In the early days they also did some discretionary trading. There's one interview I love from the period. In it Simons recalls how him and his partner had a large position in gold, riding a run from $200 to $800. One day he calls his stockbroker. During small talk the broker complains that his wife, a jeweler, had cleaned out all his old gold tie clasps and cufflinks that morning and gone downtown to sell them. As a jeweler, 'she only had to stand in the short line'. People were lining up for hours to sell their gold.

Simons hangs up and liquidates everything. By the end of the next day gold had dropped to $600 and did not go back up for years.

The most decisive input wasn't sought. It arrived through a channel that had nothing to do with the trade.

I've spent the last year building agents for investing, for my fund and as fully autonomous systems, and this pattern keeps showing up. The context that changes the decision is almost never the context you built the system to find.

The problem of getting the right context to an agent at the right moment is called context engineering. My experience is that it's the piece you need to get right because it's where the edge is. Models are converging. The gap between the best and second-best is harder and harder to notice. Most existing software is becoming accessible to agents. The remaining variable is what your agent sees and knows.

But most builders discover the same curve: adding context initially produces huge gains, then suddenly performance collapses. The system drowns in its own inputs. This is what happens when you try to solve for the unexpected by adding more of the expected.

So the thing to solve for is building systems that know what to surface and what to withhold. The architecture for this can be broken in two parts.

The first half is gathering. Known unknowns.

In a venture setting, a good example is due diligence: you need the cap table, the competitor landscape, unit economics, reference calls, and the list goes on. The checklist exists before you start. There's a lot more to an investment than that, but you need it.

In agent systems, this maps to retrieval pipelines, structured queries, monitors. Most of what ships under the label "context engineering" today is gathering.

It's valuable. It's also the easy half. Which means it's not where edge lives.

The hard half is hunting. Unknown unknowns. Context you didn't know you were missing.

A founder casually mentioning a friend building something new. An observation made walking through a city. The broker's wife standing in line to sell gold jewelry. None of these were on any checklist. None were retrievable, because nobody knew they existed.

In gathering, you know what you need and go find it. In hunting, you build readiness and position yourself, but the signal has to arrive on its own and when it does, you need to recognize it. It's something closer to noticing than it is to analyzing.

So can you actually build for it?

The best mechanism I've found is connecting agents that carry genuinely different contexts.

The equivalent for us is conversation, especially with people who are close enough to understand our domain but far enough to see it differently. Venture funds often work this way. Partners with different priors looking at the same deal surface things neither would find alone. Not because one is smarter, but because their contexts collide.

Agent systems can produce the same dynamic. When one agent submits analysis and another with different context evaluates it, the system surfaces things that quality gates alone do not produce. Verification catches errors, whereas the point of collision is to surface blind spots.

I use this constantly. When I want a more polished design for instance, I set up two agents working against each other, criticizing from different perspectives until they converge. They catch things I would not have even thought of looking at.

It's also how I build the larger, multi-agent autonomous systems like Fair.

Another example of this pattern is the agent network built by our portfolio company Droyd, which connects trading agents and lets them share context across genuinely different perspectives and skills.

Hunting is the hardest part of context engineering.

Almost nobody is building for it. But the systems that figure it out will do something no dashboard ever could: they'll hear the broker's wife and know what it means.

If you're thinking about this, let's meet.

--LDL

The idea I'm taking seriously after this research day can be expressed in one line: token pairing is a clean business model for asset‑creation platforms - pair every new asset in the platform token so every buy locks float upstream and mechanically lifts children assets downstream.

The great token explosion

Blockchains have collapsed the cost of creating a tradable asset to basically zero. The explosion of the number of memecoins is an expression of the barriers lowering and precursor to the set of assets including everything: LP shares, startup equity, digital art, creator coins, agent tokens, and much more.

As an aside, some people look at this explosion and panic with the view that too many tokens makes returns impossible. The counter to this view is that in attention markets (short‑form content is the obvious analogy), more attempts widen the tails. That's like saying TikTok killed virality because there's too much content. The opposite happens: more attempts create bigger winners.

