Fundamentally, blockchains are asset ledgers. This suggests that they are good at three things:

1. Issuing assets
2. Moving assets
3. Programming assets

Objectively, any crypto use case that harnesses any of these features necessarily benefits from a structural advantage by nature of being on-chain. Similarly, any use case that does not harness these features does not gain a structural advantage. Most of the time, the unlock is instead ideological.

While decentralization, privacy, and censorship resistance are by all means admirable pursuits, the latter meaningfully reduces the TAM of programmable asset ledgers to a subset of idealists. It has become increasingly clear that the path to mass adoption will be paved by pragmatism — not idealism. Accordingly, this essay aims to focus on the former use cases — those that would be objectively worse products without blockchains:

1. Tokenization
2. Decentralized-Virtual-Infrastructure-Networks (DeVin)
3. Decentralized-Physical-Infrastructure-Networks (DePin)
4. Stablecoins and Payments
5. Speculation

Before diving in, I want to underscore two things.

First, the following theses are meant to be derived from first principles. This means not simply retrofitting crypto as a solution to a problem. Instead, it means identifying problems that will remain — irrespective of crypto — and then proactively underwriting whether or not crypto can enable a structurally superior solution.

Secondly, the following essay aims to be as nuanced as possible. As humans we have a natural tendency to be reductive. Our brains like things that sound nice and simple. Reality, however, isn’t simple. It’s nuanced.

Thesis #1: Tokenization

Financial assets can generally be split into two categories:

1. Those that are securitized
2. Those that are not

While this may seem like a trivial distinction, it is essential to understanding how our existing financial ledgers work. Assets that are already securities have two important characteristics that non-securities do not.

First, they have a CUSIP. A CUSIP is a unique 9-character alphanumeric code assigned to financial instruments like stocks, bonds, and other securities. The CUSIP for Apple’s common stock for example is 037833100. While North America uses CUSIPs, the rest of the world uses ISINs, which incorporate the CUSIP as part of a broader 12-character code. Importantly, both codes are used as a means of fostering trust through standardization. As long as an asset has a CUSIP everyone is able to operate on the same page.

The second unique feature of securitized assets is that they almost all settle with a canonical clearing house. In the US (and globally to some degree) this is the Depository Trust & Clearing Corporation or DTCC. The principal job of the DTCC and its subsidiaries is to ensure that all trades are cleared and settled smoothly.

Let’s say you buy 10 Tesla shares on Robinhood for example. The trade is sent to an exchange or market maker to match with a seller. The DTCC’s National Securities Clearing Corporation (NSCC) then steps in to clear the trade, ensuring both sides follow through. Finally, the Depository Trust Company (DTC), another DTCC arm, settles it the next day (T+1) by moving your $2,500 to the seller and transferring the 10 shares to Robinhood’s account held at DTC. By the next day, your Robinhood app shows that you own the shares.

When people say blockchains will replace our financial rails and enable faster and cheaper settlement, they’re either implicitly or explicitly referring to replacing DTCC and its closed, centralized asset ledgers. However, while blockchains may offer numerous structural advantages given their open and programmable nature — e.g. eliminating batch processing and T+1 settlement, improving capital efficiency, embedded compliance etc — there are two reasons why blockchains will struggle to replace DTCC:

1. Path dependence: The combination of (1) an existing security standard (CUSIP / ISINs) with (2) the two sided network effects underpinning DTCC as the canonical settlement layer, make replacing the incumbent model nearly impossible. DTCC’s switching costs are incredibly high.
2. Structural incentives: The DTCC operates as a highly regulated clearing house owned by its users — a consortium of major financial institutions, including banks, broker-dealers, and other participants in the securities industry (e.g. JPMorgan Chase, Goldman Sachs, etc.). Said differently, the same entities that would collectively have to agree to adopt a different settlement system concurrently have a vested interest in the existing one.
3. T+1 nuances: Similar to payments (as we will discuss), there are reasons beyond antiquated infrastructure for t+1 settlement that blockchain may not explicitly solve. First, brokers don’t always have available liquidity to fill orders instantly. A one day buffer gives the necessary time to secure funds via loans and bank transfers. Second, DTCC’s netting settlement process reduces the amount of trades that need to be executed (e.g. 1,000 Tesla buys and 800 sells net to 200). Intuitively, this process is more effective over a longer time horizon. Settling trades instantly would meaningfully increase the total amount of transactions executed — something that most (if not all) blockchains could not handle today. For context, DTCC did $2.5 quadrillion in annual volume in 2023.

