, and that number is a challenge, not a footnote. While we cannot make an AI that works on 20watts we can definately reduce the amount of energy consumption.

4- The root of it is profit. Not science. Even OpenAI leader is confirming it by saying that AI will eventually be sold like electricity and water — by companies like OpenAI. Article link: https://www.businessinsider.com/sam-altman-ai-utility-electricity-water-openai-2026-3

Now let’s talk about what it was supposed to be from the start

The goal of this field was always to reproduce what a brain does. Somewhere along the way “artificial” stopped meaning inspired by the real thing and started meaning nothing like it — enormous, power-hungry, and opaque.

I think we need to take the biology seriously instead of metaphorically: real neural mechanisms, a memory that consolidates the way a hippocampus does, a neurochemistry that actually modulates behaviour, learning that happens as the system runs rather than only in an offline training run. Those are design constraints, not decoration. And will lead to the “second generation” of AI.

The myth of AGI

a very convenient one if what you need is a reason to keep raising money.

While I have been plain, here’s where I don’t stand: AGI. The industry’s favourite three letters do a lot of quiet work — a general, human-beating machine, forever a few years and a few hundred billion away. It’s a wonderful story — or a frightening one, depending on where you stand — and a very convenient one if what you need is a reason to keep raising money. It’s a poor description of what these systems actually are, and a worse goal to organise a field around.

It’s a poor description of what these systems actually are, and a worse goal to organise a field around.

And the way today’s models are built won’t get there — not for lack of ambition, but for reasons you can put numbers on. Large language models improve along a scaling curve, and that curve has a shape: the returns diminish. Each new increment of capability takes not a little more compute but multiples more; the graph everyone cites bends the wrong way, flattening as the bill climbs. Every training run costs more than the last and buys less than the last one did. That isn’t a detail better engineering erases. It’s the shape of the method itself.

Every training run costs more than the last and buys less than the last one did. That isn’t a detail better engineering erases. It’s the shape of the method itself.

Now set that against a hard limit: power is finite. You can’t answer a curve of exponentially rising cost with an infinite supply of energy, because there isn’t one. A method whose only real lever is “make it bigger” runs into a wall that isn’t philosophical — it’s thermodynamic. Somewhere on that curve the next run stops being affordable, then stops being physically possible, long before it stops being merely better at text.

You don’t get a different kind of thing by making the same thing bigger

And that’s the deeper point: what scales here is fluency, not understanding. A model trained to predict the next word learns the statistics of language extraordinarily well. It doesn’t thereby acquire a grounded model of the world, a cause it can reason about, or a memory it can update — and no amount of the same training conjures those out of more of the same text. You don’t get a different kind of thing by making the same thing bigger. You get a costlier version of the same thing. A transformer is, underneath, a very good text generator; scale it and you get a better text generator — not a mind that understands, and not consciousness quietly emerging from the weights. Fluency is not comprehension, and no quantity of the first ever becomes the second. Something like general intelligence, if it’s reachable at all, will come from a different design — grounded, able to reason step by step, able to learn as it runs.

The point of this work was never to conjure a god

The point of this work was never to conjure a god. It was to build something genuinely useful — that reasons, remembers, and helps — and to run it on hardware people can actually afford. Intelligence doesn’t have to be general to be worth having, and it certainly doesn’t have to be a superbeing to earn its keep. Chasing AGI is how you end up with the bill on the other pages. Building something useful, efficient, and yours is how you don’t.

What a discovery is for, and how it gets used, stays a human call — the machine widens what we can see; the judgment is still ours.

None of this means the tools are useless — the opposite. An AI can read across billions of documents and surface a link between two of them in seconds, connections no person would ever stumble on alone. That is a genuinely powerful research instrument, and we build with it every day. But it won’t know what to do with what it finds unless someone told it beforehand what to look for and why. Finding is not deciding. What a discovery is for, and how it gets used, stays a human call — the machine widens what we can see; the judgment is still ours.

The danger isn’t the tool

If one ever goes autonomous and causes genuine harm, it will be because a person somewhere pointed it that way —

Some people will tell you AI is the real long-term danger. We’d put it the other way around: the danger is us. A model does what it is built and instructed to do. If one ever goes autonomous and causes genuine harm, it will be because a person somewhere pointed it that way — wrote the objective, wired it to something it should never have touched, or pulled out the guardrails that other people had put there in the first place. Even the runaway story needs a human at the start of it: someone to build it, aim it, and take it off the leash. Even if it escapes, a human had to set it loose or dare it to.

That isn’t a reason to be careless — it’s the opposite. It means the responsibility is ours and stays ours, which is exactly why we should keep the reasoning legible and the controls somewhere a person can see them. A tool you can read is a tool you can hold to account. That matters far more than pretending the machine has a will of its own.

Now time for a little shameless self-promotion ;) I built Grillcheese Research Laboratory exactly to study, learn and solve those problems and share how to do it with as much people as possible. I invite you to check the link to our website if you are curious. https://grillcheeseai.com

Let me know in the comment what you think and if you have more ideas / different views / links.

Thanks for reading and have a wonderful day!

Yours, Nick

Beyond A.I. was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story.

Written by Dan Feaheny, Fintechie

In the 1960s sitcom, Get Smart, Agent 99 and Maxwell Smart are a spy duo working for CONTROL. Across five seasons, we never learn Agent 99’s name. Sixty years later, agentic AI has the potential to utterly transform how work gets done and society functions. One asks, how can AI scale sustainably without a massive rethink around digital identity? AI agents are already trading tokens, managing treasuries, deploying capital, optimising yield and executing strategies. If AI can autonomously move data and value across the internet, agentic agent identity (KYA or know-your-agent) will quickly become the litmus test. Indeed, at a recent conference Nicolas Kokkalis, founder of Raspberry PI talked about one of the most urgent challenges in the AI era: how to maintain trust and verify real human identity as AI systems become capable of generating convincing bots, profiles and interactions at scale.

Source: X

Real-time systems

Real-time systems of intelligence converge across instant data streams, autonomous AI generated agents and tokenisation. As we transition from batch to real-time and from human to machine, then envision existential risks to the internet as we know it. Automation and orchestration without effective guardrails or strict governance is a recipe for disaster; with many more bots than humans processing data online, then an urgency for decentralised, user-controlled identity wallets increases from all corners. From data munching big techs to big government surveillance, there is an ever growing trust gap. Global angst amongst the next generation rises as AI embeds into workflows, decisioning and results. The opportunity for global banks is now. There are potentially two primary contenders for the custodial benefits of issuing identity wallets online and at scale: they are JPMorgan Chase and Revolut — both have global ambition, top talent and long-term vision. Let us square, therefore, the circle between privacy and security.

Payments (analogue to digital)

From card-based electronic payments of the ‘get smart’ era to today’s smart contracts, identity access and governance has become patchwork at best and reactive at worst. The levels of fraud and scams continue to rise exponentially; networked individuals and state actors penetrate weak defences and poorly designed architectures; financial regulators supervise reactively from antiquated advice and manual guidebooks. Visa Direct and Mastercard Move are swiftly becoming instant data exchange networks and platforms — leveraging global trust and brand, they aim to become default ecosystems for the internet of value. However, these two behemoths have little ambition in becoming identity issuers or wallet custodians.