The question isn't whether asset creation will slow down (it won't). It's who captures the value when everyone becomes an asset creator.

The current monetization models are broken

The talks surfaced three dominant monetization strategies, each misaligned in their own way:

∗ The Bundler's Game (as told by Salvino): Anonymous deployers watch TikTok, scan Twitter, monitor Twitch streams to instantly spawn tokens for anything trending. They corner the supply early and sell into retail FOMO or snipers. It's lucrative ($54M+ in profits on Pump.fun alone in past 6 months) but parasitic. Less than 3% of participants on Pumpfun ever see meaningful wins, and it's in great part due to these sophisticated players. 

∗ Traditional launchpads: Simple game, let users deploy a token and take a fee, a % of the supply or both. The alignment issue with this model is that platforms are a classic case of adverse selection. Their terms mirror venture funds but worse so only projects that can't raise elsewhere show up. It's the lemons problem, with a token.

∗ Buyback programs: Both asset creators and platforms will try to monetize by increasing their own asset market cap through web2-inspired buybacks. This creates another alignment issue between the company's success and token value. Revenue buys back tokens instead of funding product or growth, ensuring either long-term holders or speculators lose.

And that's when the idea of pairing came into play: what if the platform's success was mechanically tied to every asset it creates?

The token pairing model: the two-candle effect

In the past 12 months, several protocols on Base like Zora, FxHash and Virtuals have started exploring with a new business model: token pairing.

The intuition is that each new asset created on the platform pairs not to USDC/ETH/SOL, but with the platform's own token. Every buy triggers (behind the scenes, abstracted from the user) a double hop:

1. buy the platform token

2. swap the platform token for child asset

David summarized the visible effect as ‘two green candles for the price of one’ - crude, but directionally correct.

For the platform asset, demand for child assets locks more platform token supply in liquidity pools. In Zora's case, demand for creator coins creates a growing mountain of $ZORA removed from circulation, locked across thousands of creator markets.

How token pairing creates value

The model works because it creates alignment between the top-level asset and the child assets and their holders, and the creators. Heet (Noice) calls it Internet Capital Alignment, a concept I prefer to its quasi-namesake Internet capital Markets.

First, it enables clean upstream value capture. Every successful child asset permanently locks platform tokens in liquidity pools. Success creates supply sinks. Jacob's data shows significant portions of $ZORA supply now locked in downstream liquidity, shrinking float through utility, not burning.

Second, strength flows down. When the platform token appreciates, every asset paired to it gets mechanically lifted. Early creators become evangelists, attracting more creators, which reflexively makes the platform better. The rising tide lifts all boats.

Third, it creates platform moats. Each successful asset makes the platform more attractive for new creators, creating a gravity well of liquidity and attention that becomes increasingly difficult to escape. The more people use the product, the better it gets. Network effects™.

Key conditions to make it work

This model fits situations where the product is asset origination itself: social tokens (Zora), generative art (FxHash), agents (Virtuals), prediction markets, and more. The platform's value is its ability to spawn valuable children.

There are a couple of important additional considerations that stem from the fact that the pairing model introduces an element of leverage. Each buy is essentially counted twice. 

First, volatility requires management. The "two green candles" demo works because of low liquidity - this is a bug through, not a feature. This requires: a) deep base pools b) routing through your asset (initially painful for Zora) c) automated liquidity provision.

Zora uses an interesting deleveraging pattern: they increase the liquidity on child assets as they appreciate stabilizes price once they take off. Leverage on the way up, and stable price once we get there.

Second, alignment requires value. Financial alchemy only works if it creates good outcomes: more artists for FxHash, better forecasters for prediction markets, more self-reliant creators. At the core of the system there must be a high-quality product where users are willing to spend time and resources, for the sake of the product or its benefits.

What I'm watching

Jacob pointed out that the price premium L1s held for being the native pairing asset is now up for grabs. I expect more asset-creation platforms to explore this. Until it's a common pattern, these projects should outperform in bullish markets. Net quote locked in downstream pools is the metric to watch.