Put succinctly, it seems a lot more likely that the existing financial rails will get updated by DTCC themselves rather than replaced by blockchains. Consequently, this means that any securities traded on-chain will remain secondary-issued by definition. In other words, they will still need to settle with DTCC on the back-end. Not only does this undermine any structural advantage theoretically provided by blockchains, but moreover, tokenization comes with additional costs and complexity of oracles to reconcile price feeds.

As a result, this reduces the value prop of on-chain securities to something far less compelling: opening a regulatory arbitrage for non-KYCed entities to access and use securities in DeFi. While there is certainly unmet demand here, especially in emerging markets, it is a fraction of the market for primary issued assets.

However, this is not to say that blockchains have no role to play in the context of tokenized securities. While domestic clearing houses work “good enough” today and won’t get disrupted for structural reasons, global interoperability across these clearing houses is nonetheless suboptimal (settlement is often t+3). Perhaps a more compelling opportunity for blockchains is serving as the global reconciliation layer between domestic clearing houses. Given their borderless nature as open asset ledgers, blockchains could reduce the settlement of international trades from T+3 to near-zero. More interestingly, this could be a strong wedge to eventually eat into domestic settlement without the infamous cold start problem. As we will discuss later, this same logic seems to apply in the context of payments as well.

Unlocking Long-Tail Liquidity

This brings us to our second type of financial asset — those that are not securities. By definition these assets do not have CUSIPs and are not reliant on DTCC and our existing financial rails. Most of these assets instead are traded through bilateral transactions (or aren’t traded at all). Examples of non-securitized assets include private credit, real estate, trade finance receivables, intellectual property, collectables, and stakes in private funds (e.g. PE, VC, and hedge funds). Today, there are a two main reasons why these assets are not securitized:

1. Heterogeneity: Securitization requires homogenous assets that can be easily pooled and standardized. The aforementioned assets are mostly heterogeneous — each real estate property, private loan, receivable, fund stake, or painting has unique features, making them hard to aggregate and standardize.
2. Lack of active secondary markets: These assets also lack a canonical secondary markets akin to NYSE for stocks. Therefore, even if they were securitized and settled with DTCC, they wouldn’t have the exchanges to ultimately connect buyers and sellers.
3. High barriers to entry: The process to securitize an asset often takes over six months and costs issuers $2M+ in fees. While some of these steps are necessary to ensure regulatory compliance and trust, the process is unnecessarily lengthy and costly.

Coming back to the broader premise of this piece, as programmable asset ledgers, blockchains are good at three things — each of which solve the aforementioned pain points:

1. Issuing assets: While the barriers to securitizing assets may be high, tokenizing these assets on-chain comes with less friction. Moreover, this doesn’t have to come at the expense of regulatory compliance as this logic can be embedded into the assets themselves.
2. Moving assets: By providing a shared asset ledger, blockchains provide the back-end infrastructure for front-ends to build unified liquid marketplaces. Other markets (e.g. lending, derivatives etc.) can also be built on top to foster greater efficiency.
3. Programming assets: While DTCC runs on decades-old systems, including languages like COBOL, blockchains unlock the ability to program logic directly into assets. This suggests that heterogenous assets can be simplified or packaged into tokenized vehicles through embedding more nuanced logic into these products.

Put simply, while blockchains may provide a marginal improvement to DTCC for existing securities, they offer a step function unlock for non-securities. This suggests that the logical adoption arc of programmable asset ledgers may start with the long-tail. Not only does this intuitively make sense, but it’s also consistent with the adoption of most emerging technologies.

The MBS Moment

One of my more non-consensus takes is that the mortgage backed security (MBS) was one of the most important pieces of technology of the past 50 years. By simply turning mortgages into standardized securities that can be traded on a liquid secondary market, the MBS improved price discovery through a more competitive pool of investors, eroding the illiquid premium historically baked into mortgages. Said differently, we owe the ability to finance our homes meaningfully cheaper to the MBS.

