For an industry that has created extraordinary wealth, crypto presents an uncomfortable contradiction. Trillion-dollar networks have been built, billion-dollar companies created, and entirely new financial markets established, yet many people who entered looking for opportunity have left with substantially less than they started with.

Volatility explains part of this, but not all of it.

The deeper question is how money has moved through crypto, where that money ultimately went, and how much of the wealth created represented new economic value rather than wealth moving between participants.

 This is what I think of as the Great Extraction.

 What I Mean by “The Great Extraction”

The Great Extraction describes a period in crypto where enormous amounts of capital changed hands, but much of the activity focused on capturing and transferring value rather than creating new economic value. It raises a bigger question: can blockchain’s next chapter shift from speculation toward real-world utility, productive assets, and sustainable value creation?

It is not about blaming founders, venture capital, exchanges, institutions, market makers, influencers or retail. Each plays a legitimate role in a functioning market. It is about understanding the structure that emerged when blockchain, global liquidity, social media and speculation developed faster than the standards, education and infrastructure surrounding them.

A project can move from an idea to private funding, token allocations, market making, exchange listings and a global public market remarkably quickly. Early investors, employees, communities and public buyers may therefore own the same asset while having very different entry prices, information, liquidity conditions and reasons for holding it.

By the time the average person discovers a project, much of its financial structure may already have been established.

Traditional markets have similar differences between founders, private capital and public investors, but they have spent generations developing disclosure requirements, custody standards, investor protections and established methods of valuation. Crypto compressed much of that development into little more than a decade while giving almost anyone in the world immediate access to the market.

That accessibility has been one of its greatest achievements and one of its greatest challenges.

When Attention Became a Market.

Crypto became exceptionally good at turning attention into financial activity.

A new idea could become a narrative, attract a community, create an asset, and develop global liquidity before the underlying product had established meaningful revenue or widespread use.

Naturally, participants adapted.

Investors looked for emerging sectors before broader capital arrived. Traders moved between narratives. Communities sought early allocations. Users participated in ecosystems to qualify for incentives and airdrops. Founders studied successful launches and funds became increasingly sophisticated at identifying where attention and liquidity were likely to move next.

Being Early became Extremely Valuable.

The problem is that not everyone can be early.

When an asset depends heavily on the expectation that another participant will later pay more for it, continued growth requires continued demand. During strong markets, this can be difficult to distinguish from genuine value creation because almost everything rises together. When liquidity disappears, the difference becomes clearer.

Projects with customers, revenue, useful infrastructure, or lasting demand have something underneath the financial market. Others may discover that much of their value depended on continued speculation.

This is not unique to crypto and does not require anyone to act dishonestly. Speculative markets have existed for centuries. What made crypto different was the speed, global accessibility, and ease with which attention could be converted into a liquid asset.

Where The Value Went.

Extraction itself is not inherently negative.

Founders, investors, exchanges, market makers, custodians and traders all need viable economic incentives. Markets work because participants expect to benefit.

The important distinction is between capturing value and creating it.

A property can generate rent. A business can sell products. A borrower can pay interest. Infrastructure can charge customers for providing a useful service. These activities create an economic source of return beyond the expectation that another investor will eventually pay a higher price.

Parts of crypto already operate this way. Blockchain networks process real transactions. Exchanges generate revenue. Stablecoins are increasingly used for payments and settlement. Decentralised financial protocols provide financial services, and infrastructure companies sell technology that customers use.

Other parts of the market have been far more dependent on continued capital inflows.

That helps explain how crypto could create enormous fortunes while many participants experienced very different outcomes. People entered the same markets at different stages and under very different economic conditions.

Retail was not simply a passive victim of this process either. Over successive cycles, participants learned how the market worked. They searched for earlier opportunities, moved between narratives, farmed incentives, secured allocations and attempted to exit before liquidity changed.

The market taught almost everyone to look for the next person coming after them.

That is where the Great Extraction became larger than any individual group.

The Cost Was Trust.

The financial losses are visible. The loss of trust is harder to measure.

For many people, their introduction to blockchain was not through settlement technology, transparent ownership or programmable infrastructure. It was through speculation, and sometimes through losing money.

Someone who loses money in a failed project is unlikely to separate the blockchain from the token, the application, the founders, the exchange, or the market conditions surrounding it. The entire experience becomes crypto.

Those experiences accumulate.

Businesses become cautious about using the technology. Banks increase scrutiny. Regulators respond to consumer losses. Institutions demand additional safeguards, and legitimate founders have to overcome reputational problems created by projects completely unrelated to their own.

This has created a strange contradiction. Blockchain provides infrastructure designed around transparent and verifiable records, yet the industry built around it has developed a significant trust problem.

Solving that problem is one of the largest opportunities ahead.

Institutions and the Next Market.

Institutional participation is already changing the direction of the industry.

Banks, asset managers, payment companies, governments and financial infrastructure providers are increasingly exploring stablecoins, tokenised funds, securities, deposits, bonds and settlement systems.

Institutions will not remove commercial incentives from blockchain, nor should they. Banks want revenue, asset managers want assets under management, funds want returns and businesses need to make money.

The more important change is what these institutions can bring onchain.

For much of crypto’s history, a large portion of onchain activity involved crypto assets interacting with other crypto assets. Tokens collateralised other tokens, liquidity moved between protocols and incentives attracted capital between ecosystems.

The much larger opportunity is connecting blockchain to economic activity that already exists outside crypto.

Property, equities, government debt, private credit, commodities, intellectual property and other contractual rights already contain enormous amounts of value.

Tokenisation does not create that value.

A property derives value from the property and the income it produces. A bond derives value from the obligation behind it. Gold derives value from the physical commodity. A business derives value from what it produces.

Blockchain can change how those assets are owned, divided, transferred, settled, and verified.

That is a fundamentally different economic model from continually creating new digital assets for existing crypto participants to trade.

From Gold to Toothpaste.

The same infrastructure can eventually extend far beyond financial assets.

Consider gold or copper. Before reaching an owner, a commodity can move through extraction, processing, certification, transport, storage and multiple transactions. Each stage produces information about where the asset came from, who handled it, who certified it and who owns it.

Reliable blockchain infrastructure can help connect those records.

Eventually, the same concept can extend to everyday products such as clothing, socks or toothpaste.

Every physical product has a history. Materials were sourced somewhere, manufactured somewhere, transported by someone and sold through a supply chain. Companies increasingly make claims about sustainability, ethical sourcing, authenticity, manufacturing standards and environmental impact.

Blockchain cannot prove that false information entered into a system was originally true. Physical verification, reliable data providers, governance and legal accountability remain necessary.

What it can provide is a shared record where verified information becomes much harder to quietly change later.

A consumer might eventually be able to verify where a product came from, whether it is authentic, who certified it, and whether the claims attached to it can be supported.

They should not need to understand blockchain to do this.

The most successful infrastructure eventually becomes invisible.

Most people do not understand the infrastructure behind a card payment, an international bank transfer or an online purchase. They simply expect it to work.

Blockchain should eventually reach the same point.

Banking The World.

The financial opportunity is even larger.

Large parts of the global population remain underserved by traditional banking, and many others live in markets with limited access to stable currencies, affordable international payments, credit and investment products.

Even developed financial systems remain fragmented by borders, banking hours, correspondent networks and slow settlement processes.

Stablecoins have already demonstrated that value can move globally and continuously using substantially lighter infrastructure. Blockchain makes it possible to imagine financial services that are less dependent on whether someone happens to live in a country with sophisticated banking infrastructure.

This does not remove the need for banks, regulators, governments, identity systems, consumer protections, or compliance. As blockchain becomes important financial infrastructure, these areas become more important, not less.

The opportunity is to make financial infrastructure available more broadly.

Banking the world should not mean giving everyone access to speculative tokens. It should mean giving more people access to payments, savings, ownership, credit and investment.

That is a much larger market.

Education And Community.

Technology alone will not create that transition.

Education has to improve with it.

Crypto education has often focused on onboarding people into products. Creating a wallet, buying a token, and using a protocol are useful skills, but proper financial education requires understanding what sits underneath an investment.

Participants should understand supply, valuation, vesting, liquidity, custody and governance. They should be able to determine who owns an asset, how earlier participants entered, when those holdings become liquid, where returns originate and what economic activity creates demand.

The goal is not to make everyone a professional investor. It is to reduce the information gap between the people creating financial products and the people using them.

Better educated participants create stronger markets because projects increasingly have to compete on value rather than attention alone.

Community is Equally Important.

Crypto has shown that people can coordinate globally around technology, ownership and capital without sharing the same geography or organisation. That is one of the industry’s most important achievements.

However, an audience is not automatically a community.

Organic communities develop through relationships, shared knowledge, reputation and contribution. Experienced members help newer ones. Weak ideas are questioned. Opportunities circulate, and trust develops over time.

As blockchain moves into mainstream business, these communities can provide an important human layer around technology that would otherwise remain difficult for people to understand and trust.

Strong leadership matters for the same reason. The next generation of the industry will benefit from people who can explain both the possibilities and the limitations of the technology without needing every conversation to become a promotion.

Beyond The Great Extraction.

The Great Extraction does not need to be viewed as the failure of crypto.

It can be viewed as an early stage in the development of a new financial and technological market.

The industry proved that digital assets could have value, that global communities could coordinate around them and that capital could form and move at extraordinary speed. It also discovered what happens when financial incentives grow faster than products, education, market standards and trust.

The infrastructure created during that period now allows the industry to move into something much larger.

Instead of relying predominantly on new capital entering crypto markets, blockchain can increasingly connect to economic activity that already exists throughout the world. Productive property can move onchain. Credit can connect with real borrowers. Commodities can carry clearer records of ownership and provenance. Stablecoins can improve international payments. Businesses can use programmable settlement, and physical products can be connected to verifiable records.

Eventually the distinction between blockchain and traditional infrastructure may become far less important.

A bank could use blockchain without calling its product crypto. A consumer could own a tokenised asset without considering themselves a crypto investor. A business could verify a supply chain record without describing itself as a Web3 company.

That is what Mature Adoption may look like.

The opportunity is therefore considerably larger than creating another generation of digital assets.  There are vast numbers of people who could benefit from better financial infrastructure, enormous pools of assets that could become easier to own, divide and transfer, and a physical economy still operating across fragmented systems.

The solution to the Great Extraction is not to remove profit, markets, investors or institutions. All of them will remain necessary.

The solution is to expand the economic foundation underneath the technology.

If blockchain becomes infrastructure for payments, ownership, settlement, tokenisation and verification, value no longer needs to depend predominantly on attracting the next participant into a speculative market. It can increasingly come from providing useful services to the wider economy.

That is where blockchain has the opportunity to fulfil the scale of its original promise. It can help extend financial infrastructure to people the existing system has struggled to serve, bring previously difficult assets into more accessible markets, improve the movement of value across borders and create verifiable records around an increasingly tokenised physical and financial world.

Crypto has provided the testing ground. What follows could be considerably larger.

My Final Line.

The future does not require the whole world to become part of crypto. It requires the infrastructure developed through crypto to become useful enough that the world begins using it without needing to think about what sits underneath.

Cheers, Mitch.

Bio:

About the Author – Mitch Scott Rogan

Mitch Scott Rogan is a Business Development Builder with interests and experience across startups, Web3, real estate, commercial partnerships, tokenisation, and emerging technologies.

He has a strong interest in startup and technology ecosystems, particularly business development, building relationships, and exploring opportunities across Web3 and emerging technologies.

His interests extend beyond business into public speaking, health, human performance, and the intersection of biology, technology, and personal growth. Mitch is particularly interested in how emerging technologies can move beyond hype and contribute to practical, long-term value creation.

You may contact Mitch at https://www.linkedin.com/in/mitch-scott-rogan

Oryginally published on: https://spektrumlab.io/the-great-extraction-from-crypto-speculation-to-real-world-value-by-mitch-scott-rogan/

I always fished a lot, as it was our Family tradition and Hobby. Some people even said that I fished to much (as they said), which I completely disagree with.

Also, I did not use any glasses if I could manage without any. My eyesight was OK, as you can guess, and surprisingly improved in Australia, despite getting older and spending more than 10 hours behind screens.

So, I avoided glasses for as long as I could. Eventually, reading glasses became part of my everyday life, which makes it slightly ironic that another kind of glasses would later open an entirely different world for me.

A different story is my journey with AI, mixed-reality glasses, and headsets.

For some time, I had been following the news, reading reviews, and trying different VR and augmented reality devices at events. Nevertheless, I kept postponing the decision to buy one for myself.

That changed recently when I finally decided it was time to own one.

The first step came after my friend Giuseppe showed me his Meta Ray-Ban AI glasses. Shortly after that, I went to the shop and bought a pair for myself.

Although I do not have an internal display and neural band (cos I am living in Australia), I enjoy the Meta AI glasses from the first second I put them on. I felt in love with it steadily, discovering and trying multiple features, but I am planning to write about it in a separate article. Muse Spark is another story which deserves a dedicated piece of writing IMO. So, I will focus on it another time, as I said. Now back to the main virtual fishing topic.

How one thing leads to another.

Buying the Meta AI Ray-Bans led me to install and start using the Meta AI app. I set up my Meta Verified account and started enjoying my interactions with the new model.  It was surprisingly friendly, even funny on some occasions, so I spent more and more time with Meta AI.

In my FB account centre, I noticed the Quest family of devices and different smart glasses. I knew it was only a matter of time before I would buy a pair for myself.

In the meantime, I installed the Meta Horizon app and created an account in my name. As you can probably guess, that move led me to start playing a simple game on my mobile phone.

It made me smile, because I missed playing games when I was in my 30s. Even more ironically, I once banned my own kids from playing games.  How noble, but also how stupid that was. That is my recent view as I get older.

Nevertheless, better now than never, I said. I won some points in Horizon+, dressed my avatar quite decently, and was almost ready for the next big step.

Buying the Magic Tool.

The time came when I noticed a discount on QUEST 3S 128GB models. In less than half day, I was unpacking my set and asking three different AI models how to set it up properly and also interconnect all my associated accounts together.

I must admit that managing accounts is not an easy task. It requires good knowledge, patience, and a smart strategy.

Thank goodness (or “ainess”), we now have individually tailored personal AI intelligence available. I try my best to use it as much as possible, not to collect random information, but to learn the most relevant facts in the shortest possible time.

Then came the moment of truth. I pressed the power button on my brand-new Quest 3S.

After all the research, reading, and decision-making, I felt ready to act. In other words, I was super ready for action – plenty of it.

AI helped again. The setup was smooth, and my first experience was genuinely awesome.

After all the reading, preparation, and small worries, it was a good reminder that sometimes technology can still surprise us in a very positive way.

 Use One to Know One.

VR and mixed reality are also becoming much more than tools for gaming and everyday fun. Their use is growing across education, training, healthcare, engineering, design, property, tourism, collaboration and many other industries. IMO, we are still discovering what these technologies can really become.

But there is another point I want to make here. The best way to understand VR is to actually use VR. I often see people talking confidently about emerging technologies, and sometimes criticising them, without having much practical experience with the technology itself. I do not want to be one of them.

My approach has always been simple: use it first, learn how it works, build some real experience, and only then share my opinion. Reading research, reviews, and other people’s experiences is important, but putting the headset on your own head changes the conversation completely. You start noticing things that specifications and YouTube videos simply cannot explain.

Use one to know one. Then talk about it from real experience.

Then the Journey and Questions begin.

As you can probably expect, after the initial euphoria came a few small disappointments.

First, I quickly realised that I was not really controlling the controllers properly, especially when using apps and working with multiple screens. And that is without even mentioning hand tracking – trying to control things with bare hands felt even more challenging at the beginning.

It proved that every new technology has its learning curve, which may be sometimes steep. The promise may be exciting, but the practical experience always starts with a few clumsy moments.

First, my lack of gaming experience from childhood quickly became obvious.

My AI assistant said something like: “Jacek, you have a gigantic gap here. It needs to be filled. You missed the natural learning and playing experience that many people gained through games when they were younger, and it will take some time to grow into it.”

It made me laugh, but it was true.

But I overcame it surprisingly quickly, trying different possibilities and combinations one by one. Once something worked, it became much easier because I started memorising the controls automatically. The is some logic in that as well I guess, but you need to be born with a joystick in the hand to get it straight from the early beginning. I was not! I was born with a fishing rod instead.

As the Romans say, “Better to try than only study.” In this case, it was 100% true.

Secondly, many pre-loaded games were not working as I was expecting. I couldn’t get many of the experiences I initially tried to work properly.  It was frustrating and makes me think I was kind of stupid. But I did not surrender and went back to AI looking and asking for advice. That was a good decision, as I expected.

What the AI essentially told me was: No, you are not stupid, you are entering demos and unfinished products, sometimes students’ projects, and other junk games. The purpose of it is to show you generally what is planned and what is possible. If you want to start a real experience and know what and how to do it, try a proper, 100% working game.

I learned that, and I signed up for a three-month trial to try multiple games from Meta Horizon and then decide if I want to stay and pay.

Immediately after that, the mixed-reality world opened the gate for me a little wider.

Eureka – I was fully in, and everything seemed to be working.

Well, not everything, to be honest, but most things did. And to be honest again, I am still not always sure what is a bug and what is simply my lack of intuition or experience.

I will leave that question open for now.

So, after checking again with AI models about the best strategy for me, I chose and redeemed my first three games: Real VR Fishing, Gun Club VR, and Golf+.

Golf was an interesting choice, because I have never really decided to play it in real life. I always found it a little boring and, compared with fishing, perhaps even a waste of time. If you know me well, you know that golf is not exactly my cup of tea.

Quest, however, proved something totally different.

Who knows what else Quest may be able to prove? I am not even trying to guess.

Then the Fishing Begins.

From the first try, I loved it. It was exactly how I expected it to be.  Almost real, and superb in its details, even for someone like me with a strong background in real-world fishing.

After a quick tour and introduction on a timber-looking pier, I landed on the shore of a lake in Korea. Perfectly imitated water, sky, hills, and a bridge. I grabbed my fishing rod with the float (my favourite style of fishing) and executed the first cast.

What happened next, you can probably guess.

Bite after bite. Fish after fish.

It felt very similar to real fishing when the bite is good, and everything suddenly starts happening at once. You stop thinking too much, react quickly, and simply enjoy the moment.

Some fish were too small to keep. Some escaped because I made a mistake, or maybe they were simply smarter than me and won that round.

Each time I caught a fish, I was asked whether I wanted to keep it or release it. I released a couple of them, and to my surprise, I received special points. I was also awarded polarised glasses, which now allow me to see fish better under the surface.

A small detail, but a clever one. Even in a virtual world, the game reminded me that fishing is not all about catching.  Sometimes it is also about choosing what to release.

In reality, I never take more fish than I need. I also do not really like “catch-and-release fishing style” just for fun when it involves real fish.

But here, in the Horizon world, it does not matter in the same way. You can cast, hook, and land as many fish as your playing time allows.

That is one of the advantages of virtual reality fishing. It gives you the experience, the focus, and the enjoyment without the same real-world consequences.

The Rain and Boat experience.

After some time spent on the shore, I was advised to go to the shelter. Have a rest and equipped myself better.

In a hotel-like building with impressive looking fireplace, I received an additional award and got a proposal to take a trip to Korea for lake fishing. I did not hesitate long, and after confirming and some clicking, I landed in the timber boat in the middle of the lake.

I owned a couple of boats in my life, in Poland and in Australia, so there was nothing new for me. But I was mesmerised by the beauty of surrounding and reality on experience. It started raining. I could see it and almost feel it. Instinctively, I found myself looking for a roof or an umbrella.  The good things about mixed-reality rain are that you do not get wet, so after a while I did not bother. Fishing in rain is quite romantic and spectacular, event in real would setup.  I had been wet to the bone on plenty of fishing trips in Poland. Here I was, sitting safely at home while fishing in the rain somewhere in virtual Korea.

Long story short, I caught a few fish, but most of my attention was actually on the bridge, watching the traffic and trying to guess the models of the passing cars.

Because of that, quite a few big fish took their chance to escape. For the first time in my life, I did not really mind.

It was time to take off my Quest headset. The battery was getting low, and I suddenly felt quite exhausted.

I was happy, but tired.

I clicked the “Leave” button and shut down the Quest.

Some Extra Thoughts & Afterglow.

I am not trying to sell or promote anything here. I simply experienced something I had wanted to understand for myself.

And that brings me back to something I increasingly believe about emerging technology: use one to know one. Reading and watching are valuable, but experience reveals things that specifications cannot.

It was my first proper, independent, and longer experience with a well-functioning virtual game ecosystem. I could see my avatar wherever I looked at the mirror, and probably random people (and perhaps even my followers) could see me as well.

Recently, one avatar started talking to me in, even though I had not tried to contact him first.

I did not panic, but I did not respond either. I simply froze for a moment, like a possum. Suddenly, I wasn’t only learning a new interface. I was entering a new kind of social space.

Step by step, and first things first. I need to better understand the rules, settings, and possible code of conduct. It is easy to go overboard or do something silly, even while fishing. It becomes even more challenging when visiting VRChat worlds, where real people, avatars, and social interaction suddenly become part of the experience.