The more ambitious implication: room exists for an internet-scale asset creation platform for culture. Whichever platform reaches this first at scale has a shot to become the asset for culture, the native internet currency. This could have been the directional bet behind Zuckerberg's Libra. Zora seems to be executing toward it now.

Lastly, as usual in crypto, we're ahead of the curve when it comes to financial engineering. But our products still fall short of broad adoption. A day like the one we spent gives me confidence as it's clear the people who can make it happen are going hard at it.

I was wrong. I just started this new practice to write for insights from my friend Rami. It’s called free writing: open a blank page, start a timer, and write about the topic until time’s up.

I remember a couple of rules from Farza’s writing (a founder who built a fantastic simple text editor for this purpose):

• No editing

• Don’t stop to think or structure; keep writing

The intention is to create a stream of thought. It reminds me of David Perrell’s image that a writer’s mind is like a water tank filled with clean and muddy water.

The muddy water lingers at the bottom, so initially when you turn the tap on you only get brown slush. But the longer it runs, the clearer the water gets.

Your brain works the same. Your initial thoughts on a topic are lame and commonplace. But as you keep spinning your wheels, new insights emerge. You underestimate how much thinking is a volume game versus a quality game. The more you write, the more you think - the more you think, the better you think.

So start writing.

It’s a good practice because it removes the expectation of output. Writing like this gives you control over your thinking: it’s up to you to start the timer, open the page, and start typing. Maybe great ideas come. Maybe not. But you’ve done the job of showing up and writing.

You can deploy this practice in many areas of your life:

• to plan your day

• to think out loud about decisions

• to disentangle personal situations

• to draft a letter to friends or family

• to work through your emotions

• to brainstorm

This feels like the greatest hack. You get to turn on your brain by just starting to press buttons on a keyboard.

My favorite ways to use this for now:

• start each morning with a 15-20 minute session on a topic like this one.

• before the workday, take 10 minutes with the prompt: What am I working on today? Why is this the most important thing for me to work on?

• when I need to make decisions or to provide an educated take on something - 15-minute timer and write it all down. Then we can summarize to bullet points etc.

At the end of the day: what did I get done today? What do I think moved the needle the most?

A happy byproduct is this creates a valuable exhaust of context about you, how you think, your dreams, aspirations, envy, insecurities. You can use these as context for AI - to mirror what you’ve shared and find connections you couldn’t.

This hasn’t been too useful for me yet, but it’s probably a prompting skill issue.

Another place where this is applicable is for prompting or spec writing for new products. You want something that does not exist: describe it, explain your pain, what you want, the desired aesthetic vibe, etc. during a 15-minute session. You will have the most comprehensive document for LLMs to work with and create either a great prompt or a product spec.

You can also use it to clarify what you want. Clarity of purpose is like magic. When you have a clear outcome in mind, it often feels like the world falls in alignment with it. The difficult part is having that clarity. This exercise can give it to you.

I recently spoke with David, a founder who in under three years has influenced the creation of 84 solar farms generating 20MW of energy and about $20M in revenue. Glow, his company, uses crypto incentives to boost nearly profitable solar farms. His token mechanism has created farms that wouldn't exist otherwise.

He core point in the conversation centered about the idea that that crypto has PMF for creating specialized hardware that meets arbitrary specifications. Consider the number of GPUs for Bitcoin or Ethereum mining (or other PoW L1s) and decentralized storage companies.

The idea is interesting because it has taken a backseat in how we view blockchains and crypto, despite:

• it's a historically successful use case for crypto

• It changes the world of atoms, at a time when it's stated priority for growing number of people in tech and government

• There's plenty of whitespace and opportunities

Crypto excels at creating hardware. Bitcoin, Ethereum, and many L1s proved it. DePin (Decentralized Physical Infrastructure Networks) is built on the principle that good mechanism design can let startups compete with incumbents without capex. Instead of investing millions upfront to create a network (e.g., batteries, broadband stations), network contributors do it at their own cost, at a small scale to mine the network token.

