Over the past year, one of the world’s largest institutional staking providers stopped validating on at least eight Cosmos networks. It wasn’t pushed out by a hack or a slashing event. It simply left: on Celestia, for example, on-chain data shows the validator was shut down by its own operator.

Dates are counted back from the end of each on-chain unbonding period (±1 day).

What was left behind is even more interesting. As of 14 October, more than 3,600 addresses on Celestia still had about 1 million TIA staked with the provider’s closed validator. Those tokens no longer earn any rewards, and most of their owners probably don’t know it.

Last week, Mete wrote that a quiet shakeout lies ahead for validators. It has already started. So why would one of the biggest players in the industry walk away from networks? In this piece, we look at the economics of running a validator: where the revenue comes from, why it moves so much, and what keeps a validator in business.

How does a validator make money?

A validator’s revenue is the product of four numbers:

Delegators stake tokens with a validator. The network pays a yield on that stake, and the validator keeps a small share of it as commission. That share is paid in tokens, so what the validator actually earns depends on the token’s price on the day. Let’s take the four in turn.

Stake. Delegation is won and lost. A validator cannot control it; it can only try to earn it.

Yield. The protocol sets it. On most networks, the inflation schedule falls every year, and the yield drops as more of the supply is staked. Ethereum’s network yield was around 2.8% at the start of 2026 and 2.6% by October. Newer networks usually pay more to attract stake. On Celestia, which we will come back to below, the gross yield is about 5.4% today.

Commission. As we wrote in The Market for Lemons in Staking, commission is usually the first number delegators compare, and competition keeps pushing it toward zero. Some networks set a floor at the protocol level. On Celestia, for example, no validator can charge less than 20%.

Price. This is the biggest variable in the equation, and the validator has no control over it.

The cost side is much simpler. Servers, data centers, engineers, audits and certifications are all paid in dollars or euros, and none of them get cheaper when the token falls.

Revenue in tokens, costs in dollars

Economists call this a currency mismatch: your revenue is in one currency, your costs or debts in another. Türkiye knows the problem well. In 2018, many companies that earned in lira and borrowed in dollars watched their balance sheets melt as the lira fell. In 1999, Barry Eichengreen and Ricardo Hausmann gave the inability of emerging economies to borrow in their own currency a memorable name: “original sin.”

A validator is in the same position, only with a far more volatile currency. To see how volatile, we built an equal-weighted basket of the tokens of the 10 networks node101 validates and compared its last two years with Bitcoin.

Source: Gate.io daily closes, node101 calculations

The daily correlation between the basket and Bitcoin is 0.70, and the basket’s beta is 1.27 (1.20 on weekly data). In other words, the basket moves roughly 1.2 to 1.3 times as much as Bitcoin. Over the last 12 months, Bitcoin fell 31% while the basket fell 68%. Every token in the basket did worse than Bitcoin over the same period.

The lesson is simple. Validating on many networks spreads token-specific risk, but not market risk. A validator spread across ten networks is effectively holding one leveraged position on Bitcoin. When the market falls, revenue drops on every network at once, while costs stay exactly where they are.

How much stake does a network need?

Working out how much stake a validator needs to break even on a network is straightforward arithmetic. Divide the annual cost by the yield times the commission:

Using Celestia’s 5.4% yield and a few assumptions for cost and commission, the picture looks like this:

Costs are hypothetical and include servers, data center, staff and a share of audits.

These figures are in dollars, but delegators stake tokens, not dollars. When a token falls 68%, the dollar value of the same delegation falls 68% too. A validator that was exactly at break-even a year ago now covers only about a third of its costs with the same delegation. To get back to break-even, it needs three times as many tokens.

For a large institutional provider, the problem is even harder, because compliance teams, audits and insurance push its cost per network much higher. We don’t know the internal reasons behind the exits above, but the numbers explain why leaving long-tail networks can be a rational decision.

Who survives?

When revenue is this volatile and costs are this fixed, running good nodes is not enough on its own. In our experience, the validators that last tend to have five things in common.

Scale per network, not network count. Sitting below break-even on twenty networks is a worse business than being strong on five. Every new network adds a fixed cost, while, as we saw above, the revenue from all of them moves together.

Treating rewards as a currency position. Holding token rewards and waiting for the price to go up ties the balance sheet to the crypto market with leverage. Selling rewards regularly, or hedging them with futures, is the validator’s version of the lesson Turkish companies with dollar debt learned the hard way in 2018.

Income beyond commission. Revenue lines such as RPC, data services and products keep the company running when commission income shrinks with the price.

Cost discipline. A validator running its own bare-metal servers instead of the cloud does the same work for less, and lower costs mean less stake is needed to break even.

Trust. As Mete wrote last week, institutions now choose their validators on trust. Institutional delegation tends to be long-term and does not leave easily when the price drops. As we argued in The Market for Lemons in Staking, the way to earn it runs through signals that are expensive to fake.

Where does node101 stand?

node101 has validated 25 networks to date, with no slashing in 43 months of validator history and an average uptime of 99.9%. We went through the fall in the chart above too, since that basket is made up of the networks we validate.

Staking Rewards data as of 2 November 2026.

What carried us through was investing in the points above before the downturn arrived. In its October 2026 assessment, Staking Rewards rated us AAA for resilience to market downturns, pointing to our profitable operating history, our cash reserves and our income beyond validator commissions. Our financial statements are also independently audited.

We see the delegator side of this as well. As of 2 November, more than 1,400 delegators have around $6.4 million staked with node101, according to Staking Rewards. Like every other number in this piece, that figure moves with token prices.

If there is one thing growing has taught us, it is this: how big a validator really is shows most clearly when the market is bad.

What does this mean for delegators?

When a validator shuts down, nobody may tell you. The 1 million TIA still staked with a closed validator on Celestia is the clearest example. Its owners haven’t lost their tokens, but they haven’t earned a single reward in weeks. A few simple checks are enough to avoid this:

• Is your validator still in the active set? Any explorer will tell you in seconds.

• If it isn’t, redelegate rather than unstake. On Cosmos networks, moving your stake to another validator is immediate, while unstaking means waiting out the unbonding period (21 days on Celestia) with no rewards.

• Could the company behind the validator survive a bad year? Audited financial statements and independent ratings answer this question, at least in part.

So, is running a validator a good business? Done right, yes. But as with any business that earns in tokens and pays in dollars, the real test comes when the market is bad, not when it is good. That is also the moment to look most closely at the validator you choose.

Sources

• Barry Eichengreen and Ricardo Hausmann, “Exchange Rate Regimes and Financial Fragility”, NBER Working Paper 7418, 1999.

• Gate.io spot daily closing prices, 1 October 2024 – 6 October 2026; basket and correlation calculations by node101.

• cosmos.directory and Celestia on-chain data: active-set exit dates and stake left with the closed validator (14 October 2026), validator rankings (2 November 2026).

• Staking Rewards, node101 Staking, October 2026 assessment.

• CoinCentral, “Ethereum Staking Statistics 2026”, October 2026.

• Mete Koray Gergin, “The Future of Validators Lies in How Much Trust They Can Create”, node101, 27 October 2026.

• node101, “The Market for Lemons in Staking”, October 2026.

In March 2026, BlackRock, the world’s largest asset manager, launched its first Ethereum ETF that passes staking rewards on to investors. The product’s materials named not only the custodian but also the validator operators staking the fund’s ether (crypto.news 2026). That same month, Visa became one of the Super Validators with the highest voting weigh…

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Proof of Stake (PoS) is no longer a “new” concept, yet I find that people most often confuse the reason why we had it in the first place, and what are some very important advantages of PoS, especially for global adoption of blockchain.

Thus, in this writing we will take a closer look at the PoS consensus model and its most fundamental component: validators. Starting with the core reason why we had PoS in the first place, we will discover how validators changed with the evolution of this new consensus model and the importance of this change for blockchains.

Why did we leave Proof of Work?

You can read a lot of arguments on why to use PoS over PoW, such as that it has huge energy consumption or that the computation power is not a very decentralized source of truth. Although these arguments have some truth in them, this is not the biggest advantage of PoS over PoW.

PoW networks are slow: Take Bitcoin for example; a few minutes per block is not very efficient already, but the real problem is the concept of finality. Before you can be sure that your Bitcoin block is finalized, you need to at least wait a few blocks, so in practice sending BTC to someone takes around an hour. This is exactly the time before Binance accepts your Bitcoin deposit, precisely because they cannot be sure that the money has arrived before this time passes.

A natural argument is to make these networks faster, for instance by making their block times shorter. For instance, if a Bitcoin block was 12 seconds long, like in Ethereum, then a BTC transfer would take around a minute. Not super fast, but much better than the previous.

The problem is that the shorter a Bitcoin block becomes, the more blocks you need to actually wait before you can make sure there will not be a fork. So if a Bitcoin block was 12 secs, then we would wait for a much higher number of blocks to avoid a fork, resulting in the same finality time in terms of hours.

This is not a design issue, but an inherent feature of PoW networks: As the source of finality is outside the network, i.e., we do not know how much computation power is available, we need to give the world some time so that it can communicate this knowledge to us.

How does Proof of Stake solve this problem?

In Proof of Stake, finality and leader election are done through stake, i.e., amount of coins locked in the network. This amount is always known by the network, so it is an intrinsic property. Thus, the network does not need to wait for knowledge to arrive, and we can even achieve single-block finality and subsecond block time.

Of course, PoS models have very different security assumptions, and some people argue that this faster speed comes with the cost of a less secure network than PoW. This is not a security paper, so I will not argue this here. For now, let us just focus on what PoS networks need to properly work.

First of all, we need the tokens on the network, that are controlling the consensus, to be locked. This is the reason why we can avoid forks, since locked tokens can be burned if there is a problem on the network, i.e., slashing. Interestingly, not all PoS models have slashing, but in one way or another they all require this idea of “unusable tokens” that are locked to secure the network.

This makes PoS networks require an incentivization to lock these tokens.

Stake rewards and APY

Finally, we have come to the financial importance of PoS models, which is the staking rewards. By defining an APY (annual percentage yield), which is what staked tokens earn periodically, the network rewards participants who stake their tokens and participate in the security of the network. Moreover, this APY is usually calculated dynamically: If there are a lot of stakers, APY decreases so that some of them are motivated to liquidate their assets, while if staking ratio is low, APY is increased to incentivize locking coins.

The emphasis here is that APY is not a security mechanism, but an incentivization over what PoS networks require to be secure. If people had other motivations to stake than financial, like a social reputation system for instance, then APY would not be a requirement, as people would still stake without gaining any tokens.

Nevertheless, rewards are the most common (or only?) way of incentivization in PoS networks today. And this mechanism has a huge effect on the nodes that are running the blockchain.

Validators and their responsibilities

Validators are the parties that token holders lock their tokens into, i.e. stake, to receive rewards. It is costly and very hard for regular people to run their own nodes, so they trust validators to operate a node for them, while they lock their tokens in this validator on the network. Validators are incentivized by a small portion of the earnings of the staker, named as the commission. Note that this model is not true for all PoS chains, but a wide-enough generalization.

Up until here, we have seen that PoS was a solution to the performance problems of PoW, without any financial advantages over it. This new role of validators, that are defined thanks to APY, changes this, and PoS suddenly becomes a much stronger model than PoW in terms of adoption.

With PoW, all miners need to do is to mine. They can be anonymous, or even hated by the world if they like, but as long as they mine, they are a part of the network. This is not the same with PoS. Validators are staked by people, and their look on people’s eyes, like their contributions or their performance, matters.

This is actually a delegated voting model, where each individual delegates their power to secure the network to a validator. If the validator wants this delegation to last, it must actually prove that it is doing a good job, much like governments in democracies: A government can make sure it will be reelected by only keeping the voters happy.

Why is this an advantage of PoS over PoW?

The observation was simple: If you give people the right to choose, then the chosen entities, that is validators, have the financial incentive to work for the best interests of people.

This is the reason why PoS can be adopted in the world much faster than PoW. PoS validators have the motivation to work for their network, contribute to the ecosystem. As a validator, we at node101 have been working for the community in many ways in the networks we delegate, such as developing products, organizing events, or educating people. The reason behind these actions was not moral, but purely financial. If we want our delegators to support us, then we need to make sure that we give the best possible service to them and the network.

On the other hand, this may also be taken as a disadvantage, totally based on your perspective. Public, regulated validators are not like free, unaudited miners. Personally, I find that the only way blockchain will be adopted is through becoming public and recognised in the law, and I find the anonymity of Bitcoin miners as a huge obstacle to Bitcoin becoming a financial payments system instead of digital gold.

Of course, just because many PoS validators are public and chosen, this does not mean PoS is ultimately better than PoW. There are still a lot of problems to be solved, and a lot of steps to take.

Conclusion

If you are a validator, probably you already knew about all these here, and not much to change. But if you were just a regular crypto user, then you should take a lesson here.

With all the tokens you have and you stake, you have power over the network. Of course, you do not need to use this power, and you can just continue with the default validator that your exchange is offering you. Yet, I believe it is nice to know you have an option to choose for something better.

I still remember running my first blockchain node years ago: it was a Celestia testnet node, and I spent most of my time figuring out how to get it running. Configuring the client, keeping it synchronized, dealing with errors I had never seen before. I was quite happy once it started working, and I suppose most people running their first node would feel the same.

Things are a bit different when you operate infrastructure that other people actually depend on. An RPC endpoint might respond to every request and still serve outdated information, while a validator might have excellent uptime without revealing much about how its signing keys are managed or what happens if something goes wrong.

I find this distinction particularly interesting because we tend to judge infrastructure by what we can observe. We see whether a validator is participating in consensus or whether an endpoint is responding within its promised SLA, but much of what determines its reliability happens somewhere else.

And that’s what I want to discuss here: the work that goes into operating blockchain infrastructure beyond simply keeping the nodes running, why some familiar engineering practices become more complicated in blockchain, and why I believe that the less visible parts of an operation deserve more attention.

When Good Engineering Is Not Enough

Blockchain infrastructure relies on much of the same engineering knowledge as any other distributed system, there’s no reason to pretend otherwise. You still need to understand the operating system, networking, storage, monitoring, and quite a few other things before you can operate anything reliably.

Yet there are situations where following a perfectly reasonable engineering practice without considering the underlying protocol can cause more trouble than it solves.

Take redundancy, for example. Running multiple instances of a service is normally a good way to improve availability. But if two validator instances using the same consensus key sign the same block, the validator may be slashed with some serious financial consequences. The usual approach to avoid a single point of failure suddenly requires a few additional precautions, some of which are very specific to the network you’re operating.

Similar challenges exist on the RPC side as well. Keeping an endpoint responsive is one thing, but making sure that the node behind it is synchronized, following the canonical chain, and providing the data a customer needs is another. And if a node loses its peers or ends up following the wrong fork, restarting it might not help much unless you’ve understood why it got there in the first place.

