A huge thanks to Amin, Cooper, Hannes, Jacob, Michael, Norbert, Omer, and Teemu for sharing their feedback on the model and the article (this doesn’t mean they agree with the presented numbers!).
Zero-knowledge proofs are entering a period of rapid growth and widespread adoption. The core technology has been battle-tested, and we have begun to see the emergence of new services and more advanced use cases. These include outsourcing of proof computation from centralized servers, which opens the door to new revenue-generating opportunities for crypto infrastructure providers.
How significant could this revenue become? This article explores the proving ecosystem and estimates the market size in the coming years. But first, let’s start by revisiting the fundamentals.
ZK proofs are cryptographic tools that prove a computation's results are correct without revealing the underlying data or re-running the computation.
There are two main types of zk proofs:
A zk proof needs to be generated and verified. Typically, a prover contract sends the proof and the computation result to a verifier contract, which outputs a "yes" or "no" to confirm validity. While verification is easy and cheap, generating proofs is compute-intensive.
Proving is expensive because it needs significant computing power to 1) translate programs into polynomials and 2) run the programs expressed as polynomials, which requires performing complex mathematical operations.
This section overviews the current zk landscape, focusing on project types and their influence on proof generation demand.
Demand Side
Supply Side
For the privacy-focused rollup Aztec, only one proof per transaction will be generated in the browser, as depicted in the proving tree below. A similar dynamic is expected with other projects.
Monetization strategies will include fees and token incentives.
The primary revenue model will rely on charging base fees. These should cover the compute costs of proof generation. Prioritization of proving work will likely require paying optional priority fees.
The demand side and proving marketplaces will offer native token incentives to provers. These incentives are expected to be substantial and initially exceed the market size of proving fees.
To understand the proving market, we can draw analogies with the proof-of-stake (PoS) and proof-of-work (PoW) markets. Let’s examine how these comparisons hold up.
At the beginning of 2025, the PoS market is worth $16.3 billion, with the overall crypto market cap around $3.2 trillion. Assuming validators earn 5% of staking rewards, the staking market would represent approximately $815 million. This excludes priority fees and MEV rewards, which can be a significant part of validator revenues.
PoS characteristics have some similarities to zk-proving:
The PoW market can be roughly gauged using Bitcoin’s inflation rate, which is expected to be 0.84% in 2025. With a $2 trillion BTC market cap, this amounts to around $16.8 billion annually, excluding priority fees.
Both zk-proving and PoW rely on hardware, but they take different approaches. While PoW uses a “winner-takes-all” model, zk-proving creates a steady stream of proofs, resulting in more predictable earnings. This makes zk-proving less dependent on highly specialized hardware compared to Bitcoin mining.
The adoption of specialized hardware, like ASICs and FPGAs, for zk-proving will largely depend on the crypto market’s volume. Higher volumes are likely to encourage more investment in these technologies.
With these dynamics in mind, we can explore the revenue potential zk-proving represents.
Our analysis will be based on the Analyzing and Benchmarking ZK-Rollups paper, which benchmarks zkSync and Polygon zkEVM on various metrics, including proving time.
While the paper benchmarks zkSync Era and Polygon zkEVM, our analysis will focus on zkSync due to its more significant transaction volumes (230M per year vs. 5.5M for Polygon zkEVM). At higher transaction volumes, Polygon zkEVM has comparable costs to zkSync ($0.004 per transaction).
Approach
Results
A single Nvidia L4 GPU can prove a batch of ~4,000 transactions on zkSync in 9.5 hours. Given that zkSync submits a new batch to L1 every 10 minutes, around 57 NVIDIA L4 GPUs are required to keep up with this pace.
Proof Generation Cost
Knowing the compute time, we can calculate proving costs per batch, proof, and transaction:
The above calculations can be followed in detail in Proving Market Estimate(rows 1-29).
Proving costs depend on the efficiency of hardware and proof systems. The hardware costs can be optimized by, for example, using bare metal machines.
2024: Current Costs
2025: Optimizations Begin
2030: Proving costs fall to $0.001 per transaction across all rollups.
2024: Real Data
The number of transactions generated by rollups and other demand sources:
2025: Market Takes Off
The proving market begins to gain momentum. Estimated number of transactions: ~4.4B, including:
2030: zk-Proving at Scale
Proving will have reached widespread adoption. Estimated number of transactions: ~600B
We estimate the proving surplus based on previously estimated proving costs. This surplus is revenue from base and priority fees minus hardware costs. As the market matures, base fees and proving costs decrease, but priority fees will be a significant revenue driver.
