Speed alone does not establish value. As Richard Tromans points out, saving half an hour with AI achieves little if a lawyer then spends half an hour checking the result. Joshua Yuvaraj calls this the “verification-value paradox”. AI can produce legal work faster, but lawyers remain responsible for checking that it is accurate, complete and relevant. The more work AI generates, the more there is to verify. He argues that this checking can consume much of the promised efficiency gain, leaving little net value once the cost of verification is counted.

That raises a different question: Are we overlooking the part of the legal value chain where AI could have its greatest impact? Furthermore, rather than producing more legal documents, should legal AI focus on delivering higher quality results — more precision and accuracy for attorneys?

AI’s greatest value may be as an auditor

The dynamics and economics of the legal AI industry have made drafting a natural starting point. Its outputs are visible, and time savings are readily measured. The value of auditing is harder to demonstrate in a product demo: it lies in errors prevented and failures that never happen.

What if the most valuable application of AI is checking the work that humans already do?

An AI-native firm’s advantage lies in its ability to invest in long-term auditing capabilities whose return comes through better service, even when these investments and product features are not directly revenue generating.

In his analysis of 9,241 merger agreements, Dave Hoffman reported that typo patterns are more strongly associated with particular lawyer teams than with the firm itself. That strengthens the case for making quality control part of a firm’s infrastructure. An AI auditing system could apply the firm’s required checks across teams and matters, making the standard of checking less dependent on which lawyers handle the file.

A good draft does not prove a properly completed process

The distinction I am making is between evaluating an AI-generated draft and recognizing the value of AI as a general auditor in legal processes. As Richard Tromans puts it: “A law firm is not just an economic pyramid of legal labour, it is a pyramid of quality control.”

The idea of legal “evaluations” has frequently been applied in the context of evaluating outputs. However, checking an output does not establish that every required step in the matter was completed.

A good draft is not proof of a properly completed process. A demand letter may read as complete while leaving out the client’s MRI entirely. The client mentioned it at intake and the firm requested the report, but it never arrived and nobody followed up. Nothing in the letter reveals the gap; it only shows up when the draft is checked against the intake notes and the records log. An AI auditor could check the file against the evidence required for the matter.

A point many engineers miss is that much of the work in a law firm involves running processes that sit outside a Word Document, including developing and executing legal strategies and processes across high-volume personal injury, e-discovery or complex, multifaceted due diligence.

Auditing should therefore cover people, processes and outputs. Were all the required procedural steps completed? Did information get lost between teams? Is a legal conclusion adequately supported, or was material evidence overlooked?

The auditing task starts with defined checks and evidence, then identifies departures requiring attention. In law, each step matters even when the answer seems obvious and checking feels pointless. Triple-checking that a legal process has crossed the t’s and dotted the i’s is part of the work itself.

This audit task represents the work of a junior- and mid-level associate. The attention to detail that lawyers require develops from lengthy, time-tested checklists of specific items to verify. This work, unlike the more complex nuances of swaying judicial opinion based on caselaw, can easily be verified and improved with AI.

Secondly, much of this audit work is where human error occurs. Attorneys burning the midnight oil often wait to verify citations or triple-check for typos at the last minute when they are simply too tired to function effectively. An audit function well defined with AI does not make those errors.

AI can make comprehensive quality control affordable

Comprehensive auditing was historically constrained by the cost of expertise. A more experienced lawyer had to check a junior person’s work. Trust was the currency of the realm, because it was one of the highest leverage tools for a senior partner to grow their business without being constrained by the time required in quality control.

Writing down the checks did not remove the cost of performing them, but AI changes the economics of running those checks. We can separate the expertise required to design an audit from the effort required to run it across thousands of matters.

Errors prevented matter more than hours saved

The economic case depends on catching more errors than existing supervision at an acceptable cost of human review. Each finding should point to evidence, and the audit itself should be tested against known failures.

Success should be measured through errors prevented, missing evidence identified, incorrectly rejected cases recovered and process failures caught before harm occurs. Hours saved and documents generated tell only part of the story.

Applied to quality control, AI could improve the reliability of the whole legal value chain. We can now build audits designed by humans but run by machines.

The winning firm will turn legal expertise into auditing infrastructure

The AI-native law firm that wins will build the best infrastructure to lower the friction in turning the legal expertise trapped in lawyers’ heads into automated AI auditing systems.

The friction in extracting that expertise is the hard part. As Josh Kubicki puts it, “Most law firms rolled out AI without ever defining the exam it needs to pass.” We need to take the checks experienced lawyers carry out in their heads and build them into an AI auditing system that performs those checks automatically across matters. That means asking lawyers what they check and why, then translating their answers into instructions the system can follow: which documents to examine, what evidence to look for, which procedural steps to verify and when to alert a lawyer.

Human oversight remains essential, but the opportunity is to make expert supervision more consistent and scalable. Thomas Potter proposes using AI for structured verification, with lawyers concentrating their judgment on the issues those checks flag. Lawyers must review significant findings, resolve ambiguity and take responsibility for decisions.

Law firms have long rationed quality control because experienced lawyers have limited time. AI gives us a way to apply their expertise across every matter through audits they design and oversee. The value lies in making that level of care routine. A single material error caught can be worth more than hundreds of hours saved drafting.

Running into a number of edge-cases, errors and unreliability issues when using Retrieval Augmented Generation (RAG)?

In "RAG Debugging 101", I look into some of the challenges and pragmatic solutions for debugging RAG systems.

I wrote this very short piece to shed light on the pain points developers encounter when building and pushing RAG products to production.

This guide provides a toolkit to tackle debugging. From bad queries to hallucinations, it's tailored to anyone vested in creating reliable tools for customers.

I am trying something new by having live documents in Google Document for others to contribute to this repository, and to keep building on top of it over time.

Source: LlamaIndex

RAG Debugging 101: See link to Working Document on RAG Debugging.

The field of conversational AI is moving fast. Yet, short prompts used by AI models, like GPT, have limits. They often don't fully capture what a user intends, the full context of the questions, and the thought process that should be employed to reach the output. This is where mega-prompts come in. These are longer instructions, typically above 300 words. They detail the user's intent, helping AI generate more accurate responses.

This makes sense. For the longest time, constraints in technologies have meant that we are used to building and working with technology solutions that aim to constrain and restrict the scope of interaction between human and software. We click 5 buttons to purchase something on Amazon. We use as few words as possible to search - “Restaurants open nearby” in Google Maps. We use product management and UI/UX to create complex products that look and feel simple to use for the average user.

With LLMs, our ability to express our intended outcome in natural language is now unbounded. Structured workflows within software are no longer the only way to generate machine-enabled outcomes. We can now use LLMs to help us interact with the broader world.

Software has always had one key advantage - Software has inadvertently helped to not just automate workflows but also standardize workflows. The workflows within software tools reflect a structured way for ingesting data, and producing data outputs that prevent variance in the data outputs.

When interacting with LLMs that do not have the same boundaries of workflow, the question is - how can humans interact with LLMs to ensure that the machine is able to understand the fundamental intent of what is desired? LLMs do not read minds and predict intent - they simply reproduce what is likely to be a desired output based on available data.

In the event of a highly customized intent that is not precisely worded by the user, LLMs understandably would not be able to understand the user’s intent or the context of the work. As an example, look at the previous example of a CMO mega prompt.

It is clear that if we had simply asked a general LLMs, with a 1-sentence prompt, for the output ‘give me a checklist to be a good CMO,’ and ask me any questions I need’, it is unlikely that the general LLMs would be able to have developed the implied underlying system needed to provide the type of granular output that the user truly intended.

Mega-prompts therefore provide the structured framework that bely a high-quality thought process that is able to better frame the user’s intent. As tooling becomes more powerful, the onus lands on the user to be able to more precisely express their intent.

Will Prompt Engineering ever become obsolete?

I think this is very unlikely due to the continued need for a creative expression of intent as the cost of production goes down.

“Continued Improvement of Great”: Firstly, as LLMs raise the floor of what ‘good’ performance looks like, there will always be the competitive need to increase the ceiling of what ‘great’ looks like. New, innovative ways of thinking, expressed in conjunction with sophisticated prompts, may help produce an outcome that is in line with the frontier of ‘greatness’. For example, while LLMs may replace a lot of strategy consulting work, we wouldn’t necessarily say that an LLM is a replacement for Clayton Christensson’s way of thinking. This is as LLMs are great for replicating output, but not determining what ‘great’ looks like.

“Last-Mile Fine-Tuning”: Secondly, niche use cases will always emerge which require the last mile of contextual fine-tuning. This fine-tuning requires business expertise, logic, and context, which can be expressed through mega-prompts. Fine-tuning isn’t just about understanding a sample output, but also about the thought processes behind a great output. Embedding thought processes and process supervision for fine-tuning purposes will be one of the most important areas of development, as OpenAI noted in May.

& “Workflows are documented, thought-processes are not”: While many workflows may be documented and executed through SaaS, many complex thought processes that experts have in their heads are not documented. SaaS is simply workflow encapsulated by products, while mega-prompts are thought processes encapsulated by words. Think crisis management, private equity, branding strategy, etc. While textbooks may exist for some of these niche industries (if at all), many of these mental models/ thought processes are largely passed down through experience and work, with textbooks and formal workflows either general or too high level, since they require a high level of contextual information at each stage of the thought process, which humans are able to actively seek out. A large part of the difficulty of capturing and digitizing thought processes is the sheer level of unstructured data, both as a function of data input, and data output.

For example, while Clayton Christensson bots popped up about 10 seconds after GPT’s APIs went live, it is clear that Clayton-bot’s outputs were extremely limited in scope, and certainly not used for solving real problems. It is limited in scope because it is trying to replicate an output it does not understand the methodology of and because Clayton’s written methodologies are likely at too high a level to apply. Clayton had been optimizing for humans to understand his work and certainly hadn’t written his work in a way that helps LLMs maximize a replication of his exact thought process.

“There are different expert philosophies for the same problem”: Thirdly, even in the event of AGI, many people will have different thought processes they want to incorporate into an output. Domain experts in the event of AGI, and in the medium term of LLMs, will likely change insofar as the most effective experts are those who are able to best communicate and incorporate their decision processes to work with and through LLMs.

Back to the Clayton analogy, smart, reasonable people will weigh different input factors differently largely due to philosophical reasons, even with the same information and problem. Clayton Christensson utilizing his thought process will come up with a different answer than say, Bill Ackman. Both will look to incorporate and digitize different thought processes to the same answer, and potentially/likely reach different conclusions they are comfortable with.

Chain of thought reasoning, as replicated somewhat through Mega-Prompts, is thus the most likely way we should see sophisticated/expert thought processes captured by LLMs, as humans get accustomed to incorporating thought processes rather than simply workflows digitally. (To be clear - I am not saying chain-of-thought reasoning is the best or most efficient way to fine-tune LLMs, simply that it is probably a key component in a specific task - helping experts digitize and replicate their thought processes at scale)

So what should a Mega-Prompt look like?

Mega-prompts reflect an opinion and additional context through 3 things:

• The context of which the LLM should best approach the question, including but not restricted to:

  • The Persona the LLM should take

    • “You are a backend technology consultant”

  • The goals of the user

    • “I am looking to optimize my cloud storage system, focusing on reducing cost over speed as I am budget but not time sensitive.”

• The process in which different elements of the answer should be obtained.

  • The chain-of-thought reasoning that should be employed

    • “First, examine the structure of my systems, being conscious of X”

    • “Second, compare that to the infrastructure design of some of my competitors, particularly Y & Z”

  • The structure of the back-and-forth, including which elements of the answer should be confirmed before proceeding

    • “Generate a number of additional questions that would help more accurately answer the question”

    • “Before proceeding to the cost calculations, please summarize the pros and cons of different systems, and confirm what factors I care more or less about.

• The format & structure of the answer

  • Vocabulary, grammar, etc

    • “Use short sentences and explain any acronyms as my audience is non-technical”

  • Any constraints and guardrails that should be noted in the answer

    • “Avoid relying on any information prior to 2018 as a new advancement in 2019 has made all developments prior redundant”

    • “Please use this template that I rely on for the format of your answer”

    • “From now on, whenever you generate code that spans more than one file, generate a Python script that can be run to automatically create the specified files or make changes to existing files to insert the generated code.”

Do mega-prompts meaningfully change output?

(Source: https://www.makerbox.club/marketing-mega-prompts) 

As we can see, mega-prompts dramatically increase the quality of the output, as judged by the user, particularly for qualitative and creative tasks.

Although many of the cited examples above are text-creation, we can also see them being extremely relevant for image generation.

The above describes a workflow encapsulated into a series of prompts

Over time, we are likely to see prompts evolve from one-sentence prompts, to mega-prompts, to chained prompts, as prompts become the atomic unit of work. 

I have seen folks from all types of industries and backgrounds working on mega-prompts and am creating a community of folks (you don’t have to be technical) who are spending time on mega-prompts. To join, drop your details here:  https://forms.gle/ARCrk4sSBw9RreLf8 

Redefining Interaction with Data through GPT

At its essence, GPT’s functionality diverges significantly from conventional data search and discovery mechanisms like Google. While Google serves as a tool for information search, the onus still remains on the user to understand, interpret, and transform the data into their unique language. GPT, on the other hand, revolutionizes this process by establishing a dynamic, two-way information workflow. It becomes less about merely searching for data and more about data extraction and interaction. Users effectively shape the responses that GPT produces based on their prompts, essentially crafting the output in their own words. This feature elevates GPT beyond a search engine; it becomes an interactive tool that delivers personalized and contextually relevant information, not just raw data but transformed insights that mirror the user’s language and thought processes. The transition from Google to GPT is not just an upgrade in search technology; it is a fundamental shift from passive data retrieval to active, personalized data interaction.

The Advent of Personalized Prompt Libraries

At the core of this seismic shift in our interaction with technology is the development of personalized prompt libraries. These libraries represent a unique collection of inquiries, instructions, and conversation starters, tailored to an individual’s or an organization’s specific needs, interests, and ways of thinking. The variety and specificity of these libraries could end up being extremely diverse, reflecting the unique mental models we use to understand and interact with the world around us.

The creation and curation of these libraries will necessitate new tools and systems to manage them effectively. We might see the development of innovative software applications that enable users to build, refine, and organize their prompt libraries with ease, even offering suggestions or templates to guide them. These tools might also incorporate robust security features, protecting the intellectual property contained within these libraries and ensuring that users can safely store and transport their “brain-in-a-box”.

Changing the Landscape of Management and Communication

Given the potential of personalized prompt libraries, the way we think about management may also change dramatically. Let us dive into this concept of a ‘brain-in-a-box’.

As we continue to develop a portable ‘brain in a box’ that encapsulates our way of thinking, we could plug that into existing enterprise workflows to create customized automated workflows, presenting unique opportunities for management and leadership. For example, a new executive from Uber joins Lyft and plugs in their prompt library and it analyzes existing workflow and Lyft data to come up with a new plan of action that simulates what an Uber executive would have done to improve Lyft.

It is not that we will all suddenly have digital twins of ourselves that are able to automatically solve problems in the real world. Rather, we would have a set of prompts that reflect the nuances of how a particular leader thinks — what are the questions they first ask themselves when they look at a business problem, the level of depth they expect from those answers, and the information they like to look at when trying to tackle a particular question. This personal ‘brain in a box’ is more about capturing the pattern of questioning and thinking that a leader brings to any problem.

Traditionally, top leaders have understood what they don’t know and surrounded themselves with experts, optimizing for spotting and leveraging expertise. However, in a future where AI plays a significant role, the best leaders might be those who excel at strategic thinking and know how to build and extract information from highly automated systems. This could upend the management concept of ‘knowing less is more’.

Furthermore, all leaders, even those who primarily interact with humans rather than AI, could significantly benefit from using tools like GPT. By practicing and refining their ability to issue clear, effective instructions through interactions with AI, leaders can enhance their communication skills and improve their overall leadership effectiveness. This suggests that daily interaction with AI could become a valuable exercise for managers, promoting the ability to issue clear and crisp instructions, whether to human teams or AI systems.

Future Infrastructure and Opportunities

To support this shift towards personalized prompt libraries and AI-assisted management, new infrastructure and opportunities will likely emerge. We might see a surge in workshops and training programs designed to help individuals and teams think more structurally and effectively use prompt libraries. New mental models for prompts could be developed and taught, helping users to get the most from their interactions with AI.

Investment opportunities may arise in companies developing these tools and infrastructure, as well as in businesses that successfully leverage prompt libraries to drive innovation and efficiency. In the longer term, we might also see the emergence of new business models centered around the creation, curation, and licensing of prompt libraries. These could range from individual “thought-leaders” selling access to their personal prompt libraries, to corporations offering bespoke libraries tailored to specific industries or tasks. These libraries could become a new form of intellectual property, with their own legal protections and trade laws.

Beyond just business, these developments could also have significant societal implications. As the use of AI and personalized prompt libraries becomes increasingly commonplace, we may see shifts in education, with curriculums incorporating more focus on structured thinking, clear communication, and AI interaction. There may also be potential for these libraries to be used in broader societal discourse — imagine if public figures or politicians shared their prompt libraries as a way of demonstrating their thought processes and policy approaches.

Privacy and Ethical Considerations

However, along with these opportunities come important questions about privacy and ethics. As our prompt libraries become increasingly reflective of our thought processes and personal perspectives, they could also become a target for data theft or misuse. This raises critical questions about how these libraries should be secured, who should have access to them, and how they should be regulated.

Moreover, the use of personalized prompt libraries could potentially reinforce existing biases or create new ones. If people primarily interact with AI through prompts that reflect their own thought processes and perspectives, they could become more entrenched in their viewpoints, reducing exposure to diverse ideas and perspectives. Therefore, it will be crucial to consider these ethical implications and develop strategies to mitigate potential negative impacts.

