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In September 2022, the Ethereum Foundation (EF) successfully merged a $250bn blockchain into a new chain. The technical gravity and stewardship of this event cannot be understated. Fortunately, this paper is not concerned with technical concepts such as distributing networking, consensus mechanisms, subjectivity, finality, block-building, etc, etc.
What is interesting however is how Ethereum’s monetary policy has fundamentally changed, and how the behavior of user’s will change with respect to this new paradigm.
Some of the more pointed questions we will seek to answer are: How will market participants of Ethereum react to PoS versus Pow? How does EIP-1559 contribute? What are the expected yields for staking? What percentage of Ethereum will be staked? Is Ethereum net-deflationary? How much is Ethereum worth?
That said, the below report is long (~20 minutes). However, at the end of it, I feel the reader will be rewarded with a better understanding of Ethereum and will be better positioned to trade the next big event in Ethereum history, Shanghai. And if that doesn’t exhilarate, I think the Ethereum long-term staking yields will be 1-3%.
I, however, won’t be the first to try to demystify Ethereum 2.0. In fact, many intelligent analyst have approached the Merge with an economic and financial lens. Some have even placed price-target of $150,000 in a goldilocks scenario. Others have offered a thesis on how the structural drivers of the asset itself, $ETH, undergoes a rapid transformation and have espoused a PE multiple. And some of the most ardent investors believe that Ethereum will be "net-deflationary", remaining affixed on "watching the burn" or salivating on the "APR%" they will receive for staking their $ETH. In all cases, however, I believe there has been a combination of miscalculation and a ‘painting of the scene with rose-colored lenses’ in these prior analysis.
Let’s first examine the popular, UltraSound.money
We’re presented with a chart that shows the 200 year projection and a 2yr projection of ETH’s monetary supply. And conveniently, investors are not able increase the staking rate below 44M ETH in the 200 year chart, just 36% of the current float, and above 69 M ETH in the 2 year chart, a little better, but still only 57% of the current-float. In any case, investors are being biased to believe that issuance will remain low. And for the un-initiated, issuance is a function of the # of validators, which is a function of staking %, and the barriers to entry to staking are incredibly low. Then, there’s a Gwei slider, which defaults to 1.9% burn-rate per year. The sliding mechanism isn’t so much the issue, but it’s difficult to have a intuition behind these inputs. We will seek to correct this.
Next, there have been a few investors who have projected out the ETH staking APR %. Most estimates were in the range of 7-11%.
While I don’t believe this is wildly over-estimated, I do believe the analysis is rudimentary, particularly as economic activity on the base layer has collapsed due to macro-conditions and new Layer 2 and alternative L1 solutions have begun processing more transactions. The difference between run-rate fees of 5M ETH and 500K ETH can yield large divergences in yield. Yet ultimately, even if there is a discrepancy in forecasting -- even if there is a return to a higher-fee ETH L1 in 2023, which is certainly possible and fair to project out -- these analysis then don’t seek to adjust the projected staking rates. If fees remain high, yields should remain attractive, which forces staking. Ultimately, the “simple-present” or “current” is not enough.
Moreover, I do believe the analysis fails at deep-fundamental level as well — more on this in a later section.
The previous section does not intend to solely criticize. It moreover seeks to highlight the need to go beyond simple ideas and and bring opacity. This is because ideas in of it themselves are cheap-currency; they are riddled with assumptions and leave the reader in the void of implicitness.
So it is imperative that a model is used to explicitize these ideas, to quantify these economic impacts. And it's important to do this not just in the present or historically, but the distant and dark future, where it is most uncertain. And going even further beyond this, a better model seeks flexibility, one that allows the user to a tell a story of the asset from the investor's singular perspective while still ensuring they are constrained by economic reality.
Therefore, this report intends to examine, borrow, respectfully criticize, and build upon some of these ideas. And ultimately, this report seeks to provide the simplest form of that model, accompanied by economic theory and financial analysis.