None of this means conventional DevOps practices are somehow inadequate for blockchain. On the contrary, they need to be applied with an understanding of what the protocol is doing underneath. Every network has its own assumptions, and even two clients serving the same blockchain can behave quite differently when something goes wrong.

Making Reliability a Habit

An upgrade is a good example of how much work can sit behind an otherwise ordinary operation. Replacing a binary and restarting a service might take a few minutes. Figuring out whether the release is safe to deploy, whether the database remains compatible, what needs to be backed up, and how to recover if the upgrade fails can take considerably longer.

Of course, none of these concerns are unique to blockchain! Change management, access control, documentation, and recovery planning have been part of infrastructure engineering for a long time. Still, when an unsuccessful upgrade can interrupt a customer’s access to blockchain data or affect a validator’s participation in consensus, it’s worth being particularly careful about them.

At node101, quite a lot of our work goes into these less visible parts of the operation. Alongside running validators and RPC services, we’ve invested time in reviewing our security procedures, improving how changes are managed, and having our operational practices examined externally. Our engineering team has also put considerable effort into areas such as infrastructure architecture, automated deployments, and troubleshooting, which are not necessarily blockchain-specific but are indispensable to operating these services properly.

I don’t think this is the kind of work anyone gets particularly excited about from the outside. There’s not much to announce when an upgrade goes as planned, an access policy does its job, or a recovery procedure turns out to be useful. But these things matter, especially when the alternative is finding out during an incident that something was never properly considered.

Trust Needs Evidence

Something else worth considering is how little information customers or delegators actually have about the infrastructure they rely on.

If you’re delegating to a validator, you can look at its uptime, commission, and on-chain performance. If you’re using an RPC provider, you can measure its latency, throughput, and availability. These are perfectly reasonable things to examine, although I would not say they tell you enough about the operation itself.

For example, how are the validator’s signing keys protected? Who has access to production infrastructure? How are incidents handled, and are the recovery procedures ever tested? Unless you have access to the operator’s internal systems and processes, you probably have no way of answering most of these questions.

This is partly why independent evaluations are useful. They allow operational practices to be examined against a set of criteria rather than relying entirely on what an operator says about itself. Security controls, internal procedures, infrastructure design, and even the way responsibilities are distributed within a team can all be subject to external scrutiny.

We’ve put our own operations through such evaluations at node101, and I consider them useful precisely because they’re not limited to whether our nodes happen to be performing well. They examine parts of the operation that would otherwise be difficult for someone outside the company to assess.

Naturally, none of this means an operator cannot fail after passing an assessment. But there is still a meaningful difference between saying that an infrastructure is reliable and being able to show how it is operated.

The Work That Never Ends

I wouldn’t say there’s a particular point at which blockchain infrastructure can be considered completely figured out. Client implementations change, protocols introduce new requirements, and different networks occasionally present problems that don’t resemble anything you’ve dealt with before.

The more interesting question is how operators can maintain a consistent standard of service despite all of this. And there’s something slightly ironic about it: blockchain is built around the idea that participants shouldn’t have to trust one another, yet most people accessing a blockchain through an RPC provider or delegating to a validator still know relatively little about the infrastructure they’re relying on.

We spend quite a lot of time at node101 thinking about that gap, and I suspect it’ll become more important as blockchain infrastructure continues to find uses beyond its original audience.

After all, there’s a lot more to operating a node than making sure it’s running.

Every market needs some way to tell good products from bad ones. When buyers can check quality themselves, prices do the rest of the work. When they cannot, something counterintuitive happens: the bad products slowly push the good ones out.

In 1970, George Akerlof explained this with used cars. Editors had turned his paper down two or three times before that, some of them because they found the idea too trivial for a serious journal. Thirty-one years later, it was part of the work that won him the Nobel Prize in economics, shared with Michael Spence and Joseph Stiglitz “for their analyses of markets with asymmetric information.”

The same logic fits staking surprisingly well. Delegators cannot see what makes a validator safe, so they choose on price, and the market slowly rewards the operators who cut corners. The way out is certification, audited accounts and real liability, and these work for the same reason validators complain about them: they are expensive.

How a market fills with lemons

Picture a used car market. Some cars are fine, and some are “lemons,” cars with problems you only discover after you buy them. The seller knows which kind he has. The buyer cannot tell.

Because buyers cannot tell, they will only pay a price that reflects the average car. That price is too low for someone selling a good car, so many of them keep their cars or sell them some other way. The cars left on the market are worse on average, buyers lower their offers again, and more good sellers drop out. Taken to its end, the market is left with lemons.

In a simple version of Akerlof’s model, each round of lower offers drives out a quarter of the remaining sellers. After eight rounds, only one car in ten is still for sale.

What is worth noticing is that nobody has to lie for this to happen; the seller of a lemon only has to stay quiet. In economics, this is called adverse selection. When buyers cannot see quality, they cannot pay for it, and a market that cannot pay for quality ends up with less of it.

Staking has the same problem

Now let’s look at how people choose a validator. What can a delegator actually see? The commission, the advertised APR, maybe an uptime number on an explorer, and a website with a nice dark theme.

What they cannot see is what decides whether the validator will fail. How are the keys stored? Who can log into the servers? Has anyone ever tested the incident response plan? Is the whole setup in one data center, with one cloud provider? Would the company survive a bad year in the market? None of this shows up until something breaks. On-chain data helps, but it records outcomes, not the controls behind them, so a validator with badly protected keys has the same clean record as a careful one until the incident happens.

Everything on the left is easy to compare. Everything on the right decides whether the validator fails.

So delegators do what Akerlof’s buyers do and decide on what they can see. Commission is usually the first number people compare. An operator that spends on security can only show that spending in one place, a higher commission, while an operator that skips it can charge less. Over time the careful operator either lowers its standards or loses stake.

Staking adds a second problem. When a validator is slashed (penalized by the protocol, which burns part of the stake behind it) or goes offline, most of the loss falls on the people who delegated to it, not on the operator. Economists call this moral hazard: when someone else pays for your mistakes, you have less reason to avoid them.

Retail delegators accept this as part of staking. Institutions usually cannot. A bank will not stake client assets with an operator whose quality it has no way to check, so in a market full of lemons its sensible move is often to stay out altogether. This is probably one of the reasons institutional staking has moved more slowly than the technology alone would allow.

Signals have to be expensive

Spence, who shared the prize with Akerlof, worked on the other side of the problem: how the better-informed side can credibly tell the other side what it knows. His answer was signaling. The informed side takes a costly action that the other side can observe. In his best-known example, a degree works as a signal in the job market partly because it is costly to get, and costlier for people who would struggle with the work.

The cost is what makes a signal believable. Anyone can write “enterprise-grade security” on a website, which is exactly why nobody reads it as information.

Validators have a few signals that pass this test. An independent security certification such as ISO/IEC 27001 is one. An outside auditor checks how the company controls access, handles changes, monitors its systems and responds to incidents, and comes back every year to check again. Passing takes months of work and real money, and it is much harder for an operator whose day-to-day practice is sloppy. That difference in cost between a careful and a careless operator is what lets the certificate carry information. A certificate is no guarantee, since certified companies still get hacked, but a signal does not have to be perfect; it only has to cost a careless operator more than a careful one.

Audited financial statements are another. They tell a client that there is a real business behind the validator, one that will still be there next year.

The strongest signal is liability. An operator can commit to compensating delegators for slashing it causes, or to paying service credits when it misses an uptime target. A promise like this is cheap only for an operator that rarely fails; a careless operator making the same promise would eventually go bankrupt keeping it. That is Spence’s logic in its purest form.

Regulation belongs on the list too. A licence is a signal issued by someone with the power to take it away.

Reputation works as a signal too, but it takes years to build and mostly rewards size. Each of these can fail on its own. Together, they give a delegator something to price besides the commission.

The wider the gap between the two dots, the more a signal tells you. A claim on a website costs everyone the same, so it tells you nothing.

The same problem in Turkish funds

The same mechanism is playing out in Türkiye right now, in a market that has nothing to do with crypto. In our September Regulation Journal, we wrote about the 131 funds of seven portfolio management companies that were placed into liquidation after the decisions of 17 September, with 455,758 investors between them. The decisions followed a series of scandals.

Fund investors face a version of the used car buyer’s problem. Most of them cannot see how a fund values its assets, what related-party exposure it carries, or how much of its portfolio could be sold in a hurry. When a scandal hits a market like that, the damage does not stay with the funds involved. Investors start asking the same questions about every fund, including the well-run ones.

The Journal argued that blockchain-based audit trails could help rebuild confidence, and also warned that a record on a blockchain is only as reliable as the data entering it. Transparency helps, but investors also need someone credible and accountable behind the numbers. That, again, is a signal.

What the costs buy

Blockchain companies tend to talk about certification and regulation as costs they put up with. Akerlof’s paper suggests another way to see them. In a market where quality is invisible, these costs are what make it visible, and only visible quality can be paid for.

Without them, staking drifts toward whoever charges least. With them, a bank can tell two validators apart and decide that one of them is worth more. Last month we wrote that regulation is not something to escape, but something to catch. This is the economic reason behind that line.

In a market where nobody can see quality, the cheapest validator wins whether or not it deserves to.

On Thursday we’re opening node101’s Trust Center, where we’ll be publishing our own signals. See you there.

Sources

• George A. Akerlof, “The Market for ‘Lemons’: Quality Uncertainty and the Market Mechanism”, Quarterly Journal of Economics, 1970.

• Michael Spence, “Job Market Signaling”, Quarterly Journal of Economics, 1973.

• NobelPrize.org, “The Prize in Economic Sciences 2001”, press release, October 10, 2001.

• NobelPrize.org, George A. Akerlof, Biographical.

• node101, “Blockchain Regulation Journal September 2026”, September 29, 2026.

Can blockchain based products create trust when the market in Türkiye needs it?

Following decisions issued on 17 September, 131 funds belonging to seven portfolio management companies were placed into liquidation. According to the Capital Markets Board of Türkiye, those funds had 455,758 unique investors. The specified liquidation period was later extended from three months to six months so that portfolio assets could be sold under appropriate market conditions. [Source: CMB - investor count] · [Source: CMB - liquidation period]

As confidence in the markets is questioned after this decision, which was prompted by a series of scandals, we believe this is precisely the time to discuss how innovative technologies can be used to restore confidence in Turkish capital markets. In particular, rebuilding the market with blockchain based digital asset technologies, while preserving the existing capital markets framework in full and moving all Turkish capital market activity onto blockchain infrastructure, could be one of the most important elements in restoring trust.

Imagine a Turkish Capital Markets blockchain in which a permissioned distributed ledger converts fund unit issuance and redemption requests, custody, cash and settlement movements, and the data and valuation version used to calculate net asset value into a time stamped audit trail. It might not make every market problem visible, but investors, auditors and regulators could follow when an order was made, at which stage it was pending and which record it was reconciled against, without having to reconstruct files after the fact. Zero knowledge proofs could make this architecture more commercially viable. Without disclosing all of its positions or counterparties, a fund could prove that it complies with stated investment restrictions, has passed specified collateral or concentration tests, or has not breached a predefined limit. The purpose of a ZK approach is to establish correctness without revealing unnecessary data.

However, blockchains do not, under any circumstances, independently establish that data entered into them is correct in the real world. Incorrect valuation, incomplete custody records, undisclosed related party risk, low liquidity or weak governance would remain problems on chain. Without reliable data sources, custody discipline, independent audit and legal accountability, an immutable record is, of course, only an immutable claim.

CLARITY is pushed to next spring uncertainty in the US

On 15 September, the US Senate failed to reach the required majority in the 49 to 50 procedural vote that would have allowed it to proceed to consideration of the CLARITY Act. The result delayed more than the bill becoming law: the federal framework that would permanently define issuance, trading platforms, custody and regulatory jurisdiction in the digital asset market was once again left in limbo. [Source: US Senate]

This does not mean that the US tokenisation agenda has stopped. A few days later, the SEC issued its Innovation Exemption, allowing onchain trading in tokenised NMS stocks under certain conditions. But the decision does not take the place of the legislature. [Source: SEC]

September Flow

• SEC opens a controlled route for tokenised equity trading: The Innovation Exemption allows certain Tokenized Securities Venues to facilitate permissioned onchain trading in tokenised NMS stocks on a conditional and limited basis. Transparent transaction data, recordkeeping, technology safeguards and volume limits are positioned as parts of the framework. [SEC]

• The United Kingdom is entering its authorisation period: The FCA has published guidance on the scope of its cryptoasset regime. The application window opens on 30 September 2026, and the full regime will take effect on 25 October 2027. [FCA]

• Lending and tokenised deposits are on the MiCA review agenda: The EBA presented to the Commission its priorities on scope and definitions, stablecoin issuers, multi function groups, interaction with payments rules, crypto lending and tokenised deposits. [EBA]

• Singapore is moving its stablecoin regime into legislation: MAS is seeking feedback on legislative changes covering value stability, capital, par redemption, stress testing, wind down plans and limited recognition of foreign stablecoins. [MAS]

• Remote customer onboarding is becoming more constrained in Türkiye: The 3 September rule requires remote identification of foreign natural persons through NFC enabled passports compliant with ICAO 9303, alongside higher risk monitoring and defined SWIFT flows. [Official Gazette]

This publication is for information only and does not provide investment, legal or compliance advice.

Blockchain space has this often confusion, that decentralization should always go besides anarchy and lack of rules. This is not right, and institutional trust has actually a lot to gain from true distribution.

In this article, I will discuss the importance of a decentralized design in institutional and conventional financial systems.

TL;DR Decentralization is a tool for institutions, and its adoption is already speeding up with regulations and certifications over validators.

What does decentralization actually mean for institutions?

In my many articles before, I have given various definitions to decentralization. Although the word has the same meaning in the essence, its capture actually differs fundamentally on use case.

For institutions, decentralization is the distribution of trust. Instead of trusting a single company, real decentralization allows institutions to distribute their trust over various entities, i.e., validators of the network.

Let us consider an example: In a normal financial exchange, the entire flow is controlled by a single company, which is bounded by the laws and regulations of a single country. Although this may seem like an advantage at first, this is actually a less superior design to a distributed network. When you take a financial blockchain, like Ethereum, you get distributed trust over validators of the network.

Validators do not represent a single entity. Each has its own regulations, company, and funding. One validator shutting down does not mean anything for the network as long as the majority of the consensus stays intact.

This has two important advantages.

First, this increases security and hardness. Conventional companies can be shut down overnight, and all of us have seen or heard of giant infrastructure companies breaking in an instance. When you are a financial system, trusting on a single hardware provider is a giant risk, and no bank or fund is actually secure enough for you to trust with your entire money. Blockchains are not bound to a single entity, and its security is not in the hands of a single DevOps team.