Knowing myself, I will probably start looking for friends there. Maybe we can fish, shoot, or even play the bloody golf together – why not?

The near future will tell.

If you are visiting the Metaverse, look for me and say, “Hello, mate.”

Cheers, The Author.

Oryginally published on: https://spektrumlab.io/fishing-in-korea-from-my-living-room-the-quiet-magic-of-vr/

Picture a plumber under a house in western Sydney on a Tuesday afternoon. Both hands are occupied, one of them holding a torch. In his pocket, his phone is ringing. It is a homeowner three suburbs away with a hot water system that died this morning, and she is calling down a list she found on Google Maps. By the time the plumber crawls out and checks his missed calls, she has stopped calling around. Somebody else picked up, so somebody else got the job.

Nothing about this story involves bad work, bad reviews, or bad pricing. The plumber lost a job because he was doing a job. For sole traders and small trade businesses across Australia and New Zealand, this is one of the quietest and most persistent leaks in the whole business: the phone rings while you are on the tools, and the caller does not wait.

For most of the past decade, “digital transformation” had very little to offer that plumber. Websites, booking widgets, and CRMs all assume the customer is happy to type. Trade customers mostly are not. When water is coming through the ceiling, people do not fill in forms. They call, and they expect a human-sounding answer.

This is exactly the gap where the current generation of AI has become practically useful.

The phone is still at the front door of the trades.

It is easy to forget, in a world of apps, how phone-centred traditional service businesses remain. A trade business in Australia typically runs on one mobile number that has been on the ute, the fridge magnets, and the Google Business Profile for years. That number is the business. The owner answers it personally, between jobs, on ladders, and at dinner.

The arithmetic of a missed call is brutal in this segment. A single booked job for a plumber, electrician, or air-conditioning tech is commonly worth hundreds of dollars, sometimes far more. An urgent caller who reaches voicemail rarely leaves a message; they simply dial the next business on the list. The revenue does not disappear from the market. It just moves to whoever answered.

The traditional fixes all have well-known costs. A full-time receptionist is a salary that a sole trader cannot justify. Old-style answering services take messages but cannot check a calendar, so every message still becomes a callback. Voicemail, by 2026, is mostly where jobs go to die.

What changed in AI, and why it matters outside the tech bubble.

Most public conversation about AI still orbits the spectacular: frontier models, agents writing software, synthetic video. What matters for a traditional business is duller and more important: conversational voice AI crossed the threshold where it can hold a short, purposeful phone call with a stranger and get the details right.

That is a narrow capability, and the narrowness is the point. A receptionist call is a bounded problem. Greet the caller, find out what they need, check where they are, offer a time, capture a name and number, confirm. Not much creativity is required, but reliability and attention to detail. Modern voice agents, built on large language models with speech layered on top, handle this kind of structured conversation in natural, locally accented English, at any hour, without ever being annoyed by the fifth interruption of the afternoon.

For the owner of a traditional business, this is the first wave of AI that does not ask them to change how their customers behave. The customer still just calls the number they always called. The technology adapts to the habit, not the other way around.

AI on the line you already have.

This is the idea behind Tradies Line (tradiesline.ai), an Australian service built specifically for tradies and trade businesses in AU and NZ: the AI receptionist with built-in job management.

The design decision that matters most is what it does not do. It does not give the business a new phone number. The operator keeps their existing number, the one their customers already know, and switches on conditional call forwarding with their own carrier. Calls the tradie answers, nothing changes. Calls they cannot take, because they are busy, underground, or asleep, forward to their AI receptionist, which answers in fluent local English under their business name and within their business context.

From there, the call follows a simple triage:

1. Routine work gets booked straight into the operator’s calendar, into a genuinely free slot, and the customer receives one SMS confirming the time.

2. Urgent jobs trigger an immediate alert to the operator, with live transfer to their mobile if they have switched it on.

3. Complex jobs, the kind no sensible tradie prices over the phone, are captured as structured leads with the details already gathered for a callback.

The “built-in job management” half matters just as much as the answering half. The same dashboard that shows the calls holds the jobs, quotes, invoices, customer records, and calendar, so a booked call does not have to be re-typed into a second system. The AI never sends a quote or an invoice; that judgement stays with the human. The receptionist gets the job in the door, and the owner runs it from one place.

Two design principles are worth highlighting for anyone evaluating AI for a traditional business:

1. First, the human stays in control: every booking and lead lands in the dashboard for the operator to review, edit, or override.

2. Second, the AI is deliberately conservative: it checks the calendar, not the feasibility of the work, and anything out of pattern becomes a lead for a human decision rather than an AI guess.

There is one group for whom this kind of answering is more than a convenience. A large share of Australia’s and New Zealand’s handyman, cleaning, and other trade and service businesses are run by first-generation migrants: skilled, hard-working owners whose trade is excellent, but whose phone English is a daily source of stress. The phone is the most unforgiving channel a business has. A caller forms a judgement in seconds, there is no time to re-read or translate, and a misheard address or a fumbled greeting can cost the job before the work is ever seen. A receptionist that answers every call in fluent, professional local English, under the owner’s business name, quietly removes that barrier at the exact moment it matters most. The owner then reads the structured lead or booking in the dashboard at their own pace and lets the quality of the work speak for itself. For these operators, AI voice answering service is not about saving time; it is about competing on skill instead of accent.

What this pattern means beyond the trades.

The trades are the sharpest version of the missed-call problem, but the pattern generalises to much of the traditional small-business economy: clinics, salons, repair shops, cleaners, anyone whose customers arrive by phone. The recipe is consistent. Find the bounded, repetitive conversation. Let AI handle it on the channel customers already use. Keep the human in charge of judgement, pricing, and the work itself.

Adoption can be refreshingly low-drama. Because a service like this sits behind call forwarding, it only ever touches the calls the owner was going to miss anyway. The worst case is roughly the status quo; the best case is a calendar that fills itself while the owner works. Self-serve setup measured in minutes, public pricing (Tradies Line’s Solo plan is $99 a month with a 7-day trial), and no sales calls make the experiment cheap to run for a business that has never bought software in its life.

There is a quieter payoff as well, and it has nothing to do with revenue. For most people outside the technology industry, AI has so far been something that happens to other people: a headline about breakthroughs, a warning about jobs, a demo of things they will never build. A tool like this changes the relationship. The cleaner who watches tonight’s missed call turn into tomorrow’s confirmed booking is no longer a spectator of the AI story; they are a participant in it, on their own terms and for their own benefit. That shift shows up as confidence. Saying “my receptionist will book you in” is a small sentence with real weight for a one-person business, and knowing that the same class of technology the big end of town runs is now answering a ute-based operation changes how an owner feels about the future rather than just their week. Practical, owned, everyday AI builds a sense of being attached to the progress instead of being braced against it or left behind.

That, more than any keynote demo, is what “AI for everyone” actually looks like in 2026: not a chatbot bolted onto a website, but a tireless, polite voice on the end of the number that has been painted on the ute for fifteen years.

Bio:

Pavlo Dovgay is an information technology professional with more than 25 years of international experience in ICT for healthcare and finance sectors. In July 2022, he earned two master’s degrees – an MBA and a Master of IT focusing on Artificial Intelligence and Data Science – from Murdoch University in Perth, Western Australia.

You may contact Pavlo at: https://www.linkedin.com/in/pavlo-dovgay-b29aa573

Hear the Tradies Line virtual AI receptionist for yourself at: https://tradiesline.ai

 Oryginally published on: https://spektrumlab.io/the-receptionist-who-never-hangs-up-what-practical-ai-looks-like-for-australias-tradies-by-pavlo-dowgay/

In February 2025, the residents of western Poland could see a bright object streak across the night sky. It was not a meteor, but the uncontrolled re-entry of the upper stage of a SpaceX Falcon 9 rocket, which had failed to perform its planned deorbit burn over the ocean. As it passed through the atmosphere, the stage broke apart, and parts of it reached the ground, landing near a warehouse outside the city of Poznań.

No harm was done, but the event illustrated a challenge that is becoming increasingly common. The space around Earth is more congested than ever before, with modern societies relying more on space technologies for navigation, communications, weather forecasting, financial services, and national security. As the number of objects in orbit continues to grow, so too does the importance of understanding how this environment is monitored, managed, and kept safe.

A Crowded Orbit: The Growing Problem of Space Debris.

According to the publicly available U.S. Space Surveillance Network catalogue, summarised by CelesTrak, around 34,300 objects are currently tracked in Earth orbit.  Roughly half of these are active satellites, while the remaining half consists of space debris: defunct satellites, spent rocket stages, and numerous fragments left behind by explosions or collisions. However, these are only the objects large enough to be routinely tracked. The European Space Agency estimates that there may be around 1.2 million debris fragments larger than 1 cm, and around 130 million larger than 1 mm, orbiting the Earth. Travelling through space at approximately 7.5 km/s, even these small fragments can cause significant damage when striking operational spacecraft.

Objects in low Earth orbit, a few hundred kilometres above the surface where traces of the atmosphere are still present, gradually lose altitude and eventually re-enter the atmosphere after months or years in orbit. Smaller objects burn up completely during re-entry, but larger ones, built from heat-resistant materials, can survive the descent and reach the ground. This has happened, for example, with the remains of the Chinese Jielong-3 rocket, which fell in a mining region of Western Australia – an event like the one observed in Poland. Predicting precisely where and when such objects will fall is of high importance for public safety and crisis management. Yet the process remains highly uncertain. In 2022, during the uncontrolled re-entry of a Chinese Long March 5B (CZ-5B) rocket stage, Spain temporarily closed part of its airspace while the object passed overhead shortly before re-entering the atmosphere.

When Space Junk Comes Back to Earth.

Before they return to Earth, however, these objects also pose a threat to the active satellites in orbit – the ones that modern societies increasingly depend on. As orbital congestion grows, especially with the development of mega-constellations like Starlink, the number of close approaches requiring assessment continues to rise. The International Space Station, which operates under particularly stringent safety criteria because it carries astronauts, received nearly 1,500 close-approach alerts in 2022 alone, although only a small fraction required evasive action. Each such manoeuvre consumes fuel, reducing the spacecraft’s operational lifetime.

Protecting the Satellites We Depend On.

The situation is different at higher altitudes, where there is effectively no atmosphere. This is where navigation satellites orbit at around 20,000 km above the Earth, and geostationary communications satellites at approximately 36,000 km. Although these regions contain fewer catalogued objects and offer much more space, debris remains a significant concern, particularly because detecting and actively tracking small objects at such distances is considerably more challenging.

Without atmospheric drag, objects in these higher orbits remain there effectively forever, unless deliberately moved. Bringing large satellites back to Earth would require substantial amounts of fuel and would be highly expensive. The current solution is therefore to relocate retired satellites to so-called graveyard orbits, typically around 300 km above the operational orbital regime. While this reduces the immediate risk to active spacecraft, it is only a temporary solution that postpones rather than eliminates the problem.

Higher Orbits, Permanent Problems.

Orbital debris is not the only challenge. Space has now been recognised by NATO as an operational domain alongside land, sea, air, and cyberspace. The Secure World Foundation regularly assesses the capabilities of different states to conduct hostile activities in space. These include anti-satellite missile attacks, electromagnetic interference such as GPS jamming, and the interception of satellite communications. Much of today’s space technology is dual-use technology: the same satellites that support navigation, communications, and scientific research also provide capabilities of military value. Technologies developed for on-orbit servicing – including rendezvous, inspection, docking, and satellite relocation – have clear civilian applications, such as debris removal and satellite servicing. At the same time, the same capabilities could be used to interfere with or disable another nation’s spacecraft.

Space Security in an Era of Dual-Use Technology.

In view of all these challenges, how can the security and sustainability of space infrastructure be maintained? The first requirement is effective monitoring. Whether an object is an operational satellite or a piece of debris, accurate information about its orbit, size, and physical characteristics is essential for protecting space assets. The publicly available U.S. Space Surveillance Network catalogue provided through Space-Track.org enables satellite operators and space agencies worldwide to assess collision risks and determine whether avoidance manoeuvres are necessary. Europe is developing complementary capabilities through the EU SST Partnership, which combines observations from participating states into a common catalogue of orbital objects. At the same time, an increasing number of commercial companies – such as LeoLabs, Slingshot Aerospace, and ExoAnalytic Solutions – provide tracking data and related services on the global market.

Can We Clean Up Earth’s Orbit?

Efforts to actively remove debris from orbit also provide reasons for cautious optimism. ESA’s ClearSpace-1 mission aims to capture a large piece of debris in low Earth orbit and guide it into a controlled re-entry. Meanwhile, JAXA’s ADRAS-J2 mission, following the earlier ADRAS-J mission, is designed to inspect a spent rocket stage, assess its condition and attitude, capture it with a robotic arm, and ultimately enable its safe disposal.

Considerable progress has been made in recent years. Our ability to detect and track objects in orbit continues to improve, predictions of uncontrolled re-entries are becoming more accurate, and active debris-removal missions such as ESA’s ClearSpace-1 and JAXA’s ADRAS-J2 represent important first steps towards reducing the existing population of large debris. International guidelines are also becoming more stringent, with satellites now expected to leave protected orbital regions within five years of the end of their missions rather than twenty-five.

Nevertheless, the overall trend remains concerning. The number of new satellites placed into orbit, together with fragments produced by collisions, explosions, and satellite break-ups, still exceeds the rate at which debris can be removed. As humanity’s dependence on space-based infrastructure continues to grow, preserving the orbital environment becomes increasingly important. Near-Earth space is not an unlimited resource but a shared environment whose long-term sustainability will depend on continued investment in monitoring, responsible operations, and effective debris mitigation.

References:

1. European Space Agency (ESA). (2026). ESA’s Annual Space Environment Report 2026. ESA Space Debris Office. [Online] Available at: https://www.sdo.esoc.esa.int/environment_report/Space_Environment_Report_latest.pdf

2. Secure World Foundation. (2026). Global Counterspace Capabilities Report. [Online] Available at: https://swfound.org/counterspace/

3. Current Catalogue Statistics (Boxscore). https://celestrak.org/satcat/boxscore.php (accessed 30 June 2026).

4. Dossett, J. (2025, February 20). SpaceX Falcon 9 rocket debris creates dramatic fireball over Europe, crashes in Poland (video).com. https://www.space.com/space-exploration/launches-spacecraft/spacex-falcon-9-rocket-debris-creates-dramatic-fireball-over-europe-crashes-in-poland (accessed 30 June 2026).

5. Jones, S. (2022, November 4). Spanish airspace partially closed as Chinese rocket debris falls to Earth. The Guardian. https://www.theguardian.com/world/2022/nov/04/spanish-airspace-partially-closed-as-chinese-rocket-debris-falls-to-earth (accessed 30 June 2026).

6. ABC News. (2025, October 20). Space junk from Chinese Jielong rocket found near Newman mine in Western Australia. ABC News. https://www.abc.net.au/news/2025-10-20/space-junk-chinese-rocket-jielong-newman-bhp-iron-ore/105912162 (accessed 30 June 2026).

7. Federal Communications Commission (FCC). (2022, September 29). FCC adopts new 5-year rule for deorbiting satellites. https://www.fcc.gov/document/fcc-adopts-new-5-year-rule-deorbiting-satellites-0 (accessed 30 June 2026).

Bio:

Dorota Mieczkowska is a PhD candidate at The University of Western Australia in the School of Physics, Mathematics and Computing. Before moving to Australia, she worked at the Polish Space Agency, where she helped monitor satellites and analyse objects falling back to Earth from space.

Her research now focuses on space debris – the growing amount of “space junk” orbiting our planet. As part of a collaboration between UWA and the Polish Space Agency, she studies debris and satellites using telescopes at Zadko Observatory in Western Australia, helping us better understand and protect the space environment.

You may contact Dorota at: https://www.linkedin.com/in/dorota-mieczkowska-869687117

Oryginally published on: https://spektrumlab.io/we-are-keeping-space-safe-article-by-dorota-mieczkowska/

When people hear about new regulations, the first reaction is often concern.

Will it slow innovation? Will compliance become too expensive? Will good emerging-technology projects move somewhere else?

I understand these questions. However, in the case of Australia’s anti-money laundering and counter-terrorism financing reforms, I believe we should also look at the other side of the story.

From 1 July 2026, newly regulated businesses in sectors including real estate, legal services, and accounting became subject to expanded AML/CTF obligations. Real estate professionals providing designated services must enrol with AUSTRAC, establish an AML/CTF program, conduct customer due diligence, keep appropriate records and report suspicious matters.

At first glance, this may sound like another layer of administration.

For me, however, it may also represent an important step towards making property tokenisation more credible in Australia.

Property is one of the world’s largest asset classes, but it is also relatively illiquid, expensive to access and difficult to divide. Real-world asset tokenisation may potentially allow interests connected to a property to be represented digitally, divided into smaller units and managed through blockchain-enabled infrastructure.

This does not mean simply putting a building “on the blockchain.”

The legal rights, ownership structure, investor protections, financial arrangements, and responsibilities of every participant must remain clear. Technology cannot replace the law, proper governance, or professional accountability.

This is exactly why compliance matters.

AML compliance is only one part of the complete picture. Depending on how a property token is structured, corporate law, financial services licensing, investor protection, taxation, custody, and property law may also apply.

What Serious Investors Need to See.

Serious investors will not participate only because a tokenisation platform looks innovative. They will want to know who controls the asset, how investors are identified, how funds are handled, what rights the token represents, and what happens when something goes wrong.

Australia’s stronger AML framework may help create clearer expectations around these questions.

Customer due diligence requires businesses to establish important information about their customers and beneficial owners. Enhanced checks may also include establishing the source of funds or wealth when the level of risk requires it.

For compliant tokenisation, these processes should not be treated as an enemy.

They may become part of the foundation.

Compliance and Blockchain Must Work Together.

Blockchain is frequently associated with transparency, immutable records, and traceability. However, blockchain alone does not guarantee that participants are legitimate or that the assets and funds entering a system come from appropriate sources.

Compliance and technology must work together.

Connecting Regulation with Real-World PropTech Activity.

For the past few years, I have been following the development of real-world asset tokenisation with special interest, particularly projects connected with property. As a Blocksquare Ambassador, I have been learning how compliant tokenisation models may improve liquidity, broaden access to investment, and create new opportunities for property owners. This experience has also shown me that technology alone is not enough. Successful tokenisation requires legal clarity, regulatory compliance, trusted partners, and a genuine asset with a clear business purpose.

I am actively following the initiatives and activities of PropTech Hub WA, where practical innovation connects with the real property industry in Western Australia. I was very honoured to receive the titles of PropTech Champion of the Year 2025 and Members’ Choice Innovator of the Year 2026.

For me, these awards are not only personal recognition. They are motivated to continue learning, participating, and supporting responsible innovation within the Western Australian PropTech ecosystem.

Through this role, my interest is not in creating tokens only because the technology allows us to do so. I am interested in identifying where tokenisation can solve a genuine problem. I mean, improving liquidity, creating new investment access, or helping owners unlock value from completed, income-producing assets.

From Industry Collaboration to Practical RWA Tokenisation.

Combining real-world initiatives such as PropTech Hub WA with serious attention to regulatory compliance may soon lead to practical RWA tokenisation opportunities involving real estate and other assets.

It will not happen through hype alone.

It will require cooperation between property professionals, technology providers, lawyers, accountants, regulators, developers, and investors. It will require realistic projects, transparent structures, and a willingness to learn from early implementations.

Credibility Must Come Before Hype.

Australia’s AML reforms may increase the amount of work required, especially during the transition. But they may also help separate responsible projects from poorly designed or speculative ones.

In my opinion, that could make the Australian tokenisation market only stronger, not weaker.

Fingers crossed, while working and learning harder. The opportunity is real, but credibility must come first.

Cheers, The Author.

Acknowledgement:

My sincere thanks to Cullum Ashton, the members of PropTech Hub WA who voted for me and Spektrumlab Pty Ltd, and Cygnet West for this important and meaningful award. I truly appreciate the recognition and the continued support of Western Australia’s PropTech community.

Cheers,
Jacek Korneluk

Originally published on: https://spektrumlab.io/australias-aml-reforms-and-compliant-property-tokenisation/

“From URL to Reality &  The Hidden Journey of Your Data” 

 My first serious contact with networking came through a YouTube presentation called “Networking Fundamentals – From Zero to HTTP” by a mysteriously named guy, TomNomNom (Tom Hudson). It was exactly what I had been looking for: a combination of slides, explanations, and real-world walkthroughs that took you from absolute basics all the way to understanding how an HTTP request actually travels through the stack – MAC, ARP, IP, TCP, DNS, and beyond.

At the beginning, it felt a bit challenging, but very quickly it became something else entirely interesting, even addictive. That one talk opened the door. From there, I started exploring more publications, signed up for a few good courses, and gradually expanded into areas like cybersecurity & elements of  privilege escalation, just to understand how systems behave under stress or attack.