Token incentives add further value boost, While it’s difficult to foresee the size of these investments, the estimate is based on the information collected from the projects.
2024: Early Market
2025: Expanding Demand
The total market is projected at $97M, including:
2030: Almost a Two-Billion-Dollar Market
The total zk-proving market opportunity is estimated at $1.34B.
A detailed analysis supporting the calculations is available in Proving Market Estimate(rows 32-57).
The estimates with so many variables and for such a long term will always have a margin of error. To support the main conclusion, we include a sensitivity analysis that presents other potential outcomes in 2025 and 2030 based on different transaction volumes and proving surplus. For the sake of simplicity, we left the proving costs intact at $0.059 and $0.001 per transaction in 2025 and 2030, respectively.
In 2025, the most pessimistic scenario estimates a total market value of just $12.5M, with less than a 10% proving surplus and 2B transactions. Conversely, the ultra-optimistic scenario imagines the market at $55M, based on a 50% surplus and 6B transactions.
In 2030, if things don’t go well, we could see a proving market of roughly $300M, from 10% proving surplus and 300B transactions. The best outcome assumes a $1.7B market based on a 90% surplus and 900B transactions.
Estimating so far into the future comes with inherent uncertainties. Below are potential error factors categorized into downside and upside scenarios:
Downside
Upside
After PoW and PoS, zk is the next-generation crypto technology that complements its predecessors. Comparing proving revenue opportunities with PoW or PoS is tricky because they serve different purposes. Still, for context:
We estimated that the zk-proving market could grow to $97M by 2025 and $1.34B by 2030. While these estimates are more of an educated guess, they’re meant to point out the trends and factors anyone interested in this space should monitor. These factors include:
Let’s revisit these forecasts a year from now.
A special thanks to Vishal from Multicoin and Max from Anza for their insights and discussions on this proposal.
Main Dashboard:
Token emission mechanisms play a critical role in the economic security and long-term sustainability of blockchain networks. In the case of Solana, the current fixed emission schedule operates independently of network dynamics, potentially leading to inefficiencies in staking participation, liquidity allocation, and overall network incentives. This proposal introduces a market-based emission mechanism that dynamically adjusts SOL issuance in response to fluctuations in staking participation.
The rationale for this adjustment is twofold: first, to enhance network security by ensuring that validator incentives remain sufficient under varying staking conditions, and second, to foster a more efficient allocation of capital within the Solana ecosystem, particularly in the DeFi sector. By linking token issuance to staking participation, the proposed model aims to mitigate the adverse effects of fixed inflation, such as excessive dilution of non-staking participants and unnecessary selling pressure on SOL.
SIMD228 introduces a dynamic adjustment mechanism based on staking participation. The model replaces the fixed emissions schedule with a function that responds to the fraction of the total SOL supply staked. The equation describing the new issuance rate is:
where r is the current emission curve, s is the fraction of total SOL supply staked, and
The issuance rate becomes more aggressive at around 0.5 of the total supply staked to encourage dynamic equilibrium around that point. Indeed, the multiplier of the current emission curve r shifts from ~0.70 at 0.4 to ~0.29 at 0.5. This means that, at a fraction of the total supply staked of 0.4, the new model mints ~70% of current inflation, at 0.5 just ~29% instead.
The corresponding APR from staking is represented below.
Despite the 0.5 shift seeming arbitrary, only data can adequately assess the real stake rate to trigger. We don’t have data we can use to understand the dynamic of SOL stakers due to issuance since the current issuance is stake-insensitive.
However, other ecosystems trigger inflation based on staking participation to have a fixed staking rate, balancing network usage and chain security. A prominent example of this is the Cosmos Hub. However, although Cosmos aims for 67% of the total supply staked, actual user behavior depends on network usage. For example, the Hub - meant to be a hub for security - has a current bonded amount of 57%. Also Ethereum has an issuance that depends on the amount of staked ETH, that at the time of writing is at 27.57%.
Some may argue that having an issuance rate that fluctuates can make returns on staked assets unpredictable. However, we believe that is just a matter of where the new equilibrium will be.