There's a popular joke, "1ETH = 1ETH". And its true. Yet, it is incredibly sarcastic as no one seriously believes that's true. And that's because we've all been conditioned to think in fiat-terms. This is because monetary-assets, or currency, as most monetary economist believe, follow this trajectory: Store-of-Value, Medium of Exchange, and Unit of Account. We first must trust that these assets will retain value, that there is no monetary debasement, and that the collective will, either enforced by law or driven by sheer-scarcity, enforce this relationship. Next, that store-of-value must exhibit other unique properties, such as divisibility, portability, etc., which allows it be transacted easily — that the currency itself can transmit itself across the network of participants. After consecutive conditioning, then, and only then, do goods-and-services become a unit-of-account, a lens in which you view value of the world.
The lens in which we view the value of the Ethereum economy is driven by fiat-currency. This is part-and-parcel why, when transactions fees, which are resource metered by the Ethereum Virtual Machine (EVM) in Gwei and gas consumption (data), rise from economic activity while the price of Ethereum is also appreciating against USD, there tends to be an economic collapse. Essentially, between the EVM resource metering and investor’s facing a higher unit of account in fiat terms, the Ethereum network modulates itself. Said differently, one begins to ask if trade be will worth the fees and the risk undertaken, or if the payment is worth the high cost of settlement assurances? The answer largely depends on aggregative behavior of the participants using the Ethereum network.
Everything about the model provided below is anchored upon that assumption: that participants have still not crossed the chasm of Ethereum as their default lens of the world. This is to say, they are price sensitive in both directions to the price of Ethereum. (Remember, if you disagree with this assumption, you are welcome to disregard this and think of 1ETH as 1ETH and purely project future Ethereum TXN fees in ETH terms. The choice is yours).
So let us begin by thinking about the economic activity of Ethereum in the context of the fees that participants pay, in ETH terms, and USD terms. The table below provides the total transaction fees from 2015-2023.
We can see a range of fees, in $ terms and ETH terms. At its high in 2021, investors were willing to pay $4.85bn worth of fees in that year, and then as the market reflexively unwound due to macro-economic risk, crypto-economic risk, and intra-eth reasons, the drop off of fees was a staggering 87%.
So what does a target range of Ethereum fees look like? How much are people willing to pay in steady-state (or our Terminal Year) in fiat-terms on Ethereum? Well, it largely depends on your view of Ethereum as a network. If total transaction volume is high and transaction fees are low -- both measured in fiat-terms -- then it is efficient network. Importantly, Ethereum does not just optimize for efficiency, instead prioritizing security and decentralization. This is why Ethereum's block-space is considered high-quality.
That said, there are two approaches here. One can think about transaction volume, and divide by a multiple to get transaction fees, or one can out right estimate total transaction fees investors are willing to pay on an annualized basis.
Let’s choose the latter for simplicity sake. A reasonable target is that investors are willing to pay $15 billion of fees in a single year. What does this assume? That Ethereum becomes an alternative financial settlement layer for institutions. This would imply fees per block will be approximately $5,700, assuming there are ~7200 blocks per day.
Is this unreasonable? Investors paid $4.85bn in fees in 2021 on Ethereum. This was primarily due to high ROIC opportunities and limited scalability solutions which meant the Cambrian explosion happened on L1 and many investors were forced to pay-to-play.
And, for comparison, Visa FY 22’ Fee Revenue was $29bn. One cannot compare these two networks without nuance because Visa does thousands of TPS and takes basis points cuts on relatively small transaction values. They function uniquely, but it is still a useful frame of reference.
However, one can expect that in the future roll-up centric Ethereum, these L2s will post copious amounts of call-data on Ethereum L1, effectively amortizing fees across their respective silo'd network(s). And there will be the occasional transactions directly settled on L1. The $15bn of fees assumes that economic throughput of the aggregate sum of roll-ups will go beyond NFTs, DeFi, and leak into more of the real economy. Whether this is possible or not requires a separate analysis. Nevertheless, we are setting the upper-bound at Visa's take-rate , something that the global economy is comfortably paying for the services Visa provides them, and striking it around half of that to be conservative.