Financial fraud and scams

Nasdaq Verafin just released its annual Global Financial Crime Report: illicit financial activity is now at a staggering $4.4 trillion; fraud and scams account for over $500 billion causing material losses for the victim and further erosion of institutional trust; and, criminal organisations and state actors move illicit funds across borders, jurisdictions and sectors in just seconds. Meanwhile, regulated institutions remain buried in technical debt and blinkered by siloed culture. Ultimately, which regulated banks are poised to capture both the commercial and societal benefits from issuing identity credentials via digital wallets for cross-border value exchange? Possibly, Revolut and JP Morgan Chase lead the pack — both have global ambition, top technology and financial platform thinking.

Fintech evolution

One must admire the speed of change since 2008. The smart phone has become the operating system for cross-border value exchange. Chinese leaders launched WeChat and AliPay via QR code, bypassing card networks and opening up vast fintech potential. Bitcoin and other derived blockchain protocols enable P2P stablecoins linked to base fiat currencies — hence all these leap-frogging innovations and digital identity becomes ever more patchwork and fragmented.

Digital identity

At sovereign level Europe, Australia and India are leveraging digital identity systems for both accessibility and inclusion to support citizen services online:

· European Digital Identity Framework (eIDAS 2.0) — Europe is building digital identity wallets allowing citizens to prove identity and credentials across borders.

· Australian Trusted Digital Identity Framework (TDIF) — a framework of rules and standards enabling secure, trusted and consistent digital identity verification, so forming the foundation of national Digital ID legislation.

· Indian Unique Identification Authority of India (UIDAI) — India’s digital identity platform now supports over a billion citizens and underpins financial inclusion, payments and digital public services.

Technology vendors, including Okta to Ping, deliver identity access and governance to protect stakeholders, customers and employees from hackers and scammers; operating systems from closed Apple iOS to open Google Android continuously monitor their ecosystems of applications to maintain data safely and securely. Moreover, banks use a patchwork of federated systems, third party support and proprietary databases to reduce fraud and protect their customers; SWIFT moves government fiat, and stablecoin platforms move digital assets. We picture a lack of interoperability between networks, systems and applications — the internet was never designed with an identity layer, but here we are. What would Agent 99 do?

Apps and infrastructure converging

Fintechs have taught legacy banks how to better serve their customers via better front end experiences. From cash to stablecoins and from batch to instant, digital rails collapse monolithic IT architectures replacing static core systems of record; agentic AI enables autonomous workflows horizontally across departments, borders and even jurisdictions; modern and scalable IT systems are continuously executing, highly automating and tightly interconnecting; table stakes are graph matrices and algorithms of BigTechs such as Facebook aka Meta; cloud technologies combine with data-intensive AI for instant decisioning without human inputs. Hence, we need far more data governance and codebase maintenance as data lineage and leakage get worse and the financial services industry needs KYA or know-your-agent tooling immediately to identify these machines and bots transferring money online on behalf of humans and entities. As the dream of Web3 and decentralised finance nears, identity wallets issued by trusted and regulated banks should help us all cross the divide resulting in a safer online world, including:

· systems that are transparent and verifiable

· networks that are global from day one

· economic models that align users, creators, developers and operators.

Infrastructure that does not depend on a small number of intermediaries

This half of this decade will shape the internet’s future for generations to come, so let’s help the banks issue identity and restore institutional trust for all. For decades banks protected money, governments protected identity and technology firms-controlled access to information. Yet agentic AI may collapse these boundaries into a single problem. An autonomous machine trading assets, initiating payments, signing contracts and interacting with governments cannot simply rely on usernames and passwords designed for humans. The internet was built around connectivity, not trust. And that design decision mattered little when people moved information; it becomes far more consequential when machines begin moving money, assets and legal rights. The institutions that issue and verify trusted digital identity may not simply control authentication. They may ultimately determine who can participate in the economy itself. So, the question is no longer whether AI needs an identity layer — the question may be whether future citizens, companies and AI agents require permission from whoever owns it.

The next banking war is not about money: it is about identity was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story.

How law and regulation are responding to technological change in digital assets and money: what does it mean for businesses?

Written by Brett Hillis, Partner Reed Smith LLP

Technological change is nothing new and legal systems have been responding to it since at least the introduction of the printing press. Changes in technology gives rise to questions that the law has not needed to answer before, or not at the same scale. To take an example, how should law and regulation respond to driverless vehicles? Should such vehicles be allowed on public roads? What safety requirements do such vehicles need to comply with? Who is liable for accidents caused by such vehicles?

Whilst there is an interesting history to how law and regulation respond to technological change, the purpose of this article is to identify different approaches that law and regulation is taking to technological change today, looking specifically at digital assets and digital money. These are important issues for business; the carrying on of transactions between AI agents is going to require some form of programmable money as measure of value and a means of exchange. Tokenisation has the capacity to reduce settlement times and make many transactions more efficient. At the same time, the stakes in decisions on where to invest feel higher than ever before, as such decisions face conflicting trends. Capital feels more mobile than ever and can search out opportunities across jurisdictions. Businesses have greater opportunities to create brand value globally and network effects create “winner take all” markets, where a Taylor Swift is dominant in a way no one else has been for decades. At the same time, countries are taking much more varied approaches to how these markets affect their economies. Some are adopting an “open doors” policy, others are pragmatically adopting regulatory regimes, whereas others have rejected these markets in favour of centralised national paradigms.

CBDCs vs private stablecoins

At present, the most obvious distinction is between those jurisdictions which are embracing private stablecoins, chiefly the US, and those looking to develop their own central bank digital currencies (all be it not using blockchain technology to do so), most notably China. Through the GENIUS Act, the US has developed a comprehensive regulatory framework for USD denominated stablecoins. The same time, the US has taken steps to prevent the establishment, issuance and use of CBDCs within the US. This ban affects not just foreign issuers but the US Federal Reserve itself.

China bans unapproved yuan stablecoins

At the other end of the spectrum, China has maintained a ban on cryptocurrency transactions since 2017, which continues to be extended. For example, earlier in 2026, China was reported to have banned unauthorised offshore issuance of yuan-pegged stablecoins. At the same time China has been promoting the digital yuan, which is seen as part of a strategy to reduce reliance on the US dollar. The two superpowers represent opposites in their approach and, while interesting geopolitically, their different approaches to this most obvious issue are not the most elucidating for businesses since the choice likely amounts to being ‘open for business’ or not. Of more interest are some of the more subtle distinctions regarding how countries are responding to digital assets and programmable money.

Laying the groundwork?

Before one gets to regulation, a fundamental question is the legal nature of digital assets — in particular, are they a form of property and, if so, what form of property? Answering these questions are key to establishing dependable ways in which digital assets can be used. A legal regime that does not reliably address these questions can leave the most basic questions for business uncertain. Whilst this may not stop innovation, it puts a break on investment especially where the underlying issue manifests itself. The way to approach these issues can vary between countries based on the legal system with courts, legislators, academics and trade bodies all potentially playing a role. In England, whilst there are critical voices, a response to these questions has received broad acceptance. Work on the issues proceeded through the UK Jurisdiction Taskforce’s (“UKJT”) Legal Statement on cryptoassets and smart contracts, Law Commission projects and decided cases, and included a short piece of legislation (the Property (Digital Assets etc) Act 2025) to address one specific uncertainty. Whatever the questions about regulation, attention to these essential issues of legal classification is vital.

