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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.

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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.

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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.