At $15bn of fees on Ethereum, this is a reasonable target assumption.
Note: It has come to my attention that the fee data from Etherscan did not include burn fees. Therefore this is an underestimation of Y0 fees and, perhaps, an over-estimation of the multiple. There is an addendum below that explains the net-effects. I urge everyone to read it, but only after you complete the full-reader. This does not change the model. It only changes the outcomes.
Up until this point, we've made the assumption that users of the Ethereum network are willing to pay $15bn of fees (in USD terms). Yet, this tells us nothing about what we expect Ethereum will be worth. If we were to simply take a Price/Earnings multiple of Visa, we are looking at 25x-35x. Following this logic, if we use the higher end of the earnings multiple, we arrive at a target-market capitalization of $525bn. Ethereum is currently worth around $200bn.
However, while both Visa and Ethereum are networks to settle transactions, Ethereum is both a network and economy. Said differently, Visa is a financial intermediary sitting on the borders of the global economy and clipping fees, whereas Ethereum provides an economy and currency, a form of money, that is supported by the properties that no other economy offers in the modern fiat-world. There is no need to belabor this point, but it is the confluence of smart-contract programmability, the characteristics of a blockchain, and a predictable monetary and fiscal policy that allow for an economy to exist. If everyone were to pay in Visa shares and if Visa offered benefits that no other monetary transmitter could offer, then and only then would we begin to think about Visa as more than a simple network.
In an entirely new sort of view, one can think of Ethereum as economy that is a net-importer of Dollars (reserves) via stable coin balances on-chain, and exporter of censor-ship programmability and digital value transfer.
Alas, the question then becomes what is the correct Ethereum monetary premium to attach to Ethereum, the economy? There's no easy answer. However, let's see what the market has historical implied by looking at annualized transaction fees in USD terms. The market has valued Ethereum at 1000x fees and all the way as low as 10x (using annualized txn fees from a daily input).
The range is large so let's instead look at the average market-cap and then divide by the average txn fees to get a more smoothed-out ratio. Looking at the 4-Year Average (2018-2022) of the avg. market cap / fee ratio, it is 268x. Importantly, in 2020 and 2021, the multiple hung around 55x. This suggest that when fees are high, there is less monetary premium attached, as the demand flows from discretionary and structural buyers cannot rise further to push price and maintain economic activity.
Once again, if we were to simply rationalize and extrapolate a Visa P/E, our valuation would not be indicative of human behavior. Ethereum stands in a separate category than a simple payment network. A financial forecaster must ask themselves whether this will remain true in the future or not, and it is not shrivel in the face of historical incongruency, however irrational it may seem. Instead, they must ask if they are in the range of possibilities.
Our forecast will use a 150x ratio which targets a $2.25 trillion market-cap. Ethereum would be one of the most valuable technological assets in the world, eclipsing Bitcoin's all time high.
It is best to think about two entities in our model, but with three different purposes for each of those two entities. From a security perspective, Stakers and Non-Stakers. From a transaction perspective, Validators and Transactors. And from an economic perspective, Savers and Spenders. I believe these distinctions will be useful when thinking about the analysis going forward. And I may use these terms interchangeably.
Now that the top-line drivers are complete, we need to focus on the current-state of Ethereum and set two more drivers (yellow inputs) in our terminal year.
The five inputs (in purple) are as follows:
TTM Avg. Price. ($)
TTM Txn. Fees (E in MM)
Current Outstanding Float (# in MM)
Staked % (aka Staking %, Staker Ratio %, Staking Ratio %)
EIP-1559 % of Fees Burns
Using Justin Drake's 70% burn assumption
The Two Drivers (yellow) are as follows:
% Staked in the Terminal Year
EIP-1559 % of Fees Burns in Terminal Year
Before we can discuss the totality of the model, let's examine the life-cycle of a simple transaction in Ethereum.
When users demand a transaction be settled, they send their transaction to the mem-pool attached with a transaction fee, which can be further decomposed into three ideas.
