There’s something to be said about the reality of how helpful AI can be within running a business, and as someone who has been on the hunt to discover this for themselves, I speak from experience when I say that the path to AI-run success is not exactly what the internet makes it out to be.
A few months ago, as a business owner myself, I became consumed with the idea of building something truly special with AI assistance a real, legitimate business with actual revenue generation but I couldn’t find any real information on what the actual process of doing this looked like
Most articles written on the subject were either promotional pieces for SaaS tools or sensationalist opinion pieces focused on how the technology could replace humans in the workplace; no one actually seemed to want to discuss the day-to-day reality of building an AI-driven company and what it actually meant to run such an entity. Thus, I set out to answer these questions for myself, and in this piece, I’ll do my best to relay what I learned without pushing any particular tool or method as being somehow superior to the rest.
Image generated by ChatGPT
What Does it Mean to Build a Company with AI?
When I set out on this journey, I didn’t have a business plan. I had an idea, a hope, really that perhaps a single individual with the right tools could be able to achieve the same results as a small, mid-level team.
I had seen too many companies waste money and resources on tasks that a competent individual could complete and struggled to find much in the way of viable options for streamlining my own processes at the time, and so that’s what drove me to seek out information on how to build a company with AI assistance in the first place.
The truth is, I didn’t have a particularly strong grasp on what exactly it meant to build a company in this way at first — a majority of people don’t. What I failed to realise at the time was that to build a truly digitalised, AI-driven company meant to adopt a fully digitalised approach to every aspect of my own operations as well.
In practice, this looked like research and validation became a continuous, daily process rather than an event that only happened occasionally — content writing became exponentially faster and far more experimental, as I could churn out ten different headlines for the same article in the same day rather than spending a week agonising over a single one, customer communications became far more immediate and efficient, and administrative tasks that previously bled into my entire day were consolidated into far more manageable chunks that took up, altogether, less than an hour.
It’s important to note, however, that none of this eliminated the need for judgement or critical thinking within my own operations — I simply replaced my own repetitive, time-wasting tasks with those that could be automated, which ultimately gets to the root of what I feel is the most important lesson within the entire experience in general.
The Lesson that Not Everyone Talks About
When it comes to actually learning how to build a company with AI assistance, I think the most important lesson to be learned is that AI doesn’t do the work for you it only removes the roadblocks that previously kept you from doing the work you needed to do at an acceptable pace.
My early experiments with AI were riddled with failure emails that I sent to prospective clients had been generated by AI and were immediately off-putting due to their robotic nature; market research conducted by my chatbots turned out to be wildly inaccurate when cross-referenced with actual data from other sources, etc. In the end, none of this was actually AI’s fault it simply reflected that people who are using these tools tend to make the same mistakes over and over again, and those mistakes are usually process-related. If you rush through the process of generating content, you’ll end up with content that sounds rushed. If you fail to fact-check your research, you’ll end up with research that’s full of easily avoidable errors.
So, Can You Actually Build a Company with AI Assistance?
Yes, but not in the way that most people seem to think
You can’t magically outsource your own judgement or decision-making to some magical algorithm, but what you can do is automate the drudgery of actually executing on an idea to let your brain focus on the more important tasks that require thought. The companies that have successfully utilised this method in practice have done so by building proper feedback loops into their processes and never putting out any work without first double-checking the AI’s work to ensure it meets their standards.
In essence, these companies know that the best way to use AI assistance is to treat any work that comes out of it as a first draft of something that will eventually need to be reviewed by a human being — this way, they’re able to maintain quality control throughout their operations while still enjoying the benefits of increased productivity and decreased overhead. It’s a delicate balance, but with the right approach, it’s entirely possible.
What Would You Say to Someone Considering This Approach?
If you’re reading this article, odds are you’re considering learning how to build a company with AI assistance one day, and so I urge you to think carefully about exactly what it is you hope to accomplish before investing too much time into learning the ins and outs of these tools.
Above all, I think it’s important that you realise that the end goal should always be a company that only requires your own brainpower to make decisions. Ideally, most of your daily tasks should be automated so that you only have to spend minimal amounts of your own time on them throughout the day.
Start small: take one repetitive task from your own operations and plug it into an AI-powered program before you try to scale up and automate your entire business at once and be prepared to spend some time troubleshooting before you begin seeing results. After all, the companies that will end up succeeding in this space aren’t the ones that claim to be “100% AI-run,” but rather the ones that use the technology as a tool to move faster than everyone else while keeping a close eye on what needs to be improved in their own operations.
This is the reality of learning how to build a company with AI assistance, and I hope that this piece serves not as some magical end-all-be-all guide, but rather an informative look at the actual process that people rarely ever seem to talk about, as well as an actionable starting point for anyone who wants to begin exploring the benefits for themselves.
Forget “which one is smarter.” The real shift is happening quietly, under the hood, and most people won’t notice until it’s already changed how they work.
I keep seeing the same debate pop up: is Claude smarter than Gemini, is Chat GPT still ahead, whatever. Honestly? Wrong question entirely. The stuff that’s actually going to matter is happening quietly, in places most people aren’t even looking.
I’ve been using these tools since they were basically novelties the kind of thing you showed your coworkers as a party trick. Ask around now and most people will tell you the future is “better answers” or “smarter writing.” That’s not really where this is going.
The bigger shift is in what these things fundamentally are, not how well they perform on some benchmark. Here’s my read on it, based on where the money and the engineering effort have actually been going.
Image Generated by chatgpt
We’re moving past chatbots into agents that do stuff
Right now you type a question, you get an answer, that’s the whole interaction. That model has an expiration date on it.
The next phase is AI that actually does things instead of just describing them: books your flight, cleans up your spreadsheet, pushes a code fix. This isn’t a prediction; it’s already happening in early form. The labs have shipped versions of this that can browse the web, click through interfaces, run code.
What’s holding it back isn’t capability, it’s trust. Nobody wants software that deletes the wrong file or emails the wrong person by mistake. So a lot of what’s coming isn’t going to be flashier intelligence — it’s going to be boring stuff like permission systems, confirmation steps, undo buttons. The unglamorous plumbing that makes people comfortable handing over real responsibility.
Memory that doesn’t reset every conversation
Most AI still forgets you exist the second you close the tab. A few companies have bolted memory features on top, but it’s early.
What’s coming is assistants that actually track your ongoing projects and how you write and what you keep running into problems with — without you re-explaining your whole situation every single time. That’s genuinely useful. It also raises uncomfortable questions about data retention and consent. My guess is the tools that win here won’t just remember more — they’ll let you actually see what’s stored and delete it, rather than just saying “trust us.”
Multimodal stops being a bragging point
“It can look at pictures now” used to be a headline feature. Soon that’ll just be table stakes. Voice, video, live camera feeds — these are going to merge into one conversation rather than sitting in separate menus you have to hunt for.
Point your phone at something broken, get spoken help back instead of typing out three paragraphs describing the problem. This stuff already exists in rough form. What’s actually improving is speed and reliability, not whether it’s possible at all.
A quieter race: running well on your own device
There’s a whole separate competition happening that has nothing to do with which model tops the leaderboard. It’s about which company can get something genuinely useful running on your phone without needing a data center behind it.
On-device matters because it’s faster, it’s private, and it’s cheaper to run. Expect a split forming — giant models for heavy lifting, small efficient ones baked directly into your phone for everyday tasks.
Personality is turning into an actual product decision
Most assistants sound pretty interchangeable right now — competent, a little bland. That’s going to change. Some will stay blunt and no-nonsense. Others will lean warm, or get tuned specifically for law or medicine or teaching.
This matters more than it sounds like it should, because tone is tied directly to trust, and trust is what decides whether someone actually uses this thing for something that matters health, money, their kid’s homework.
Regulation is going to shape this more than any competitor will
This is the part that gets ignored in most of these takes. Governments in the US, EU, and across Asia are actively writing the rules right now around transparency, copyright, data use. These aren’t theoretical debates. They decide what actually ships.
Expect more labeling on AI-generated content, clearer ways to opt out of training data, tighter restrictions around healthcare and hiring and anything involving kids. The companies that get ahead of this instead of fighting it are probably going to end up with an advantage that outlasts a few missed product launches.
In the end, it’s a trust problem, not an intelligence problem
Benchmark scores make for good headlines. They don’t decide who actually wins long-term. What decides that is whether people trust a tool enough to hand it something real.
That trust gets built through consistency and honesty about limitations and through how a company handles it when something breaks. An assistant that says “I’m not sure” when it isn’t sure will probably earn more loyalty over years than one that scores a point higher on some test nobody outside a research lab has heard of.
So what should you actually expect?
Not some dramatic leap forward. More like a slow accumulation of smaller changes tools that remember more, act more on their own, run faster locally, and get shaped as much by regulators as by engineers. What you’re using today is a rough draft, not a finished product.
The real race isn’t about who has the smartest model. It’s about who builds something boring enough, reliable enough, that you stop noticing you’re even using it.
Curious what you think — five years from now, do these feel more like tools to you, or more like teammates? Drop your take below.
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.
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.
ChangXin Memory Technologies, better known as CXMT, is preparing for one of the most closely watched semiconductor listings of 2026.
At the same time that China’s public markets are establishing an official price for the company’s shares, on-chain traders have begun forming a separate view of what CXMT could be worth after its listing.
The result is an unusual experiment in global price discovery.
One market is selling regulated equity through a formal IPO on Shanghai’s STAR Market. The other is trading a perpetual contract linked to expectations surrounding the company before its public debut.
They are not the same asset. They do not provide the same rights. But together, they reveal how traditional financial events are increasingly becoming tradable on-chain narratives.
With Ave.ai integrating Hyperliquid perpetual markets, users can now spot emerging contracts such as the on-chain CXMT perp alongside crypto assets, tokenized market opportunities, stock-related contracts and other real-world trading themes.
This is not simply another market listing. It reflects a much larger shift in how traders discover and price global assets.
CXMT’s Blockbuster Shanghai IPO
CXMT is one of China’s most important semiconductor companies and a leading domestic producer of dynamic random-access memory, or DRAM.
DRAM is a critical component in computers, smartphones, data centers and AI infrastructure. The global market has historically been dominated by Samsung Electronics, SK Hynix and Micron, making CXMT’s rapid development strategically significant for China’s semiconductor ambitions.
The company priced its Shanghai STAR Market IPO at 8.66 yuan per share. CXMT is expected to raise approximately 57.9 billion yuan, or US$8.5 billion, by selling nearly 6.7 billion shares. The offering implies a post-listing valuation of about 579 billion yuan, or US$85.2 billion.
If the overallotment option is fully exercised, the offering could raise as much as approximately US$9.8 billion. The deal is positioned to become the largest A-share IPO completed by a Chinese semiconductor company.
The scale of the offering reflects more than investor demand for another technology stock. CXMT sits at the intersection of several major themes:
Artificial-intelligence infrastructure
Global memory-chip demand
China’s semiconductor self-sufficiency strategy
Domestic capital-market expansion
Competition in the global DRAM industry
Reuters has described CXMT as China’s DRAM champion, while the company’s listing is expected to rank among Asia’s largest share sales of 2026.
But before the company’s shares begin trading publicly, a separate market has already started expressing an opinion.
CXMT Is Already Becoming an On-Chain Market
A Hyperliquid HIP-3 ticker representing CXMT was reportedly acquired for 500 HYPE, with plans to introduce a CXMT pre-IPO perpetual market.
This means crypto-native traders do not necessarily need to wait for the official Shanghai listing before taking a position on market expectations surrounding CXMT.
However, the distinction is critical:
The on-chain CXMT perpetual is not CXMT stock.
