SIMD-228: A Critical Analysis

Thank you to Brady, Mert, Guney, Koen, Vishal, and Max, for reviewing earlier drafts of this report.

Actionable Insights

• Solana’s current inflation schedule operates under a fixed time-based model that begins at 8% annually and decreases by 15% yearly until it reaches a floor of 1.5%. Inflation currently stands at ~4.68%. SIMD-228 seeks to replace this with a formula that adjusts inflation dynamically based on the staking rate.
• Our impact simulations suggest SIMD-228 stabilizes inflation, maintains staking, and sustains yield in the best- and median-case scenarios. In the worst-case scenario(s), low staking and high inflation run the risk of threatening network security, which is also the case today.
• Our impact simulations suggest SIMD-228 achieves its aim of market-based emissions in high-staking scenarios by minimizing sell pressure, adapting to disruptions, and ultimately reducing inflation.
• Our impact simulations indicate that SIMD-228’s main weakness emerges in environments with low stakes and penalty-driven incentives (i.e., economic pressures such as aggressive validator unstaking). These incentives could create a feedback loop that could potentially threaten network stability, increase token issuance, and cause more validators to exit. Note that, in a vacuum, any curve would suffer in such scenarios. However, the proposed curve would recover faster than the current inelastic inflation curve.
• 647 out of 1,316 validators (49%) have a staking rewards commission rate of zero, meaning changes in inflation will have little direct impact on their economic viability. This percentage increases slightly to 51% for validators outside the supermajority, where 635 out of 1,243 validators have a zero staking reward commission rates.
• Our validator economics model suggests that the introduction of the SIMD-228 inflation curve will lead to a modest contraction in the validator set. Far more significant factors influencing the economics of Solana’s long-tail validators include SOL price, network activity, block reward sharing rates, and voting costs.
• Voting costs remain a validator’s primary expense (~2 SOL per epoch). Currently, the protocol charges a uniform base fee to all vote transactions regardless of stake weight. Anza is working on significantly reducing voting costs, potentially through a new vote inclusion credit SIMD that is expected to reach mainnet by the end of the year.
• Long-tail validators tend to exhibit lower staking reward commission rates and rely less on inflation commissions than other validator cohorts, including exchange-operated and institutionally focused validators. The current average inflation commission rate, excluding private validators with a fixed rate of 100%, is 2.75%. Outside the supermajority, this rate is slightly lower at  2.68%.
• Our projections for future validator revenue sources suggest that post SIMD-96, only 175 validators would derive more than 20% of their revenue from inflation commissions on an annualized basis.
• Concern has been expressed over SIMD-228’s original 10-epoch transition period, which has now been extended to 50 epochs. Our simulations suggest that longer transition periods or delays could diminish SIMD-228’s efficacy, thereby extending high inflation and reducing its ability to stabilize staking and yields in worst-case scenarios.
• Solana’s current time-based inflation schedule was initially modeled after peer networks like Cosmos, relying on fixed parameters rather than dynamic market forces. Ideally, inflation should be determined by the demand curve for SOL staking yield rather than arbitrary human decisions. Markets are inherently more efficient at price discovery.
• High token inflation creates selling pressure as stakers in many jurisdictions must sell rewards to cover tax obligations. At the current ~4.69% annualized inflation rate and an $80 billion fully diluted valuation, annual emissions total approximately $3.75 billion. Even modest reductions in issuance would prevent hundreds of millions in yearly value leakage.

What Is SIMD-228?

SIMD-228: Market-Based Emission Mechanism is a Solana Improvement Document (SIMD) proposed by Tushar Jain, Vishal Kankani of Multicoin Capital, and Max Resnick of Anza. It aims to introduce a market-based inflation mechanism by replacing the current fixed disinflation schedule with a dynamic model more responsive to network participation. Solana’s current inflation operates under a fixed model that begins at 8% annually, decreasing by 15% each year until it reaches a floor of 1.5%. It currently stands at ~4.68%.