The first, the Base-Fee, which is burnt and therefore logically reduces the total outstanding supply of Ethereum. This mechanism that determines the base-fee amount is formally known as EIP-1559, which is an algorithmic function that remediates user-transaction uncertainty and scales given the Ethereum's network congestion levels. Justin Drake, a well-known Ethereum Researcher, estimates that 70% of the total transaction fees will be burnt (and therefore is our current default assumption).
The second portion of the transaction fee is known as the Priority-Fee. In aggregate, this ETH can be thought of as a redistribution of ETH from transactors to validators. This point cannot be understated. Framed differently, ETH, the consumption-asset, is transferred from spenders to savers.
And finally, based on the number of validators, new ETH is minted into the circulating supply and distributed directly to stakers.
To caveat, issuance and burning are independent mechanisms, but on second-order level thinking, they are related to each other. And ETH, the asset, is only net-deflationary when the Base-Fees are larger than the ETH issued in a given block, or a over a specific time-period.
Now that we have an understanding of life-cycle of transaction fees combined with EIP-1559 and ETH Issuance mechanics, we can begin to complete our model.
Without going through every single calculation, we know the following: % are staked, OSU-ETH, TTM fees (E in MM), TTM Avg. Price, EIP-1559 Burn %, and lastly, perhaps most importantly, we've imagined a future Ethereum.
In Y0, if we multiply our TTM Avg. Price by our TTM Txn. Fees, we come up with an implied Total TXN fees in $544mm. The model then amortizes the difference from our terminal fees $15bn from our Y0 fees, meaning our model linearly builds up transactions fees from $554m to $15bn.
Then, we let the EVM and EIP-1559 dynamically redistribute, burn, and issue supply based on these inputs. Here is what is happening is in a different model.
Once we have a new total outstanding shares, we can imply an Ethereum Price in the following Year, or Y1 (in this instance) based on Y1's target market-capitalization and new outstanding shares.
The model repeats. And again, notice (see above) how it linearly scales to our terminal year smoothing the process:
Non-stakers increase their staking %,
Transaction Fees Scale,
The share count changes,
And the monetary premium -- embedded in Mar-Cap / Fee (x) -- tapers off to a more normalized premium.
Of course, we can see the implied price of Ethereum. Using the assumptions, Ethereum is worth approximately $18,000 per share (accounting importantly for the delta in float) at a $2.25 trillion market cap.
First, what is APR, or Annual Percentage Rate %. It’s formally defined as, “the interest rate charged by a lender on a yearly basis, expressed in the form of a percentage.” The inverse of this is APY % *, which is the interest earned.
Nevertheless, our model needs to be absolutely precise in our calculation, ensuring we are not double-counting return. And more paramount, being explicit about which entity receives that return in aggregate.
To do this, we will return to our "Simple-ETH-Model", where we can clearly see where float flows and analyze in more depth.
As a refresher, when Ethereum is minted, those new shares are directly minted to stakers. And naked $ETH holders, spenders, are debased. Moving forward in the flows analysis, there is a redistribution of a portion of transaction fees from spenders to savers, or non-stakers to stakers. This is better known as Validator rewards. And finally, to remain consistent with ETH issuance yield toward stakers, the burned ETH is retired from existence, which actually reduces the yield for non-stakers or savers. See below:
Returning to our broader model, we can see how our APR changes as our model captures the dynamism of Ethereum's monetary policy along with our projected fees.
We can see yields at 4.59% in Y0 and slowly drop over to 2.02% in steady state for savers. (We will discuss this return in more detail later).
So, when an analyst tells you that yields are expected to be 4-5% or 8-10%, that Ethereum will be worth $150K, that Ethereum will be net-deflationary... the question(s) one should be asking are: For how many years? And which variables need to remain true for that time period for this to happen? What is the market-capitalization at that price level? What are the aggregate fees in ETH terms and USD terms?
Ethereum is not complex, but the behaviors within the Ethereum economy are dynamic. Upon examination of our model, here are some examples of the levers that affect yields (ceteris paribus):
For instance, if we start with high-fees in our Y0, say $5m of total ETH Fees, then our yield in our terminal year increases to 2.07%. A slight difference from 2.02%.