Buying CXMT shares through the Shanghai IPO gives an investor formal ownership in the publicly listed company, subject to the rules, eligibility requirements and settlement structure of China’s securities market.
Trading a CXMT pre-IPO perpetual gives the trader exposure to a derivatives contract whose price reflects market expectations. It does not provide equity ownership, shareholder voting rights, dividend rights or access to the official IPO allocation.
Reports indicate that the CXMT HIP-3 ticker was acquired for 500 HYPE and prepared for launch in a pre-IPO market segment.
The difference can be summarized simply:
These markets should not be treated as substitutes. They represent two different forms of price discovery.
Two Markets, Two Price-Discovery Mechanisms
CXMT’s official IPO price of 8.66 yuan was established through a regulated offering process involving the issuer, underwriters, institutional demand and exchange requirements.
The on-chain market works differently.
Perpetual traders continuously submit bids and asks based on their expectations of CXMT’s future value. Their decisions may incorporate the IPO price, expected first-day performance, comparable-company valuations, semiconductor demand, AI-related sentiment and short-term speculation.
One market asks:
What price should CXMT use to issue its shares?
The other asks:
Where might the market value CXMT once trading begins?
That distinction makes pre-IPO perpetual markets especially interesting — but also especially risky.
There may be limited liquidity, uncertain reference prices, rapidly changing settlement expectations and large gaps between bids and asks. A quoted perpetual price cannot automatically be translated into a reliable corporate valuation.
For example, reports of large CXMT bids on Hyperliquid generated theoretical valuation comparisons far above the official IPO valuation. But those figures were based on pre-IPO derivative orders rather than completed equity transactions, and should not be interpreted as definitive market capitalization.
In other words, the on-chain market can be informative without necessarily being accurate.
It captures expectations, positioning and speculation in real time. It does not replace formal valuation work.
Why HIP-3 Matters
The emergence of CXMT on Hyperliquid is possible through HIP-3, Hyperliquid’s framework for builder-deployed perpetual markets.
HIP-3 allows qualified deployers to create and operate new perpetual markets. The deployer is responsible for defining the market, selecting the oracle structure, establishing contract specifications, setting leverage limits and managing settlement when required.
This model expands the range of assets that can potentially become tradable on-chain.
Historically, crypto perpetual markets concentrated on digital assets such as Bitcoin, Ethereum and major altcoins. Builder-deployed markets make it possible to explore contracts connected to a wider universe:
Public equities
Stock indices
Commodities
ETFs
Private-company expectations
Pre-IPO events
Other real-world financial themes
Hyperliquid currently presents itself as a fully on-chain, non-custodial venue supporting hundreds of spot and perpetual markets across crypto and other asset categories.
CXMT demonstrates what happens when permissionless market creation meets a major global IPO.
The market can begin forming expectations before traditional public trading officially starts.
Ave.ai Brings Hyperliquid Perps Into a Unified Trading Entry Point
The challenge for on-chain traders is no longer simply gaining access to more markets.
It is discovering the right market at the right time.
New contracts frequently appear across different protocols, chains, interfaces and market operators. Traders may need to move between social media, analytics dashboards, block explorers, wallets and decentralized exchanges before they can even understand what is available.
Ave.ai is addressing this fragmentation by integrating Hyperliquid perpetual trading into its broader on-chain platform.
Ave Wallet Pro’s iOS perpetual DEX integration allows users to access Hyperliquid market data, manage assets and interact with perpetual markets through a mobile on-chain trading experience.
For users following CXMT, this means the emerging on-chain perpetual can be discovered within the same ecosystem they already use to explore other trading opportunities.
Through Ave.ai, traders can increasingly move across multiple market categories:
Meme coins
Newly launched tokens
Multi-chain spot assets
Smart-money signals
Hyperliquid perpetuals
Stock-related contracts
Pre-IPO narratives such as CXMT
Ave.ai’s main platform already combines real-time blockchain data, wallet monitoring, smart-money tools, price alerts, copy trading and trading interfaces. It reports integrations across more than 130 blockchains and 300 decentralized exchanges.
Adding Hyperliquid perps expands that model beyond traditional crypto-token discovery.
Users can now spot an emerging market such as the CXMT perpetual without treating stock narratives, on-chain derivatives and crypto trading as completely separate worlds.
Ave.ai Is Not Moving Away From Crypto
Ave.ai has historically been strongly associated with meme-coin discovery, on-chain analytics and early token opportunities.
Its expansion into stock-related perps, ETFs and pre-IPO markets may appear to be a change in direction.
A better interpretation is that the definition of an “on-chain asset” is expanding.
Stocks are becoming tokenized. Commodity and equity indices are appearing as perpetual contracts. ETFs are entering blockchain-based trading environments. Private-company expectations are becoming tradable through pre-IPO derivatives.
As more traditional assets move on-chain, the infrastructure originally built for crypto discovery becomes relevant to a much broader financial market.
Ave.ai is therefore not abandoning its original positioning. It is extending the same core capabilities — discovery, analysis and execution — to new asset categories.
What connects these categories is not their legal structure. It is their growing availability through on-chain infrastructure.
Ave.ai’s role is to make those fragmented opportunities easier to discover and access through one integrated entry point.
Why CXMT Could Be a Defining Example
CXMT is especially significant because it combines three powerful market narratives.
1. Artificial intelligence
The growth of AI infrastructure has increased demand for memory chips across servers, data centers and advanced computing systems.
2. China’s semiconductor strategy
CXMT represents China’s effort to build a stronger domestic memory-chip industry and reduce reliance on foreign suppliers.
3. On-chain real-world markets
The Hyperliquid contract gives crypto-native traders a way to express a view on a major Chinese IPO before the underlying shares begin public trading.
This creates a market that may attract several different groups:
Semiconductor-focused investors
China technology watchers
AI infrastructure traders
Crypto derivatives traders
Event-driven speculators
On-chain real-world-asset participants
For Ave.ai users, CXMT is not only another ticker. It is an example of how globally important financial events are becoming visible within on-chain trading platforms.
What Traders Should Watch
Pre-IPO perpetuals involve substantial uncertainty. Before interacting with a CXMT-linked contract, traders should examine several factors carefully.
Contract specifications
Confirm what the contract represents, how its index or oracle is calculated, and what happens when the underlying shares begin trading.
Settlement rules
Understand whether the contract continues after the IPO, transitions to a different reference price or settles under specific conditions.
Liquidity and order-book depth
A visible price does not guarantee that a large position can be opened or closed near that level.
Funding rates
Perpetual positions may generate recurring funding payments. Holding costs can become significant when positioning becomes highly one-sided.
Leverage and liquidation
Pre-IPO contracts can experience extreme volatility. High leverage may result in liquidation even when the trader’s longer-term thesis is ultimately correct.
Basis risk
The perpetual contract may trade at a substantial premium or discount to the official IPO price. There is no guarantee that the two prices will converge immediately.
Market access and jurisdiction
Availability may vary depending on a user’s location, platform eligibility and applicable regulations.
The Bigger Story: Traditional Finance Is Moving On-Chain
The most important part of the CXMT story is not that another perpetual contract has been launched.
It is that an IPO taking place on Shanghai’s STAR Market is simultaneously becoming an on-chain trading event.
Stocks, ETFs, commodities and pre-IPO expectations were once almost entirely confined to traditional financial infrastructure. Today, their price exposure is increasingly being represented through blockchain-based markets.
This transition will not eliminate traditional exchanges. Nor will perpetual contracts replace regulated equities.
Instead, the financial market is developing an additional layer of price discovery — one that operates globally, continuously and on-chain.
Traditional markets establish ownership.
On-chain derivatives establish exposure.
Traditional IPOs allocate shares.
Pre-IPO perpetuals aggregate expectations.
The two systems may coexist, interact and sometimes disagree.
That disagreement is exactly what makes them valuable to watch.
Ave.ai: One Entry Point for the Expanding On-Chain Market
CXMT offers a preview of what the next generation of on-chain trading could look like.
A trader may begin by monitoring a semiconductor IPO, compare its formal offering price with an on-chain perpetual market, examine real-time positioning and then act through a connected trading interface.
With Hyperliquid perpetuals integrated into Ave.ai, users can spot CXMT and other emerging on-chain markets alongside the broader crypto ecosystem.
The opportunity is no longer limited to discovering the next meme coin.
It increasingly includes discovering how the next stock, ETF, commodity or pre-IPO event is being priced on-chain.
As traditional financial assets move onto blockchain infrastructure, platforms that unify discovery, data and execution will become increasingly important.
CXMT may be one of the first major Chinese IPOs to receive meaningful on-chain price discovery before its public debut.
An AI agent may select a counterparty, negotiate terms, interact with a smart contract and authorise payment. Yet it is not generally recognised as a legal person, therefore its outputs need to be attributed to a human being or organisation. The UNCITRAL Model Law on Automated Contracting, adopted in 2024, supports contracts formed or performed through automated systems, including AI and machine-to-machine transactions. It establishes rules for attributing automated outputs and addressing unexpected outcomes without requiring the system to possess legal personality. And the emerging direction is clear: autonomous execution does not remove human or corporate accountability.
Roman law distinguished between people who were legally independent (“sui iuris”) and those subject to another’s authority (“alieni iuris”). The “paterfamilias” was the legally independent head of the household and principal holder of its property. He was not a ‘beneficial owner’ in the modern legal sense but can be compared cautiously with a principal asset owner, trustee, company or family office. Nevertheless, commerce required others to manage farms, ships and businesses and so the peculium was a fund placed under another person’s practical administration whilst remaining connected to the principal. The Roman jurist Gaius, Institutes, Book IV, sections 69 to 74, explained that liability depended on the authority granted; where the principal expressly ordered a transaction or appointed someone to operate a business or ship, liability could extend beyond the peculium. In other circumstances, recovery might be limited by reference to that fund. Justinian’s Institutes, Book IV, Title VII later restated this graduated approach and, in today’s climate, the resulting lesson is clear:
The greater the authority given to an AI agent, the greater the potential exposure of the principal behind it.
In the case of wallets, a separate wallet does not itself determine authority or liability; asset segregation, attribution and recourse remain distinct questions.
What modern cases tell us
In the case ofQuoine Pte Ltd v B2C2 Ltd, algorithms entered cryptocurrency trades after a platform failure activated a fallback price. The Singapore Court of Appeal treated the deterministic programs as mechanisms selected by their human operators, rather than inventing a separate legal mind for the software. The case suggests that using an automated system does not necessarily allow its deployer to disown a resulting contract, with these limits of unchecked automation having been exposed by US global financial services firm, Knight Capital. In 2012, faulty software sent more than four million erroneous orders in forty-five minutes, producing losses exceeding $460 million. Unsurprisingly, the SEC found inadequate safeguards, testing and supervisory controls and imposed a $12 million penalty. The lesson is that an AI peculium needs more than a capped wallet — it requires transaction limits, cumulative exposure controls, approved counterparties, price tolerances and an effective suspension mechanism. Another example can be seen in the case of Moffatt v Air Canada, where a tribunal held the airline responsible after its chatbot gave a customer inaccurate information about bereavement fares. These decisions are not universally binding but illustrates that a business cannot assume its AI interface is legally separate from the organisation deploying it. Meanwhile, the Ooki DAO litigation has provided a related warning — a US court held that a decentralised organisation could be sued as an unincorporated association and treated as a person under the Commodity Exchange Act. Similarly, the SEC’s 2017 DAO Report emphasised that regulatory treatment depends on economic reality, not technological terminology. A wallet, smart contract, DAO or SPV may segregate operations but it cannot automatically override securities law, sanctions obligations, consumer protection or fiduciary duties.