Conclusion

The concept of personalized prompt libraries represents a revolutionary shift in our interaction with AI. As we move towards a future where each person has an extensive written prompt library that reflects their unique way of thinking, we will likely see profound impacts on business, management, education, and society. This future presents exciting opportunities, but also challenges that must be thoughtfully addressed. In the end, the successful integration of these libraries into our daily lives will depend on our ability to adapt our thinking, develop effective tools and infrastructure, and navigate the ethical and privacy considerations that arise.

The dawn of personalized AI interaction is upon us, and it holds the promise to reshape our world in ways we are only beginning to imagine.

ZK bridges are bridges that rely on ZK proofs to create trust-minimized cross-chain swaps. Examples of these include platforms like Succinct.

USBs essentially work by aggregating dexes on a sending chain as well as a receiving chain. When a cross-chain transaction is initiated, the bridge swaps assets into an intermediary canonical stablecoin on the sending chain, conducts its value transfer in stablecoins through the intermediary centralized/decentralized stablecoin issuer, and then swaps finally into whatever asset you want on the receiving chain through a dex on the receiving chain.

Illustrated, an ETH to SOL transaction looks something like this:

Why are these the 2 superior solutions out there?

Hacking continues to be a major issue for bridges. One reason for this is what Vitalik has pointed out as the “fundamental security limits of bridges” - essentially that the higher volume and the more critical a bridge becomes, the more attractive it is to hack that bridge (as the reward increases dramatically).

There are 3 central issues with bridges that need to be solved, and essentially one way to solve it.

• Liquidity

• Cross-chain finality

• Cross-chain security

With USBs, instead of requiring liquidity pairs for each swap, you are relying on one of the largest liquidity pools (stablecoins) as the settlement layer. This is similar in concept to Thorchain’s settlement system, except that one is relying on stablecoins instead of RUNE as the settlement layer. Here, liquidity is not an issue since the ‘bridge’ which requires stablecoin liquidity is its own stablecoin issuer.

Cross-chain finality: A problem with cross-chain swaps is that all swaps are not instantaneous and are constrained by the receiving chain’s finality. This is generally solved if the bridge or liquidity provider is willing to relax security constraints in exchange for a faster transaction, though this increases/introduces risk to those parties. However, to create a trust-minimized and more efficient environment, it becomes far easier to scale if the stablecoin issuer is able to bear the finality risk instead of the bridge. This is especially true, given that slippage may occur during the cross-chain transaction. There is less slippage involved to use a stable asset as the settlement layer since there is less value variance that needs to be accounted for.

Cross-chain security: From a technical perspective, the way the stablecoin issuer decides to build their bridge will affect how one begins to think of the added security concerns of cross-chain transactions. The options range from a ZK solution to an oracle-type solution similar to LayerZero, although it is arguable that using a mechanic that relies on a stablecoin issuer’s bridge should not additional trust assumptions. From an economic perspective, it becomes simpler to think about. If one is already comfortable with the centralization risk of Circle or Tether, then any stablecoin bridge solutions (likely semi-centralized in practice), will simply inherit the existing trust assumptions and be no more or less secure. Indeed, one can make the argument that a centralized stablecoin issuer will be obligated in practice to cover any hacked capital, if we regard USDC as a tokenized receipt for deposit tokens.

In short, we will have a bridge that will use stablecoins as the most liquid, secure, and stable means of taking on economic and security risk during cross-chain swaps.

ZK bridges will also help reduce a lot of the trust assumptions required in a cross-chain swap and are certainly in the future of bridge designs. However, even if trustless and secure, ZK bridges may have an economic disadvantage to Unified Stablecoin Bridges, given the need to accumulate liquidity for swaps.

There are two interesting takeaways:

• There is an opportunity to build a stablecoin focused on cross-chain settlement

• There is an opportunity to build the Unified Stablecoin Bridge

It is important to note that we should reduce reliance on non-native or wrapped stablecoins over time. Only canonical assets reduce trust assumptions since you are relying on the same entity that you previously trusted for the pre-existing stablecoin.

What do we currently have that can fulfill this need?

• 1inch (Pantera Portfolio) - Doesn’t do cross-chain swaps

• Li.Fi - Bridge aggregators - aggregates, so doesn’t provide their own liquidity and so relies on the fundamental security/liquidity of the underlying bridges. Although they can reduce liquidity fragmentation, this is done via a number of different underlying bridge channels, which come with their own hacking risk. 

• Centralized stablecoin providers - Circle (Pantera Portfolio) provides native stablecoins on 8 chains. Tether provides native stablecoins on 9 chains.

• Decentralized stablecoin providers - DAI. Work is still ongoing on their multi-chain expansion, with work existing on Arbitrum (Pantera Portfolio), Optimism and Starkware (Pantera Portfolio).

It is interesting that we currently do not have any player that really fulfills this need. A large bottleneck to this is the relatively slow growth of multi-chain support on the long-tail of chains, which is driven by a few factors including cost-effectiveness for stablecoin issuers. Without native stablecoin support, the incentive is for bridge providers to provide wrapped equivalents instead, thus further fragmenting liquidity. Even if cross-chain aggregators/multi-chain bridges wanted to act in a more altruistic manner and aggregate rather than issue their own stablecoins, they would still be inheriting (and transmitting) security risk of that wrapped stablecoin. This of course, ignores the fact that native stablecoin issuance requires scalable collateral management which is not something that any protocol can simply ‘do’.

One limitation of this argument is that it is likely that stableswap bridges will continue to retain an advantage for slippage/liquidity at least for some time.

There is an interesting potential for existing cross-chain infrastructure players like LayerZero to occupy this space. Given that cross-chain infrastructure players are already being used by dapps with cross-chain activity, and many of them have their own venture built bridges (like Axelar’s Satellite or LayerZero’s Stargate), they are in a strong position to distribute such cross-chain stablecoins. However, given the complexity needed to manage the regulatory and financial risk of stablecoins, it is far more likely one of these players will partner with a large institutional player to produce such a multi-chain native stablecoin for settlement.

In short, USBs have the potential to solve for the bridge trilema of liquidity, finality, and security, which could unlock meaningful cross-chain volumes and composability.

Thanks Will Reid, Arjun Chand, Alex Shefrin, Matt Stephenson, Amir Bandeali for thoughts/comments on the piece. Comments does not imply endorsement.

• The first is that the composability of Web3 allows features to find adoption in a way that we typically only see with products, obscuring investor/founder’s ability to distinguish between crypto products and crypto features.

• The second is that tokenomics can further obscure the typical filters used to differentiate a feature from a product. 

First, let’s look at some of the questions that help define what is a product versus a feature?

• Is this tool a standalone product that customers would purchase?

• How valuable is this functionality as a standalone application?

• How much of a nice-to-have vs a must-have is this functionality?

Essentially, it boils down to how valuable is the problem you are solving, and whether or not this problem is solved in a way that allows you to continue growing and expanding the product you have created to solve adjacent problems. A feature is a characteristic or function of a product that adds value, but only incrementally. The size of the problem being solved is also a reflection of the market size, which is a reflection of how much of a fit it is for venture capital.

An example can be in the personal wealth management space in Web2 Fintech. Is a dashboard to track all your bank accounts a feature? Probably. What about a dashboard that allows you to track all your subscriptions? Probably. What about a dashboard that allows you to do all the above, allow immigrants to remit money back home, and get their first mortgage? If nothing close to that exists out there, that probably starts to look more like a product with a thicker value proposition.

Let’s talk about 3 famous examples of the Feature v Product debate, as described by Yannick Oswald:

1. Dropbox

2. Calendly

3. Spotify

1. The famous example that ignited the debate was when Steve Jobs called Dropbox a feature back in 2009. To the Dropbox founders, Dropbox is a product, but to Steve Jobs, dropbox is a feature like iCloud. What Steve didn't think of was what was behind Dropbox's atomic unit of backing up files. Dropbox created a platform that makes all of your digital content (images, videos, documents, etc.) easily accessible across different situations spanning different people (sharing, collaborating, etc.) to various places and technologies (Mac, Android mobile, etc.). To

2. At first, Calendly feels like a feature on top of our digital calendars. But there are so many people out there who find use in such a feature that it was a great wedge to build a larger independent company. Currently, some 10 million people are using Calendly every month. And, with the pandemic, the number of meetings we now need to set up has gone up. All of the serendipitous and impromptu encounters we used to have around an office, or a neighborhood coffee shop?

3. In its early days, Spotify launched a killer feature that lets users share songs and playlists without worrying about who is and isn't currently a member. The atomic unit of Spotify was and is allowing users listen to any song they want without having to buy it, of course. So, playlists were only a social feature, but a brilliant one that made the platform attractive against competitors such as Apple. Features can make products great and get them ahead of rivals. It is important to note however that Spotify was more than just a playlist of songs. There were many startups that tried to go to market as a ‘standalone’ playlist creator, that failed, because it did not own distribution and streaming.

How does this apply to Web3?

Web3’s never-before-seen hyper composability has helped obscure the distinction between a product and a feature. In Web2, a new product with only an incremental feature, would face resistance in adoption, given the switching costs needed (i.e. you would not switch banks just to use a personal finance dashboard). In Web3, the interoperability of products meant that new features could be built on top of existing dapps, ensuring ease of adoption within a single release, but failing to address the core question of whether something was a product or a feature. Users are able to shift between products very easily and get the exact same experience and service as on the previous product with an additional feature benefit. 

This allowed many Web3 companies that were really building incremental improvements and features to claim a high level of user growth, despite fundamentally having a narrow product focus that was difficult to expand from.

An example of this can include auto-compounding vaults on top of Uniswap. Auto-compounding vaults are incredibly useful for users looking to increase yield without having to manually compound yields, at the expense of repeated gas transactions. However, this is arguably a useful feature rather than a standalone product.

Another example of this can include semi-custodial exchanges. As MPC technology matured at the same time in the aftermath of FTX, ideas around building a semi-custodial exchange that allow users to interact with a centralized exchange through a non-custodial wallet began to proliferate. Such platforms, utilizing a semi-custodial interface that is likely using third-party technology, is a feature rather than a product, especially given the likelihood that this tech and process will become a market standard.

I had raised earlier that there are many great products that can start out as features. How does this apply to Web3? It can be good to ignore the question of products vs features in the context of a fast-growing market. It is possible to employ a first-mover advantage with a feature to occupy mindshare and distribution in a nascent market, and accumulate the resources and customers to continue building a sticky product. This is to say - there are instances where investing in features in a fast growing large market can be beneficial. Going back to semi-custodial exchanges, they are again an example of this trend that may benefit from the boom as first-movers of semi-custodial solutions gain some headwind. It is then entirely possible that a semi-custodial exchange born today, through good/fast execution can become a new overall market leader.

There are a few great stories to understand how this range has played out.

Convex

• The entire value proposition of Convex is that it enables bribery as a service entirely around a single platform, Curve. Essentially a voting mechanism built on top of Curve, it was able to ride on top of the importance of Curve and have its own Convex tokens be an important source of governance for liquidity. Efforts by others to replicate Bribery as a service for other platforms have largely stagnated.

Blur

• The entire value proposition of Blur is that it gets to lean on the volume and liquidity on top of marketplaces like Magic Eden and Opensea. Blur was essentially a trading UX that was able to build its own liquidity and generate a liquidity moat against the marketplaces it was drawing liquidity from.

Aave

• We can see how limitations to composability can affect how to think about the feature vs product debate across different types of crypto projects.

• Across Aave V2 and V3, liquidity is fragmented, with one potentially arguing that each version of Aave should serve as a standalone feature, while Aave as a collection of pools LPs can interact with, serves as the product

As Yannick mentioned, “The product needs to be so good that they can acquire a big piece of the market for their atomic unit and rapidly extend their family of features and products over the coming years. It is this dominance over the atomic unit which enables these companies to build category-defining businesses in the long term. As a result, incumbents and newcomers should quickly face difficult-to-cross moats in their attempts to compete.”

It can also work the other way. Ideas that were products can also become features as the problem initially tackled shrinks in urgency. A potential example of this in Web2 is bill payments. Where previously bill payments were a problem to solve requiring a standalone product, in this day and age where there are so many tech-enabled and plug-and-play solutions, bill payments became a feature rather than a product.

An example of this happening in Web3 can include the many platforms that have scaled horizontally quickly to become the de-facto platform offering a range of services that ultimately allow them to be sticky, revenue-generating core infrastructure like Biconomy.

Why feature-type platforms may have more longevity in Web3 than Web2

In Web2, tools that were more features than products struggled to monetize. There was a feedback loop, typically around Series A, that existed where it became clear that product expansion was difficult, or monetization became difficult as the feature was not sufficiently interesting enough to get a larger segment of customers to switch over to a new product with the feature entirely.

In Web3, one key economic innovation was the ability for tokens to be used as a reward mechanism to sustain open-source protocols that were previously incredibly difficult to economically incentivize development. This essentially meant that as long as you could prove utilization or simply hyped it up, there was an ability to monetize your activity through the sale of tokens. This led to the joke that crypto featured hype as a business model, as tokenomics became an obscuring force that required us as an industry to adopt new measures of what it means to be a product vs. a feature. Obviously, there are plenty of teams that are building and solving real problems in crypto. There are plenty of crypto businesses with real product market fit, with significant 9-figure fiat revenues. However, given the ease of monetizing tokens, many companies simply do not need to face the same issue of understanding if they are a product versus a feature. Furthermore, given the speed at which the industry grew during the boom years, it hardly seemed to be an issue, as many tokens launched managed to hit mouthwatering valuations even at early stages of product development.

As such, we should expect and continue to expect many more well-capitalized feature-type platforms exist in Web3 than Web2.

How does this affect the way we think?

• As Founders:

  • Moats, particularly around switching costs, that once protected Web2 products are no longer viable in Web3. Due to composability, teams operating in Web3 are going to have to find new ways to develop and maintain their user base.

  • Product ecosystem thinking may become more prevalent as businesses initially building features grow out a range of features that allow them to stand as products.

• As Investors:

  • Thinking through whether something is a feature versus a product has become a lot harder in crypto, with the key being able to understand what features are more likely than others to retain and occupy important mindshare. With certain tailwinds, feature businesses may leverage fast growth to be something much more.

  • Tokens have also made it far easier to make money on something that simply has a lot of marketing.

  • Long-term oriented investors are more focused on hype-resistant business models that ultimately lead to products versus staying as a feature.

Thanks Daniel Fallon-Cyr, Karthik Rajeswaran, Sunny Singh, Brandon Galang, Mihir Kulkani, Carl Hua, Sehaj, Will Reid, etc for comments/input. Input does not imply endorsement.

The main lesson I learnt from those experiences was that success meant an intense ability to build a product roadmap that focused on occupying mindshare of extremely valuable customers within existing markets, and which had an incremental ability to monetize through a wider range of revenue streams than traditional startups.

There are two points I want to make for fintech and crypto investors and founders reading this. The first is comparing the monetization routes for businesses on UPI versus Crypto, and the monetization meta that arises. The second is on the product ecosystem philosophy that arises due to monetization issues.

An Introduction to UPI

UPI is an instant real-time payment system set up by the Indian government that is lauded as one of the most adopted, near costless, and effective payment systems in the world. The adaptability and adoption of UPI blow most digital wallet ecosystems out of the water by comparison. As an aside, it has always been interesting to me that crypto has been compared to digital wallets when UPI is the better comparable. An example of this is payment interoperability. In the case of wallets, the sender and the receiver have to be registered with the same wallet app to make a transaction. If the sender uses Brand #A wallet, the receiver is required to have the same Brand #A wallet to receive the money. In comparison, the UPI transaction takes place between any two bank accounts, irrespective of the UPI application used to make the transaction. For example, using Google Pay, a sender can transfer money to a receiver who has Phonepe (another UPI payment provider) installed. Further, the largest wallet ecosystem is in China, which has a centralized duopoly wallet ecosystem, completely antithetical to an open-source, composable standard that is crypto.

One problem that arose through the creation of UPI is the difficulty that companies have in monetizing the products they build on top of it.  Driven by government mandates and by the open competition that UPI enabled, UPI payment features are near impossible to monetize. There is stiff competition for P2P mindshare and nobody wants to introduce a fee that would certainly hurt competitive positioning. Switching between UPI applications is very easy because the apps do not control the user’s bank account or underlying data and cannot create a switching cost based simply on access to previous transaction data. This is like the crypto world where applications cannot hold users captive because access to underlying data is open. 

A similar problem exists in crypto. Where there is an easy ability to fork a swap contract and provide it with zero fees, the ability for crypto companies to monetize is threatened. This was less of an issue during the bull cycle, given how nascent and few the existing dapps were, as well as monopolistic tendencies for liquidity. As we began to see with the fork of Uniswap by Sushiswap, and with the influx of venture-backed companies, and the lowering of liquidity during the bear market, it became more difficult to become a monopolistic rent-seeker.

Understanding Monetization in UPI and lessons to bring to Crypto

Let’s look at what some of the UPI-based businesses in India have done to create value-added moats, in the context of a harder monetization path:

Before diving into the table, I want to mention the Monetization Meta. With difficulty monetizing in fintech, there was always an arms race to discover the next product that could potentially help to break monetization stagnation.  Occasionally, a new monetization meta will emerge with higher take rates that become an attractive monetization feature to add to every roadmap, regardless of cohesiveness to the product roadmap. In fintech, lending (monetize through interest or origination) and card products (transaction fee) quickly became one of these go-to feature sets as infrastructure to embed lending and card products matured. I used to joke that card-issuance-as-product-market-fit was a meme, as almost every fintech had card issuance in their roadmap, regardless of the business model. How many cards/bank accounts did everyone expect the average person to have? For fintech folks in the room, I always joked that staking and MEV are the new lending/credit card. 