Second, the liveness is much higher with distributed systems. You have probably seen the tweet that Ethereum had zero downtime in the past 10 years, while all infrastructure companies, including the biggest ones like Meta or AWS, have suffered from temporary shortages. This is not an implementation problem, but a core architectural advantage that a properly decentralized network cannot be taken down by the failure of a single operator.

Thus, a true decentralized system is not banks and others trying to escape, but actually to use.

But do institutions actually realize this?

We can talk about an ideal world here all day long, but the important question is if others actually know about it.

Luckily yes, and this is the reason why the institutional adoption of blockchain is so rapidly increasing in the past two years despite all of its inconveniences.

It is not a secret that blockchains are not ready: There are still many technical problems, and their UX is still way worse than any “normal” system can offer. Yet, we see that with laws and regulations, all countries adopt the existing blockchains to their systems.

The real reason behind this is technological advantage. From Swift to J.P. Morgan, major financial institutions are no longer treating blockchain as an experiment. They are building their own infrastructure, launching tokenization platforms, and integrating blockchain-based settlement into conventional financial systems.

Stablecoins are another strong example. With the GENIUS Act, the United States gave dollar-backed stablecoins a much clearer institutional framework. The important point is not that the U.S. suddenly created a digital dollar, but that regulation helped legitimize a form of digital dollar infrastructure that was already global. By embracing the technology instead of rejecting it, the U.S. strengthened the dollar’s role in an increasingly digital financial system.

And the tech is enough on its own?

This is another common mistake. Just because blockchain technology is great on its core, it does not mean that institutions will accept it as it is.

Blockchain companies, but in particular validators and RPC providers, need to actually satisfy the asks of regulations. This has an extensive coverage, from certification like ISO/IEC 27001 to locally managed servers, validator companies have a lot of rules to satisfy if they want to be a part of this new, decentralized institutional world.

It is again very important to not confuse decentralization with lack of certification: A validator being regulated or audited does not make them less decentralized, but instead actually proves how secure and strong they are in their services.

This is also our main motivation with node101, keeping the values and power of decentralization while being a pioneer in the certification of validators. Being blockchain native, it is no excuse to postpone the hard process required to be regulation-compliant.

The big takeaway

Today, it is not possible to claim blockchain is just useless. It is one of the biggest technological advancements that we have had in finances and many other areas in decades, and it will be used. Not because we push it hard, but because it is better.

This being said, we should not mistake great technology with great experience. What blockchain is lacking today is not mainly speed or adoption, but order. Law, regulations and certificates are just part of the bigger picture of how blockchain will be integrated into the world of the future.

Having understood the fundamental threat created by this new era, which I addressed in my previous article, we can outline the hypothesis behind our answer to the question: “What should we do now?”

Our short answer is: The Turkish lira should gain a programmable and strategic monetary infrastructure, issued on a digital-asset foundation built on blockchain technology.

Why would people prefer local deposits when globally accessible, higher-yielding stable-dollar investment pools are available?

At node101, we have worked for many years with global actors shaping blockchain technologies. We share the stage with founders, C-level executives, and decision-makers from this ecosystem, and have the opportunity to exchange ideas with them directly in various meetings.

For some time, we have been analysing the risks that the new digital-dollar system could create for the rest of the world and for Türkiye. To test the soundness of these analyses, we ask questions directly of U.S.-based global decision-makers whenever the opportunity arises.

At a meeting held earlier this year, I asked Tomasz Stańczak, the former director of the Ethereum Foundation, one of the most important institutions in the Ethereum ecosystem, the following question: In a future where SWIFT can provide every member bank with access to stable dollars through blockchain-based payment infrastructure, and where investors holding stable dollars are offered high yield returns, what becomes of the deposit bases built on local sovereign currencies?

Stańczak said that he saw no serious barrier to hyper-dollarisation and believed this process could take place globally through stable dollars and Ethereum.

I posed a similar question to Jeremy Allaire, founder of Circle, the issuer of USDC, one of the most widely used stable dollars in the world today. Allaire stressed that this is not only a financial rupture but also a technological one. He said that Circle visits regional hubs such as Istanbul to explain to public- and private-sector institutions that they need to adapt to these new financial technologies, and that Circle wants to support them through this transition. Otherwise, he said, economies may inevitably be defeated by time and technology.

All of this shows us one thing: for global economies, the threat is no longer a distant possibility; it is a near, tangible, and serious reality.

It should not be forgotten that if Turkish-lira deposits weaken, the economic foundation of the Turkish lira weakens as well. If that foundation weakens, financing for roads, bridges, hospitals, restaurants, industrial facilities, and every other kind of local investment comes under greater pressure. Türkiye should therefore not be merely a follower of the new digital-dollar regime; it should become a conscious, strong, and strategic partner in this transformation.

The Digital Turkish Lira should be part of this new system, not left outside it

This infrastructure should add value not only to Türkiye’s economy, but also to its economic relationships with allied and friendly countries in Europe, the Middle East, Africa, and the Far East—especially its trade relationship with the United States. Within the digital financial architecture of the 21st century, the Turkish lira should be positioned not merely as a local currency to be protected, but as a strategic instrument that gives Türkiye new political power at the level of monetary policy.

A blockchain-based Digital Turkish Lira infrastructure could give the Turkish lira a true technological foundation for circulation and settlement for the first time. In this respect, it is a candidate to become one of Türkiye’s most important technical infrastructure projects of the 21st century. It should be designed as an infrastructure that meets the needs of both the public and private sectors and, through its programmable structure, enables the development of new financial products, derivative instruments, and digital assets.

This system should be accessible while also being manageable, when necessary, in a manner compatible with Ankara’s supervisory and oversight capacity. It should be designed to facilitate the flow of external resources into Türkiye and to establish a secure bridge between the Turkish lira and global digital-asset markets.

The Turkish lira should have a strong presence on a network where stable dollars, stable euros, tokenised real estate on the Arabian Peninsula, tokenised Russian gas, Chinese goods, and similar real-world assets can be settled. With an economy approaching $1.5 trillion, a population nearing 90 million, a strong military, and institutional capacity, Türkiye can become a centre that inspires confidence in this new digital financial order.

Such an infrastructure could be a powerful tool not only for economic growth, but also for combating money laundering, terrorist financing, unregistered money transfers, and illicit capital movements. The supervisory capacity of institutions such as MASAK, the CMB, and the BRSA could become more effective internationally through blockchain-based transparency and traceability. The blacklists, risk lists, and compliance mechanisms established by Türkiye could gain the capacity to produce results not only locally, but also regionally and globally.

In a world where east-west and north-south trade is increasingly conducted through digital assets, Türkiye should not be merely an observer of these flows. The Turkish lira, Turkish assets, and Türkiye’s financial institutions should become active participants in this new network economy.

A New Economic Network for the Turkish Lira

The Digital Turkish Lira is not merely a digital representation of money; it should be a strategic infrastructure choice that determines Türkiye’s place in the new global financial architecture. The aim is to move the Turkish lira beyond being a currency that remains on the defensive in the face of technology, and place it at the centre of an economic network with growing circulation, use cases, and international reach.

This transformation should be approached with a strong legal framework, security, privacy, financial stability, and the principles of public oversight in mind. If designed correctly, the Digital Turkish Lira can help Türkiye protect its financial sovereignty while building stronger connections with global capital, trade, and digital-asset markets.

In my next article, I will examine the use cases that make this infrastructure’s economic contribution concrete: the areas in which the Digital Turkish Lira can create value, from foreign trade and cross-border payments to export finance, from tokenised assets to public payments, and from SMEs’ access to finance to next-generation capital markets. The real question is not whether this technology will arrive, but what role Türkiye will assume in it.

In this article, I will share my experience presenting a blockchain-based CBDC at the ETHBelgrade main stage and the insightful comments I received. Since this article will be lengthy, I will divide it into two parts. This was my first visit to Belgrade, and I was truly impressed by the hospitality I experienced. I would like to extend my gratitude to all my friends, especially my longtime friend Marko, who stayed in touch with me throughout this trip.

This summer, I had the opportunity to speak with senior executives from many banks, public institutions, and technology solution providers in regional capitals such as Abu Dhabi, Riyadh, Belgrade, and Ankara. The digitalization of finance, accelerated by the Washington-based GENIUS and CLARITY Acts, is affecting the entire world in waves, and executives in these cities want to understand how this new monetary system will affect them and how they should position themselves accordingly. The central question is what must be done to build our own digital asset ecosystems in harmony with American digital assets, especially stable dollars that are globally accessible through blockchain networks such as Ethereum, Tron, and Solana. Our thesis at node101 for integrating the rest of the world into this new order in the most appropriate way is blockchain-based local CBDCs.

Blockchain-based CBDCs were discussed in Belgrade

For many years, the largest blockchain event in the Balkans has been ETHBelgrade ( https://ethbelgrade.rs/ ). This year’s event, as in previous years, took place in a new format with a large audience and strong international participation. In the same venue complex, not only ETHBelgrade but also the Solana Summit was held. Over two days, many researchers, academics, and professionals working in DeFi and finance met one another and listened to inspiring talks.

This year, during my first visit to ETHBelgrade, I also had the opportunity to give a Main Stage presentation titled ‘From Lira to Ledger: Where Blockchain-Based CBDCs Fit in a Stablecoin World’.

In my presentation, I shared our original work on how the Turkish lira can be positioned within the global monetary system that is being reshaped by regulations such as GENIUS and CLARITY.

Focused on the role that the Turkish lira and Turkish assets can play in a future where the new dollar regime is built on blockchain infrastructure, and where real-world assets, from real estate to financial assets, are increasingly tokenized and carried into the digital world. This transformation is not only a matter for Türkiye, but a global issue affecting all countries, including Serbia, Arabia, and Europe, regardless of nationality.

In the first part of the presentation, I discussed where the monetary system stands today and how it may evolve in the future. Then explained the approach we have developed to provide the Turkish lira with a strong infrastructure platform, to reinforce its use in the 21st century, and to enable the Turkish lira and Turkish assets to interact with the new monetary regime in the healthiest possible way.

CBDCs were the solution of the last decade; blockchain-based digital currencies are the solution of this century

Throughout the 2010s, the decline in cash usage across most BIS-member central banks, the spread of digital payment systems, and the increasingly visible need for these systems to rely on more resilient settlement infrastructures brought the idea of CBDCs to the forefront globally. In the same period, the need to settle real-world assets expected to be tokenized on secure, publicly backed, and scalable infrastructures further strengthened this search.

As part of this trend, visible across different parts of the world from Brazil to the European Union and from Russia to China, Türkiye also began taking its first steps toward the Digital Turkish Lira through international coordination. Although the idea of a Digital Turkish Lira first entered written records in 2018, more than eight years later it has still not fully reached a concrete implementation reality.

Over these eight years, world politics, the global economy, and technology have changed significantly. Naturally, the meaning, function, and feasibility of CBDCs have also been affected by this transformation. While confidence in CBDCs and the benefits these technologies promise have been questioned from various angles, blockchain-based financial products and alternative digital asset infrastructures have increasingly emerged as stronger options.

Toward a new global monetary regime with stablecoins...

Billions of people living in different parts of the world have long faced various difficulties in accessing the American dollar easily and reliably. High inflation in local currencies, fiscal indiscipline in private and public institutions, and other macroeconomic factors have driven a flight to the dollar; yet high commission rates, black-market exchange rates, and other frictions have made access to dollars difficult even in countries where access to the banking system is relatively easy.

Stablecoins, which entered our lives in the late 2010s, made it possible for anyone with internet access to reach dollar-like digital assets much more easily, thanks to their fixed values pegged to fiat currencies. Demand for these stablecoins issued on blockchains has grown rapidly over time on a global scale. Given the level of efficiency that blockchain technologies have reached today, even within the United States it has become cheaper and faster to transfer dollars from New York to California via stablecoins than through traditional banking methods.

While blockchain-based financial products have advanced technologically to this extent, while their user bases have reached hundreds of millions of people and their total market volumes have climbed into the hundreds of billions of dollars, central bank digital currency projects, by the mid-2020s, still appear to be struggling to move beyond limited pilots and test phases.

Stablecoins gain legitimacy with the GENIUS Act and indispensability with the CLARITY Act

Since the 2024 election cycle, the direction of monetary policy in the United States, especially the debate over whether interest rates should be lowered, has become one of the important items on the political agenda. Lower policy rates would allow the American government to borrow at a lower cost and sustain its spending capacity more comfortably. Yet because of the Fed’s independent structure, the political pressure placed on the Fed Chair and board members did not produce a direct result in the short term.

At precisely this point, stablecoins emerged as a new channel that could both reduce the United States’ borrowing costs and increase global access to dollar liquidity. By giving payment stablecoins a federal regulatory framework and requiring these assets to be backed largely one-to-one by American dollars or short-term U.S. Treasury securities, the GENIUS Act turned stablecoin issuers into a natural and continuous source of demand for the American debt market. Indeed, today more than 350 billion dollars in total stablecoin issuance is backed by three-month short-term U.S. Treasury bills. This means more than 1.2 trillion dollars in additional annual borrowing capacity. To put it more clearly, one study shows that if 350 billion dollars in stablecoin issuance backed by three-month bills were realized, short-term U.S. Treasury bill yields could fall by approximately 200 to 250 basis points (2 to 2.5 percent), creating direct annual interest expense savings of approximately 7 billion to 8.75 billion dollars for the U.S. Treasury on that volume alone. [1]

As can be seen, this new structure has enabled the U.S. administration to position the stablecoin market, which has already reached hundreds of billions of dollars in global volume, as an important investor base for short-term Treasury securities. As the endless global demand for dollars and American Treasuries becomes more accessible through blockchain infrastructure, that demand also adds further depth to the U.S. borrowing market.

This development has not only financial but also political consequences. Theoretically, the Fed’s authority over the policy rate remains intact; in practice, however, the stablecoin market creates an alternative and increasingly powerful channel for the circulation of dollar liquidity. The fact that U.S. Treasury securities are regarded by global public opinion as among the safest assets also supports this mechanism.

As stablecoins gain a clearer legal footing and digital asset markets become institutionalized through regulations similar to CLARITY, a new monetary regime is becoming visible in which the dollar circulates globally not only through the traditional banking system and central bank channels, but also through blockchain-based financial networks. Even if this does not eliminate long-term dependence on Fed dollars entirely, it may lead to the emergence of new centers of power in the United States, formed by private enterprises, around the production, distribution, and access channels of dollar liquidity.

A new American dream

A future in which anyone living anywhere in the world can access the American dollar from where they sit appears, at first glance, quite “democratic.” Moreover, because these dollars are issued as stable dollars on public blockchain networks such as Ethereum, Solana, Avalanche, and Tron, which have validators distributed across different parts of the world, the structure may even look from the outside like a decentralized field of financial freedom.

A new, liberal American dream: digital dollars for everyone, financial access for everyone.