SIMD-228 seeks to replace this with a formula that adjusts inflation dynamically based on the staking rate (i.e., the fraction of SOL tokens staked by validators) using the formula: 

i(s) = r * (1 - √s + c * max(1 - √(2s), 0)), where:

• s is the staking rate.
• r is the static inflation rate (derived from a time-decay function starting at 4.68% and declining to 1.5%).
• c is a constant (~3.146, approximately π) that fine-tunes the inflation adjustment. This makes the curve aggressive below 50% staking, surpassing the static rate at 33%.

To ensure a smooth transition from the current to the proposed schedule, SIMD-228 proposes interpolating between the old and new rates over 50 epochs using the formula:

i(s) = r * (1 - 𝛼√s + 𝛼c * max(1 - √(2s), 0)), where:

• 𝛼 increases from 0.1 to 1 over the first 50 epochs.

The proposal argues that stakers increasingly earn SOL from other sources, such as block rewards and MEV, as the network matures. This has resulted in an oversupply of emissions relative to the demand for staking, potentially leading to centralized ownership, reduced SOL usage in DeFi due to high inflation rates, and downward price pressure. The primary goal is to minimize token issuance and sell pressure by reducing inflation when staking is high, indicating strong network participation, and increasing it when staking is low, incentivizing participation and maintaining network security. SIMD-228, therefore, aims to minimize issuance to the Minimum Necessary Amount (MNA) required to secure the network. This would reduce sell pressure and foster SOL usage in DeFi while maintaining decentralization. 

Alternatives Considered

Four other alternatives were evaluated but rejected due to their limitations:

• Fixed-Rate Adjustment: a new fixed issuance rate would be set (e.g., via community input). However, this lacks market responsiveness, risks arbitrary decisions, erodes trust in the community’s decision-making process, and could potentially compromise consensus safety by ignoring real-time staking dynamics.
• Fixed Target Staking Yield with MEV: issuance would be adjusted based on a 30-day moving average of MEV tips to maintain a target yield. This could align with market conditions, but it risks incentivizing alternative MEV payment methods to evade tracking, rendering the design ineffective. The proposal explicitly avoids measuring MEV and is solely focused on staking-based emissions.
• Proportional Controller Function: issuance by a controller function, allowing for a dynamic response to staking participation fluctuations. However, this approach risks increasing emissions even if staking participation naturally falls below the target rate.
• Single-Term Formula: issuance would be determined by the first term of the proposed SIMD-228 formula only. However, this would leave an unaddressed edge case where staking rates continue to drop and terminal inflation hits 1.5%. In such scenarios, the second term increases inflation beyond the current curve.

Modeling the Potential Impact of SIMD-228

The following code, available here, aims to model SIMD-228 and its potential impact on Solana’s inflation. Anyone can run these simulations to verify the findings themselves.

The code can be broken down into the following sections:

• Time Decay Function
• Current Solana Inflation Rate
• SIMD-228 Inflation Rate
• Staking Rate Adjustment
• Simulation Function
• Visualization Functions

Time Decay Function (`time_decay_static_rate`)

This function gradually reduces the static inflation rate from an initial value (i.e., r0, e.g., 8%) to a minimum value (i.e., r_floor, e.g., 1.5%) over a set number of epochs (i.e., dynamic_epochs).

It uses a linear decay formula to simulate a controlled reduction in inflation over time.

Current Solana Inflation Rate (`current_inflation_rate`)

This function represents Solana’s existing inflation model. It starts at 8% and decreases 15% annually until it reaches a floor of 1.5%.

This is a fixed schedule.

SIMD-228 Inflation Rate (`simd228_inflation_rate`)

This function implements the proposed SIMD-228 inflation formula:

i(s) = r * (1 - √s + c * max(1 - √(2s), 0))

• s: the staking rate (i.e., the fraction of tokens staked)
• r: the static inflation rate from the time-decay function
• c: a constant (~3.146, approximately π) that adjusts the curve

The formula is expected to behave in a way that decreases inflation when staking is high and increases when staking is low. This is intended to make inflation responsive to network participation.