In crypto, the initial infrastructure meta around monetization through transaction-take swaps or issuing governance tokens, has shifted recently to stablecoins, MEV capture, and staking. New monetization metas will continue to evolve and shift, as narratives have already shifted to compressing staking take rates during this bear market. As with any infrastructure meta, as competition increases, take rates compress, and the industry develops a new meta for adoption or as a monetization narrative.

B2B Business Models: Premium Services

In this model, UPI businesses create superior features that are more reliable, robust, and have higher success rates, and charge a fee for such higher performance, on existing rails.

JusPay (Softbank/Accel-backed) and RazorPayX (TCV/GIC/Tiger-backed) are great examples of businesses that have successfully created a moat by enhancing API access to existing public rails. They do this by building solid infrastructure and features that premium clients demand and are willing to pay a fee for. Juspay works with FI partners to create infra that sits within the bank environment. They speed up the processing of UPI services, resulting in much faster, and more reliable throughput. Similarly, RazorPay offers merchants a bank account with deep integrations to ancillary services like accounting software, etc.

Similarly in Crypto, while the blockchain is theoretically open and permissionless, there is work that goes into interacting with the blockchain more efficiently. Alchemy (Pantera/Lightspeed) exists to help provide an easier and more enriched way of interacting with the multitude of blockchains that exist out there in the market, with APIs that allow better data extraction, faster transaction execution, and transaction monitoring.

B2B Business Models: Proprietary networks

While UPI is inherently interoperable, some companies create moats from their proprietary networks on top of the open network, typically around data, and consumer cross-sell.

One crucial difference in crypto versus UPI is that while the value of proprietary networks in UPI/fintech is on the data they collect on the transactions they help process, the value of proprietary networks in crypto is the transaction itself that they help to control.

For example, while plenty of fintech companies derive their value in the data they collect, allowing them to cross-sell products, crypto companies can largely only capture value in the control of the transactions that flow through their platform. Crypto analytics businesses of course exist (Nansen/Messari), which have a role to play in the ecosystem, but it is certainly true that the data moat that crypto companies have over fintech companies are relatively thinner.

In Crypto, the ability to control transactions allows platforms to do interesting things like capture MEV revenue through private memepools, bundle transactions, etc, in ways that are reminiscent to payment for order flow that non-exchange fintech companies are not exposed to, but which all crypto companies have a gateway into, from wallets to defi smart contracts, to centralized exchanges. Rook, a private memepool, run by Amber Group, is an example of transaction flow being captured in a proprietary network, with MEV rewards redistributed to users, reducing their transaction fees.

B2C business models: Club/Membership based model

Creating exclusivity for access to specialized products is another way in which some companies have successfully created a niche. In this model, users get access to special privileges or features only if they are part of a club or membership program. Companies are experimenting with different ways to define club membership requirements, with Cred being an example of this. Cred’s base feature allows users to easily pay all kinds of bills via UPI for free and uses their club membership model to gate access to special deals and more advanced social features. This creates a social network moat allowing them to attract better deals and advertising revenue.

This is a very natural fit with Crypto today, with DAOs and NFT gating becoming a popular strategy to create a strong core set of users and grow outwards from there. However, creating a sustainable and continuously evolving/engaging membership benefits program is hard. This is also applicable in DeFi, with companies like Cega offering certain risky DeFi products only to their large NFT whale customers, who have presumably indicated a higher risk profile by investing large amounts into their NFT collections.

B2C business models: Super App model

In this model, users are nudged to trust the app for payments and money, and over time trust the same app for many other daily activities such as food orders, groceries, shopping, financial solutions such as bill payments, buying insurance or credit cards, etc. The user benefits are that the app aggregates several user needs in 1, which would otherwise require several different apps. For service providers this is a great source of new user acquisition. Building the initial foundation of trust - strong tech, cheap transactions, great security, etc. are critical to building this super app ecosystem. Examples that have successfully built strong businesses based on this model are Google Pay, PhonePe and PayTM. WeChat remains the largest Super App success story in Asia.

In Crypto this model naturally fits well with Wallets, CEXs, DEXs, and Onramps which are typically the gateway to web3 for most users. As a result, we have seen some of the larger, more profitable business models leverage their user-base to become an all-in-one service platform. Especially given that the core limitations of crypto remain trust and distribution, super-app dynamics tend to work especially well in crypto.

We will explore the super-app/product ecosystem idea in detail later.

So what are the takeaways that this comparison teaches us?

For Founders:

• Keeping a close eye on the monetization meta. Network effects for capital and distribution advantages exist2, and this becomes a backbreaking operationally intensive business to focus on shipping a series of products that have short-term supernormal returns, and long-term fee compression.

For Investors:

• Understanding that we can observe many fintech markets in India and other emerging markets like China to understand how monetization in crypto evolves.

• Understanding that monetization in crypto will likely not be concentrated from a single product

Product Ecosystems/ Super-App models

The main lesson about building fintech companies on UPI has been about the need to expand across product sets, and to focus on building a network effect across product suites. At the core of this is understanding how to build strong product cross-sell and if the product ecosystem appropriately captures the full current and future product needs of its customers.

The underlying principle here is that where the underlying rails prevent opportunities to monetize, the acquisition of transaction volume becomes a mindshare battle to cross-selling other products where monetization can be found. In the best-case scenario, feature sets should overlap to create a deep network effect and stickiness. In the worst-case scenario, simply acquire resources and act aggressively enough to win a range of ‘shallow’ market opportunities.

Such product ecosystems or super-app dynamics should be far more prevalent in crypto because of the increased need to monetize through a range of different products. The realities of building on public permissionless rails compress take rates for the vast majority of products. Having personally seen a handful of products where 7-8 figure subscription revenue is the norm, the product roadmap for most infrastructure players default to product ecosystems. The underlying question product teams have to answer are: Are the feature roadmaps within our ecosystem best reflective of how the ecosystem will eventually develop?

Network effect within product feature ecosystems, brand value, community development, developer experience/mindshare, and capital as a moat all become underlying principles for differentiation.

A few examples of this can be found across UPI and crypto.​

PayTM: One of the leaders in UPI adoption, the publicly listed PayTM has created an ecosystem around payments, e-commerce, advertising, and asset management.

Razorpay: Similarly, Razorpay, the $7.5B Temasek-backed merchant payment provider on top of UPI has created a product ecosystem approach, across payment, deposit, cards, and payroll features. Razorpay can be seen as the Stripe for India, but with a far more extensive product suite.

Alchemy: Alchemy began as RPC node infrastructure by providing faster and more reliable Ethereum node infrastructure, that ended up expanding across the stack to become the AWS for crypto, and a $10B Silver Lake-backed developer platform.

ZeroHash: ZeroHash has built an extensive product suite, leveraging its position in the market to offer a range of services, given the relatively nascent demand for each point-solution.

So what are the takeaways that this comparison teaches us?

For Founders:

• Realizing that a product ecosystem approach is inevitable for many businesses to reach VC scale in crypto today, understanding that you need to build mindshare with the first feature set, and immediately be competitive on monetization with the subsequent features shipped.

For Investors:

• Understanding the realities of the product ecosystem / super-app approach. The good thing is that although it seems like an incredibly difficult thing, and far different from the ‘singular killer use-case that builds a product moat’ approach that we see in other industries, the resultant product ecosystem moat is hard to outcompete with a good team.

As founders building in crypto double down on finding product market fit, I have a strong belief that many traditional fintech lessons will return to the fold of crypto, and many lessons from other traditional fintech payment ecosystems around the world, and particularly in emerging markets, will be highly relevant to crypto moving forward.

====

Thanks a lot to Aditya Gupta [PM @ Google Pay], Mihir Kulkarni [SVP @ Truist], Utkarsh Bajpal [Investor @ Elevation] for their comments/input. Input does not imply endorsement. 

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First, a quick recap - Liquidity provisioning represents one of the main ways to earn yield in crypto while holding a basket of tokens. Liquidity provisioning works off the concept of automated market makers (AMMs), which allow anyone to create pools of two pairs of assets that instantly provide liquidity to that trading pair. This pool allows others to trade between the two assets deposited, allowing traders more liquidity to freely trade out of positions and allowing depositors to earn transaction fees. Liquidity providers typically need to deposit 50% in value of each asset. In the early days of frantic trading activity, liquidity provisioning was a significant source of yield.

However, as liquidity provisioning became more competitive, it became clear that it was very easy to lose money as a liquidity provider (LP) without active sophisticated management. The main reason for this was impermanent loss, where due to the nature of trading pools, you ended up with more of 1 of the 2 assets that was worth less over time, as traders traded out of the less profitable asset.

Key risks also included design flaws for newly developed liquidity pools, transaction fee rewards that may be denominated in the platform’s native tokens (which may depreciate quickly), as well as providing liquidity for tokens that may be rugpulls and be ‘dumped’ on, draining liquidity and leaving LPs with the worthless tokens.

The solution is two-fold - proactive or structural. From a proactive perspective, you either help create complex derivatives (or one-click strategies) and trading interfaces to help sophisticated traders hedge their exposure to create delta-neutral strategies. This exists, is a decent solution, but can sometimes require LPs to have a deep mathematical understanding of the type of exposures they are getting themselves into or require proactive management. This is more feasible for professional rather than retail traders. Simpler solutions like NIL Protocol attempts to automate much of the hedging of potential impermanent loss. There are, however, some structural obstacles (potentially solvable) that come with attempting to hedge IL, including the difficulty in predicting/calculating IL.

The second solution is structurally single-sided liquidity provisioning where their retail liquidity providers can provide liquidity for trading pools, with guaranteed preservation of principal as well as some level of transaction fee revenue, through structures that mechanically prevent impermanent loss (as opposed to hedging against it).

The development of single-sided liquidity has so far been relatively slow but I’ve noted 5 ways that single-sided liquidity can be developed, with the aim of structurally reducing the losses that derive from impermanent loss.

5 types of structural single-sided liquidity

Method 1 - Stableswaps

The first, and most obvious way to provide single-sided liquidity provisioning (‘SSLP’) is to only provide this for 1:1 assets. 1:1 assets are asset pairs where the value between the two assets should always be fixed (i.e. USDC<>USDT, ETH<>WETH, WBTC<>renBTC, etc), due to the inherent relative value of both assets. 

Coupled with a Curve-type stableswap AMM design (which reduces but does not eliminates slippage between 1:1 assets), you can essentially create a situation where LPs can reasonably expect to not encounter any impermanent loss since they would always receive value (through either token) that are equal in relative value.

The downside of this approach is that it is inherently limited to providing liquidity for assets that have a 1:1 equivalent, which is the minority of tokens. It also forces LPs to bear the de-peg risk of the counterparty, which became increasingly common through StETH, UST, etc, which would have caused losses to counterparties in the pool. Protocols that employ this include Stargate (which has focused only on blue-chip assets like ETH, USDT, USDC), and Platypus Finance (which uses oracles to monitor and halt trading during de-peg events).

Method 2 - Native Token SSLP

The second way is to provide what I term native-token single-sided liquidity pairing. This can be seen in protocols like Bancor and MonoX.

LPs would essentially deposit against a native token provided by the underlying platform. For Bancor, you would provide liquidity against the BNT governance token. For MonoX, you would provide liquidity against the vUNIT native token created by MonoX.

The thinking is that in case of impermanence loss, the platform can print more of its native tokens at a lower rate than the revenue coming from transaction fees.

The flaw of this model was of course, that this created a constant downward sell pressure, despite calculations to the contrary. Furthermore, the platform was essentially creating a leveraged bet on continued revenue streams boosting native-token prices. In case of either a general DeFi sell-off or a huge drop in native-token prices, the cost to cover impermanent loss would exceed what the protocol could print in native tokens. This happened to Bancor during the 2022 DeFi sell-off, and Bancor at the time of writing, has still suspended operations.

MonoX suffered a critical hack that is unrelated to its economic design, and has recently relaunched. Interestingly though, vUNIT is not a governance token, but rather a representation of protocol-owned liquidity and assets. Arguably, a similar flaw might have happened here during downward markets affecting liquidity and asset availability.

Tokemak arguably has a similar design, although with a seemingly more sustainable model. LPs can stake on a single-sided basis into a pool that is paired with the native token TOKE. TOKE has utility as a bribery token similar to Convex, thus providing utility and a base value for the token. It remains a question if this will be sustainable in the long run.

Method 3 - Customizable IL Pairing

The third way would be what I call customizable IL pairing (“IL pairing”). IL pairing is essentially the acknowledgment that there are players in the market who are willing to take on some form of price fluctuation but willing to provide liquidity for long-term purposes.

This includes a lot of the DAO liquidity management solutions like Rift Finance. Here, the key insight is that DAOs are willing to provide liquidity for one side of the liquidity pair in their own native tokens and be willing to absorb as much of the IL that happens to the entire pool. The reason for this is that this is a more capital-efficient way to provide liquidity for those who are looking to trade their DAO token (as opposed to paying for a market maker, or immediately selling their DAO token for ETH, depressing token prices, and providing both ETH and the DAO token to trade within a pool).

Of particular interest is the fact that Rift allows the DAO to absorb any IL that may emerge in the pool. The ratio of losses to be first absorbed by the DAO can be customized (hence customizable IL pairing), but the default on Rift is for the LP to have completed principal protection of capital. LPs are therefore protected on their principal LP amount unless the token pair value drops by 75%.

So, the three rules defining the return profile for Rift's initial vaults are the following:

1. The ETH side gets off back at least the APY floor.

2. The DAO side gets at most their ceiling.

3. The ETH side gets any additional yield beyond requirements (1) and (2).

We will increasingly see mechanics like this that shift the burden of impermanent loss to one party or the other within an AMM pool. Arguably, a similar principle is at play when we think about fixed versus variable interest rate bond products as we saw in Ondo Finance where the burden of IL is essentially shifted to the variable interest rate bondholder.

I believe single-sided liquidity management in this design mechanic will eventually become a core primitive that sits below bond instruments, interest rate swaps, and other emerging instruments.

Method 4: Halting unprofitable trades through oracles and dynamic concentrated liquidity

The whole idea of an AMM is that you can bootstrap liquidity and thus trading, by allowing price discovery to occur naturally through arbitrageurs.

Each trade that is used to facilitate arbitrage (that takes a view on prices of assets in other markets to understand if an arbitrage opportunity exists), fundamentally represents a loss.

Given that AMMs do not exist in a vacuum, and that an AMM can take reference to prices occurring in other markets, it is, therefore, possible to act against arbitrageurs by only allowing LP-favorable trades to occur in the pool.

This can be done through a mixture of two things - dynamic concentrated liquidity and through oracle backstops.

Maverick Protocol is one of the pioneers in dynamic concentrated liquidity, and there are easy ways that the math behind dynamic concentrated liquidity would be broadly useful for single-sided liquidity mechanics that aim to reduce or democratize access to low IL liquidity provisioning.

GooseFX has an interesting model where LPs get to benefit from dynamic concentrated liquidity provisioning to reduce the arbitraging that happens on their AMMs (since liquidity will be dynamically concentrated around an oracle-set price, which should be the market price as taken from other parts of the market). Furthermore, an oracle backstop solution exists where the oracle will charge traders of the AMM the worst price from the market price (as judged by the oracle from prices in the other markets) and the price within the AMM. This ensures that there will be no situation where LPs are providing liquidity at a price that allows them to be taken advantage of. There are limitations with relying on oracle systems such as front-running and centralization risk, which however should be solvable over time.

Method 5: Pool-Specific Market Making

Hashflow employs an interesting model for single-sided liquidity by allowing users to lend into pools that are issued as capital to market-makers to create liquidity for the pools that users lent capital into.

This is slightly controversial in my head, because if you’re issuing capital to market makers, then that is simply a quasi-unsecured lending marketplace, or conversely not an ‘automated’ market maker. Traditional lending protocols also lend to market makers who generate yield by providing liquidity on CeFi and DeFi mechanics. Theoretically, this makes the risk profile no different from uncollateralized permissioned lenders like Clearpool who ultimately lend out to market makers.

Impermanent loss is therefore structurally protected against by a centralized entity that is actively hedging and providing liquidity in a smart, non-automated way. In another way, this is similar to Method #4, but relies on a centralized entity as opposed to a decentralized algorithm to help avoid impermanent loss. There’s a very useful feedback loop where the platform fundamentally attracts liquidity for its own trading pools, and LPs are simply indirectly providing liquidity to an identified pool.

If we fundamentally are optimizing for safer structures for liquidity provisioning, Hashflow’s model should be something we can look to as a potential model for replication.

What I don’t consider single-sided liquidity provisioning

The most misleading form of single-sided liquidity provisioning are the ones provided by Alpha Homora/ KyberSwap/ Thorchain. Despite an option to provide only one side of the token pair, what instead happens is that the deposited assets get swapped for the counter-token pair at the point of deposit, forcing the LP to gain exposure to the token pair, which is against the principle of single-sided liquidity provisioning (and thus single-asset price exposure as far as practicable).

LP Provisioning and the swap at the point of deposit for Thorchain. Note the difference in total supply and the assets in position.

Areas of further research/other ideas

If we think about what IL is - it is essentially a short on volatility. Low/no volatility pairs (i.e. a token pair of stablecoins like USDC<>USDT) do not suffer IL. 

The total loss by a liquidity provider is also a net position (i.e. one only suffers loss when the impermanent losses of the value of the assets in the pool exceed the transaction fees gathered by LPs to a pool). This means that IL is also predicated on time. Exiting a liquidity pool soon after a period of high volatility will likely mean crystalizing IL, as opposed to a point in time in the future.

