Valuation Approach

We derive ETH’s valuation from first sizing the TAMs that ETH participates in, and then approximating ETH’s potential market share within such TAMs to calculate network value. We then divide network value by our forecasted ETH supply to derive a per unit price. Consistent with our BTC valuation, we use 2030 as our terminal benchmark year.

We believe ETH’s network value is rooted in:

“Store of Value” (SoV) “Gas Fees”, from:

(a) Transactions

(b) General Computing

Before diving into our specific assumptions, we’d like to shed light on an alternative valuation methodology that has surfaced in several other places on the web. There’s a point of view out there that the way to value ETH is by applying a “staker multiple” to the amount of the network’s annual gas fees. So for example, if the ETH network facilitates $30B of transactions in a year (i.e. the network generates $30B of fees for stakers), then one should apply a multiple — call it, 10x (or another multiple implied by a view on expected return) — to the $30B fee base to justify a $300B value. This $300B would be the market cap of ETH attributable to the Gas Fees use case.

We have debated this point internally and ultimately believe that the “staker multiple” framework is incorrect because we’re NOT valuing a node. We are valuing the network, and a standalone token is just a piece of the network that does not generate a node’s dividend-like periodic return. If we’re valuing a node that generates $1M worth of ETH fees every year, then yes, an investor comfortable with a ~10% yield should be willing to pay $1M * 10x = $10M for that node.

Valuing ETH is more like valuing a commodity than a company with prospect of future cash flows, where the market prices each unit based on supply and demand. Since we’re valuing a token (i.e. the network value) the math should instead be: ($Gas Fees / year) / network velocity = ETH market cap needed to facilitate annual gas fees. This is similar to the MoE framework laid out in our BTC article linked above.

Store of Value

Building on our view on Bitcoin’s valuation, we believe that Ethereum shares similar characteristics as Bitcoin as a SoV, despite this not being the intended primary use case. We also believe current owners and investors of ETH are treating the token as a SoV. Further, the SoV use case is less of a natural “monopoly” than it is a natural “oligopoly”, with ETH being among the few tokens with relative liquidity, broad mindshare, and easy accessibility. ETH could also potentially be a better SoV / MoE / Unit of Account token over BTC given:

Currently faster network (15–25 TPS, vs. BTC’s ~7) and cheaper transaction fees than BTC (less than a dollar, vs. BTC’s $20+), with a path to order of magnitude gains from near term scaling solutions. Rollout of Proof-of-Stake (“PoS”) consensus mechanism granting the prospect of near-term scalability / throughput, prevention of wasted electricity / hardware resources, and economic finality. Governance confidence, in light of actual organizational and enterprise leadership

(a) Ethereum Foundation — demonstrated commitment to continual innovation via planned milestone releasers, core developers soliciting feedback from larger community, thoughtful experimentation of new features

(b) EEA — large organizations that have banded together to ensure ETH is a suitable platform for reliable, enterprise grade tech

(c) Less politicking than BTC, at least for now

We’ve used the same assumptions on the SoV TAM as our previous BTC article. As a reminder, our base case BTC model was premised upon BTC taking 16% of crypto SoV value in 2030. For ETH, we’ve assumed a much lower 5% share, as the SoV use case since isn’t the explicit cited goal of the Ethereum Foundation. Ironically, our valuation still overweights ETH’s SoV value over its “core” Gas Fees use case. While this may be a little concerning on the surface, the combination of its SoV and Gas Fees use case can also serve as a diversified bet on appreciation potential, as upside can come from two relatively decoupled use cases.

Gas Fees

The Ethereum Foundation’s intended primary use case for ether is “crypto-fuel” — ether represents the scarce economic unit that can be used to purchase computing power on the decentralized world computer. Ether fees, denominated in smaller units called gas, must be paid whenever someone makes a request to utilize the network’s resources, be it sending transactions across wallets, or deploying a smart contract onto the mainnet. We break down the network value attributable to Gas Fees into 1) Transaction fees (people sending ethers to each other) and 2) General Computing fees (non-transaction-related requests), the latter of which should comprise the majority of aggregate Gas Fees as the network matures.