Staking Rate Adjustment (`adjust_staking_rate`)

The staking rate adjustment dynamically adjusts the staking rate based on staking yields. It compares the effective yield to a target yield. If the yield deviates, the staking rate changes with a cap on how much it can shift per epoch to avoid drastic swings.

Simulation Function (`simulate_with_delay`)

The simulation function runs the full simulation over a set number of epochs (i.e., total_epochs). It uses the current inflation model for a specified number of epochs (i.e., delay_epochs) before switching to the SIMD-228 formula after the delay.

At predefined epochs (i.e., shock_epochs), the staking rate drops by a factor (i.e., shock_factor, e.g., 0.8 for a 20% reduction) to simulate episodic shocks. We define episodic shocks as temporary reductions in the staking rate applied during the dynamic period. This is done to mimic real-world disruptions, testing how resilient the inflation model is under stress.

If the effective yield drops below a threshold (e.g., 3.5%), additional “penalties” are applied. By penalties, we try to mimic economic consequences such as aggressive validator unstaking in response to a given event.

Visualization Functions

plot_scenario plots results (e.g., inflation, staking rate, token supply) for a single scenario across different delays.

compare_Scenarios compares best-case, median-case, and worst-case scenarios for a fixed delay.

sensitivity_analysis tests how parameter changes (e.g., c, adjustment speed, initial staking rate) affect outcomes.

How Can We Interpret These Simulations? 

The code generates plots across three time horizons (6, 12, and 24 months), multiple delays, and different scenarios. Scenarios include:

• Best-Case:  an initial staking rate of 70%, no penalties, no shocks, and validator commission set at 5.87%
• Median-Case: an initial staking rate of 65%, no penalties, a shock at epoch 200 resulting in a 20% decrease in the staking rate, and validator commission set at 4.38%
• Worst-Case: an initial staking rate of 30%, a penalty of 3%, shocks at epochs 100 and 300 resulting in a 30% decrease in the staking rate, and validator commission set at 3.66%
• Worst-Case-No-Penalties: an initial staking rate of 30%, no penalties, shocks at epochs 100 and 300 resulting in a 30% decrease in staking rate
• Worst-Case-Small-Penalties: an initial staking rate of 30%, a smaller penalty of 1%, shocks at epochs 100 and 300 resulting in a 30% decrease in staking rate

We can interpret the key metrics as follows:

Total Supply

• What It Shows: Growth of the token supply, which starts at 550m SOL
• Interpretation:
• • Delay Period: Supply grows based on the current disinflation model (i.e., 8% start, 15% annual decrease)
  • Dynamic Period: Post-delay, growth slows if SIMD-228 reduces inflation (i.e., higher staking results in lower inflation)
• Example: In the best-case scenario (i.e., 70% staking), supply growth should be slower than in the worst-case (i.e., 30%) with similar penalties, shocks, and other influential conditions

Staking Rate

• What It Shows: Fractions of tokens staked
• Interpretation:
• • Delay Period: Constant
  • Dynamic Period: Adjusts based on yield. High yields increase staking; low yields or penalties decrease it
• Example: In the worst-case scenario, shocks (i.e., shock_std=01) and penalties (yield < 3.5%) may cause volatility or a downward trend

Inflation Rate

• What It Shows: Annual inflation rate as a percentage
• Interpretation:
• • Delay Period: Follows the current model, dropping from 4.68% to 1.5%
  • Dynamic Period: Switches to SIMD-228, where staking rates lower inflation via 1 - √s.
• Example: Median-case (i.e., 65% staking) should show a smoother drop than the worst-case scenario (i.e., 30% staking)

Staking Yield

• What It Shows: Annual effective yield for stakers after an assumed 4.12% commission, aligning with the trend of validators offering zero fees.
• Interpretation:
• • Delay Period: Yield reflects the current inflation divided by staking rate
  • Dynamic Period: Yield adjusts with inflation and staking changes. Higher staking can sustain yields by reducing inflation.
• Example: The best-case scenario should maintain higher yields longer due to stable staking

Sensitivity Analysis

• Parameter c:
• • Higher c (e.g., 2.5 vs 3.5) amplifies staking’s effect on inflation. We need to check if it overly suppresses yields.
• Adjustment Factor:
• • Larger values (e.g., 0.01 vs 0.1) make staking more responsive. We need to look for oscillations or sluggishness.
• Initial Staking:
• • Higher starting rates (e.g., 30% vs 70%) lead to lower inflation and better yields. We need to assess the system’s dependency on this.