Why England and Wales could lead
The Law Commission has concluded that the law of England and Wales can generally support smart legal contracts without wholesale statutory reform. It also identified areas requiring further attention, including deeds, jurisdiction, interpretation and remedies. The Property (Digital Assets etc) Act 2025 has further confirmed that digital or electronic assets are not prevented from being objects of personal property rights merely because they fall outside the traditional categories of things in possession and things in action. That improves certainty over digital property but it does not determine who is responsible when an AI transfers it. The commercial opportunity is to combine existing contract, property, trust, company and financial-services law with a technically enforceable AI mandate.
Building a modern peculium protocol
A modern AI peculium should be a legal and technical control framework where it would identify the principal and define the AI’s objectives, permitted assets, counterparties, jurisdictions and transaction types in a digitally signed mandate. Capital could be placed in a segregated wallet or account and smart-contract permissions would impose per-transaction and cumulative limits. Borrowing, pledging assets, using an unapproved protocol or exceeding a threshold would require human authorisation and instructions, data sources, decisions and transactions would be logged so the agent’s conduct could be reconstructed. Lawyers, trustees, directors, compliance officers or regulated custodians could validate authority, approve exceptional actions, preserve evidence and activate emergency suspension and insurance could then be priced against a measurable mandate and maximum exposure. Furthermore, ring-fencing would still have limits as it could not automatically exclude claims arising from fraud, negligence, sanctions breaches, regulatory violations, fiduciary misconduct or express authorisation by the principal. This all echoes Rome where liability depended not only on the assets allocated, but also on what was ordered, who benefited and how much authority had been granted.
The EU AI Act requires proportionate human oversight for high-risk systems, including the ability for authorised people to intervene or stop systems that are not operating as intended. The UK’s principles-based framework emphasises safety, transparency, accountability, governance and redress; both approaches point toward controlled autonomy rather than artificial personhood.
Autonomy without unaccountability
Roman law did not solve AI governance two thousand years in advance. It did, however, recognise that commerce could be delegated without leaving authority and liability undefined. AI agents do not need fictional personhood to contract and move value — they need intelligible mandates, restricted access to assets, transparent records, effective human control and credible recourse. Jurisdictions that build this architecture first could provide the trusted infrastructure through which autonomous commerce, machine-to-machine payments and AI-managed wealth operate at scale. Rome’s enduring lesson is that delegation becomes commercially useful only when authority, assets and accountability have clearly defined boundaries.
Robinhood Chain was built to bring tokenized stocks and real-world assets on-chain. But less than two weeks after launch, its biggest source of momentum is coming from somewhere else entirely: meme coins.
The network launched its public mainnet on July 1 as a permissionless Ethereum Layer 2 designed for tokenized assets, decentralized trading, lending, and broader on-chain finance. Robinhood describes the chain as AI-native infrastructure for financial services and real-world assets, including stock tokens linked to companies such as Apple, Google, and Nvidia.
Yet traders did not wait for the long-term RWA vision to develop.
They arrived for the memes.
From Tokenized Stocks to a Retail Trading Frenzy
Robinhood Chain’s early growth has been fast.
CoinDesk reported that the network generated approximately $3.1 billion in decentralized exchange volume within its first week, placing it among the top blockchain networks for DEX activity. The chain also attracted nearly 800,000 lifetime active addresses, processed millions of daily transactions, and accumulated hundreds of millions of dollars in assets and stablecoins.
Ave.ai data also indicates that Robinhood Crypto DEX volume crossed $2 billion, with approximately 300,000 daily active addresses and more than 800,000 lifetime addresses during the network’s initial growth period.
Those numbers are impressive for any newly launched chain. What makes them more interesting is the composition of the activity.
Robinhood Chain was designed primarily for tokenized stocks and RWAs, but tokenized real-world assets currently represent only a small portion of the network’s overall activity. Meme coins, stablecoins, spot trading, and speculative liquidity are driving much of the early demand.
The most visible example is CASHCAT, a cat-themed meme coin inspired by Robinhood’s earlier branding. CoinDesk reported that CASHCAT climbed more than 2,000% over seven days and reached a market capitalization significantly larger than the total value of tokenized stocks on the chain at the time.
Robinhood CEO Vlad Tenev summarized the unexpected launch dynamic clearly: the chain is being built for RWAs, but it also “works great for memes.”
Why Meme Coins Often Arrive Before Utility
For experienced crypto traders, this pattern is familiar.
New chains rarely begin with mature lending markets, institutional asset flows, and deeply integrated financial applications. Their first phase is often driven by speculation.
Meme coins are particularly effective at creating that first wave because they are:
Easy to understand
Fast to launch
Highly shareable
Community-driven
Sensitive to attention and momentum
Accessible to retail traders
They give users an immediate reason to bridge funds, open wallets, test DEXs, follow token launches, and interact with new infrastructure.
PYMNTS describes meme coins as behavioral instruments that reveal where traders are willing to take risk, how quickly capital can move, and whether a new chain has enough liquidity and cultural momentum to attract attention.
In that sense, the Robinhood Chain meme boom is not necessarily a distraction from the network’s RWA strategy. It may be the first stress test of the infrastructure.
The more important question is what happens after the initial excitement.
The Real Opportunity: Converting Speculation Into Infrastructure
Meme coins can bring users and liquidity. They cannot guarantee that either will stay.
The long-term opportunity for Robinhood Chain depends on whether speculative activity becomes the foundation for a broader financial ecosystem.
That means converting meme-driven traffic into sustained usage across:
Tokenized stocks
Real-world assets
Stablecoin liquidity
Lending markets
Perpetual futures
Cross-chain trading
Portfolio and risk-management tools
This is where Robinhood Chain differs from a typical meme-first network.
Robinhood already has a large retail trading audience, a recognizable financial brand, and an established position across equities and crypto. Its blockchain strategy is designed to connect those strengths with open, on-chain infrastructure.
Robinhood’s official materials position the chain as a bridge between traditional assets and DeFi, with stock tokens, decentralized lending, perpetual trading, and agentic financial tools as key parts of the roadmap.
The meme coin wave may therefore serve as the network’s liquidity engine rather than its final identity.
What Crypto Traders Should Watch
1. DEX volume quality
High trading volume is encouraging, but traders should determine how much is organic and sustainable.
A new network can generate strong initial numbers through incentives, subsidized gas, bots, launch events, and short-term speculation. The more meaningful signal is whether volume remains active after early rewards and hype begin to fade.
2. Liquidity concentration
Large headline volume does not mean every token has deep liquidity.
Many early-stage meme coins may have:
Thin liquidity pools
Wide spreads
High price impact
Concentrated ownership
Limited exit liquidity
Traders should examine pool depth, holder concentration, buy-and-sell activity, and liquidity changes before entering a position.
3. Smart-money behavior
Wallet activity often reveals more than social media sentiment.
Useful signals include:
Early wallets accumulating before major price moves
Large holders gradually distributing
Repeated profitable entries by the same addresses
Sudden changes in top-holder concentration
Coordinated buying across related wallets
Large liquidity removals
A token may look strong on a price chart while experienced wallets are already exiting.
4. Meme-to-RWA rotation
One of the most important trends to watch is whether capital begins moving from meme coins into tokenized stocks and other RWA products.
If users who entered through speculative tokens begin trading stock tokens, supplying liquidity, borrowing against assets, or using structured financial products, Robinhood Chain may be building a more durable ecosystem.
If activity remains almost entirely meme-driven, the chain may struggle to retain users after the speculative cycle cools.
5. Infrastructure adoption
The strongest chains are rarely defined by one successful token.
They are defined by the tools surrounding the tokens:
DEXs
Wallets
Bridges
Trading terminals
Launchpads
Analytics platforms
Bots
Lending protocols
Risk-management tools
PYMNTS argues that infrastructure ultimately determines which meme coins become liquid markets and which disappear into the long tail.
Where Ave.ai Fits Into the Robinhood Chain Opportunity
For traders, a rapidly growing chain creates both opportunity and information overload.
New tokens launch quickly. Liquidity moves between pools. Wallet behavior changes in real time. A position that looks attractive at entry can become difficult to exit within minutes.
Ave.ai was among the early on-chain trading platforms to integrate Robinhood Chain, giving traders a single interface for discovering, analyzing, and trading assets across the network.
Through Ave.ai, traders can:
Bridge assets to Robinhood Chain
Discover newly launched Robinhood Chain tokens
Trade spot assets directly on-chain
Monitor token prices and liquidity
Analyze holder concentration
Track smart-money wallets
Review transaction history
Access AI-powered signals and real-time market data
This matters most during the early stage of a new ecosystem, when traders need to evaluate opportunities faster without sacrificing visibility into on-chain risk.
Instead of relying only on social posts or headline price movements, traders can use Ave.ai to study who is buying, how liquidity is changing, and whether profitable wallets are accumulating or distributing.
A Practical Robinhood Chain Trading Framework
Before trading a new Robinhood Chain token, consider a simple five-step process.
Step 1: Confirm the token
Verify the contract address and make sure the token is the correct asset. New chains frequently attract copycat contracts and misleading tickers.
Step 2: Review liquidity
Check the available liquidity, trading volume, spread, and estimated price impact. Avoid assuming that a high market capitalization automatically means the token is easy to exit.
Step 3: Analyze holders
Look for excessive concentration among the largest wallets, developers, insiders, or bundled addresses. A small number of wallets controlling most of the supply creates significant downside risk.
Step 4: Track wallet flows
Identify whether high-performing wallets are buying, holding, or selling. Repeated selling from early holders can be more important than bullish social engagement.
Step 5: Define the exit before entering
Decide how much you are willing to lose, where you would take profit, and what change in liquidity or wallet activity would invalidate the trade.
In meme markets, discipline matters more than conviction.
The Bigger Picture
Robinhood Chain’s early success illustrates a recurring truth in crypto: infrastructure may be built for utility, but speculation often arrives first.
Meme coins have helped the network generate attention, liquidity, addresses, and trading activity at remarkable speed. That does not automatically validate the chain’s long-term RWA vision, but it gives Robinhood something every new ecosystem needs: active users testing the rails.
The next phase will determine whether Robinhood Chain becomes a temporary meme venue or a meaningful bridge between retail trading, tokenized stocks, and decentralized finance.
For traders, the opportunity is not simply to chase every new token. It is to understand how attention, liquidity, wallet behavior, and infrastructure interact.
Robinhood Chain may have been built for tokenized finance.
For now, meme coins are opening the door.
And with early network support, real-time analytics, smart-money tracking, and integrated trading tools, Ave.ai gives traders a clearer way to navigate what comes next.
Ready to elevate your trading experience? Try Ave AI now:
Disclaimer: This blog post is for informational purposes only and does not constitute financial advice. Cryptocurrency trading involves significant risk. Always conduct your own research before making any investment decisions.
Robinhood Chain’s Meme Coin Boom was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story.
Have you ever noticed how much of a business day disappears into phone calls? A customer wants to book an appointment, another needs an order update, a new lead asks the same questions your team answered yesterday, and someone else calls just to confirm business hours. None of these conversations are difficult, but when they happen hundreds of times every week, they quietly consume valuable working hours. This is why many businesses are turning to AI Calling Agents in 2026. Instead of replacing employees, these intelligent systems handle repetitive conversations so teams can focus on work that requires human judgment. In organizations where routine calls make up a large portion of daily communication, automating those interactions can reduce manual call handling by up to 60%, depending on business processes, call volume, and how automation is implemented.
What Is an AI Calling Agent?