This means that better tooling should be provided to retail LPs that are embedded into AMM pools to allow LPs to know when they can exit positions. This can take the form of forced lock-ups by AMMs (likely controversial), and projection calculators (to allow users to automatically calculate when and whether it can mathematically recover IL losses from their LP position). We can also consider other issues such as capital efficiency (ROI of revenue for TVL provided) calculators to understand how much liquidity within a dynamic concentrated liquidity band is being appropriately utilized, instead of laying idly not collecting fees with sub-optimal concentrated liquidity.

As a separate point, there is an increased appreciation that liquidity provisioning is not only a type of financial instrument/position that an investor is taking but a position that can be embedded into other types of financial instruments (bonds, interest rate swaps, etc).

Conclusion

Maturity around single-sided liquidity provisioning promises to usher in a new age of safer DeFi, especially for retail users. I am particularly excited by the way that DeFi will mature in a safer way while creating the foundation that will unlock further financial instruments.

I believe a mixture of Method 3, Method 4, and Method 5, enhanced by embedded calculator tools and potentially fixed term lock-ups, are directionally correct and will help to structurally reduce the amount of capital lost through impermanent loss. I would love to talk to anyone who has done any thinking or building around SSLP, and if there are any mechanics that are missing, please do ping me at chia@panteracapital.com. 

Thanks to James Key, Lauren Stephanian, Ronald Jan Schuurs, Ash Zenhom, GooseFX, Maverick Protocol, etc for comments/inputs. Comments do not imply endorsement.

First, a recap of my journey so far:

Despite trying to buy Bitcoin with a credit card back in 2012 — and failing — I eventually purchased my first Bitcoin and officially joined the crypto class of 2017. I have been fascinated by the space ever since, and started professionally investing in crypto in 2019.

My journey has been a mix of VC, operating, and writing. I have an woefully underutilized law degree from Cambridge, built e-commerce companies in Sri Lanka and Pakistan, and have been in Fintech / Crypto / Consumer VC for the past 4 years, most recently as a Principal at Saison Capital, a multi-stage (Early/Growth/LP), multi-geography (US/Asia) investor. As the first employee at Saison Capital, I am proud to have worked with its excellent team (and a few other secret degens) to help make it one of the top 10 Fintech CVCs globally (alongside Coinbase, Alameda) in just 3 years.

The first year of my MBA involved juggling academics while diving down the DeFi rabbit hole. Helping Bessemer Venture Partners dive into crypto, and doing research with Messari — a feature of my analysis of the Fat Protocol Theory here — I ended up spending a lot of time on stablecoins, AMM design, and DeFi, writing about design choices in cross-chain infrastructure, crypto treasuries, and MMO Dynamics in Web3, as well as experimenting with ideas to build around b2b stablecoin yield farming.

I love diving into the technical aspects of the next wave of DeFi, and firmly believe that developing one’s ability to quickly understand complicated mechanisms is an investor’s only real long-term moat.

Why now?

It is easy to invest in a bull market. It is hard to make money in a bear market — even though many of the best projects were born and nurtured in the depths of crypto winters.

Dropping out of Harvard to work closely with founders while the hype dies is the best way to focus on learning everything I can while supporting the founders building the next cohort of industry-defining companies.

Crypto moves fast — remember when everyone was trying to be an OHM expert? There is a large inflow of world-class talent from Web2 and TradFi flowing into the crypto ecosystem, and I want to be with these folks as they combine the best Web2 building principles with the best of Web3 to help create the permissionless, transparent, global, and iterative tech foundation of our economy.

Why Pantera?

After speaking to various TradFi and crypto-native funds, it became clear to me that I wanted to join a diverse team of crypto-native investors and builders who had seen, invested in, and survived past bear markets, positioning themselves to capitalize on the next run-up. The team at Pantera embodies that perfectly.

Dan Morehead, Pantera’s founder, combined extensive TradFi credentials with the vision required to have recognized the potential of crypto and blockchain technology as early as he did. Dan knows how to spot and move fast to invest in new tech paradigms, but brings wisdom and experience from his time at Goldman, Deutsche Bank, and Tiger Management.

Joey Krug, Pantera’s co-CIO, comes from the opposite spectrum. Joey has an operator/builder background and extensive technical skills. As a Thiel fellow, he co-founded Augur, one of the first protocols built on Ethereum. He’s since founded Eco, and continues to leverage that operator mindset to identify the best founders and getdeep in the weeds with them on thorny technical or business challenges.

Paul V, Partner at Pantera, needs no introduction. As one of the most well-connected crypto OG investors, Paul has had 14 years of experience investing, more than 8 of which have been in crypto. He has an extensive blog on crypto where he’s dropping the latest developments in Web3, which I have been following for years (which I highly recommend).

Over the past 8 years, Pantera has built one of the largest and most recognizable crypto portfolios, with over 200 portfolio companies, and was also once again the top crypto VC in 2022. Besides some of the industry-defining companies that Pantera has historically invested in like Circle, Alchemy, 1Inch, Amber Group, etc, I’ve been really excited by some of the recent investments like Psyoptions, Swim Protocol & InfiniGods.

Why Crypto?

The big unlock for me about why to dedicate my time to crypto was really about how open source culture tied into the concept of ‘Open Source Money’ & ‘Open Source Disney’.

A. Open Source Money

As a TradFi fintech investor looking at backend banking and payments infrastructure, I realized what an inefficient financial system existed. We have created siloed databases (some that depend on obscure programming languages like COBOL) to help keep track of who owns and owes what.

Crypto promises an open-sourced financial system built on the promise of a transparent, global and interoperable system of value. A financial system which is inherently functional 24/7, low maintenance, constantly iterating and improved upon from a set of global eyes rather than 5 engineers in a bank, with permissionless engagement with the system.

This system has the potential to turn intermediaries into permissionless protocols, and remove friction from the way we exchange value. As we saw from the Internet incrementally removing friction from communicating information (people were asking why we needed information that fast back then), an incrementally frictionless financial system allows for the exponential growth in second-order business models.

This point on incremental improvement is an important one. The wrong question to ask is ‘what is the unique use-case that is solved?’, but rather ‘what behaviors could be unlocked if this was easier?’. Here’s an example of that fallacy where 1995 David Letterman points out that the Internet was not useful for broadcasting and recording sports games, since we already had radio, television and tape recorders. He missed that the creation of a technically superior (but not previously non-existent) communication technology would unlock greater use cases, if streaming technology was easier. This was not about the specific use-case of watching sports on your desktop monitor, but about how this communication protocol would one day power information directly to laptops, handphones, and other devices. We do not need to imagine that crypto will eat up the entire financial world tomorrow — we just need to believe it can grow as fast as the Internet, which after 40 years, is still only 20% of retail sales.

Is the current state of crypto the end-state of this new financial system? Probably not, which is why I am excited to be investing in new crypto companies. It is indisputable that large parts of crypto today are solutionism or fundamentally regulatory arbitrage. Sometimes, crypto leads to fragile economic systems that blow up to the detriment of retail investors. That is bad. However, we can create better payments systems, better remittance platforms, and more transparent, permissionless financial ecosystems when we use blockchain technology in the right way.

I simply ask the question: If we were to design a more efficient global financial system from scratch, what technology, principles and mechanics am I more likely to use? What would the exchange of value look like if it were easy?

B. Open Source Disney

I was a kid of the 90s, and grew up around forums, being in school when Wikipedia was launched, and understanding how open source knowledge can make the world a better place.

I grew up in an Internet world where the digital pursuit of knowledge was a selfless goal. If you needed to find out more about something, you went to forums and talked to forum mods who would help redirect you within an ocean of crowdsourced information. Besides the free flow of information,was the free flow of social interactions.

I obsessed over video games growing up, almost playing Halo 2 competitively, immersed in Runescape, Neopets, MapleStory, and really nerded out. I grew up in Singapore, a small island, and interacted with my cultural world through the Internet. I spent countless nights staying up reading Halo fan fiction on Fanfiction.net. Through pure passion, the fanfiction community brought the Halo universe to life, through hundreds of thousands of pages of elaborate back-stories and fantastical writing.

The biggest takeaway was that a lot of cultural value that exists in society and culture are driven by people using the Internet to coalesce communities around niche passions to build a common fantasy world. That our culture is driven as much by our grassroots community, as it is by giant cultural behemoths like Disney and Hollywood, and that they can work together. Web3 offers the promise of creating structures that allow grassroots communities to capture value together, around community-directed cultural properties.

I have always been a firm believer in the power of keeping knowledge free, and how it can propel society forward. This is why I write extensively about tech, and build open-sourced projects that give back to the community and help others. I believe that Web3, leveraging open source principles, fundamentally represents an opportunity to fulfill these values, while helping founders build great companies.

Lastly — The Asks:

• Are you a Web3 founder/ investor, particularly around DeFi and its adjacent verticals?

Hit me up at Chia[at]panteracapital.com. Although I get the opportunity to look across sectors at Pantera, my background was from DeFi research, and TradFi fintech, and I would love to dive deep into those spaces with you. I love talking about how existing mechanics could be improved, and how we can design incentives to bring more institutions into crypto.

• My goal is to spend more time talking to CTOs & Engineers.

Please send me more technical white papers and academic finance papers. I went through law school by getting drunk at 9pm, and waking up at 1am to finish 50 pages of reading for a 10am meeting with my professor. I was built for reading white papers, and want to be reading as many of them as physically possible during this bear market. One of the most fun I had was diving into understanding the design choices in cross-chain infrastructure.

The easiest way to have a conversation with me is for us to dive into new ideas and primitives to build. See you all around the internets.

Debate #2: A New Model of Tokenomic yield: Using dapp native tokens to secure the chain

Debate #3: Comparative Costs Structures: Variable v. Fixed costs structures

By now, most people would have heard of LayerZero and a large number of announcements in the cross-chain generalized interoperability space (“L0”). There are a thousand articles (Anti-Ape) about how powerful the L0 space can be, given its ability to abstract away every L1 in existence.

I wanted to give some clarity on how to think about design choices between Axelar, LayerZero, and other cross-chain projects, and the overlooked nuances of these projects, by focusing on 3 key pieces of differentiation that will be key: Global v Local Security, Native tokenomics, and Comparative Costs Structures, and answer the question about how to think about who would win.

The Modularity Vision: The Global v. Local view of Blockchain security

LayerZero’s core differentiator and the chief lens to view the industry is on design choices on modularity, of which LayerZero represents the extreme end of providing modularity. LayerZero passes transaction messages through a relayer and secures them with an Oracle that must confirm the veracity of the transaction message. LayerZero provides the infrastructure to coordinate cross-chain messaging but allows dapps to choose both the relayer and the oracle that a dapp may use, rather than mandating a specific actor for either.

One of the criticisms about LayerZero is on their existing security set-up, currently centralized, as the oracle used is the Industry TSS Oracle, and the relayer used is the relayer run by LayerZero. 

These are misconceptions as LayerZero is over time, looking to be a completely agnostic modular infrastructure, with a ‘marketplace’ of relayers and oracles for developers to choose from.

In essence, LayerZero is not prescribing the full security profile that multi-chain dapps will use, but rather takes a modular approach to allow dapps to decide how much security they want to design and adopt, with the burden of security design choices on the developers.

The bull view is that LayerZero is giving greater security design choices for developers. The bear view, which is also a humorous description, is that LayerZero is thinking about security by not thinking about security. Instead, they aim to provide the choice and probably over time, provide content and education around best practices that developers can choose to adopt when choosing their own set-up on top of LayerZero.

It is interesting that this debate on blockchain modularity is not new, either to blockchain or even in tech. We had similar debates between Linux and Microsoft, fundamentally presenting 2 different views on creating ecosystems - more customizable solutions to provide choice or more standardized solutions for ease of adoption. DevConnect 2022 had a fantastic panel on this exact conversation.

The key contention is whether you believe the right way to build infrastructure is to follow Celestia and other modular infrastructure that let developers design their underlying infrastructure or follow Solana and focus on increasing adoption by building more monolithic and standardized blockchains that restrict choice, but may be easier to adopt and understand. As David Phelps phrases it - “[this] is two very different visions of the future: do we want universal consensus for global connectivity or local consensus?”

This brings us to Axelar. Axelar and LayerZero present the essence of a similar debate, with LayerZero providing the infrastructure to enable more ‘local’ consensus networks, and Axelar providing a more global approach, as each Axelar node helps to secure each other, creating more universal consensus/security. Axelar uses an intermediate consensus layer to demand that there is a basic standard of consensus (i.e. rules that determine whether a message is verified to be true) that must be set, that is applicable to any message transfers on Axelar. This is in contrast to LayerZero which sets up the infrastructure but lets dapps decide on the rules and consensus required to approve a transaction on each dapp.

The founders of Axelar and LayerZero debating whether Axelar’s universal security model is the better approach.

The specific trade-off for users, founders and developers would be:

• Users: Mainly security, with a greater variance in security expectations. There would exist a possibility that the dapp’s utilization of LayerZero may be poorly designed, or have specific security vulnerabilities. On the other hand, this might represent greater security if developers use LayerZero to institute higher dapp-specific security for their relayers. 

• Developers: The ability to implement/create your choice of relayer and oracle (including a centralized relayer, or a third party decentralized relayer), though increasing the need for each developer to understand and make a decision on why one choice is better than the other. 

Modularity in itself also has security implications. A monolithic security structure like Axelar, with its intermediate consensus chain, might create more formidable security since resources are pooled into a single universal security structure. On the other hand, the failure of the intermediate consensus chain may result in the catastrophic loss of all assets secured by Axelar nodes. In contrast, as LayerZero is merely a modular messaging infrastructure, any single dapp being compromised using LayerZero has no knock-on effect to compromising assets being managed by another dapp using LayerZero.

A New Source of Tokenomic yield: Native Tokens & Value Capture

One thing I am particularly interested in seeing moving forward is how dapps building on top of L0s will make decisions on using their native tokens to secure validators. My view is that validator tokenomics will be one of the next huge topics that founders will have on the top of their mind when LayerZero utilization increases. This also has huge implications for LayerZero’s own value capture framework.

Founders will increasingly understand that they can build additional utility for their dapp’s native token by using using their native tokens as a slashing penalty to secure cross-chain validators. This essentially turns every native token into a potential proof of stake dapp, allowing a dapp like Aave to allow users to stake the Aave token when Aave processes cross-chain transactions. This provides another token sink mechanism by incentivizing lock-ups, and even an income stream for stakers and the protocol itself by charging a transaction fee that feeds into the staking yield for validators of cross-chain transactions. So far, only LayerZero above has discussed this publicly although other cross-chain protocols like Axelar have privately noted that this is a utility that founders can also build on top of their own cross-chain infrastructure. It is potentially slightly more complicated to build this feature on non-LayerZero infrastructure, given that most other cross-chain infrastructure have their own native validator tokens that need to be built underneath the ‘native proof of stake’ mechanics.

For example, Axelar will be launching their native token soon, and the AXL token will be used to help secure the AXL network through staking rewards/slashing penalties.

Since LayerZero has not publicly launched their token, another interesting tokenomics question is whether ecosystem incentives are aligned when individual actors may use LayerZero without LayerZero tokens. This is as there are no obvious token incentives to build on LayerZero. The LayerZero team may be attempting to purely focused on building for the community, and achieve a pure form of product market fit, unencumbered by tokenomics, specifically providing infrastructure to dapps who need cross-chain infrastructure rather than profiting from the next hot crypto project. When asked, the cofounder of LayerZero, Bryan, hints at this view by stating that big grant programs at the onset may attract mercenary developers that may crowd out and distract core believers who are aligned with LayerZero’s value proposition.

One question that may be posed given the current structure would be what the value capture mechanics would look like for LayerZero, given their light tokenless model. A governance token model would bring speculative value, but given the lack of utility for a L0 token, it is unclear what the value capture mechanics for LayerZero would look like. My personal guess is that over time, the LayerZero team will see value in building their own dapps on LayerZero, or end up leveraging a LayerZero token as a currency that dapps may choose to use to stake their validators (in spite of incentives by dapps to do the same for their own native tokens).

On the other hand, with a native token, Axelar’s value capture mechanics are more straightforward. Growth of the AXL network promotes the AXL token value, rewarding early dapps and adopters of the AXL network, unlike early adopters of LayerZero. It is therefore ironic that the market currently views LayerZero as the ‘sexier’ infrastructure, despite a lower ability for the community to monetize from LayerZero’s growth. With the launch of the AXL token in June 2022, there is scope for this to change.

Comparative Cost Structures: The cost trade-off

LayerZero’s model also assumes a specific cost-trade off which may be more applicable to specific business models and transaction types. With their current model, LayerZero’s reliance on a third-party oracle incurs a higher variable per-transaction fee. Oracle networks have a marginally increasing cost-per-use, especially as there is increased demand for Oracle services. On the other hand, Axelar runs light nodes, which have a high fixed cost, but can be accessed with little to no variable cost per use.

Tascha notes the differences in costs also have implications on the growth rates of both L0s, with Axelar taking a ‘slower but steadier’ approach to building out the infrastructure layer.

Anti Ape lays it out here nicely, arguing that IBC and L0 will likely co-exist to serve different use cases. Arguably, lighter models like Axelar or IBC solutions will be more suited for high volume transactions in:

• GameFi

• NFT mints

• Consumer payments

Anti-Ape’s argument implicitly assumes that there will be a limited number of Oracles used in practice, leading to congestion of the Oracle network over time, and thus the variable cost per message. It is important to note that Anti-Ape’s argument does not hold in a hypothetical universe where there is an infinite number of Oracles to choose from in practice.

LayerZero is likely to find a stronger product-market-fit for larger transactions and for DeFi dapps which may want specific types of consensus mechanics, especially for high-gas fee chains where the variable cost is high, for example in, ETH-Solana cross-chain DeFi loans.

Design choices affect use cases. Nomad, as a cheap optimistic cross-chain messaging platform requiring 30 minutes for finality, is potentially suited for large whales and dapps that are willing to wait for 30 minutes for a transaction to be completed in a cheap manner.