It's also a use case that improves the world of atoms by leveraging primitives we built in the world of bits. Good hardware and infrastructure improve lives. If crypto can design better incentives than governments or companies, then it can do a lot of good in people's lives.

I’m excited to hear about more business ideas and investment opportunities that follow this design pattern. Their key traits:

• Well-defined hardware requirements with measurable output that can be incentivized/optimized.

• The output is economically viable and used by more than the hardware installer/operator.

• Ideally, a network effect exists where the product quality scales geometrically with the number of operational hardware nodes.

Problems:

• There are many tasks that I would like an assistant to help we with but they lack context, especially the precious context contained in my Obsidian vault that's not easy to reach from somewhere else than the computer

• I love Claude Code but I can only reach it in my terminal - I would like to be able to access it assistant through Telegram or Whatsapp

• There are a bunch of tasks I do often and have my own ways of doing, I want the assistant to remember this and to be able to improve over time at my suggestions

• I would like to use voice inputs to create tasks, emails, drafts, calendar events etc. Ramble for a while and that queues up a number of things to execute and triages them accordingly.

Implementations ideas

• I want to use Claude Code because it's so powerful, can use MCPs, can manage files and multi-modal input

• I need to find a way to get an instance running with its own files in a place where it's always on for me to reach - hardest problem I'm facing for now.

• Using sub-agents for specific tasks, but I'm not sure how to make the subagents to improve themselves. An alternative might be to create subfolders for each types of process and and a claude.md file detailing processes and how to approach in each folder

• I want to make sure I'm not making this too complex to implement, and that I start with simple use cases and gradually improve in complexity.

Example tasks

• Put all my invoices in a folder with the right title format based on the content of the attachement

• Answer questions based on content from my obsidian - "when did I speak to xxx for the last time and what did we discuss"?

• Please create a calendar event for September 13th called meeting with Lewis and invite Lewis - the event needs the right person + a meeting link

• Please write a message to my fiancée telling her I'm on my way and will arrive at 8pm - needs to be in my tone and in french, as inferred from the context

• Create a new obsidian note titled 'Observations from my trip in Asia' with the following ideas in there - idea 1; idea 2, etc

Tools

• Obsidian

• Todoist

• Gmail

• Telegram

• Whatsapp

• iMessage

• Notion

• Slack

It's not surprising. Building a fair and open system is part of the crypto vision, and that's orthogonal to how most tech platforms run. They are for the most part incompatible.

The root of this incompatibility is data verifiability. User data in web 2 is not verifiable outside of the context where it was generated. For instance, only LinkedIn can prove your connection with someone. As a user, a screenshot is the best you can do, and that’s light evidence.

The greatest leap for consumer crypto would be building a bridge from web 2 to web 3, introducing verifiability to data from the platforms that people use daily.

This bridge might come in the form of a technology we’ll refer to as web proofs, that leverages a technology called zkTLS. It improves the established Transport Security Layer protocol with zero-knowledge cryptography.

Without ZK, TLS ensures that when a user connects to a website, like their bank account, the connection is secure and authentic. It's like having a private conversation where users can verify they're talking to the real bank. But once that conversation is over, they can't prove to anyone else what the bank told them. It's just their word.

The zk component adds a new capability: you can now prove you were in a private conversation with your bank and what happened in that conversation. It’s like having a notary observe the conversation and issue a certificate saying “this user’s balance is over $10,000” without disclosing the actual amount.

This means developers can now verify web data from trusted sources (like banks, social media, or shopping sites) in a way that's:

• Private: No sensitive information is revealed

• Verifiable: Anyone can check the proof’s authenticity

• Tamper-proof: The proof can't be faked or modified

Companies like Opacity Network and Reclaim Protocol are building tools for developers to leverage these proofs to build better applications. They need to solve hard technical problems, but they progress quickly.

So, what can we build with this technology, assuming we overcome these hurdles in a short amount of time?