But appearance and reality are not always the same. At first glance, one might think that the Fed-centered distribution of dollars is being decentralized through private companies and public blockchains. Yet when we look a little deeper, the balance of power in the new digital asset regime points to a far more centralized structure than assumed. While today’s dollar regime operates relatively through the Fed, the Treasury, and institutional checks and balances, the new stable dollar regime allows the president sitting in the White House to exert much more direct and sharper influence over the global financial order. But how is this possible?

With the GENIUS Act, the U.S. administration gains a new channel through which it can strengthen demand for Treasury securities via stablecoin issuers. Through the Treasury Secretary appointed by the president, a continuous and globally growing buyer base can be created for short-term U.S. Treasury securities issued by the Treasury, through stablecoin companies. This creates a new architecture that reduces the practical need for the Fed in the circulation of dollar liquidity.

Moreover, this new regime creates not only a financial but also a field of political power. The fact that a stable dollar issuer operating in the U.S. market is subject to the American regulatory framework gives Washington significant political leverage. For example, a U.S. president could appear before the cameras one morning and say, “Tron is a Chinese network; we do not want stable dollars circulating on that network.” If such political will emerged, stablecoin issuers headquartered in the United States might have little room for maneuver other than moving their stable dollars on Tron to other networks.

At this point, we must ask: when it comes to the dollar, to what extent can we really speak of the decentralization of blockchains?

In the new digital dollar regime shaped by regulations such as GENIUS and CLARITY, the U.S. government is increasingly consolidating its power to determine who can issue stablecoins, which individuals, institutions, and countries can use these assets in commerce, on which networks they can circulate, and which reserves can support them. In other words, while political influence over the dollar deepens, this new regime is being built under the appearance of decentralization.

Ultimately, what stands before us is a digital dollar architecture that appears decentralized, but is in reality more centralized, more controllable, and far more politically intensive.

And in this dream, there is no room for CBDCs!

As can be seen, while the U.S. dollar is being privatized and spread across the world through issuers such as Circle and Tether, the actors in this transformation will not be limited to blockchain-focused companies. Alongside American Big Tech companies such as Amazon, Tesla, and Google, major financial institutions, especially Morgan Stanley, Citi, and others, will also become part of this private dollarization wave and add new layers of profitability to their businesses.

In this process, the U.S. government also gains a significant advantage. Thanks to global demand for stable dollar assets, it finds a way to lower borrowing costs while building a new channel through which it can spread the inflationary burden of the dollar to the rest of the world. In this new regime, the dollar becomes more accessible than ever; and as digital asset markets mature through regulations similar to CLARITY, the ability of users holding stable dollars to earn yield may make them more attractive than ever.

In such a future, the question becomes unavoidable: will there really be room for central bank digital currencies, which we call CBDCs? More importantly, will the Fed be allowed to develop its own digital dollar as a CBDC?

The current policy direction of the United States largely answers this question with a “no.” The executive order published in January 2025 and the “Strengthening American Leadership in Digital Financial Technology” report published in July 2025 show that the United States has taken a clear policy position against CBDCs both domestically and globally, and explicitly states that it will not permit CBDC projects either within the United States or in overseas countries. The American administration prefers the development, expansion, and use of dollar-backed stablecoins in ways that strengthen global dollar dominance, rather than central bank digital currencies. [2]

So how does this affect our CBDC project, namely the Digital Turkish Lira?

If the infrastructure of the Digital Turkish Lira project is not reconsidered in light of this new American digital asset regime, the project may become a product of an old world before it is even born. Therefore, what must be done today is not to abandon the Digital Turkish Lira, but to redesign it as a blockchain-based infrastructure that is compatible with the new global financial architecture and strengthens the use of the Turkish lira in the digital economy.

In other words, the Digital Turkish Lira should no longer be treated merely as a central bank digital currency project; it should be approached as a strategic infrastructure question that will position the Turkish lira, Turkish assets, and Türkiye’s financial sovereignty within the new digital dollar regime.

As node101, and also personally, we believe that the digitalization of the Turkish lira is the most important project to be undertaken for the future of the lira in the 21st century. As people who have long been thinking about these issues, asking questions, listening to different perspectives from various parts of the world, and speaking directly with those building this new regime, we are publicly discussing our hypotheses for moving the Turkish lira from a spreadsheet-based lack of infrastructure to a blockchain-based infrastructure. In my next article, I will explain in detail what our thesis for a blockchain-based Digital Turkish Lira infrastructure looks like and what kind of economic contribution it could make, first to the Turkish economy, then to the economies of allied countries, and ultimately to the world economy.

Thank you for reading. I look forward to your views and criticism.

References

[1] Liang, Nellie, and Brent Neiman. 2024. “Stablecoins after GENIUS: Macroeconomic and Financial Risks.” Brookings Institution Working Paper, September 20. brookings.edu.

[2] The White House. “Strengthening American Leadership in Digital Financial Technology.” The White House. Accessed September 6, 2026. https://www.whitehouse.gov/crypto/.

In the series of articles I’ve been writing since the beginning of the year, we have always focused on where a blockchain is headed: the block production mechanisms, consensus, peer-to-peer latency, etc. However, for this article, I want to go back to something much simpler and rather old: the first blocks of a blockchain.

Like everything else, a blockchain has a beginning: a first block, or a genesis block, so to say. Then another block gets added to it, and another. In time transactions modify balances, contracts change their states, and the network gradually builds a history of everything that has happened since its beginning. At first, keeping this history is not much of a concern because there is simply not that much to keep.

The situation becomes different after years of operation as the blockchain may have produced millions of blocks and processed billions of transactions, while every new block continues adding more information to what was already there. We usually consider this history permanent because once a block becomes part of the canonical chain, changing what happened becomes increasingly difficult.

Bitcoin is a good example of how physical this problem eventually becomes. Today, synchronizing Bitcoin Core requires downloading more than 740 GB of blockchain data. Ethereum is more complicated because of its changing state: depending on the client and the type of historical state being retained, an archive node can require multiple terabytes of storage, with some implementations requiring considerably more than others.

However, the machines storing this history do not grow together with the blockchain. Storage is limited and expensive, while the chain is expected to continue growing indefinitely. At some point, node operators therefore have to make a decision: how much of the past do they actually need to keep in order to follow the present?

Leaving the Earlier Blocks Behind

A node synchronized with the tip of a blockchain does not necessarily need every previous version of its state to continue running. What matters for most of its daily operation is the current state and the information required to verify the blocks that come next. And at this point, node operators make use of something called pruning. Instead of keeping all historical information indefinitely, a node can remove data that is no longer required for its operation of adding new blocks to the blockchain. Depending on the blockchain and the way the node is configured, this can significantly reduce storage requirements without preventing it from remaining synchronized with the network.

How aggressively this can be done also depends on the architecture of the blockchain. Bitcoin Core, for example, can reduce its storage requirement from hundreds of gigabytes to only a few gigabytes in pruning mode while still validating the blockchain. In Cosmos SDK chains, operators can go even further with historical state: the software can be configured to retain only two-to-three blocks.

Nevertheless pruning does not make the current consensus less secure. A properly configured pruned node does not become a weaker participant simply because it cannot answer a query about the state from several years ago. It can still verify the information required by the protocol and follow the current chain with the same consensus rules.

All in all, from the perspective of a node operator, the decision is understandable. The blockchain grows continuously but hardware does not and keeping information that is rarely queried comes with a real and high cost. The node therefore follows the chain towards its tip and gradually leaves some of the earlier state behind.

But this creates another question: what happens when we want to go back?

What Happened Five Years Ago?

Suppose we want to understand exactly what the blockchain looked like at a particular block five years ago. This is not necessarily an unusual request. Financial records may remain legally relevant for years, an old transaction may become part of a court case, or an application may need to prove what a particular account or contract looked like at the time an agreement was executed. In such cases, knowing the current state is simply not enough. We may need to inspect an account balance, the value of a contract, or some other part of the state exactly as it existed when that block was produced.

Thousands of nodes might have received and verified that block at the time. Yet, years later, many of those same nodes may no longer be able to answer our question. They can be perfectly healthy, synchronized with the latest block and independently verifying the network while no longer keeping the historical state we are looking for.

Here, we need to distinguish between a few concepts that are usually put together when we describe blockchains as permanent: immutability, availability, and verifiability.

Blocks and states are connected through cryptographic commitments. Merkle-based structures, for example, allow enormous amounts of blockchain state to be represented by a relatively small root commitment. This means that even if we do not store the historical state ourselves, someone who preserved it can later provide us with a particular piece of information together with the necessary proof. We can then check that proof against the commitment preserved by the blockchain and verify whether the information really belonged to that historical state. In other words, we may depend on someone else to provide the old data, but we do not necessarily have to trust them about what that data contains.

However, a commitment to information and the information itself are different things. Let’s say if I give you a file together with its hash: you can verify whether the file is the one I originally committed to. If every copy of the file disappears and only its hash remains, however, the hash cannot recreate the file.

This distinction applies to blockchain history too. Cryptography can allow us to verify historical information once we obtain it, but cryptography cannot make unavailable information available.

In other words, immutability does not automatically imply availability.

Who Keeps the Past?

Of course, historical blockchain data does not normally disappear simply because ordinary nodes prune it. There are archive nodes specifically maintained to preserve historical state, while indexers, explorers and RPC providers make different parts of that history accessible to applications and users.

But this changes the decentralization problem in an interesting way. There may be thousands of nodes capable of independently verifying what happens on a blockchain today, while only a much smaller number of machines can answer what an account looked like five years ago. Those archive operators cannot simply invent another historical state and convince us that it is legitimate thanks to the cryptographic commitments.

They can, however, choose not to provide it at all.

This means that historical integrity and historical availability have different security models. Consensus and cryptography protect us against someone changing the past, while access to that past still depends on someone continuing to store and serve it. In that sense, the older parts of a blockchain can gradually become more centralized in their availability even though they do not become more centralized in their truth.

This is not necessarily a problem. Requiring every node to preserve every historical state forever would mean that the hardware requirement for participating in a blockchain would continuously increase simply because the network survived another year. After decades, operating an ordinary node could require storing an enormous amount of information that has almost no relevance to current consensus. Therefore, for a blockchain expected to operate indefinitely, some form of forgetting is almost unavoidable.

Conclusion

We often treat permanence as if it were one property of a blockchain, but it is actually the result of several different guarantees. A blockchain can make its history extremely difficult to modify without requiring every participant to preserve every historical state forever.

If every node were required to carry everything the network had ever known, the cost of participating would increase indefinitely with the age of the blockchain. Pruning prevents this by allowing the majority of nodes to concentrate on verifying the present while a smaller part of the infrastructure preserves the past.

This creates a trade-off that is easy to overlook. As history gets larger, its integrity can remain protected by the blockchain while its availability becomes dependent on fewer participants. And those participants cannot easily lie about what happened but they may eventually become the only ones left who can show us.

Then, the question becomes not whether a blockchain can preserve its history forever, but who will still be there to provide it when we want to look back?

For the past few years, developers have often been discussed as if they were changing teams. AI was attracting talent, money and attention; crypto was losing all three. It made for an easy story, especially when every week seemed to bring another model release while the blockchain market was still recovering from its last cycle.

GitHub shows how large the AI shift became. In 2023, contributors to public generative AI projects increased 148% from the previous year. By August 2025, more than 1.1 million public repositories were importing an LLM SDK. AI moved quickly from a specialist field into the everyday software stack.

The less obvious question is what happened to blockchain development during the same period.

The data behind Blockchain Developer Report tells a more durable story than the headlines suggest. BDR’s average count of recently active GitHub accounts rose from roughly 20,400 in 2022 to 21,900 in 2023. It climbed to almost 28,000 in 2024 and reached 29,700 in 2025.

Most of that growth arrived in 2024. The pace slowed the following year, but activity remained far above its 2022 level. Public blockchain work did not disappear as AI took off. More of it became visible.

Two technologies spreading in different ways

AI adoption travels easily across the software industry. A support platform can add a language model to summarize tickets. A finance product can use one to read documents. A developer tool can generate tests or explain an unfamiliar codebase. None of these products needs to become an “AI company” for its repository to carry a visible AI dependency.

Blockchain enters a codebase differently. It tends to appear through smart-contract languages, protocol clients, wallets, cryptographic libraries and chain-specific SDKs. The work is often more tightly connected to the architecture of the product. Adding an LLM API to an existing application may take an afternoon. Introducing account abstraction, a proving system or a new execution environment usually demands a deeper commitment.

That difference matters when we try to read developer attention from GitHub. AI can spread horizontally across almost every category of software while blockchain continues growing inside a smaller technical domain. One trend does not have to consume the other.

The same developer may also contribute to both. Someone working on wallet infrastructure can use an AI coding assistant, build an internal agent or add an LLM-powered interface to the product. GitHub will record the repositories and dependencies, but it will not tell us that the developer has crossed from one professional identity into another.

The idea of a clean migration assumes that developers choose one industry and leave the other behind. Modern software work rarely looks that tidy. Technologies overlap. Tools travel faster than job titles, and a developer’s public contributions may cover several parts of the stack in the same month.

AI may even be helping more blockchain experiments reach GitHub. It has become easier to understand an unfamiliar SDK, draft a basic contract interface, generate tests or translate documentation into working code. That lowers the cost of trying something new.

It does not remove the hard parts. Security reviews, protocol design, production maintenance and ecosystem knowledge still take time. AI can shorten the path to a first prototype without guaranteeing that the prototype becomes a lasting project.

That distinction begins to show up inside the BDR data.

The base widened faster than it deepened

The total number of recently active accounts grew, but the composition of that activity changed.

At the end of 2022, BDR’s “one-time” category represented 74.1% of active accounts. By the end of 2025, its share had reached 78.3%. Over the same period, the “full-time” share moved from 13.9% to 11.3%. The average number of full-time accounts was almost unchanged between 2024 and 2025.

These are contribution-frequency categories, not job titles. Even so, the direction is useful. The broad edge of the ecosystem expanded faster than its higher-frequency core.

There are plenty of ways for occasional activity to enter the public record. A developer may build a hackathon project, test a new SDK, contribute to a grant prototype or integrate a wallet into an existing application. Some will return. Others will leave the repository untouched after the first working version.

That early activity still has value. Experiments create feedback, reveal documentation problems and put tools in front of new developers. Every durable contributor starts somewhere.

The harder work begins after the first repository appears.

For ecosystem teams, the useful question is no longer simply how many developers showed up. It is how many found a reason to continue. Did they return after the event ended? Could they get help when the sample code failed? Was there a real project waiting beyond the tutorial? Did maintainers review their contribution while it was still relevant?

Hackathons and grants are good at creating a visible spike. Documentation, issue triage and responsive maintainers are what turn that spike into a working community.

The BDR trend suggests that blockchain has not lost its ability to attract experimentation. The next challenge is converting more of that experimentation into repeated contribution.