That said, it is hard to tell if 50% of the total supply staked is what Solana needs to grow with a healthy ecosystem. Thus, we believe 50% is no better or worse than any other sensible number to trigger “aggressiveness” since security is based on price. Indeed, a chain can be considered secure if the cost to attack it (Ca) is greater than the profit (P)
Currently, Solana's inflation schedule follows an exponential decay model, where the inflation rate decreases annually by a fixed disinflation rate (15%) until it reaches the long-term target of 1.5%. This model was adopted in February 2021, with the inflation rate reaching 4.6% as of February 2025 (cfr. ref). To achieve smooth disinflation, current issuance decreases by ~0.0889% per epoch until it reaches its long-term target.
The current curve is not sensitive to any shift in stake behavior; the only change is happening at the APR level. Indeed, the APR for a perfectly performing validator is
The current curve discourages a staking dynamic, with the sole aim of diluting the value of those who do not stake independently from the fraction of the total supply staked. This implies a dynamical change influenced by time rather than network needs.
It’s clear that to understand if Solana needs a change in the inflation model, we must assess how the current model influences the network's activity.
Our first consideration regards the dynamic evolution of the stake ratio. As we can see, despite decreasing over time, it shows a period of stillness, moving sidewise and confined in specific regions. Examples of this are epochs’ ranges [400, 550] and [650, 740], where the stake rate stays between [0.7, 0.75] and [0.65, 0.70], respectively, cfr. here. Notably, assuming epochs last for ~2 days, both periods are comparable with a year length.
Despite the prolonged static behavior, the stake rate shows a strong correlation with inflation (correlation coefficient of 0.78), meaning that when inflation decreases, the stake rate also decreases.
Since inflation is insensitive to the staking rate, the slow change in inflation triggers a shift in the fraction of total SOL staked. This indicates a willingness of users to move stake only when dilution for non-stakers decreases.
To assess if non-staked SOL moves into DeFi, we can study the elasticity of total value locked (TVL) over stake rate (SR)
where %ΔTVL is the percentage change in TVL in DeFi, and %ΔSR is the percentage change in the stake rate.
Elasticity E, in this context, measures the responsiveness of the change in TVL in DeFi, to change in the staking participation. A negative value indicates that TVL and SR move in the opposite direction, meaning that the decrease in SR correlates with liquidity moving into DeFi.
We need to see an increase in TVL and a decrease in SR to have a hint that non-staked SOL moves into DeFi. However, the elasticity indicates a low correlation between the staking rate and TVL.
If we compare DeFi TVL on Solana and Ethereum, we see that Solana still has a lot of room to grow.
The difference between TVL stems from the lack of adoption of lending protocols on Solana, while DEXes are catching up.
If we focus on DEXs’ TVL per traded volume, we see how Solana results in a more efficient network when dealing with trading activity.
On the contrary, TVL per active user is still low, indicating users’ preference for low-TVL interactions, like trading.
This indicates users’ willingness to use the chain and points to a high potential for growth. However, combining this observation with the staking behavior, there could be friction in depositing capital into DeFi due to dilution.
This may be due to DeFi yields still low compared with staking. For example
The reason why SOL is needed for DeFi growth is its low volatility compared to other assets prone to price discovery (i.e. non-stablecoins). This property has several implications, like:
This makes SOL indispensable for growing DeFi activity, especially for DEX liquidity provision for new projects launching their tokens.
In this section we have seen how DeFi grows because of externally injected capital. Further, the majority of TVL is locked in DEXs - with possible fictitious TVL into illiquid memecoins. Solana has 33% of TVL in DEXs, Ethereum just ~8%. Ethereum has 24% of TVL in Lending, Solana 13%. As a comparison, at the time of writing, just on AAVE you have 2% of ETH supply, on Kamino + Solend you have just 0.2% of SOL supply.
A key argument in favor of SIMD-228, as articulated by Anza researcher Max Resnick in his recent X article, is that inflation within the Solana network functions as a "leaky bucket," resulting in substantial financial inefficiencies. The theory contends that the current excessive issuance of SOL, approximately 28M SOL per year valued at $4.7B at current prices, leads to significant losses for SOL holders, including stakers, due to the siphoning of funds by governments and intermediaries.