If we assume the current staking ratio dramatically increases over-night from 13.59% to 50%, while keeping our target of 75% for our end-year, the Terminal Year return is 2.01%, not much difference, but still a difference. And if we play with the long-term staking rate, lowering it, then yields increase for stakers as they receive more issuance on a pro-rata basis. Further complicating this input is the existence of entry and exit queues.
If we assume a lower monetary premium, meaning, lowering our Avg. Market-Cap / Fee multiple, to 50x, yields increase to 2.66% in our terminal year. This is because we're keeping target fees constant at $15bn while there are more fees (in ETH Terms) as a % of total float outstanding being redistributed from non-stakers to stakers. And because this is the case, Ethereum is likely to be more net-deflationary in these periods where valuations remain low, but fees are high.
This list above is not exhaustive, and the interplay between these variables are worth really thinking about, which is the point of the model. (The Ethereum target price is not the point).
Nevertheless, in the beginning of this paper I discussed making the implicit, explicit. That said, I believe our model assumes some path-dependence as it builds linearly to a target. Furthermore, its important to remember that our model also captures sliver-of-time, or, an average of that time period. There will be many variations in between. This is not to say it is useless, but path-dependence can play a role, cascading into pricing, total outstanding float, returns, etc. To conclude, our terminal year continues to make explicit what our expectations are in the long-term, where that volatility is likely to be more subdued.
First, what are Staking-Ratios a function of, or, which factors drive their changes? Here's what I believe drives the relationship between staking and non-staking. These drivers are all somewhat interrelated:
Importantly, the change from PoS to PoW magnifies risk, as slashing burns large sums of your principal investment. This is the intention of Proof-of-Stake.
First, the risk-free-rate is a theoretical concept concerning credit-risk, not currency risk. Nothing is ever truly risk-free.
Most Ethereans would argue that the Ethereum Staking Return % is the RFR. Yet, if slashing can occur, this is similar to default risk. And it is even perhaps more magnified as default-recoveries are much lower than even the lowest spec-grade credit. As Vlad Zamfir said (paraphrased),"it's as though your ASIC farm burned down if you participated in a 51% attack".
What logically follows then is that one can, and should, think about Ethereum as a sovereign currency bond, as the cash-flows that are being distributed are in ETH terms. This is not to say, Ethereum cannot be become a risk-free-entity, there just needs to be enough credit history and some lindyness.
Ultimately, given our model still assumes a fiat unit of account, the staking ratio will respond to the differences between USTs yields and Staking yields.
These can be thought of as the cash-flows that the sovereign currency bond produces.
Ethereum decided to implement rate-limits to protect against attacks. Nevertheless, this policy choice can create sticky staking % ratios, and create lagged market-clearing equilibrium events.
So is 75% too high? Relative to other L1s, this seems reasonable.
However, I would argue that it is actually on the lower end, assuming rational market participants.
Returning to our model, there is a line item, "Fee Buffer Check".
Hypothetically, let's imagine there were 100M ETH outstanding and 75M of ETH were staked, which would imply there were 25M Non-Staked ETH circulating. Next, run-rate fees are projected to eclipse 25M ETH. In this scenario, spenders will have run out of currency, or cash, or digital-oil, to spend in the economy. This would force stakers un-bond and withdraw their staked ETH.
The Fee Buffer in our model ensures there is enough legal-tender circulating to pay the Ethereum Network, and a % greater than 100% is necessary. That is the upwards bounds. In general however, we would expect market-participants to hold excess-cash, just as we do in the real-world in case of financial exigency.
While validator risk and entry/exit queues create both credit and illiquidity risk for these choices, I believe during periods of normal economic growth, savers will increase their staking ratio where fee buffer ratio greater than 110% and less than 200%, generally implying staking ratios in 90% range.
(Note: I think the reason why current staking ratios are low are because of how much the cash is being held in exchanges, or other areas in the economy. Also market inefficiencies).