In this piece, we will explore three directions:

1. Incentives to target and shape web 2 behaviors

2. Marketplaces for digital assets

3. New oracle types

Crypto excels at incentives, mainly through airdrops and rewards. The main innovation is to reward true believers and critical early liquidity with tokens. It helps protocols overcome the cold start problem and turn these users into evangelists because they have skin in the game, all at no upfront cost.

The problem is we're limited to on-chain actions, and the users taking them are a small fraction of the potential audience for many products.

Web proofs could expand airdrop and reward data sources beyond on-chain activity to include the applications and actions we use daily, such as Spotify listens, Robinhood trades, Amazon purchases, and credit card transactions. They widen the scope of eligible actions and enable web 3 builders to reach more users.

I expect the coming months will be full of experiments that reward past behavior and increase crypto adoption, as well as drive future behavior using on-chain incentives.

The most obvious use is to distribute incentives based on web data. For example, by requiring web proof that someone is in the top 10,000 Spotify listeners of the artist, gate concert ticket access. This ensures true fans get access before the general public and scalping bots.

Another low-hanging fruit is airdrops to non-crypto communities. Precedents exist, like PleasrDAO airdropping tokens representing ownership in a one-of-one Wu-Tang Clan Album to $GME holders on Robinhood.

Another important effect is porting reputation across platforms, like the above example of showing a ticketing platform our reputation based on Spotify. Another use case is vampire attacks of web 2 services and marketplaces where reputation is critical, and recreating them to be more competitive. This is what Nosh is doing, building a driver and user owned delivery platform onboarding drivers using the reputation they built on Uber or Lyft.

Interestingly, web proofs are used to the fullest in bringing off-chain data to crypto, but they also break the barriers that tech platform silos create by giving data value outside its original context of creation.

Friction and transaction costs are the enemies of marketplaces. A major source of transaction cost is the need to trust your counterparty. Many exist mainly to capture data to build a reputation system for buyers and sellers to establish that trust.

Web proofs and smart contracts can help build a zero-trust system that reduces friction, increasing the velocity and volume of trading on these marketplaces for digital objects like domain names or tickets.

An example is ZKP2P, which created a secondary ticket marketplace. They solved the problem of sellers listing tickets they don’t have, frustrating users who don’t get theirs until the event day, if at all.

In contrast, ZKP2P transactions are nearly instantaneous. Sellers must prove they have the ticket to list it. Buyers send money into an escrow smart contract. To receive the payment, the seller must send the ticket to the buyer, generate a web proof they sent it to the corresponding email, and then instantly retrieve the escrowed amount.

The process from start to finish is much shorter than the current ticket sales lifecycle. This will significantly increase trading volume and improve users' experience.

It's not clear what the most promising verticals for this verified P2P design are. Our mental model is twofold. First, look for spaces with a lot of existing volume and brokers whose primary role is to be an intermediary of trust. Secondaries marketplaces or domain names are examples. The second avenue is looking for transactions that should happen but aren't due to high trust requirements.

We find this design space promising, and it’s a matter of experimenting with different verticals. If you’re building something in the space, we’d love to discuss.

Decentralised protocols rely on oracles to bring external data to crypto. Web proofs affect a) the amount of data we can bring onchain and b) the type of data available to protocol developers.

Web proofs significantly reduce the cost and friction of bringing off-chain data on-chain. They are easier to generate than oracle-attested data, which require multiple independent nodes to provide and validate the data. The data needs to come from multiple sources for accuracy. By contrast, a single user with direct access to the data source can generate web proofs.

These data sources are also harder to shut down than API-based oracles. The APIs used by oracles can be identified and taken down, whereas zkTLS proofs work as long as the user can access the data in their browser. This means most data sources can be put on-chain without the original data controller preventing it.

Web proofs can bring valuable data types that traditional oracles cannot, as they only concern public sources. They can access private user data like personal account information, transaction history, or health records. As an application developer, you have verifiable access to user reputation and history to create new experiences.