An AI Calling Agent is a voice-powered AI system designed to communicate with customers over the phone in a natural way. Unlike traditional IVR systems that force callers through long menu options, modern AI understands spoken language, recognizes customer intent, and responds conversationally. It can answer questions, collect information, schedule appointments, and even transfer complex requests to the right employee when necessary. The real advantage is not that AI speaks like a human, but that it can manage thousands of repetitive conversations with the same accuracy and consistency. This allows businesses to improve customer service while reducing the amount of routine work handled by their teams.
Can AI Calling Agents Really Reduce Business Workload?
The answer is yes but the result depends on what type of calls your business receives every day. The biggest gains come from reducing work that follows predictable patterns rather than conversations that require negotiation or expert advice.
For example, businesses commonly automate:
Appointment booking and confirmations
Lead qualification calls
Frequently asked customer questions
Order status and delivery updates
Payment reminder calls
Customer feedback collection
When these repetitive interactions represent a significant share of total call volume, businesses may reduce manual call-related workload by up to 60%. The exact outcome varies from one organization to another, which is why successful automation always begins with understanding existing workflows before introducing AI.
Why Businesses Are Investing in AI Calling Agents
Every business wants to improve productivity, but hiring more employees is not always the most practical solution. Rising customer expectations, growing call volumes, and the need for faster responses have encouraged organizations to rethink how phone communication is managed. An AI Calling Agent provides a scalable way to handle routine conversations while allowing employees to focus on customer relationships, sales discussions, and complex problem-solving. This approach also supports broader business workflow automation strategies. Instead of treating phone communication as a separate process, businesses can connect AI with scheduling systems, CRM platforms, and customer databases, creating a smoother experience for both employees and customers.
AI is best when used as a teammate
One of the biggest misconceptions about AI is that its purpose is to replace people. Routine conversations are handled automatically, while sensitive situations, negotiations, complaints, and important customer decisions remain with experienced team members. This balanced model improves productivity without sacrificing the personal interaction customers still expect in many situations. Rather than removing the human element, AI creates more opportunities for employees to spend time where their expertise makes the greatest difference.
Connect AI Calling Agents with Your Existing Business Systems
An AI Calling Agent becomes even more valuable when it works alongside the software your business already uses. Instead of functioning as a standalone tool, it can connect with CRM platforms, appointment scheduling systems, help desk software, and customer databases. As a result, employees no longer need to switch between multiple applications or manually enter the same information after every conversation. The entire communication process becomes faster, more organized, and less dependent on repetitive administrative work.
Business Benefits Beyond Reducing Workload
Reducing manual work is only one advantage of implementing an AI Calling Agent. As businesses continue adopting intelligent automation, they often discover improvements in customer experience and operational efficiency as well.
Some of the most noticeable benefits include:
Faster response times for incoming customer calls.
Consistent communication without variations between agents.
24/7 availability, including weekends and holidays.
Better lead qualification before reaching the sales team.
Reduced waiting time during peak business hours.
Improved employee productivity by eliminating repetitive conversations.
These advantages help businesses create a smoother customer journey while allowing employees to focus on strategic tasks that contribute directly to business growth.
Common Mistakes Businesses Should Avoid
In reality, successful automation begins with understanding existing business processes. If workflows are poorly organized, automation may only speed up inefficient practices rather than improve them. Businesses should also avoid automating every conversation. The most successful companies use AI to manage routine interactions while allowing employees to handle conversations where experience and personal communication make the biggest difference.
The Future of AI Calling Agents
The next generation of AI Calling Agents will do much more than answer phone calls. They are expected to understand customer history, remember previous interactions, communicate across multiple languages, and coordinate with other AI systems to complete business tasks automatically. Instead of acting as simple virtual receptionists, they will become intelligent business assistants capable of supporting sales, customer service, and internal operations. As voice AI technology continues to improve, businesses that begin adopting these solutions today will be better prepared for future innovations without needing to rebuild their communication systems from the ground up.
Final Thoughts
Phone calls remain one of the most important ways businesses connect with customers, but they can also consume a significant amount of employee time when routine conversations are handled manually. An AI Calling Agent offers a practical solution by automating repetitive tasks, improving response times, and supporting employees instead of replacing them. For businesses with a high volume of predictable customer interactions, implementing AI-driven call automation can reduce manual workload by up to 60%, depending on operational workflows and implementation strategy. As organizations continue investing in smarter business communication during 2026, AI Calling Agents are becoming an essential tool for improving efficiency, enhancing customer experiences, and building scalable operations for the future.
Welcome back!!! If you have been following my RAG series, you already know that chunking is where most retrieval pipelines quietly break. You split a document into neat little pieces, embed each one, store it in a vector database, and assume the system now understands your content. It does not. Each chunk sits alone, stripped of the paragraph before it and the paragraph after it, and that missing context is exactly where your RAG system starts failing.
In September 2024, Anthropic published a technique called Contextual Retrieval that tackles this problem directly. It is simple, it is cheap to run, and it cut retrieval failures by 49%, and by 67% when combined with reranking. That is not a small improvement. That is the difference between a RAG system users trust and one they quietly stop using.
Say you are building a RAG system over a company’s financial filings. One chunk says:
“The company’s revenue grew by 3% over the previous quarter.”
Sounds fine until someone asks “What was ACME Corp’s Q2 2024 revenue growth?” Your retriever has no idea this chunk is even about ACME Corp, let alone Q2 2024. The company name and the quarter were mentioned two paragraphs earlier, in a chunk that got split away during preprocessing. The embedding for this chunk carries none of that information, so it barely resembles the query, and it gets buried under chunks that are only superficially related.
This is not a rare edge case. It is the default behavior of naive chunking, and it is the single biggest reason RAG systems return “I could not find relevant information” or, worse, confidently hallucinate an answer.
What Contextual Retrieval Actually Does
The fix Anthropic proposed is almost embarrassingly simple: before you embed or index a chunk, prepend a short piece of context that situates it within the full document. Instead of embedding
“The company’s revenue grew by 3% over the previous quarter.”
you embed
“This chunk is from an SEC filing on ACME Corp’s Q2 2024 performance. The previous quarter’s revenue was $314 million. The company’s revenue grew by 3% over the previous quarter.”
Now the chunk carries the company name, the time period, and the surrounding numbers, all inside the text that gets embedded and indexed. Both your vector search and your keyword search suddenly have something real to match against.
The two components that make this work are:
Contextual Embeddings Every chunk gets a short, chunk specific context written by an LLM before embedding. This context is generated using the whole document as reference, so it captures things like the document’s subject, the section it belongs to, and any entities or dates that got separated from the chunk during splitting.
Contextual BM25 The same contextualized chunk is also indexed for keyword search using BM25. This matters because embeddings alone still miss exact matches like order numbers, product codes, or specific dates, the same limitation we covered in the keyword search article in this series. Contextual BM25 gives you that exact match capability on top of contextualized text.
Run both together in a hybrid retriever, and you get chunks that are both semantically rich and precisely searchable.
Why This Works Better Than Alternatives
You might be thinking, why not just make chunks bigger so they carry more context naturally(That was my first thought too 🤔).A few reasons this does not hold up:
Bigger chunks dilute relevance. A 2000 token chunk covering five different topics gets a muddy embedding that does not represent any single topic well. Precision drops even as recall might improve slightly.
Bigger chunks blow up your context window. If you retrieve the top 10 chunks and each one is huge, you are burning tokens on irrelevant surrounding text instead of giving the LLM more distinct, relevant pieces of information.
Contextual retrieval keeps chunks small and precise, and adds context as metadata baked into the text itself, rather than solving the problem by brute force. You get the recall benefits of bigger chunks without the noise.
Implementation, Step by Step
Here is a working pipeline you can adapt to your own documents.
Step 1: Generate Context for Each Chunk
from anthropic import Anthropic
client = Anthropic() CONTEXT_PROMPT = """<document> {full_document} </document> Here is the chunk we want to situate within the whole document: <chunk> {chunk_content} </chunk> Please give a short, succinct context to situate this chunk within the overall document for the purposes of improving search retrieval of the chunk. Answer only with the succinct context and nothing else.""" def generate_chunk_context(full_document, chunk_content): response = client.messages.create( model="claude-opus-4-8", max_tokens=150, messages=[{ "role": "user", "content": CONTEXT_PROMPT.format( full_document=full_document, chunk_content=chunk_content ) }] ) return response.content[0].text
If you are worried about cost, Claude’s prompt caching handles this well. You cache the full document once and reuse that cached version across every chunk in that document, so you are only paying full price for the document tokens a single time.
Step 2: Build the Contextualized Chunks
def build_contextualized_chunks(document_text, chunks): contextualized_chunks = [] for chunk in chunks: context = generate_chunk_context(document_text, chunk) contextualized_chunk = f"{context}\n\n{chunk}" contextualized_chunks.append(contextualized_chunk) return contextualized_chunks
Step 3: Index for Both Embedding Search and BM25
from rank_bm25 import BM25Okapi import numpy as np
def build_hybrid_index(contextualized_chunks, embedding_model): # Embedding index embeddings = embedding_model.encode(contextualized_chunks) # BM25 index tokenized = [chunk.lower().split() for chunk in contextualized_chunks] bm25 = BM25Okapi(tokenized) return embeddings, bm25
Step 4: Retrieve With Both, Then Rerank
def hybrid_retrieve(query, chunks, embeddings, bm25, embedding_model, top_k=20): # Semantic search query_embedding = embedding_model.encode([query])[0] semantic_scores = np.dot(embeddings, query_embedding) # Keyword search tokenized_query = query.lower().split() bm25_scores = bm25.get_scores(tokenized_query) # Combine, normalize both to 0-1 range first semantic_norm = semantic_scores / (semantic_scores.max() + 1e-8) bm25_norm = bm25_scores / (bm25_scores.max() + 1e-8) combined = 0.5 * semantic_norm + 0.5 * bm25_norm top_indices = combined.argsort()[-top_k:][::-1] return [chunks[i] for i in top_indices]
For the final accuracy boost, add a reranker (Cohere’s rerank model or a cross encoder) on top of these top 20 results before passing the final 3 to 5 chunks to your LLM. This is the step that took Anthropic’s numbers from 49% failure reduction to 67%.
Best Practices for Contextual Retrieval
Use a cheap, fast model for context generation. You do not need your most expensive model to write a two sentence summary of where a chunk sits in a document.
Cache the full document when generating context for multiple chunks from the same source. This is where prompt caching saves real money at scale.
Keep the generated context short, 50 to 100 tokens is usually enough. Long context defeats the purpose of keeping chunks small.
Always pair contextual embeddings with contextual BM25. Using only one leaves accuracy on the table.
Add a reranking step if your use case can tolerate the extra latency. The accuracy gain is significant.
Test on your own domain before trusting published numbers. Anthropic’s benchmarks used codebases, fiction, ArXiv papers, and science papers, your documents may behave differently.
Monitor your actual retrieval failure rate before and after, do not assume the improvement transfers without measuring it.
Conclusion
Contextual retrieval is one of those techniques that feels obvious once you see it, and that is usually a sign it is worth adopting. It does not require a new vector database, a new architecture, or months of engineering work. It requires rethinking one step in your pipeline: what actually gets embedded and indexed.
If your RAG system has been giving vague or wrong answers and you have already tuned your chunk size, tried different embedding models, and added reranking, this is very likely the missing piece.
Thank you for following this RAG tutorial series! If you found this helpful, please give it a clap 👏 and share with others building RAG systems.
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.
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:
· 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.
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 Netherlands has become one of Europe’s leading technology hubs, with businesses increasingly embracing digital innovation to stay competitive. Among the technologies driving this transformation, AI stands out as one of the most impactful. Organizations across industries are investing in AI Development to improve efficiency, streamline operations, and unlock new opportunities for growth.