While on the subject on staking ratio's, we need to once again re-visit to concept of staking return %, or APR %. (I previously mentioned there would be a need to correct some fundamental misunderstandings of Staker Return % projections).
In the previous section, we've established that there is a pragmatism to Staking Ratio %, one where the fee buffer should be greater than 100% at a given point of time. How about theoretically? Is it possible that 100% or 99.9999% of the supply is staked? Yes. Although, this scenario would resemble a colossal depression where there is no economic activity, where the entire economy has decided to save instead of spend. (However, the economic implications are not important for estimating returns, which is the point of this section).
In our model using 100% as the staking ratio, the issuance APR % is around 1.50% (top-right of the model below). (Note: this is extremely close to the maximum % amount of Ethereum that can be emitted based on Ethereum's monetary policy). Adding this to the total return simply did not make sense because, logically, "if 100% of the network receives new issuance, then it's as if no one received issuance.
Therefore I resolved to backing out the issuance APR in each year to get a real-yield. Now, the model suggested a real-yield of less than 1% across our model. This initially felt right, as it was a nominal vs. real-yield thing. But upon further introspection, this also didn't make sense. They seemed to be receiving more than .48% in Y0, how could they not?
There was an intense bout of internal cognitive dissonance: how could I believe that yield does not exist in the theoretical 100% staked scenario, but also believe in the existence issuance yield when there is less than 100% of staked?
The answer lies in understanding a stock-split and being precise with our definitions.
For the former, a stock-split is just how it sounds, the shares are split, sometimes 1/2 and sometimes even further.
Let's examine a stock-split with another simple model. Let's say there Alice owns 50 shares (Share A) and Bob owns 50 Shares (Share B), and there is a stock-split, which issues 10%, or 10 shares, and distributes it pro-rata to Alice and Bob. The ownership of the company does not change. However, there is a re-pricing of the shares.
It bears repeating, if everyone receives new shares on a pro-rata basis, it is a simple re-pricing of shares as the market-capitalization hasn't changed
Now let's imagine there is new issuance, but instead of it being a traditional stock-split, the issuance only hits Alice (Share A).
What's happening? Well, new shares are being issued, but unlike a traditional stock-split, the issuance is not pro-rata.
And perhaps even more saliently, let's examine APR % in both scenarios.
Notice how both Alice and Bob in the traditional stock-split capture share-issuance yield of 10%. And notice how in the second scenario, where shares are issued only to Alice, Alice still receives the equivalent APR % of 10%. This is because APR % or Staker Return % measures the raw % change in shares rather than the change in ownership. This, in my opinion, is a fundamental miscalculation (which I've alluded to above).
The conclusion is that Staker Return % or APR % is an incredibly crude metric to measure returns when what we're really seeking to measure is dilution, or the change in ownership. This is to say, Alice's change in ownership is 4.76% as opposed to a 10.0% APR, and it should be thought of as her return!
If we continue to follow our Simple ETH model through EIP-1559 and fees, we can really begin to see how ownership of the Ethereum network changes, as well as seeing how APR % functions. We'll use our Y0 assumptions and inputs.
Alice, or Stakers, have increased their ownership by 4.15% as opposed of the 4.57% APR figure. And Bob, or Non-Stakers have decreased their ownership by .65% as opposed to a APR % decrease of .25%.
The differences may not seem like much, but they do matter, particularly because when Ethereum is net-deflationary, APR% over-states the gain and vice-versa.
How do we make even more sense of what's happening?
In 2020, there was an extraordinary amount of stimulus in the US's Covid-19 American Rescue Plan. And it was there that the Cantillon Effect became rather evident, where portions of the stimulus were distributed out in different time periods and different amounts, yet all market-participants were aware of the scale of the stimulus. To make matters worse, equity markets were soaring and there were many non-affluent workers who were delinquent on their mortgages or rent payments.