On a LTM basis, 94% of Gas usage is derived from Transaction fees, with only 6% from General Computing fees. In other words, a long position in Ethereum is a bet that General Computing usage would substantially rise in the future. Given the amount of ICO activity in 2017 and current ICO pipeline, as well as the pending scaling solutions, we believe Ethereum’s fundamental usage should help it to grow into its current valuation.

Transaction Fees

Transaction fees currently represent a small portion of Ethereum’s network value. On a LTM basis (as of December 2017), we estimate that just slightly north of $20mm in ether/gas has been spent as transaction fees (versus total gas used of $22mm), based on our analysis of daily blockchain data from etherscan.io. This figure reflects ~500% growth over the same LTM period last year. Needless to say, Transaction fees are currently a negligible contributor to ETH’s $75 billion network valuation and will likely maintain its small contribution relative to other value drivers going forward.

Volume: In our base case, we assume the growth in transaction volumes will mirror the growth in our General Computing fees as described below, as more general computations should encourage increase transaction volume (e.g. B2B or B2C payments for computational services). Transaction volume growth could very well be higher than our current forecast, but we assume a one-to-one correlation with General Computing fee growth for conservatism.

Average Fee: Based on our analysis of blockchain daily metrics, we have implied that the average transaction fee hovered just below 30 cents per transaction in 2017, an order of magnitude greater than a penny in 2016 (as a result of rising gas prices). Going forward, we assume a modest 10% YoY increase in the average transaction fee.

Network Velocity: Armed with these assumptions, we project that Transaction fees will amount to around $9 billion in 2030, and contribute only $1.6 billion to network value after accounting for network velocity of 4.0x. Recall that we used a higher 5.5x velocity for BTC. For BTC, we thought that arguments for low velocity (HODLers, SoV use case) and high velocity (high frequency technical traders) would roughly offset each other so that M2 velocity of 5.5x would be a reasonable proxy for BTC velocity. The lower velocity assumption for ETH at 4.0x reflects its run-rate Proof-of-Stake (PoS) mechanism. This incentivizes longer term holdings (over BTC’s proof-of-work system), as PoS validators must lock up significant amounts of ether during a bonding period wherein they are entitled to earn fees for securing the network.

General Computing Fees

As Ethereum has the potential to be broadly disruptive and is still in a “beta-esque” stage of development, it is extremely difficult to gauge which sectors specifically its technology will take share from, and quantifying such share gains are even harder. As a reminder, our analysis is conducted more as a thought experiment to give us directional conviction than a true model worthy of underwriting returns in the context of a traditional investment committee. Still, we believe there is merit in forcing ourselves to think about key levers of value.

We believe that 2030 is a reasonable target for network maturity and operation under a steady state — this may very well be realized earlier but we prefer to err on the side of caution. At present, annual Gas Fees are only slightly above $20mm, with the vast majority coming from Transaction fees as described above. Our analysis of blockchain daily data yields an estimate of 2–5% of current Gas Fees attributable to non-transaction, General Computing fees. This makes sense as no ERC-20 dApps are currently really past a beta stage. We believe 2018 will see the beginning of commercial dApp rollouts. General Computing gas fees will be gradually fleshed out, increasingly representing proportionally more network value relative to Transaction gas fees.

Benchmarking: We have benchmarked our analysis against broader metrics in order to sanity check our assumptions. In summary, we think our estimates are already optimistic in getting to ~$2T of annual gas fees by 2030. One would have to be pretty aggressive to get conviction around a more bullish General Computing fee forecast than what we’ve already laid out.

As it is, we assume that by 2030, 1/3 of global GDP would fall under ETH’s TAM, and that ETH would take ~5%-10% of this TAM. In other words, ETH contributes ~1.7%-3.3% of global GDP by 2030. This is consistent with the World Economic Forum and Deloitte’s estimate that “by 2025, ~10% of global GDP will be stored on blockchain”^2 (which implies the figure would be greater by 2030). Today, Ethereum represents more than 12% of total crypto market cap. We also benchmark our projections to global oil demand — another reference helpful in illustrating potential market size, and particularly apt as ETH bulls like to compare the asset to “digital oil”. One would have to believe that ETH facilitates an equal amount of demand as global oil demand of $2T, the latter of which (incl. its derivatives such as gasoline and diesel) has a near monopoly on transportation fuel. While it is unlikely that ETH, AWS, or Azure would command this level of monopolistic clout, these players may share an oligopoly in a larger market, if cloud computing were to take an increasingly greater share of global spend.