It is important to note that both the Axelar and the LayerZero teams dispute the above characterization, arguing that their model are better suited for the entire spectrum of both DeFi, GameFi, etc.

• Axelar argues that standardized security is especially crucial for building trust in DeFi as an industry. Users and even other developers would not have the time to necessarily examine all potential loopholes if each dapp builds their own customized security. 

• LayerZero argues that a standardized security approach is inflexible and potentially especially costly for high-frequency transactions like GameFi. With a single consensus mechanic shared by all types of dapps, the protocol would need to establish a high basic level of security. This will mean that dapps with higher security requirements would essentially be subsidized by lower-security/higher-frequency dapps that inherit and pay for, but not require such high-security requirements.

How to think about who will win?

The L0 wars are not won by code, but by adoption. It is not just about how much transaction volume goes through LayerZero and Axelar’s bridges (Stargate/Hop) because then you are just thinking about building better bridges, but how you are able to build the foundational infrastructure for native multi-chain dapps to build on top of.

The main driver of thinking about who will build on which L0 is to think about the value of developer choice. Do dapp developers really want to make security design choices? This is the fundamental stake in the ground that LayerZero is tying itself to. LayerZero’s edge is to introduce choice and modularity, which will only win if developers and the community appreciate the customizability of security. Recent headlines of high-profile on-chain compromises seem to indicate that while security will be a greater area of focus, the way people believe best to achieve such security is still unclear.

From this view, two easy things to track are:

1. How much experimentation are developers doing with the oracle/relayer combination (either using LayerZero or using different choices as the oracle or relayer)?

There are 2 sub-questions here. First is an ideological view: is modularity worth pursuing even if it leads to less total net secure systems (i.e. high variance of security across dapps)? Second is a utilitarian view: will modularity lead to competitive market forces that will lead to greater total net secure systems? LayerZero will exist where the answer to any of these questions is yes.

An early indication of engagement with these questions is through developer experimentation. Lower experimentation indicates developers are happy to use default trust assumptions. One fear I have is that while there will be a desire for dapps to have a customized relayer, there is not a wide range of reliable oracles to choose outside of Chainlink, and LayerZero will end up only providing modularity over the relayers, and relying on only Chainlink by default, limiting the total net value of modular infrastructure. This increases the potential for Chainlink’s CCIP to disrupt LayerZero since they provide very similar infrastructure with Chainlink’s CCIP infrastructure providing slightly deeper off-chain data infrastructure on top.

1. How excited are founders to leverage their tokens for cross-chain validator revenue?

How governance tokens can continue to capture value, especially with increasing SEC scrutiny, is already beginning to be one of the key questions dapps are looking to answer. LayerZero has the potential to allow every single dapp to build Proof-of-Stake-type revenue streams by conferring staking revenue streams to their governance tokens. I have yet to see much founder attention to this topic, but it will likely continue to grow once founders start to understand the power of this mechanic.

One interesting implication that I have yet to see popularized is a more modular compromise within the ecosystem, with an Axelar-type infrastructure with universal security but with LayerZero-type custom tokenomics. Universal security and custom tokenomics are largely opposing objectives, but I think that a middle ground can potentially be achieved through a dual staking model (staking of native bridge tokens + staking of native dapp tokens).

Thanks to Mings.eth, Michelle Lai, Bryan Pellegrino, Ryan Zarick, Galen Moore, James Friel, and Georgios Vlachos for input. Input does not mean endorsement or agreement with article.

Links to the best reads in the L0 space:

AlphaPls, Anti-Ape, CoinYuppie, LayerZero Medium, Axelar Blog, Mings.eth

This thesis was critical in framing where value will be captured in crypto, where investors should invest, and how to think about the application vs the protocol layer. Unfortunately, due to a range of recent developments, we have seen that this thesis is under significant pressure, and explore if the death kneel for the Fat Protocol Theory can be sounded. The importance of understanding this thesis is where it affects our view on where we should allocate investment focus across protocols and dapps, and the stage of first entry. Ashwath of Delphi Research noted that the “legitimacy of the fat protocol thesis is key to Ethereum’s continued dominance.”

The main drivers of this theory’s decline include the following:

• Reduced monopolism: Multi-chain Dapps/ Dapp branding

• Overstated network effects: Long-term protocol competition leading to lower fees

• Reffal Curve: The role of Roll-Ups reducing total demand

• New framework of thinking of L1 value capture: Currency v Nations

Why the Fat Protocol Theory made sense

The Fat Protocol Theory was based on 3 main descriptions of the interaction between protocols and dapps:

• Any dapp building on top of a protocol will require the protocol’s native token to power any transactions within the dapp.

• It is far easier to clone dapps and attract users than it is to clone protocols and attract users, allowing protocols to take on a more monopolistic competitive position relative to dapps

• Protocols will thus capture more value than dapps, since the protocol’s value capture will be reflected in any transactions that the dapp layer processes (value creation), let alone the value that the dapp itself will capture.

Measuring the cracks in the Fat Protocol Theory

Joel John of LedgerPrime noted that the first cracks of the Fat Protocol Theory emerged when capital-intensive use-cases like stablecoins emerged. With stablecoins like Tether driving adoption of ERC-20 tokens, the market cap of ETH did not rise as much in proportion to the rise in market cap of stablecoins, indicating a fundamental break in the relationship between protocol/dapp value capture (at least for stablecoins).

The main way that the Fat Protocol Theory is proved is that we should expect the market capitalization of dapps on Ethereum to be a fraction of the market capitalization of Ethereum itself. The alternative situation would be described as a ‘flippening’.

The thinking is that temporary speculative bubbles in dapp valuations can exist, but must fundamentally revert to being under ETH’s market cap. However, since the thesis has been published, we have seen multiple long-term instances where the ERC-20 dapp layer market cap has exceeded ETH’s market cap since 2020, 2021, and today in 2022 (320b v 311b).

(Snapshot: Calculated by summing the 308 largest ERC-20 token market caps on CoinMarketCap on 20 Feb 2022)

On the one hand, it can be argued that the dapp market cap ‘flippening’ is a result of temporary speculative bubbles in dapp valuations, and we should expect a long-term reversion of ETH’s market capitalization to be above that of its underlying dapps. On the other hand, it may be reflective of the increasing dapp-product-market-fit in a maturing crypto ecosystem that will look to stress the Fat Protocol Theory even further. Furthermore, it can be argued that the current divergence is understated. Centralized organizations that interact with protocol may contribute to the protocol development, but not necessarily have their value-capture reflected in tokens. A good example of this is Opensea or Coinbase, where value capture by these organizations is reflected in the equity value of their company, rather than the tokens that were not issued.

Overstated network effects: Long-term protocol competition leading to lower fees

Joel’s thesis was written in a time when there was an overwhelming sentiment toward single-chain maximalists. It seemed that protocols provided the ultimate network effect that would be hard to replicate. We see today that due to ETH scalability issues, and general capital/ financial incentives, there was a strong motivation and wedge for competing, increasingly specialized, protocols to emerge.

One of the main ways that value capture by the protocol level would take place would be through the transaction/gas fees that the protocol captures in a transaction it facilitates.

The emergence of cross-chain infrastructure helped to promote an increasingly sustainable multi-chain world. Protocol competition then sparked a drive for lower protocol fees, even if the mechanism for value capture existed (see Solana’s waived rent fees).

Multi-chain Dapps & Limitation of Forking

A dapp building on a single ecosystem restricts their user base, which is increasingly non-sustainable given the popularity of multiple competing protocols. Dapps will increasingly be multi-homing, the act of ensuring native interoperability across multiple protocols, to prevent being ‘stuck’ on one protocol. For example, increasing ETH gas fees saw a migration of Dapps and crypto holders into alternative L1s like AVAX, SOL, etc. Dapps, seeing the trend, are able to diversify their product offerings/user base by providing multiple integrations to serve communities across multiple protocol ecosystems. AAVE building on Polygon as well as AVAX is a good example of this phenomenon. As Dragonfly Capital noted, “Binance Smart Chain taught everyone: if you don’t launch here, we’ll just launch a fork of you and take the revenue you would’ve gotten”. 

Furthermore, there was increasing recognition of the value of the brand equity for dapps. Uniswap expanding into another chain like AVAX would in all likelihood be more successful than a 3rd party Uniswap fork on Polygon, due to Uniswap’s ability to leverage existing liquidity relationships, talent, capital, distribution, etc. Consumers would also be more likely to trust Uniswap given their prior brand equity built up (even with no experience on Polygon itself).

The premise of monopolistic value capture by the protocol layer has been weakened by these developments. 

The role of Roll-Ups

“But you mentioned L2s like Polygon that are on ETH!”

L2s are helpful in helping to solve some of the key constraints of ETH, and retain transaction volume on ETH. However, as L2s roll-up and consolidate a larger number of transactions into a single L1 transaction, it will reduce the amount of ETH that is needed for the same number of transactions. 

One argument is that, similar to tax revenues, lower transaction costs (tax rates) can still produce a higher aggregate demand for ETH (tax revenues), as it would help increase the rate of dapp development/transaction. 

Creating the above, I call this theory the Reffal Curve of Protocol Value Capture, in respect of my economist teacher, Mr. Reffal (/s). Although we talk about this theory in relation to ETH L2s and their impact on demand on L1 ETH, it is also broadly applicable to any alternative L1s and their internal gas policy. It is important to note that the Reffal Curve, particularly the areas to the right of B, could not have existed in the view of the Fat Protocol theory since it assumed a monopolistic view of protocols. Here, the curve to the right of B indicates users and dapps forking or switching to alternative L1s to conduct transactions.

Accordingly to the Reffal Curve, alongside increased protocol competition and reduced gas fees from L2s, I believe we are far more likely to see a situation closer to point A, rather than point B. Ethereum maximalists will disagree and believe that we are headed to a scenario closer to point B from point C, while proponents of a multi-chain world may find themselves on the side of point A, given the speed at which alternative L1s have been able to take market share away from ETH, or alternatively believe in point B if they believe that the crypto macro-trend will drive transaction volume/demand far more than existing capacity is able to serve. 

One pushback could be that, given what we know about the price elasticity of ETH gas costs (last calculated by Vitalik in 2018) being above 1 (i.e. is price elastic, where a change in demand is greater than a change in price). Therefore, a drop in gas price will be more likely to move us into the side of B, rather than the side of C.

My personal belief is to A as we are far more bottlenecked in terms of good crypto projects than in transaction throughput, especially taking into account essentially infinitely scalable alt-L1 throughput. 

Currency v. Nations

One of the most useful criticisms to have arisen out of the Fat Protocol Theory debate has been the use of currency rather than GDP as a means to evaluate protocols.  As noted by Nick Hotz of Arca, in the US for example, money supply is worth about $20T while equity capitalization is $50T, with the ratio of money supply: equity capitalization varying based on how much value that government (layer 1) is providing to its companies (native apps). 

The reframing of a fundamental view of protocol value has been helpful in getting a more intuitive understanding of protocols, but also served as a further weakening of the Fat Protocol Theory. 

This is given that if we view protocols as currency rather than GDP, the total market capitalization of the underlying blockchain/currency money supply would not necessarily need to exceed the market capitalization of the dapp layer.

Nick Hotz further notes that while the Fat Protocol thesis has been very profitable and ahead of its time, the main driver for its success has been due to a lack of alternative places to invest, and a lack of traction from the applications themselves, not necessarily value capture dynamics from the protocol level. Given the huge amount of relative dapp product-market fit that we have seen since 2016 when Joel wrote the Fat Protocol Thesis, the Fat Protocol Thesis is showing its limitations.

Why it matters for portfolio construction

On the one hand, what the Fat Protocol Thesis fundamentally gets correct is that there is a larger value capture by the protocol level, but we have seen value capture compression on the protocol level.  However, it is indisputable that even today, the best-performing investors have seen their returns come from protocols rather than dapps. Solana for example reached 100B market cap in token value after 4 years.

At the very least, we can at least conclude that the growth of TVL/utilization beyond a certain point does not correspondingly increase protocol value. This has interesting implications for whether growth investments in mature protocols would necessarily make financial sense (in the Venture Capital sense), given the repudiation of a monopolistic single-protocol world. Its repudiation also increases the value capture potential of the dapp layer, and increased exposure to dapps within an investment portfolio.

The Fat Protocol Theory has left us with a few principles that have so far stood the test of time, and while we can expect investing into emerging protocols to continue to be lucrative in the short term, we should expect heavier portfolio allocation into dapps as a way to hedge against increased protocol competition. Cracks and limitations of the theory have developed since 2018, and is under pressure from a wide range of developments, such as increasing dapp product-market-fit, multi-chain competition, and overstated blockchain network effects, as evidenced by the dapp:protocol market cap ratio. Yet, protocol returns have so far spoken for themselves, indicating some soundness in the investment strategy in the short/medium term, though we no longer hold conviction in its long-term viability.

Thanks to Jose L Sampedro Mazon, Chiyoung Kim, Mike Giampapa for their thoughts/comments (thoughts does not imply endorsement)

What is Venture building? Venture builders are organizations that build startups, using their own ideas and resources. Technical and management teams of a startup would typically be under varying levels of employment by the main Venture builder entity, which reflects the range of ‘venture building’ models that exist in the market.

Ecosystem engagement (ranging from the level of resource commitment) can be seen below to better contextualize Venture Building from the range of other options: 

Diving into the different models of Venture Building specifically:

As I’ve written before - If you can’t find it, build it. This has been an attitude taken by a number of venture funds (crypto/non-crypto) who find that they hold the keys to a number of important factors of building a highly successful company: Access to talent, capital, and an understanding of what needs to be built.

On the highest spectrum of venture building, companies like Rocket Internet have famously built and sold billion dollar companies like Lazada, hiring co-founders to build from scratch to acquisition. These funds tend to own the vast majority of the cap-table.

Moving downwards, venture builders like Superlayer, Advanced Blockchain have been known to work with promising founders on the initial idea, provide the initial Seed capital, and with key members of the founding team under the employment of the parent venture builder. These venture builders typically then continue their involvement with the company as an investor into their later rounds and typically own outsized but not significant majority stakes in the company.

Working in a similar manner, though on a shorter-term basis, companies/programs like Antler, EF work with individual founders in a programmatic manner, and help them find cofounders to start companies together, investing an initial pre-Seed/Seed capital. Post-investment/program, the founders are typically left to build their companies, with some support from their investors (who typically generate hundreds of companies through this model).

For Crypto, a range of venture building options are available, though I generally suspect the VC-as-a-Co-founder approach (Early-stage/ Short-term) will be the approach that best balances founder incentives and capturing opportunities reliably for crypto organization, given the wider range of needs that a Web3 company would need (liquidity, tokenomic design, multi-chain support, etc).

The main principle for why one should go deeper into Venture Building would be driven by external factors of industry bottlenecks - specifically on a relative scarcity of resources. In an environment with high levels of industry bottlenecks, where a player owns a relatively disproportionate level of resources (capital & non-capital) to overcome said bottlenecks, venture building is recommended. These resources can include information, capital, talent, and distribution, which see an unusual concentration of such resources among the crypto kingmakers of protocols, exchanges and tier 1 crypto-native funds.

In the traditional Web2 world, with the examples given above, the resource scarcity that those venture builder examples had were fundamentally around talent, capital. The resource that Rocket Internet was gathering was talent and capital arbitrage of emerging markets (back when it was hard to find both in places like Africa, South Asia, LATAM, Southeast Asia in the early 2010s). The resource that EF/Antler have is optimizing for cofounder talent density and the difficulty of finding co-founders.

Furthermore, crypto’s unique characteristic of leaning towards being an open-source decentralized platform also contributes to the likelihood of venture building proliferating. Founding teams that have successfully created a dapp or protocol can find themselves in the position to decentralize both governance and product building for that dapp or protocol, let the community take over, and focus on a new project without necessarily compromising its growth (For example, as seen in Sushiswap which continued growing as a prominent dapp despite the founder leaving). This is in contrast to a closed organization where the departure or even split focus of the management team would dramatically affect the output of the organization. The capacity of leadership teams to switch project focus gives greater space for venture-building to exist.

Protocols in particularly have the highest strategic reason to venture building. From the spectrum of light-touch evangelizing to heavier-handed venture building, we will see protocols increasingly more to the latter, in a ‘crypto super-ecosystem’ approach to venture build D.apps/use-cases across their own ecosystem to increase protocol adoption. Protocols have a single incentive - To increase utilization of the protocols. Protocol teams are doubly motivated to venture build on their own protocol - because they can capture the value of a newly built D.App, but also increase the utilization of their protocol, increasing the number of use-cases for their consumers and increasing protocol mindshare. 

Within Crypto specifically, we have already seen this trend of venture building by protocols. The most prominent example of this was where the team behind Luna built out Chai payments, Anchor and Mirror, all applications which helped to increase consumer adoption of the Terra ecosystem, which increased the value for others to build on Terra. The Generalist notes that this unusual approach allowed Terra to build products in which it can act as its “own first, best customer”, similar to how Amazon similarly created tech products like AWS for which they were their first customers.

Solana has also recently announced the launch of Solana Pay, built by the Solana Labs team. Solana Pay is not only a needed business model in itself (leveraging cheaper transaction costs to allow crypto merchant acceptance, with instant settlement, lower merchant fees, and an ability to serve lower margin industries that already struggle to accept credit cards [e.g. Gas Stations, Cross-Border payments, etc]) but will also strategically drive the adoption of SOL and the Solana ecosystem.

In contrast, Ethereum has historically not been a place where we see such high-profile venture building by the ETH foundation, though displaying some elements of incubation and grant-making. Vitalik has been a driving force for new ideas on crypto-primitives that 3rd party founders have built out on Ethereum (Vitalik’s thoughts on AMMs inspiring Uniswap for e.g.). 