What Do These Simulations Tell Us?

For the purposes of this analysis, we’ve limited our time horizons to the next 24 months. Please feel free to edit the modeling code, add new scenarios, and plot over differing time horizons. Note that these scenarios are done ceteris paribus without considering MEV, network activity, and the myriad of other SIMDs that could be proposed and implemented before SIMD-228 is implemented if it passes. We are solely interested in assessing SIMD-228's merits and how it tackles inflation under different conditions. We will tackle more comprehensive validator set economics in later sections.

The following charts are screenshots of a select few scenarios plotted by our Python script via Matplotlib:

We can continue tweaking parameters and plotting charts to simulate various potential conditions. However, the trends are evident. We can interpret the following from the simulations:

• SIMD-228 stabilizes inflation, maintains staking, and sustains yield in the best- and median-case scenarios.
• In the worst-case scenario(s), low staking and high inflation run the risk of threatening network security, which is also the case today.
• Longer implementation delays amplify these risks, particularly in worst-case scenarios, by increasing the initial token supply and reducing yields, as shown in the scenario comparisons. 
• Large validators benefit from stability in the best- and median-case scenarios, whereas smaller validators face severe risks in the worst-case scenarios. This is because yields fall below profitability after commissions (2-3.5% vs. 3.66% long-tail), likely forcing exits, consolidations, or validators to seek other forms of revenue unless mitigated by vote cost reductions or other incentives.
• SIMD-228 achieves its aim of market-based emissions in high-staking scenarios by minimizing sell pressure, adapting to disruptions, and ultimately reducing inflation.
• Our simulations indicate that SIMD-228’s main weakness emerges in environments with low stakes and penalty-driven incentives (i.e., economic pressures such as aggressive validator unstaking). These incentives could create a feedback loop that could potentially threaten network stability, increase token issuance, and cause more validators to exit. Note that, in a vacuum, any curve would suffer in such scenarios. However, the proposed curve would recover faster than the current inelastic inflation curve.

In principle, SIMD-228 works as intended—it’s a promising innovation. However, it should be followed by other complementary measures to mitigate possible bad scenarios, or the impact on smaller validators could be deleterious. This warrants a discussion of validator set economics.

Validator Set Economics Modeling

We also modeled validator set economics across various forward-looking scenarios to further our analysis. Our primary goal was to understand better the potential impact of SIMD-228 on the size of the validator set. Given the complexity of validator economics and the many variables involved, readers unfamiliar with the fundamentals may find it helpful to refer to our previous Helius blog post. The model leverages real economic data from the past twelve months, sourced from the Blockworks Solana dashboard, and current validator set data from the Stakewiz API.

A publicly accessible version of this analysis is available in this Google Sheet. We would like to acknowledge the Pine Stake validator team for developing an earlier version of this analysis.

Validator Set Scenarios

Our validator economics model considers low-, median-, and high-stake rate scenarios. Under SIMD-228, a low staking rate is positive for validator economics as it increases issuance, thereby increasing commission revenue. The model classifies validators into one of three buckets: profitable, unprofitable, and break-even, considering the three key revenue streams: inflation commissions, block rewards, and MEV commissions. Break-even is defined as an annual profit ranging between -10 SOL and 10 SOL.

Current Scenario

• Stake rate: Current stake rate ~63%
• Current inflation curve
• Model estimate: 706 Profitable validators, 13 break-even

Low Stake Rate Scenario

• Stake rate: 35%
• SIMD-228 inflation curve
• Model estimate: 706 Profitable validators, 12 break-even

Median Scenario

• Stake rate: 50%
• SIMD-228 inflation curve
• Model estimate: 689 Profitable validators, 8 break-even

High Stake Rate Scenario

• Stake rate: 70%
• SIMD-228 inflation curve
• Model estimate: 685 Profitable validators, 10 break-even

Validator Set Model Assumptions

We assume all validators operate with equal efficiency. While this is not true in practice, incorporating real-world operational differences would add unnecessary complexity to the analysis.