Market Share: Our forecast methodology involves taking a handful of existing markets that we think are most eligible for an ETH-based solution as a proxy for ETH’s total market size. We believe of course, that the potential applications may range outside these sectors, but that these represent the largest industries in which decentralized computing has relatively straightforward utility.

To derive the above estimates, we have taken industry market sizes from research reports and other third party estimates, growing industry revenue forward at their prescribed rates. We have proposed ETH market shares of 5–10% across these sectors as a straw man, which reflects our internal assumptions and accounts for the fact that 1) a platform like ETH may be able to compete for such services at a lower cost than current incumbents and 2) ETH may not be a natural monopoly so competing platforms like NEO/QTUM may make inroads in these markets as well. Cloud computing is the most obvious use case for ETH’s breed of blockchains, but the use cases are much broader as this technology may also ultimately disintermediate more traditional markets than have not been automated by centralized solutions.

We may publish a more detailed post outlining our rationale for each of these “disruptable” industries in the future, but have included only summary descriptions and thoughts for now:

Cloud Computing

Distributed runtime and distributed computing with 24–7 resiliency and redundancy

Renting out storage, idle clock cycles — crowdsourced computing power

Control over data and anonymous computations

Current ERC-20 projects include Golem, SONM, Storj

Note: While this represents the most logical use case, we did not assume a larger share for conservatism, as certain computations that run on centralized clouds probably still stay centralized. For instance, certain sequential calculations may lend themselves more to vertical scaling (enterprise-grade centralized hardware), vs. ETH’s horizontal scaling (lots of consumer grade hardware)

Financial Services

Disintermediation of trusted third parties who are making markets, brokering transactions, and otherwise facilitating currently complex payment rails and networks, and other financial services

E.g. several intermediaries involved in SWIFT transactions who all take a cut

Democratize services previously only available to HNWIs / developed world to underserved and unbanked segments (e.g. tax services, active investments, general banking services)

Automated settlement of financial contracts (e.g. derivatives), decentralized trading, fraud protection

Current ERC-20 projects include ICONOMI, OmiseGo, Stellar Lumens, Binance Coin, SALT, 0x

Insurance

Blockchain as trusted third-parties in P2P insurance networks for underserved populations / hard-to-place risks or for current insureds but with lower fees

Improvement of trust, higher quality underwriting from crowdsourced data

More efficient claims handling and fraud protection via smart contracts

Current ERC-20 projects include Wetrust, iXledger, Zeusshield

Legal Services

Displacement of lower-level, commoditized legal services through smart contracts

Potential obviation of certain legal practices (e.g. IP, if it can be tracked immutably on the blockchain)

Current ERC-20 projects include BlockCat, Etherparty, Agrello, Mobius

Gambling and Prediction Markets

Private, cheap, and accessible; odds guaranteed to be fair (transparency)

Broad points of failure (i.e. no single “house”)

Bespoke micro-bets and lower fees than current solutions (e.g. Vegas takes a large cut of books)

Forecasting and data analytics agencies via wisdom of the crowd

Current ERC-20 projects include Augur, Gnosis, DecentBet, Unikrn

Other / New Markets created

All other basket — ranging from retail to social media to digital plants^3

Current ERC-20 projects include CryptoKitties, Shopin, Basic Attention Token, Status, Spankchain

Ideally, we would have also liked to look at a bottoms-up build in sizing the potential ETH market by mapping out the unit economics of gas fees derived from both transactions and computations. However, it is extremely hard to do so with sufficient conviction based on the nascence of this use case and the lack of transparency and data behind average computation volume and average computational complexity (to estimate gas cost per operation).

For some context, we have included the below table outlining selected operations and their associated gas fees, based on the article here^4 and the Ethereum yellow paper (appendices G and H).