Early regulation vs “wait and see”

Some jurisdictions moved early to set up regulatory regimes for digital assets. An interesting example was the EU and its MiCAR regulation. In setting out a regime early, MiCAR gave market participants a level of predictability about the scope and content of regulation. Having a clear target as to what businesses need to do and, crucially, certainty that it will not change with the political weather, has encouraged many international digital asset companies set up MiCAR regulated entities in response. That early approach can also act as an anchor, pulling the regimes of other jurisdictions towards it, in terms of the scope and content of regulation. The EU’s approach has generated a lot of institutional interest, and early regulatory adoption can build credibility. But early adoption risks rules becoming out of date. Much of MiCAR was already written by the time of the FTX collapse. It appears that the EU digital assets industry has achieved good growth with no obvious failures, but there is a perception (fair or unfair) of unnecessary friction in the EU regime.

An obvious comparator to the EU is the UK’s approach, which has been to move later and in a more piecemeal fashion seeking to learn lessons from other countries’ approaches. The UK introduced AML requirements for cryptoasset firms at the same time as the EU, then moved to regulate financial promotion and is bring cryptoassets fully within the UK regulatory perimeter, with effect from October 2027. The theory behind this approach is that it will better enable the UK to calibrate its regime to reflect the experiences of other jurisdictions. Certainly, the UK’s consultations on the new regime have been extensive and industry has been given a good opportunity to consider and comment on the potential new rules. Whether that effort is worth it will partly come down to the extent to which this work has produced a better regime, or one that industry and the public better understand.

But that is not the only factor. The “wait and see approach” has allowed some crypto businesses to develop and grow in the UK whilst complying with the more limited current or developing regime and gain traction and size whilst not imposing full regulation on them from the outset. On the one hand, these businesses face a more complex and changeable path to dealing with emerging regulation; on the other hand, some of that greater complexity only arises when they are in a better position to address it. The approach has also given the UK the time and space to work out its views regarding digital assets. There was considerable scepticism at the regulatory level regarding these products and markets but those views have become somewhat more balanced, although there is room for further movement. There is also evidence that UK authorities have been listening to industry (see its response to criticism of holding limits on stablecoins discussed below).

Embrace the substitutes?

One way to distinguish different countries’ approaches to this area is how comfortable they are with products and services that are substitutes (sometimes less than perfect substitutes) for existing products and services. More specifically, to what extent are they comfortable with holdings of stablecoins as a substitute for deposits? The US has established a comprehensive prudential regime for stablecoins through the GENIUS Act and appears unperturbed about any potential for holdings of stablecoins to reduce bank deposits and its effect on US financial stability. Stablecoins appears to be an acceptable substitute for bank deposits — indeed, the point seems hardly to have been raised. The UK approach has been different in that the Bank of England has been exercised about the effect on bank deposits. In part, this has been to avoid customer confusion — setting up guardrails to reduce the risks a stablecoin issued by a bank is, in fact, a deposit with deposit protection sitting behind it. This lies behind the Bank of England preventing banks issuing stablecoins except through a separate company. But the UK approach has gone beyond this and the Bank has proposed strict holding limits on stablecoins, a move which provoked industry backlash — even from the House of Lords. In response, the Bank has said it is examining alternative means of ensuring financial stability (e.g. through issuance limits). It will be interesting (and important) to see where it lands.

Are there lessons for firms from this experience?

I think there are several general points for those firms navigating policy in this field:

· understand how policy can shift — firms need to think through and hedge against how the policy approach can change, as demonstrated by the variety by the shifts in US policy.

· good regulation can build credibility — there is comfort in dealing with firms that are well-regulated.

· respond to consultations — whether through trade associations or on your own. It may well make a difference.

The battle over stablecoins, CBDCs and tokenisation is often presented as a technology story. It is not. It is a competition for economic influence. Just as previous generations fought to host stock exchanges, payment networks and internet platforms, today’s race is about who controls the rails of programmable value. The jurisdictions that get law and regulation right will attract capital, talent and innovation. Those that get it wrong may discover that in the digital age, financial leadership can migrate far faster than anyone imagined.

How law and regulation are responding to technological change in digital assets and money: what… was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story.

Introduction

In today’s digital age, artificial intelligence (AI) is not just a buzzword; it’s a game-changer. As an online entrepreneur, I’ve seen firsthand how AI has revolutionized the way I earn money online. I’ve been in the online money-making game for quite some time now, starting out on now popular subreddits such as /r/beermoney or /r/signupsforpay and even /r/churning, as well as having been a seller on Facebook marketplace (reselling thrifted items or even items found while dumpster diving) and also actively selling on OfferUp, Mercari, Poshmark, and Ebay. Nowadays I am focusing more on Ecommerce, such as drop shipping and affiliate marketing so at the risk of “tooting my own horn” as they say- I would like to think that I have picked up quite a few nuances or tips and tricks along the way. I am more than happy to share all I have learned, for in the beginning it was articles like these that helped me understand what I needed to start investigating etc. in order to start making money online on my own. The point here is that recently

‘/ I have made the discovery that there is one single factor in my online money making that has never failed me and only ever made things better- that is AI. Hands down, AI has probably saved me years of time on work since I started using it- not exaggerating. From optimizing my website’s SEO to enhancing my online marketing strategies, AI has become an invaluable tool in boosting my online income. By analyzing data and predicting customer behavior, AI helps me identify the most relevant keywords and content ideas, ensuring that my articles and website rank high on search engines. This drives organic traffic and attracts a larger audience to my online business. Furthermore, AI-powered chatbots have improved customer support, allowing me to provide personalized assistance to visitors on my website 24/7. This not only enhances the customer experience but also increases conversion rates. With AI in my corner, I’m able to automate time-consuming tasks like social media posting and data analysis, freeing up more time for creativity and growth strategies. It’s a win-win situation for me and my online business. Join me as I delve deeper into the transformative power of AI and discover how you too can leverage this technology to maximize your online income (and hopefully we can both be internet GAZILLIONAIRES in a few years tops, hehe.)

Understanding the Basics of AI and Machine Learning

AI, or artificial intelligence, is a revolutionary advancement in computer science. It focuses on empowering machines to handle tasks that typically require human intellect. Within the AI domain, machine learning emerges as a crucial component, enabling machines to learn from data and improve their performance over time.

Machine learning algorithms play a vital role by analyzing extensive datasets to recognize patterns and make accurate predictions. This capability is especially beneficial for online entrepreneurs like yourself, providing valuable insights into customer behavior and preferences.

The speed and efficiency at which AI algorithms can process large volumes of data are remarkable. This allows you to make informed, data-driven decisions and optimize your online income strategies effectively. By harnessing the power of AI, you can not only outperform competitors but also swiftly adapt to dynamic market conditions, ensuring sustained success in the online business landscape.

How AI is Revolutionizing Online Marketing and Advertising

The impact of AI on online marketing and advertising is huge. Thanks to AI tools, we can dig into customer data, group audiences, and whip up personalized marketing campaigns. One of the coolest things about AI in online marketing is how it helps us find and target the juiciest keywords for SEO. By checking out search trends and how users behave, AI algorithms can pick out the keywords that are most likely to bring organic traffic to our sites. This means our content gets a nice spot in search results, making us more visible and drawing in a bigger crowd.ms automate the buying and selling of online ad space. By analyzing user data and real-time bidding information, AI algorithms can optimize ad placements and target the right audience, maximizing the return on investment (ROI) for online advertisers.