This model assumes that the current stake ratio among validators remains constant. This ratio dictates their relative share of total block rewards, inflation commissions, and MEV commissions. We take a snapshot of these ratios at the present moment and then adjust key parameters related to SIMDs and network activity across different scenarios. While stake adjustments are modeled as occurring uniformly across the validator set—an unlikely scenario in practice—this approximation should still provide a reasonable estimate.

With the activation of SIMD-96 on mainnet last month, 50% of block rewards are no longer burned. This model assumes no change in user behavior when selecting between priority fees and Jito tips, given that transaction costs and user experience remain unchanged. Under this assumption, validators will collect twice the priority fees they would have had pre-SIMD96 for the same level of network activity moving forward. As a result, block rewards now play a more significant role in modeling future validator economics than before.

To simplify reasoning and comparison with the current inflation schedule, the model assumes immediate enforcement of SIMD-228. However, in reality, should the proposal pass, its activation on mainnet will take at minimum, several quarters.

Additionally, the analysis does not take into account the following factors:

• Private deals to share block rewards
• Profits from sandwiching
• Stake pool commissions
• Solana Foundation Delegation Program (SFDP) subsidies
• Any alternative income source outside of issuance, block rewards & Jito MEV

Regardless of the scenario, readers may observe that many long-tail validators operate unprofitably. This could be for various reasons: 

• Unprofitable validators operating at a loss in the hope of becoming profitable soon.
• Test validators running alternative client software configurations
• Validators receiving voting cost support from the Solana Foundation Delegation Program
• Validators with alternative revenue sources (e.g., sandwichers)

Validator Set Economics Modeling Observations

The first observation drawn from the data is that long-tail validators do not make significant revenue from staking reward commissions. Long-tail independent and ecosystem team validators tend to exhibit lower staking reward commission rates and rely less on inflation commissions than other validator cohorts, including exchange-operated and institutionally focused validators.

• 647 out of 1,316 validators (49%) have an inflation commission rate of zero, meaning changes in inflation will not impact their economic viability. 
• This percentage increases slightly to 51% for validators outside the supermajority, where 635 out of 1,243 validators have a zero inflation commission rate.
• The current average inflation commission rate, excluding private validators with a fixed rate of 100%, is 2.75%. Outside the supermajority, this rate is slightly lower at  2.68%.
• Our projections for future validator revenue sources suggest that post SIMD-96, only 175 validators would derive more than 20% of their revenue from inflation commissions, assuming network activity remains consistent with the past 12 months.
• In the low 35% stake rate scenario, the number of profitable and break-even validators decreases by just one compared to the current situation. In the median 50% stake rate scenario, this number decreases by 22 (3%). Meanwhile, in the high 70% stake rate scenario, the number of profitable and break-even validators drops by 24 (3.4%).

The model suggests that the introduction of the SIMD-228 inflation curve will likely lead to a contraction in the validator set, though this effect is expected to be limited (3.4% under the high-stake scenario). More significant factors influencing the economics of Solana’s long-tail validators include SOL price, network activity, block reward sharing rates, and voting costs—variables that this model holds constant.

Arguments Against SIMD-228

Validator Economic Risks

Many smaller validators are concerned that if SIMD-123 passes, there will be a “race to zero” for block rewards, making their operations less profitable. And if SIMD-228 also passes, long-tail validators may become even less profitable. As our validator set economics modeling reveals, many long-tail validators operate unprofitably. This would force them to pursue other economic avenues, join stake pools, be subsidized, seek alternative sources of revenue unaligned with the network, or shut down altogether.

