Zoya_0

The first thing I notice when I watch a zero-knowledge (ZK)–driven chain over a longer time horizon isn’t user growth or headline transaction countsit’s how uneven the computation footprint is. Activity doesn’t scale linearly with usage. Instead, it comes in bursts: proof generation spikes, verification settles into a steady rhythm, and liquidity seems to orbit around those cycles rather than around simple demand for blockspace.

That asymmetry tells you early that this isn’t a typical execution environment. It’s a verification market.

When I track wallets interacting with these systems, I see two distinct classes of participants forming pretty quickly. On one side, you have infrastructure-aligned actorsprovers, node operators, and teams running specialized hardware. Their behavior is slow, deliberate, and capital-intensive. They’re not rotating in and out; they’re positioning for throughput dominance. On the other side, you get more familiar crypto-native flows: capital that treats the network like a yield surface, moving in when incentives expand and fading when they compress.

What’s interesting is how little overlap there is between those two groups.

Speculators and yield farmers tend to cluster around moments when proof demand increasesairdrops, ecosystem campaigns, or onboarding waves. They’re not interacting with ZK as a primitive; they’re interacting with the economic layer built on top of it. Meanwhile, the infrastructure side is playing a completely different game. They’re optimizing latency, proof costs, and hardware efficiency, which has very little to do with token price in the short term but everything to do with long-term positioning.

That split reveals something deeper about the structure: the network’s core value isn’t in execution, it’s in compression of trust. And that changes how capital behaves.

In most chains, execution drives fees, fees drive validator rewards, and rewards anchor token demand. Here, verification is cheap by design, while proof generation is expensive and offloaded. So the economic gravity shifts away from the chain itself and toward whoever controls the proving layer.

This is where incentive design becomes critical.

If you look closely at how tokens are distributed in these ecosystems, a large portion is often directed toward subsidizing proof generation or incentivizing participation in early-stage infrastructure. That creates a temporary bridge between speculative capital and long-term infrastructure buildout. But it also introduces fragility.

Liquidity pacing, in particular, becomes non-trivial. When emissions are high, you see rapid capital inflows chasing yieldTVL spikes, transaction counts rise, and the network appears to be gaining traction. But that liquidity is rarely durable. It’s there for the spread, not for the system. more interesting signal is whether infrastructure commitments continue after emissions normalize.Running a prover isn’t like staking tokens in a typical PoS system. It requires real-world resourceshardware, energy, optimization expertise. That creates a higher barrier to exit, which in theory should lead to stickier capital. But only if the economics justify it. If proof generation rewards compress too quickly, even these participants start to scale back, and you can see it in subtle ways: longer proof times, reduced throughput, or increased reliance on centralized operators.From a market microstructure perspective, this creates very distinct liquidity windows.I’ve noticed that activity tends to cluster around discrete events: major integrations, upgrades to proving systems, or changes in cost efficiency. These aren’t just narrative catalyststhey directly impact the cost structure of the network. When proving becomes cheaper or faster, you often see a temporary surge in activity as new use cases become viable. Liquidity flows in, but it’s opportunistic. Traders position around these shifts, knowing that the window may not stay open for long.

There’s also a feedback loop between verification costs and application design. When verification is cheap, developers push more computation off-chain, increasing reliance on proofs. That, in turn, increases demand for provers, which can tighten margins unless efficiency improves. It’s a constant balancing act between cost compression and demand expansion.Compared to earlier cycleswhere blockspace itself was the scarce commoditythis feels like a different regime. Scarcity is no longer about execution capacity; it’s about computational efficiency and who can deliver it at scale.The long-term question is whether this model creates a durable economic layer or just a transitional phase.

If incentives remain heavily emission-driven, then a lot of the current activity is likely to fade as those emissions taper off. We’ve seen this pattern beforecapital that looks committed until the yield disappears. But if the network can reach a point where proof generation is both efficient and economically sustainable without heavy subsidies, then you start to get something more interesting: a self-reinforcing system where infrastructure investment leads to lower costs, which drives more usage, which in turn justifies further investment.
That’s the flywheel everyone is implicitly betting on.The risk is that the system never quite escapes its bootstrap phase. If proving remains too expensive, or if rewards don’t adequately compensate for the capital required, then participation consolidates. You end up with a small set of dominant operators, and the decentralization narrative starts to weakennot because the protocol failed, but because the economics pushed it in that direction.What I think the market is underestimating right now is how sensitive this entire structure is to cost curves.

People tend to focus on adoption metricsusers, transactions, ecosystem growthbut in ZK systems, those are downstream effects. The real driver is whether the cost of generating proofs can consistently decline faster than demand increases. If that happens, the system scales naturally. If it doesn’t, you get periodic bursts of activity followed by stagnation.

From where I’m sitting, watching flows and behavior over time, this doesn’t look like a typical L1 or L2 growth story. It looks more like an emerging compute market, where the winners aren’t necessarily the ones with the most users today, but the ones who can most efficiently turn computation into verifiable outcomes.And that’s a much harder game to price.