Over the coming 5 years, I expect nearly all illiquid asset classes will undergo its “mortgage-backed security (MBS) moment”. Tokenization will lead to more liquid secondary markets, more competition, better price discovery, and most importantly, more efficient allocation of capital.

Thesis #2: Decentralized-Virtual-Infrastructure-Networks (DeVin)

For the first time in human history artificial intelligence will eclipse human intelligence across almost every domain. More importantly, this intelligence won’t remain static — it will continuously improve, it will specialize and collaborate, and it will be near-infinitely replicable. In other words, imagine if we took the most effective individuals across every domain and we replicated them near-infinitely (bound only by compute) and then we hyper-optimized them to seamlessly collaborate with one another.

Put simply, the impact of AI will be big — and likely a lot bigger than our linearly-programmed minds want to intuitively anticipate.

Naturally, this begs the question: will blockchains, as programmable asset ledgers, have a role to play in this emerging agentic economy?

There are two ways that I expect blockchains will augment AI:

1. Resource coordination
2. Serving as the economic substrate for agentic transactions

For this thesis we will principally unpack the former use case. If you’re interested in the latter, I wrote a dedicated piece a few months ago here (TLDR: blockchains may underpin the agentic economy but it will take some time)

Commodities of the Future

Fundamentally, there are five core inputs that AI, and agents more specifically, need to function.

1. Energy: Electricity is the power sustaining AI hardware’s operation. No energy implies no compute, which means no AI.
2. Compute: Compute is the processing capacity driving AI’s ability to reasoning and learning. Without it, AI can’t process inputs or function.
3. Bandwidth: Bandwidth is the data transfer enabling AI connectivity. Without it, agents can’t collaborate or update in real time.
4. Storage: Storage is the capacity holding an AI’s data and software. Without it, AI can’t retain knowledge or state.
5. Data: Data provides AI with the necessary context to learn and respond.

For sake of this thesis, we will focus on the first four. To understand one of the more compelling use cases for programmable asset ledgers in the context of AI, it’s important to first understand how compute, energy, bandwidth, and storage are procured and priced today.

Unlike traditional commodity markets where pricing dynamically adjusts according to supply and demand, these markets generally function off in-flexible bilateral agreements. Compute for example is predominantly sourced via long-term cloud contracts with hyperscalers like AWS or direct GPU purchases from Nvidia. Energy procurement is similarly inefficient. Data centers negotiate fixed-rate power purchase agreements (PPAs) with utilities or energy wholesalers (often years in advance). Storage and bandwidth markets also suffer from similar structural inefficiencies. Storage is purchased in predetermined blocks from cloud providers, with companies often over-provisioning to avoid capacity constraints. Likewise, bandwidth is acquired through inelastic commitments with ISPs and CDN providers, again forcing companies to prioritize peak capacity needs over average utilization.

The common thread across all these markets is the absence of granular, real-time price discovery. By selling resources through rigid tiers rather than continuous price curves, the existing system trades off predictability for deadweight loss as buyers and sellers cannot efficiently coordinate. By definition this leads to one of two things: either (1) capacity is wasted or (2) business are constrained. Notwithstanding, the net effect is suboptimal resource allocation.

Programmable asset ledgers offer a compelling solution to the aforementioned problem. While these resources will likely never be securitized for the reasons mentioned in the previous section, they can nonetheless be easily tokenized. By providing the substrate for compute, energy, storage, and bandwidth, to be tokenized, blockchains are theoretically able to unlock liquid markets and real-time dynamic pricing for these resources.

Importantly, this is not something existing ledgers can accomplish. By nature of being programmable asset ledgers, blockchains solve have five structural advantages in this context:

1. Real-time settlement: An asset ledger that takes days or even hours to settle the exchange of these resources undermines the efficiency of these markets. Blockchains, by nature of being open, borderless, 24/7, and real-time, ensure these markets are unencumbered by latency.
2. Open: Unlike traditional resource markets controlled by incumbent oligopolies, blockchain-based resource markets have inherently low supply-side barriers to entry. By creating an open marketplace, any infrastructure provider — from hyperscale data centers to small-scale operators — can tokenize and offer their excess capacity. Contrary to what most people assume, the long-tail makes up a much larger share of data centers.
3. Composability: Blockchains enable other derivative markets to exist on top of these markets which fosters greater market efficiency as buyers and sellers can hedge similar to traditional commodities.
4. Programmability: Smart contracts enable complex conditional logic to be embedded directly into resource allocation. For instance, compute tokens could automatically adjust their execution priority based on network congestion, or storage tokens could programmatically replicate data across geographic regions to optimize for latency and redundancy.
5. Transparency: On-chain markets provide visibility into pricing trends and utilization patterns, enabling market participants to make more informed decisions and reducing information asymmetry.