Growth creates a retention problem

A larger entrance can hide a weak middle.

When an ecosystem is small, bringing in new developers feels like the obvious priority. As the top of the funnel expands, the constraint moves elsewhere. More people encounter incomplete documentation. More prototypes reach the point where they need security advice, infrastructure credits or a maintainer’s attention. More grant recipients have to decide whether the second version is worth building.

AI may increase that pressure. If coding tools make the first prototype cheaper, ecosystems will receive more experiments of uneven quality. The teams that benefit will be the ones that can recognize promising work early and give it a path forward.

That path does not need to be elaborate. A useful starter issue, a reply from an experienced maintainer or a clear production example can matter more than another broad developer campaign. Small pieces of support arrive at exactly the moment when someone is deciding whether to keep going.

The measurements should follow that journey. First contributions are worth tracking, but so are returns after 30, 90 and 180 days. Ecosystems should know how often an account moves from a single contribution into regular work, begins contributing across repositories or starts reviewing the work of others.

Those transitions describe an ecosystem’s depth better than a launch-week total.

The next BDR analysis can make them visible. It can follow account-level cohorts over time, distinguish established repositories from newer additions and examine how many blockchain contributors also appear in public AI projects. That would replace the abstract idea of a developer migration with observable behaviour.

For now, the public record already tells us something useful.

AI became one of the fastest-growing areas of software development without bringing public blockchain work to a halt. BDR recorded a much larger active-account measure in 2025 than it did in 2022. The pace eased, and the higher-frequency segment did not expand as quickly as the wider base, but developers continued to build, test and publish.

The opportunity is no longer just to prove that blockchain developers are still here.

It is to give more of them a reason to stay.

Data and sources

Blockchain figures use the Blockchain Developer Report export generated on August 10, 2026 and cover the completed calendar years 2022–2025. Annual values are arithmetic averages of daily trailing-28-day active-account snapshots. BDR is a node101 product; its definitions and collection approach are described in the BDR methodology. Historical figures are retrospective and may update as public repositories are discovered or classified.

AI figures come from GitHub’s Octoverse 2023 and Octoverse 2025.

Zero-Knowledge was definitely one of the biggest topics of blockchain once, and we have seen many great projects developing their technologies. Yet, there is very little data on what is the state of ZK’s adoption today.

As a part of our developer analysis in blockchaindeveloper.report, we inspected what is the state of ZK developers as of August of 2026, and we present our findings below, for general and each ZK ecosystem.

Active Developer Comparison to All-Time Metrics

The zero-knowledge ecosystem had so far just a little less than 29 thousand repositories created, with 33 thousand developers who have tried ZK technologies in total. However, only 1341 of these developers are active as of August 2026.

This means that currently 4.0% of developers in the ZK ecosystem are still active, which is not a promising overall ratio at all. Moreover, the overall global active developer share for blockchain developers (which is again the number of active devs over all-time devs) is 27.9%, showing us that the ZK ecosystem is not doing very well even inside the blockchain ecosystem in general.

However, it is important to notice that the biggest drop in this ratio is caused by old ZK technologies that do not currently host any active developers, so this does not necessarily mean that the ZK ecosystem is losing traction, but only some ancient big ZK projects are now not very strong.

This final conclusion is also supported by ecosystem specific data that we share in later sections.

Distribution of Activity in the ZK Ecosystem

When we look over the historical charts, we observe that most of the repository count in ZK is generated by two ecosystems: Starknet (12k repos) and Circom (7k repos). Alone, the two explain 70% of all repository count. Their role is similar in the total developer count as well.

However, we see that activity is considerably more distributed, with Midnight and Starknet leading, followed by Aztec and Circom.

This observation leads us to two important conclusions: The first is that even though the two biggest ecosystems of ZK are not as active today, they still own an important developer segment. The second is that some of the newest ZK ecosystems are actually matching the old favorites in activity, which is very positive news for new technologies.

Now, let us move on to some ecosystem specific analyses to better understand the situation.

Ecosystems in the Order of Active Developer Count

Midnight

TL;DR 1367 all-time repos, 1127 all-time devs, 544 active devs

To summarize simply, we can say that Midnight is rapidly gaining traction. Their active developer count has increased very rapidly in the past 2 months, and now they own the biggest active ZK developer segment. So far, their 2-months retention is also very high with 91.8%.

Another interesting metric of Midnight is the increase in the number of returning and reactivated developers in their chart:

This graph indicates that the recent increase of Midnight is not only made-up of new developers, but older Midnight developers are also returning to the ecosystem.

One final graph to share is the multi-ecosystem distribution of Midnight developers.

The graph shows that about half of Midnight developers are working only in the Midnight technology, which is also very high compared to most of other ZK ecosystems.

Overall, we can say that Midnight is trending fast at the moment, but the sustainability of their growth will be seen in the later months of 2026.

Starknet

TL;DR 12398 all-time repos, 13678 all-time devs, 393 active devs

Starknet is the biggest ecosystem of ZK in terms of all-time repos and devs, but their active developer count has been decreasing since the beginning of 2026. The trend is even more clear when we look at their all-time data:

Nevertheless, there is also enough evidence to believe that this stop is finally ending, and Starknet active developer count is reaching a stable point. The high retention of 80.5 % also supports this idea.

Moreover, Starknet developers have one of the highest tenure levels of the ecosystem:

Here, we observe that Starknet has many established and emerging developers in its ecosystem, also supporting the idea of a stabilizing ecosystem. Note that last 2 weeks are not a fully accurate representation since the tenure data is often updated retrospectively.

Moreover, just like Midnight, Starknet ecosystem also hosts a significant portion of single-ecosystem developers, which is especially important considering they are an Ethereum L2.

To conclude, we can say that the Starknet ecosystem is looking to be stabilizing since the beginning of 2026. Later months will determine if Starknet will be able to protect its developer base.

Aztec

TL;DR 3203 all-time repos, 2862 all-time devs, 164 active devs

Aztec’s charts are quite surprising. Until the last month, we see that Aztec’s active developer count was having a steady growth, reaching 400 active developers in total, which is higher than Starknet’s current numbers. However, there was a sharp decrease last month, causing Aztec to drop up to 100 active devs.

We do not know the exact reason for this drop. One possible explanation may be that being a privacy-first ecosystem with institutional projects, many Aztec repos are still private, and we will see a retroactive increase in Aztec’s numbers in the coming weeks as these repos go public. Of course, data cannot relate to any cause directly.

However, it is also interesting to compare these results with multi-ecosystem segmentation.

We observe that while the count of single-ecosystem developers of Aztec decreases much less, multi-ecosystem developers are the main cause of the decrease in Aztec’s charts. The fact that Aztec hosts many more multi-ecosystem developers than the two previous ecosystems also implies the importance of this difference.

Knowing that the total active developer count of the blockchain has also witnessed a significant drop in the last month, we can conclude that Aztec is affected by this change more than others, as its ecosystem co-exists with the general blockchain as well.

Coming months will show whether this decline is temporary or part of a longer-term trend.

Circom

TL;DR 7300 all-time repos, 8373 all-time devs, 132 active devs

Now, let us look into one of the oldest ZK technologies that is still in use.

The same surprising picture of Aztec meets us also in Circom’s charts. We see that in the past month, Circom experienced a drastic drop, from 600 active devs (ecosystem high) to 132. The most optimistic explanation is again the frequency of institutional projects in Circom, and that these numbers will be updated retroactively with later pushes.

However, another possibility may be that Circom is finally leaving its place to the new technologies of ZK. Being slower and less-convenient than many, it is not a surprise for developers to start leaning towards newer technologies these days. The low retention rate (40.4%) of Circom also supports this idea.

Not surprisingly, as Circom does not have its own network, most Circom developers are also multi-chain, which is also another correlation between Circom and Aztec.

As an important difference, we can also notice that single-ecosystem developers of Circom are also leaving for the past weeks, with almost no single ecosystem developer left currently.

Of course, such a recent update is not a trend, and this does not mean anything definitive on Circom.

Succinct SP1

TL;DR 617 all-time repos, 1789 all-time devs, 59 active devs

And as a final bonus, let us look at the 5th most active ecosystem of ZK, which is also unique among the others above being a zkVM instead of a programming / circuit language.

At first, we can also easily mistake Succinct being also very similar to Aztec and Circom, as it has lost many developers in the past weeks. However, the high retention rate of Succinct (70.3%) makes us wonder if we were too quick to jump into this conclusion.

Indeed, the developer segmentation charts may be pointing to a different conclusion:

Indeed, until very recently the big majority of Succinct’s ecosystem is made up of multi-ecosystem developers. During the past month, these devs left, while single ecosystem developers of Succinct showed a significant improvement.

This chart may actually be showing us that Succinct is finally getting its own ecosystem, and later Succinct development will be much more stable.

Conclusion

The ZK ecosystem is clearly going through a transition rather than a simple decline. While much of its historical developer activity was concentrated around older technologies such as Starknet and Circom, current activity is becoming more distributed, with newer ecosystems beginning to capture a meaningful share of active developers.

The strongest signal in the data is this shift in where developers are building. Midnight has grown rapidly, Starknet appears to be approaching a more stable developer base, while Aztec, Circom, and Succinct show more volatile recent patterns that will need additional months of data before drawing strong conclusions.

Overall, ZK remains an active but changing part of the blockchain developer ecosystem. The next stage of its growth will likely depend less on the size of its historical developer base and more on which ecosystems can attract developers, retain them, and turn short-term interest into sustained activity.

Thank you for reading this report. You can reach us at hello@node101.io for any questions, or inspect all the metrics yourself in blockchaindeveloper.report.

You are not a suspect, and nobody is watching a screen. A camera on your route home photographs your car anyway, and the image becomes a searchable record: plate, make, color, time, place. On its own, that record says almost nothing about you, and it won’t stay on its own for long.

The structure is simple, which is exactly what makes it consequential. Each layer takes a piece of ordinary behavior and turns it into something an institution can act on.

Strictly speaking, a score is only an input. The output is the interest rate, the insurance premium, the rejected application, the investigative priority. “They know your price” means that these systems can translate fragments of your behavior into consequences you never see being calculated.

So here is the argument I want to make. One industry has already built the infrastructure that turns ordinary life into machine-readable records. Another turns those records into scores, and institutions increasingly let the scores set the terms of participation: what you pay, what you can rent, how closely you get examined. The cases below each carry one step of that claim, and together they point at a single question: who holds the right to score whom.

The machine that turns movement into data

The capture layer comes first, and it already runs at scale. Flock Safety sells automated license plate readers that operate, by the company’s own count, in more than 6,000 US communities. The cameras record vehicles passing through an area, and participating agencies can grant one another access or run searches across the wider network, depending on how sharing is configured.

The public-safety argument deserves a fair hearing, since plate readers do recover stolen cars, locate missing people, and help police find suspects, and a powerful tool can still be a legitimate one.

The search logs, however, told a fuller story once officials and journalists started reading them. An Illinois audit in 2025 found that Customs and Border Protection had accessed data originating in the state, which sat uneasily with a law restricting such use in immigration and abortion-related investigations. In Texas, a sheriff’s office searched cameras for a woman whose family said she had undergone a self-administered abortion. The logged reason referenced the abortion itself; the sheriff later described the search as a welfare check.

Flock paused its pilot programs with federal agencies and added new restrictions. Even so, several cities cancelled their contracts.

The cameras themselves never changed. Infrastructure justified by stolen cars had quietly become capable of answering questions about immigration status and reproductive health, because the record stayed neutral while the queries carried the intent. That settles the first step of my claim: capture at scale exists, and its purpose travels with whoever writes the query. The harder question is who converts records into decisions.

Two layers, not one machine

Companies like Flock make the physical world legible by converting events into data points. Companies like Palantir make those data points actionable, connecting scattered records into decision-ready profiles. Banks, insurers, employers, and government agencies then turn the profiles into decisions about actual people.

Palantir has supplied software to US immigration enforcement for more than a decade. In April 2025, ICE awarded the company roughly $30 million to develop ImmigrationOS, including tools for enforcement prioritization and self-deportation tracking. By September, another $30 million order sat inside a broader contract vehicle worth up to $157.5 million, one that explicitly referred to “identification and targeting for enforcement.”

One industry watches, another interprets, and the institutions downstream make the consequences real. Keep that division of labor in mind, because it later decides whom a person could even complain to when a score goes wrong.

Why scoring exists

Before going further I should concede the strongest objection to my argument: scoring exists for defensible reasons. Since lending to strangers requires estimating risk, standardized credit models made those estimates more consistent while letting lenders evaluate people who arrive without personal connections or collateral. Insurance cannot function without pricing the probability of loss. Fraud systems have to tell an ordinary payment apart from a stolen card.

My objection begins where a score travels beyond its original purpose, resists inspection, and produces consequences nobody can meaningfully challenge. The next two cases show exactly that migration.

When the score leaves home

In 2024, reporting revealed that General Motors had been sharing data collected through its OnStar Smart Driver feature with consumer reporting agencies used by insurers. The data covered speeding, hard braking, nighttime driving, and precise location, in some cases collected every few seconds.

The Federal Trade Commission alleged that GM’s enrollment process was misleading and that many drivers never gave informed consent. Some of those drivers said the resulting reports affected their insurance rates. GM discontinued Smart Driver, and in January 2026 the FTC finalized an order barring the company from sharing certain location and driving-behavior data with consumer reporting agencies for five years.

A feature presented as driver coaching had become an input for underwriting, and no grand plan was required to get there. Once a useful dataset existed, another industry found a price for it. For my argument the details matter less than the direction of travel: the score left home, and nobody who generated the data watched it go.

From score to price

A 2023 decision by Türkiye’s data protection authority shows the next step with unusual clarity. A car rental company’s website described a decision-support algorithm for first-time customers, one that weighed the applicant’s Findeks credit score, recent credit applications, payment performance, debt ratio, and even the socioeconomic profile of the neighborhood where they lived.

The company denied conducting the disputed Findeks inquiry. The authority found otherwise: access to the customer’s Findeks report had been made a condition of receiving the rental service, and it imposed an administrative fine.

The shift here is easy to miss. Information developed to assess borrowing risk had crossed into an unrelated market; the question thus stopped being whether someone would repay a loan and became whether they could rent a car at all.

This, concretely, is how scoring becomes pricing and what my title means. One system estimates risk, and another uses the estimate to alter the terms of participation. Even incentives follow the same logic, because a discount for accepting monitoring doubles as a surcharge for refusing it. As a result, privacy starts to resemble a luxury good.

The speculative next step: liquid people

Nothing described so far requires a blockchain. Its role comes later, and because this is the most speculative part of my argument, let me be explicit about which parts are documented and which are projection.

The documented layer is data capture and scoring. The experimental layer already exists in miniature: in 2020, entrepreneur Alex Masmej raised about $20,000 by selling $ALEX tokens, promising holders 15 percent of his income for three years along with access and voting privileges connected to his career.