From startups and small businesses to large enterprises, companies are recognizing the value of intelligent technologies that can analyze data, automate repetitive tasks, and support better decision-making. As adoption continues to grow, AI is reshaping how businesses operate and compete in the Dutch market.
Why AI Adoption Is Growing in the Netherlands
Several factors have contributed to the rapid growth of AI across the Netherlands. The country benefits from a strong digital infrastructure, a highly skilled workforce, and a culture that encourages innovation. Businesses are constantly looking for ways to improve productivity and deliver better customer experiences, making AI a natural choice.
The increasing availability of data has also played a major role. Organizations now generate vast amounts of information every day, and Artificial Intelligence Solutions help transform that data into actionable insights. This enables businesses to make informed decisions, identify trends, and respond quickly to changing market conditions.
How AI Is Transforming Businesses Across Industries
AI is no longer limited to technology companies. Today, organizations in healthcare, finance, retail, logistics, and manufacturing are using AI to solve real-world business challenges.
Many businesses are implementing Custom AI Solutions designed to address their unique requirements. These solutions help automate routine tasks, improve operational efficiency, and enhance customer interactions.
Some common applications include:
AI-powered customer support systems
Predictive analytics for forecasting demand
Automated document and data processing
Fraud detection and risk analysis
Personalized product recommendations
Inventory and supply chain optimization
Intelligent workflow automation
As businesses continue exploring new use cases, AI is becoming an essential part of everyday operations.
Key Benefits of AI for Business Growth
Improved Efficiency
One of the biggest advantages of AI is its ability to automate repetitive processes. Tasks that previously required hours of manual effort can now be completed more quickly and accurately. This allows employees to focus on higher-value activities that contribute directly to business growth.
Better Decision-Making
AI systems can process and analyze large volumes of data much faster than traditional methods. By identifying patterns and trends, businesses gain valuable insights that support strategic decision-making.
Enhanced Customer Experiences
Modern consumers expect fast, personalized, and convenient experiences. AI helps businesses understand customer preferences and deliver more relevant interactions. Whether through recommendation engines or intelligent chatbots, AI can improve customer satisfaction and engagement.
Cost Optimization
Automation reduces manual workloads and minimizes operational inefficiencies. Businesses can optimize resources, reduce errors, and lower costs without compromising quality or performance.
Greater Scalability
As organizations grow, managing increasing workloads can become challenging. AI enables businesses to scale operations more effectively by handling larger volumes of data and customer interactions without significant increases in resources.
Industries Leading AI Innovation in the Netherlands
Healthcare
Healthcare organizations are using AI to support medical research, improve diagnostics, streamline administrative tasks, and enhance patient care. AI-driven systems can help healthcare professionals make faster and more informed decisions.
Finance
Banks and financial institutions rely on AI for fraud detection, risk management, customer support, and financial forecasting. AI helps improve security while delivering more personalized financial services.
Retail and E-commerce
Retail businesses use AI to understand customer behavior, optimize inventory levels, and create personalized shopping experiences. These capabilities help increase customer satisfaction and operational efficiency.
Manufacturing
Manufacturers are implementing AI to improve quality control, predict equipment maintenance needs, and optimize production processes. These improvements can reduce downtime and increase productivity.
Logistics and Transportation
AI helps logistics providers optimize delivery routes, forecast demand, and improve warehouse operations. This leads to faster deliveries and more efficient supply chain management.
Challenges Businesses Face During AI Adoption
While the benefits of AI are significant, successful implementation requires careful planning and execution.
Some common challenges include:
Managing and organizing business data
Integrating AI with existing systems
Ensuring data privacy and security
Addressing skill gaps within organizations
Measuring return on investment
Selecting the right technologies and strategies
Many organizations begin by evaluating available AI Development Services to better understand which solutions align with their business objectives. Taking a strategic approach helps reduce implementation risks and improves long-term outcomes.
The Future of AI in the Netherlands
The role of AI in business is expected to expand significantly over the coming years. Emerging technologies such as machine learning, computer vision, natural language processing, and generative AI are opening new possibilities for innovation.
Businesses are increasingly viewing AI not simply as a tool for automation but as a way to improve competitiveness and create long-term value. As technology continues to evolve, organizations that invest in AI today will be better prepared to adapt to future market demands.
The demand for Artificial Intelligence Solutions is expected to increase as businesses seek smarter ways to manage operations, improve customer experiences, and drive sustainable growth.
Why AI Matters for Businesses in 2026 and Beyond
AI is becoming a key component of modern business strategy. Organizations that embrace AI Development can gain advantages through improved efficiency, better decision-making, and enhanced customer engagement.
As competition continues to increase across industries, businesses are exploring Custom AI Solutions that help them remain agile and responsive to market changes. Companies that successfully integrate AI into their operations are likely to be better positioned for long-term success.
Working with an experienced AI Development Company can also help organizations identify practical opportunities for AI adoption and ensure solutions are aligned with their business goals.
Conclusion
The rise of AI in the Netherlands marks an important shift in how businesses approach growth and innovation. Organizations across industries are using AI Development to improve operations, gain valuable insights, and deliver better experiences for customers.
As AI technologies continue to evolve, the adoption of Artificial Intelligence Solutions and Custom AI Solutions will become increasingly common. Businesses that understand and embrace these changes will be better equipped to compete, innovate, and grow in the years ahead.
The Netherlands is well positioned to lead this transformation, making AI one of the most important drivers of business success in the modern digital economy.
The current macro technological landscape is defined by an unprecedented consolidation of capital. Mega-cap technology conglomerates are executing multi-hundred-billion-dollar infrastructure strategies centered entirely on the procurement of advanced silicon, localized power infrastructure, and localized processing centers. However, institutional asset allocators are systematically overlooking the secondary structural constraint of this paradigm: data provenance, immutable audit trails, and data-availability integrity for artificial intelligence frameworks.
Centralized cloud environments fail to guarantee mathematical permanence or verifiable historical sequencing for massive LLM training sets. Arweave ($AR), a decentralized data-availability protocol developed upon a hard-capped cryptographic blockweave framework, presents a pure #dhandho architecture. It resolves an acute, high-uncertainty technological bottleneck via low-risk, permanent infrastructure utilities, offering massive structural asymmetry.
The Architecture: Decentralizing Storage Permanence
Traditional cloud storage relies entirely on a recurring operational expenditure framework (SaaS subscriptions). If a subscription payment fails, or if a centralized counterparty undergoes liquidation, the anchored records are purged. Arweave circumvents this structural flaw via its proprietary Storage Endowment Model.
When a user writes data to the Permaweb, they pay an upfront fee that covers the hard cost of storing that data on the protocol for 200 years. This cost calculation is derived via conservative data-storage degradation curves (assuming storage cost declines at roughly 0.5% per annum). The vast majority of the fee bypasses immediate miner distribution and goes directly into a decentralized Storage Endowment Fund.
Defensive Capital Pool: The endowment accumulates value in the native asset ($AR).
Miner Stabilization Logic: If storage physical costs ever exceed the baseline projections, the protocol programmatically taps the endowment to subsidize mining operators.
Deflationary Supply Sink: As physical storage demand scales exponentially with enterprise migration, massive chunks of the protocol’s native token are locked indefinitely inside the endowment, permanently removing supply from liquid markets.
The Paradigm Shift: The AO Decentralized Supercomputer
For years, the legacy market classified Arweave exclusively as a niche Web3 cloud storage backup layer. This fundamental mispricing has been completely invalidated by the deployment and maturation of the AO Network Layer, a hyper-scalable decentralized computing infrastructure constructed atop Arweave’s immutable data engine.
Unlike legacy base-layer networks that demand every node on the network execute sequential transactions synchronously to maintain a singular global state, the AO engine introduces an actor-oriented programming paradigm. This framework achieves horizontal scale by decoupling the core compute components:
Messenger Units (MUs): Decentralized relays that ingest, sign, and route un-computed client instructions seamlessly across parallel environments.
Scheduler Units (SUs): Independent, hyper-fast sequencing nodes that accept messages, assign an incremental slot number, and guarantee cryptographic chronological ordering without resolving transaction states.
Compute Units (CUs): Distributed computing nodes that pull data and state histories on-demand, resolving computation lazily and providing cryptographic proof of execution back to the user.
Because state resolution is handled via independent processes rather than global state constraints, AO processes can scale infinitely. The underlying Arweave blockchain functions as the ultimate, tamper-proof, high-capacity hard drive, maintaining the permanent ledger of every step in the compute cycle.
Financial Metrics and Asymmetric Risk Profiles
Evaluating Arweave through a strict business lens reveals an institutional-grade asset operating with minimal macro inflation and extreme scarcity:
Operational ParameterBaseline MetricInstitutional SignificanceCirculating Supply~65,650,000 $AR~99.4% of maximum hard cap is already in active circulation.Maximum Hard Cap66,000,000 $ARZero programmatic structural token dilution or venture-backer emission unlocks.Market Capitalization~$138,000,000 USDDe-risked baseline evaluation relative to multi-billion-dollar speculative L1 blockchains.Network MoatProof of Access (PoA)Miners are economically incentivized to hold unique, historical data to win block rewards.
The asymmetry matches the foundational Dhandho framework perfectly: “Heads I win, tails I don’t lose much.”
At a compressed market capitalization of roughly $138 million, the market is pricing Arweave at a deep discount, confusing structural crypto-market consolidation with actual terminal risk. The absolute downside is floor-supported by the real-world dollar utility value of the underlying permanent storage demand. Conversely, the asymmetric upside is powered by the protocol captureship of the autonomous AI agent ecosystem, processing millions of complex state computations natively on the AO layer while archiving their datasets permanently on Arweave.
The Red Team Counter-Thesis & Operational Risk Mitigations
A robust risk assessment dictates tracking several core critical dependencies:
Bandwidth Bottlenecks: While storage costs are deterministically solved, network data retrieval speeds under heavy parallel load across decentralized gateways must maintain parity with Web2 infrastructure speeds. This is being countered by the rollout of network availability staking models, forcing node operators to collateralize assets to guarantee high uptime.
Enterprise Adoption Interfacing: Legacy enterprise tech stacks do not natively recognize decentralized data protocols. Bridging this gap requires specialized middleware pipelines, which the open-source dev landscape is actively abstracting out via native SDK integrations.
Institutional Legal Notice
The content provided within this publication is strictly for informational, structural, and educational research purposes. It does not constitute, and shall not be interpreted as, formal financial, investment, legal, or tax advice. The underlying data points and protocol mechanics are derived from publicly accessible on-chain network statistics. Asset allocations in digital infrastructure and decentralized cryptographic protocols contain significant architectural, operational, and structural risk profiles. Individuals and institutions must perform independent due diligence or consult with licensed financial professionals prior to executing market allocations.
Everyone says AI can “catch fraud.” Almost nobody explains what that actually means. Let’s open the black box.
Naked Market breaks down macro finance, blockchain infrastructure, AI systems, and automated trading to help you understand the future of global finance before the mainstream catches up.
Picture this. You’re a junior auditor, and it’s late.
In front of you: a general ledger with four million line items in it. Behind you: a manager who needs your section signed off by Monday. In your hand: a sampling table that tells you, officially and scientifically, that checking 120 of those four million transactions is enough to “reasonably assure” the whole file is clean.
So you pick your 120. You go through them line by line. They’re fine. You sign off.
Somewhere in the other 3,999,880 transactions you never opened, a vendor that doesn’t actually exist has been quietly billing the company for “consulting services” every month for two years.