Moving forward, we can expect to see organizations increasingly diversify their ecosystem engagement strategy, with a spread of grants, venture capital investments, and venture building organizations, potentially as separate entities. Given that venture building, for example, cannot be executed well as a part-time strategy by developer teams, we will see an increased focus for standalone venture building organizations that are specialized in running processes to identify, build and capture value through dapp venture-building.

In conclusion, the relative resource scarcity, dominance of crypto kingmakers, and the nascent nature of the Web3 ecosystem have set the scene for the disproportionate growth of venture building in Web3 as a means for strategic competition. Newer protocols have paved the way for some of this activity and moving forward, I expect to see more Exchanges and Tier 1 Crypto-native funds build out venture building teams and organizational capabilities as a means of competing, and as the future for Web3 value-creation.

Thanks to Shaun Heng, Chiyoung, Jack Chong, Matt Sorg for their comments/input. Input does not mean endorsement.

In particular, I wanted to dive into 3 characteristics of MMOs that have crossover potential and could expand access to and adoption of Web3:

• Tutorial systems

• Navigation/curation platforms (like a Web3’s Steam Workshop or App Store)

• Competitive/collaborative incentives

A few other obvious business models that have already been ported over from MMOs include Guilds (DAOs), personal showrooms (CryptoVoxels, NFT Showroom), gambling/sports betting (BetFury), and virtual pets (CryptoKitties, BattlePets). These characteristics can provide a road map for untapped opportunities in web3.

Tutorial Systems of Web3

Crypto is confusing. There are a wide number of Layer 1/2 protocols & ecosystems to deal with while navigating a fairly nascent bridging experience.

How did we solve this problem in gaming? Handbooks were provided to help users understand how to play a new computer or console video game. Eventually, we understood that tutorial systems needed to be a part of a seamless user experience.

Web3 is a similarly complicated world. For the next billion Web3 users to be onboarded, we need to build processes and experiences that help them understand how to navigate crypto tools and the value they can get. The Web3 equivalent of tutorial systems currently revolves around using Discord, Youtube, Reddit or paying for Coursera-type crypto education courses. These “handbooks” are too formal, highlight gaps between theory and practice, and may not be fully optimized for those trying to make a decision of whether they should be spending more time in this new Web3 environment. 

In thinking about best practices drawn from the gaming ecosystem, as Matthew Miller, former Lead Designer for City of Heroes notes in the context of games, “You need to teach as much as possible, while simultaneously teaching as little as possible. It’s a balancing act. You want to ensure the player knows how to play your game, while at the same time you don’t want the tutorial to feel like a lecture where you should be taking notes because you never know what’s going to be “on the test.”

Bree Royce of Massively Overpowered, an MMO magazine, notes that “the best newbie onboarding system is a game that encourages community at all levels, rewards teaming up across levels, and actively works to foster and support player relationships.”

There are a few platforms that are focused on building gamified onboarding systems to different aspects of Web3 (from lending to purchasing NFTs, to being a liquidity provider, etc)  with a built-in reward mechanism (“Learn to Earn”). Earn.com (now part of Coinbase), Rabbithole.gg, and Balaji’s 1729.com (ex-founder of Earn.com) aim to a clearer onboarding and tutorial system for new Web3 users. The gamified experience, with financial incentives at each step, helps to find the balance of what Matthew Miller mentioned between teaching as much and as little as possible.

Much like an MMO tutorial, users are rewarded for using tools and systems that they would need to learn to fully immerse in an environment. Rabbithole.gg gamifies the process with experience points and rewards for small tasks/actions and breaks down the range of activities that a user can choose to undertake within Web3 (Lending, joining DAOs, buying NFTs, etc). 1729.com rewards users for completing various tasks with Bitcoin. Community-based aspects of the Web3 tutorial system are still fairly nascent. One potential example is Buildspace, which has built a gamified community-based learning platform for those looking to start learning in Solidity, with NFTs given out as rewards, and homework answers required to be shared with the Discord community while advancing through the course.

We have also started to see wallets (like Zapper) adopt a quest-like functionality to onboard users into various crypto use-cases. DAOs have also seen a recent rise in platforms focused entirely on helping to streamline onboarding users into DAOs, to help them discover 

A great tutorial system for crypto would be one that teaches beginners how to safely purchase assets, rewards experimentation with different use-cases, allows easy visibility to all assets across wallets, while encouraging community-based interactions.

The Steam workshop of Web3

When users search for games within the Steam gaming platform, they navigate the service using the Steam Workshop, which standardizes the onboarding and distribution of various game projects, akin to the iPhone App Store. Users are confident they are able to search and discover a curated set of projects and filter according to need, reviews and functionalities. We are missing a Steam Workshop for Web3.

An equivalent Steam Workshop for web3 could solve the problem of Search for users, and the problem of Distribution for developers, and streamline the onboarding of new crypto users.

Some core features that could be built include:

• Solving for trust (potentially via reviews)

• Solving for Search/Discovery (via curated lists)

• Solving for activity aggregation (for e.g. via a personal dashboard of assets/activity across ecosystems/DApps)

DAOs are also emerging as an area where an App Store approach providing curated search and discovery is needed. DaoHaus.club, for example, allows users to search for DAOs according to a range of criteria and natively interact with the DAOs on-platform. 

Technical onboarding support and standardization is one key benefit of an ecosystem App store approach. For example, navigating the “workshop” for Polkadot may navigate the wallet onboarding process in an easier way, similar to how the Steam Workshop/ App Store have standardized the way consumers think about onboarding and consuming new Apps. An App store approach, if developed, would also allow for a more seamless UI, and provide a vector for single-sign-in (i.e. Sign in with Ethereum) consolidation (especially imagining the App Store for Ethereum and all Ethereum L1/L2s).

Newer Layer-1 networks such as Solana and Avalanche have incrementally worked towards an ‘App store’ approach by trying to solve discoverability on their own websites (as seen below). I believe we will see a multi-chain App store equivalent that will allow consumers to dive deeper into the user experience through curation and reviews.

Steam workshop/App Store for Web3 dynamics would also be a catalyst for embedding the existing Creator Economy into all aspects of crypto businesses.

As the ‘browser’ to Web3, Wallets may also find value in creating their own Web3 app store to allow developers to build and sell plug-ins (e.g. lightweight analytics, alerts) that improve the native experience and utility of their wallets.

Play to Earn games will also find this concept increasingly relevant. Given the growing debate on the viability of Play-to-Earn games, User Generated Content has been seen as one of the potential avenues for sustainable economics. This is given the increased attention to turn Play to Earn games into platforms for players to exchange value independent of any Play to Earn dynamics in the game itself. For User Generated Content to work, Steam workshop dynamics will arise, either on a protocol level (i.e. interoperable GameFi marketplace) or within specific Play to Earn games.

Embedding Player-to-Player Competition & Collaboration (“P2P”)

In games like Fortnite and Runescape, games were fundamentally used as a platform to allow users to compete against each other and collaborate with each other. In a NewZoo study of gamer personalities, a strong resonance for collaborative and competitive behaviours was found among a significant percentage of surveyed gamers.

Encouraging collaborative and competitive dynamics among Web3 users is a crucial missing ingredient in the Web3 ecosystem. This is not unique to games. If we think about the Web2 financial ecosystem, we see r/wsb retail investors collaborating and picking sides against Wall Street, banding together within a community, and fighting against an “other.”

I believe player-to-player (P2P) competitiveness and collaboration will unlock a wave of innovation and adoption within Web3. P2P is defined here as creating structured environments for players to compete or collaborate with each other in individuals or identified teams, rather than against the broader environment/ market.

There are three ways to think about how platforms can embed P2P mechanics in the Web3 context:

• Firstly, utilizing existing crypto-native communities and building P2P mechanics on top of these communities

• Secondly, utilizing existing IRL communities and porting them over to Web3. 

• Lastly, utilizing crypto-native innovations that have P2P potential, like Martingale Shares.

Channelling crypto-native communities

We are still in the early days of a crypto zeitgeist, but NFT culture has been actively pushing the creation of communities, with the emergence of various famous NFTs, including Bored Apes, CryptoPunks, etc. NFTs for example, have been used as instruments for social coordination and resource pooling, for a variety of purposes including supporting social impact, investments, or enabling collective ownership of public goods.

While these communities are emerging, we have already started to see the development of P2P utility between various NFT communities in crypto gaming. For example, Onisquad is developing games to work on top of pre-existing NFT-based communities. They aim to co-opt IP and NFT assets into their game mechanics, as opposed to creating an entirely new NFT universe from scratch.

Given the interoperability of crypto ecosystems, we are likely to see more tools and platforms pop up looking to serve the existing and emerging crypto brands and strengthen crypto-native communities.

Leveraging existing In-Real-Life communities

In addition to crypto-native communities, existing affinity and fan groups in sports and celebrities can be ported over into a Web3 context. In the field of sports, one of the first emerging areas of crypto, it is unsurprising that we have seen a huge amount of attention to porting over IRL communities into the crypto world. Sorare raised a large Series B rounds tackling NFT football cards and fantasy football management.

Users on Sorare can build fantasy football squads and play them off each other, providing a P2P use-case for NFTs.

Other potential P2P functionalities may also include crypto live sports betting with live chat functionalities (bearing in mind potential regulatory constraints around sports-betting), or music fans betting on the outcome of livestream music duels/battles. 

An example of a ‘livestream battle’ in China on a Web2 platform, which does not exist to a large extent outside China.

The ability for crypto to facilitate a seamless exchange of value means that betting dynamics can now be embedded into existing consumer-facing platforms.

Embracing crypto-native innovations

We are in the early days of seeing how a seamless exchange of value can create innovative ways of engaging parties in a competitive way. One of the most recent experiments that fall into this category is the concept of Martingale shares.

Martingale shares are a mechanism that gives you a share of any underlying asset, as well as essentially a time-based lottery ticket for the rest of the shares of the asset. As each day passes, the algorithm gives a 50% chance that you receive another share from someone else, or you might lose your existing share to another shareholder.

These competitive mechanics can be gamified and modified for a P2P experience. During Call of Duty Mobile’s popular free Tournament events, users can join and perform various in-game tasks for a temporary “clan” to increase the probability of winning a set of rewards. This in-game mechanics has parallels to Martingale shares, in that users can be incentivized to perform actions to increase the probability of winning a rare prize on behalf of their team. In this context, Martingale shares disincentivize inactivity since a team gaining an increased probability to win increasing fractions of a prize can compound quickly. The actions that Web3 users can undertake to increase their probability of winning might include performing various organizational tasks (marketing, ops, etc) within a project/DAO or within P2E games. 

The Wolf game is another recent example of crypto-native innovation around probabilistic ownership between parties. Players can own one of two types of NFTs: Wolves or Sheep. Sheep NFTs can be staked to earn the game’s native token, but wolves have the ability to probabilistically steal both the native token and even the Sheep NFT from the opposing sheep team. Although firstly designed an incentive mechanism to encourage long-term holding, this also forms a foundation for team-based combat.

The ability for Martingale shares and other types of crypto-innovations to incentivize competitive actions that are a net positive for a project/DAO will help with the evolution of Web3 from a single-player experience to a multiplayer game.

In conclusion, in drawing parallels between the user journey of MMOs and the gamified experiences of Web3, we see business model opportunities emerge, both in platforms currently being launched, as well as opportunities that could emerge. As UI/UX continues to dramatically improve in Web3, an area that has historically lagged in development, we can also expect to see many of the mechanics in onboarding, collaboration, and navigation.

Thanks to Dan Shipper, Kirill So, Ryan Foo, Lucas Tcheyan, Lauren for input/comments. Input does not imply endorsement.

Treasury management refers to the broad act of managing the pool of funds that are on their balance sheet. Software in this space are essentially CFO-in-a-box type services that help companies understand the state of their treasuries. Legacy general enterprise companies in the TradFi space include the large enterprise tech players, Oracle, Microsoft etc, and smaller specialized legacy players like Kyriba, HazelTree, etc, that help CFOs understand the state of their treasuries. Owning CFO mindshare of treasuries is important as the opportunity to win the IoS for Finance.

There are 3 main things that I wanted to understand today:

A. Who are the main category of customers for crypto treasuries?

B. What are the different products/initial business models that are a wedge into crypto treasury mindshare?

C. What are history lessons from TradFi that can help us understand treasury mindshare?

#A:

The creation of crypto currencies have given rise to 4 main categories of corporate customers that would touch crypto in some way:

1. Fiat -> High yield stablecoin staking accounts (Traditional companies that want crypto yield but not assets)

2. Fiat -> Crypto -> Crypto (Institutions that want to active trade crypto)

3. Fiat -> Crypto (Institutions that want crypto assets but not day-trade)

4. Crypto -> Crypto (Crypto-native orgs that are mainly crypto-treasuries without touching much fiat)

1 are Corporate defi mullets. Defi mullets are defined as platforms that appear to feel like traditional fintech companies from the perspective of the user (in terms of user experience and processes), but that leverage Web3/crypto mechanics on the backend. The target customers here are traditional businesses with little interaction with the Defi ecosystem.

With the rise of an entire ecosystem of stablecoin savings accounts, corporate defi mullets like Meow have emerged, offering crypto-like interest rates for fiat deposits.

2 refers to financial institutions that are actively trading and have a need for portfolio visibility across a range of ever-changing assets. The target customers here are financial institutions that could include hedge funds and family offices that are actively trading crypto assets.

3 refers to financial institutions that are looking to get long-term exposure to the crypto asset class, but do not see themselves as active traders. The target customers here are financial institutions that could include large corporates who are looking to merely diversify their treasury base and would typically hold their crypto positions for a long period of time.

4 refers to crypto-native organizations that have little interaction with fiat, and predominantly hold native-tokens, ETH/BTC, stablecoins, etc. The target customers here are organizations that would include companies that raised solely from ICOs, DAOs, and generally crypto-native organizations. Within this segment, we have seen DAO treasuries expand from 400m to 16B in less than a year in 2021, and we are only just getting started.

#B/C:

We will now explore the product features that give rise to winning in crypto treasuries across the different customer segments above in #A.

Typically, corporate treasuries have been won in the following ways:

• Custody

• Dedicated Accounting/Payroll Software

In Traditional Fintech, we have recently seen the emergence of the following features to win Treasury Mindshare

• Payments

• Lending

• Remittance

• Corporate Credit Cards/Expense Management

• Incorporation

• And discuss the unique Web3-native aspect of Stablecoins

Summary of Right to Win - If I were to build a crypto treasury company, what features can I first build-out to appeal to which customer segments?

Custody

TradFi: Custody would appear to be the most obvious and easiest way to own the treasuries space. Banks are the most obvious example of how they became the main interfacing layer between Financial Institutions and their crypto treasuries. All SMB neobanks fundamentally see being able to be the primary deposit account for SMBs as the holy grail for trust and thus being the IoS of Finance. Most TradFi Fintechs try to focus on improving the user experience of opening or navigating the banking experience.

Crypto: Similarly in crypto, and exchanges like Kraken, Coinbase, and Matrixport were able to quickly be the main interfacing layer between Financial Institutions and their crypto treasuries. Although the primary use case of exchanges was to trade assets, many of these exchanges also became the de-facto custodians for financial institutions.

Even non-custodial solutions like hardware wallet companies, solving the problem of custody, found themselves in a great position to upsell enterprise treasury solutions. Ledger, for example, built out the Ledger Enterprise software solution off the back of its hardware cold wallet solution.

Hypothesis of Opportunity: Custody is a well-understood process and an obvious wedge. We have seen treasury management solutions emerge naturally from custodians in both TradFi and Crypto, and should continue to see them as being able to win the lion’s share of the treasury management space.

Dedicated Accounting/Payroll SaaS

TradFi: Dedicated Accounting Software can range from the expected - from leveraging Microsoft/Oracle/Kyriba/Novantas - to the unexpected - with players like Khatabook and Bukukas leveraging simple accounting software to win over SMBs.

Crypto: Dedicated Accounting infrastructure is still relatively new but an obvious pain point. Companies like Multis, Utopia, Coinshift, Gilded Finance are trying to build accounting and payroll offerings for crypto-native treasuries.

Hypothesis of Opportunity: Starting with payroll, we are starting to see nuances in crypto accounting SaaS development. Just as payroll (especially for crypto native companies where labor is typically the main costs) was the natural wedge product for most TradFi fintechs to break into corporate treasury management, crypto payroll has represented the most obvious wedge product to first solve. Taking inspiration from Tradfi treasury management plays, other product vectors that are emerging will likely include treasury risk analytics, liquidity management for incentive programs, tax and audit analytics, and account receivable management. More financially focused generally applicable analytics like tax and risk analytics would be more broadly appealing to all segments of potential customers (e.g. traders, tradfi and crypto-native companies).

A specific use-case that is uniquely found among crypto-native treasuries would also be around providing transparency standards. In order to uphold the ethos of decentralization and transparency, crypto native treasuries will increasingly find themselves pressured to provide easy transparency into DAO treasuries, representing an opportunity for the ‘Blockexplorers’  for DAO/crypto-native treasuries.

Payments

TradFi: Payments has emerged as a fairly unusual way to win over treasury. Bharatpe became a 3B unicorn by leveraging their QR code payment system to persuade MSMEs in India to avail of their high-yield (12%) interest account offerings. The initial trust derived from helping businesses move money allows them to naturally upsell treasury services, typically starting with the float already captured within the payment ecosystem. 

Crypto: Interestingly, at least for consumers, payments has also appeared for ‘retail treasuries’ with Chai payments offering a high-interest rate for fiat that users deposit into their Chai payment e-wallet. 