Specifically, the theory highlights U.S. tax policies that treat staking rewards as ordinary income, subjecting them to a top tax rate of 37%—considerably higher than the 20% long-term capital gains rate—creating a "leaky bucket" effect that erodes value. Additionally, Resnick points to the role of powerful centralized exchanges like Binance and Coinbase, which leverage their market dominance to impose high commissions, such as 8%, on staking rewards, further draining resources from the network. The conclusion is that, by reducing inflation through SIMD-228, Solana could save between $100M and $400M annually, depending on the degree of leakage, thereby aligning with the network's ethos of optimization.
The current snapshot seems to point to an overpayment for security. Indeed, the current SOL staked value amounts to ~$53B, which is securing a TVL of ~$15B. Since the cost to control Solana is 66% of SOL staked, we have ~$35B securing ~$15B. However, it’s a common misconception that is the current 4.6% of inflation that determines the overpayment, leading to a ~28M SOL minted per year, or $4B at today's prices. This has nothing to do with security overpayment, and other ecosystems like Ethereum prints ~$8B for securing the network.
Our task is then to assess under which condition the overpayment statement holds. To assess if the current curve is prone to overpayment of security, we need to study the evolution of the parameters involved. This is not an easy task and each model is prone to interpretation. However, based on the above data, we can build a simple dynamical model to quantify the “overpaying” claim.
The model is meant to be a toy-model showing how the current curve (pre-SIMD228) can guarantee the security of the chain, overpaying for security based on different growth assumptions. The main idea is to assess security as the condition described in Eq. (3), where the profit is estimated assuming an attacker can drain the whole TVL. In this way, the chain can be considered secure provided that
which define the security ratio.
In our model we consider the stake rate decreasing by 0.05 each 150 epochs, based on the observation done in the previous section. We further consider an amount of burnt SOL per epoch of 1,800 SOL (cfr. Solana Transaction Analysis Dashboard), and a minimum stake rate of 0.33.
The first case we want to study is when TVL grows faster than SOL price. We assumed the following growth rates:
The dynamical evolution obtained as an outcome of these assumptions is depicted below.
We can see how, with the assumed growth rate, the current inflation curve guarantees a secure chain up to 2.5 years. Notably, this happens at a stake rate of 0.5 and SOL price slightly below SOL ATH. This corresponds to an inflation rate of 3% and an APR of 6.12%. After this point, the curve is not diluting enough non-staked capital to bring back the chain at security level.
Of course, changing the growth rates for DeFi’s TVL and SOL price changes the outcome, and we don’t have a crystal ball to say what will happen 10 years from now. For example, just assuming a SOL price growth higher than the TVL growth, the current curve results in a 10 years of overpayment for security.
This model shows how the current overpayment for security can drastically change over time, based on different growth assumptions. To enable the reader to draw their conclusions, we have built a dashboard that allows users to modify our assumptions and analyze the impact of adjusting various growth parameters. The dashboard is available here.
Solana requires beefy machines to run well. This is because there is a dilution of stake for non-optimally performing validators, decreasing their APR in favor of top-performing validators (see, e.g., here and here).
For example, let’s consider that 60% of the stake has an uptime of 99.8% — i.e., 60% of the stake has a TVC effectiveness of 99.8% — while 40% of the stake has an uptime of 95%. When accounting for APR share, we have a multiplier of
meaning 99.8% of the stake takes 61.1% of the total APR (i.e., of inflation) at the expense of the non-optimally performing 40% of the stake.
Despite this being in line with Solana's needs for top-performing validators, such a mechanism implies higher costs for validators. These machines are relatively expensive, ranging from $900/month to $1,500/month. To ensure that a validator can continue to validate when a machine fails or needs to reboot, a professional node operator needs two machines per validator identity. Furthermore, Solana uses a lot of network bandwidth. The costs vary by vendor and location, estimated at $100–200 per month. On top of that, there are voting costs of around 2 SOL a day. Assuming a SOL price of $160, this corresponds to an overall cost of between $128,800 and $137,200 a year. This is without accounting for engineering costs!
Assuming an 8% commission on staking rewards, a validator with 0.1% of stake needs — at 1 SOL = $160 — an APR ranging between 2.60% and 2.77% to break even. However, at the current staking rate, SIMD228 pushes the APR to 1.40%, making 1,193 out of 1,317 validators unprofitable from sole inflation. Clearly, lowering SOL price changes the APR needed to break even!
It is worth noting that, if SIMD228 is implemented in a year from now, assuming a stake rate of 0.5, the current curve would produce an APR of ~6%. At the same level of stake rate, the proposed curve would produce an APR of ~2%.