Importantly, while this idea may have faced headwinds a few years ago, the emergence of increasingly autonomous AI agents will dramatically accelerate demand for tokenized resource markets. As agents proliferate, they will inherently require dynamic access to these resources.

For example, consider an autonomous video processing agent tasked with analyzing security footage across thousands of locations. Its daily compute requirements might fluctuate by orders of magnitude — requiring minimal resources during periods of normal activity, while suddenly needing to scale to thousands of GPU hours when an anomalous event triggers deep analysis across multiple feeds. In traditional cloud models, this agent would either waste significant resources through over-provisioning or face critical performance bottlenecks during peak demand.

However, with tokenized compute markets, this same agent could programmatically acquire precisely the resources it needs, when it needs them, at market-clearing prices. Upon detecting an anomalous event, it could instantly bid for and secure additional compute tokens, process the footage at maximum speed, then immediately release those resources back to the market when analysis completes — all without human intervention. The economic efficiency gained multiplied across millions of autonomous agents represents a step-function improvement in resource allocation that traditional procurement models simply cannot match.

Perhaps most interestingly, this could breed emergent use cases not possible before. Today’s agents remain tethered to organized companies with pre-established access to compute, energy, storage, and bandwidth. However, with blockchain-enabled markets, agents can autonomously source these critical resources on-demand. This flips the existing model on its head by enabling agents to become fundamentally independent economic actors. This in turn could enable greater specialization and experimentation as agents optimize for increasingly narrow use cases without institutional constraints.

The net effect is a fundamentally different paradigm where the next generation of breakthrough AI apps emerge not top down, but rather bottom up from the autonomous interactions between the agents themselves. Again, this is uniquely enabled by programmable asset ledgers.

展望未来，这一转变最初可能会缓慢且渐进，但随着 AI 代理变得更加自主，并在经济中发挥更重要的作用，链上资源市场的结构性优势将愈发显现。

Thesis #3: Decentralized-Physical-Infrastructure-Networks (DePin)

While the previous thesis made the case for programmable asset ledgers serving as the digital substrate for these emerging resource markets, this thesis will make the case for how blockchains can simultaneously disrupt the physical substrate. While we won’t dive too deep into each respective vertical (@PonderingDurian and myself did a deep dive here) the following logic generally applies to any DePin vertical (e.g. telecom, GPUs, positioning, energy, storage and data).

Porter’s Five Forces

One of the best frameworks to understand the economics of physical infrastructure companies and how blockchains and DePin specifically can possibly disrupt them is to view things through the lens of Michael Porter’s five competitive forces.

Porter’s framework is a more granular framing of the numerous forces that inherently erode any business’s margins to the cost of capital in the absence of some structural moat. The five forces are as follows:

1. Rivalry Among Existing Competitors: Is the industry subject to intense competition among incumbents which could catalyze a race to the bottom? Infrastructure giants tend to operate in cooperative oligopolies where they either implicitly or explicitly keep prices high enough to maintain lucrative margins.
2. Threat of New Entrants: How easy is it for new competitors to enter the market, diluting profitability by increasing supply? Intuitively, infrastructure giants are also insulated by low barriers to entry through capital-intensiveness and scale economies.
3. Threat of Substitutes: Are there substitute products that undermine the value of the existing product? By nature of being commodity businesses, infrastructure giants generally don’t face the threat of substitutes.
4. Bargaining Power of Buyers: How much a business can charge for its product is a key input into the numerator of the profitability equation. Do buyers — customers or enterprises — have leverage to demand lower prices or better terms, squeezing provider profits? Infrastructure giants are subject to low switching costs. This usually suggests that the lowest cost producers win in commodity markets.
5. Bargaining Power of Suppliers: How much a business pays for its inputs is the denominator in the profitability equation. Does the business have leverage over the suppliers of the key inputs that ensures input costs stay modest? Infrastructure giants have three major inputs: (1) land (2) labor and (3) hardware. While supplies certainly hold some leverage, large infrastructure incumbents generally mitigate these risks with fixed contracted and bulk deals.