That’s not a made-up scenario. It’s close to how most audits, everywhere, still actually work. And once you sit with that for a second, something strange happens: you stop being surprised that fraud gets caught late, and start being surprised it gets caught at all. The numbers back this up in an almost embarrassing way. A typical fraud scheme runs for about a year before anyone notices it. And when it’s finally caught, it’s usually not the audit that catches it — it’s a tip. A coworker who got suspicious. A vendor who let something slip. Plain office gossip catches more fraud than the entire system built specifically to catch it.
I’ve written before about AI acting like a “watchdog that never sleeps” reading every contract, every invoice, every payment, at a scale no human team ever could. I said that and moved on quickly, because there was a lot of other ground to cover. This week, I want to go back and actually open the watchdog up. Not “AI catches fraud” but what is it doing, mechanically, all day? What does it actually mean to “read” four million transactions? What is it looking for? And because I always promise you the honest version, not the sales pitch where does it still fall apart?
Here’s the whole idea in one sentence, before we go step by step: an AI auditor doesn’t spot-check a small sample and hope for the best. It checks everything, all the time, and it has to explain exactly what it finds. Let’s see what that actually looks like.
The old audit was never really looking
Here’s something nobody says out loud: a traditional audit was never really built to find fraud. It was built to give “reasonable assurance” which, in plain words, means “we checked enough of it to feel comfortable putting our name on it.” Sampling isn’t a flaw in the system. It is the system, and it has been since the days when checking every single transaction by hand simply wasn’t possible. There weren’t enough hours in a year, let alone enough auditors.
Which means someone committing fraud doesn’t need to be clever. They just need to be smaller than the sample. Keep the fake invoices small. Keep them regular. Keep each one just under whatever amount would trigger a second signature. Spread them thin across a few thousand line items out of millions and you’re not outsmarting the auditor. You’re just betting they’ll never check your corner of the file. For a long time, that’s been a remarkably safe bet.
Think of it like a teacher who grades only 3 out of 40 exam papers and assumes the other 37 are just as good. If you copied your answers and you’re not one of the 3 she happened to pick, you’re safe not because you were clever, but because she simply never opened your paper.
Open the watchdog up, and here’s what’s inside
So what does an AI auditor actually do differently? Strip away the buzzwords, and it comes down to four things.
1. It reads the whole file, not a sample of it
This sounds almost too simple to matter, which is exactly why it matters so much. An AI system built for auditing doesn’t pick 120 transactions out of four million. It reads all four million. Then it reads next month’s four million too. Some modern platforms can handle hundreds of millions of transactions even billions in a single pass. That’s not a future promise. That’s the starting point. There’s no dice roll anymore over whether the fraud happened to land inside the sample, because there is no sample. There’s just the whole file, every single time.
That one change checking everyone instead of checking a few does more of the real work here than anything you’d actually call “intelligence.” Before the system even starts looking for clever patterns, it’s already closed the exact gap our fake vendor was hiding in.
2. It hunts for the fingerprints a liar leaves behind
Okay, so it checks everything. But checking everything is only useful if you know what you’re looking for. This is the part where people’s imaginations tend to run wild, picturing something almost magical. In reality, it’s a handful of clever tricks, stacked one on top of another.
Trick one is the closest thing to an actual magic trick here. Take any large set of real world numbers — city populations, invoice amounts, electricity bills, company revenues, anything and look at what digit each one starts with. You’d expect 1 through 9 to show up roughly equally often. They don’t. Numbers that start with 1 show up about 30% of the time. Numbers that start with 9 show up only about 5% of the time. This holds true, almost eerily, across nearly any large set of real financial numbers. It’s called Benford’s Law.
Here’s why that matters for catching a liar: nobody knows this pattern exists, so nobody can fake it. When a person invents numbers for a fake invoice, a cooked expense report they tend to spread their made-up digits out roughly evenly, because that’s what “random” feels like to a human brain. Real life doesn’t work that way, but fake numbers usually do. An AI auditor runs this exact check across an entire ledger, instantly. An account can get flagged simply because its numbers are too evenly spread out to be real which sounds backwards, but is exactly the giveaway.
Trick two is learning from past liars. Feed the system thousands of already-confirmed fraud cases, and it learns the general shape fraud tends to take. Not one single rule more like a fingerprint made up of dozens of small warning signs, all showing up together. An invoice that lands just barely under the amount that would need a manager’s approval. A brand-new vendor that gets paid within days of being added, and never again after. Suspiciously round numbers, when real invoices are almost always messier and more specific. None of these prove anything on their own. Stack enough of them together, and the risk score climbs.
Trick three is something a plain spreadsheet formula could never do on its own: looking sideways, not just down a column. This is called relationship analysis — checking whether a “new” vendor’s bank account secretly matches an existing employee’s own account. Checking whether three supplier companies that look unrelated actually share the same office address, the same phone number, the same registration date.
This exact blind spot is what let “ghost worker” scandals happen around the world for years. Nigeria’s federal government, for instance, once discovered it had been paying full salaries to more than 23,000 workers who simply didn’t exist, invented on paper by whoever controlled the payroll. What made that possible for so long wasn’t a clever scheme. It was that nobody had ever built a system to compare every single record against every other record, all at once, looking for connections like this. That’s exactly what relationship analysis does and it’s not just a faster version of an old trick. It’s something genuinely new.
None of this is hypothetical, and none of it is years away. It’s already running today, inside the world’s biggest audit firms. EY has a system that started out catching unusual journal entries in a single Tokyo office and has since spread across the whole firm. KPMG runs an AI agent that decides which expenses need a closer look, pulls the paperwork on its own, and drafts most of the report before a human ever opens the file. Even tax authorities are doing this, one estimate found the number of AI tools used inside the IRS grew more than tenfold in about three years, mostly to help decide who gets audited in the first place.
3. It never closes the file
A traditional audit is like a single photograph: once a year, months after everything already happened, checking whether last year was clean. By the time anyone finds out it wasn’t, the money is usually gone. Often, so is the person who took it.
An AI auditor doesn’t wait for year-end. It watches the ledger the way a smoke detector watches a room quietly, constantly, in the background and it goes off the moment something breaks the normal pattern. A vendor’s bank details change right before an unusually large invoice goes out. A sudden run of expenses that all land just under the approval limit. Someone logging in at 3am from an account that’s never once done that before. None of these prove anything by themselves. But each one is exactly the kind of small, early warning sign that a once-a-year audit simply can’t catch in time to matter.
And this changes more than just how fast fraud gets caught, it changes how people behave in the first place. Once someone knows every transaction is being watched, all the time, not just a random few, the math of temptation shifts. “There’s a small chance anyone ever checks this” feels very different from “something is checking this right now, tonight.”
4. It has to show its work
Here’s the part that breaks the sci-fi image of “the algorithm decided, end of discussion.” A serious AI auditing system isn’t allowed to just whisper “fraud” and walk away. Every single flag comes with a risk score and a plain-language explanation of exactly what caused it, this invoice, that unusual digit pattern, that matching bank account. There are entire techniques built just to crack open a machine-learning model and show which factors actually drove its answer. If a system can’t explain itself this clearly, no human auditor is allowed to rely on it. Regulators have said, flatly, that “the computer said so” is not evidence of anything.
This is worth pausing on, because it’s the opposite of what you might expect. You might assume the whole point of AI is to remove the human being from the decision entirely. Here, regulators have drawn the line in exactly the opposite place, on purpose. Rule-makers in both the US and Europe have said, in effect: the human auditor is still the one legally responsible for the final opinion, no matter how sophisticated the tool underneath them gets. The AI’s entire job is to point at something, explain why, and step back. The human’s job is to look at what it’s pointing at, and decide.
Here’s the part that should give you pause
So far, I’ve shown you the tidy version. A system that reads everything, spots hidden fingerprints, watches around the clock, and explains itself clearly that sounds almost unbeatable. It isn’t. There are three reasons why, and each one is uncomfortable in its own way.
Reason one: the machine can only be as honest as what you feed it. A system can be brilliant at protecting a record, while having no way of knowing whether that record was ever true to begin with. An AI auditor is extremely good at spotting a number that looks statistically off. It’s much less good at spotting a document that’s simply, convincingly made up from nothing and AI tools have made that dramatically easier to do. One auditor recently described a routine document review that almost sailed straight through: professional formatting, believable signatures, every field lined up neatly. Something just felt a little too perfect. A closer look revealed the entire document signatures and all had been generated by an AI tool, built carefully enough to even fool the technical metadata behind it. The tools built to catch a liar, and the tools available to a liar, are increasingly close cousins of the exact same technology.
Reason two: it’s still a game of cat and mouse, just a much faster one. Rule-based fraud detection has always had the same weakness. The moment fraudsters figure out where the line sits, they simply learn to stand just behind it. Machine-learning models are harder to reverse-engineer than a fixed rule, but the same basic instinct still applies. An employee who realizes unusually round numbers get flagged will simply stop using round numbers. The chase doesn’t end. It just moves up a level, again and again.
Reason three: nobody has fully figured out who’s accountable when the watchdog itself is wrong or quietly rigged. The regulators who oversee public company audits have openly admitted there’s no finished rulebook yet for exactly how much independent judgment an AI system is allowed before a human has to step in. The rules are being written in real time, while the tools are already in daily use across the profession. This gap matters twice over. It matters if the system is simply wrong by accident. It matters even more if someone quietly sets it up to look away from one particular vendor, one particular account, one particular name. A rigged AI auditor might be worse than having no AI auditor at all because it shows up wearing the costume of objectivity, and almost nobody thinks to double-check the very thing they were just told is the double-checker.
So does an AI auditor actually stop fraud?
Let’s answer that honestly. No. Not by itself, and not completely. It can’t stop a determined, well-resourced person from lying convincingly at the exact moment the data first enters the system nothing downstream can fully undo a lie once it’s already in.
But “not completely” is doing a lot of quiet work in that sentence. What an AI auditor can do is change the math of getting away with it. Right now, fraud runs for about a year, on average, before anyone notices and it’s usually found by luck, not by design. Now shrink that window from a year down to days. Replace a small chance of ever being checked with a near-certainty of being seen. Do that, and you haven’t made fraud impossible. You’ve made it a dramatically worse bet than it used to be. Most people who commit fraud at work aren’t master criminals. They’re ordinary employees who, in one weak moment, convinced themselves that nobody would ever look closely enough to notice. Take away the “nobody’s looking” part, and a lot of those weak moments never turn into an actual decision at all.
Who audits the auditor?
Which leaves the real question sitting underneath all of this. It isn’t “does the technology work?” it clearly does, more of it, every quarter. It’s this: who gets to look at how the AI auditor itself was built, trained, and configured and who checks that nobody quietly tuned it to look the other way?
Nobody, anywhere in the world, has a clean answer to that yet. A detection system that’s a black box built and controlled by one party, with zero outside visibility is only ever as trustworthy as that party’s own incentives. An open, inspectable process that someone else can independently check is the real difference between an auditor you can actually trust, and an auditor you’ve simply been told to trust.
The watchdog is real, and it’s already working today, inside major audit firms, tax authorities, and government procurement offices around the world. Just remember to ask, every single time: who trained the dog and whose hand is it actually watching?
Four things worth remembering
1. Sampling was never really built to catch fraud. It was built to make an impossible workload possible. Fraud didn’t need to be clever, it just needed to be smaller than whatever slice actually got checked.
2. An AI auditor’s real advantage isn’t “intelligence” it’s coverage plus persistence. Reading everything, all the time, is a bigger shift by itself than any clever algorithm sitting on top of it.
3. Explainability isn’t a nice bonus feature. It’s the entire reason a human is legally allowed to rely on the output at all. A flag with no reasoning behind it is just a rumor wearing a risk score.