If we analyze the distribution of commission dividing validators by cohorts, we see that 50% of validators with less than 0.05% of stake have commissions higher than zero, and 40% of validators with stake share between 0.05% and 0.5% have commissions higher than zero. Here cohorts are defined as
If we look at the Cohorts’ dynamical evolution, we can see how the median of Cohort 3 started to adopt 0 commissions around epoch 600 (Apr 9, 2024), meanwhile Cohort 4 just started to opt for this solution recently. Cohort 1 and 2 are more stable with time. This is a clear sign that commissions are set based on market conditions, probably indicating that these are zero when value extracted from MEV and fees is enough to guarantee profitability.
This ties validator revenues to MEV and network fees, making the fraction of total supply staked a parameter highly dependent on the market and broader network activity.
Indeed, these add extra revenue to stakers, and there is no need for higher inflation. However, this assumes fairness among MEV and fee share, but we know these are long-tailed distributions. This property implies that having a higher stake unfairly exposes bigger validators to a higher likelihood of being leaders of juicier blocks.
By considering the distribution of MEV and fees from the start of the year, we can run simulations to see the effect of stake share on this “Market APR.”
From the plot above, it’s clear that low stake has a higher variance and lower median, incurring a non-null probability of ending the year with a low-generated Market APR. Considering that most of the revenues come from MEV and most are shared with delegators, the dynamic around it could enhance centralization. Other possibilities are
It is also worth noting that the simulations above are highly optimistic since they include MEV and PF from the January “craziness”. By excluding those very profitable days, we have a smaller Market APR.
This is still eventually optimistic, since at time of writing - epoch 747 - APR from Fees and MEV is respectively at 0.79% from fee and 1%. If we run simulations considering just data from the end of February we have an overall market APR further decreasing.
Notoriously, low stake validators cut costs on machines, operating on non-performing infrastructures for the operation of Solana. This results in an overall lower TVC effectiveness and higher skip rate. The first has an impact on network APR, requiring a higher APR to make profits. The second, instead, has implications on extracted Market APR, exacerbating the “MEV unfairness” between stake shares.
Another risk we see is that the market APR per staked SOL will drastically increase if there is a shock in fraction of staked SOL. Despite the amount of MEV and fees depending on block proposals, and then from the share of staked SOL, the relative gain per SOL depends on the SOL staked. In other words, a share of staked SOL of 1%, S1, produces on average M from MEV and fees. If the fraction of SOL staked goes down, the same share of 1%, this time S2, still makes M from MEV, this time with S2< S1. Since, M/S1 < M/S2, revenue for staked SOL increase. This behaviour is depicted in the image below - fixed share of staked SOL of 1%.
Despite this seems to be a point in favour of aggressively lowering issuance, we think that, combined with the risks of encouraging “bad” MEV to increase proceeds, this may lead to more staked capital used to frontrun users.
This makes Solana vulnerable to dilution from bad actors, since APR for staked SOL coming from market activity will be drastically higher than APR from inflation. This is risky because you can now make more profits in relative terms from MEV. Put it simply, larger actors can accumulate SOL with discounts coming from unstaking.
However, it is worth mentioning that, assuming a period of ~200 epochs to see SIMD228 implemented and a stake rate of 0.4, the proposed curve produces an APR of 6.47%, meaning that the effect induced by MEV is mitigated.
Introducing a market-based emission mechanism for Solana represents a fundamental shift from a fixed issuance schedule to a dynamic, staking-sensitive model. This proposal aims to align SOL issuance with actual network conditions, optimizing security incentives, removing unnecessary inflationary pressure, and fostering ecosystem growth. By adjusting emissions based on the fraction of total SOL supply staked, the model seeks to maintain an equilibrium that balances validator rewards with broader economic activity within the Solana ecosystem.
The analysis highlights key insights regarding the rigidity of the current staking rate, its correlation with inflation, and the limited elasticity between staking and Total Value Locked (TVL) in DeFi. The findings suggest that Solana's existing inflation structure primarily dilutes non-stakers rather than dynamically responding to network needs. Moreover, despite the increasing role of MEV and transaction fees in validator revenues, the distribution remains skewed, raising concerns about potential centralization effects under the new regime.