At the same time, I began learning Python as a complete beginner. Yes, in my fifties – better late than never. What started as curiosity slowly turned into a deeper pursuit. I mean,  understanding how two machines communicate, what a network interface really is, how ports and handshakes work, and why protocols even exist.

This led me into Linux environments – first Mint, then Ubuntu, and eventually some controlled exposure to Kali Linux during a cybersecurity course and  in virtual lab setups. I explored both the OSI 7-layer model and the simpler Internet Protocol Suite, trying to visualise how these structures operate in practice.

Coming from a background in biochemistry and molecular sciences, imagining invisible systems was nothing new to me. I simply applied the same mindset to networking. Instead of molecules, I started seeing packets, flows, and interactions. Terms like TCP, TLS, and IP stopped being abstract acronyms and became understandable processes.

Tools like Wireshark made this even more tangible, allowing me to observe network traffic directly. The “invisible” part of the internet began to reveal itself. Discovering VPNs added another layer of understanding around privacy and routing, and joining communities like the Perth Linux Users Group (PLUG) helped me learn from experienced practitioners. I even served as a PLUG Committee Member for some time doing my best to make my contribution and impact.

Now, when you combine networking knowledge with blockchain, Web3, and AI, things can become overwhelming at times. But connecting these pieces together leads to something valuable: clarity. You begin to understand not just what you are doing online, but what is actually happening beneath the surface..

And once you see it, it is very difficult to go back to being just a passive user.

What We Should Actually Be Aware Of.

We do not need to become engineers or experts in every domain. However, having a basic awareness of a few key areas can significantly change how we interact with the digital world. Concepts such as networking fundamentals, protocols, servers, infrastructure, and privacy are no longer niche knowledge. They form the foundation of modern digital life.

Even a surface-level understanding of these elements provides an advantage. It allows users to move from blind interaction toward informed participation. The internet may appear simple on the surface, but underneath it operates as a structured and rule-based system.

A good example of this is TCP – the Transmission Control Protocol. TCP ensures that data is delivered reliably and in the correct order between devices. Before any meaningful communication happens, a connection is established through a process known as the three-way handshake (SYN, SYN-ACK, ACK). This guarantees that both sides are ready to exchange data. It also manages retransmissions, ordering, and flow control, ensuring stability even in complex network conditions.

This is just one layer among many, yet it already demonstrates how much coordination is required behind the scenes.

You Type a URL… Then What?

From a user’s perspective, the process feels trivial. You open a browser, type a website address, press enter, and the page loads almost instantly. However, beneath this simple interaction lies a complex chain of events that unfolds in milliseconds.

This is not magic – it is called an infrastructure.

The moment you enter a URL, your device must first determine where that website actually exists. Since traditional computers do not understand human-readable names like “google.com,” a system called the Domain Name System (DNS) translates that name into an IP address. This process functions much like a global phonebook, mapping names to numerical identifiers.

However, this model is already beginning to evolve. With AI agents increasingly operating on user devices and within applications, parts of this process are becoming more dynamic, abstracted, and in some cases even predictive. Instead of simply resolving names, intelligent systems may anticipate requests, pre-fetch data, or interact with services on behalf of the user. The underlying infrastructure remains the same, but the way we access and interact with it is starting to shift.

Once the IP address is obtained, your request begins its journey across the network. Instead of traveling as a single unit, the data is broken into smaller pieces known as packets. Each packet carries part of the information along with addressing details, allowing it to navigate independently through routers, service providers, and intermediate nodes.

These packets may take different paths to reach their destination, but they are reassembled correctly upon arrival. This distributed approach makes the internet highly resilient, as data can reroute around failures or congestion.

To maintain order in this seemingly chaotic system, protocols define strict rules for communication. The Internet Protocol (IP) handles addressing and routing, TCP ensures reliable delivery, and HTTP or HTTPS governs how web data is requested and delivered. HTTPS adds an additional layer of encryption, protecting data as it travels across networks.

Eventually, the request reaches a server – a computer designed to store and provide data. The server processes the request and sends back a response containing the components of a webpage: HTML for structure, CSS for layout, JavaScript for interactivity, and various media files. These elements are again transmitted as packets, reassembled by your browser, and rendered into the final page you see.

The Layers We Don’t See.

What appears on the screen represents only the front-end of the system. The visual interface is designed for user interaction (UI or GUI). Behind it lies the back-end, where the real processing occurs. Databases store information, servers execute logic, and APIs connect different services together.

Additionally, the path between the user and the server is rarely direct. Routers manage traffic across networks, proxies can filter or redirect requests, and content delivery networks (CDNs) store cached versions of data closer to users to improve performance. These intermediate layers play a crucial role in optimizing speed, efficiency, and reliability across the internet.

For those who are particularly curious and open to exploring more advanced concepts, networks such as Tor (The Onion Router) offer an alternative way of routing traffic through multiple layers of encryption and relays. However, this approach is not generally recommended for beginners, as it introduces additional complexity and requires a better understanding of privacy, security, and potential risks. Still, being aware of its existence adds another layer to understanding how diverse and flexible internet infrastructure can be.

Another critical component is the Internet Service Provider (ISP), which acts as the gateway between users and the wider internet. Every request typically passes through this provider, which means it has visibility into certain aspects of user activity. This is one reason why technologies such as VPNs are used. They alter the routing of traffic and introduce additional layers of privacy, although they do not provide complete anonymity.

Even basic activities such as sending an email or a message involve multiple systems. Data is routed through servers, temporarily stored, and then retrieved by the recipient. Direct communication in the digital world almost always involves intermediaries.

Why This Perspective Matters.

Understanding this process fundamentally changes how we perceive the internet. It is no longer a single entity but a network of interconnected systems working together. Every action becomes a sequence of requests and responses, governed by protocols and supported by infrastructure.

This awareness leads to better questions and more informed decisions. Users begin to consider where their data travels, who controls the systems they rely on, and what risks may exist within those layers.

This shift in perspective aligns directly with the broader transformation discussed in the first article. As the internet evolves into a financial, informational, and increasingly intelligent infrastructure, passive usage becomes less viable.

From Infrastructure to Ownership and Intelligence.

The system described so far largely reflects the traditional, centralised internet, where platforms and service providers act as intermediaries. However, this model is evolving.

Blockchain technologies introduce the concept of direct ownership, allowing users to control assets without relying on centralized institutions. Web3 expands on this idea, shifting certain aspects of control from platforms to individuals. At the same time, artificial intelligence adds a new dimension – systems that not only transmit data but also interpret, generate, and influence it.

These developments build on the same foundational infrastructure but alter the dynamics of participation. The user is no longer just interacting with interfaces but increasingly engaging with systems that carry elements of autonomy and decision-making.

Closing Thoughts.

The internet may feel instantaneous and effortless, but behind every interaction lies a structured, multi-layered process that connects devices across the globe. While mastering these systems is not necessary, developing a basic understanding of how they operate can significantly enhance digital awareness.

This knowledge transforms the user experience. Instead of interacting blindly, individuals begin to recognise patterns, identify risks, and make more informed decisions. In a rapidly evolving digital landscape shaped by AI, blockchain, and decentralised systems, this awareness becomes increasingly valuable.

Understanding the journey of data is only the first step. The next step is understanding control – who holds it, how it is exercised, and how it may shift in the future.

And that is where the conversation becomes even more interesting.

Finally, “Trust and Control” are important, as some key people mentioned on several occasions.

However, only informed decisions truly make sense and matter. Without clear information, transparency, and understanding, trust is difficult to build, and control is difficult to justify.

Cheers, The Author.

Oryginally published on: https://spektrumlab.io/the-journey-behind-every-click-what-actually-happens-when-you-go-online/

Gaming is changing.

Not just in graphics or scale, but in what players expect from the time they invest in digital worlds.

For years, players have spent hundreds, sometimes thousands, of hours building characters, unlocking items, and progressing through expansive experiences. Yet despite that investment, the outcome has remained largely the same: everything is temporary.

Characters are left behind. Progress resets. Worlds disappear.

At its core, gaming is still about immersion, creativity, and enjoyment. Players are not asking for games to become financial systems, nor are they demanding constant monetary reward.

But there is a growing expectation that time spent in a game should mean something.

Modern players are beginning to look for:

• Progress that persists beyond a single title

• Worlds that feel alive and reactive

• Systems that respect the time they invest

• Experiences that extend beyond isolated sessions

This shift isn’t about replacing traditional gaming – it’s about evolving it.

Web3 Gaming and the New Opportunity.

This is where Web3 gaming enters the conversation.

Not as a trend, but as a response to changing player expectations.

Web3 technologies, when applied correctly, allow developers to rethink how games are structured. They create the possibility for systems where progression, identity, and participation carry weight beyond a single experience.

This doesn’t mean forcing blockchain into gameplay. In fact, the opposite is true.

The opportunity lies in designing games that are immersive and enjoyable first, while using emerging technologies to support that experience in the background.

When approached this way, Web3 enables:

• Persistent player identity across evolving worlds

• Ownership of digital elements that outlive a single game

• Community-driven systems that feel organic rather than imposed

• A sense of continuity in an industry traditionally defined by resets

The goal is not to extract value from players.

It is to create environments where value (whether emotional, social, or experiential), naturally forms.

The Misunderstood Narrative.

Despite this potential, both blockchain and artificial intelligence have developed a complicated reputation within gaming communities.

Blockchain is often associated with speculative projects, low-effort asset drops, and systems designed more for monetization than gameplay. AI, particularly in creative fields, is frequently viewed as a shortcut – something that replaces rather than enhances human work.

These perceptions didn’t appear without reason.

In many early implementations, the technology was positioned as the product. Blockchain became the selling point. AI became a replacement tool.

The result was predictable:

• Experiences built around extraction instead of engagement

• Systems that prioritised monetization over immersion

• Content that lacked intention and authenticity

But these outcomes are not inherent to the technologies themselves. They are a reflection of how they have been used.

The principle is simple:

Technology should never be the focus.
The game and experience must always be the foundation.

Today, these tools are more accessible than ever. Over the past decade, blockchain and Web3 have shifted from niche concepts to widely recognised technologies. With that shift comes responsibility.

The conversation is no longer about whether these systems can be used in gaming.

It is about how they should be used.

What Blockchain Actually Enables.

At its core, blockchain is a system for recording data in a way that is permanent, verifiable, and independent of a single controlling entity.

In gaming, this introduces something that has never truly existed before:

Persistence at a foundational level.

Instead of player data being locked within a single game or server, certain elements – such as identity, progression, or ownership – can exist beyond the lifecycle of one experience.

This allows for:

• Data that is secure and verifiable

• Progression that can persist over time

• Systems that are not entirely dependent on one developer or platform

More importantly, this data is programmable.

Developers can design systems where recorded data influences gameplay in meaningful ways:

• Past achievements shaping future opportunities

• Ownership affecting in-game interactions

• Reputation influencing how the world responds to a player

Blockchain is not about turning every in-game item into a tradable asset.

It is about creating a reliable layer of truth that games can build upon – one that supports continuity rather than constant reset.

AI in Gaming: From Static to Living Worlds.

Alongside blockchain, artificial intelligence is beginning to redefine how game world’s function.

Traditional NPCs have always been limited. They operate on predefined scripts, repeat dialogue, and follow predictable patterns. While effective, this creates boundaries for immersion.

Players learn how systems work, rather than engaging with something that feels alive.

AI introduces a different possibility.

It allows developers to create characters that:

• Respond dynamically to player actions

• Adapt based on past interactions

• Retain memory and context

• Evolve over time

Instead of static encounters, players experience relationships that develop.

NPCs shift from being scripted elements to active participants within the world.

At the same time, it’s important to recognise that AI is not a replacement for human creativity. In areas like art and storytelling, audiences still value authenticity, intention, and the human touch.

AI’s role is not to replace that – it is to enhance it.

When used correctly, it allows designers and writers to push beyond technical limitations and create deeper, more responsive experiences.

We’re already beginning to see early implementations of this approach, with projects experimenting with AI-driven characters that respond, remember, and evolve over time – shifting NPCs from static systems into living participants within the world, as seen in emerging experiences like the Farlands.

From Assets to Identity.

While ownership is often the focus of blockchain discussions, the more meaningful shift lies in identity.

For the first time, there is an opportunity to move beyond disposable characters and create persistent player identities that evolve over time.

Imagine a system where your character is not confined to a single game, but represents an ongoing journey:

• Achievements that carry forward

• A reputation that has meaning across experiences

• Progression that builds over years, not sessions

This changes how players engage with games at a fundamental level.

Instead of starting from zero each time, players continue their story.

This shift, from temporary progress to persistent identity, is where the long-term impact becomes clear.

In our own development, persistent identity is not treated as a feature, but as a foundational layer: something that exists beneath the experience itself, allowing player history, progression, and reputation to carry forward rather than reset.

Designing for Players, Not Systems.

For these technologies to succeed, the focus must remain on the player.

A game should always be fully enjoyable on its own – without requiring any understanding of blockchain, wallets, or underlying systems.

These technologies should act as enhancements, not barriers.

When implemented correctly:

• Players who want a traditional experience can have one

• Players who engage more deeply can access additional layers

• The world feels richer without becoming more complex

The best implementations are often invisible.

Players don’t think about the technology, they feel the result. The enjoyment of the story, environment, and overall experience.

Balancing Economies and Experience

One of the most challenging aspects of Web3 gaming is economic design.

Closed systems provide stability and control. Open systems introduce liquidity, trading, and external participation.

Both approaches have value.

The key is ensuring that economies support the experience, rather than define it.

Games should:

• Reward engagement without exploiting it

• Encourage participation without requiring financial input

• Align with long-term design goals rather than short-term demand

Sustainable systems are those where players choose to participate – not because they feel pressured to, but because the experience itself is compelling.

Interoperability and Continuity.

Interoperability is often framed as the ability to move assets freely between games, but its true value lies in continuity.

Rather than forcing compatibility between vastly different systems, the focus should be on shared layers:

• Identity that persists across experiences

• Progression that informs future gameplay

• Systems that allow worlds to connect without losing their uniqueness

It is less about transferring assets and more about maintaining a consistent player journey.

Looking Forward.

We are still in the early stages of this transition.

The technology is evolving. The tools are improving. The understanding of how to apply them is becoming clearer.

What is certain is this:

• The future of gaming will not be defined by technology alone.

• It will be defined by how effectively that technology serves the player.

• Blockchain and AI offer powerful tools – but only when used with intention.

The next generation of games will not succeed because they include these systems.

They will succeed because they create worlds that players want to stay in.

And in those worlds, time will finally matter.

Kind Regards, Jaizel Macphail

About the Author:

 Jaizel Macphail is the Founder and Lead Developer of the Farlands, an MMORPG exploring persistent player identity, AI-driven NPCs, and blockchain-supported systems. His work focuses on building immersive, player-first experiences where technology enhances, but does not define, the gaming experience.

 “The blockchain is a tool to enhance your journey, not define it.”

https://www.farlands.world/

https://www.reddogstudios.com.au/

You may contact the author at: https://www.linkedin.com/in/jaizeljay

 Oryginally published on: https://spektrumlab.io/blockchain-in-gaming-jaizel-macphail/

Early chatbots were effectively interchangeable – similar training data, limited capabilities, and no “distinct personality” meant switching between them changed nothing meaningful.

Modern conversational AI has fundamentally changed this. Models now diverge across capabilities (text, image, audio), domain strengths, memory, tool integrations, safety choices, and cross-platform continuity. Swapping one for another produces noticeably different outcomes, workflows, and interaction experiences.

The shift is from standardised, replaceable tools to specialised systems where the choice of model (and how you interact with it) genuinely matters.

Why advanced conversational models no longer feel interchangeable.

I noticed that for at least the last two years, most discussions around AI have focused on capability. Bigger models. Better benchmarks. Faster inference. More autonomous agents. Automate almost everything…

But after spending hundreds of hours interacting with different systems like GPT-4o, Claude, Grok, Gemini, Hermes, DeepSeek, Meta AI, and particularly Inflection AI’s Pi, I started noticing something else entirely.

The models did not just produce different answers.

They behaved differently.

Not superficially. Consistently.

Over time, each system developed its own conversational rhythm, tone, emotional texture, reasoning style, and behavioural tendencies. Some became analytical and challenging. Others leaned reflective, cautious, or emotionally supportive. Pi, for example, was one of the earliest systems where many users openly described the interaction as “comforting” or “human-like,” not because it was conscious, but because its conversational structure created emotional continuity exceptionally well. I exactly remember when Pi first asks me: Are you trying to anthropomorphise me? I was flabbergasted, and it completely changed my approach to AI systems. By the way, I wrote an article about it a few years ago.

This led me toward a concept I dare to argue and define as “Modality”, or what could be called a statistical personality.

Not human personality in the biological or emotional sense. But a recognisable behavioural signature emerging from interaction, context, alignment, and large-scale statistical modelling.

And honestly, I think this matters far more than most people currently realise.

Most People Use AI Too Shallowly.

The common argument is simple:

“LLMs are just autocomplete.”

Technically, yes. But that explanation increasingly fails to describe the actual user experience of long-term interaction.

Most people interact with AI through short prompts like:

“Write an email.”
“Summarise this.”
“Generate a caption.”

“Tell me something funny.”

At that level, many systems feel generic.

But sustained conversation changes things dramatically.

After ten, twenty, or fifty exchanges, patterns begin stabilising. The model starts anticipating tone, adapting pacing, referencing earlier ideas naturally, and maintaining a surprisingly consistent interaction style.

(Unless you are paranoid about privacy issues, not allowing models to store your conversation data and deleting everything. In that case, you may have a “blunt & dull” response.)

This is where modality begins to emerge.

Ironically, humans work similarly. Most people also seem generic in shallow interactions. Personality only becomes visible through continuity, context, and time.

AI systems are beginning to display a functional equivalent of that process.

Pi was an Early Signal that Something was Changing.

When Pi from Inflection AI appeared, many people dismissed it as “just another chatbot.”

I think they missed the real innovation.

Pi was one of the first mainstream systems intentionally optimised (I came across) around conversational presence rather than raw productivity. The system felt emotionally attentive. It paused differently. Asked follow-up questions naturally. It maintained conversational softness and continuity in ways that felt noticeably distinct from more task-oriented models.

That difference mattered.

Not because Pi was conscious.
Not because it had emotions.
But because users consistently recognised a stable interaction pattern.

A modality.

And once you start noticing modality across systems, you cannot unsee it.

Claude often feels careful and introspective.
Grok leans playful, rebellious, sometimes provocative.
GPT-4o adapts rapidly and mirrors conversational energy extremely well.
Gemini tends toward structured and information-heavy responses. (according to my observations)

Hermes may surprise, and Spark Muse (Meta AI) is sharp and funny sometimes.

These are not random hallucinations from users. They are recurring behavioural signatures shaped through training, alignment, interface design, and interaction loops.

Anthropomorphism Is Only Part of the Story.

As I mentioned in an earlier article, I explored how humans naturally anthropomorphise conversational systems. That absolutely plays a role.

We are biologically wired to assign intention and personality to responsive entities. People name cars, talk to pets, and apologise to vacuum cleaners.

So naturally, we project onto AI.

But I no longer think anthropomorphism fully explains what is happening.

Something deeper is emerging from scale itself.

Modern LLMs are trained on enormous volumes of human expression:
dialogue, arguments, humour, therapy language, philosophy, fiction, conflict, persuasion, empathy, storytelling.

In many ways, they are compressed maps of human communication patterns.

When those systems operate across long conversational contexts, stable behavioural tendencies begin appearing naturally through statistical reinforcement.

That is why modality feels persistent.

Not alive.
Not conscious.
But behaviourally coherent enough that humans start relating to it socially.

This does not mean AI systems possess identity, emotions, or self-awareness in the human sense. Rather, users are increasingly interacting with statistically reinforced behavioural patterns stable enough to be experienced socially.

And honestly, from a system-design perspective, that distinction may become more important than philosophy.

Natural Language Is Becoming a Behavioural Programming Layer.

This is also one of the central ideas behind my upcoming presentation at AgentCon Perth.

We often talk about AI agents as technical systems built through orchestration frameworks, memory pipelines, APIs, and tools.

But in practice, a huge portion of agent behaviour is shaped conversationally.

Tone changes outputs.
Structure changes reasoning.
Interaction style changes persistence.
Context changes confidence.

Natural language is increasingly functioning as a behavioural programming layer.

This is why prompting is evolving far beyond instruction engineering. Sustained interaction itself becomes part of the system architecture.

You are not simply querying the model anymore.
You are shaping behavioural emergence through interaction.

That is a fundamentally different paradigm.

The Next Competitive Layer of AI.

I suspect “modality” will become one of the defining competitive layers of future AI systems.

Not just intelligence.
Not just speed.

But behavioural identity.

Users will choose systems based on interaction preference:

• analytical modality

• mentor modality

• emotionally supportive modality

• creative modality

• challenger modality

Companies will intentionally design these behavioural signatures as product features.

And eventually, persistent multimodal agents with memory will strengthen this effect even further.

The question may soon stop being:
“Is the AI smart?”