In any case, the Cantillon Effect means that those who are close to the creation of the money-supply -- GSEs, Commercial Banks, Corporations, HNWIs, Money and Hedge Funds, Small-Businesses, Special interest -- are able to essentially hedge the effects of that monetary debasement as they are the first parties to spend on good-and-services which have not yet been inflated. Importantly, the Cantillon Effect is not exactly concerned about monetary inflation, or even monetary deflation, but the distribution of that monetary inflation and/or deflation.
As is the case in our "Simple-ETH-Model", the ETH issuance is not distributed pro-rata, and is only distributed to stakers. There is, in my opinion, a Cantillion Effect occurring, and a Cantillon Yield being harvested. Importantly however, in Proof of Stake, there are no barriers to entry to becoming a Staker, whereas in Proof of Work, the barriers were outsized capital requirements that created outsized returns for Miners. This is to say, Miners were able to harvest much of the Ethereum issuance as they were the first network articipants to capture the monetary inflation. Non-Stakers then must ask themselves why they wouldn't capture that yield? The answer to this question is a little more complicated, but in general, this is why I believe the staker % ratio will increase, and it will be bounded more or less by the fee-buffer mechanism.
And this is all the more reason to use the % change in ownership as a return %. So returning to our model, here is the true return.
The point of this paper is not to talk about price targets. But it is worth noting how market-behavior will largely respond to the change in Ethereum monetary policy.
First, it is undeniable that Ethereum Issuance from PoS to PoW will drop by 80-90%. And it is largely true that there will be less Cantillon Yield that miners can harvest, as the cost-structure for PoS validating is much more forgiving than PoW ASIC farm mining. Additionally, PoS returns are much lower-duration. So, there are indeed less structural sell-pressures. However, I believe there could be over-estimation of the liquidity effects between staked-ETH and naked-ETH, where certain analyst believe that between staking and decentralized-finance, there will be limited supply of float on the market. This projection is wanting.
Second, the demand side is still manifesting. And it is still dependent on the "Ultra-Sound-Money" meme, or, in model terms, increasing the monetary premium multiple as opposed to only the actual fee-generating capabilities. This still ultimately separates itself from Bitcoin, an unviable financial layer combined with a static and at-risk monetary policy. In our model, we lowered the monetary premium, but it is possible that Ethereum becomes much more valuable as a form of currency as more fees are generated in the economy -- (MV = PQ) would imply this.
Ultimately, I believe Ethereum has created a large supply reduction, and the monetary policy is well-designed, algorithmically adjusting beautifully based on economic activity.
There are still some lingering questions that I did not touch upon in this piece. However, I believe enough has been answered here. Below is the final model.
As we discussed in the introduction, I believe this is the simplest model one could construct (and much more complexity could be built into it). I hope there will be more opacity behind staking return % and staking ratio %, and more investors will be cognizant of how Ethereum economics behave.
And last but not least, I am deeply grateful to:
@CometShock for walking me through the transaction life-cycle in the context of the Simple Ethereum Model.
@StefanPatatu for reminding me of the Cantillon Effect in Twitter DMs. Hopefully Cantillon-Yield becomes a term.
@Chasedeven for a first-draft pass.
@3pointO_Cap on Twitter
@alphaketchum on Twitter
It has been brought two my attention that the data I sourced from Etherscan was not inclusive of the burn-fees. Therefore my model underestimates transaction fees.
Does this change the integrity of the model?
Does this change the valuation?
Yes and No. Yes, because what this means is that the Mrkt-Cap / Fee Ratio should be lower, meaning the market values Ethereum on average much lower, perhaps 50x -- using the last 3 years. So instead of $15bn * 150x, we would would multiply by 15bn * 50x.
And no, because, we know investors were wiling to pay not ~5bn (in 2021) but actually $10bn in 2021. One could argue that the terminal year fees could be higher. And one could even argue that the multiple should be larger than 50x as Ethereum increases its SOV status as it becomes much more mature. If fees rise but the multiple lowers, we could come up with a similar valuation.
As I stated numerously, there are many ways to estimate a multiple, and it depends on your view of Ethereum in the future.
Does this change the yields?
Ultimately, again the point of the piece is to see how the drivers of Ethereum behave. And it is not about the valuation.