Evidently, this framework suggests that physical infrastructure giants are incredibly defensible businesses. This is consistent with the fact that most incumbents have maintained their market position over 30 years. However, there are three reasons why the DePin model is a formidable challenger.

DePin’s Structural Advantages

First, DePin harnesses a novel capital formation model whereby upfront capital costs for building out the network are outsourced to individual contributors. In return, these individuals receive tokens representing future equity in the growth of the network. This allows DePin projects to reach a threshold scale where unit economics can in fact be competitive, without having to initially raise capital in a centralized fashion. Importantly, this suggests that, when executed effectively, the DePin model can create viable entrants by penetrating the economies of scale that insulate existing incumbents.

Secondly, DePin fundamentally improves the economics of Porter’s fifth force: bargaining power of suppliers. By harnessing a distributed network of humans, the DePin model not only reduces, but entirely circumvents two (and possibly all three as we will discuss later) of the biggest input costs for physical infrastructure businesses:

1. Land: By tapping into individual contributors — who themselves own the land — the DePin model eliminates this cost completely.
2. Labor: Similarly, DePin evades labor costs by outsourcing the set-up and maintenance of nodes to network participants.

The third structural advantage of the DePin model is in its ability to more granularly match supply and demand and in the process reduce deadweight loss. This advantage is especially apparent for geographically dependent networks (e.g. DeWi). These projects are able to first see where demand for bandwidth is highest and subsequently concentrate token emissions to incentivize supply-side build out in that area. Moreover, if demand spikes elsewhere, they can dynamically adjust incentives accordingly.

This is in stark contrast to traditional infrastructure businesses that build out supply in the hopes that demand follows. If demand falls, telecoms are nonetheless stuck paying the costs to maintain infrastructure, resulting in deadweight loss. By nature of being decentralized, DePin networks have more granularity over matching supply in demand.

Looking Ahead

Going forward, on the demand side, I expect the DePin model will continue to shine in two key area (1) B2B applications where businesses are inherently more cost sensitive (e.g. computing, data, positioning, storage) and (2) consumer commodities where, similarly, consumers don’t have subjective preferences and instead optimize principally for cost (e.g. bandwidth, energy).

Thesis #4: Stablecoins and Global Payments

In 2023, global GDP was ~$100T. That same year, over $2T was spent on global transaction fees. In other words, for every $100 spent globally, on average $2 went to global payment fees. As our world becomes increasingly unbounded by geographical constraints, this number is estimated to continue its steady growth at a 7% CAGR. Arguably one of the greatest opportunities lies in serving demand for cheaper global payments.

Similar to domestic payments, the high transaction fees associated with moving money globally is less a function of network infrastructure but rather risk. Contrary to what you often hear, the messaging layer that facilitates global payments — SWIFT — is actually quite cheap (h/t @sytaylor). SWIFT’s network fees generally range from just $0.05 - $0.20 per transaction. The remaining cost — often upwards of $40 - $120 — is downstream of two sources.

1. Risk and compliance: The burden of ensuring cross-border transactions adhere to KYC/AML requirements, sanctions, and other currency restrictions is placed on banks by regulators. In the event that a bank violates these rules, they can be fined upwards of $9B. Consequently, if you are a bank facilitating cross-border payments, building out teams with dedicated infrastructure to ensure you don’t accidentally violate these sanctions is essential.
2. Correspondent banking: To move money globally, banks must have correspondent banking relationships with other banks. Given different banks manage risk and compliance according to their own jurisdiction, there are additional costs associated with reconciling these differences. Entire teams and dedicated infrastructure also need to be built out to manage correspondent banking relationships.

Ultimately, these costs get passed on to end users. To simply say, “we need cheaper global payments” therefore misses the point. Instead, what’s needed is a structurally better way to audit and manage the risks associated with global payments.

Intuitively, this is one of the few things that blockchains are good at. By not only circumventing the need for correspondent banks, but providing an open ledger whereby all transactions can be audited in real time, blockchains s provide fundamentally superior asset ledger with respect to managing risk.

