Matthew Sigel: Now there may be a point when that turns, and I do think that we’re going to need institutional adoption and we can get into how we define that to power the next leg of this bull market.
Dominic Weibel: Did we ever have such an asset? A retail driven asset?
Matthew Sigel: Lots of collectibles are like that. If you look at how people in China, for example, want to get their money offshore, a lot of times they’re using Rolex watches. That’s a type of asset class that individuals use to transport and transact. There’s less institutional participation. It’s collectible commodities with unique characteristics that preserve purchasing power. And Bitcoin now, it’s 15 years of franchise value. It’s a brand now. Stanley Druckenmiller was just talking about that last week. I think that’s what appeals to young people.
Moreover, Bitcoin does have a use, it absorbs stranded energy. The oil, gas and coal companies, especially outside of the U.S., that are facing bigger barriers to get their product to market either because of carbon intensity targets or U.S. sanctions, find a beneficial set of characteristics in Bitcoin. It provides an incredible sink to monetize stranded energy and move it anywhere in the world. I noticed that Argentina’s largest private oil and gas company that accounts for 15% of domestic Argentinian production just announced that they will be mining Bitcoin with the excess gas that is created in the flaring process.
Dominic Weibel: I would like to recycle back to Ethereum arguably being on a path to a valuation model that might be more akin to the Bitcoin one. Interestingly Bitcoin might also come to the Ethereum cash flow side now that we see elevated development activity around L2s, the BitVM or zk-rollups for Bitcoin. Of course, that would be awesome without changing opcodes at the base layer, but even if it’s necessary, it will be overall beneficial, as Ethereum proves that fees follow functionality. Currently however, Bitcoin does not offer too much functionality, but at some point, that might change, bringing cash flow aspects towards Bitcoin too.
In Ethereum substantially different aspects are at play. For instance, we have reward emissions in the form of staking rewards, and a dynamic that is akin to buybacks in the stock market, Ethereum’s burning mechanism. Both play a vital role in your valuation models and fairly so.
And so do transaction fees and MEV (Maximal Extractable Value, editor’s note), basically every value that can accrue at the base layer. if you don’t mind, please guide us through your Ethereum analysis framework and don’t hesitate to share your bull and bear cases for ETH based on your cashflow multiples.
Matthew Sigel: One way that might be helpful is comparing and contrasting the Solana price target to the Ethereum price target. Our base case for Solana implies about a 10x upside. And that happens with Solana achieving just a 30% market share in terms of the value being intermediated among all open source blockchains. For ETH, we get 5x upside, and that’s assuming that ETH captures double the market share, hence double the market share of Solana. We need 70% market share for that 5x. With simple math like that, you can see that in a vacuum, risk reward for Solana appears to be a lot better than Ethereum. There’s very little in the price in terms of what the market is expecting on their terminal market share.
Now you also have to account for the volatility of the token, some of the unique supply demands of the token, the level of uncertainty around the future roadmap, or the lack of any meaningful TVL on Solana. But in a vacuum, you’d have a higher Solana weighting than Ethereum in our fundamental model because of the upside that can be generated with half the market share.
The key variables that we look at, I mentioned those three pillars earlier, are penetration, market share, and monetization. In our Solana model, we’re assuming that the token captures value at one fifth the level of Ethereum. Thus, the blended Ethereum token take rate comes out to about 2% in our model when you combine finance, metaverse, social, gaming, and infrastructure. For Solana, we’re assuming about 60 bps blended, therefore a much lower take rate. The network is engineered from a perspective of abundance, whereas Ethereum appears to be engineered from a perspective of scarcity by pushing all those transactions to L2s.
Dominic Weibel: That’s highly interesting Matthew. In the last weeks, ETH snapped back into a slightly inflationary environment and at the same time we see increased rollup activity.
Matthew Sigel: There’s not too much activity on the base layer. That’s one problem of this month’s rally. The flows are coming from ETNs. We can also see the ETH to BTC ratio breaking down. It looks quite similar to me as it did in late 2019 when BTC had bottomed and was kind of in a choppy but upward range. The market didn’t sound all clear that we’re into this new bull market until the BTC halving and then ETH dominance rose along with the entire market cap. My framework is that we’re in that late 2019 parallel. Same thing’s happening with Bitcoin miners. They’re lagging the Bitcoin price pretty dramatically here, which also occurred pre-halving in 2020. For ETH, I just think we need to see more on-chain activity. That’s what drives gas fees. That’s what drives monetization of the token while for BTC, the use case is hodling.
Dominic Weibel: I fully agree from the cycle perspective, it’s very similar. However, while being in a slightly inflationary environment of ETH issuance, we also see Ethereum’s rollup centric roadmap materializing. Transactions happening on L2s are now at a scaling factor of 4.8 compared to base layer transactions. Is there a case for Ethereum, where blockspace becomes abundant via L2s and we face a scenario similar to Solana?
Matthew Sigel: I think that’s the thesis, but even L2 transactions started tailing off. I think it was in August, which really corresponded with the new leg lower that we’ve seen in the ETH-BTC ratio.
Dominic Weibel: They peaked, yes. Moreover, I guess a good amount of activity is not organic based on airdrop farmers.
Matthew Sigel: You can tell from our approach that we’re trying to make money by taking a slightly longer-term view than the high frequency folks. The volatility of market share within the L2s is quite high and unpredictable to justify large positions in those types of tokens. From my perspective, it’s more of a wait and see on those.
Dominic Weibel: The layer two narrative is very alive. Monitoring how much value accrues to these rollups is highly important. Several dashboards indicate that we are and will deal with substantial cash flows to L2s as they rake the delta between transaction fees paid to the base layer and the transaction fees collected in the first place, but also the MEV accruing to rollups, something to watch.
Moreover, there are rather established players already. From early on, Arbitrum and Optimism could gather quite significant market share and made important progress on the decentralization side. So yes, while a volatile space and no clear winner yet, Lindy Effects are at play.
I’d like to refer back to valuation models, especially looking at cashflow assets. How do we consider the difference in supply impact compared to stocks? What I’m talking about is that neither dividends in the most common case nor buybacks in stock markets change the total supply of the underlying stock. There’s no burning of supply for instance. And if there’s a dividend paid, it’s usually in fiat terms and not based on inflationary emissions. Yet for both Ethereum dynamics like burning and staking rewards, we have a material supply impact that alters the total and the free float supply.
Another important difference is that digital assets have way less overhead and substantially higher margins than stocks. I wonder, how did you account for these two mechanics in your valuation models?
Matthew Sigel: Well, I would slightly disagree with the premise that equities who buy back their shares don’t occasionally see declining share count. They do. And that declining share count can make their earnings per share look a lot better. You can see that with many equities that ended up having negative equity. It is possible to buy back shares, decrease your share count and make your stock go up because of that. In the case of digital assets, determining the terminal supply is one of the biggest unknowns that we have. Every other Ethereum model essentially uses the price of Ethereum in order to predict the price of Ethereum, which doesn’t make any sense to me. You have to start with some top-down beliefs about what the penetration levels of this asset class can be, and then what the take rate for the network can be benchmarking it against existing networks with similar functionality. The whole use case here, I think, is that this is a deflationary asset class that brings automation to existing workflows. It should enable cost savings and consumer welfare because of that. So that’s of how we think about it.