#night @MidnightNetwork $NIGHT

The first thing that stands out when I spend time watching a ZK-focused chain isn’t the headline metricsit’s the rhythm of activity. Unlike typical L1s or even optimistic rollups, the flow here feels punctuated. You don’t get a smooth distribution of transactions; instead, you see bursts tied to proof generation cycles, batch submissions, and settlement windows. It’s not just usageit’s computation crystallizing into discrete moments.

Wallet behavior reflects that. There’s a clear split between participants. On one side, you have infrastructure-heavy actorsprovers, sequencers, and entities clearly optimized for compute throughput. Their wallets aren’t just moving tokens; they’re coordinating resources. On the other side, you’ve got capital that behaves more like opportunistic liquidityrotating in around incentive programs, bridging campaigns, or ecosystem launches, then thinning out once the immediate yield compresses.

What’s interesting is how little overlap there is between those two groups.The infra side tends to be sticky. Once someone invests in proving hardware or integrates deeply with the stack, they don’t churn easily. The capex alone creates inertia. But the liquidity side behaves almost identically to what I’ve seen across multiple cyclesfast-moving, yield-sensitive, and largely indifferent to the underlying tech beyond its ability to generate return.

That divergence tells you a lot about the economic structure. ZK systems, by design, separate verification from execution. That sounds like a technical detail, but in practice, it reshapes how value accrues. Execution can be cheap and scalable, but verificationespecially when proofs are complexintroduces a cost center that has to be compensated somewhere.

So the question becomes: who’s paying for verification?In most of the ZK ecosystems I’ve tracked, the answer is still evolving. Early on, it’s heavily subsidized. Token emissions, grants, and ecosystem funds absorb a large part of that cost. You can see it directly in on-chain flowsrewards clustering around entities that contribute proofs or support network throughput.L subsidies create a very specific kind of liquidity profile.Capital comes in aggressively when emissions are high, but it doesn’t commit. You see TVL spikes that coincide with incentive announcements, followed by gradual decay as yields normalize. It’s the same pattern we saw with early liquidity mining on other chains, but with an added layer: here, the underlying cost structure (proof generation) doesn’t disappear when incentives do.

That’s where things get more nuanced.

If you track liquidity pacing over time, you’ll notice that ZK ecosystems tend to develop predictable windows of activity. Bridging spikes, staking inflows, governance participationthey often cluster around known events. Proof system upgrades, new circuit deployments, or even changes in fee models can trigger temporary surges in both usage and speculation.

Traders pick up on that quickly.

I’ve seen short-term capital position ahead of these windows, not because they care about the protocol long-term, but because the microstructure becomes somewhat gameable. When you know that a certain upgrade will reduce proving costs or increase throughput, you can anticipate shifts in fee dynamics, liquidity depth, and even token velocity.

But the deeper question is whether any of this translates into durable economic activity.

Right now, I’d argue the answer is mixed.

On one hand, ZK systems introduce something fundamentally different: verifiable computation as a service. That’s not just another execution layerit’s a new primitive. If demand for privacy-preserving or off-chain computation grows, the need for efficient proof generation could anchor real, persistent value.

On the other hand, the current market behavior doesn’t fully reflect that yet.

A large portion of activity is still incentive-driven. When emissions compress—and they always dothe system gets tested. Do provers continue operating at scale when rewards drop? Does liquidity remain when yields fall below market averages? Or does the ecosystem hollow out, leaving only the most committed participants?

You can already see early signals.

In periods where incentives taper off, transaction counts don’t collapse entirelybut they do normalize quickly. The speculative layer exits first. What remains is a thinner, more purpose-driven set of users: builders, infra operators, and a smaller base of organic demand.

That’s actually a healthy sign, but it comes with a tradeoff. Growth becomes slower, less explosive. The narrative loses some of its momentum because it’s no longer fueled by easy capital.

From a market perspective, this creates an interesting asymmetry.

Most participants are still valuing these networks based on surface-level metrics—TVL, transaction count, ecosystem size. But those metrics are heavily influenced by short-term incentives. The harder thing to measureand the thing I think the market is underestimatingis the gradual buildout of proving infrastructure and the cost curves associated with it.

If proving becomes significantly cheaper over time, the entire economic model shifts. Verification costs drop, margins improve, and the need for heavy subsidies diminishes. At that point, capital doesn’t need to be bribed to stayit stays because the system is economically efficient.

But we’re not fully there yet.

Right now, ZK ecosystems sit in an in-between state. They’re past the purely experimental phase, but not yet at the point where their economics are entirely self-sustaining. That’s why you see this constant tension between sticky infrastructure and mercenary liquidity.

What I keep coming back to is this: the market is very good at pricing narratives, but much slower at pricing cost structures.

And in ZK systems, cost structure is everything.

If the trajectory of proving efficiency continues to improve, then what looks today like a subsidy-dependent ecosystem could quietly transition into a durable economic layer. Not because of hype or user growth alone, but because the underlying economics finally make sense without external support.

That’s the part I think most people are still overlooking.

#night @MidnightNetwork $NIGHT