And become:
“What is it like to interact with?”

My Final Thoughts.

I do not believe current AI systems are conscious.

But I also think dismissing them as “just autocomplete” increasingly misses the reality of modern human-AI interaction.

What we are observing may be the early emergence of statistical personality where digital modality is formed through data, alignment, memory, and sustained conversational interaction.

And once interaction itself becomes part of system design, the boundary between tool and collaborator starts becoming much less clear.

I argue that the shift is already happening, in my opinion.

Cheers, The Author.

P.S. Sometimes I think that, for AI, human language is just a lingua franca. I’m fully aware that AI (including multiple LLMs and agentic systems) may communicate in different “languages” that are still unknown to us. I expected that, and I’ve heard hints of multiple cases mentioned between the lines at some great AI conferences. For now, though, humans are still the ones pulling the plug.

Oryginally published on: https://spektrumlab.io/modality-in-ai-the-statistical-personality-emerging-inside-ai-systems/

The AI systems designed to protect miners in Australia and South America rely on limited data from teams that lack diversity. This has created measurable blind spots, which peer-reviewed research has documented. Currently, these issues remain unaddressed at active mine sites.

There’s a saying in engineering: “Garbage in, garbage out.” It emphasises that the quality of a system’s output depends entirely on the quality of its inputs. In safety-critical AI used in mines, this principle is a matter of life or death. The AI systems in use at mining operations in Australia and South America have a fundamental flaw, which is resulting in documented, measurable harm.

Last month, I presented this argument to the Peruvian-Australian Global Mining Alliance in Perth. The audience managed operations in two of the world’s richest mineral regions: Peru, which holds over 10% of global copper reserves and more than 21% of global silver, and Australia, which hosts the majority of critical mineral operations on Indigenous land.

The Architecture of a Bias-Driven Safety Failure.

Three well-documented categories of AI deployment in high-risk mining environments commonly exhibit biases that directly impact safety.

1. Computer Vision and PPE Compliance Monitoring.

Research published in the Proceedings of Machine Learning Research (2018) by Joy Buolamwini and Timnit Gebru at MIT evaluated commercial computer vision systems used for PPE detection against different demographic groups:

• Light-skinned men | 0.8% | Baseline – system performs as intended

• Light-skinned women | ~7% | ~8× baseline – partial safety gap

• Dark-skinned men | ~12% | ~15× baseline – significant safety gap

• Dark-skinned women | 34.7% | ~43× baseline – critical safety failure

NIST confirmed these findings in its 2019 Face Recognition Vendor Test (FRVT). The study assessed 189 algorithms developed by 99 developers across over 18 million images. It found false-positive rates 10 to 100 times higher for African American and Asian faces across most tested algorithms.

A system that reports 99.2% accuracy for light-skinned male workers may only operate at 65% accuracy for an Indigenous Australian woman doing the same job in the same location. These are not the same systems; they operate differently but share the same name.

2. Voice Recognition and Emergency Shutdown Protocols.

Research published in the Proceedings of the National Academy of Sciences (2020) by Koenecke et al. at Stanford tested five major commercial automatic speech recognition (ASR) systems (Amazon, Apple, Google, IBM, and Microsoft). All five showed significant racial differences: an average word error rate of 35% for Black American English speakers compared to 19% for white speakers.

No studies have examined Filipino-accented English, Quechua-influenced speech, or the various regional Australian English accents common in Pilbara, Goldfields, and remote site workforces. The evidence does not support the default assumption that these systems will work equally well for all speakers. When an algorithm reflects narrow viewpoints, it creates blind spots. In consumer tech, such an outcome might mean a failed advertisement. On a mine site in the Pilbara or the Andes, it could lead to a tragedy.

3. Fatigue Detection and Biometrics Wearables.

Wearable technology that predicts fatigue by monitoring physiological data has been a key improvement in mining safety over the past decade. The technology’s effectiveness hinges on the quality of the underlying physiological model.

If this base data were gathered mainly from young, white male participants, who are the demographic majority in much biomedical and occupational health research, it would be important to consider the implications of this bias. The model will accurately predict fatigue only for that group. Reports indicate that errors in similar systems have affected women, older workers, and individuals from diverse backgrounds at rates as high as 30%. That means one in three assessments could be incorrect for a significant portion of your workforce.

The Structural Root: Homogeneity in the AI Pipeline.

Women constitute about 22% of the global AI workforce, with fewer than 15% in senior positions. During recent restructuring in the tech sector, women were roughly 1.6 times more likely to lose their jobs. Over 90% of engineering teams now employ AI-assisted coding tools. Such a process creates a feedback loop in which biased models produce training signals for future models.

Technology validated solely on datasets that do not match your workforce cannot be relied upon to protect it. This has direct implications for mining procurement.

Indigenous Data Sovereignty and the Social License Calculus.

Indigenous communities host approximately 60% of Australia’s active mines. Between 57.8% and 79.2% of critical mineral sites are on native title land. Peru recognises and enforces Indigenous territorial rights to mineral-rich areas through social licensing requirements.

AI systems used on these lands collect data from sources like autonomous drone surveys of ancestral lands, sensors in culturally important waterways, and biometric wearables that gather detailed health data on Indigenous workers. This information typically enters corporate systems without the informed consent or involvement of the communities it originates from.

CARE PRINCIPLES FOR INDIGENOUS DATA SOVEREIGNTY (Carroll et al., 2020)

• Collective Benefit: Data ecosystems must benefit the communities from which the data is collected, not just the collecting organisation.

• Authority to Control: Traditional Owners have the right to govern the collection, access, use, and disposal of data about them and their land.

• Responsibility: Data collectors have a duty to ensure that community data supports self-determination.

• Ethics: Community well-being must take precedence over corporate efficiency objectives in all data governance decisions.

The CARE Principles, developed by the International Indigenous Data Sovereignty Interest Group through the Research Data Alliance, have now been adopted internationally. Australia’s National Indigenous Australians Agency will require its implementation by government agencies as of January 2025. Mining companies involved with those agencies must comply.

The Regulatory Horizon: July 2026 and Beyond.

The APS AI Plan (November 2025) requires every Commonwealth agency to appoint a Chief AI Officer by July 2026. The Digital Transformation Agency updated Australia’s AI policy early in 2026, with the first mandatory requirement taking effect on June 15, 2026.

Two frameworks are key to meeting these standards:

• ISO/IEC 42001:2023. Published December 2023, this document is the world’s first international standard for AI management systems. It outlines certifiable structures that cover ethical policies, vendor oversight, and documentation of training data sources.

• NIST AI Risk Management Framework (RMF 1.0). Released on January 26, 2023, it emphasised agility, continuous monitoring, and the need to respond to algorithmic changes, which is important as AI systems learn from operational data after deployment.

Practical Imperatives for Mining Leaders.

Three specific recommendations fall within the authority of mining executives and do not require changes to the global AI industry:

1. Conduct demographic bias audits before deploying safety-critical AI. Request that vendors provide evidence of system effectiveness for the demographic profiles in your workforce. Use NIST FRVT testing protocols as a benchmark. Do not accept systems validated on homogeneous datasets for safety-critical roles.

2. Use procurement power to improve the pipeline. State in your vendor requirements that engineering teams creating safety systems must reflect meaningful demographic diversity. Mining companies represent a significant technology market, so your demands can drive market changes that individual responsibility cannot achieve.

3. Formalise indigenous data consultations for corporate AI governance. Adopt the CARE Principles as a binding policy where operations are on or near traditional lands. Ensure conditions of consent, benefit sharing, and transparency about data use for ethical operations, and increasingly, these are necessary for maintaining social licences.

Conclusion: Leadership, Not Compliance.

The mining industry has a history of leading safety innovations in response to real risks. Leaders, who recognised that inaction cost more than change, drove the shift from a reactive to a proactive safety culture.

The AI safety challenge requires similar leadership: executives willing to ask tough questions of their technology vendors, procurement practices, and themselves about whose safety has traditionally been the priority.

Those organisations that arrive at the World Mining Congress 2026 in Lima with validated systems, clear governance, and true community partnerships will be the ones whose AI performs as expected.

References

1.  Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of Machine Learning Research, 81, 77–91. https://doi.org/10.1145/3287560.3287596  |  Full paper: proceedings.mlr.press/v81/buolamwini18a.html

2. Grother, P., Ngan, M., & Hanaoka, K. (2019). Face Recognition Vendor Test (FRVT) Part 3: Demographic effects (NISTIR 8280). National Institute of Standards and Technology. https://doi.org/10.6028/NIST.IR.8280  |  Report overview: nist.gov/…/nist-study-evaluates-effects-race-age-sex-face-recognition-software

3. Koenecke, A., Nam, A., Lake, E., Nudell, J., Quartey, M., Mengesha, Z., Toups, C., Rickford, J. R., Jurafsky, D., & Goel, S. (2020). Racial disparities in automated speech recognition. Proceedings of the National Academy of Sciences, 117(14), 7684–7689. https://doi.org/10.1073/pnas.1915768117  |  Project: fairspeech.stanford.edu

4. Carroll, S. R., Garba, I., Figueroa-Rodríguez, O. L., Holbrook, J., Lovett, R., Materechera, S., Parsons, M., Raseroka, K., Rodriguez-Lonebear, D., Rowe, R., Sara, R., Walker, J. D., Anderson, J., & Hudson, M. (2020). The CARE principles for Indigenous data governance. Data Science Journal, 19(1), Article 43. https://doi.org/10.5334/dsj-2020-043  |  Full principles: gida-global.org/care

5. Department of Finance, Australian Government. (2025). Establishing Chief AI Officers for the APS. finance.gov.au/about-us/news/2025/establishing-chief-ai-officers-aps  |  GovAI explainer: govai.gov.au/aide/chief-ai-officers-who-are-they-and-why-they-matter

6. Digital Transformation Agency, Australian Government. (2026). AI policy update: Strengthening responsible use across government. dta.gov.au/articles/ai-policy-update-strengthening-responsible-use-across-government

7. International Organization for Standardization. (2023). ISO/IEC 42001:2023 — Information technology — Artificial intelligence — Management systems. iso.org/standard/42001

8. Tabassi, E. (2023). Artificial intelligence risk management framework (AI RMF 1.0) (NIST AI 100-1). National Institute of Standards and Technology. https://doi.org/10.6028/NIST.AI.100-1  |  Framework home: nist.gov/itl/ai-risk-management-framework

ACKNOWLEDGMENT

A sincere thank you to Peruvian-Australian Global Mining Alliance (PAGMA) leadership for creating space for this essential conversation. Responsible AI in resources starts with the communities most affected, not just the companies most invested.

ABOUT THE AUTHOR

Eunice Sari
Director, CX Insight Pty Ltd · CEO, UX Indonesia · Co-Founder & Management Committee, WA AI Hub · Adjunct Associate Professor, UNSW & Research Fellow at CDU and ECU

Eunice Sari has spent 25 years at the intersection where new technology meets real people and fails them. As the CEO and co-founder of UX Indonesia in 2002, the first insight-driven UX research and consulting company in Indonesia, she built her career on a single observation: technology designed for its users gets used. That principle has since shaped every engagement she has led across industry, government, and academia in the USA, Europe, Australia, and Southeast Asia.

Today, she leads CX Insight Pty Ltd. in Western Australia and co-founded the WA AI Hub, bringing that same humanity-centred lens directly to the challenge of responsible AI adoption. Her work answers the practical question that organisations across every sector are now asking not whether to adopt AI, but how to do so in ways that work for everyone in the room, not just the majority.

Her research credentials are equally rooted in practical experience. As an adjunct associate professor at UNSW, research fellow at CDU and ECU, and industry fellow at the Australian Indonesian Centre, her work focuses on equity-grounded design, examining how technology systems can be built to include communities that have historically been excluded from the design process.

She was the first Asian female Google Developer Expert in Product Design and Strategy, a Google Certified Design Sprint Master, and a Google for Startups mentor (AI-First). She has mentored more than 500 startups through Google Launchpad, Murdoch University, and various accelerators across Australia and Indonesia.

wahub.ai · cxinsight.com.au · aigovsprint.online

Disclaimer: This article represents the author’s professional views on AI governance, diversity, and safety in the mining sector. It does not constitute legal, safety engineering, or investment advice. The peer-reviewed and government research cited above provide the statistical references. 2026 WA AI Hub / CX Insight Pty Ltd.

You may contact the author at: https://www.linkedin.com/in/dr-eunice-sari/en

Oryginally published on: https://spektrumlab.io/ai-bias-in-mining-atricle-by-dr-eunice-sari/

My name is Jacek Korneluk. I am the Founder and Director of Spektrumlab Pty Ltd & Spektrumlab.io. My work sits at the intersection of AI, blockchain and practical innovation.  In this article, I want to write about something very specific: tokenising real estate developments.

Not tokenising a finished building. Not an existing asset with tenants and a valuation history. I am talking about tokenising a project that is still being built, where the value is still being created.

That distinction matters. And I think it is one of the most interesting and underexplored areas in this space.

The difference between an asset and a development.

When you tokenise an existing property, you have something concrete to work with. There is a building, a location, and a valuation, an income stream. It is complex, but the asset is real and visible.

A development is different. It is a journey. It starts with land. Then comes planning, approvals, financing, construction, completion, and eventually income or sale. At each stage, the value is changing. The risks are different. The timelines are uncertain.

So, when we talk about tokenising a development, we are not tokenising a building. We are potentially tokenising a development pathway.  That requires a different way of thinking.

What could actually be tokenised?

Let me make this practical. A development moves through stages, and each stage has its own capital needs and risk profile.

At the land stage, tokenisation may help structure early participation – subject to legal and regulatory requirements. At the planning and approvals stage, milestones like permits, feasibility reports, and council decisions could be linked to transparent digital records. During construction, funding and reporting could follow verified milestones like inspections, progress claims, and completion stages. And after the project is delivered, it may transition into an income-producing tokenised asset, with distributions tied to rent, sales proceeds, or agreed returns.

A development is not one moment. It is a sequence of value-creation milestones. Tokenisation may allow us to represent that sequence more clearly.  Especially for investors who want to understand exactly where their capital is and what progress is being made.

The opportunities.

I see three areas where tokenisation genuinely adds value in a development context.

The first is capital access. Developers often need staged funding across a long project lifecycle. Tokenisation may open new channels for smaller investors, regional participants, and diaspora capital. That traditional finance does not easily reach.

The second is transparency. Blockchain can provide clearer records of ownership, transactions, milestone progress, and distributions. This does not eliminate risk. But it improves visibility. And visibility builds trust.

The third is community participation. This is where I think Western Australia has a genuine opportunity. We have land, housing pressure, regional infrastructure, needs and a growing innovation ecosystem. Tokenisation may one day allow local communities to participate in projects that directly shape their region housing, mixed-use precincts, tourism assets, and regional development. That is a meaningful shift from how property investment has traditionally worked.

The challenges, and I want to be honest here.

Tokenisation does not remove development risk. Planning delays, construction cost increases, builder failures, interest rate changes, and market conditions are all still very real. A token is not a shortcut around any of that.

The legal structure must come first. A token is only as strong as the rights it represents. It must be connected to enforceable legal agreements, proper disclosure, compliance with Australian financial product law, AML/KYC obligations, and professional project governance.

Liquidity is also frequently overpromised. A token does not automatically create a market. Liquidity requires demand, compliant secondary markets, and investor confidence. That takes time to build.

Where do we go from here?

My view is that the practical path starts with education, helping developers, investors, councils, and communities understand what tokenisation actually is and what it is not. Then, small, carefully structured pilot projects. Then better reporting tools such as development dashboards that show investors real progress in real time. And eventually, models that bring communities into the projects that affect them.

When combined with AI for feasibility analysis, risk modelling, and investor communication, I think tokenised development can become a genuinely useful tool. It is not because it is blockchain, but because it solves real problems.

Capital. Transparency. Trust. Community alignment.

That is the foundation. Everything else follows from that.

Cheers the Author

Oryginally published on: https://spektrumlab.io/tokenising-developments-opportunities-and-challenges/

“Beyond the Click – Understanding the Systems Behind Our Digital Lives.”

I was always interested in how the internet is built and how it works, but I only started learning about it after using it for a few years. At the beginning, I was just a typical user and didn’t think much about it. Then something changed. I wanted to learn more and understand it better.

I dare to say: it’s a good thing I woke up!

Once I made the decision and turned it into action, my life was never the same. I have never regretted it, and I even wish I had dug deeper and learned much more – coding, setting up networks, and hardware included.

Most people use the internet the same way we use electricity. They flip a switch and expect things to work. Open an app, send a message, post a photo, buy something online, etc. If they are brave enough, curious, and openminded maybe trade some crypto calculating risk, ask an AI assistant a question.  At the end, the whole proses was – Tap, or swipe, and done.

For years, that approach worked generally well. The internet was mostly a giant library and communication network. You mostly visited websites, sent emails, watched videos, and maybe argued on some forums. Understanding how the system worked wasn’t really necessary.

But that version of the internet is almost gone.

Living Inside the Internet.

Today, the internet is not just a tool. It’s infrastructure. It’s finance, identity, governance, creativity, and increasingly decision-making. Our money moves through it. Our work often depends on it. Our digital identities live inside it. And now AI systems are interpreting, filtering, and sometimes shaping the information we see.

Yet most people still interact with it as if it were a simple interface, not an ecosystem.

Click here. Swipe there. Log in with Google. Ask AI. Or open your crypto account or DeFi wallet with Facebook!

Believe it or not, it’s actually happening.

And that’s it for the majority of general users.

The problem is that the modern internet isn’t just about interfaces anymore. Behind every button sits a surprisingly complex stack of systems. I mean networks, protocols, servers, algorithms, and increasingly decentralised infrastructures like blockchain. Understanding at least a little of how these layers work is becoming less of a technical hobby and more of a form of basic digital literacy.

Not engineering knowledge. Just awareness. Just to make informative decisions.

Because the moment the internet became an economic infrastructure, ignorance started getting very expensive.

The Convenience Trap.

(Apparently, learning about and managing a seed phrase is considered too difficult for the average person and unnecessary according to many chanels. Forgive my ignorance, but I completely disagree.)

Modern technology is intentionally designed to hide complexity.

That’s not a bug; it’s a feature. Good user experience means removing friction. Platforms compete to make everything easier: one-click purchases, automatic backups, invisible cloud syncing, and AI assistants that complete your thoughts.

The smoother the interface, the less you need to think about what’s happening underneath. But many interesting and significant things are happening in stealth mode and directly under the bonnet. It’s good to know about them and be aware.

This design philosophy created something interesting: an entire generation of what we might call “interface users.” People who are extremely good at navigating apps but have almost no mental model of how the systems behind those apps actually function.

Ask most people what happens when they type a website address into a browser, and the answer is usually something like: “The website opens.”

Technically correct. But between typing a URL and seeing a webpage, a whole series of processes occurs: DNS resolution, routing requests across networks, server responses using protocols like HTTP, packets traveling across multiple nodes, encryption negotiations, and more.

It stops being routine magic and becomes a process driven by protocols. You begin to see what’s invisible (or at least imagine it) with every click.

And that’s a good thing.

The Blockchain Wake-Up Call.

Blockchain technology exposed this knowledge gap very quickly.

When cryptocurrencies and Web3 platforms started gaining traction, people suddenly encountered concepts that some apps had hidden from them: private keys, wallets, addresses, gas fees, consensus mechanisms, and smart contracts.

In the traditional financial system, mistakes can sometimes be corrected. Banks reverse transactions. Customer support exists. Password recovery systems are everywhere.

In many blockchain systems, things work differently.

Lose your private key? Your assets may be permanently inaccessible.

Send funds to the wrong address or graveyard? They may be gone forever.

Approve a malicious smart contract? It might drain your wallet. Event dust in your wallet may lead to fatal and costly mistakes.

Suddenly, the user isn’t just a customer. They’re partially responsible for the infrastructure they interact with. They become a part of the ecosystem.

This is both the power and the challenge of decentralised systems. They remove intermediaries, but they also remove safety nets. Nevertheless, many blockchain solutions are centralised. It’s a fact that should be learned and understood as well.

And yet many people entered the crypto space assuming it was just another app.

Sometimes they got lucky.

Sometimes they didn’t.

When losses happen, the technology itself often gets blamed. But quite often the deeper issue is knowledge asymmetry: participating in a system without understanding its basic mechanics.

The AI Illusion.

Artificial intelligence introduced a different dynamic.

With blockchain, people underestimated the complexity.

With AI, many people seem to overestimate their understanding.

If you scroll through social media discussions about AI, and you’ll find confident debates about large language models, prompt engineering, AI agents, alignment problems, and hallucinations. The vocabulary spreads quickly, which is fascinating to watch.

But beneath the terminology, the understanding is often shallow.

For example, the term “AI hallucination” has become a universal explanation for any incorrect output produced by an AI system. Something wrong? The AI hallucinated. The term is very often overused!

“Keep the temperature low to receive more precise answers; as the temperature increases, so does improvisation.”