While the proposal addresses inefficiencies in capital allocation, its impact on validator sustainability remains a critical concern. The simulations indicate that under SIMD-228, a significant fraction of validators may become unprofitable, making revenue generation more dependent on MEV and network fees. This shift introduces new risks, including possible off-chain agreements to manipulate MEV distribution or incentives for adverse behaviors.
In conclusion, while SIMD-228 introduces a more responsive and theoretically efficient emission mechanism, its broader implications on validator economics, staking participation, and DeFi liquidity require further empirical validation. Although we believe that dynamical inflation tied to the fraction of the total supply staked is more aligned with network needs, we advocate a less aggressive reduction in order to make overall validator profitability less dependent on market conditions, reducing security issues. This less aggressive reduction may be achieved if SIMD228 takes around a year to be implemented.
Welcome back to Behind the Blocks, where we dive into the journeys of the talented individuals shaping the future of Chorus One. In this series, we highlight their stories, explore their roles in decentralized technology, and share why Chorus One is the perfect place to make an impact in Web3.
Today, we’re thrilled to feature Maria, a Software Engineer at Chorus One. From running a kindergarten in Romania to discovering her passion for Web3 development, Maria's journey is a testament to adaptability, resilience, and the transformative power of curiosity.
Q: Can you tell us a bit about your role as a Software Engineer at Chorus One and what drew you to join the team?
Maria: My journey into Web3 started during a transitional phase in my life. After co-founding a kindergarten and working at a startup focused on pre-school education, COVID forced me to pivot. I started learning how to code in 2020 and about a year and a half later, I discovered DappCamp, a Web3 bootcamp.
I didn’t know at the time that the scholarship I received for the course was sponsored by Chorus One, but it changed everything. Jen, a team member from Chorus One, approached me about interviewing for an engineering position, and I couldn’t turn the opportunity down.
The interviews were incredible—our CEO's focus on transparency and kindness stood out. I didn’t have much experience in Web3, but the environment at Chorus One is so welcoming and growth-focused. It’s been fascinating to work in a place where my voice is heard, there’s no unnecessary bureaucracy, and ownership over your work is encouraged.
Q: What does a typical day look like for you in this role, and what kind of projects do you usually work on?
Maria: My days are split between coding and code reviews —about 70% of the time—and meetings or interviews. I love that we keep meetings to a minimum, just twice-weekly team huddles where we connect, align objectives, and discuss architecture.
I’ve worked on ETH-based projects like a rewards reporting tool and monitoring solutions for our staking clients. One of the most exciting projects was the Opus pool, where I got to focus on monitoring tools and client-facing features. It’s a dynamic environment, and no two days are the same.
Q: What has been one of the most challenging projects you’ve worked on at Chorus One, and what did you learn from it?
Maria: The first project I worked on—the rewards reporting tool—was incredibly challenging. It involved reading, interpreting, and processing block data into a format that was useful for users. I had to learn about Ethereum protocol and the kind of information contained in a block.
That project taught me how to dig deep into blockchain mechanics, and it gave me a solid foundation for understanding how the networks operate.
Q: How does Chorus One support your professional growth, whether through learning opportunities, tooling, or other resources?
Maria: The learning budget here is a game-changer. I’ve taken courses, gotten coaching, and attended conferences—all encouraged and supported by the company.
Another key aspect is the variety of projects we work on. Each one introduces me to new technologies and protocols, keeping my work exciting and challenging. The team’s focus on certain networks also helps me stay sharp without being overwhelmed by the sheer scope of Web3.
Q: For engineers interested in Web3, what advice would you give about joining a company like Chorus One?
Maria: My advice is to build something concrete using Web3-specific tools. For frontend developers, experiment with libraries like RainbowKit, WalletConnect, Wagmi, or Viem to integrate wallets and build dApps, to understand the user flow.
I would use Infura to get nodes to read from, hardhat or foundry for developing and testing smart contracts, get used to using Etherscan to read contracts, view transactions etc.
A good resource: https://cryptozombies.io/. I also like Nader Dabit’s content.
If you’re into backend, create a monitoring tool that extracts and visualizes blockchain data—it’s a great way to show your skills. Develop a blockchain scraper, extract useful data from blocks, export the data as metrics to Prometheus, build a Grafana dashboard etc.
For DevOps, try hosting your own node and focus on one protocol to understand it deeply. Develop monitoring tools for it.
Having hands-on experience with Web3 tools is incredibly impressive in interviews.