Case Studies of Successful AI-Powered Online Businesses

Let’s take a closer look at some standout examples of AI-driven online businesses that are making waves in the digital world: 1. AI-Powered Chatbots Revolutionizing Customer Service: Companies like Shopify have implemented AI chatbots on their websites, providing instant customer support 24/7. These chatbots use AI algorithms to understand customer queries and provide personalized responses, leading to improved customer satisfaction and increased sales. 2. Personalized Product Recommendations: Online retail giant Temu utilizes AI algorithms to analyze customer behavior and preferences. By offering personalized product recommendations based on past purchases and browsing history, they have seen a significant boost in conversion rates and customer engagement. 3. Dynamic Pricing Strategies: GoDaddy, an e-commerce platform, leverages AI to adjust pricing in real-time based on market demand, competitor pricing, and customer behavior. This dynamic pricing strategy has helped them stay competitive and maximize profits. 4. Predictive Analytics for Marketing Campaigns: CJMarketing, a digital marketing agency, employs AI-powered predictive analytics to optimize their marketing campaigns. By analyzing data trends and customer behavior patterns, they can target specific audiences more effectively, leading to higher ROI for their clients. These case studies demonstrate the diverse applications of AI in driving success for online businesses. By harnessing the power of AI technologies, these companies have been able to enhance efficiency, improve customer experiences, and achieve significant growth in their respective industries.

The Role of AI in Improving Customer Experience and Engagement

In the digital world, customer experience is paramount. AI-powered tools and technologies can enhance customer experience and engagement in several ways. Personalization is one area where AI truly shines. By analyzing customer data, AI algorithms can create personalized recommendations, offers, and experiences. This level of personalization not only delights customers but also increases their loyalty and likelihood of making repeat purchases. AI can also improve customer engagement through chatbots and virtual assistants. These AI-powered tools can engage with customers in real-time, providing them with personalized recommendations, answering their questions, and even assisting with the purchasing process. This level of personalized assistance creates a seamless and enjoyable customer journey, increasing the chances of conversion and repeat business. Furthermore, AI can analyze customer feedback and sentiment to identify areas for improvement. By understanding customer preferences and pain points, online businesses can tailor their products and services to better meet customer needs, further enhancing the overall customer experience.

Leveraging AI to Optimize Website Performance and User Experience

In the highly competitive online landscape, website performance and user experience are critical factors that can make or break an online business. Thankfully, AI can help us optimize these aspects. AI-powered tools can analyze website performance metrics, such as page load times and bounce rates, to identify areas for improvement. By optimizing website speed and usability, we can ensure that visitors have a smooth and enjoyable browsing experience. This reduces the chances of visitors leaving our website prematurely and increases the likelihood of conversion. Additionally, AI algorithms can analyze user behavior on our websites to identify patterns and make recommendations for improving website design and layout. By understanding how users interact with our websites, we can make data driven decisions to optimize the user experience and increase engagement.

Monetizing AI-Generated Insights and Predictions

AI not only helps us optimize our online income strategies but also provides valuable insights and predictions that can be monetized. For example, AI algorithms can analyze market trends, customer behavior, and competitor data to make predictions about future demand and consumer preferences. These predictions can be used to identify profitable niches, develop new products or services, and create targeted marketing campaigns. Furthermore, AI-generated insights can be packaged and sold as valuable reports or consultancy services. Online entrepreneurs can leverage their expertise in AI and data analysis to provide insights and recommendations to other businesses looking to improve their online income strategies. By monetizing AI-generated insights and predictions, online entrepreneurs can diversify their income streams and maximize their earnings.

My Personal Favorite Tools: AI That Have Truly Proven Useful

When it comes to navigating the digital landscape, having the right tools at your disposal can make all the difference. Here are some AI-powered tools that have truly proven their worth in enhancing my online experience:

1. Monica AI: A reliable assistant that simplifies complex tasks and provides quick access to a wealth of information, Monica AI has become an indispensable part of my digital toolkit. Her efficiency and versatility make her my go-to AI companion for various tasks.

2. Unicorn AI Website Builder: This tool has revolutionized the way I create websites. With its intuitive design features and AI-driven customization options, building a professional website has never been easier. (If you sign up using the link above, use promo code “viafirst20” for 25% off!)

3. Autoblogger.ai: Streamlining content creation, Autoblogger.ai utilizes AI to generate engaging blog posts and articles effortlessly. Its content generation capabilities have saved me valuable time and effort.

4. Beacons.ai: A powerful tool for creating personalized landing pages and microsites, Beacons.ai has helped me enhance my online presence and engage with my audience more effectively. It is also by far my favorite tool on this list!

5. NicheScraper: This AI tool has been invaluable for conducting market research and identifying profitable niches. Its data-driven insights have guided my business decisions and strategies.

6. Rytr: A versatile writing assistant, Rytr uses AI to generate high-quality content, from blog posts to social media copy. Its ability to streamline content creation has been a game-changer for me.

These AI tools have significantly enhanced my online capabilities, making tasks more efficient and effective. Incorporating these tools into my workflow has not only saved me UNBELIEVABLE amounts of time but also improved the quality of my work. If you’re looking to boost your online presence and streamline your processes, consider exploring these AI-powered tools for yourself!

Conclusion: Embracing the AI Revolution for Online Income Growth

In conclusion, AI has become an indispensable tool for online entrepreneurs looking to boost their online income. From optimizing SEO strategies to enhancing customer experience and engagement, AI-powered tools and technologies have revolutionized the way we do business online. By leveraging AI, we can automate time-consuming tasks, gain valuable insights into customer behavior, and make data-driven decisions that drive growth and increase profitability. As the AI revolution continues to unfold, it’s essential for online entrepreneurs to embrace this transformative technology and leverage its power to maximize their online income. By staying ahead of the curve and embracing AI, we can unlock new opportunities, stay competitive, and achieve long-term success in the digital marketplace. So, join me on this exciting journey from bits to bucks and discover how AI can revolutionize your online income. Embrace the AI revolution, and let’s boost our online income together!

From Bits to Bucks: How AI is Boosting My Online Income was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story.

The Biggest Fintech Myth Holding Businesses Back: You Don’t Need to Be a Bank to Offer Banking Services

Modern financial infrastructure has changed the rules. Today, fintechs, SaaS platforms, marketplaces, and digital businesses can deliver banking experiences without becoming banks provided they partner with the right licensed infrastructure.

For decades, there was only one generally undisputed rule about the financial industry, if you want to provide banking services, you have to open a bank.

Banking licenses, compliance departments, hundreds of regulators, and billions in capital were some of the high barriers to entry that only large multinational conglomerates could overcome

That belief, however, is now an outdated perspective that does not take into account recent innovations in the financial industry. Namely, many financial institutions now rely on specialized enablers to provide regulated banking-like services to their clients.

As such, the banking-as-a-service (BaaS) economy now enables non-financial institutions to embed payments, wallets, cards, accounts, and other financial services and functions within their own P2P and B2B commerce platforms, apps, and sites

This new industry trend ultimately results in a situation where the line between technology and finance gets blurred, often to the point where neither one is particularly obvious to the consumer

The new BaaS economy disrupts the traditional financial services industry in numerous ways, from allowing non-banks to embed financial services inside their platforms to enabling technology companies to innovate and specialize in different aspects of the financial value chain

The most basic characteristic of the BaaS economy is that it enables collaboration between financial institutions that hold banking licenses and technology companies that operate as enablers. The former provides the backbone services and products, such as custodian accounts and deposits,

While the latter embed them in their platforms to facilitate everyday P2P and B2B payments, money transfers, issuing cards, lending, and other financial services

The overall purpose of BaaS is to separate the core banking infrastructure from the front-end technologies and make it much easier for companies to adopt and customize financial services, rather than having to build them from scratch.