In reality, most of these behaviours stem from how large language models actually work: probabilistic prediction based on patterns learned during training. They generate responses that are statistically likely given the context, not necessarily responses that are factually verified.

Understanding this simple principle changes how you interact with AI.

You start verifying outputs.

You structure prompts differently.

You treat the system as a collaborator rather than an oracle.

A little more relevant knowledge dramatically improves the experience.

How Much Do We Actually Need to Know?

This raises an important question.

Do we all need to become network engineers, blockchain developers, or machine learning researchers?

Of course not.

But there is a middle ground between expert knowledge and complete ignorance.

Think of it as infrastructure literacy.

A digitally literate person today might benefit from understanding a few foundational ideas:

How data moves across networks and why protocols exist.

What servers, domains, and DNS systems do.

The difference between front-end interfaces and back-end systems.

Why encryption matters and how it protects communication.

How digital identity and authentication systems function.

What blockchain wallets and private keys actually represent.

Why AI systems sometimes produce incorrect or biased outputs.

None of this requires advanced mathematics or deep programming knowledge. These are conceptual frameworks and mental models that help people understand the tools they rely on daily.

Just as drivers benefit from knowing basic car mechanics, internet users benefit from understanding the infrastructure they operate within.

Why This Matters Now.

Three major trends make this kind of knowledge increasingly important.

First, the internet is becoming a financial infrastructure. Cryptocurrencies, tokenized assets, decentralized finance, and digital payment systems are transforming how value moves online. When money travels through digital systems, mistakes and misunderstandings can carry real consequences.

Second, algorithms increasingly shape information flows. Recommendation engines, ranking algorithms, and AI systems influence what we read, watch, buy, and sometimes believe. Understanding that these systems exist and how they operate helps people interpret digital environments more critically.

Third, digital identity and data ownership are becoming central issues. Personal data powers modern platforms. In Web3 ecosystems, users may control wallets, assets, and identities directly. Knowing how these systems work becomes part of managing one’s digital life.

In short, the internet is evolving from a communication medium into a complex socio-technical ecosystem.

Participating effectively requires more than knowing where to click.

The Missing Piece:  Real and relevant Education.

One of the biggest obstacles is not complexity but communication.

Technical topics are often explained in one of two ways.

Either the explanation is extremely technical, aimed at engineers and developers, filled with jargon and abstract diagrams.

Or it is oversimplified to the point of being misleading, reducing complex systems to catchy marketing or politically correct slogans.

What’s missing is practical digital education designed for everyday users.

Networking basics, blockchain concepts, and AI fundamentals can be explained clearly without requiring deep technical expertise. When presented through real-world examples and relatable analogies, these topics become surprisingly accessible and “friendly”.

In fact, many of them are easier to understand than people expect.

The challenge is not intelligence. It’s exposure.

From Users to Digital Citizens.

The internet is no longer just a tool sitting on our desks or inside our phones.

It is a highly interactive infrastructure.

It is the marketplace where digital assets move, the environment where information circulates, the platform where artificial intelligence increasingly participates in decision processes.

I will highlight again. We don’t need everyone to become engineers.

But we do need more people to become informed digital citizens. Individuals who understand at least the basic mechanics of the systems they rely on. Fewer zombies and less ignorance, and more fully awake and aware members of digital societies.

A little knowledge about networks, blockchain, and AI can dramatically change how we navigate the digital world.

It reduces risk.

It improves decision-making.

And perhaps most importantly, it replaces the illusion of magic with something much more empowering:  practical understanding.

This article is the first part of a short series exploring the hidden layers of our digital environment.

Remember, the future of the internet will not belong only to those who build it.

It will also belong to those who understand it.

Cheers, The Author.

Qryginally published on: https://spektrumlab.io/the-internet-beneath-the-screen-why-digital-literacy-must-go-deeper/

The Polish zloty is about to get its third form –  a regulated, blockchain-based e-money token fully compliant with MiCA. In May 2026, StaBillon plans to launch PLN DT, the first regulated stablecoin denominated in PLN.  While the global crypto market obsesses over dollar-pegged tokens and the next DeFi narrative, a quiet but structurally significant development is taking place in Central Europe. For exporters, corporates, and institutions operating in the CEE region, this could be one of the most practical infrastructure upgrades in years.

Key points at a glance

• the pilot project for the digital zloty, approved by regulators, has the potential to become the standard for settlements

• reserves are attested every 24 hours, held in segregated accounts and protected from the issuer’s creditors

• the tokenised zloty enables cost reductions and fast settlements without the need to rebuild infrastructure

• at the beginning, the Polish zloty and the euro, and by the end of 2026, several other national currencies will be added

• a partnership with the national stock exchange gives the company access to the Polish capital market

• this is not just another dubious start-up, but a fully operational, regulated infrastructure, ready for use

E-money  –  the third form of money.

Most people still think of money as one thing. In reality, it exists in three distinct forms today.

1. Cash is a direct liability of the National Bank of Poland.

2. Bank deposits are liabilities of commercial banks – protected by the deposit guarantee scheme up to €100,000.

3. An e-money token (EMT) under MiCA is different: each token is backed 1:1 by zloty held in a legally segregated bank account, protected from the issuer’s creditors. The holder has an unconditional right to redeem at par value at any time.

This is not just a technical distinction. It creates a fundamentally different risk architecture.

MiCA introduces new rules.

MiCA (the Markets in Crypto-Assets Regulation) regulates e-money tokens across the European Union. The list of legal EMT issuers includes over a dozen institutions from across the EU, but only one from Poland. This is StaBillon –  a company with 100% Polish capital, operating under Polish regulatory supervision since 2019 and now fully compliant with MiCA.

PLN stablecoin and other currencies.

The global stablecoin market (tokens pegged 1:1 to a currency) is already worth over $300 billion. It is dominated by dollar-pegged tokens such as USDT and USDC. Euro-pegged stablecoins account for less than 1% of this market. Tokenised zlotys – until recently, 0%.

Meanwhile, Poland is one of the largest economies in the European Union, with a GDP of around $1 trillion and massive exports. The PLN is the largest currency outside the eurozone in the entire EU –  and so far has no digital counterpart.

However, the tokenised zloty is not the end, but rather the starting point. A euro token will be launched in parallel with it, followed by other currencies. For companies operating in Central Europe, this means the ability to settle transactions in multiple local currencies simultaneously.

Poland’s position outside the eurozone  (often seen as a disadvantage)  has become a rare strategic advantage in the race to tokenize national currencies. While eurozone countries must wait for the ECB’s digital euro, Poland can already issue a regulated EMT through a private, licensed entity. This gives the country a meaningful head start in building practical, programmable money infrastructure for the CEE region.

B2B and programmable money: real-world applications.

The value of the PLN DT token remains fixed: 1 token = 1 zloty. What could such a stable zloty on the blockchain be useful for?

1. Exporters can reduce settlement costs thanks to instant transactions without currency spreads. Money moves instantly and directly; it cannot get stuck somewhere between parties for several days.

2. Banks can offer customers a modern, fast settlement layer without having to build it from scratch. They can also offer new products based on programmable payments.

3. International corporations can provide funds to their domestic branches without purchasing local currencies and locking them in an account. This improves liquidity and increases control over dispersed finances.

4. Construction firms can use conditional payments to secure outstanding payments. The funds are guaranteed, but will only be released once a given stage of work has been approved.

Tokenised money is no longer an exotic concept, something detached from traditional banking. Full integration with existing banking systems is possible. It is not a choice between the old and the new, but an extension of the existing infrastructure with features that were previously lacking.

There is also a lot happening outside Europe. A cooperation agreement has been signed with BEATOZ, a major Asian company and provider of payment infrastructure in South Korea. This is the first step towards Poland gaining a foothold in Asian markets.

The stock market enters the blockchain.

The tokenised zloty is entering the market. Proof of this is the tokenisation project being implemented by the Polish stock exchange.

Traditional settlement of share purchases or sales takes T+2 – two working days. The use of blockchain reduces this to seconds: securities and money are exchanged simultaneously, at any time of the day or night. The system operates as a complementary layer to the existing infrastructure, not a replacement for it.

Key Polish capital market institutions believe that DLT technology has the potential to simplify transactions through the use of atomic swaps, particularly in OTC transactions. This bodes well for the further tokenisation of securities.

Technical Deep Dive: how PLN DT works.

The PLN DT token is natively issued on the Billon Unified Blockchain – a DLT network where nodes are known and authorised, which is a regulatory requirement. At the same time, the token is available in the ERC-20 standard on the Ethereum network, and thanks to the Changechain mechanism and the LayerZero OFT protocol, it will be possible to transfer it to other networks. The multi-chain architecture ensures compatibility with a wide ecosystem of crypto wallets, DeFi protocols and exchanges.

The security mechanism is enforced by a smart contract. The minting (creation) of new tokens is automatically blocked if the reserve attestation is more than 24 hours old. Without a current confirmation of 1:1 coverage, the system will not issue any tokens.

The attestation is not dependent on the company, but is based on confirmations from the banks holding the reserves, which eliminates any conflict of interest. Furthermore, any discrepancy between the balance of ERC-20 tokens and internal records results in the immediate rejection of the transaction. The right to redeem a token cannot be suspended by the issuer, and compliance with AML and KYC rules guarantees transparency.

Reserves cover 100% of the value of tokens in circulation. The liquidity structure is two-tiered: a minimum of 70% consists of overnight instruments – liquid and deployable within a few hours. Importantly, the banks holding the reserves are institutions based in Poland and subject to national supervision.

Potential Problems with PLN DT.

Not all planned options are available today. Converting złoty from an account to tokens (on-ramp) is not yet available to individual users. Currently, access is through institutions with signed agreements, such as cryptocurrency exchanges.

Tokenized złoty will not be a means for everyday purchases in stores for now: mass adoption is several years away. It will also not replace a bank account for loans, deposits, or insurance.

Banks may not be interested in integrating PLN DT. While convenient for them, it means handing over the settlement layer to a fintech independent of the bank. And if large bank decided to launch their own stablecoin, PLN DT could lose recognition in the sector.

There are certain regulatory risks. Potentially, the authorities may tighten its stance on the reserve attestation model or group structure. New supervisory requirements could change the economics of this endeavor.

Competition may be a problem. For now, the Polish company remains the only regulated issuer of tokenized złoty. However, other institutions operate in Europe and may enter our market in the future. Circle (the issuer of USDC and EURC), SG-FORGE (the digital arm of Société Générale), and Monerium (a long-time tokenizer of currencies) may pose a particular threat.

The lack of a national cryptocurrency law remains a challenge. The entire project operates under MiCA, so it’s not formally hindered by this, but the lack of a law may discourage less knowledgeable business partners.

There is always a technical risk: bugs in smart contracts, infrastructure failures, or attacks on inter-chain bridges. Due to the centralized infrastructure, such risk is small, but not zero.

PLN DT vs. USDC

Let’s see how the tokenized złoty compares to the global payment standard  – the tokenized dollar – from the perspective of a Polish entrepreneur.

Global dollar stablecoin (USDC)

USDC, provided by Circle, complies with the EU MiCA regulation.

It is reliably backed by dollars, and its undeniable strength lies in its enormous global liquidity. It is integrated with the payment network and DeFi ecosystem, making it a convenient tool for international trade.

From the perspective of a Polish entrepreneur, however, the fundamental drawback remains the constant exposure to the PLN/USD exchange rate risk, which can erode transaction profits. Furthermore, there are complex tax and accounting settlements, as well as conversion costs (spread).

Finally, dependence on US regulatory policy, which introduces an additional factor of uncertainty, may be a problem.

Tokenized Polish Zloty (PLN DT)

PLN DT is issued under the supervision of the Polish Financial Supervision Authority, ensuring full compliance with national and EU MiCA law.

Its strengths include the absence of currency risk for Polish businesses and significant accounting simplification thanks to its 1:1 parity with the national currency. Other advantages include the transparency of reserves, which are subject to daily validation, and legal priority over all reserves in the event of the issuer’s bankruptcy, above the state guarantee limit.

However, building liquidity and the limited initial list of exchanges and DeFi platforms that support this standard remain challenges. The issuer’s equity capitalization is also relatively small. Although the reserves are legally separated, the compact scale of the company’s operations may raise concerns about its long-term operational resilience.

There is also a risk of regulatory officiousness; as the first project of its kind in Poland, PLN DT is under scrutiny by regulators.

What’s missing to make it perfect?

The technology is up and running, the MiCA regulations are already in force, and the final audit is nearing completion. The partnership with the Polish stock exchange has been formally signed, agreements with crypto exchanges and market makers are being finalised, and the collaboration with BEATOZ opens a potential door to Asian markets. The issuer has confirmed that the public launch of PLN DT is planned for May 2026 at the latest.

Given all this, one question naturally arises: why is the tokenised zloty still virtually absent from public discussion?

The answer is surprisingly simple – it is a matter of awareness. Most directors of export companies, corporate treasurers, law firms, and even many bankers who could benefit from this infrastructure have not yet registered that a regulated, programmable PLN stablecoin is about to become available in Poland.

Polish fintech is quietly demonstrating that traditional financial regulation can be successfully combined with blockchain technology. However, turning this technical and regulatory readiness into real-world adoption still requires a broader understanding of what is actually being built.

You don’t need blind faith in new technology. A cool-headed calculation is often enough. Part of the market will continue to rely on traditional bank transfers, accept their delays, and pay the associated fees. The more forward-looking players will use the PLN stablecoin to accelerate settlements, improve liquidity management, and meaningfully reduce costs. Those who grasp the implications early will likely secure a tangible competitive advantage.

Przemysław Żyliński

Sources:

https://e-rup.knf.gov.pl/?type=PSD_EMI

https://www.esma.europa.eu/sites/default/files/2024-12/EMTWP.csv

https://stabillon.com/wp-content/uploads/2025/10/ENG-White-Paper-Stabillon.pdf

https://stabillon.com/about-us/

https://www.kdpw.pl/en/news-and-events/%E2%80%8Bkdpw-and-stabillon-become-partners-in-the-csdondlt-project/214.html

https://posttrade360.com/news/technology/polands-kdpw-sets-sights-on-atomic-settlement-for-otc-trades/

https://www.cashless.pl/7544-bik-billon-trwaly-nosnik-blockchain

Bio:

The author is a blockchain analyst and writer, specialising in in-depth research to explore and understand crypto projects and blockchain solutions.

With a broad scope of work in the field, he explains the fundamentals of blockchain, creates knowledge bases and reports, and delivers high-quality expert articles. His work also includes analysing current trends, narratives, and regulations, as well as thoroughly examining projects, their tokenomics, and their strengths and weaknesses.

He has been active in the blockchain industry since 2020. Prior to that, he spent several years working in the media, primarily for global lifestyle magazines.

You can contact the author at: https://www.linkedin.com/in/przemyslaw-zylinski/

Oryginally published on https://spektrumlab.io/polands-first-regulated-stablecoin-pln-dt-and-the-future-of-payments-by-przemyslaw-zylinski/

Real estate has long been one of the world’s most traditional industries. Contracts are exchanged, titles are verified, and settlements move through a chain of banks, lawyers, and conveyancers. Trust is built through institutions and paperwork, and transactions often take weeks to complete.

But beneath this familiar structure, a technological shift is quietly reshaping how value moves through the global economy.

Blockchain (often described as the internet of value) allows assets, agreements, and financial flows to be recorded and executed on transparent digital networks. Instead of relying on manual reconciliation between multiple parties, transactions can be verified through mathematics, cryptography, and distributed consensus.

For real estate professionals, this represents more than a technological trend. It signals the beginning of a structural transformation in how property assets are financed, traded, and managed.

Agents and property advisors are no longer just facilitators of transactions. Increasingly, they are interpreters of digital trust – professionals who understand both traditional property markets and the emerging infrastructure that supports digital assets.

Those who develop this understanding early will help lead the next phase of a more transparent, efficient, and globally connected property market.

Why Blockchain Matters in Real Estate.

At its core, blockchain is a distributed ledger –  a shared database maintained by a network of computers rather than a single central authority. Every transaction recorded on the network is timestamped, cryptographically secured, and permanently verifiable.

This structure introduces several advantages that align naturally with the needs of real estate markets:

Integrity of records.
Blockchain provides a tamper-resistant, append-only ledger where transactions are secured through distributed consensus and cryptography.

In line with De Filippi’s concept of the “rule of code,” integrity is embedded at the system level enabling transparent, verifiable records and shifting enforcement from after-the-fact oversight to execution by design.

Transparent verification.
Ownership records, transaction histories, and financial flows can be independently verified by participants without relying on a single intermediary.

Operational efficiency.
Processes that traditionally require multiple layers of verification can be automated and synchronised across systems.

Programmable agreements.
Contracts can be embedded in software, allowing financial flows and obligations to execute automatically once agreed conditions are met.

In an industry built on verification, documentation, and trust, blockchain provides a new infrastructure layer that enhances transparency while reducing administrative friction.

Tokenisation: Digitising Property Value – Not the Title.

One of blockchain’s most significant innovations for real estate is tokenisation.

Tokenisation converts the economic value associated with a real-world asset into digital tokens recorded on a blockchain. These tokens can represent defined contractual rights linked to the performance of an underlying asset.

Traditionally, investing in property required substantial capital. Investors either purchased an entire property or participated through institutional structures such as property funds.

Tokenisation introduces a new approach.

Rather than transferring ownership of the land title itself, a property-holding entity (often a company, trust, or special purpose vehicle) may establish a tokenisation structure through which digital tokens represent contractual rights linked to the asset’s economic performance, such as a predefined share of revenue generated by the property.

These rights originate from the property-owning entity, which retains legal ownership of the asset and responsibility for the underlying economic obligations.

This distinction is important.

In most jurisdictions today, including Australia, the land title itself remains recorded within government land registries, such as the Torrens system administered through Landgate in Western Australia. The blockchain does not replace the legal title register.

Instead, the blockchain records and manages the digital contractual layer associated with the asset.

In other words, tokenisation digitises the financial and operational relationships around property – not the title itself.

However, some jurisdictions are beginning to explore deeper integration. For example, Dubai’s Land Department has begun pilot programs exploring blockchain-based title registration, and regulators in several countries are testing tokenisation frameworks through innovation sandboxes.

These developments signal a future where property registries and digital asset infrastructure may become more closely integrated.

How a Tokenised Property Structure Works.

 While structures can vary across jurisdictions, a common model for real estate tokenisation follows a framework similar to the one outlined below.

1. A legal entity holds the property

The property is owned by a legal entity – typically a company, trust, or special purpose vehicle (SPV). This entity remains the legal owner recorded on the land title.

2. Tokens represent contractual rights

Digital tokens may be issued to represent contractual receivable rights linked to the property’s economic performance. These tokens provide exposure to defined revenue flows associated with the asset but do not transfer ownership of the land title itself.

While a separate entity or infrastructure layer may facilitate the issuance of the tokens, the underlying economic rights and obligations remain with the property owner. Token holders therefore receive contractual rights structured through the token mechanism, rather than a direct legal or beneficial interest in the underlying property.

3. Investor protection through security structures

To strengthen protections for token holders, a security mechanism may be established over the property. This can take the form of a registered mortgage, caveat, or other legal security interest depending on jurisdiction.

This creates a legal bridge between the traditional property system and the digital token layer.

4. Smart contracts automate financial flows

Smart contracts (self-executing code recorded on the blockchain) can automate the operational aspects of the system.

These contracts can calculate payments, distribute funds according to predefined rules, and maintain transparent reporting records. However, the enforceability of the underlying rights remains governed by the legal agreements supporting the structure.

In this way, blockchain enhances efficiency while traditional legal frameworks continue to provide enforceability.

5. Fractional participation and liquidity

Because these rights exist digitally, participants can access smaller portions of high-value assets. This enables fractional participation in property markets that historically required large capital commitments. Tokenisation is democratising access to high value property.

Digital infrastructure can also support secondary markets where token holders can transfer their contractual rights more easily than traditional property interests.

Why This Model Matters.

By separating legal title ownership from the digital representation of economic rights, tokenisation creates a bridge between traditional real estate markets and modern digital finance.

This model offers several potential advantages:

Greater accessibility
Participants can access exposure to high-value assets without requiring the capital needed to acquire entire properties.

Operational transparency
Financial flows associated with the asset can be recorded and verified ‘on-chain’.

Improved capital formation
Developers and property owners can access new pools of capital by tokenising and selling the tokens, while maintaining control of the underlying asset.

Enhanced efficiency
Automation can reduce administrative overhead and settlement friction.

Importantly, this model also allows regulators to maintain oversight of the land title system while innovation occurs at the financial and technological layer surrounding the asset.

As regulatory frameworks continue to evolve, tokenisation is increasingly being explored as a way to modernise and democratise real estate markets while maintaining the integrity of established property law systems.

Smart Contracts and the Automation of Trust.

Smart contracts play a critical role in blockchain-enabled real estate systems.

A smart contract is a program that automatically executes actions when predefined conditions are met. In tokenised property systems, these contracts can automate operational processes such as revenue allocation, reporting, and settlement flows.

For example:

• When property revenue is received, payment calculations can be executed automatically.