While independent validators such as Laine and Solana Compass have made significant contributions to the ecosystem, expecting validators to pursue other economic avenues may not be the best alternative. In a post on X (formerly Twitter), Chainflow, a validator with approximately half a million in stake, revealed that they “remain heavily dependent on SFDP delegation to continue operating, in spite of [their] best efforts to attract stake” by being net contributors rather than extractors. This aligns with our analysis of the Solana Foundation Delegation Program (SFDP) posted to the Helius blog earlier this year. Seemingly, there aren’t a lot of realistic avenues for smaller validators to attract stake.

For long-tail validators, voting costs remain the primary expense, often surpassing their earnings from issuance commissions. Currently, the protocol charges a uniform base fee to all validator vote transactions regardless of stake weight. To alleviate this burden, Anza researchers have indicated they are working on a significant reduction in voting costs. A proposed upgrade, potentially involving a new vote inclusion credit SIMD, is expected to reach mainnet by the end of the year. However, depending on when SIMD-228 will be implemented, assuming it passes, some are worried these reductions will come too late. A long-term solution, completely overhauling vote transactions as we know to address the core issue meaningfully, will take some time.

Economic Stability Risks

The proposal assumes MEV rewards in the form of Jito tips reduce the reliance on emissions but explicitly avoids measuring it. Despite the proposal’s dependency on its assumptions, this omission risks underestimating staker revenue. This could lead to over-issuance if MEV declines unexpectedly or under-issuance if MEV surges, potentially destabilizing validator economics, and SOL’s market stability, as the Q1-Q4 2024 Jito tip fluctuations ($42m to $430m USD) mentioned in the proposal suggest.

Additionally, the proposal’s aggressive curve below 50% staking, which surpasses the static rate at 33%, could lead to excessive inflation spikes in low-staking conditions, as seen in our worst-case scenarios. This could increase issuance beyond the MNA and, ultimately, contradict SIMD-228’s aims.

Implementation and Predictability Concerns

SIMD-228 introduces a more complex, market-based formula that may confuse the average layperson accustomed to Solana’s predictable inflation schedule. This added complexity, combined with the epoch transition period and c constant’s nuanced impact, could erode community trust if people misinterpret or mistrust the model’s behavior. That is, this formula is much harder to understand than “number go down every year until we hit 1.5%.”

The epoch transition period is of particular concern. Some cite that the initial 10-epoch transition is too extreme and essentially “changes the rules” of Solana’s emissions. However, our simulations suggest that longer transition periods or delays could diminish SIMD-228’s efficacy, thereby extending high inflation and reducing its ability to stabilize staking and yields in worst-case scenarios. This transition period has since been changed to 50 epochs and announced publicly by Vishal in a post on X. 

Arguments For SIMD-228

The Leaky Bucket: Inflation's Hidden Costs

A key argument in favor of SIMD-228 is that inflation functions as a "leaky bucket," a concept recently highlighted by Anza researcher Max Resnick in his X article. One of the primary downsides of high token inflation is increased selling pressure, as some stakers—especially in certain jurisdictions—interpret staking rewards as ordinary income and sell a portion to cover taxes. At the current ~4.69% annualized inflation and a $80 billion fully diluted valuation, new emissions amount to approximately $3.75 billion per year. The top individual income tax rate in the United States is 37%, while the long-term capital gains rate is 20%, effectively creating a ~17% "leaky bucket" tax on issuance. Both income and capital gains are taxed at the same rate for corporations, but capital losses cannot offset income, leading to a 21% haircut on issuance if the SOL market cap remains unchanged.

Beyond governments, centralized exchanges and custody providers also take a significant cut of staking rewards through anti-competitive practices. Large exchanges such as Binance and Coinbase charge an 8% commission on staking, a rate unthinkable for smaller validators in the current competitive environment. These platforms leverage user lock-in to extract more from Solana’s issuance. With Solana emitting ~28 million SOL per year (~$3.8 billion at today's prices), even a small reduction in issuance could save the network hundreds of millions annually.

Inflation’s Market Distortion and the Power of Price Signals

Excessive inflation beyond what is necessary to secure the network creates persistent downward price pressure, distorting market signals and hindering fair price comparison. In traditional financial terms, PoS inflation is akin to a publicly traded company executing a small share split every two days. Many market participants—charts, dashboards, casual observers, and retail investors—fail to account for this inflation in their analysis, leading to misleading price perceptions.