The projected layer goes further. Blockchains can make credentials portable, and tokenization can create tradable claims on the income, access, or reputation attached to them.

Portability by itself could do real good. It could help a refugee prove qualifications after losing access to the institution that issued them, or let a freelancer carry a work history between platforms. A portable credential and a liquid market are different things, though. Portability lowers the cost of reusing information, while tokenization goes a step further and lets claims built on that information be divided and traded.

Design this badly and a person’s worst year stays legible indefinitely. Tie a market to reputation and a temporary controversy shows up as a visible selloff; falling confidence damages opportunities, and the lost opportunities then look like confirmation of the market’s judgment. At that point the price stops measuring reputation and starts producing it.

Rights on paper, review in practice

Contesting these systems is possible, at least on paper. The GDPR restricts certain solely automated decisions with significant effects. In its SCHUFA judgment, the EU’s highest court held that a credit score can fall within those rules when the recipient relies on it decisively, and its later Dun & Bradstreet judgment required an explanation meaningful enough for a person to understand and challenge the decision, making clear that trade secrecy offers no blanket excuse for refusing access. Türkiye’s KVKK gives people a comparable right to object when exclusively automated analysis produces an adverse result.

The harder problem, however, is practical. To exercise any of these rights, a person has to know that a score was used, identify who is responsible for it, and obtain review before the consequence becomes irreversible. Each of those steps can fail when data providers, model operators, and decision-makers are separate organizations. A right exercised after the apartment, the job, or the loan is gone therefore arrives too late to prevent anything.

Who gets to score whom?

Still, none of this makes the outcome inevitable, and I want to be precise about what I am defending. Purpose limits, inspectable scores, correction rights, selective disclosure, and review before consequential decisions could preserve the benefits of scoring without creating a permanent market in human worth.

Rather than separating data from no data, or blockchain from no blockchain, the dividing line separates systems that help people prove something about themselves from systems that let others turn those proofs into control over their future. The deepest inequality of the coming decades, I suspect, will hinge less on who owns the data than on who holds the right to score everyone else.

Sources

• Flock Safety, “Customers Own and Control Their Flock Data”, and Frequently Asked Questions.

• Associated Press, “License plate camera company halts cooperation with federal agencies among investigation concerns”, August 25, 2025.

• Forbes, Flock Safety company profile, updated March 3, 2026.

• Wired, “ICE Is Paying Palantir $30 Million to Build ‘ImmigrationOS’ Surveillance Platform”, April 2025.

• Forbes, “ICE Awards Palantir Another $30 Million for ‘Voluntary Return’ Program”, September 30, 2025.

• Federal Trade Commission, “FTC Finalizes Order Settling Allegations that GM and OnStar Collected and Sold Geolocation Data Without Consumers’ Informed Consent”, January 2026.

• Türkiye Personal Data Protection Authority, Board Decision No. 2023/1234, July 20, 2023.

• Roll, “Hi, my name is $ALEX”, 2020.

• Court of Justice of the European Union, SCHUFA judgment, Case C-634/21, December 7, 2023.

• Court of Justice of the European Union, Dun & Bradstreet Austria, Case C-203/22, February 27, 2025.

A technology can be useful and a disastrous investment at the same time. Railways and the internet changed the world, but both produced enormous financial bubbles that later caused sleepless nights for the people who financed them.

This essay is about a narrower question: how much of the value AI creates will be captured by the companies paying for the buildout, and how much will leak to customers through lower prices. AI does not need to fail for its investors to lose. It only needs to become cheap faster than they can earn their money back.

On some measures, the AI boom is approaching the 1999 dot-com bubble in terms of valuations, and it may be even larger when measured by the share of the economy’s investment it is absorbing. But this time, there is a fundamental difference: the economic life of the capital being built.

Physically, an AI chip can remain operational for years. Economically, its most valuable life may be much shorter, possibly as short as one to three years under heavy use, compared with dot-com-era fiber and telecom infrastructure that remained useful for decades. This is the most important and least settled assumption in this essay, and I will stress-test it below. The direction still matters for the infrastructure argument: dot-com fiber could be bought cheaply after the bust and operated profitably for years, whereas AI chips lose their highest-value uses much faster as newer generations arrive. They can be moved to lower-value workloads, but the buildout is still likely to leave behind a shorter-lived capital stock, even if the chips are not literally worthless.

The size of the bet

To understand what is going on, first we need to understand the numbers.

According to their latest guidance, Alphabet, Amazon, Meta, and Microsoft plan to spend roughly $695 to $725 billion in total capital expenditure in 2026: about $200 billion at Amazon, $180 to $190 billion at Alphabet, around $190 billion at Microsoft, and $125 to $145 billion at Meta. For the record: The companies do not break out a clean AI-only line; these are total capex figures.

For comparison, Sweden, a literal country with 10.6 million people, produced about $669 billion of GDP in 2025. Four companies now plan to out-spend the entire annual output of Sweden.

In addition to the enormous amount of money involved, AI infrastructure adds a utility-like layer to the technology industry: lots of physical investment upfront, uncertain returns later. Although these companies generate extraordinary profits and hold enormous cash reserves, the scale of AI investment is so vast that even this sector has increasingly turned to the bond market. The five largest hyperscalers, meaning Amazon, Alphabet, Meta, Microsoft, and Oracle, issued about $121 billion of US corporate bonds in 2025, roughly four times their annual average of $28 billion during the previous five years. Some of that borrowing funds ordinary business, but the surge lines up with the capex plans. By mid-2026, issuance had already passed $159 billion, and full-year forecasts sit around $230 to $240 billion.

The money is also starting to move in circles. Nvidia invests in its own customers, and those customers sign multi-year compute commitments worth hundreds of billions of dollars with the clouds that buy Nvidia’s chips. A growing share of the data centers is financed through special-purpose vehicles that keep the debt off the sponsors’ balance sheets. There is nothing illegal about any of this. But vendor financing and circular capital flows are the same patterns that made earlier infrastructure booms look healthier than they were.

The depreciation question

Earlier I said the most valuable life of an AI chip may be one to three years. That claim needs support, because the companies’ own books say otherwise. Microsoft and Google depreciate their servers over six years. Meta extended its servers and network assets to five and a half years in January 2025. If those schedules reflect economic reality, the infrastructure argument gets much stronger and this essay gets much weaker.

One data point cuts the other way. In February 2025, Amazon, which runs the largest cloud fleet in the world, shortened the useful life of a subset of its servers and networking equipment from six years back to five. The reason it gave was the faster pace of technology development in AI and machine learning. Amazon had spent the previous decade extending server lives. It is the first hyperscaler to walk one back, and it did so specifically because of AI.

Why does an accounting footnote matter? Because if the true economic life of AI hardware is shorter than the book life, depreciation is currently understated, which means today’s reported profits are overstated. Skeptics like Michael Burry (the investor who called the 2008 housing crash) argue the gap could reach roughly $176 billion of understated depreciation between 2026 and 2028, though that number cannot be verified from public filings. A JPMorgan stress test found a more modest effect: applying a three-year life to AI hardware would cut earnings per share and operating margins by roughly 6 to 8 percent for most hyperscalers, with Oracle as the bigger exception. That would hurt earnings without breaking them. Still, nobody is arguing that the chips will outlast their schedules.

Also, not all of this capex is chips. Alphabet said about 60 percent of its recent quarterly capex went to servers and 40 percent to data centers and networking, and buildings, power infrastructure, and fiber genuinely do last decades. The short-life problem applies to the silicon. The silicon just happens to be the biggest and fastest-growing slice.

The price is collapsing

Obviously, these companies are not spending this money merely so that you can generate an anime-style image of your cat for free. They need the infrastructure to earn an adequate return on their investment.

To increase their profits, they need to increase volume, improve margins, or ideally do both. The problem is that the price of model access is collapsing.

To take one benchmark-based comparison, from a16z’s “LLMflation” analysis: when GPT-3 became publicly accessible in late 2021, it was the cheapest widely available model capable of reaching an MMLU, or Massive Multitask Language Understanding, score of about 42, at roughly $60 per million tokens. By late 2024, the cheapest model clearing the same bar cost about six cents.

The price at that specific capability level has barely moved since 2024, but for a telling reason: the decline moved up the capability ladder. The same few cents now buy substantially higher benchmark scores. In fact, you no longer need a model provider at all to reach GPT-3’s level. Small models that beat it can run on an ordinary Mac with 16 to 24 GB of memory, where the marginal cost is mostly electricity.

As the price of a fixed level of benchmark performance approaches commodity levels, there are two broad ways to justify this massive investment: sell a more capable frontier model at a premium, or increase usage fast enough to offset falling unit prices. Ideally, they do both.

At a fixed capability level, a thousandfold fall in price would require a thousandfold increase in volume to preserve revenue. But revenue is not profit. Profit also depends on the cost of serving each token, the utilization of the infrastructure, depreciation, and financing costs.

It also matters why prices are falling. If prices fall because the cost of serving each token is falling just as fast, thanks to better hardware and better software, margins can survive the collapse. If they fall because competitors are undercutting each other, margins die with them. So far the evidence points more toward the first story: Epoch AI, which tracks these price declines, found no clear evidence that shrinking profit margins explain them. The second force is real too, though, because open-weight models put a ceiling on how much of the efficiency gains providers can keep for themselves.

Of course, as AI advances, people are using it for tasks that were not possible with GPT-3. Providers are not limited to selling more of the same capability. They can sell the newest model at a premium.

The problem is that each premium may be temporary.

The Intelligence Premium

The intelligence premium is the extra price customers are willing to pay for capabilities that cheaper or older models cannot yet provide.

Setting aside the other advantages of open-weight models (like privacy, if you self-host them), if two models can achieve the same task, who would choose to pay more?

The problem is that the premium has a short half-life. As competitors catch up and smaller models improve, yesterday’s frontier becomes today’s commodity, widely available at a fraction of the price. The premium does not disappear entirely, but it moves to the next capability level.

As I was finishing this essay, Moonshot AI announced Kimi K3, a 2.8 trillion parameter open-weight model that, by its own account, trails only the two strongest proprietary models. The weights become public on July 27 under a permissive license. Moonshot priced its own API at western frontier rates, but that hardly matters; once the weights are out, anyone can host the model, and the price competition starts there.

The market graded the announcement within hours. Shares of Zhipu, a rival Chinese lab, fell 22 percent by midday, and MiniMax fell 14 percent. Notice who paid the price: the other model companies. The chipmakers and the clouds kept selling.

This creates a difficult business model. AI companies must keep spending to reach the next capability level before the premium on the current one disappears. Standing still is not an option, because the product they are selling becomes cheaper even when it does not become worse.

The real question, then, is whether the premium lasts long enough, and is large enough, to recover the cost of producing the next generation of models and the infrastructure behind them.

The bull case

However, I want to mention two strong arguments on the other side, this time with the numbers behind them.

First, falling prices do not necessarily mean falling revenue. If lower prices make AI useful for far more tasks, demand may grow faster than the price declines. A model that costs ten times less can still produce more revenue if it is used a hundred times more. And demand really is exploding. Microsoft’s AI business passed a $37 billion annual revenue run rate in early 2026, growing 123 percent year over year. Google Cloud grew 63 percent in the same quarter, with a contracted backlog that nearly doubled to over $460 billion. Both companies say they are capacity constrained: they could sell more compute if they could build it faster.

Cheaper models do not just replace expensive ones in existing tasks; they make entirely new uses economical.

Second, the hyperscalers do not need to recover their investment only through model access. Microsoft can use AI to make Azure and Office more valuable. Amazon can sell more cloud capacity. Meta can improve advertising and engagement. Alphabet can protect or expand its search, advertising, and cloud businesses. In these cases, AI may generate returns indirectly, even if the price of the model itself keeps falling.

This makes the investment case stronger, but it also makes it harder to measure. A company can claim that AI protects revenue, increases productivity, or improves an existing product without showing how much of that value came from the infrastructure itself.

So let me say what would prove this essay wrong. Usage keeps growing faster than prices fall, year after year. Serving costs drop as fast as prices. Old hardware stays busy instead of idling. Indirect AI revenue shows up in cloud and advertising numbers in a way you can measure. The backlog and run-rate figures above are early evidence for that side of the argument.

This is where the short life of the premium becomes important again. The companies are not making one large investment and then collecting returns from it for decades. They are entering a cycle in which each new generation requires more capital, while the previous generation quickly becomes cheaper and less valuable.

The result may be a business that grows rapidly while producing disappointing returns on capital. This would not make AI a failed technology, but rather a difficult industry to invest in.

Who captures the value?

The history of technology is full of examples where users captured more value than the companies that built the infrastructure. Competition pushed prices down, capacity became abundant, and the economic benefits spread across the rest of the economy. The technology succeeded, but many of the investors who financed it did not.

AI may follow the same pattern. The models can become more useful, more widely available, and more important to the economy while the companies producing them struggle to earn returns that justify the capital invested. In fact, the faster AI improves and spreads, the faster older capabilities may lose their scarcity value.

There is more than one bet hiding inside “the AI trade,” though, and the economics differ. Chipmakers earn high margins for as long as the buildout continues, whatever the buyers’ returns turn out to be. Frontier model labs face the intelligence premium problem in its purest form: enormous training costs against a premium with a short half-life. The hyperscalers sit in the middle as both the gold diggers and the landlords; they dig with their own models, but much of their AI revenue comes from renting compute to the labs. And businesses with existing distribution, customer relationships, and profitable products can plug cheap intelligence into what they already run.

This is why the central question is not whether AI will change the world. It probably will. The question is how much of the value created by AI will be captured by the companies financing the buildout, and how much will be passed on to customers through lower prices.

If intelligence continues to become cheaper, the economic value of AI may rise while the market value of producing it falls.

That is the risk behind the current investment boom. The industry may be building something enormously useful, but too cheap to sell at the prices needed to justify what it cost to build.

Sources

• 2026 capex guidance: Alphabet Q1 2026 earnings call ($180-190B); Microsoft FY26 Q3 earnings call ($190B for calendar 2026, via CNBC); Amazon Q4 2025 results (~$200B); Meta Q1 2026 results ($125-145B).

• Hyperscaler bond issuance: M&G Investments, “Tech issues: The AI debt deluge hitting bond markets” (Bank of America data); H1 2026 issuance and UBS full-year forecast via press reports.

• Price decline at fixed capability: a16z, “Welcome to LLMflation” (2024); Stanford AI Index 2025; Epoch AI, “LLM inference prices have fallen rapidly but unequally across tasks.”

• Useful-life accounting: Amazon Q1 2025 10-Q (useful life change, six to five years); Meta 2025 10-K (5.5 years); Microsoft and Alphabet filings (six years); JPMorgan depreciation stress test via press reports.

• Sweden GDP: World Bank / IMF, 2025.