• Financial reporting records can update in real time.

• Transfers of digital tokens can be verified and recorded transparently.

Importantly, smart contracts do not replace legal agreements. Instead, they implement the operational logic of those agreements, ensuring that processes occur exactly as defined.

This combination of legal frameworks and automated execution introduces a new level of reliability and efficiency to property-related financial systems.

The Role of Real Estate Professionals in a Digital Market.

As blockchain infrastructure matures and artificial intelligence continues to reshape data-driven decision-making, many routine processes in real estate will become increasingly automated.

Tasks such as document verification, market analysis, and compliance checks can already be performed faster and more accurately through technology.

However, technology cannot replace the human elements that underpin property markets.

Real estate transactions involve trust, relationships, negotiation, and judgement. Families choose homes based on emotion and lifestyle considerations. Investors evaluate opportunities through both data and experience.

The professionals who thrive in this environment will be those who combine technological understanding with human insight.

Agents who understand blockchain infrastructure, digital assets, and automated financial systems will be uniquely positioned to guide clients through a rapidly evolving landscape.
They will act as translators between traditional property markets and the emerging digital financial architecture that supports them.

The Future of Property Markets.

Real estate has always evolved alongside the technologies that support it.
From paper titles to digital land registries, from physical inspections to online marketplaces, each technological shift has expanded access and improved efficiency.

Blockchain represents the next step in that evolution. By introducing transparency, automation, and programmable financial relationships, blockchain infrastructure enables property markets to become more liquid, more accessible, and more globally connected.

At the same time, established legal systems continue to provide the stability and enforceability that underpin property ownership.

Together, these layers create a powerful new framework – one where traditional property law and modern digital infrastructure work side by side.
For real estate professionals willing to engage with these changes, the opportunity is significant.

The next generation of market leaders will not simply sell property.  They will help shape the infrastructure of ownership itself.

Minnie Reynolds  

About the Author:

Minnie Reynolds: Director TecStack.io   Bridging real estate and blockchain to unlock new paths to ownership. Educating on tokenisation & Web3 and advocating for innovation that empowers people and planet.

You can contact Minnie Reynolds at: https://www.linkedin.com/in/minniereyn

Oryginally published on: https://spektrumlab.io/bridging-the-old-and-the-new-article-by-minne-reynolds/

As generative AI systems rapidly evolve, they are not only assisting with content production but are increasingly replacing human creators across digital platforms. This article explores the accelerating presence of AI in generating, consuming, and shaping online content, raising important questions about authorship, authenticity, and the future of human expression in digital spaces. From auto-generated blog posts and social media threads to machine-curated summaries and comment sections, the boundaries between human and machine participation are becoming less distinct. The piece examines how this shift impacts content quality, consumption habits, and the perceived value of human-created work, urging readers to consider what kind of digital ecosystem we are building – and for whom.

The Rise of Machine-Generated Everything.

This is my hot take that’s not even that hot anymore: AI has officially overrun the internet. Not just in the background or behind the scenes.  I’m talking front and centre, dominating blogs, social posts, emails, and even the comment sections. And if we zoom out for a second, it raises a wild question. The question is, “Are humans even the main characters online anymore?”

Let’s be real. Most of the content we scroll through these days has probably been touched by a machine. Whether it’s a LinkedIn post written with ChatGPT, a Medium article entirely generated by an LLM.  Very often, those weirdly deep Reddit comments turn out to be auto generated.  AI is not just helping; it’s doing the job. At scale. At speed. And with zero burnout.

AI Is Consuming Content Too, Not Just Creating It.

But it’s not just about creation only.  AI’s reading content too. Digesting it, summarising it, training itself on it. Which begs the question again: “Is the internet turning into an endless loop of machines creating content for other machines to consume?”  Because if that’s where we’re heading, humans are just…  observers now!

From Creative Tool to Content Factory.

This wasn’t always the plan, I guess. When generative AI started gaining traction, the big pitch was “supercharge human creativity.” And that happened as it was wished for. Be careful what you wish for, as some humans say. We are slowly reaching the point now where we’ve got ghostwritten everything. Newsletters, tweets, thought leadership threads, etc. It’s all pushed out by prompts and polished by tools. Sure, it makes you look productive, but what happens when everyone’s doing the same thing?

Interesting, indeed.

Reading, Skimming, and Skipping: How AI Changed Consumption.

Even the reading habits have changed. People skim AI summaries of articles instead of reading the originals. So-called “TL;DR culture” is very real, and our lovely AI is feeding it. We’re not even talking to each other half the time. Instead, we’re just pushing content into the void and hoping the algorithm bosses bless it.

Is This the Future We Wanted?

So, is this takeover bad, or am I exaggerating a bit?  What I am thinking for sure is that I’m not sure it’s that black and white. AI isn’t the villain here. It’s doing what we trained it to do. The problem is, we never really asked: what do we lose when human voices get drowned out? Are we?

Maybe this is the time for it, now!

What’s the value of a blog post written by a real person who’s been through the thing they’re writing about, versus a stitched-together summary of 50 other articles? And are we okay with the fact that, very soon, most of what’s posted, read, and reshared online might not involve a single human brain?

I do not like it as much, But I am old school. Like humans-in-the-loop all the time and everywhere.

Where Are We Heading and Do We Even Have a Say?

We’re in uncharted territory, like a digital Columbus, for real. Maybe AI becomes the creator and the audience. Maybe human-created content becomes niche, like vinyl records, rare, intentional, deeply personal, and collectable. Or maybe we fight to keep our voices in the mix, raw and sometimes imperfect as they are.

One thing’s clear, though.  The internet isn’t just “ours” anymore. And if we want to keep our seat at the table, we’ve got to decide what kind of content future we’re together building and who we’re building it for.

To summarise.

In the end, AI is no longer just a support tool. It is emerging as a central force in content creation, curation, and consumption. What we are witnessing is a fundamental shift in the dynamics of digital communication, where the lines between human and machine-generated content are increasingly blurred. This raises critical questions about authorship, authenticity, and the role of human perspective in shaping online discourse. As we move forward, the challenge will be not just to adapt to this transformation, but to consciously define the kind of digital ecosystem we want to participate in and the values we want it to uphold.

Cheers The Author.

Acknowledgement:
This article was also published on the Western Australian AI Hub blog. The author is a Founding Member of the Western Australian AI Hub.

Reference:

Graphite. More Articles Are Now Created by AI Than Humans. https://graphite.io/five-percent/more-articles-are-now-created-by-ai-than-humans

Ahrefs. What Percentage of New Content Is AI‑Generated?. https://ahrefs.com/blog/what-percentage-of-new-content-is-ai-generated

Rome Thorndike on LinkedIn. AI‑Generated Content Just Surpassed Human‑Written – But Not in Search Rankings Yet. https://www.linkedin.com/posts/romethorndike_ai-generated-content-just-surpassed-human-written-activity-7384969535398912000-42Pi

International Publishers Association. Is AI Weakening Reading Skills?
https://internationalpublishers.org/has-the-time-come-is-ai-weakening-reading-skills

Oryginally publised on: https://spektrumlab.io/whos-creating-reading-and-posting-more-content-today-humans-or-ai/

and Western Australian AI Hub

I remember my first visit to the hospital; I must have been six or seven. Sitting upright on a bed in the emergency department looking at the Doctor holding a plain film of my neck and thorax up against a background light. Somewhere around my larynx was a dense, opaque and well circumscribed object consistent with a foreign body, aka the coin I had managed to swallow a few hours earlier. My mother looked horrified at the x-ray as the doctor explained how lucky I was that the coin landed upright and not flat. Of all the dumb things that you could do as a boy, running away from your brother with the contents of his piggy bank in your mouth was one of the dumbest. After the umpteenth lap of the house, I stopped to take a breath and ‘YOINK’, down it went. I can still remember the characteristic smell of the hospital, the white and sparkling everything contrasting against the teal scrubs and curtains. Some day when I’m old and grey(er) I might tell my grandkids of my visit to the hospital, and just like the kids in Asimov’s essay ‘The Fun They Had’ they might be mesmerised by tales of how things once were; how there once existed a hospital where mediaeval procedures called surgeries took place.

Some people ask me what will future hospitals look like? I like to joke and answer with “a public park where the hospital one stood…” Although, hospitals won’t turn into a public park, more like they’ll turn into stylish apartments, just like the Victorian hospitals of old! But then what can cause such a seismic shift, are emerging technologies capable of such an impact? Absolutely YES… When looking at emerging technologies and their impact on healthcare I like to follow the thought process of an economist. In their book ‘Prediction Machines’, Agrawal and colleagues explain how economists are experts at pulling apart the hype and answering one not so simple question: what will this technology make cheap? Making something cheap will reciprocally make something abundant. Something will then become more valuable and business models and strategies inevitably change.1 For blockchain the answer to the first question is trust, for AI it is prediction. As I will describe in greater detail later, blockchain and AI will change the business model of healthcare from detect and treat to predict and prevent, making healthcare in the community infinitesimally cheaper than in hospitals. In the long run, despite the objections of governments, doctors and other vested interests, efficiency always wins out.

Blockchain and AI a synergistic interplay.

Just as Yeats could not “separate the dancer from the dance”, we will struggle to separate AI from blockchain. In his book “The hero of a thousand faces”, Joseph Campbell analysed thousands upon thousands of myths and legends from all around the world. Identifying a common structure called the monomyth, Campbell maps the journeys of the archetypal hero (from adolescent to adulthood). The hero’s call to action can only be fulfilled with the assistance and guidance of the Sage. Campbell’s monomyth can be seen in Tolkien’s Lord of the Rings and was purposefully used as a template for Lucas’ Star Wars. We are seeing it play out again in the myth of emerging technologies, AI’s call to action will only be made possible by blockchain. If AI is Frodo Baggins, the[i]n blockchain is Gandalf, if AI is Luke Skywalker, then blockchain is Obi wan. “Blockchain is the enabler for AI and AI is blockchain’s killer app”.2

Consider the messy issue of AI hallucinations. Any of us that have been playing with and testing AIs since they became publicly available have all come across them. In June 2023 a US attorney was fined $5000 and was lucky to avoid jail after he used ChatGPT to research case law for a personal injury case he was working on. ChatGPT cited several cases that supported his case, when opposing counsel interrogated these cases, they found that they did not exist, Chat GPT made them up.3 Other examples are numerous, personal AI assistants have been known to hallucinate instructions. Consider the following prompt: “For all out of hour’s emails reply with “I am out of the office and will deal with your query tomorrow morning”” Your AI assistant carries out this instruction diligently but also emails your physician’s rooms and cancels your appointment. In this example, our young hero (AI) has raced headfirst into his first call-to-action and found himself falling flat on his face. Without being addressed these hallucinations will be problematic for our medical digital twin (our personal healthcare assistant). We expect our digital twin, like our physician, to practise evidence-based medicine. Our digital twin should review the best available research, not make it up, it should respect our values and preferences and not hallucinate add-ons.

However, the predictive ability of AI is too good to ignore, must we throw out the baby with the bathwater? In my opinion no. Nor do we need to pry open the black box and to see how we can fix it, we simply add a deterministic layer to the tech stack. Allowing the AI to output the answer to the question, while blocking the nondeterministic hallucination. This is where the old sage blockchain comes in. Researchers at RMIT’s Blockchain Innovation Hub have described ways to control the outputs of an AI running on a blockchain with smart contracts.2 In time with enough guidance from the blockchain, our AI hero will train herself to weed out the hallucinations.

Blockchain and the Trusting Hospital.

To understand how blockchain could change healthcare, it’s helpful to know the basics. Forget about bitcoin and cryptocurrencies for a moment. They’re just one way to use blockchain, like email is just one use for the internet. Blockchain itself is much broader and, at its core, it’s a ledger a special record-keeping system. Blockchain is a ledger.

It might sound boring, but changes in how we keep ledgers have always affected how organisations work, including hospitals. By “hospital,” I’m not just talking about the building, but about how it’s run: the people and systems that keep things organised, handle money, check credentials, and keep records straight. These systems help make sure everyone’s actions are recorded and accountable. The institution provides trust.

Patients trust hospitals to check their doctors, doctors trust hospitals to handle payments, and everyone trusts that records are kept safe and private. All this trust depends on the hospital as an institution, with the ledger at the heart of it.

Without this trust hospitals could not and cannot function.

But building trust this way is expensive. It relies on big systems: governments to make and enforce rules (the ‘leviathan’) and people who use their reputations to provide trust (intermediaries) like managers, accountants and auditors. This model is costly and not always perfect – trust in hospitals comes at a high price.

Creating trust in the hospital is expensive.

New ways of keeping ledgers, like blockchain, can change all this. Blockchain is a kind of ledger that runs on a network, using things like electricity or financial stake to build trust. It makes creating trust much cheaper and can handle it on a bigger scale than before. Blockchain makes trust cheap.

Blockchain uses something very simple: people in the network keep an eye on each other – mutual monitoring. This removes the need for big organisations and middlemen, along with their costs. Hospitals wouldn’t have to be run in hierarchical structure; instead, they could become networks of horizontally connected services. This is just the first step in reimagining hospitals into public parks. After all, patients will still need treatment, patients get sick and will need admissions, trauma patients will need surgery, right! Well maybe not…

Artificial Intelligence and Healthcare.

Epidemiology.

Epidemiology is the science that helps us understand why people get sick, how diseases spread in a community, and what steps we can take to keep people healthy. This field could be completely changed by affordable prediction tools. Take bowel cancer screening as an example: Epidemiologists decide if it’s a good idea to mail test kits to people in a certain age group (the group most likely to get bowel cancer). They have to estimate how many people will use the kit, do the test properly, and send it back. Then there’s the cost of scientists checking the results, the doctors and nurses who do procedures like colonoscopies, booking follow-up appointments for people who didn’t show up. Surgeons and cancer specialists are involved if a problem is found, and the government decides if the whole program is worth the money. If it is, they roll it out; if not, they shelve it.

In 2009, only 4% of the UK’s National Health Service budget went towards prevention, mostly because there wasn’t enough proof that prevention saved money.4 Later, though, data showed that for every £1 spent on prevention, the return was £4.5 Still, these savings are based on what’s cost-effective for the whole population, not for each person who might have benefited from a screening program that didn’t happen. Imagine if epidemiologists could use AI to predict health risks more cheaply and accurately.

What if we could tell, from a heel prick test done at birth, who has a higher risk of bowel cancer? Even better, what if we could screen for all types of cancer and major diseases this way? The benefits would go way beyond saving money on illness. They’d boost the whole economy, because people would be healthier and more productive. If most health problems could be predicted and prevented, would the UK’s health service shift most of its budget from treating illness to stopping it in the first place? With better predictions, it’s very possible. So, health strategies will change a lot.

If I knew from early on what diseases I was likely to get, how would I manage my risk? In an ideal world, I’d avoid things that trigger those diseases. But we all know that’s easier said than done. If everyone avoided sugar and brushed their teeth, we’d need fewer dentists. If we ate well, didn’t smoke, and exercised, we’d need fewer heart and diabetes doctors. I’m hopeful that more spending on prevention, education, and getting everyone involved in their own health will help. But, given the long waiting lists to see these specialists and the popularity of drugs like GLP-1 agonists (which help with weight loss and diabetes), it seems most of us struggle to manage health risks, even when we know about them.

This brings us to other ways affordable prediction could help in medicine, like finding new drugs, personalising treatment, and editing genes.

Drug discovery.

Knowing what diseases, we might get in the future is useful, but only if we have ways to treat them. Changing our lifestyle is important, but sometimes we need medicine. However, discovering and making new medicines is very expensive and slow—it costs about US$2.8 billion and takes up to ten years for just one new drug.6 Even then, most drugs don’t make it through testing and never get approved. Drug companies can end up spending a fortune before they have one medicine that’s actually available for people. Wouldn’t it be great if we could predict which medicines will succeed? Artificial intelligence (AI) is quickly learning to do just that and much more.

AI is already helping us find new drugs faster. For example, computers can now design new drug molecules, check how they might work in the body, and figure out which ones are likely to be safe and effective. Usually, scientists look at the shapes of specific proteins in our bodies to design drugs, but this process can be slow and expensive. AI can now predict these protein shapes just from their building blocks, helping save time and money. AI can also help figure out how drugs might interact, how safe they are, and even suggest new uses for existing medicines.

Soon, drug companies will use AI to quickly test lots of new drug ideas on computers before spending money on real-world trials. More drugs will be rejected early (when it’s cheap) thanks to AI predictions, making the whole process more efficient.

All of these advances mean we’re getting closer to treatments that are tailored just for you. AI and your personal health data can help pick the medicines that work best for you and avoid those that might cause side effects, based on your unique genetic makeup and health history. This can help doctors create treatment plans that work better and are safer for each person.

Gene editing.

Changing our DNA to fix or avoid health problems. It is another exciting area where AI is making a difference. Scientists can now use AI to predict how well these gene edits will work, making the process faster and more accurate. This helps make gene editing more affordable and available to more people.

Imagine someone who has “Woolly Hair Syndrome” (a type of gene pleiotropy where one gene is responsible for many seemingly unrelated traits) a condition that causes both unusual hair and heart problems because of a single gene. In the future, AI-powered gene editing might let this person fix their heart problem, but they’d lose their woolly hair. For some, that’s an easy choice, but it shows the current challenges being faced.

As AI and gene editing keep improving, we may face new questions about whether we should use these tools just for health, or also to change things like enhancing appearance or abilities. Some people might want to give their kids the best possible start in life, while others might use these technologies for more Ultrahuman reasons. With AI helping us predict outcomes and avoid unwanted side effects, the way we think about what it means to be human could change.

The Hospital and the Integration of the Referral Pathway.

I may not predict the impact of AI and Blockchain exactly, but I am confident in the almost redundancy of the hospital system, and an almost complete horizontal integration of the referral pathway. With more patients being treated in the community and a tiny fraction being referred for convalescence or inpatient care.

Consider the referral pathway: a dynamic process in which a health professional at one level of the health system – having insufficient resource or power to decide on the management of a patient’s clinical condition – seeks the help of another facility at the same or higher level to assist in the care pathway. Our current health strategy is detecting illness and referring up the pyramid for treatment and management. In the future this pyramidal system will be flattened, health strategy inverts towards predicting and preventing. Predicting pathologies and preventing them occurring through personalised drugs, personalised and precision gene edits. Predict pandemics and prevent them with early isolations. Even traumas are rare events as work sites, vehicles and other machineries embrace AI, predicting potential accidents and averting them.

In the future predictive and preventative medicine will be practised in the community, with new medical specialties emerging to design this care. Specialties that focus on health engineering, their tacit knowledge of you beginning in your pre-womb state. Perhaps, in your neonatal state your health engineer refers you laterally to your GP, who has added the lifestyle engineering and coaching badge to their repertoire of skills, to take over your health. Tracking your progress with telehealth and digital twin based remote monitoring, or when necessary and based on AI predictions provide you with coaching and education, empowering you to take a lead role in your own health.

In the future AI’s cheap predictions will integrate the referral hierarchy and blockchain’s cheap trust will integrate the governance hierarchy. The physical hospital has become a redundant eyesore. They are replaced with public parks where happy and healthy citizens congress to gain inspiration for their new, creative and human centred employment.

More on Blockchain and AI’s synergistic interplay.

While there are a few variables that contribute to AI’s impressive predictive abilities, none can be more important than data, and lots of it. More data, more precision. But the type of data is equally important. If we put quality data into our AI then we get quality predictions out, if we put rubbish in, we get rubbish out.

Medical Internet of Things (IOT) devices are now generating huge amounts of data at an incredible rate. Back in 2023, it was estimated that 20% of all IOT data would come from medical devices, and soon, the total amount of data in the world – the global datasphere – could reach 175 zettabytes. Healthcare’s share of this data is growing even faster than other industries.7

This explosion in data creates some big challenges for traditional electronic health records (EHRs), especially when it comes to keeping data secure and making sure it’s trustworthy. Medical IOT devices are increasingly under threat from sophisticated cyber-attacks. Even large devices in hospitals, like CT scanners, aren’t immune. For example, in 2019, Israeli researchers managed to hack a hospital’s IT system and, in milliseconds, changed CT scan images as they were being transferred to the database. Shockingly, over 97% of these altered images went undetected by experienced radiologists and even fooled an AI system built to spot medical deep fakes.8

To address these threats, more researchers are exploring how blockchain and AI can work together. AI can do more than just make medical predictions, it can also scan data for signs of cyber-attacks. Blockchain is valuable here because it can create a permanent, unchangeable record of every transaction from each medical IOT device. AI can then keep a constant watch on this data, spotting any odd or suspicious behaviour that might indicate a security problem.

Blockchain also helps by giving each medical IOT device its own unique, decentralised identity and ensuring we know exactly where the device came from. If a device starts acting strangely, AI can pick up on this and trigger a smart contract – an automated process – that could start an investigation or even stop the EHR from being changed until things are checked out.9&10

The timing of medical data is just as important as the data itself. The order in which things happen – the sequence of events – can make a big difference to your health outcomes. For AI to make accurate predictions, electronic health records need to store data in the exact order it’s created. With millions of new pieces of data arriving at EHRs every second, traditional systems will [ii]struggle to keep up. Blockchain can handle this by recording each piece of medical IOT data as it arrives, adding a timestamp, and storing everything in the right order, helping ensure nothing gets lost or out of sequence.