The BaaS economy ultimately makes a wide variety of financial services accessible to a much broader audience of innovators and entrepreneurs. Some examples of such companies include technology-native financial platforms that embed cards and accounts as a way to make their business-to-business and business-to-consumer transactions more efficient, secure, and transparent

For example, many of the largest technology companies today offer their business clients an option to open business accounts and receive payments directly through their digital platforms.
In that way, BaaS ultimately empowers the technology industry to disrupt the financial services industry by embedding financial infrastructure as a way to improve products and services offered by non-financial companies.

At the same time, the BaaS economy is not removing the importance of financial institutions, as they remain critical enablers of the digital economy.

A fundamental change brought by the BaaS economy is that it focuses on the needs of the consumer. Embedded finance ultimately puts the consumer at the center of the financial experience, which means the overall experience has to be much more intuitive and more compelling
The BaaS economy therefore ultimately shifts the paradigm to create value by complementing existing products and services with financial services and functions

The opportunities for such financial complementarities are countless, as they can be found in virtually every industry and every company, regardless of their size or specialization.

An e-commerce marketplace can allow its merchants to receive instant settlements, rather than having to wait for several days for the money to clear. A payroll company can allow its workers to open mobile accounts and receive payments instantly, as well as issue cards that can be used to make purchases.

A logistics company can make it much easier for its business clients to settle international payments, while a SaaS company can allow its clients to send and receive money directly through the SaaS platform. In each of these examples, the financial infrastructure enhances the core vertical, which ultimately results in a much better client experience.

Ultimately, the embedded finance model can be seen as much more efficient and effective way to distribute financial services, as it ultimately makes them more accessible and easier to use.

It is important to note that financial regulations have not gone away, despite the rapid rise of the BaaS economy. Financial services have always been one of the most heavily regulated industries worldwide, and they continue to be subject to extremely strict anti-money laundering (AML), compliance, transaction monitoring, and data privacy regulations.

However, many of those regulations can now be handled by BaaS enablers (i.e., financial institutions that specialize in reselling their infrastructure and technology to other companies). Such enablers handle the banking license, custodian accounts, deposits, transaction clearing, and other aspects that were traditionally the responsibility of the financial institutions that provided those services directly to the consumer

Therefore, the BaaS economy ultimately lowers the regulatory barriers for non-financial companies that want to embed financial services within their platforms and products. At the same time, the BaaS economy also reduces the implementation costs and the amount of time needed to launch new financial products and services

That is especially important for smaller technology companies and start-ups that would not be able to launch a financial services product, even if they wanted to, due to the immense costs involved. It takes hundreds if not thousands of employees for technology-native financial platforms to manage risk, comply with regulations, maintain the necessary IT infrastructure, and provide excellent consumer support.

By collaborating with BaaS enablers, such companies can significantly reduce their costs and risks by relying on the expertise of financial infrastructure providers and their extensive regulatory experience.

The BaaS economy ultimately lowers the barriers to entry for everyone involved. New entrants can launch more innovative financial products and services with reduced risk and cost.

Simultaneously, larger financial services companies can use the BaaS economy to scale their operations faster by relying on the business-to-business (B2B) infrastructure provided by technology enablers. At the same time, the widespread adoption of the BaaS economy allows even non-financial and non-technology companies to embed wallets and payments solutions within their business-to-consumer (B2C) and business-to-business (B2B) operations.

Such opportunities ultimately allow diverse sets of companies to compete more effectively while improving products and services offered to their consumers.

One of the reasons why the BaaS economy is misunderstood is because some of the most basic principles have not been fully acknowledged. The banking industry has long held the belief that only banks can offer banking services.

Yet, in the twenty-first century, the most valuable financial services innovations are being driven by companies that are not financial institutions, even if they collaborate with banks and other financial institutions.

There is nothing mysterious or counterintuitive about this trend the banking-as-a-service economy ultimately reflects the fact that the finance industry has started to behave like any other technology-driven industry.

Just like many other technologies, finance is now being unbundled between different specialized enablers, each of which plays a specific role in the client experience. The core infrastructure remains the domain of financial institutions, while the front-end technology is now being developed by companies that care to customize the financial experience for their clients.

By enabling those enablers, the BaaS economy ultimately promotes competition, lowers the costs and complexity of financial services, and provides those services to a much broader audience.

The finance industry no longer has a duopoly between large technology companies and big banks, with the competition between the two often stifling the innovation at the intersection between the two domains. Instead, the BaaS economy enables a much more dynamic and diverse financial services ecosystem that ultimately benefits everyone involved.

Perhaps the most important insight regarding the BaaS economy and the embedded finance space is that the entire financial services industry will ultimately become dominated by non-bank enablers that embed financial services within their products and technologies.

This development is ultimately driven by the demand for convenience and ease of use, as consumers are much more likely to use financial services when they do not have to deal with the hassles and complexities of the traditional finance industry.

The BaaS economy ultimately recognizes that the most valuable financial services are the ones that are embedded within other technology products and services. As such, the future of financial services is no longer dictated by banks, but rather the companies and platforms that utilize banks’ infrastructure to create compelling financial products for their clients.

The Biggest Fintech Myth Holding Businesses Back: You Don’t Need to Be a Bank to Offer Banking… was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story.

When Fibonacci, the 200-week average, on-chain cost basis, and momentum all point to the same price, the tape is telling you something.

Bitcoin printed a low of $57,767 on the first day of July. To most of the market, that was just another red number at the tail end of a brutal quarter. A 54% haircut from October’s $126,296 peak, capped by a June that shed 20.5% on its own; one of only eleven months since 2013 to fall 20% or more. But to anyone who had been mapping the levels, $57,767 wasn’t just a low. It was the number.

The 0.618 Fibonacci entrancement of the entire 2022-to-2025 bull run, which is measured from the ~$15,476 cycle low to the ~$126,296 all-time high, sits at $57,809. Price didn’t drift toward it. It tagged it to within forty-two dollars and immediately tried to turn. On a six-figure asset, that’s not a near-miss. That’s a bullseye.

And the precision is the whole point; not because Fibonacci is magic, but because the 0.618 was only one of half a dozen completely independent roads that all happened to end in the same neighborhood.

The golden pocket, defined

In technical analysis, the “golden pocket” is the narrow band between the 0.618 and 0.65 retracement levels. It’s where the deepest corrections within an intact uptrend tend to find their floor before continuation; the last high-probability shelf before a move is considered fully retraced. For this cycle, that pocket spans roughly $54,300 to $57,800.

Bitcoin has now entered it from the top. And the reason this particular pocket matters more than a typical one is that it isn’t standing alone. It’s stacked on top of nearly every other floor the market has.

The confluence: where the roads meet

Strip away the narratives and look only at where independent, unrelated methods placed their line in the sand. Six of them converge on the same $54k–$58k shelf:

• Fibonacci 0.618 - $57,809. Tagged at $57,767.
• 200-week moving average - ~$61,000. The single most reliable macro floor in Bitcoin’s history; it marked the bottom in 2015, 2018, and 2020. Price broke below it for the first time since 2022 on this flush.
• Realized price (network cost basis) - ~$54,000. The average price at which every coin last moved. It forms the lower edge of the golden pocket almost exactly.
• Long-term holder supply - a record ~16 million BTC. Up from 14.12 million at the October top, snapping a two-and-a-half-year downtrend. The strongest hands are absorbing coins, not shedding them.
• LTH-MVRV - ~1.5. Long-term holders sit on only modest unrealized profit, nowhere near the levels that historically trigger distribution. On-chain, this is accumulation, not a top.
• Momentum and sentiment - RSI bullish divergence with the Fear & Greed Index at 12. Price ground to a lower low into late June while daily RSI held a higher low, and sentiment hit “extreme fear.”