Agenda

🟥 Türkiye entered a new phase of crypto asset market data transparency.

MKK started publishing daily core market data reported by crypto asset service providers through KVMKS. As of 15 July, it was announced that 53 CASPs had completed their MKK membership processes, approximately 5.6 million investors had traded on platforms to date, and around 3.2 million investors had balances. *

🟥 MiCA is now fully in implementation across Europe.

With its 16 July 2026 update, ESMA’s Interim MiCA Register continues to bring authorised crypto asset service providers, whitepaper records, e-money token issuers and non-compliant entities into a single reference point. *

🟥 ESMA selected custody as one of the first major supervisory focus areas after MiCA.

ESMA launched a common supervisory action to examine the digital operational resilience of crypto asset service providers in their custody activities. Focus areas include key management, custody architecture, transaction controls, incident detection, smart contract risks and third-party dependencies. *

🟧 The European Commission continues to collect feedback for the MiCA review.

The targeted consultation aims to assess whether MiCA remains fit for purpose in light of its first implementation outcomes and market developments. The deadline is 30 September 2026. This process may lay the groundwork for MiCA 2 discussions around stablecoins, tokenisation, DeFi and service provider obligations. *

🟧 BIS is bringing stablecoins back into its core agenda.

A study dated 21 July 2026 discusses how stablecoins could strengthen digital dollarisation in emerging economies and create implications for monetary policy transmission and financial stability. *

🟧 BIS treats blockchain infrastructure as financial infrastructure.

It also frames blockchain fragmentation not merely as a technical issue, but as a market structure problem. The 6 July 2026 bulletin highlights that different consensus designs make different trade-offs between security, scalability and decentralisation, which can increase fragmentation across liquidity, assets and infrastructure. *

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🟥 EU sanctions directly targeted crypto services.

The 21st Russia sanctions package, adopted on 23 July 2026, introduces the possibility of third-country bans for crypto asset services and transaction bans on certain third-country crypto platforms. This shows that crypto infrastructure is becoming a more direct target in sanctions evasion risk. * *

🟧 The CLARITY Act reached another critical point in the US, but the timeline tightened.

The Senate is struggling to bring the crypto market structure package to a vote before the summer recess. The debate continues around stablecoin rewards, ethics provisions, consumer protection, and the SEC-CFTC jurisdictional perimeter. * *

🟧 The UK crypto regime moved into the operational preparation phase.

The FCA reminds firms that it has published final rules and guidance for the new crypto asset regime, that the authorisation gateway will open on 30 September 2026, and that the new regime will begin on 25 October 2027. * *

🟧 The federal-state jurisdiction fight over prediction markets is intensifying in the United States.

A federal court temporarily blocked enforcement of Minnesota’s prediction market ban. The CFTC, Kalshi and Polymarket argue that these products should be treated under the federal commodities derivatives regime, while states are trying to limit them under gambling regulations. This debate shows that jurisdictional boundaries between regulators remain unresolved for tokenised markets and event contract products. *

🟩 The Bank of England made its tokenised money agenda more explicit.

Andrew Bailey’s July speech made tokenised deposits, the Digital Securities Sandbox and the digital gilt link more visible in the UK. The Bank of England connects programmable payments with real economy problems through tokenised money design and synchronisation infrastructure. This suggests tokenisation is increasingly being viewed not only as a capital markets efficiency tool, but also as a way to address payment delays, fraud and trade processes. *

Post-MiCA EU market

New authorisations, non-compliant entities and which countries become more active under ESMA’s custody supervision should be tracked through ESMA’s Interim MiCA Register. * *

Türkiye KVMKS data

MKK’s daily KVMKS data may become Türkiye’s first regular public dataset for the crypto market in terms of investor count, investors with balances, total market value and CASP memberships. *

CLARITY Act

As the US Senate heads into summer recess, the September timeline for the CLARITY Act, the SEC-CFTC jurisdictional split and the stablecoin reward debate should be monitored. *

UK authorisation gateway

Ahead of the FCA authorisation gateway opening on 30 September 2026, platforms serving the UK should review their application strategy and cross-border service models. *

EU sanctions and crypto

How the EU uses its third-country crypto services ban tool will be critical for crypto platforms’ sanctions risk management. *

node101

Staying ahead in blockchain requires continuous learning. Blockchain Regulation Journal provides a concise monthly flow of the key information you need to know.

Over the past months, we have implemented a large-scale tracker and analyzer for the blockchain developer ecosystem. Until now, we have found over 950 thousand repositories and 450 thousand developers across 26 ecosystems.

In this article, we will briefly summarize what the data is telling us, and answer some of the most common questions such as where developer activity is concentrated or what is the most common trend in developer activities.

If you do not agree with our observations, you can find our live dashboard here:

blockchaindeveloper.report.

We also describe our methodology and limitations of the data, in case you want to learn more about the details.

BDR is currently in beta, and its coverage continues to expand. More than 100,000 repositories were added during the past two weeks alone. As new repositories are discovered, some historical figures may also be updated. Follow us for the latest dataset releases and analyses.

How many developers are in the ecosystem?

Interestingly enough, when we observe the number of active developers (i.e. committed at least once in the past 28 days) in the ecosystem, we see that the overall number did not decrease significantly over the past years.

Instead, since late 2024 numbers have remained around a stable baseline (about 25k) with some oscillations.

This graph is very encouraging since the common belief in the blockchain ecosystem is that the number of developers is mostly correlated to the token price. It is certain that the token price is not going very well over the past months, but the number of developers is remaining broadly stable. Can this mean the ecosystem is finally gaining resilience?

Other trends for active developers

Currently, the retention rate of developers is ~ 40%, meaning that 4 out of 10 of each developer remain active from the last month. This interpretation is also reflected in the churn shown below.

This figure suggests that even though the total number is not changing too much, fluctuations are quite high, and they are following a very similar trend for the past 2 years. This is a negative sign for maturity, showing that almost half of developers are working on blockchain for only around a month.

What does growth look like?

Another important point is the distribution of new developers joining the ecosystem.

Here, a similar observation follows the previous. Even though the fluctuations are much stronger, new developer growth also seems to be keeping inside some boundaries, which is again very important for the maturity of the ecosystem.

Of course, a natural question that follows is which ecosystems are attracting the most new developers.

Probably not many would be surprised to see EVM in the first place, but it is peculiar enough that the two close followers, Stellar and Polkadot, are actually approaching EVM’s new-developer count. This is particularly interesting when you realize that Stellar is actually a substantially different stack than EVM and it is actually using a different programming language.

How about the distribution across ecosystems?

Coming back to the active developers of blockchain, it is important to answer how many developers are actually shared across ecosystems.

This final graph shows that it is not only the number of developers stabilizing, but also developers that are working on multiple ecosystems at the same time. This may not be as positive as before, since ideally we would like to see old developers working on new technologies as the time passes. Nevertheless, it is still reassuring to see that the number is at least not decreasing.

What’s next?

In this brief text, we have shared a few figures for the global data.

If you are looking to better understand individual ecosystems or developers, you can find more analyses and ecosystem specific dashboards in blockchaindeveloper.report.

Data is important; to analyze, understand and improve.

Now that we have analyzed and understood, what’s next is to improve.

In my previous article, I discussed how regulations must be built on the principle of the “Separation of Powers” for local blockchain ecosystems to grow in a healthy and sustainable manner. Whether in the US, UK, EU, Turkiye, or Hong Kong, regulatory bodies need to position local exchanges, applications, infrastructure providers, and users to mutually feed into one another, rather than issuing unilateral, prohibitive mandates.

Therefore, for a healthy blockchain ecosystem, we must first correctly define our internal balances and “boundaries of power.” This means establishing a cluster where the powers of exchanges, custodians, and data providers are strictly separated, infrastructure is supported by domestic servers, and data sovereignty is secured through local blockchain infrastructure providers.

While properly defining this cluster is a valuable step, it alone will not suffice to breed digital asset companies capable of competing globally. A local separation of powers might prevent systemic collapses,like the FTX disaster,but it won’t inherently drive actors to enrich one another. For instance, while Turkiye boasts giants like Paribu and Midas capable of locking horns with global competitors, many local companies operating in niches like custody, infrastructure provisioning, zero-knowledge (ZK) tech, wallets, and neobanking clearly need support to reach international maturity. Global giants in these verticals, backed by foreign capital and commanding worldwide customer bases, operate within the Turkish ecosystem, absorbing the very nutrients local counterparts need to take root, thereby creating near indestructible monopolies.

This brings us to the core theme of today’s article How do we protect local actors against the destructive competition of foreign tech giants? More importantly, how can we transform these global behemoths into a “lever” that feeds our own domestic ecosystem?

The Small of Today, The Giant of Tomorrow

Take today’s giant exchanges as an example. What does it take to compete with platforms like Binance, OKX, Paribu, or Midas and grab a slice of their customer pie? You would need tens of millions of dollars in investment, massive teams, licenses, aggressive marketing campaigns, and countless other resources. It’s akin to trying to challenge Google in search, battle Apple and Samsung in mobile hardware, or take on Anthropic and OpenAI in the LLM landscape,an incredibly steep uphill battle.

On the flip side, the blockchain and cryptocurrency ecosystem is not composed entirely of exchanges and custodians. There are numerous emerging subsectors shaped around the needs of these heavyweights. RPC and data providers feed them blockchain data, validators supply white-label staking solutions, zero-knowledge products deliver privacy fixes, and various wallets build the user interfaces. Because their total trading or operational volumes are exponentially smaller, these sub-sectors often appear trivial compared to the main pillars. For example, while the global custody sector generates a staggering $500 billion in value annually, the RPC and blockchain data provisioning sector, a vital component of those very services, is currently hovering around just $4 billion a year.

To build a custodian today that can rival global incumbents, you must fight tooth and nail for a slice of that $500 billion pie against deeply entrenched solutions. Conversely, if you are a company investing in zero-knowledge technologies, a sector that has only recently hit a $20 billion market size, you can ride the wave as the industry multiplies tenfold over the next five years. By scaling alongside the market, the products you develop can turn you into a global player. This is exactly why we must nurture the “small of today, giant of tomorrow” companies within our ecosystem.

But how do we pull this off? Even in niche sub-sectors, global monopolies still exist. For instance, half of the total volume in validation is controlled by just a handful of massive firms, a pattern mirrored across other verticals. Major custody and exchange platforms in Turkiye currently funnel tens of millions of dollars annually to these foreign-sourced companies to access these critical tools. However, redirecting these capital flows to local alternatives would keep foreign currency within the country while paving the way for a self-sustaining blockchain cluster right here in Turkiye. By growing through domestic investments in their own products and services, today’s small enterprises will naturally become more competitive in international markets, securing a defining stake in the future of their industries as they expand.

Therefore, we must erect time-bound protective barriers for ancillary sectors whose global market sizes currently fluctuate between $50 million and $250 million. These are early-stage financial niches that will ultimately shape tomorrow’s technological architecture: Zero-Knowledge (ZK) cryptography, domestic validator networks, RPC providers, and privacy-centric cybersecurity products. Think of this as a smart, limited import substitution regime. We need to shield the Turkish market from the crushing competition of global monopolies so that our local data providers, validators, and encryption startups have the breathing room to flourish on home soil.

The Turkish Case Study ‘Today’s Global Giants Must Anchor in Turkiye’

Engaging in a direct war of resources and capital against global giants in trillion-dollar markets, like Layer 1/Layer 2 (L1/L2) blockchain networks, global payment rails, or Large Language Models (LLMs), is simply irrational. Imagine trying to impose import substitution on LLM companies that already boast millions of users, active enterprise subscriptions, and the protective backing of the US government and Senate. Proposing this would be short-sighted Turkiye would be starved of both the economic gains and the sheer efficiency these technologies unlock.

With these giants backed by trillions of dollars, how realistic is it for Turkiye to build an LLM from scratch to rival them with just a few billion dollars? While a slim chance exists, it only holds true if those LLMs solve hyper-specific problems current models cannot touch. Instead, Turkiye’s strategy here should be built around attracting international finance and global venture capital (VC) into the country.

However, letting these giants enter the Turkish market and access our local user base should not operate like an open-ended capitulation. The presence and licensing processes of global players in Turkiye must be explicitly tied to adding value to the local ecosystem, specifically through a Turkiye-In Country Value (TICV) model.

When the Big Fish Feeds the Small Fish

The magical instrument that bridges these two layers, the need for global financing and the protection of local niche sectors, is this very TICV model.

Let me map out the equation, and since this is a draft, I look forward to your feedback. Any global blockchain giant or AI powerhouse wishing to operate and secure a license in the Turkish market must maintain a high TICV score; otherwise, they face exclusion from state tenders. To boost this score, they would be required to open data centers in Turkiye, employ local engineers, and, crucially, procure products and services from our local ZK, RPC, and validator startups, the very niches we shielded under our protective umbrella.

Consequently, global capital transforms into a direct lever that fuels the growth and cash flow of our budding local startups.

Creating Tomorrow’s Global Players Today

Today, niches like ZK, validator networks, or decentralized cybersecurity are constrained within a global $50 million to $250 million band. However, over the next decade, they will evolve into massive, multi-billion-dollar core industries.

Because Turkiye will have implemented the right protective shields and TICV levers ahead of time, it won’t be a passive market playing catch-up when these niches balloon into giants. On the contrary, by regulating and safeguarding local actors today, the country will have rooted its own future billion-dollar global tech giants. Just as the seeds planted by a forced import-substitution regime due to past defense industry embargoes have blossomed into game-changing global enterprises today, it is entirely in our hands to create tomorrow’s champions in blockchain and cutting-edge tech from this moment forward.

In the previous articles, I explored how distributed systems evolved into blockchains [1], why and how blockchain architectures eventually became modular [2], how the access layer quietly shapes decentralization [3, 4], and how each generation of protocols inherits the constraints of the one before it [5]. Though with these discussions we approached the ecosystem from different view points, they all revolved around a common idea: blockchain architecture is about managing constraints rather than eliminating them.

One constraint, however, remained in the background and we only slightly mentioned it: time.

We usually encounter it as latency, block times, confirmation or settlement delays, and RPC response times. These metrics are often treated as isolated performance indicators, optimized independently and used to compare one blockchain with another. Yet they all describe the same underlying phenomenon. So, before asking why one blockchain is faster than another, we should first ask a more fundamental question.

Can a decentralized system ever have a single present, or is latency simply the price of many participants trying to share the same reality?

One Network, Many Presents

Raphael’s The School of Athens portrays dozens of philosophers gathered in the same hall, discussing the same ideas, yet no two figures occupy the same position. Some argue, others observe, a few teach, while several remain immersed in their own thoughts. Knowledge emerges because independent viewpoints gradually converge through dialogue.

A blockchain network behaves in a remarkably similar way: When we describe a blockchain, we usually imagine it as a single system progressing through time. A transaction is submitted, a block is produced, and everyone observes the same new state. But no one really experiences that moment simultaneously.