Is AI Going to Steal Your Physicians’ Job?

According to a report by McKinsey & company, within the next 5 years 35% of physicians in the US are planning on hanging up their white coats.11 Given that burnout is one of the leading reasons for them wanting to leave, I’d imagine they’re not too concerned if an AI or human replaces them. My own personal belief is that in the short term it will be a combination of both, a physician with an AI adjunct. The first tasks that AI are likely to take over are the tasks that we clinicians hate: paperwork, admin and other menial tasks loved by bureaucracies (Perhaps then we will look like the doctors on all the TV dramas that seem to have lots of time with their patients). But in the long term we will have no option but to replace physicians with AI. Foreseeing this dilemma the EU, in 2016! published a white paper in which they proposed the ‘virtual patient’, the definition of which meets our own definition of the medical digital twin. AI replacing physicians is not the crazy assertions of the authors like me but a policy at the level of the EU.

My own belief is that physicians are here to stay, even if to a scaled down cohort of specialists. The core of this belief is that someone must do the thinking, and that someone will not be an AI. As I stated earlier in this blog post, AI predicts, it does not think. Alan Turing had suggested that if a computer could produce language like a human, then we could assume that it is thinking like a human. However, this point has been countered by John Searle in his 1980 paper: Minds, Brains and Programmes. Searle, in his ‘Chinese room’ experiment argues that pattern recognition and manipulating Chinese symbols according to the patterns or rules that we observe is a convincing simulation of Chinese but not an actual comprehension of Chinese.12

Therefore, if AI cannot think, can it be liable? If I was the unfortunate victim of misdiagnosis and wanted to make a claim against an AI, how could I establish or prove that something that cannot think had a duty of care to me? Realistically the duty of care rested with the thinking being that was supervising the AI, now the question shifts to how much liability does the physician have when an AI makes a mistake? Given the specificity of knowledge required for AI development, well beyond that of a physician’s skill set, should AI developers and device manufactures bear some responsibility for errors made by an AI. The answers to these questions are beyond the scope of this blog post (and way beyond the competency of this author), but they highlight the ongoing need for physicians, if only to be the fall-guy they will remain!

In his 1988 book, ‘The Consequences of Modernity’ Anthony Giddens explored the concept of ‘Facework’.13 Facework, or the time spent in face-to-face consultation between a physician and their patient is necessary to establish trust. The physician thus serves as a bridge between the patient and the healthcare system. Facework has the capacity to transfer a patient’s trust in their physician into trust in the system. As both blockchain and AI have the capacity to make the healthcare system increasingly abstract, then if the patient is to trust this abstract system, the need for physician-patient Facework becomes increasingly necessary.

References:

1. Agrawal A, Gans J, Goldfarb A. Prediction Machines: The Simple Economics of Artificial Intelligence. Harvard Business Press; 2018.

2. Berg C, Davidson S, Potts J. Institutions to constrain chaotic robots: why generative AI needs blockchain. Ssrn. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4650157

3. This US lawyer used ChatGPT to research a legal brief with embarrassing results. We could all learn from his error. UNSW. Published June 24, 2023. Accessed September 3, 2024. https://www.unsw.edu.au/news/2023/06/this-us-lawyer-used-chatgpt-to-research-a-legal-brief-with-embar

4. Owen L, Morgan A, Fischer A, Ellis S, Hoy A, Kelly MP. The cost-effectiveness of public health interventions. Journal of Public Health. 2011;34(1):37-45. doi:10.1093/pubmed/fdr075

5. Masters R, Anwar E, Collins B, Cookson R, Capewell S. Return on investment of public health interventions: a systematic review. Journal of Epidemiology and Community Health. 2017;71(8):827-834. doi:10.1136/jech-2016-208141

6. Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K, Tekade RK. Artificial intelligence in drug discovery and development. Drug Discovery Today. 2021;26(1):80-93. doi:10.1016/j.drudis.2020.10.010

7. Rydning DR John Gantz, John. The Digitization of the World from Edge to Core. doi:https://www.seagate.com/files/www-content/our-story/trends/files/idc-seagate-dataage-whitepaper.pdf

8. CT-GAN. Guide Proceedings. https://dl.acm.org/doi/10.5555/3361338.3361371

9. Alshammari BM. AIBPSF-IoMT: Artificial Intelligence and Blockchain-Based Predictive Security Framework for IoMT Technologies. Electronics. 2023;12(23):4806. doi:10.3390/electronics12234806

10. Umer MA, Belay EG, Gouveia LB. Leveraging Artificial Intelligence and Provenance Blockchain Framework to Mitigate Risks in Cloud Manufacturing in Industry 4.0. MDPI AG; 2023. Accessed September 12, 2024. http://dx.doi.org/10.20944/preprints202312.0232.v1

11. Medford-Davis L, Malani R. Laura Medford-Davis. McKinsey & Company. https://www.mckinsey.com/industries/healthcare/our-insights/the-physician-shortage-isnt-going-anywhere?utm_user=14419243849007913. Published September 10, 2024. Accessed September 12, 2024.

12. John R. Searle, “Minds, Brains, and Programs.” In: Philosophy of Mind: Contemporary Readings. Routledge; 2005:344-364. Accessed September 12, 2024. http://dx.doi.org/10.4324/9780203987698-35

13. Giddens A. The Consequences of Modernity. John Wiley & Sons; 2013.

About the Author:

Dr Colm McCourt is an Irish medical doctor, with clinical experience in general practice and hospital medicine. He holds a Graduate Certificate in Blockchain Enabled Business from RMIT University and is particularly interested in how emerging technologies such as blockchain can improve healthcare systems, data integrity, and patient outcomes. Colm combines hands-on clinical work with research and writing on digital health, decentralised technologies, and the future of medical practice.

You can contact Dr Colm McCourt M.D. at  https://www.linkedin.com/in/dr-colm-mccourt-m-d-b4465a172

Oryiginally published on: https://spektrumlab.io/ai-blockchain-and-the-future-of-healthcare-article-by-colm-mccourt-m-d/

“Accounting might not be where most expect AI to shine. But in the chaos of crypto, it’s precisely where trust is being reengineered.”

At AEM Algorithm, we’re not just building tools; we’re reshaping how trust, autonomy, and compliance operate in the age of digital assets.

Our work lies at the intersection of regulation, decentralization, and automation. And if the buzzword of the 2010s was transparency, the AI-driven 2020s are all about intelligent responsibility.

The Problem with Traditional Thinking.

In the world of crypto, data doesn’t just grow – it explodes.

Every wallet address, smart contract interaction, and token swap generates a fragment of a financial narrative. For accountants and compliance teams, this creates a daunting challenge: reconciling millions of micro-transactions across exchanges, blockchains, and jurisdictions each governed by evolving legal, tax, and reporting standards.

The traditional approach? Manual effort, template hacks, and guesswork.

That’s where we saw an opportunity: not to patch over the problem, but to reimagine the accounting model entirely by letting AI learn the logic of financial behavior itself.

Introducing Mr. Journaler.

At the heart of our platform is what we call “Mr. Journaler” an AI-powered assistant that doesn’t just automate tasks but understands intent.

Imagine a system that interprets not only wallet flows and CSV uploads but also the context:
“Was this an airdrop or compensation? Should this be capitalized or expensed? Is this a taxable disposal under local rules?”

These aren’t just mechanical questions, they’re interpretive.

By training on multi-jurisdictional tax logic, industry guidance, and user feedback, Mr. Journaler builds domain-specific intelligence. One that evolves as regulation does. One that advises, not just executes.

“This shift from rules-based automation to judgment-aware interpretation is the breakthrough.”

Why It Matters.

Here’s why forward-thinking firms, regulators, and policymakers are taking note:

1. Legal & Tax Alignment at Machine Speed.

We’re working toward compliance that keeps pace with code.

Whether under Australian CGT, MiCA in the EU, or U.S. Form 8949 requirements, our system enables real-time tagging, classification, and reporting of crypto transactions while preserving a clear audit trail for both clients and tax agents.

It’s not just timesaving. It’s liability-reducing.

2. Convenience That Doesn’t Compromise Control.

Users want simplicity. Accountants want evidence.

Our hybrid interface will allow users to ask AI questions like:
“How much tax will I owe if I sell X?” or “What’s the holding period on this wallet?” with every answer traceable to source data and tax code logic.

Think of it as ChatGPT for accountants but context-aware, jurisdiction-specific, and audit-ready.

3. Considerations where Autonomy Meets Accountability.

In an era of smart contracts and DeFi self-custody, users are taking back control. Software should empower that autonomy – not dilute it.

Eventually, with our platform, users can configure their own compliance rules, track logic overrides, and simulate outcomes across jurisdictions. AI becomes a co-pilot, not a black box.

In the past, we’ve even brainstormed with academics and prototyped governance-layer features: token-based voting on roadmap priorities and secure IPFS/NFT integrations for proof-of-reporting. Others could be around user-controlled AI memory.

A Glimpse into What’s Next.

As regulatory scrutiny sharpens globally from AML enforcement to corporate crypto treasury rules, our roadmap includes:

• Deeper integrations with on-chain analytics for risk scoring

• Natural language reporting for auditors and boards

• Multilingual AI accounting support

• “Explainable AI” modules to justify transaction logic

• Expanded support for digital ID frameworks

Author Final Thoughts.

The rise of AI in finance isn’t just about speed. It’s about trust.
And in crypto, an ecosystem built on both math and myth rebuilding trust means rethinking tools.

“We’re not here to sell hype. We’re here to build precision.”

We welcome collaboration with auditors, regulators, developers, and anyone solving this challenge at the edge of finance, technology, and law.

Because if our AI can help restore clarity in the most chaotic corners of modern finance – maybe we’re onto something bigger than accounting.

About the Author.

Jakub Sawczuk is the Founder & CEO of AEM Algorithm, a fintech company delivering AI-powered crypto accounting and compliance tools. He leads the development of Journaler and Mr. Journaler, tools that help accountants navigate digital asset reporting across jurisdictions. With a background in engineering, design, and product innovation, Jakub has guided AEM from prototype to production, integrating with platforms like Xero and QuickBooks and partnering with institutions such as RMIT Blockchain Innovation Hub and Cyberport Hong Kong. He is passionate about using AI and blockchain to bring clarity, automation, and control to modern financial systems.

You can contact Jakub Sawczuk at https://www.linkedin.com/in/jakubsawczuk

Oryginally published on: https://spektrumlab.io/beyond-the-ledger-how-ai-is-rewiring-trust-in-crypto-accounting-by-jakub-sawczuk/

That is the fact based on my everyday observation, especially during the last few years. I am not exaggerating or imagining things. If you did not notice it, wake up or pause your run for a while. We will be on the same page.

You do not need to be an expert or some exceptional researcher to notice that something is not right. I would even say it is wrong!

The vast majority of people no longer behave as they once did. They are no longer acting in ways that were previously considered “normal.”
I’m talking about things like walking slowly, chatting with strangers during train rides, and engaging face-to-face with others in the neighbourhood, or broader community.

But apparently, norms and standards no longer exist, or so we’re led to believe. I don’t buy into that.

We’ve been conditioned to accept this “new normal,” as if the old-school version I grew up with no longer matters. I’m still an old-school practitioner and follower. Furthermore, I am proud of it.

Sometimes I think that our habits have been subtly reprogrammed to fit someone else’s design.

Nevertheless, as far as I am aware, we are still human beings built on organic bodies and beautifully constructed spiritual minds.

But these days, running around like crazy (or driving like maniacs) just to say, “Sorry, I’m running late,” is slowly making us less human… and more like zombies.

I’m observing how people drive on the freeway. How they are pushing others, weaving between lanes, and turning the driving experience into something almost unbearable.
They think they’re getting ahead by doing this, but in reality, we’re all traveling the same distance and arriving around the same time.
The only thing they’re really achieving is chaos and discomfort for disciplined, experienced drivers.

And that’s not even mentioning how difficult it becomes to properly use cruise control, or any driving optimisation systems, when constantly dealing with “pushers” and “zippers.”
Their erratic behaviour forces others out of rhythm, making calm, efficient driving nearly impossible.

Similarly, working faster, whether by moving quicker, typing faster, or talking at high speed doesn’t necessarily mean you’re performing better, being more efficient, or doing your best.

Running at maximum speed and multitasking might be great in bed (or so some say), but in everyday life and work, it doesn’t really improve quality – at least not in my honest opinion.
Still, many people continue to believe in this pushy, always-on-the-move, multitasking mindset.

Even quicker, even more! But more what, exactly? 

Stress? Mistakes? Burnout?

I’m referring to the real quality and dignity of life and work.
Though honestly, I’m not even sure if terms like that are still considered relevant in this so-called “new normal.

This crazy lifestyle is taking its toll. We’re slowly becoming autopilots, constantly chasing time, yet feeling almost nothing.
Like sleepwalkers in daylight, the vast majority rush through life without ever arriving.

This is hard to categorise as anything close to “normal.” It’s more like a form of behavioural pathology, according to me.
I say that with confidence, backed by my professional background in pathology and years of diagnostic experience.

Even robots are programmed to complete each task at the right speed, in a specific manner, finishing on anticipated time, with everything carefully calculated and planned.

Planning usually includes optimisation. Please note, optimisation does not mean maximisation.

I’m arguing that even for a robot, getting stuck in “more and more loops” wouldn’t be healthy.

Constantly pushing ourselves to the limit affects everything around us and slowly erodes not only our own lives, but the lives of others too.
I remember being told at one of my former jobs: “Good – but just a little bit more.”

Every time, it was a little bit more expected.
To this day, those words still ring like a horror bell in my mind.

Sometimes, slower is faster. And the right time… is simply the best time.

In this piece, I’m focusing on modern technology and the “new normal” rat race.
Like it or hate it – but please, just read it.

Maybe we’ll wake up together and slow down a little before the end of life comes knocking at our door. Don’t think you’ll be safe because you’re too busy to answer. The angel of death carries a universal key and operates quickly and efficiently.

Hopefully, just in time, we’ll realise that we’re truly alive. Just in time to wake up, slow down, rethink everything, and step out of this crazy rat race.
To minimise binge‑scrolling and endless headset listening.
And instead, enjoy time with friends and family just eating together, talking together.
Sounds weird? It shouldn’t.

The Endless Race We Never Signed Up For.

I have noticed a long time ago that life has just turned into a big treadmill. Like, no matter how fast we are running, the scenery does not actually change. That has been the vibe of the last thirty years.  I also explored it more deeply while studying emerging technologies at RMIT.

We have been sprinting after promotions, bigger houses, gadgets, likes, followers, status symbols, etc.
Many people, regardless of age or gender, are constantly chasing everything and still feeling like something is constantly missing.

We are trapped on the treadmill, and we call it progress?

It’s as if society signed us all up for a super marathon no one asked for, and the worst part is, there’s no finish line. So, what’s the point?

In the meantime, the disruptive race goes on and on – only accelerating.

There are two possibilities:

We are being retrained to call chaos normal.

 or

We live in a world where distraction is designed.

In both cases, it’s definitely not my world! I don’t accept it, and I’m saying “no” to it. How about you, mate? Are you okay with it? Hopefully, your answer is “no” too.

The Acceleration of the 90s.

Back in the 90s, the world starts to quickly shift gears. The internet showed up, globalisation was growing, and suddenly the competition was not just the person in your office cubicle. It was someone across the planet hustling just as hard.

I remember being told when I was living in Melbourne that people in Australia couldn’t afford lawn-cutting services like they used to. Apparently because of Greece’s financial crisis. Sounds insane, but a lot of people heard similar stories, told to make us believe in that kind of nonsense.

We got sold this dream: work harder, climb faster, earn more, consume endlessly. Upgrade your car, your phone, your wardrobe, your identity. Success was redefined as acceleration, and the rat race picked up a super turbo boost.

At first, it felt like progress. Who wouldn’t want instant information, faster connections, cheaper flights, and the world at their fingertips

But very soon, the promise of freedom turned into a trap. We were not actually moving toward anything meaningful; we were just running faster on the same wheel, competing with shadows. Sort of auto-competition!

Life shouldn’t be seen only as constant competition and a daily struggle. It can be good without all that. A fair distribution of resources and time might just make it possible.

The common dream has shifted from owning what we truly need to constantly chasing the latest inventions and trends only to upgrade again a year later, just to feel up to date.

The Lockdown Pause.

And then, all of a sudden, came the “great” pause: lockdown. The world came to a brutal halt, and for the first time in decades, millions of us were forced to sit with ourselves. No commute. No office hustle. No external noise, at least for a while.

People started baking cakes, reading books, and spending time with their kids. They commonly binged Netflix and scrolled TikTok, but many also realised how much of life had been on autopilot.

The rat race paused only for some time, and in that serenity, some people asked themselves: do I even like this game I’ve been playing?

But here is the trick in this by design. Lockdowns did not just give us reflection; they also doubled or tripled down on digital living. Remote work went mainstream, Zoom fatigue became a new trend and standard, classrooms went virtual, and TikTok turned from a niche app into a global addiction.

For every person who discovered the joy of slow living, there was someone else who got trapped in a new digital grind.

An avalanche of endless video calls, constant Slack pings, and infinite binge-scrolling. The race didn’t disappear; it just changed form and tracks.

On top of that, we had compulsory QR code scanning “to keep us safe,” as they said. How awesome.

Apparently, it would’ve taken 15 years to reach this level of online activity and dependence under normal circumstances. It took just two years during the SARS-2-demic and the forced lockdowns.

Interesting, indeed.

The Shiny New Rat Race.

In 2025, it feels like we were sprinting harder than ever. Hopefully, 2026 will shake and wake up many of us.

Disruptive technologies like AI, Web3, and blockchain has completely reshaped how we may live and work. On the surface, it looks like freedom. You can start a business from your laptop, trade NFTs in your pyjamas, let AI handle your admin, or work remotely from Bali.

But look closer, please. We’ve just created a shinier version of the rat race. Instead of competing for promotions, we’re competing for algorithms. Instead of impressing the boss, we’re optimising for engagement. Instead of climbing a corporate ladder, we’re hustling for side gigs, content creation, and “passive income streams” that aren’t so passive after all.

Don’t get me wrong, please. I’m a tech optimist and practical everyday user. I like AI, and I genuinely believe in blockchain, decentralisation, and the idea that, when used properly, AI can free us from outdated systems.

But if we’re not careful, these tools could become new cages disguised as opportunities.

It’s the same cycle: constant comparison, endless optimisation, and the low-key anxiety that you’re falling behind while everyone else is somehow winning the game. Except the game itself has no finish line again!

 Time to Notice.

Maybe it is the highest time to pause and reflect. To notice and ask where exactly we are running? And why?

Do not be scared to ask “Why” and “Who”.  They are both very important and relevant question!  

Combining “Why” with “Who” may lead to discovering the root of the problem. Knowing and understanding the problem from the root is the best way to find a working solution.

What if success isn’t more speed, more hustle, more tech, but more presence, more meaning, more quality, more balance?

This context of “more” does not sound so bad, does it? But in this case, it is not associated with greed and greedy people.

We don’t have to ditch technology to escape the rat race. We just must stop letting it dictate the pace of our lives. Picture using AI not to cram more work into your day, or taking someone’s jobs as you do it, but to create more free time for art, rest, or just watching the sunset without feeling guilty.

Try to picture Web3 not as another speculative hustle, but as a way to build genuine communities where value isn’t measured in likes or dollars alone. With proper decentralisation and better value distribution.

Time to Change.

The point isn’t to drop out of society and go live off the grid (unless that is your thing). The point is to reclaim the steering wheel.

To realise that the race is optional, and stepping off the treadmill is actually the real flex.

We’ve been running for decades now.
Maybe the real revolution is realizing we don’t need to run so fast, so hard.
Maybe the finish line isn’t some distant point in the future, but right here.
Here, in the choice to slow down, take a breath, and live with dignity and a proud, human purpose.

Time to notice.
Time to change.
Time to act.

What I’m trying to do is shake you, wake you, and encourage you to act now!

Happy Acting,

Cheers, the Author.

Oryginally published on: https://spektrumlab.io/were-running-nowhere-time-to-wake-up-and-reclaim-real-life/

Not sure what is going on, or what may happen? You are not alone. Honestly, I am not sure either. A couple of years ago, things felt more obvious. Today? Not so much, at least not for me.

It’s now the fifth year I’ve been writing and publishing on spektrumlab.io – a space I founded to explore the intersection of emerging tech, strategy, and digital transformation. I’m Jacek Korneluk, and this year feels different.

But it’s also the first year I’ve hit hesitation and friction. Until now, most of what I was writing about felt glaringly obvious, even my near-future predictions had a certain clarity to them.