Moreover, and perhaps more interestingly, by nature of being programmable, blockchains can go as far as embedding any requisite rules or compliance around payments into the transactions themselves. The programmable nature of blockchains also enables native yield on collateral assets to be distributed back to cross-border payment facilitators (and possibly even end users). This is in stark contrast to traditional money transmitters like Western Union where capital is locked-up in pre-funded accounts globally.

The net effect is the cost of underwriting risk should compress towards the cost of programming the open ledger to handle compliance and risk management (plus the additional cost of on and off ramping if needed) net of the yield generated by the stablecoin collateral. This is an objective structural advantage over both existing correspondent banking solutions as well as other more modern cross-border solutions that rely on closed and centralized databases (e.g. Wise).

Perhaps most importantly, unlike domestic payments, there seems to be no incentive for governments to build out globally interoperable payments infrastructure themselves that canabalizes the stablecoin value prop. In fact, I’d argue that governments have a strong structural incentive to not built out interoperable payment rails as a means of keeping value held principally in their own currency.

This is perhaps the most bullish tailwind for stablecoins — cross-border payments is uniquely a public market problem looking for a private market solution. As long as governments have a structural incentive to maintain poor global payments infrastructure, stablecoins will remain well-positioned to increasingly facilitate global commerce and eat at the $2T+ of annual cross-border transaction fees.

The Path to Adoption

Lastly, it is worth speculating on the path to adoption. Ultimately, there are two vectors that will govern the arc of stablecoin adoption:

1. Type of payment (i.e., B2B, B2C, C2C, etc.)
2. Payment corridor (i.e., G7, G20 minors, long-tail)

Intuitively, the payment corridors that are subject to the highest fees and worst banking / payments infrastructure will likely lead adoption (e.g. Global South, LaTam, Southeast Asia). Addtionally, these areas also tend to be the same regions subject to irresponsible monetary policy and historically volatile domestic currencies. Adoption of stablecoins in these regions comes with the dual-benefit of cheaper transaction fees and access to dollars. The latter is arguably the biggest driver of demand for stablecoins in these regions today and will likely continue to be going forward.

Secondly, given businesses are historically more cost-sensitive than consumers, B2B use cases will also lead along the former vector. Today, 90%+ of all cross-border payments are B2B. Within this vertical, SMBs seem to be best suited for stablecoin adoption given they operate on thinner margins while also being willing to take on more risk than larger enterprises. SMBs who may not have access to traditional banking infrastructure and simultaneously demand dollars seem like the sweet spot for adoption. Other notable use cases for stablecoins in a global context include treasury management, trade financing, international payouts and receivables.

Going forward, as the long-tail increasingly adopts stablecoins as a structurally superior cross-border payment method, we should see the rest of the distribution slowly follow suit as the structural advantages become too obvious to ignore.

Thesis #5: Speculation

The final thesis is perhaps the most obvious and straightfoward. Humans have an inherent desire to speculate and gamble. This is something that has remained true for thousands of years and will only continue to remain true.

Moreover, it is becoming increasingly clear that blockchains are uniquely positioned able to fill this void. By nature of being programable asset ledgers, blockchains once again lower the barriers to issuing assets — in this case speculative assets with non-linear payouts. This includes anything from perps to prediction markets to memecoins.

Going forward, as users venture out in the risk curve and seek increasingly non-linear outcomes, blockchains seem well positioned to meet this demand with increasingly novel means of speculation. This could include anything from markets for athletes, musicians, songs, social trends, or something as granular as TikTok posts.

Humans will continue to demand new ways of speculating and blockchains are the optimal first-principles means of servicing this demand.

Looking Ahead

Throughout history, the adoption of new technologies reflects a similar arc:

Some emerging technology enables a structural advantage -> a small subset of businesses adopt the technology to improve their margins -> incumbents either follow suit to remain competitive or they lose market share to more nimble adopters -> the adoption of the new technology becomes table stakes as capitalism naturally selects the winners.

In my view, this is why the adoption of blockchains as programmable asset ledgers is not only likely, but inevitable. By providing a clear structural advantage across these five vectors — tokenization, DeVin, DePin. payments and speculation — the adoption of blockchains is more of a question of when. While the timeline remains unclear, what is clear is that we have never been closer.

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