Hypothesis of Opportunity: Similar to how Chai executed for consumers, and Bharatpe executed for SMBs, merchant payment gateways may represent a way to enter treasury management for both crypto-native companies, as well as the most natural crypto on-ramp for traditional companies looking for cheaper payment rails. As an example, a merchant gateway can offer to provide yield for the float that merchants have on that merchant gateway.

Furthermore, because of the vast nature of tokens out there, crypto-native companies looking to make payments in native tokens would see value in payment gateways that provide automatic settlement into a currency that the merchant wants to receive in (i.e. only USDC), as opposed to having to incur gas fees twice, in first converting into the receiving currency and then sending said currency across to the receiving merchant.

Lending

TradFi: Lending has also emerged as an interesting wedge case in a similar way to Payments. SMB neobanks like Koinworks, Validus and Aspire emerged out of an initial SMB lending platform, building the trust to justify managing treasuries. Factoring/lending companies like Capchase are also seeing opportunities to build out treasury management solutions.

Crypto: Due to the nature of how lending emerged in crypto, first emerging (and currently still mainly focused) around overcollateralized lending. Overcollateralized Lenders have tended to emerge out of custody-first companies, and have started to offer some treasury services - like Tesseract’s Earn product. We have yet to see undercollateralized lending-first crypto companies mature to the point that treasury management products have emerged from these platforms. 

Hypothesis of Opportunity: This could however emerge as newer forms of lending emerge and popularized. It is interesting to note that the same dynamic of undercollateralized credit constraints that created lending-first neobanks in emerging markets Asia is replicated in the undercollateralized credit constraints found in crypto, hinting at the potential for undercollateralized lending models moving into crypto SMB neobanks. Undercollateralized lending platforms could be well-positioned to win over treasury management in the same way that SMB lenders in TradFi branched out to win treasury management. Companies that focus on providing lending services could branch themselves out into a SMB neobank. 

Specialized crypto lenders like undercollateralized lenders to DAOs may also represent a great wedge to win over DAO-specific treasury management.

Remittance

TradFi: Remittance has emerged as an area to win over export/import oriented neobanks. SMB Neobanks like Airwallex, Neat and Wise all began as companies fundamentally solving remittance issues before expanding into a neobanking product suite.

Crypto: Remittance has never emerged as a key area of concern for companies moving digital assets across borders, given the borderless nature of digital assets. The closest relevant business model would likely be off-ramps, but this would represent a poor wedge given that the value of off-ramps is derived from firms looking to exit into the TradFi ecosystem. 

Hypothesis of Opportunity: However, one customer segment remittance would be relevant to would be for traditional companies looking for cheaper remittance solutions. By leveraging the crypto promise of cheaper remittances, corporate treasuries could be won over in a similar way that TradFi remittance-first neobanks developed. A product evolution along this logic could be imagined as the following: Crypto remittance mullet -> defi mullet neobank offering defi-returns based on stablecoin staking.

Corporate Credit Cards

TradFi: The opportunity set of corporate credit cards as an initial wedge to win over corporate treasury is clear - multiple unicorns with incredible traction have been born in this space in a relatively short time through Brex, Divvy, Ramp, etc. Credit Cards as a way to win expense management software, as a way to win general treasury management is well understood. Companies like Brex have even moved into providing TradFi companies crypto exposure 

Crypto/Hypothesis of Opportunity: The opportunity in Crypto is less direct. Brex for Crypto-native companies has certainly been discussed actively, but I believe there are a few temporary obstacles that currently suppress this opportunity set. KYC for employees/DAO contributors who wish to remain anonymous, the immaturity of DAO organizational processes today, and workflow processes around multi-sig wallet security represent some of the structural and temporary difficulties for building a crypto-native Brex. It is arguable that as DAOs/crypto native companies mature, we will see a Brex for crypto overcome some of these obstacles.

Stablecoins

TradFi: The equivalent of Stablecoins are the Central Banks, which would not play a major role in the daily operations for corporate treasuries. Corporations receive the fiat currency that are needed for daily operations simply from their equity or debt stakeholders.

Crypto: In Crypto, however, the need to hold or access stablecoins for working capital requirements (for example, payroll) is a need more acutely faced by crypto-native companies. Although it is possible to build a stablecoin treasury by exchanging native tokens or ETH/BTC through exchanges, companies may also simply work with stablecoin issuers directly like Circle, Maker, etc. 

Hypothesis of Opportunity: As a result of such a direct relationship between stablecoin issuers and companies (in a way that does not exist in TradFi), stablecoin issuers are in a great position to upsell treasury management solutions (since they are directly feeding into the treasury). We have seen this with Circle launching enterprise treasury solutions for both crypto-native companies and traditional companies looking to get crypto exposure/yield.

Incorporation

TradFi: Fintechs have been looking to incorporation services as part of an easy way to win over custody, by bundling custody/bank account services with incorporation services as seen in Stripe Atlas, Mercury, Aspire.

Crypto: Incorporation is a less clear problem statement in crypto, especially as the regulatory framework, and market appetite for censorship risk is still being developed. As of the time of writing, especially in the case of DAOs, some level of legal entity is desired for reasons of providing liability shields, and employee benefits. As a result, there has been an entire industry of platforms, like Syndicate DAO, dedicated to helping spin up/incorporate legal entities for DAOs and other crypto-native companies.

Hypothesis of Opportunity: Understanding legal and incorporation needs represents a tricky problem statement for crypto-native companies and represent a well-understood wedge to occupy treasury management.

In conclusion, we can see that lessons from TradFi are useful to understand the range of product features that might emerge to win in crypto treasuries, across multiple customer segments with very different use-cases, needs, and starting points.

Crypto treasuries is certainly a multi-winner space, even in specific customer segments, and it would be interesting to see how, as businesses continue to accumulate significant crypto assets and demand greater tooling to manage their balance sheet.

Thanks to Saket Kumar, Mike Giampapa, Zeynep Yavuz, Wellington Scully, Pavir Patel, Sharon Lourdes Paul, Eric Lim, Dan Schlabach for thoughts/input (Input does not mean endorsement)

• High-Level drivers of Livestream Commerce

• Understanding the historical context: The US Livestream Commerce Landscape

• Business Driver #1: Building Trust by matching Product to Platform

• Business Driver #2: Building a Call-to-Action

• Business Driver #3: Sales through Entertainment

• The 3 Stakeholders’ Jobs to be Done Framework

• The bull case for livestream commerce: Comparison to QVC

If you are reading about live streaming commerce — you probably know the headline basics. It is huge in China at 600B USD GMV. In 2020 GMV Douyin did 60B, Kuaishou did 51B, Taobao Live did 62B. It is emerging in the US. You are trying to find a more comprehensive breakdown on where this industry will go next.

In this article, we wanted to lay out the historical context of livestream commerce, the competitive landscape today, a framework for understanding the business model, specifically its main levers for success and what the end-state business model looks like (diving into the economics of QVC)

If you are a child of the ’80s or ’90s, you probably remember QVC or the Home Shopping Network. Before every retailer had a website, and before Amazon became the “everything store,” people who wanted to shop without leaving the house would turn to one of those cable channels for programming designed to sell products to them. 

High-Level drivers of Livestream Commerce

Livestream commerce has its origins in the distinct but related concepts of trust and entertainment. Livestream commerce finds its niche in low-trust products - agriculture and cosmetics. You could fall sick by eating fruits with too much pesticides, or have fruits with bad taste/sugar levels. You could also ruin your skin with sub-quality cosmetics.

The best framework to think about it is as an agency business model with a payments interface. The three main drivers in this business is its ability to attract, with the right incentives, influencers who know how to sell and entertain via livestreaming, and as a platform, strike the best deals with the most exciting brands/SKUs, and a user interface that will encourage seamless conversions. 

A16Z describes ‘shopatainment’ as part art, part game show, part theater, part supply chain management, and part auction house , which is not far from it - though as we will see, a familiar agency-type business dynamic will emerge.

Livestream commerce sees significantly better performance than traditional e-commerce, and this can be seen in the customer behavior numbers. Conversion rates are higher:

As noted by 6Pages, conversion rates can be 30-40% – dramatically higher (up to 10x) than traditional ecommerce. One livestream-shopping marketplace reported that nearly 80% of customers made a 2nd purchase within 30 days. Companies are also reporting a 50% lower product return rate because consumers can ask questions and see sizes and colors.

Understanding the historical context: The US Livestream Commerce Landscape

We can break down the different types of US livestream commerce business models by their focus on Influencer Tiers, Use of Professional Streaming, and SKU focus.

In the competitor’s scan for US livestream commerce plays, we note three major waves of players:

• Wave 1: The first wave of legacy television commerce players: QVC, Home Shopping Network

• Wave 2: The 2010-16 Post-FB social network boom of players: Joyus, Dote, Down to Shop

  • XiaoHongShu becomes a unicorn in 2016

• Wave 3: The 2018 re-emergence: NTWRK, TalkShopLive

  • Snapchat IPOs, Facebook Stories launched in 2017

  • Chinese business models become an area of attention for US markets as Pinduoduo IPOs in 2018.

  • Bytedance acquires Musical.ly in 2017 and explodes in popularity in the US

There are 3 main takeaways to think about when we looked at the US competitive landscape.

Firstly, breakout winners have come from collectibles, and we will see more winners emerge in Beauty - cosmetics and skincare specifically.

Secondly, there is a diversity of players within the influencer tier and I was particularly intrigued by the use of store owners livestreams. Though bullish on their selling capability, I was less-bull on their entertainment quality. Here, flash sales and gamification tactics are likely to feature more heavily as a hook.

Lastly, livestream commerce is not new even in the US, and I remain fully bullish on standalone livestream commerce plays to develop and win in niches, expand horizontally and win big.

Matthew Lee, Partner at Progression Fund, notes that Wave 1 of QVC & HSN were largely successful as they pioneered the TV/cable medium for 35-64 year old women, many of whom were stay at home mums with an average age of 50. Wave 2 was likely unsuccessful because mobile penetration was not super high yet, and live content was extremely hard. The content needs to engage users immediately otherwise they would leave, so the market timing was not right and the tech stack was likely emerging.

From a timing perspective, Wave 3 is focused on a younger, mobile-first audience who want engaging and interactive experiences. Matthew notes that a lot of the companies in the above list did not start with livestreaming, but were early movers in adopting it, accelerated by the pandemic and the significantly higher conversion rates that livestream commerce offered.

Business Driver #1: Building Trust in the SKU

While there are more consumers shopping via e-commerce than ever before, most users still feel unsure about buying products from websites they haven’t shopped on or heard of before. A major reason for that is the fact that people are used to being able to see the product in person before buying it, or at least have someone they can talk to and ask questions. On most e-commerce websites, you’d be lucky to get a bot to answer your questions.

Livestream commerce is about real-time interaction as a vehicle to build trust. This makes livestream commerce a perfect vehicle to attack lower-trust SKU commerce. You are no longer buying a product off a static page. You are able to see someone speak about the product, and explain to you why it is right for you personally after building an element of a trusting relationship. Taking it one step further than just watching them speak about it like on the yesteryear TV shopping networks, you are even able to interact with them and ask them questions about the product. In a world where human contact is becoming increasingly rare while shopping, livestream commerce is able to bring a human element that significantly boosts trust and enables stronger brand loyalty. 

“People want to buy something from someone they trust. I have established that trust with the viewer,” Martin told Fast Company, which may sound vague and unprovable, except that NBC’s in-house analytics show that sales on its e-commerce platform go up whenever a product is associated with an image of her face.”  - Vox

If we dive into the concepts of trust, we see that trust correlate strongly with certain SKUs more than others.

This is also reflected in the SKU categories, which generally require an education process to overcome a trust deficit:

The top category for Kuaishou is jewellery - specifically gold bars and emeralds, which is a historically opaque industry. The top 3 categories for Douyin are clothing, personal care and make-up. Xiaohongshu has also been able to specifically target foreign brands looking to enter China and overcome a trust deficit.

As an aside, Fashion, specifically women’s clothing, are likely to pop up as a common SKU for influencers. However, I would argue for example that early-stage livestream commerce companies will face significant supply-chain challenges tackling fashion due to the increased complexity of maintaining a fashion supply chain and interface. Fashion is far more likely to succeed if promoting a limited line of SKUs with small size variations (i.e. shoes).

Nicole Quinn, General Partner at Lightspeed Venture Partners, holds a view on the type of SKUs that are most likely to succeed in video commerce:

The key for all forms of live shopping is to lean into collectibles. The powerful part of collectibles is that it taps into human behavior where you cannot get enough of them. With a high willingness to pay, there is a strong competitive element that produces strong emotions just as pride, envy or jealousy. Collectibles are a large part of how QVC / HSN began and it is now a key driver for the online live shopping companies we see emerging today.

Trust and Entertainment, go hand in hand with community building. This is particularly apparent in SKU categories like collectibles, and cosmetics, where we see parallels in Whatnot/Popshop.Live (Collectibles) to Bilibili and Supergreat (Beauty) to Xiaohongshu.

It can be noted that livestream commerce can also be introduced to a wide array of non-traditional e-commerce SKUs, for example of cars, houses, etc, in ways that would not have been possible on a QVC-type physically closed-set. In a similar vein, we have already seen the rise of Tiktok Financial Influencers who talk about various publicly traded stocks through livestreams, with online brokerages like FUTU embedding livestream capabilities for their influencers within their forums (which recently got banned a few days ago).

Business Driver #2: Building a Call-to-Action

“But if you call now, within the next twenty minutes cause we can't do this all day, we'll give you a second set absolutely free.”

Looking past the haunting eyes of Vince Offer (pictured above), the infamous ShamWow infomercial pitchman, we see that one of the keys to conversion of video commerce to purchase is a time-based call to action.

Given the nature of livestream commerce, it is easy to scroll past into the next video, or be satisfied at the entertaining nature of the pitch itself. One of the most overlooked lessons from livestream commerce plays has been the role of time-based discounts in moving the consumer from the discovery and entertainment phase, to actual conversion.

Time-based discounts are coupons or discounts that expire in the next few minutes/hours. These discounts can be given freely or can be acquired easily through low-efforts tasks (linked to engagement for example as seen below). In substance, they are more sophisticated forms of the type of time-based FOMO that the ShamWow commercial tries to instill in their viewers.

These time-based methods can also be seen in other features like showcasing a countdown for limited quantity SKUs, higher discounts for earlier purchasors, and many others. These time-based conversion tricks are especially key for livestream commerce, given the temporal nature of the livestream where the consumer attention span is more focused, but may flip to another channel/video quickly.

Business Driver #3: Sales through Entertainment

The two core jobs to be done here are selling and entertainment.

Lindsey Li, Investor at Bessemer Venture Partners, notes that this is not new. In the US, offline commerce is often inherently social – malls are destinations to meet friends and browse or discover goods. Shopping is naturally inclined towards solving for entertainment.

The interactability function of social media has meant that, unlike a celebrity endorsement, which typically meant posing for photo shoots, filming TV advertising and doing press releases, the influencer themselves are expected to use social media, and in the case of livestreaming, to interact themselves with their fanbase in real time. This has helped shaped fan engagement in the livestreaming space into a blend of talking and selling a product, while being entertaining.

This graphic from A16Z helps explain the model of “shopatainment.”

In certain cases, brands and platforms take it one notch above. In Asia, it has focused fully as a form of entertainment. At first glance, the set and production value of a livestream is indistinguishable from a professional set. In Asia, the powerful force of video entertainment shopping has spawned a livestream ecommerce industry akin to professional movie production. 

This represents an important bottleneck for platforms serving long-tail influencers. Although a seemingly attractive audience segment since it is relatively easy to acquire (e.g. mass DM’s via Instagram), the skill-sets for selling and entertainment via livstreaming is very different from posting curated images/posts via Instagram. 

Long-tail influencers are far more likely to work in niches where there is a shared and baseline understanding of the products in question (i.e. collectibles, ethnic fashion) where niche influencers with non-significant audiences may have extremely sticky audiences due to the specific niche they play in.

In more competitive product segments (i.e. women cosmetics), reliance on mid-higher tier influencers may be necessary, especially in the beginning, as a means for the livestream platform to differentiate itself from others.

As an aside, long-tail influencers is where horizontal content platforms like Tiktok and Youtube have an advantage since they have a lot of data on existing influencers and their potential to scale as well as an ability to leverage cross-selling as a means to attract influencers to convert into live-stream commerce. I remain bullish on verticalized livestream commerce plays, as a function of being able to carve niches both from a viewer, brand and influencer acquisition perspective, as well as supply-chain/SKU optimisation perspective. 

Matthew Lee, Partner at Progression Fund, postulates that retailers will have to deliver an omnichannel experience that includes livestream shopping because of the large increase in conversion rates compared to other existing channels. Many retailers may also find themselves turning into a media company in the coming years. In a relatively prescient move, Pinterest announced their move into livestream commerce a few days ago.

QVC has understood this and has optimized for high-end influencers on their livestream platforms. QVC hosts are personalities, many of whom have devoted fan bases, like the late comedian Joan Rivers whose QVC jewelry line sold $1 billion over 20 years. 

They are trained in the products they sell, often visiting manufacturing plants to school themselves. When on air, the host juggles promoting a product and interacting with models, guest hosts, callers, and off-screen analysts. The host physically interacts with the product, highlights its features in abundant detail, and makes their sales pitch. - Moz

Between his two weekly live shows on QVC and his broad digital platform, Mr. Venable last year sold more than $250 million worth of egg poachers, frozen crab cakes and backyard smokers. When he posted a recipe for Philadelphia cheesesteak dip, it reached 5.5 million people on Facebook. The two cookbooks in his “In the Kitchen With David” series have sold more than a half-million copies on QVC. - New York Times 

This blend can be complicated to pull off as can be seen in Amazon’s own failures with livestream commerce: Style Code Live.