Geometry, a moving average, cost-basis economics, holder behavior, momentum, and crowd psychology are not related disciplines. They don’t borrow assumptions from one another. Yet each of them, worked independently, pinned the same price zone. That is the textbook definition of confluence - and confluence is where turns are made. A single indicator flashing green is noise. Six unrelated ones flashing green at the same price is a signal.

“Deep value” is meant literally here

The phrase gets thrown around loosely, but in this case it’s precise. A weekly close beneath the 200-week moving average has only ever happened in the deepest-value windows of prior cycles. Price now trades below it and is pressing toward realized price; the level below, which the average holder in the entire network is underwater. This is a condition that has only ever appears in true capitulation. Layer on a Fear & Greed reading of 12, and you have a market priced for despair sitting directly on its historical value floor.

Deep value doesn’t guarantee an instant reversal. But it does something more useful: it dramatically compresses the remaining downside relative to the upside, because you are buying at the level the last two cycles treated as a generational floor rather than chasing at the top.

The smart money is buying the flush

The most important tell isn’t on the price chart at all ; it’s on-chain! Through the entire drawdown, long-term holder supply has done the opposite of price. It rose to a record while price fell in half. This is the same behavior that defined the 2015 and 2019 accumulation bottoms: patient capital quietly absorbing the coins that panicked sellers are throwing away.

Crucially, the metric that historically signals the end of a bull run, where are long-term holders flipping from accumulation to distribution, hasn’t tripped. With LTH-MVRV near 1.5, the cohort is barely in profit. The “sell into strength” phase that tops markets is still far away. The people who have been right across multiple cycles are treating this as a place to buy, and their footprints are on the blockchain for anyone to read.

Momentum is turning before price

Reversals rarely announce themselves with a green candle; they announce themselves with waning downside momentum first. That’s exactly what the RSI divergence shows; sellers pushing price to marginally lower lows while the force behind those lows fades. Pair that with the developing structure on the daily chart, where the second low is printing right on the 0.618, and you have the anatomy of a spring; a final flush into a major level that traps the last sellers before the reversal.

It isn’t confirmed yet. But it’s the shape you want to see, forming exactly where you’d want to see it.

The turn thesis

Bottoms aren’t a single event; they’re a checklist that fills in one item at a time. Value: present. Accumulation by strong hands: present. Momentum divergence: present. Capitulation and extreme fear: present. A major Fibonacci level and the cycle’s most important moving average, tagged together: present. When every item on the list shows up at the same price in the same week, the base rate shifts decisively toward “reversal or durable base” and away from “waterfall continuation.”

The market spent nine months and half its value searching for a floor. Every independent map it could have used pointed to the same address. Price has now arrived at that address. The targets, plural, are hit.

What confirms it - and what kills it

Conviction without invalidation is just hope, so here’s the honest frame on both sides.

Confirmation comes on a decisive reclaim of the ~$61,000 zone. This is the spot where the 200-week average and the neckline of a developing double-bottom overlaps. A weekly close back above it would stack technical structure, Fibonacci, the moving average, and on-chain accumulation into a single confirmed signal, and would strongly suggest the low is in.

Invalidation is equally clean: a weekly close below ~$54,000, the realized-price floor of the golden pocket. Lose that on a closing basis and the deep-value thesis is spent - the next Fibonacci shelf, the 0.786 at roughly $39,200, comes into play, which is the same low-$40s zone the forced-seller bears have been targeting. Holding the pocket is the bull case. Losing it opens the trapdoor.

That line ($54k) is the whole argument compressed into one number. Above it, deep value did its job. Below it, the flush wasn’t finished.

The bottom line

Whether this proves to be the cycle low or simply a major low, the weight of evidence says the zone that always mattered has finally been reached. Six unrelated methods spent months pointing at one shelf; the market has now sat down on it, with the strongest hands buying, momentum quietly turning, and sentiment being dragged along the floor like a fighter trying to pick themselves up from the mat. The targets are hit. From here, the burden of proof has shifted - for the first time in this drawdown, it’s on the bears to break the level rather than on the bulls to defend it.

This article is analysis of market structure and on-chain data, not financial advice. Technical levels are probabilistic, not deterministic; confluence improves the odds of a reversal but does not guarantee one. Price anchors are approximate and shift with the data source. Do your own research and manage risk accordingly.

Bitcoin Taps the Golden Pocket was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story.

Why programmable rails are quietly replacing the plumbing of global finance

TL;DR

• Stablecoins aren’t a crypto side-bet anymore they’re emerging as core payments and settlement infrastructure, with 2024 transaction volume estimates ranging from $15.6 trillion to as high as $35 trillion depending on methodology.
• The real shift isn’t “dollars on a blockchain.” It’s that messaging, reconciliation, and settlement three separate processes in traditional finance can now happen on a single programmable system.
• Tokenized finance extends the same logic to bonds, deposits, and fund shares, with central banks (via BIS-led initiatives like Project Agorá) actively piloting unified ledger models.
• The biggest risk isn’t volatility it’s monetary. The BIS has flagged “stablecoin dollarisation” and the erosion of the singleness of money as structural threats to bank deposits and lending capacity.
• Contrary to popular narrative, the long-term winners may not be the largest private stablecoins (USDT, USDC) but regulated tokenized deposits issued by banks themselves.
• A meaningful share of reported stablecoin volume is inflated by trading and bot activity actual real-economy payment usage is smaller, but growing from a more legitimate base.

Opening Hook

In 2024, a small remittance company processing payments between the UK and Lagos noticed something odd in its ledger. A transaction that used to take three days to settle through a chain of correspondent banks, each taking a cut and adding a delay was now clearing in under a minute. No SWIFT message. No batch cutoff. No reconciliation team manually matching line items the next morning.

The money hadn’t gotten faster because the banks got better. It had gotten faster because it stopped being “bank money” for a few seconds. It became a token moved, verified, and settled on a programmable ledger before becoming spendable cash again on the other end.

That small, almost invisible substitution is the entire stablecoin and tokenization story in miniature. It’s not about replacing currency. It’s about replacing the rails currency travels on.

Context & Problem

Traditional finance runs on infrastructure built for a pre-internet world. Money moves through fragmented ledgers held by different banks, each updated on its own schedule, often only during business hours, often only after a batch process runs overnight. Cross-border payments are worse: they pass through a chain of correspondent banks, each one a separate ledger, each one a separate point of delay, cost, and potential failure.

This isn’t a minor inefficiency it’s the default condition of global finance. A wire from Singapore to São Paulo might pass through three or four intermediary banks before it lands, with fees and delays compounding at every hop. Securities settlement has its own version of the same problem: trades, custody records, and cash movements are tracked on separate systems that have to be reconciled after the fact, which is why settlement still routinely takes one to two business days even for liquid public securities.

Stablecoins and tokenized assets attack this problem at the structural level. Instead of multiple parties maintaining separate records that need to be reconciled, everyone references the same programmable ledger. The Bank for International Settlements has been explicit about this framing, describing the appeal of a “tokenised unified ledger” as a way to integrate messaging, reconciliation, and settlement into one system rather than three.