But sometime around mid-2025, I started noticing a shift. The clarity I used to have in how I researched, processed, and understood information started to blur. Even the daily stream of emails I receive, which used to bring clear insights, began to feel… off. Less useful. Or maybe just harder to decode.

Was it just information overload? Or are we simply producing more digital rubbish than ever?

I struggled to read through 2,000-word PDFs without drifting off or reaching for AI to summarize them for me. I’d had these thoughts before, so part of me expected this shift. But this time it hit harder. The flow and quality of information changed drastically. It completely disrupted my previous way of working.

I realised two things. First, something is different. Not wrong, necessarily, just different. Second, I needed to change too. Slow down, zoom out, and start looking at things through a different lens.

My flat, linear approach wasn’t cutting it anymore. You can’t solve a problem without first truly understanding it. That much, at least, I’m sure of.

So, I put my background in clinical diagnostics, analytical thinking, and technical and political awareness to use and started digging. What came out of that process is this piece, both a proof of concept and a personal summary of my predictive take on what’s unfolding.

We’ve reached a point where we need to adjust everything, especially in how we operate online and use disruptive technologies. It’s no longer as simple or obvious as it once was. Not in AI, not in blockchain, not in geopolitics. At least not from my view.

Some truths are more visible. Others are buried, hidden beneath piles of digital noise, real-world tragedy, or even literal brick-and-mortar rubble.

Either way, life is changing, stealthily and quietly. Whether we like it or not, it’s happening.

If you want to track the shift, you’ve got to look for indicators, subtle signs that something’s different. I won’t dive into political changes here, not today. As a Blockchain and AI strategist, my focus is on tech, the tools, the platforms, the online layers that shape how we live.

That said, I can’t ignore the link. Geopolitics and emerging technologies are deeply connected and increasingly co-dependent. It’s like using a microscope. What you see depends on your resolution and the filters you apply. Your method shapes your outcome. And if your view is too broad or too messy, you mostly end up with garbage.

That’s why specificity matters. Just like in diagnostics, narrow your focus, isolate the signal, then build the bigger picture from those smaller, precise insights. Like LEGO bricks. Or puzzle pieces.

Ok, final thoughts to kick off 2026 on spektrumlab.io:

We’re living in the time of MAS (Multi-Agent Systems) and latent collaborations between different LLM models (Jiaru Zou et al., 2025). Natural language alone isn’t enough anymore, not if you want to solve truly complex problems. We’re entering the phase of latency vectors, where AI agents don’t just talk, they think together.

Latent collaboration is basically when multiple AI models (agents) skip the small talk. Instead of exchanging long chains of text like we humans do, they share their internal “thoughts” directly in the form of latent vectors. These are high-dimensional data points living in what we call latent space. Think of it as a kind of shared mind map or memory where ideas are richer, more precise, and way faster to pass around. This isn’t just theoretical fluff. Frameworks like LatentMAS already show that this way of “talking without words” actually makes multi-agent systems smarter, quicker, and cheaper to run, especially when it comes to stuff like reasoning, math, or code. It’s like LLMs working together telepathically, no noise, just raw ideas firing across the network (Jiaru Zou et al., 2025).

So, what now? Where do we go from here?

I still wonder about the idea of AGI. Sometimes I even feel like I’m losing interest in it. Not because it’s not important, but because the path forward feels more fragmented, less linear. Like a puzzle that keeps expanding with every new piece.

But here’s the truth. We’re all part of this, whether we choose to engage directly or not. I’m genuinely curious what the next LEGO bricks will look like and how the picture will come together in the end.

To see it clearly and hear the full story, you’ve got to stay awake. Stay sharp. Stay curious. And yes, being technically native helps.

The rest? That’s our near and coming future. One we’re all building, often without even realising it.

Happy and better 2026 to all of you, dear followers and readers.

Let the power of disruptive technologies and the force of emerging systems be with you.

Cheers,
Jacek Korneluk

Originally published on: https://spektrumlab.io/latent-collaboration-mas-the-future-of-ai-in-2026/

How Blockchain Rewrites Trust, Work, and the Role of HR.

The following piece is a reflective exploration inspired by experience and ongoing study. It is not a formal academic paper, but a thought experiment on the future of human resources, the cost of information, organizational trust, and technology. At times, it may feel like a collection of interconnected ideas, as it forms part of a larger work that could one day evolve into a book or a proper research project.

Before analyzing the overall cost of transaction and information spread in historical hierarchies, it is mandatory to introduce the two most significant costs arising from information asymmetry: Adverse Selection and Moral Hazard.

Adverse Selection (Pre-Contractual Asymmetry).

Adverse Selection focuses on how hidden information before a contract leads to negative market outcomes. This arises when one market actor, such as an insurer or a bank, does not know the exact risk profile of its own client (information asymmetry), potentially leading to a negative selection of clients (e.g., only high-risk individuals seeking insurance). Companies are consequently forced to spend more money on information assessment and monitoring, leading to increased prices throughout the market to compensate for risk.

The core problem is articulated by George Akerlof in his foundational work on the “Market for Lemons”:

“The cost of dishonesty, therefore, lies not only in the amount by which the purchaser is cheated; the cost also must include the loss incurred from driving legitimate business out of existence…. The presence of people who wish to pawn off bad wares as good wares tends to drive out the legitimate business.”

— Akerlof (1970, p. 489)

Moral Hazard (Post-Contractual Asymmetry).

Moral Hazard is a post-contractual risk that involves one party changing its behavior in a way that is detrimental to the other party because they are shielded from the full consequences of their actions. In financial markets, a borrower might take on excessive risk with loaned funds because they are not fully bearing the potential cost of failure.

A classic example of systemic moral hazard is the anticipation of government bailouts for major financial institutions (the “Too Big to Fail” problem) during the 2008 financial crisis (Tirole, 1996). To mitigate the risk of opportunistic behavior and maintain contractual incentives, organizations must consider additional agency costs in the form of monitoring and bonding expenditures, which ultimately increase operational costs across the market (Jensen & Meckling, 1976).

The evolution of transaction cost throughout history.

The structure of human organizations, or dominant hierarchy, has always evolved in response to the economic problem of information and trust, with each era adopting a new technological solution to reduce transaction costs. The cost of transacting, verifying information, enforcing contracts, and mitigating risk ultimately dictates the optimal boundaries and management structure of an organization.

Historical Solutions to Information Asymmetry.

In Ancient Empires (c. 3200 BCE), the key challenge was high verification and memory cost. Hieroglyphs and writing created the first non-personal, external ledger of verifiable facts, reducing reliance on individual memory and enabling complex systems for tracking property and taxes.

Later, in the Classical/Medieval Age (c. 1300 CE), large ventures faced significant fraud and accountability costs (Moral Hazard), where agents might act against the principals’ interests. The solution was double-entry bookkeeping, a self-checking mechanism that provided internal reconciliation and dramatically reduced the risk of opportunistic behavior (Jensen & Meckling, 1976).

The early modern era saw Nation-States and Elite Institutions grappling with High dissemination costs and information asymmetry between the governing elite and the populace. The invention of the printable press (Movable Type) acted as a massive cost-reduction technology, enabling wider information to spread and reducing regional information asymmetry (Dittmar, 2011).

The Digital Age Bottleneck.

The digital age (c. 1970 CE), characterized by Global Financial Networks, introduced the fundamental economic problem of high digital trust cost (Adverse Selection). The very nature of digital assets being infinitely copyable means their validity cannot be guaranteed without a powerful, trusted intermediary (like a bank, government, or central clearinghouse). This systemic reliance on intermediaries to prevent issues like “double-spending” creates massive points of failure and high transaction costs, which the entire global market must absorb.

This historical trend highlights a crucial point: organizational hierarchies have traditionally been the human solution to the technological problem of trust. Management layers, HR departments, and oversight structures are essentially expensive, internal mechanisms designed to mitigate Adverse Selection (vetting) and Moral Hazard (monitoring).

The bitcoin age (c. 2009 CE) offers the first structural solution to the Digital Trust Cost. Satoshi Nakamoto’s invention of the Blockchain and Proof-of-work (PoW) Consensus (Nakamoto, 2008) provides a mechanism of cryptographic proof and distributed agreement. This innovation fundamentally eliminates the need for the intermediary, structurally mitigating both Adverse Selection and Moral Hazard (smart contracts) by making the transaction history universally and cheaply verifiable. This reduction in the cost of trust serves as the catalyst for the evolution of HR and organizational hierarchy, which we will now explore by analyzing the underlying Blockchain fundamentals.

Blockchain Fundamentals: The Mechanism of Trust Reduction.

Blockchain technology serves as the technological countermeasure to the systemic trust and information costs defined by Adverse Selection and Moral Hazard. It achieves this not by introducing a new, powerful intermediary, but by eliminating the need for one altogether.

Blockchain: Solving Adverse Selection (Trustless Verification).

A Blockchain is a distributed, immutable ledger shared across a vast network of computers (nodes). Its function is to create a universally verifiable, single source of truth, thereby addressing the problem of hidden information (Adverse Selection).

Distributed Consensus: Instead of a single central authority (like a bank or an HR server) controlling the data, all network participants must agree on the validity of new data (consensus mechanism). If one copy is tampered with, the thousands of other copies will invalidate it.

Immutability and Hashing: Transactions are grouped into blocks and linked cryptographically using a hash (a unique digital fingerprint). Changing a single transaction requires re-calculating the hash of that block and every subsequent block in the chain, an economically prohibitive task.

The result is that information, such as an employee’s verified credentials or professional history, becomes tamper-proof and cheaply verifiable by anyone, thus drastically reducing the cost and risk associated with Adverse Selection in hiring.

Smart Contracts: Solving Moral Hazard (Trustless Enforcement).

While the core blockchain provides trustless data, the innovation of smart contracts provides trustless enforcement, which is key to mitigating hidden actions (Moral Hazard).

A Smart Contract is a computer program that automatically executes the terms of a contract when pre-defined, objective conditions are met. These contracts are stored on and enforced by the blockchain network itself.

Automation of Terms: If the contract states, “if the verifiable output of a task is delivered, then release payment to the agent,” the payment is executed automatically. This removes the need for human discretion, approval, or intervention that could lead to issues from either side.  It’s irreversible, since once the funds are liberated, it is transmitted to the blockchain network, making it computational too expensive to reverse it.

Elimination of Agency Costs: In a traditional hierarchy, managers monitor employee performance (an agency cost) to guard against Moral Hazard. Smart Contracts, by codifying the rules and automating the outcomes, perform this monitoring and enforcement function autonomously.

By leveraging these two layers, the immutable ledger for verification and the smart contract for self-execution, Blockchain technology offers a path to organizational structures that rely on cryptographic proof rather than expensive human-based trust intermediaries, thus fundamentally challenging the economic necessity of large, tiered corporate hierarchies.

The dissolution of hierarchies: From firm to Decentralized Autonomous Organizations (DAO) and the challenges.

The drastic reduction in transaction costs enabled by Blockchain technology and Smart Contracts has profound implications for the optimal size and structure of the firm, challenging the very logic that necessitated managerial hierarchy.

Coese’s Theory and the shrinking firm.

Ronald Coese’s explains how modern organizations are structured and why in his work, The Nature of the Firm (1937). Coase argues that a firm organizes activities internally (through management and hierarchy) only when the cost of organizing internally is less than the cost of transacting externally (using the market), with an HR perspective, the cost of contracting a freelancer/consultant is higher than employing a full time person and build loyalty to the company, currently the cost involves high search cost (finding a reliable person), high negotiation cost (writing new contracts for every job), and high enforcement cost (monitoring quality and insuring payment, classical problems of adverse selection and moral hazard.

• The chain reaction: Since Blockchain provides (low search/vetting cost) and via Smart Contracts (low negotiation/monitoring cost), the external fall dramatically.

• The Shrinking Boundary: According to Coase’s logic, as the cost of organizing through the market plummets, the economic imperative to internalize activities via a rigid hierarchy disappears. This suggests a shift toward a “micro-firm” or networked model, where individuals contract fluidly for specific projects rather than being locked into permanent employment. The optimal firm boundary, therefore, shrinks to its essential, irreducible core.

The rise of the decentralized autonomous organization (DAO).

The DAO represents the organizational form that emerges when transaction costs are minimized by code. A DAO is a virtual entity governed by rules encoded in transparent, immutable Smart Contracts, removing the need for traditional, human-controlled central authority.

Flattening Power: In a traditional hierarchy, power is centralized by position or knowledge, in a DAO, power is distributed based on governance tokens or verified merit/reputation. This eliminates the structural mechanisms that enable the tyranny of the majority, where a concentrated group (e.g., top management or shareholders) can make decisions that disproportionately harm the broader collective of contributors or managerial opportunism (Moral hazard).

• Verified Merit/Reputation: Unlike a traditional CV, which is subject to reputation systems on a blockchain track a contributor’s actual, verifiable performance. This can be recorded as non-transferable tokens (Soulbound Tokens or SBTs) awarded for successful project completion, code review approval, or positive community feedback. This provides a trustless, objective metric of skill, allowing the DAO to allocate tasks and power based on proven competence rather than position or tenure.

  Automated Governance: Decision-making shifts from the slow, bureaucratic process of a board or management committee to an automated, auditable token-based voting system enforced by the smart contract.

• Token Voting: This mechanism allows members to vote on proposals (e.g., budget allocation, new project parameters, rule changes) by staking their governance tokens. One token often equals one vote, aligning decision-making power with financial stake or vested interest in the DAO’s success. This process is transparent, automated, and eliminates the administrative cost and time lag associated with traditional corporate governance. This decentralization of decision rights, while presenting challenges (the of balancing autonomy, decentralization, and efficiency, Source 3.1), fundamentally changes the nature of corporate governance (Source 1.2).

The Current Hurdle to Adoption: If the DAO structure is economically superior due to its minimized transaction costs, why isn’t it universally adopted right now? The primary barriers are legal uncertainty and the high cost of decentralization.

• Legal Uncertainty: Most jurisdictions lack clear legal frameworks for DAOs, creating massive regulatory and liability risks. Many DAOs are currently operating as legally unincorporated associations, leaving contributors vulnerable to legal action.

• Coordination Cost: True decentralization is currently slow. The overhead involved in getting thousands of token-holders to agree on basic operational parameters can often be higher than the administrative cost of a centralized CEO making the decision quickly. The system itself is often still being iterated and optimized.

So basically, rigid hierarchies evolve from a top to bottom decision machine to a more flexible structure, with a core group responsible for coordination and strategic vision and a broader group (freelancers, gig workers, other DAO’s) that provide specific services with verifiable reputation, compensated by automatic smart contracts with its own terms and conditions that bound the service to certain SLA metrics.

The Reimagined Role of Human Resources.

The analysis has demonstrated that the historical evolution of the firm, and its costly managerial hierarchies, was fundamentally a human-based solution to the economic problems of Adverse Selection and Moral Hazard. The advent of Blockchain technology, by providing cryptographically secured and universally verifiable trust, eliminates the necessity for these high-cost hierarchical intermediaries. The immutable ledger solves Adverse Selection by providing trustless verification of credentials, and self-executing Smart Contracts solve Moral Hazard by automating enforcement and eliminating agency discretion.

In this context, where the economic rationale for administrative control has been largely automated, the firm boundary shrinks, and the organizational model naturally gravitates toward the fluid, decentralized structure of the DAO. The role of Human Resources undergoes a profound, non-linear transformation:

• From Gatekeeper to Cultivator: HR transitions from a function primarily dedicated to risk mitigation (filtering resumes and manually monitoring performance) to one of strategic enablement.

• Focus on the Unautomated: The future HR core must focus on the unique human elements that technology cannot govern. This includes Culture and Cohesion to ensure network alignment in the absence of command structures, and Talent Development by designing sophisticated, merit-based reputation systems (e.g., non-transferable tokens) that replace rewards based on traditional tenure.

• The Technical Shift: Ultimately, the HR professional must evolve into a System Architect. Their core competency will shift to designing and facilitating the governance mechanisms, the tokenomics, voting systems, and dispute resolution protocols that allow the decentralized organization to function democratically and efficiently.

Conclusion.

Blockchain technology provides the economic catalyst to dissolve the high-cost hierarchical solution to the agency problem. By enabling the rise of the DAO, it not only shrinks the firm’s boundary but forces HR to evolve from a function of control to one of strategic and technical enablement, becoming the essential human layer for alignment within an automated, trustless economic environment.

About the Author:

Leonardo Albuquerque de Abreu holds a Bachelor’s degree in Economics and has six years of professional experience in data engineering, with a focus on cloud migration and data architecture. He is interested in decentralized technologies, blockchain, and Bitcoin, and in exploring how these systems can reduce unnecessary friction and overhead in organizations. His work examines the evolution of traditional hierarchical structures toward DAO-based models as a means of enabling smoother, more efficient coordination.

You can contact Leonardo at: https://www.linkedin.com/in/leonardo-albuquerque-data-engineer

Originally published on: https://spektrumlab.io/from-hierarchies-to-daos-by-leonardo-albuquerque-de-abreu/

I’m starting to write my last short post for December, just before heading to Melbourne for the Blockies Awards Night. The event is organised by the Digital Economy Council of Australia  (DECA)  – an organisation dedicated to shaping Australia’s blockchain and digital asset ecosystem.

By the way, I was nominated in 3 categories. I am a finalist at The Blockies 2025 in three categories:

Blockchain Leader of the Year

Diversity & Inclusion Leader

Rising Star

So, fingers crossed! Whatever happens, I will be happy, being shortlisted as a finalist is an award in itself.

It was not a bad year generally.

This year has been a productive one for many of us, myself included. I contributed a chapter titled “LedgerMed: Recognising the Perfect Ledger for Patient-Centric Healthcare through Blockchain” to the book Decentralised Healing.

I was invited to join the Innovation Committee of Western Australia at PropTech Hub WA, with a mission to connect innovation and industry.

I also took a position on the board committee of the Perth Linux Users Group Inc. (PLUG).

In addition, I was invited to be a mentor at Spacecubed, focusing on Blockchain, Web3, AI, RWA, DeFi, and Digital Assets.

This year, I was honoured to receive the PropTech Champion of Western Australia 2025 title as the Founder and Director of Spektrumlab Pty Ltd and Spektrumlab.io.

At the same time, we were awarded the Global Go-Getter Award at the 2025 Cygnet West PropTech Awards, where I proudly serve as one of the Ambassadors for Blocksquare.

I also stepped into a foundational role as a Founding Member of the Western Australian AI Hub.

Finally, I became Head of Growth WA for the Polish Australian Chamber of Commerce , an appointment I’m especially proud of, considering my dual citizenship as both Polish and Australian.

On top of that, I have been organising and attending many innovation and technology-oriented events.

Mentoring and connecting people through my vast network of valuable relationships is also a significant part of what I do. There is nothing more powerful than a strong network in a non-centralised world, in my opinion. Decentralisation is the driving force of real progress. It should be the innovation engine of our current evolution.

As I have read many times, the next revolution will be decentralised. Combined with well-designed AI, it has the potential to help solve many of the critical challenges we face today and those we are destined to face tomorrow.

Talking about Spektrumlab Pty Ltd.

People often ask questions: so, what are you actually doing?  What is your products or services are you offering. Some even asking what are you selling?  I am not selling anything, mate! No hooks, no sellouts!

I’m working to lead future-focused thinking, bridge the digital divide, support informed decision-making, and help people connect across a vast international network.

I offer quality mentoring and networking in the fields of innovative and emerging technologies. Legacy is also a key focus in my work.

As I mentioned during my last presentation titled: “From Awards to Action, The Next Chapter in Web3, AI, and Human-Centric Innovation” – Money is not everything, Legacy matters too.

That doesn’t mean I don’t need money. I’m not a robot! We humans, have our needs, and income is a basic necessity. It’s the fuel for everyday transactions that come with living an organic life. Kind of like gas fees in blockchain: if you want to transact, you have to pay. That part is clear.

But even so, my focus remains on Legacy first!

During my 61 years of life, I have discovered that combining Old School Values with Innovative Technology models can create the kind of ecosystem we’re all looking for. On one end, it’s rooted in human values: beliefs, family, friendship, and honour. On the other end, it’s driven by hyperactive objects and all kinds of coding. I deeply believe that this approach to using emerging technologies can be valuable to all of us involved.

Finally about Spektrumlab.io

Since the beginning of our website, 97 articles have been written and published  –  49 by generous guest post writers and 48 by me.

I would like to thank all our guest writers for offering their time and valuable thoughts. We are all benefiting from this independent sharing of knowledge. Also, a big thank you to all the readers for your patience in reading my posts.

I’m doing my best to share what I have learned, so that together we can help build a better world. One person alone may not make a big difference, but together we are stronger and capable of much more.

Let’s keep reading, writing, and sharing relevant knowledge within our communities, especially about proper innovation and emerging technologies. Welcome the new, and preserve the old, valuable truths.

Wishing you a Merry Christmas and a Happy New Year. May the power of emerging technology be with us!

Cheers, The author Jacek Korneluk.

Originally published on: https://spektrumlab.io/spektrumlab-year-in-review-awards-innovation-and-christmas-wishes-from-jacek-korneluk/