In 2016, Amazon invested significantly in Style Code Live (SCL), a livestream commerce platform and brought on high-end influencers to sell. Lyndsey Rodrigues, Rachel Smith and Frankie Grande (Ariana’s brother) — all had TV and broadcast backgrounds before joining Amazon. Yet, it was shut down by 2017.

In a post-mortem published by Forbes, Paula Rosenblum noted that a poor understanding of the interaction between selling and entertainment brought down SCL.

Although Style Code Live was helmed by proven talent, the channel itself seemed to be a cross between a typical home shopping channel and a light daytime talk show that talked about style, fashion, and beauty tips. While Amazon did not release the number of viewers who tuned into the channel or how many sales were generated by the products sold, it does appear that the main reason for its failure was the lack of focus in combining two different TV genres.

In essence, people who enjoy shopping channels like QVC and HSN obviously got bored watching the tips and reality show aspects of the channel. Meanwhile, those who liked listening to tips and guest appearances by the likes of Kourtney Kardashian and Sarah Jessica Parker were probably turned off by the push to sell the products. Hard sell and soft sell rarely work well together, and this may have been the overriding reason why Style Code Live met its demise.

Success in the home shopping world is a result of the interaction between the host, a guest (ideally a celebrity of some sort) and the audience. Of course, the products have to matter, but the key is really that interaction. The audience has a chance to talk to the celebrity

The blend of real-time data on top of influencers enables this real-time juggling of SKUs, Brands, Influencers and Platforms.

Shawn Xu, Investor at Floodgate, notes that he "get[s] excited by the data driven selling that's possible on livestream commerce. With QVC, they are able to measure and benchmark how specific products are selling by the minute. With platforms like our portfolio company Popshop Live, we're able to get this data on an even more granular level. We are also excited by the community engagement that gets built up around individual livestream seller brands, which are driving shoppers back to the platform in a stickier way. We are super bullish about this category."

The 3 Stakeholders’ Jobs to be Done Framework

For Influencers

• Provide a platform that allows me to interact with my audience, and let them seamlessly purchase, so I have higher commissions

• Provide an attractive platform for Brands (and ideally introduce me to them), so they receive all the necessary data they need to be attracted to me as an influencer for them

• Provide a platform that will continue to grow significant eyeballs so I can grow my fanbase when I use your platform

For Brands

• Provide a platform that is easy for me to find and interact with these influencers

• Provide a platform that will continue to grow significant eyeballs

• Provide a platform that is easy for me to sell and ship products

• Provide a platform that can help me build and engage a community

For Viewers

• Provide the most relevant influencers for my tastes as fast as possible

• Provide an interactive and entertaining in-show user experience 

• Provide a seamless purchasing experience

The key bottleneck here is the matching between influencers and brands. Interaction protocols, especially if a platform adopts long-tail influencers, and brands are still fairly nascent and platforms would need to hand-hold both influencers and brands as to why their platform is the best way to engage their audience.

As the platform’s topline is driven by successful matches between influencers and brands, a heavy amount of handholding and matchmaking between influencers and brands are likely to take place - which is heavily reflective of the agency-type dynamics that the platform would have to deal with.

From Clubhouse, we can see that taking a light engagement model, in the face of participants who may be new to this mode of engagement, is likely to be detrimental to the platform.

“Six creators from Clubhouse’s creator program say no brands sponsored them before the end of the program, and Clubhouse failed to turn any of their shows into sustainable endeavors, as it advertised it would. None of them plan to keep doing their high-production shows, and many are refocusing their efforts on other platforms, simply because they have a better shot at being compensated for their effort...

[Influencers] received the number of unique listeners, the total number of minutes spent listening, and the number of listeners a room had at its maximum. “Obviously, if you know anything about sponsorships, that means nothing to anybody,” she says.” - The Verge

The ability for platforms to provide some element of product structuring as a key component for influencer monetization has been an important point I have made before, so I will just add that A16Z has made a good list of the types of products influencers can offer on livestream platforms.

The bull case for livestream commerce: Comparison to QVC

Incumbents are massive and are not slowing down. QVC, the largest US incumbent, generated $11.47 billion in 2020 and as early as 2015, nearly half of those sales were taking place online — The company has 16.5 million consolidated customers worldwide with strong retention. 90% of QVC’s revenue comes from loyal repeat shoppers. The average QVC shopper makes between 22-25 purchases per year.

QVC has adapted fairly well to the new consumer age, despite being a 35-year-old company. Although their focus on professional filming (similar to NTWRK) allows them to target a more mainstream audience (and if I were to guess, promotes higher AOVs), their transition into mobile commerce has been successful. In 2017, online sales made up 59% of QVC revenue in the US, and in 2018 it bounced up to 62%. Of those sales, 66% were made in a mobile app. This has placed QVC in the top 10 for e-commerce sales, including mobile sales.

Diving into QVC’s annual report, we find a fairly strong and diversified business. QVC has 16.5m customers, of which 11.6m are from the US. 26% of their revenue is international, with a Net Income of 7.4% (2020), 7.0% (2019), 7.9% (2018).  Their SKU categorization is also diversified across a large number of categories, with Home (42% of sales) and Beauty (18% of sales) coming on top.

The legacy nature of their business can be evident in a few areas. Besides a high executive compensation (2020: ~17m to the Exec Chairman and ~10m to the CEO) for a 4B market cap company with no significant long-term shareholder value capture, the year on year revenue growth has essentially been flat, growing 2% in 2 years (and during COVID on top of that).

In my view, it is notable that QVC, like their Chinese counterparts, is owning live stream mindshare for more mainstream SKUs like Home and Beauty. As US livestream commerce startups mature from collectibles and fast-fashion into more mainstream SKUs, we will see QVC’s lunch being eaten by many VC-backed startups leveraging the livestream commerce opportunity.

Looking at the financials and operational numbers of QVC made me extremely bullish that there are at least several multi-billion dollar profitable GMV companies that could be created in the short run within the livestreaming space alone. We are in the early days of this latest wave of US livestreaming, and I am super excited about what is to come.

Thanks to Professor Rajiv, Lindsey Li, Matthew Lee, Nicole Quinn, Akshay Bajaj, Shawn Xu, Shawn Teow for comments/input! Input does not constitute endorsement.

We look into:

• Current sentiment on revenue concentration risk

• Illustrating B2B concentration risk across different segments of Fintech

• Why those sentiments are wrong when examining stickiness, servicing whales, and making an alpha v beta market bet as an investor

• Long-term sustainability of concentration risk for b2b fintech infra

• Boundary conditions for understanding when b2b concentration risk matters

1. Current sentiment on revenue concentration risk

Conventional logic says that revenue concentration risk is something to be worried about. This makes sense - after all, when a customer represents a significant portion of your revenue, they hold a significant bargaining position and may drive your margins down in the long run. They may also leave suddenly, creating volatile revenue situations.

Revenue concentration risk is such a standardly accepted risk that it is one of the mandatory flags that must be discussed in a company’s SEC filing.

It has therefore been fascinating to see multiple B2B Fintech infrastructure plays go public with significant revenue concentration risk. 

How do you know if your customer concentration is high or low? A rule of thumb holds that if any single customer accounts for 10% or more of your revenue or if your largest five customers account for 25% or more of revenue, you have high customer concentration.

We have seen tech companies with concentration risk biting them. Twilio losing Uber, which was 12% of their revenue source at the time, and suffering a 30% share drop at the time, is one of them. As Yann Ranchere poses the question - is customer concentration an overstated risk, or a source of alpha to be lauded?

2. Illustrating B2B concentration risk across different segments of Fintech

We see B2B concentration risk across different segments of Fintech. As we can observe, concentration risk is applicable across multiple verticals of fintech.

3. Why those sentiments are wrong

   1. Stickiness

   2. Growing with Whales

   3. Explaining Alpha v Beta

Revenue Concentration should be seen as a positive feature, and not a bug, of B2B Fintech Infrastructure. 

The main bear factor for b2b infrastructure plays is the churn-off problem. This basically describes the situation where smaller startups use your infrastructure solution as an interim to building their own tools, or represents a potential for volatile revenues.

The churn-off problem is uniquely mitigated in Fintech Infrastructure. Fintech infrastructure is far less turnkey than other types of infrastructure provider (e.g. Communication (Twilio), CRM (Zendesk), etc). This is especially the case for highly regulated industries like Fintech (as opposed to for e.g. a standard CRM tool). The requirements in fintech infrastructure encompass not only teams with a capacity for not just regulatory expertise, but also a capacity for specifically proprietary regulatory expertise (i.e. a regulatory moat through the creation of a new financial process/product). 

This is especially true in fintech where the customers of fintech infrastructure are less familiar of the regulatory complexities of the financial product (for example in embedded finance plays), or for new financial services (Kalshi’s development of event-contracts -betting on events happening -are a good example of this).

“As for what happens if Kalshi takes off and other brokerages or other large financial institutions attempt to create their own event contract offerings, Mansour insists that it wouldn’t be so easy for them. “A lot of the work that we’ve done over the last two-and-a-half years is [intellectual property]. Every single detail of operations was built for event contracts. It would take a bit of time — especially for some of these bigger institutions — to really get into the space.” - TechCrunch

As such, fintech infrastructure is far less commodifiable, given the degree to which it abstracts the complexity and cost of regulatory compliance. In tandem with the degree of customization for customers, fintech infra is generally less likely to be a target for in-housing.

Quantitatively, this has meant that b2b fintech infra are more likely to see higher net dollar retention (NDR).

Above are the NDR numbers for the top public SaaS companies, with an average of 120%.

Marqeta’s NDR was over 200% for FY20 and FY19. Adyen has a NDR of 115-120%. Affirm has a >100% NDR.

In the SMB SaaS sector, Shopify has a 100% NDR, while its comparable SMB fintech infrastructure play like Toast has a 110% NDR. We note that a lot of our points on fintech infrastructure relate mostly to non-SMB fintech infrastructure players. Notably, although no numbers are publicly available, Toast does not have a single customer above 10% of their revenues.

(For the purposes of this hypothesis, we refer to customer revenue concentration only, and not country or product concentration)

Marqeta’s TPV growth by cohort

Long term sustainability of Fintech Concentration Risk

Is it long-term sustainable? Probably not - as companies get larger, they want to prevent single points of failure, which will include their financial services or fintech infrastructure. Further, it is true that margin compression is likely to happen in the long-run. For example, Marqeta's take rate fell from about 0.7% in 2019 to 0.5% in 2020, and has dropped further since then. However, if you see whale clients as an investment in capex/capacity building to allow a platform to scale quickly from an early-stage company to one capable of large volumes of transactions, then leaning into revenue concentration as a private company is a legitimate strategy. Having built up capex/capacity to serve other larger enterprise clients positions the infrastructure platform to be able to win over other whale-type clients (which in Marqeta’s specific case, we can see in the multiple other whales they onboarded). Frank Rotman of QED pointed out that being able to “practice” with an “at-scale” client is extremely valuable and paves the way for additional contracts. Sheel Mohnot of Better Tomorrow Ventures makes a point, which I believe is especially important for infrastructure plays, that getting blue-chip logos is an obviously great way to shorten the sales cycle at other ‘whale’ clients.

The underlying risk that an investor assumes when assuming such concentration risk is that the growth rate of the ‘whale’ client is an anomaly not reflective of broader industry growth rates. However, if the investor is already willing to make a broader macro bet on a specific industry (say card-issuing), then they are already underpinned by a rising tide that is only supported by the added growth from a concentrated client, to support infrastructure/capacity building. A short-term focus on concentration risk is therefore misleading, and sub-optimal from the lens of an investor.

To illustrate Marqeta’s ability to win over existing ’whale’ clients over time, and their ability to capture upcoming whales, we see that Marqeta has been able to capture both large enterprise clients as well as selective small clients that will become whales:

At point of onboarding:

Doordash: 500m valuation

Affirm: 575m valuation

Klarna: 2.25b valuation

Square: 5b valuation

Airbase: Seed stage

Brex: Seed stage

Uber: 80b valuation

Afterpay: 4b valuation

JP Morgan: 200b startup

Marcus: ~100B in deposits

The key here is to understand that even if fintech infrastructure plays exhibit high churn rates among a long-tail of startups, that they possess an ability to remain sticky among the core group of fast-growing companies. If they have a double-churn issue (i.e. churn on the long-tail of customers, and churn after a certain scale with their fastest-growing companies), then it is more unlikely to see a path to success.

Furthermore, one of the best arguments we can make that revenue concentration does not matter for fintech infrastructure startups is actually the very fact that so many fintech infrastructure companies IPO at large market capitalizations, with significant revenue concentration. This indicates that you can build a successful IPO-able startup over multi-year periods, with significant revenue concentration, in excess of at least 30%.

Lastly, capturing Alpha is a far greater growth driver than capturing Beta. Given the sunk cost of setting up the initial regulatory and operational moat, being able to scale with that particular whale ‘alpha’ captures a faster growth rate than ‘beta’/industry benchmark growth. Yann Ranchere, partner at Anthemis, adds nuance to this argument by pointing out that ‘riding the whale’ is less about one whale and capturing the top 5/segment of potential/current industry players.

Usage-based pricing is why fintech companies are better able to extract value from their whale clients while sticking to them. Fintech infra generally has usage-based pricing, as opposed to general software that is priced on a license/seat basis. As Insight Partners’ James Woods points out, usage-based pricing allows for a natural rate of “land-and-expand” growth higher than any other form of pricing model, noting that SaaS companies across all verticals that can justify a usage-based pricing all tend to naturally have much higher net dollar retention.

“The top eight [public SaaS] companies all utilize usage-based pricing. The next best company that doesn’t utilize usage-based pricing is Zoom, which can boast a 130% NDR – that’s still less than the average of the companies that do.” - James Woods, Insight Partners

A bear case is the emergence of infrastructure-as-a-service providers like Finix, that empowers companies to be their own pay-fac’s, allowing them to more easily escape reliance on a single infrastructure provider. I foresee the main short-term bottleneck here to be regulatory and compliance capabilities by companies in utilizing such infrastructure-as-a-service providers.

4. Boundary conditions for understanding when b2b concentration risk matters or not

Given this specific understanding of fintech infrastructure where we’ve learnt to accept concentration risk in its growth journey, what are the boundary conditions for this principle across sub-segments of fintech infrastructure?

Fintech infrastructure that will do particularly well would:

• Exhibit a strong bias for stickiness (no to low churn foreseeable for their whale-type customers)

• Can identify and sell into the fastest-growing companies of its class, translating their growth into the infrastructure platform’s growth

• Have a usage-based fee structure to grow alongside their clients

• Scalable infrastructure (incl. Compliance, risk management, etc) to prevent being the bottleneck for growth.

• Operationally or regulatorily complex

Revenue concentration risk is also highly dependent on the industry. Frank Rotman of QED pointed out that revenue concentration was, for example, not an overwhelming risk in the case of Credit Karma. Although there was a concentration of lenders to Credit Karma, the quality of Credit Karma’s leads (and in our opinion, the non-fungibility of capital sources - i.e. each lender runs similar processes), meant that there were substitute lenders that could replace any deficiency in capital sources.

An example of a B2B Fintech Infrastructure company that suffered (at least temporarily) concentration risk included Green Dot. Green Dot’s debit card program with Walmart constituted 66% of revenue and net earnings. 

Green Dot shares dropped in 2012 as, right after the IPO, Walmart announced it would also additionally partner with a competing debit card issuer.

In mid-2016, the Walmart contract was extended until 2020, and in end-2019, the contract was extended until 2026. At the same time, Green Dot’s overall revenue CAGR was relatively low at ~11% between 2012 and 2020. Arguably, the difficulty in growing revenues by winning over fast-growing clients for its BaaS products violated a core boundary condition for fintech infrastructure plays - to leverage existing resources to acquire new fast-growing clients.

As an aside, this seems to have turned around in the past few years with a new CEO installed by an activist fund. Green Dot announced partnerships with Quickbooks (Intuit) and Apple in 2019, with Wealthfront in 2020. In 2021, Amazon launched a debit card product with Green Dot.

We argue that not all fintech infrastructure is equal. Below, we highlight some common features that fintech infrastructure in certain verticals may have that make the revenue concentration argument less applicable:

• Low stickiness: Operational complexity that does not grow proportionate to scale

• Low ability to scale with whales: An ability to scale alongside whales is crucial - encompassing compliance, customer service, risk management, etc.

• Low ability to win new whales: An ability to leverage existing infrastructure to win over new whale clients.

• Connectors vs. Processors: Infrastructure that connects players rather than processes operationally or regulatorily complex information or financial services may be more likely to have their services brought in-house at scale.

• Sub-par macro-growth: For the concentration risk to be worth it, the whale client must be expanding at a super-normal rate to grow the infrastructure’s bottom line as well as justify building infrastructure at scale. This means that the infrastructure they are building (i.e. in-house payment processing, BNPL, etc) must be one that is reflective of a sudden supernormal macro growth trend.

In conclusion, concentration risk is a feature, not a bug, of best-in-class B2B Fintech infrastructure. They are:

• Great to scale with since sticky + usage-based pricing so have less short term churn concern as opposed to non-fintech b2b infrastructure, and greater ability to capture value as whale client scales 

• Cashflow from Whale 1 helps build infrastructure for a growing startup to win other whales 

• From an investor perspective, a leveraged industry bet make sense since sticky (‘Alpha’ v ‘Beta’) and you do want to make a leveraged bet as a VC 

• Ignoring the above, from a pattern recognition perspective, fintech infrastructure companies have IPO’ed with significant revenue concentration, so revenue concentration should not be a  concern for pre-IPO companies

Thanks to Frank Rotman, David Lilienfeld, Yann Ranchere, Andrew Starker, Natalie Luu, Sheel Mohnot for comments/input! Input does not constitute endorsement.

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Now a crypto investor at Pantera Capital.

I was a top writer on Quora and Medium, finally moving on to Substack.

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