System Breakdown

It helps to separate the two ideas, because they solve overlapping but distinct problems.

Stablecoins are digital tokens engineered to hold a stable value, typically pegged to a fiat currency like the U.S. dollar. A basic transaction flow looks like this: a user acquires tokens from an issuer or exchange, holds them in a digital wallet, sends them across a blockchain network, and the recipient sees near-instant settlement. Behind the scenes, the issuer maintains reserves cash, short-term government securities, or similar low-risk assets and handles redemption when someone wants to convert tokens back into traditional currency.

Tokenized finance applies the same logic to a broader category of assets: deposits, bonds, fund shares, even real estate or trade receivables. An asset is issued on-chain, ownership and transfer rules are embedded directly into smart contracts, and settlement can be automated using delivery-versus-payment logic meaning the asset and the cash move simultaneously, atomically, with no gap where one party could be left holding a partial trade.

The distinction matters because stablecoins solve a payments problem, while tokenization solves a capital-markets and asset-ownership problem. Together, they form a stack: programmable money (stablecoins) moving programmable assets (tokenized securities) on the same underlying rails.

Deep Dive

The mechanics are simpler than the hype suggests, but the implications are larger than most people assume.

Take a tokenized money market fund. In the old model, an investor’s fund shares are recorded by a transfer agent, custody is handled by a separate custodian, and if the investor wants to use those shares as collateral for a loan, that requires yet another set of agreements and reconciliations between institutions. In a tokenized model, the fund share is itself a digital asset. It can be transferred, pledged as collateral, or settled against payment instantly, because ownership and the rules governing it live in the same place as the transaction itself.

This is why major institutions have started running tokenized money-market funds and tokenized government bonds as proofs of concept not because tokenization makes the underlying asset more valuable, but because it makes the operational layer around that asset dramatically cheaper to run.

The same logic applies to cross-border payments, which is where Project Agorá comes in. This is a BIS-led collaboration involving seven central banks and 43 private-sector institutions, aimed specifically at testing whether a shared, tokenized infrastructure can make cross-border payments faster and cheaper without abandoning the regulatory and legal protections that come with central bank money. It’s a meaningful signal: this isn’t fringe crypto experimentation, it’s central banks asking whether programmable ledgers belong in the core of the financial system.

What’s easy to miss is that none of this requires “crypto” in the cultural sense most people imagine no speculative trading, no anonymous wallets, no volatility. The technology underneath stablecoins and tokenized assets is being deliberately separated from the speculative crypto market and re-applied as plumbing.

Key Metrics

The scale of stablecoin activity is large enough that the range of estimates itself tells a story. Reported transaction volume for 2024 ranges from about $15.6 trillion to $27.6 trillion, with some estimates reaching as high as $35 trillion, depending on the dataset and methodology used. That spread matters it reflects genuine disagreement about how much of this volume is real economic activity versus automated trading and bot-driven transfers.

On the supply side, one widely cited figure puts total stablecoin supply at roughly $214 billion, with active addresses climbing from 19.6 million to 30 million a 53% increase. That growth in active addresses is arguably a more honest signal of adoption than raw transaction volume, since it reflects more distinct users and wallets actually engaging with the system rather than high-frequency trading inflating the totals.

Risks

The operational risks are the ones people usually think about first: smart contract bugs, bridge failures between different blockchain networks, wallet compromises, and outright chain outages. These are real, and they’ve caused real losses in the broader crypto ecosystem.

But the more structurally important risk is monetary, and it’s the one regulators are most focused on. The BIS has warned that widespread stablecoin adoption can undermine what it calls the “singleness of money” the principle that a dollar should be a dollar regardless of which institution is holding it. If stablecoins issued by different private companies start trading at slightly different effective values, or if redemption isn’t always guaranteed at par, that principle breaks down. The BIS has also raised the possibility of “stablecoin dollarisation” in smaller economies, where local currency gets displaced by dollar-pegged tokens, potentially destabilizing local monetary policy.

There’s a banking-specific version of this risk too: if deposits move out of traditional banks and into stablecoins, banks lose a cheap and stable funding source, which directly affects their capacity to lend. This is one reason banks themselves are increasingly interested in issuing their own tokenized deposits rather than ceding the space to private stablecoin issuers.

Finally, there’s a compliance gap that’s easy to overlook. A 2023 BIS bulletin flagged the issue of bearer-style stablecoins crossing KYC boundaries — meaning tokens can circulate freely beyond the identity checks performed by the original issuer, since anyone can hold and transfer them without re-verification at each step.

Bull vs Bear Case

The bull case holds that stablecoins and tokenization represent the most significant change to financial infrastructure since electronic payments themselves. Settlement that used to take days now takes seconds. Reconciliation that used to require entire back-office teams becomes largely automatic. Cross-border payments, historically the most expensive and slowest part of the system, become a software problem rather than a correspondent-banking problem. In this view, the institutions that build compliant, well-governed tokenized rails early will own the next generation of financial infrastructure, the way Visa and Mastercard owned the card-payment rails of the last generation.

The bear case is that most of the current activity is not what it appears to be. A large share of reported transaction volume is inflated by automated trading rather than genuine payments or commerce. Tokenization doesn’t automatically create efficiency — without trusted issuers, shared technical standards, and clear legal finality (meaning a transaction, once settled, is truly final and can’t be reversed or disputed), tokenized assets just become faster versions of the same bottlenecks, dressed up in new technology. And the regulatory risk is real: a system built around private stablecoin issuers competing with central bank money is, almost by definition, a system regulators will eventually move to constrain.

Scenario Analysis

Base case: Regulated stablecoins and tokenized deposits coexist, with banks issuing their own tokenized money alongside a smaller number of heavily regulated private stablecoin issuers. Cross-border payments and securities settlement gradually migrate to tokenized rails over the next five to ten years, largely invisible to end users.

Bull case: Central bank initiatives like Project Agorá succeed in building genuinely interoperable, bank-grade tokenized infrastructure. Settlement times across both payments and capital markets compress from days to seconds as the default, and tokenization becomes the standard operating layer for institutional finance, not a niche feature.

Bear case: Regulatory fragmentation across jurisdictions slows adoption, high-profile stablecoin failures or de-pegging events erode trust, and the technology gets pushed back into a crypto-native niche rather than becoming mainstream infrastructure similar to how some earlier fintech innovations stalled out after early hype.

What Most People Miss

The most common misconception is treating stablecoins as primarily a crypto-trading tool or a way to access dollars outside the traditional banking system. In practice, the more important and durable use case is as a payments and settlement primitive the boring, infrastructural layer that most users will never directly interact with, the same way most people don’t think about the ACH network when their paycheck deposits.

The second misconception is assuming tokenization is inherently more efficient. It isn’t, by default. Efficiency only emerges when there are trusted issuers, shared technical standards across platforms, and clear legal finality. Without those three things, tokenized assets can simply replicate the fragmentation of traditional finance, just on a blockchain instead of a mainframe.

The third, and perhaps most contrarian point, is this: the long-term winners may not be the stablecoins everyone already knows. USDT and USDC currently dominate issuance and transfer activity, which has shaped the public narrative that private stablecoins are the future. But the more durable infrastructure may end up being tokenized bank deposits money that retains the legal and regulatory protections of the traditional banking system while gaining the programmability of a blockchain. That’s a much less exciting headline, but it’s arguably the more likely long-term outcome.