4. The fight doesn’t end at detection, it just moves one level up, to whoever configured the detector. Ask who trained it, on what data, checked by whom exactly as skeptically as you’d ask about a human auditor’s own independence.
When most people hear “AI caught the fraud,” they picture something almost magical, a machine that simply knows. Once you open it up, it turns out to be a much more human story than that: patient, unglamorous statistics run at a scale no person could ever sustain, combined with an old-fashioned insistence that a human still has to look at the result and be willing to put their own name on it.
That combination — relentless machine coverage, plus a human genuinely accountable for the final call is a far better anti-fraud system than either half alone. It’s also a fragile one. It only holds together for as long as both halves stay real, instead of becoming decoration on a compliance report nobody actually reads. And it’s worth remembering: the same always-on watching that catches a fake vendor can, if pointed at ordinary employees instead of the fraud itself, quietly turn into something closer to surveillance. The technology itself doesn’t know the difference. Only the people who configure it do.
The rich react to the headline. The wealthy understand the machine. This time, the machine is doing some of the reacting for you which makes it even more important to know exactly what it’s reacting to, and why.
If you want to keep reading finance this way, the machinery underneath the headline, before it gets obvious — subscribe.
One clear breakdown at a time, for readers all over the world.
Can You Trust AI to Catch Fraud? was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story.
Weve always treated corruption as a problem of bad people. Its not, its a problem of bad situations. And for the first time, we have two tools that can fix the situation itself.
Naked Market breaks down macro finance, blockchain infrastructure, AI systems, and automated trading to help you understand the future of global finance before the mainstream catches up.
Picture this. Youre standing in a government office.
Youve got the right papers. Youve waited two hours. And the man behind the glass slides your form back across the counter without stamping it. “Hmm. This one could take a few weeks,” he says slowly. “Unless…”
He doesnt finish the sentence. He doesnt have to. You both know exactly how this goes a little cash slipped under the counter, and like magic, the stamp appears.
If youve lived just about anywhere on earth, you know this moment in your bones. Maybe it was a traffic cop. A hospital desk. A permit office. A border guard. And youre not imagining how common it is, either — roughly one in four people on the planet had a version of that exact moment in the last year. Paying a little extra, to a person with a little power, just to get something they were already owed.
Now, the normal reaction is to get angry at the guy behind the glass. What a crook. And fair enough — he is one. But heres the uncomfortable thing Ive slowly come to believe, and its the whole reason for this piece: the problem was never really him.
Put almost anyone behind that glass give them that much power, over something you badly need, with nobody watching — and youd get the same shakedown. Different face, same script. Which means corruption isnt mostly a problem of bad people at all. Its a problem of bad situations.
And that little shift changes everything. You cant fix human nature — good luck with that. But you can absolutely fix a situation. And two technologies youve heard a thousand overhyped things about blockchain and AI happen to be very good at quietly dismantling the exact situations corruption needs to survive.
Theres a twist coming, though. The same two tools, pointed the wrong way, could make all of it far worse. Both halves matter so stick with me.
Corruption always needs three ingredients
Heres the strange thing about corruption: for something so universal, its weirdly predictable. It almost always needs the same three ingredients sitting in the same room. Go back to our man behind the glass and youll spot all three.
One — hes the only game in town. You cant take your form to a competing clerk down the street. He is the one and only person who can stamp it. Youre stuck with him, and he knows it.
Two — he gets to decide, and the rules are fuzzy. Theres nothing forcing him to stamp your form today. He can drag his feet, misplace your file, discover a mysterious “problem.” The rules are just vague enough that hes got room to wiggle — room to make your life hard, or easy.
Three — nobodys watching. No one is looking over his shoulder. Theres no record of what he does that he cant quietly fix later. If he squeezes you for a bribe, who on earth would ever find out?
Put those three together — the only option, free to decide, and unwatched and you get a bribe. Every single time. In every country. No matter how kind or nasty the person behind the glass happens to be. An economist called Robert Klitgaard actually squeezed this into a little formula so tidy it belongs on a poster in every government building on earth:
And heres why that formula is secretly full of hope. If corruption came from evil hearts, wed be stuck forever — youd have to make people good, one soul at a time. But if it comes from those three ingredients, you dont need better people at all. You just need to quietly remove one ingredient from the room. Take away his monopoly, or his wiggle room, or his darkness, and the whole thing falls apart.
So lets remove some ingredients. One tool takes away the darkness. The other takes away the gatekeeper. Watch.
Blockchain switches on the lights
Start with the easiest ingredient to attack: nobodys watching.
Corruption is a creature of the dark. It lives in the file only one official can open, the record that gets quietly changed at midnight, the money that slips between two desks and simply vanishes. Take away the dark, and a huge amount of it just… cant happen anymore.
This is the one thing blockchain is genuinely, boringly great at. Forget coin prices and Twitter hype for a second. Strip all that away and a blockchain is really just a shared notebook. Everybody holds the same copy. Everybody can see whats written in it. And here is the magic part — nobody can secretly rip out a page or change something thats already written. If you try, everyone elses copy still shows the original, and youre caught red-handed.
Now imagine every government contract, every payment, every land title, written in a notebook like that. Suddenly our clerk cant “lose” your file, because copies of it exist everywhere. He cant quietly hand your neighbours land to his cousin, because the real record is still sitting there for the whole world to see. And anyone can follow the money from the second it leaves the treasury to the second its spent. The shadows just got a whole lot smaller.
And this is not some far-off daydream. Its already running, in places where it genuinely matters.
The country of Georgia — long haunted by property disputes and quietly rewritten land records — moved its land titles onto a blockchain, so ownership can no longer be fudged by whoever controls the database. Colombia ran school-lunch contracts on one, so every bid was out in the open and impossible to erase. The United Nations World Food Programme sends aid to refugees over a blockchain it calls Building Blocks, so the help reaches hungry people instead of leaking to middlemen on the way. The move underneath all of them is the same: take the ledger out of one officials private drawer, and put it in a shared notebook nobody can secretly edit.
AI removes the man behind the glass
Blockchain handles the watching. But what about the other two ingredients — the guy whos your only option, and his wiggle room to say no? Thats AIs job, and it does two very different things.
Job one: the watchdog that never sleeps. A human auditor can only check a handful of files. He samples a few, crosses his fingers, and prays the fraud happened to land in the pile he grabbed. An AI doesnt sample. It reads every single contract, invoice, and payment — millions of them — and it never gets tired, never looks away, and cant be taken out to a nice lunch. It catches the things no human ever could: the supplier who doesnt actually exist, the bill split neatly in two to sneak under a limit, the one company that somehow wins every contract.
This is already live. Colombia built a system that flags suspicious contracts before the money even goes out the door. Brazil and Portugal are running their own versions. And just like that, the one thing every crook is quietly counting on — that no one will notice — stops being a safe bet.
Job two: the vending machine. This one is sneakier, in the best way. Think about our clerk again. The reason he can squeeze you is that he decides. But what if he didnt? What if getting your permit worked like a vending machine — you feed in the right documents, and out pops the stamp, automatically, with no human in the middle to haggle with?
Thats exactly what these systems can do: take a decision thats currently “whatever the official feels like today” and turn it into a fixed, automatic rule. If the aid money is set to send itself the moment you qualify, theres nobody standing in the doorway with their hand out. It turns out you cant bribe a vending machine. (This is the same quiet machinery I wrote about when AI agents got their own bank accounts and started paying for things with no human in the loop — just pointed at a government office instead of a shop.)
Put them together, and the trap closes
Now line the two up, and you can see why people get excited.
Blockchain flips on the lights, so nobody can hide. AI plays two roles at once — the watchdog that never blinks, and the vending machine that deletes the middleman. One takes away the darkness. The other takes away the gatekeeper. Do both at the same time, and youve pulled every ingredient out of the room at once. No monopoly, no wiggle room, no shadows. On paper, thats the most powerful anti-corruption machine anyone has ever dreamed up.
Which is precisely the moment you should get suspicious. Because Ive only shown you the shiny half.
Here comes the twist
Nobody selling you “blockchain will save the world” wants to say this part out loud, so I will: our corrupt friend is not stupid. When you slam his old doors shut, he doesnt quit and go home. He goes looking for new doors. And these shiny new tools quietly hand him a few.
New door one: just lie at the start. Remember the magic notebook nobody can change? It has a loophole. It perfectly protects whatevers written in it — but it has no clue whether what got written was actually true. So the clerk stops trying to change the record. Instead, he simply writes the lie in the first place. He registers the wrong owner. He types “shipment arrived” for a shipment that never showed up. Now his lie is locked in — permanent, tamper-proof, and protected forever by the very system built to stop him. The notebook guards the record beautifully. It just cant tell whether the human holding the pen was honest — and the human at that entry point is always the weak spot.
New door two: bribe the person who built the vending machine. You cant bribe the machine, true — but somebody built it. Somebody wrote the rules deciding who gets a yes and who gets a no. So the bribe simply climbs one level up, to that person. And it gets worse. When a normal corrupt clerk gets caught, the corruption stops. But when the favouritism is baked quietly into the code, it keeps running long after anyones been arrested — rigging the game while looking perfectly fair and neutral. The crook stops being a person you can catch, and becomes a line of code nobody can even see.
And new door three — the one that should genuinely give you pause. That all-seeing eye we pointed at the corrupt minister? It can just as easily be spun around to watch you. The same money that can be programmed to reach a refugee in seconds can be programmed to expire, to freeze, or to punish. Pointed at the powerful, this technology sets ordinary people free. Pointed at ordinary people, the very same technology becomes a cage — the exact double-edge sitting underneath every government digital-money project being built right now. Nothing in the code decides which way it faces. Only the person holding it does.
So can it actually end corruption?
After all that, lets just answer the question in the title honestly. Can blockchain and AI end corruption?
No. Truthfully, no. Nothing ends it, because you cant delete the part of human nature that reaches into the jar when it thinks no ones looking. But heres the thing — that was always the wrong target.
What these tools can do is almost as good: they can drain the swamp corruption grows in. Make it far riskier, far more visible, and far more of a headache to pull off. Shrink its hiding spots from “basically everywhere” down to a few tight corners you can actually guard. Thats not a perfect world. Its just a much fairer fight — one where the crook has to work ten times as hard for a tenth of the reward. And that, honestly, would change the lives of billions.
Which leaves the real question — the one this whole piece has been sneaking up on. Its not “does the technology work?” Its “who gets to hold it?”
Because the very same machine either starves corruption or supercharges it, and it all comes down to one thing: is that all-seeing eye pointed at the powerful, or at the people? Are the rails open and shared by everyone — or owned by one hand that can flip the switch whenever it likes?
And that is why the thing this newsletter keeps circling back to actually matters. Corruptions favourite hiding place in the modern world is the gap between countries — the cracks between 180 separate national money systems, where more than a trillion dollars a year quietly disappears simply because nobody can see across the seams. A shared, neutral, open money layer closes those cracks and drags all of it into daylight — but only if it belongs to everyone and no one, not to whoever grabs it first. One Earth, One Currency was never really about paying faster. Its about building something transparent enough to starve the rot, without handing any single government the master switch. Thats the whole system were tracing here — and corruption is the sharpest test of whether we build the version that frees people, or the version that watches them.
Four things worth remembering
If you forget everything else, keep these four. Theyll quietly change how you read every corruption story from now on.
1. Its the situation, not the person. Corruption is just what happens when someones the only option, free to decide, and unwatched. So stop asking “is he a good guy?” and start asking “could he get away with it?” That second question actually predicts things.
2. Watch the new doors. These tools dont delete corruption — they move it. To the moment someone types the data in, and to the people who write the code. Thats where the next fight quietly goes.