• A validator proposing a block observes the transaction before another validator on the other side of the world.

• An RPC node may already serve the updated state while an indexer is still reconstructing it.

• A wallet continues displaying an old balance even though the transaction has already been finalized.

• A blockchain explorer may report confirmation seconds before another explorer catches up.

Everyone interacting with the blockchain temporarily lives in a different version of the network’s present. The further information (the new state) has to travel across the nodes, the longer these differences mentioned above persist. Unlike The School of Athens, where independent perspectives eventually converge through discussion, blockchain networks converge through protocol.

Latency is the observable consequence of this reality. At that point, the challenge is not just making systems faster but understanding that decentralized systems cannot make every participant share the same present at the same moment.

Consensus Does Not Synchronize the Present

You may ask that consensus protocols are mechanisms that allow validators to agree on the state of the blockchain, so why is the present not synchronized? While you are technically correct, this description does not necessarily answer what consensus protocols accomplish.

Consensus protocols were never constructed to synchronize the present, they have been here for synchronizing the past.

When validators exchange votes, propagate blocks, or verify signatures, they are not attempting to make every participant observe events simultaneously. Such a goal is physically impossible in a globally distributed network. Instead, consensus establishes a common history after enough information has propagated throughout the system. This distinction matters because the network is always in motion while consensus takes place. Messages arrive at different times, packets follow different routes, and validators observe slightly different versions of the world for brief periods. Consensus gradually resolves these differences into a shared history that everyone can later agree upon. And this is why confirmation / settlement periods are introduced in blockchains.

In other words, a blockchain does not maintain one universal “now.” It continuously transforms many local observations into one globally accepted “then.”

The Present Moves Further Away

Even after consensus has been reached, a finalized block does not immediately become visible to users. RPC nodes update their internal state, databases process new information, indexers reconstruct events, caches refresh their contents, and applications request the latest data before a wallet finally displays an updated balance.

This difference explains why users often perceive blockchain performance very differently from protocol metrics. A network may produce blocks in milliseconds and finalize transactions fast and efficiently while applications still appear slow because the surrounding infrastructure has not yet reflected the new state.

Yet, we should not perceive it as a failure of decentralization. Every additional layer that separates users from consensus introduces another interval during which information continues to travel: this is an architectural reality. The protocol reaches certainty first, the user reaches it later.

How Do We Encounter It in Real Life?

This distinction becomes much more visible in financial systems.
Imagine an automated trading strategy reacting to a sudden price movement. The algorithm decides to buy because the latest market data indicates an opportunity. Yet, both the price update and the order itself need time to travel through the network before they become part of the system’s shared state. And by the time the transaction is executed and the rest of the network observes it, the conditions that motivated the trade may already have changed. The algorithm was reacting to the latest present it could observe.

Traditional software too experiences delay, but the architecture is fundamentally different:

• A centralized trading platform maintains a single authoritative database, meaning there is only one version of reality. Participants can reduce latency simply by moving closer to that authority.

• High-frequency trading firms, for example, colocate their servers inside exchange data centers because every microsecond between their systems and the exchange’s matching engine matters. The closer they are to the source of truth, the earlier they observe new information and the sooner they can react.

Blockchains deliberately reject this model. There is no single server to stand next to and no database that instantly defines reality for everyone else. Every validator must first receive the same information before agreement can emerge, and every participant waits for that agreement to propagate throughout the network. Latency therefore becomes a shared architectural constraint rather than a private competitive advantage.

Can We Eliminate Time?

Modern blockchain architecture dedicates enormous effort to reducing latency. These designs are usually referred to as real-time blockchains and some of the ways they achieve it are as follows:

• Consensus algorithms reduce communication rounds.

• Peer-to-peer protocols optimize block propagation.

• Validators improve network connectivity.

• RPC providers deploy regional infrastructure closer to applications.

• Layer 2 systems execute transactions before settlement.

• Indexers continuously optimize how quickly the new state becomes queryable.

Each of these improvements shortens a particular delay but none is enough to remove time itself. Even the fastest blockchain cannot escape the physical reality that information cannot exist everywhere simultaneously. Messages still traverse continents and requests travel through networks, participants still receive events in different orders. Every optimization reduces waiting somewhere while preserving the underlying relationship between distance and time. Even though through these improvements the constraint becomes smaller, it remains still.

Conclusion

Distributed systems taught us that information cannot exist everywhere at once. Blockchains inherited this limitation while removing the trusted operator capable of resolving disagreement externally. As a result, decentralized systems exchange a single universal present for something more subtle: many local presents that gradually converge into one shared history. Latency is basically the name we give to that convergence time.

Perhaps this is why blockchain architecture has never truly been about making systems faster. Every generation of protocols has attempted to shorten the interval between action and certainty, yet none has succeeded in eliminating it entirely. Time remains embedded within every consensus round, every propagated block, every RPC request, and every application waiting for the latest state.

The blockchain, then, is not a machine that moves through one shared moment but a network continuously negotiating its own present.

TL;DR

🟥 Akbank, Garanti BBVA, and Yapı Kredi were granted crypto asset custody authorization

The Capital Markets Board (CMB) favorably responded to the authorization requests of Akbank, Türkiye Garanti Bankası, and Yapı ve Kredi Bankası to operate as crypto asset custody institutions. This decision indicates that Turkey’s crypto asset market has entered a new phase through institutional custody infrastructure. The involvement of the three major banks in custody suggests that topics such as secure custody, operational standards, and regulated financial infrastructure will become more central in the VASP (Crypto Asset Service Provider) ecosystem. * *

🟥 The market is being reshaped as the MiCA transition period approaches its end in Europe. ESMA’s interim MiCA register makes authorized crypto asset service providers and related registrations visible at a single reference point across the EU. Binance announced that it withdrew its MiCA application in Greece and will seek authorization in another EU country. *

🟥 The CMB’s active VASP list has decreased to 56 institutions. Bithero and Minka appear to have moved to the liquidation process category. The list is still temporary, and the CMB specifically states that inclusion on the list does not mean authorization. *

🟥 Stablecoins are once again at the center of regulatory attention. A new BIS study suggests that reading stablecoin transfers as simple payments can be misleading, as the majority of flows are embedded in more complex financial transactions like trading, lending, arbitrage, liquidity provision, and settlement. *

🟧 Institutional players are prominent in the MiCA license race. Ripple announced that it received preliminary approval for a CASP license under MiCA from Luxembourg’s CSSF. Following full approval, the company aims to offer regulated crypto asset and payment services to banks, fintechs, and corporations across the European Economic Area. *

🟧 Malta announced a regulation discussion for DeFi. The MFSA published a discussion paper on decentralized finance. The text brings up topics such as the scope of MiCA, financial crime risks, software-based organizational models, the guardian agent approach, and the management of risks arising from smart contracts. *

🟧 Citi Institute’s Tokenization 2030 report estimates the 2030 base scenario for tokenized assets at $5.5 trillion, with an optimistic scenario at $8 trillion. According to the report, early adoption is expected to concentrate in public debt instruments, liquid collateral, and publicly traded securities. *

🟧 Perpetual futures are moving to regulated markets in the US. The path opened by the CFTC for Coinbase and Kalshi suggests that a portion of offshore volumes could be drawn into supervised structures in the US. This also initiates a new debate regarding traditional exchanges, leverage limits, and investor protection. *

🟧 Banks and regulated financial institutions are becoming more visibly involved in crypto services in Europe and Spain. BBVA’s launch of Bitcoin and Ether trading and custody services in Spain indicates that banks are approaching crypto asset services with a more open strategy following MiCA. *

Money runs on a kind of shared belief. A banknote is paper that a whole country has agreed to treat as valuable, and that agreement holds only as long as people expect it to keep holding. Most of the time this is invisible. You get paid and you spend without thinking about why the paper works. But in some places that belief has started to thin, and people have quietly moved the part of their money they actually trust somewhere else. What follows is about what happens when a currency keeps its legal status and loses its real job, and about the technology that made walking away from it almost effortless.

Money runs on trust

Almost all sovereign nations and territories issue their own currency: the Turkish Lira in Türkiye, the Peso in Argentina (each the local currency of its country). Although the banknote itself holds no intrinsic value, it is accepted within a territory because it can obtain goods or services, and it can do so because it is issued by the legitimate institutions of a state that people trust.

In financial markets, trust often matters as much as fundamentals. In addition to data, people react to their confidence in the institutions interpreting and acting on that data. But what happens if people start to lose that trust? Maybe they begin converting the national currency into another one in their heads, all the time. Maybe the rent on your home gets negotiated in a foreign currency. Maybe your salary is no longer even in the same currency your father was paid in 20 years ago.

In economics, this phenomenon is called “dollarization.” Dollarization happens when residents of a country use a foreign currency (mostly the US dollar) instead of, or alongside, the domestic currency for saving, pricing, transactions, or even as legal tender. Dollarization is, at its core, a response to mistrust. The legal currency and the trusted currency aren’t always the same, and the gap between them is where monetary power actually sits.

The old friction, and the new rails

The history of dollarization has two sides: before crypto and after.

The old version did its job really well, mostly by being a headache to anyone who wanted to switch currencies. You had to pay more than the market rate, maybe walk into an exchange office, face capital controls, or give up on earning interest unless you held a foreign account.

Then stablecoins came. A stablecoin is a crypto token designed to hold a fixed value, almost always pegged one to one to the US dollar, so one unit is meant to be worth a dollar at any time.

None of the frictions above exist with a stablecoin. You don’t have to state a reason for your transfer, and no one can delay it without explanation. You can save in a more trusted unit, and you can shield your savings from the local currency’s collapse. Stablecoins basically collapsed all that friction into a phone.

Case study: Türkiye

It was obvious that this new system would accelerate dollarization, and that is exactly what happened. We have a case study here: Türkiye.

Türkiye experienced one of the worst inflation rates in its history after the pandemic. But what hurt the Turkish economy more than the inflation itself was the gap in the data between the official numbers and third-party numbers.

Over the five years following the pandemic, official statistics suggest that consumer prices increased by roughly sevenfold, while the estimates of ENAG (an independent Turkish inflation research group whose figures run well above the official ones) point to an increase of approximately twenty-eightfold. The implied price level under the ENAG methodology is therefore about four times higher than that implied by the official figures.

The result: between April 2023 and March 2024, stablecoin purchases in Turkey were equivalent to 4.3% of GDP, with total purchases of $38 billion against a GDP around $907 billion. That is the highest share of stablecoin purchases relative to GDP of any economy in the world. For comparison, the second and third places go to Thailand at 1.3% and Georgia at 0.7%.

So in short, since 2018 the Lira has lost over 80% of its value against the dollar, and inflation was near 65% as of 2024. The behavioral response predates crypto: Turkish residents hold over $100 billion in foreign currency deposits, having long shifted savings into gold and hard currency.

As we said earlier, stablecoins are just the newest, lowest-friction version of an exit people were already taking.

Living in two currencies

Regardless of the data above, people didn’t abandon the Lira in their daily lives. Instead, they split their money into two: local currency for what the law forces (salary, taxes, rent on paper, daily spending), and digital dollars for what they actually trust (savings, anything cross-border). The currency survives legally while dying as a store of value. This is the move that turns “Turkey bought stablecoins” into “here’s what dollarization 2.0 actually looks like.”

A state can issue the money and force people to pay their taxes in Lira, but it cannot force them to believe in it. Monetary sovereignty is more than the legal right to issue a currency: it is also the effective capacity to govern economic behavior through that currency, which requires that the public actually wants to hold it.

How it drains an economy

But how does this hurt the national economy?

To understand this, we have to understand how deposits work. When you put money in a bank account, the bank holds only a fraction of it as reserves and lends against the rest, which is how credit gets created in the economy. As savings leave for stablecoins, the bank has fewer deposits to lend against, and credit tightens. On top of that, when your savings sit in the domestic banking system, they tend to circulate back into the local economy: banks park part of them in government bonds, reserves stay with the central bank, and the interest you earn is taxed at home.

But if you hold stablecoins, Tether or Circle (the private firms that issue the biggest dollar stablecoins) holds the reserves and keeps the Treasury interest. You end up financing US bonds and a private issuer instead of your own country’s banks.

Case study: Argentina

This is where our second case study comes in: Argentina. The Peso lost roughly 95% of its value against the dollar over the five years through 2025. Until April 2025, individuals were allowed to buy only up to USD 200 per month through official banking channels, and even that access was limited in practice by a complex web of restrictions.

So a parallel market formed: the “blue dollar,” historically a cash street trade requiring physical pesos and personal trust. The crypto era’s contribution: Argentina’s P2P (peer-to-peer) USDT market effectively replaced the informal blue-dollar exchange that previously required physical cash and personal trust networks.

Why better numbers won’t bring people back

So where does this leave a country whose people have quietly moved their savings onto someone else’s rails? The temptation is to expect a reversal. Argentina lifted the cepo (its web of currency controls) in April 2025; for the first time in six years people could buy dollars freely, and monthly inflation fell from triple digits a year to low single digits. Turkey’s inflation, too, came off its worst. If dollarization were only a response to bad numbers, better numbers should pull people back.

They probably won’t. Trust is slower to rebuild than a currency is to stabilize. A decade of watching savings evaporate teaches a lesson that one good year does not unteach, and the lesson has already hardened into infrastructure: dollar bank accounts, dollar wallets, dollar QR payments. When a state’s answer to dollarization is to build rails for the dollar, it ratifies it.

The new dollarization

This is the new dollarization. There is no collapse, no formal surrender, and no central bank disappears. People earn in the local currency, calculate in dollars, and save in stablecoins, treating the national money as something to pass through rather than hold. The banknote keeps its legal status and loses its real job. A currency does not have to die to lose. It only has to become the money people are paid in, and not the money they believe in.

It does not have to stay this way

None of this is permanent, even if it looks that way from inside it. Dollarization has receded before. Peru spent the 2000s and 2010s quietly winning its savers back into the sol, and Israel did something similar a decade earlier. Both took the same unglamorous route: many years of low inflation and a slow, deliberate effort to build deep local-currency savings and bond markets, run by a central bank that stopped surprising people.

What is worth noticing is what did not work. The governments that tried to force the dollar out by decree, by converting dollar deposits or banning foreign-currency accounts, mostly made things worse. Bolivia and Mexico both tried versions of this in the 1980s and pushed money offshore and out of the formal banking system instead. You cannot legislate belief back into a currency any more than you can legislate it out. What brought people back, where anything did, was time spent being credible.

So the path home exists, but it runs through years rather than headlines, and it asks a government for the thing that is hardest to fake: a long stretch of being boring and trustworthy at the same time. For now the dollar rails are laid, and a generation has learned to earn in one money and save in another. Whether the next one unlearns it depends on whether the local currency can spend long enough being unremarkable.