Key Variables

A few factors will determine which scenario plays out. Regulatory clarity is the biggest one whether major jurisdictions converge on consistent rules for stablecoin reserves, redemption guarantees, and KYC requirements, or whether fragmented rules force issuers to operate differently in every market. Issuer trust and transparency matter just as much: whether reserve backing is independently verified and redemption is reliably honored at par, especially under stress.

Interoperability is the quieter but equally important variable whether tokenized assets and stablecoins on different blockchain networks can move seamlessly between each other, or whether the ecosystem fragments into incompatible silos the way early internet protocols once did. And finally, bank participation: whether traditional banks build their own tokenized deposit products fast enough to remain central to the system, or whether they cede ground to private issuers.

Strategic Impact

For fintechs and payment companies, this shift changes the competitive landscape. Companies that build compliant infrastructure around stablecoin settlement and tokenized assets early gain a structural cost advantage over those still routing payments through traditional correspondent banking chains. For banks, the strategic imperative is defensive and offensive at once: defend deposit bases by offering their own tokenized products, while also building the compliance and custody infrastructure that institutional clients will eventually demand.

For treasury teams and institutional investors, tokenized money-market funds and bonds offer a preview of what capital markets infrastructure could look like with near-instant settlement and built-in collateral mobility assets that can be pledged, transferred, or repurposed in real time rather than locked in multi-day settlement cycles.

For policymakers, the strategic question isn’t whether to allow this technology, but how to shape it before private issuers shape it for them. Initiatives like Project Agorá suggest central banks are choosing to participate directly rather than simply regulate from the outside.

Conclusion

The headline framing stablecoins as a crypto product has always undersold what’s actually happening. What’s underway is a re-architecting of the operational layer of finance: the messaging, reconciliation, and settlement processes that have run on fragmented, batch-based systems for decades are being consolidated onto programmable, shared infrastructure. The dollar isn’t being replaced. The pipes carrying it are.

Whether the eventual winners are private stablecoin issuers, bank-issued tokenized deposits, or some hybrid of both, the direction is consistent: finance is becoming software, and the institutions that understand this early banks, fintechs, and regulators alike will be the ones shaping how that software gets written.

Personal Note

What struck me most while researching this piece wasn’t the trillion-dollar volume figures it was how unglamorous the actual winning use case looks. Nobody gets excited about reconciliation. Nobody writes headlines about settlement finality. But that’s exactly where the real value is being created, quietly, in the parts of finance that were never designed to be fast in the first place. The most important infrastructure shifts rarely look exciting while they’re happening they just show up, years later, as the thing nobody remembers having to wait three days for.

The Shopify of Money: How Stablecoins and Tokenized Finance Are Becoming the New Settlement Layer was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story.

The Silent Disappearance of Entry-Level Jobs in the AI Economy: A Generation’s First Career Ladder Is Breaking

As artificial intelligence reshapes industries at scale, the traditional entry-level job is quietly fading forcing young professionals to rethink how careers begin, not just how they grow.

Years went by with one clear path. Study hard, finish school, then start at the bottom. The jobs were never flashy, yet each became the base of something bigger.

Confidence grew there, slowly. Mistakes happened often and that was okay, skills formed not on paper, but while working, hands-on, day after day that structure is now under pressure.

Now machines handle jobs like typing numbers, answering questions, or writing reports tasks people once learned on the job. Firms rely more on tools that never sleep, cutting costs while speeding things up.

These starting-point duties disappear, replaced by silent software doing ten jobs at once. Learning by doing fades when algorithms take over day one. Speed wins, but newcomers lose footing before they start.

What happens next might surprise you a quiet shift that shrinks entry-level chances over time. Not sudden, yet clear when you look closely.

The Vanishing First Step

True, positions aren’t vanishing overnight. Yet the baseline for entry keeps moving.
Back then, new analysts would pass months fixing data errors, setting up sheets, one task after another stacking up. Now? Much of that work finishes itself overnight, handled silently by smart software while workers sleep.

A strange situation shows up here. Workers with skills remain necessary, yet firms look only at those who’ve done the job before. Getting that history usually means starting at the bottom. Now that path is falling apart.

Out here, fresh grads hold degrees tight in hand yet stumble into jobs asking for years they do not have. Paper credentials mean little when every opening wants proof of time served.

AI Changed How We Learn by Changing What Learning Is Worth

What really changes goes beyond machines taking tasks. Experience built through practice now holds less worth in jobs.
Back then, companies saw slow progress as part of bringing in fresh workers.

It took a beginner more hours to finish work, yet those extra minutes were considered building something.
Instant results come first these days. As machines handle jobs in moments, there is less room for people to catch up slowly. Training fades into the background when performance matters most.

This shift sneaks into decisions without noise. Rather than bringing on a pair of newcomers meant to evolve, firms now lean toward a single seasoned worker backed by artificial intelligence aids.

The New Entry Barrier Skills Without a Safety Net

Surprisingly, skill still matters just as much since AI showed up only now you have to know more before you even begin.

These days, fresh applicants must understand software tools and processes that used to be picked up slowly at work. Instead of waiting for training, people now learn by doing small jobs, trying things alone, or showing real examples of their efforts. Hiring based on proof of skill is spreading fast.

Still, that change widens gaps in who gets hands-on chances. Some never touch actual work tasks before landing a role. Old-school company learning setups are fading quicker than new routes appear to fill them.

What’s Actually Disappearing

True, some beginner roles still exist. Not every starting position has disappeared overnight. A few openings remain, though harder to spot.

Still, low-barrier jobs aren’t gone for good. Just shifted, not erased entirely.
Fading now is the workplace where learning packed every moment, yet demands stayed light. Not gone yesterday, but slipping where growth crowded in, while pressure kept its distance.

We are seeing,
Fewer positions aimed only at beginners
More hybrid “mid-level from day one” expectations
Greater reliance on automation for foundational tasks
Increased demand for self-sufficiency from new hires

Simply put, firms aren’t pushing out new hires they’re dismantling spaces that welcome them.

The Human Cost of Efficiency

Hidden in the shift, a small price slips through unnoticed by efficiency charts.
Starting out meant more than a paycheck it built habits through daily routines. Mistakes happened here without serious consequences, talking with coworkers became routine, slowly shaping how tasks got done. Showing up on time mattered, just like meeting set dates.

Working alongside others revealed different approaches to shared work. Over time, handling pressure grew easier.

Fewer safety nets mean young workers often pick things up on the job, where errors cost more and patience runs thin.
A whole group of people grows up knowing tools well yet rarely facing how offices truly function. Adaptation, Not Extinction
Just because things have changed does not mean chances vanish instead, they shift shape.

A different path opens when the old one bends; possible routes begin to show up where none existed before, project-based hiring instead of role-based hiring, Apprenticeship models in tech and business, Portfolio-driven recruitment and AI-assisted onboarding instead of traditional training programs

Freelance and micro-internship ecosystems replacing early corporate roles
What counts as entry-level now depends less on how long someone has worked and more on what they can actually do.

The Bigger Question Ahead

What matters now isn’t if machines take beginner roles. That shift happened quietly, in pieces.

Here lies a different puzzle altogether. What steps in when work stops teaching people how to grow?

Back when they were just starting out, even seasoned experts had to begin somewhere. Should those early steps grow tougher to take, fewer people will make it through over time a slow fade few notice until it’s too late.

Out here, machines aren’t pushing people out of jobs. They’re reshaping how skills grow in the first place.
That shifts how things stand now.

The Silent Disappearance of Entry-Level Jobs in the AI Economy: A Generation’s First Career Ladder… was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story.