Chorus One is one of the largest institutional staking providers globally, operating infrastructure for over 60 Proof-of-Stake (PoS) networks, including Ethereum, Cosmos, Solana, Avalanche, Near, and others. Since 2018, we have been at the forefront of the PoS industry, offering easy-to-use, enterprise-grade staking solutions, conducting industry-leading research, and investing in innovative protocols through Chorus One Ventures. As an ISO 27001 certified provider, Chorus One also offers slashing and double-signing insurance to its institutional clients. For more information, visit chorus.one or follow us on LinkedIn, X (formerly Twitter), and Telegram.
Berachain is officially live on mainnet. This marks the beginning of a transformative period for DeFi, where security and liquidity scale together under Berachain’s novel Proof-of-Liquidity (PoL) consensus.
The goal of Berachain’s proof-of-liquidity (PoL) consensus mechanism is to allow security and liquidity to scale together. In traditional proof-of-stake (PoS) blockchains, a substantial amount of capital is locked to ensure network security. This staked capital, while ensuring network security, remains idle, and does not contribute to the liquidity of the ecosystem. The fundamental idea behind proof-of-liquidity is to remove this trade-off between security and liquidity, by directly incentivizing DeFi activity with sustainable staking revenues.
Berachain’s economic design revolves around three distinct tokens:
Validators propose blocks based on their $BERA stake and distribute emissions of $BGT, which can be allocated to Reward Vaults. The amount of emissions they can distribute depends on their $BGT stake, if we want to mention this: (1) How often they propose depends on their $BERA stake. (2) How much $BGT they distribute upon proposal depends on their $BGT stake.Users providing DeFi liquidity can stake their receipt tokens in these reward vaults to be eligible for $BGT rewards.
BEX: The Berachain Exchange
BEX is a native decentralized exchange featuring House Pools and Metapools to enhance liquidity efficiency. Liquidity providers not only earn trading fees but also accumulate $BGT, which can be staked with validators to influence governance and optimize emissions.
Bends: Native Lending Markets
Bends allows users to borrow $HONEY against collateral such as ETH, BTC, and USDC. By interacting with Bends, users deepen liquidity while simultaneously earning $BGT emissions, creating a dual-incentive model for sustainable lending.
Berps: Perpetual Futures TradingBerps is Berachain’s native perpetual futures exchange, offering high-performance derivatives trading with deep liquidity and efficient capital deployment.
With Berachain’s unique emission mechanics, delegators need a sophisticated strategy to maximize returns. This is where BeraBoost comes in—an automated allocation algorithm developed by Chorus One Research that dynamically optimizes $BGT distribution to maximize rewards.
Validators on Berachain play a crucial role in emission allocation. Delegators who stake with a validator benefit from the validator’s strategy for directing emissions to Reward Vaults. BeraBoost takes this a step further by:
This mirrors how traditional DeFi yield farming strategies work but integrates them directly at the consensus level. As Camila Ramos highlighted in this thread, Berachain’s PoL effectively allows users to outsource their farming strategies to validators, providing an avenue for both sophisticated and unsophisticated users to optimize their returns without active management.
Learn more about BeraBoost here.
Berachain’s Proof-of-Liquidity introduces a fundamental shift in blockchain economics. By aligning security with capital efficiency, Berachain not only enhances validator incentives but also fosters deeper liquidity for the entire ecosystem. The introduction of BeraBoost further refines this model, allowing delegators to passively maximize returns while reinforcing the network’s decentralized security.With mainnet now live, Berachain is poised to redefine on-chain liquidity dynamics, governance participation, and validator incentives—all while maintaining seamless Ethereum compatibility. Builders, liquidity providers, and institutional players now have a powerful new platform to engage with.
To get started with staking or liquidity provisioning, reach out to us at staking@chorus.one and check out our staking guide here. The era of Proof-of-Liquidity is here.
Chorus One is one of the largest institutional staking providers globally, operating infrastructure for over 60 Proof-of-Stake (PoS) networks, including Ethereum, Cosmos, Solana, Avalanche, Near, and others. Since 2018, we have been at the forefront of the PoS industry, offering easy-to-use, enterprise-grade staking solutions, conducting industry-leading research, and investing in innovative protocols through Chorus One Ventures. As an ISO 27001 certified provider, Chorus One also offers slashing and double-signing insurance to its institutional clients. For more information, visit chorus.one or follow us on LinkedIn, X (formerly Twitter), and Telegram.