A healthy price chart is one of the most effective marketing tools for any ecosystem, influencing not just traders but all participants. In crypto’s psychologically driven markets, price serves as a Schelling point—a key indicator of an ecosystem’s overall health.

Superiority of Market-Driven Systems

Many, including Helius co-founder Mert Mumtaz, have emphasized the need for market-based systems that incorporate real-world feedback. Solana’s current time-based inflation schedule was initially modeled after peer networks like Cosmos, relying on fixed parameters rather than dynamic market forces. Ideally, inflation should be determined by the demand curve for SOL staking yield rather than arbitrary human decisions. Markets are inherently more efficient at price discovery, and systems that fail to adapt to real-world conditions become fragile—an unacceptable risk for a network securing billions in value. Solana risks central planning, hidden vulnerabilities, and long-term stagnation without market-driven adjustments.

Penalizing Network Use

High inflation penalizes the active on-chain use of SOL for activities such as participating in liquidity pools, NFT trading, or placing order book bids. While Solana’s mature and robust Liquid Staking Token (LST) infrastructure may help mitigate some of these adverse effects by allowing SOL to be used actively without dilution, it also introduces additional costs. These include user experience friction, liquidity fragmentation across different tokens, potential slippage when swapping in and out of LSTs, and the burden on users to understand staking mechanics to protect themselves from the indirect cost of dilution.

Kamino co-founder Marius echoed this perspective, stating, “Staking encourages hoarding and reduces financial activity… In a way, it’s similar to the Fed raising rates and tightening financial conditions.”

Avoiding Analysis Paralysis: The Case for Iterative Progress

In a recent post on X, Max Kaplan, CTO of Sol Strategies, argues that it is better to be roughly right than precisely wrong. Given that no perfect model exists, real-world experimentation is the best way to refine one. Waiting for ideal data before making changes leads to analysis paralysis—where excessive deliberation prevents action. Kaplan points out that this tendency has historically crippled large companies, allowing faster-moving competitors to capture market share.

The crypto landscape evolves rapidly, and Solana must adapt accordingly to this competitive environment. Progress should be made as fast as reasonably possible, understanding that the first attempt may not be perfect. Continuous iteration and learning are far superior to stagnation. This approach reflects the Solana community’s pragmatism, rapid iteration, and engineering-driven innovation ethos.

In light of this view, some view Solana as a nation-state with a sovereign economy. They posit Solana’s value comes “from its ability to unlock novel use cases and more efficient implementations of existing use cases,” which is to utilize capital. Solana needs capital efficiency because most of its tokens are locked away in stake accounts. Instead, SOL should be swapped, traded, providing liquidity, buying NFTs, investing in RWAs, etc. We should focus less on staking percentages and more on unlocking this war chest of idle capital. 

By extension, Solana is viewed as the home of internet capital markets (i.e., the next evolution of capitalism). Some go as far as saying, “[C]hoose capitalism, choose Solana.” Toly’s vision of Solana is a blockchain as Nasdaq speed. With the chain becoming increasingly intertwined with notions of better finance, capital formation and efficiency, digital sovereignty, private money, and capitalism itself, it makes sense as to why people want to improve Solana’s monetary policy—good markets need good policy, and people aren’t satisfied with the current state of affairs. Even if people disagree with the specific details of SIMD-228, they agree that inflation is too high and realize that something needs to change. SIMD-228 is, therefore, viewed as philosophically aligned with Solana’s mission. 

Conclusion

In this article, we have introduced two models to clarify the potential effects of SIMD-228 and examined key arguments for and against the proposal. The extensive discussion and debate surrounding this proposal highlight a positive development—Solana’s governance is active and engaged. The network has matured to a point where a diverse set of stakeholders actively shapes its future, debating and advocating for what they perceive to be the best path forward.

Additional Resources

• Is Solana’s inflation too high? - Helius Blog
• The Solana Foundation Delegation Program & the Challenges Facing Long-tail Validators - Helius Blog