3. Always ask which way the eye is pointing. Aimed at the powerful, its accountability. Aimed at you, its surveillance. Same exact technology — the direction is a choice a human is making, not a fact of the machine.
4. Open beats owned. A system no single person can switch off is the only kind that actually fights corruption, instead of just moving it upstairs to whoever owns the switch.
Where are you looking?
One last thought, because its the whole reason to read a newsletter like this instead of the daily noise.
When a corruption scandal hits the news, most people feel a jolt of anger, shake their heads, and scroll on. Totally understandable. But the people who really get where the world is heading arent watching the scandal at all. Theyre watching the machine underneath it — whos quietly building these new systems, and who is going to control them — because thats where the next hundred years of power, honest or crooked, is actually being decided. And this one reaches every single person reading this, in every country: one in four of us paid that hidden tax last year, and its almost always the people who can least afford it who pay the most.
We cant vote corruption out of the human heart. But for the first time in five thousand years, we can start taking apart the situations it needs to survive. Whether we end up building the version that frees people or the version that watches them is still — for a little while longer — genuinely up to us.
Thats the difference this whole newsletter is about, really. The rich react to the headline. The wealthy understand the machine.
If you want to keep reading finance this way — the structure under the headlines, before it gets obvious — subscribe. One clear breakdown at a time, for readers all over the world.
Companies that treat data privacy as a strategic investment not just a legal obligation are earning greater customer trust, reducing risk, and building stronger businesses in an increasingly digital world.
Here’s a professional, human-written Medium article with a natural tone, strong storytelling, and an engaging structure. It is designed to read like it was written by an industry expert rather than AI, while remaining neutral and informative.
Before the European Union introduced the General Data Protection Regulation (GDPR), data was often viewed as an unlimited business asset. Companies collected customer information from websites, apps, online purchases, and marketing campaigns, frequently without giving users much visibility into how that information was stored or used. As digital services expanded across borders, concerns about privacy grew alongside them. Consumers wanted more control over their personal information, while regulators recognized the need for stronger accountability.
GDPR changed that conversation completely.
When the regulation came into effect in 2018, many organizations initially saw it as another complex compliance requirement. Businesses rushed to update privacy policies, redesign consent forms, review contracts, and strengthen internal security practices. For some, it appeared to be a costly administrative exercise.
Several years later, the perspective has shifted.
Today, GDPR is increasingly recognized as more than a legal framework. It has become a benchmark for responsible business practices in the digital economy. Companies that genuinely embrace its principles often discover benefits that extend well beyond regulatory compliance.
At its core, GDPR is built on a simple concept: individuals should have greater control over their personal information. Organizations must clearly explain why they collect data, how they use it, how long they retain it, and who has access to it. More importantly, businesses are expected to collect only the information they genuinely need rather than accumulating unnecessary customer data.
This approach encourages organizations to become more disciplined in managing one of their most valuable assets types of information.
Trust has become one of the most important competitive advantages in modern business. Customers are increasingly aware of data breaches, identity theft, phishing attacks, and unauthorized data sharing. They are far more likely to engage with businesses that demonstrate transparency and respect for privacy.
When customers know their personal information is being handled responsibly, confidence naturally grows. That confidence often translates into stronger customer relationships, higher retention rates, and improved brand reputation. While privacy may not always appear on a balance sheet, its commercial value is becoming increasingly difficult to ignore.
The influence of GDPR has also extended far beyond Europe. Many organizations operating in Asia, North America, Africa, and the Middle East have adopted GDPR-inspired privacy standards, even when they are not legally required to do so. Global businesses prefer consistent privacy practices across markets rather than maintaining separate compliance models for different jurisdictions.
As a result, GDPR has helped establish an international benchmark for data governance.
The regulation has also accelerated investment in cybersecurity. Protecting personal data requires much more than legal documentation. Organizations are strengthening encryption, implementing stronger access controls, monitoring security incidents more closely, and improving employee awareness around data protection.
In many cases, GDPR compliance has encouraged companies to modernize outdated systems and strengthen their overall operational resilience. Better privacy often goes hand in hand with better security.
For financial institutions, fintech companies, healthcare providers, e-commerce platforms, SaaS businesses, and digital service providers, privacy has become a key part of customer experience. Users increasingly expect clear consent mechanisms, easy access to their personal data, and the ability to manage their privacy preferences without unnecessary complexity.
Businesses that deliver this experience often differentiate themselves in highly competitive markets.
Of course, GDPR is not without its challenges. Smaller organizations sometimes struggle with limited resources, evolving regulatory guidance, and the operational effort required to maintain ongoing compliance. Privacy is not a one-time project that can be completed and forgotten. It requires continuous governance, regular reviews, employee training, and adapting to new technologies as they emerge.
Artificial intelligence presents another important dimension. As AI systems rely heavily on data for training and decision-making, organizations must carefully balance innovation with responsible data handling. Transparency, accountability, and lawful processing have become even more significant as AI adoption accelerates across industries.
Rather than slowing innovation, strong privacy practices can actually support sustainable technological growth. Organizations that establish clear governance frameworks are often better positioned to adopt emerging technologies while maintaining customer confidence.
Looking ahead, data privacy will likely continue evolving alongside digital transformation. More countries are introducing privacy legislation inspired by GDPR, while consumers are becoming increasingly selective about which businesses they trust with their information.
The companies that succeed in this environment will not necessarily be those collecting the most data. Instead, they will be the ones that collect data responsibly, protect it effectively, and use it transparently.
In a digital economy where trust is becoming as valuable as technology itself, GDPR represents far more than regulatory compliance. It reflects a broader shift toward responsible innovation, ethical data management, and customer-centric business practices. Organizations that recognize this shift are not simply reducing regulatory risk they are building stronger foundations for long-term growth in an increasingly connected world.
In recent years, the cryptocurrency sector in Europe has faced significant turbulence, often attributed to the rollout of the Markets in Crypto-Assets (MiCA) regulation. While it’s easy to link the downfall of over 5,000 crypto startups to these regulatory changes, the truth is more complex and multifaceted.
The year 2017 marked the beginning of a crypto boom, with countries like Estonia becoming a hub for crypto innovation. Over 6,000 companies took advantage of the largely unregulated environment in Europe, driven by a wave of optimism and speculation. However, it quickly became apparent that over 35% of these companies were merely shell corporations with no real operational presence in Europe. They used licenses to facilitate various illicit activities, including fraud and money laundering.
Lack of real presence
By 2020, countries like Estonia recognized the detrimental effects of such companies on their economy and reputation. Consequently, they took decisive action, shutting down approximately two-thirds of registered crypto firms, sending a clear message: Europe would no longer tolerate fraudulent practices in the space. This dramatic reduction left around fewer companies with legitimate operations that could stand the test of regulatory scrutiny.
Lack of solid rails
The narrative surrounding cryptocurrencies continued to evolve, especially as we moved through the pandemic and beyond. By 2024, the burgeoning interest and capital that had once flowed into crypto began to transition into the AI revolution. As a result, the crypto hype began to cool, revealing a landscape devoid of robust infrastructure and operational viability. Many of the remaining companies found themselves stripped of hype and without real “rails” to support sustainable business practices. Lacking a solid foundation, many crypto founders began to question their future in the industry and in Europe.
Fast forward to today, and the question remains: will the small fraction of crypto companies still holding licenses in Europe survive? While some of these businesses might have regulatory permissions, they often lack the necessary infrastructure to thrive in a market that has increasingly shifted towards institutional players. The past year has seen a significant migration of the crypto addressable market toward institutional services, leaving retail-focused startups scrambling for relevance.
In this climate, the survival prospects of retail crypto startups seem bleak. With lower volumes and diminishing interest from everyday traders, the road ahead for these businesses is fraught with uncertainty. Founders of struggling crypto firms have begun to pivot, establishing AI-focused companies that promise more longevity and higher growth potential. As more entrepreneurs leave the remnants of their crypto ventures behind, it becomes increasingly clear that the potential for success in the space is dwindling.
Final Thoughts
While MiCA has influenced the regulatory landscape, it is not the sole reason for the exodus of crypto startups. Instead, a combination of factors — including initial over-optimism, the prevalence of shell companies, and a market pivot towards AI — has reshaped the industry in Europe. The upcoming years will reveal the fate of those remaining in the crypto space. As the crypto narrative evolves, the crypto industry must adapt or risk being left behind in a world increasingly dominated by technological advancement.
Most folks barely notice how cash flows between European countries these days. With just several touches on a phone screen, funds shift quietly through the background. What makes this possible isn’t magic, it’s a network called SEPA, built so euro transactions feel local even when they’re not. Though unseen by nearly everyone, it runs deep beneath daily finance like quiet wiring under city streets.
Image Generated by chatgpt
Now shifting isn’t the SEPA system, but the environment surrounding it. Behind the scenes, artificial intelligence starts altering transaction oversight, approval, and security processes. Though quiet, this change takes root inside core banking frameworks, slowly redefining how reliability and speed hold up in today’s financial world.
Out here, SEPA aimed to smooth things out, yet everything else in finance has gotten trickier. Fast-changing scams surprise old setups that depend on rigid logic. As payments pile higher, watchdogs want tighter control done quicker than before. Systems using static conditions fail because they spot only what’s been seen. Surprise moves slip past when habits shift faster than code updates arrive.
Out of nowhere, artificial intelligence shifts how things work. Rather than sticking strictly to fixed guidelines, it picks up knowledge by observing information. By reviewing countless transactions and spotting trends in actions, it forms a sense of typical behaviour for people and organisations alike. That means oddities, tiny ones, can now be caught more easily. What once looked fine alone suddenly seems off when seen alongside past habits.
Spotting scams ranks high among how AI helps SEPA banks operate. Today’s tricks shift fast, built to dodge fixed checks. Instead of sticking to old methods, smart software studies fresh info non stop, reshaping its thinking as behaviour changes. That keeps threat spotting alive, far sharper, cutting down mistaken flags that bother real customers.
Seconds tick by while cash zips across borders under SEPA Instant rules. Right after, artificial intelligence scans each move lightning fast for red flags. A pattern feels off. Behaviour strays from past steps. The system pauses just one piece, not the flow. Risk checks happen mid-rush, invisible to users. Safety locks in before doubt spreads. Speed stays high even when caution kicks in.
Sometimes machines spot odd behaviour in banking records before people do. They trace how payments connect through different accounts, not just one by one. This way, strange links show up more clearly. Rules meant to catch dirty money work better when software maps these trails automatically. Old ways often overlook what ties together behind the scenes.
Because of AI, how things run gets better bit by bit. When moving payments through SEPA paths, choices adapt driven by traffic levels, expense, and pace of handling. Smoother transfers happen when delays shrink. Across banks and areas, systems begin working more cleanly.
Still, changing things brings problems. The main worry? Making sense of choices. Banks need clear reasons, particularly when moves impact what customers do. Some smart programs work in ways people cannot easily follow, making it hard to balance being right with being responsible. On top of that, information gets split across borders, between companies, which holds back learning speed for these tools.
Even with hurdles, SEPA banks are quietly growing a smarter edge. This upgrade doesn’t tear out what’s there; instead, it sharpens the edges. Moving cash isn’t just about shifting digits any more. Shaped by patterns, surroundings, and threats as they happen, each transaction now adjusts on its own.
Deep down, changes run much further than what meets the eye. Simple, quick payments remain unchanged for people using them. Yet inside, machinery never stops adapting. With SEPA forming the backbone, smarts come alive through machine-driven understanding.
Side by side, these forces build what comes next payments that shift without noise, fitting themselves to tangled money routines through calm smarts. Quiet learning slips into every transaction.
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.
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