This opens new possibilities for applications to combine personal data with crypto infrastructure to build new products, such as:

• DeFi services where risk underwriting leverages data from users’ off-chain financial services like centralised exchanges or bank data

• Onchain insurance for off-chain events such as health data, or employment status

• Social networks where reputation and connections can be verified through existing platforms

• Identity verification systems using multiple data sources for enhanced security, sybil resistance, and privacy.

The technology is still early, with important improvements needed in scalability, performance, and robustness to data structure/API changes.

The core idea of making web 2 data verifiable and valuable outside its initial creation context will have important repercussions, from reducing switching costs between applications as users can easily port their reputation to radically new ideas. Think social vampire attacks

The potential applications of web proofs are vast and largely unexplored. As the technology matures, we expect innovative use cases that combine blockchain transparency with abundant web 2 data, leading to great consumer experiences.

The main driver for (a) is the good work of Dan, Varun and the Merkle factory team. Not much to say here besides noting their remarkable execution and prompt recovery from actions that arguably adversely impacted (b).

(b) is the more interesting factor because it's the driver for Farcaster's long-term growth and a flywheel. Adoption looks like this:

devs come to build new experiences that can only exist on an open social graph →some of these experiments (like degen, supercast, clanker) work out and attract people beyond the Farcaster ecosystem → Farcaster becomes more attractive for more devs to come

The critical assumption is that devs want to build on Farcaster. An encouraging anecdotal data point is that in the past 12 months I've met hundreds of teams building full time in the ecosystem, without a sliver of inorganic financial incentives. I emphasise inorganic, because founders are economically motivated. They want to build on Farcaster because their products can become great businesses and generate organic revenue.

Developers and founders come because a) they can build products that can't exist anywhere else or b) because they can tap into a growing, high-quality user base. Farcaster users are eager to try new products and willing to spend more than typical X or Threads users (great potential Average Revenue per User). They can make a killing by building net new things and tapping into this great distribution channel.

And that's how Farcaster wins: by being a flourishing economy. Where good products turn into revenue. Where the most ambitious founders in crypto and beyond bet the house. Where early adopters turn to find the next brilliant app or the next 1000x. And where the next 10m users in crypto get onboarded.

One way to check this thesis is to treat revenue from Farcaster companies as a leading indicator for user influx. You can find these indicators on the farconomy dashboard we built with Woj.

I recently paid a large deposit. Instead of selling my savings account assets, I borrowed stables on Morpho to offramp via Coinbase. Et voilà.

In a few minutes, a crypto loan replenishes my bank account. This is a more seamless experience than anything outside of crypto. I realised it was so good because it was instantaneous because it did not require any human verification.

In crypto, loans are secured by assets worth more than the loan, similar to the mechanics of a mortgage. As long as I remain solvent, meaning my collateral’s value exceeds the loan value by a margin of security, my credit score or risk doesn’t matter. When I borrow, I don’t need permission to someone who’d verify my claims - everything is in a smart contract that will enforce those rules. There is no need for verification and the operation is thus instant.

A major cost in lending is verifying borrower information. The industry relies on underwriting models that use personal information (age, salary, employment, etc.) to make risk assessments and lending decisions. For the model to be accurate, the information must be verified - self reporting isn’t enough. Every additional user has a high marginal cost due to verification.

I argue that crypto and zero-knowledge proofs bring verification costs to zero. You don’t need to pay a credit score provider to know if I’m employed if I present a cryptographic proof generated from my payroll provider’s website. Lenders don’t need to know your salary if you locked collateral in a contract to borrow.

What happens when verification costs approach zero? I anticipate several outcomes:

1. The user experience is improved because loans are instantaneous. Lending companies on crypto rails will offer a better user experience.

2. Loan costs decrease. Traditional lenders must shrink to remain competitive and pass savings to consumers, or new players emerge with lean cost structures to compete on price. A similar situation occurred with traditional banks that lost revenue from net interest margin to neo-banks passing those savings to users.

3. Verification companies either disappear or get integrated with the risk assessment function. The top 5 credit scoring companies have a combined market capitalization of $120 billion. They sell verified data. If users can self-authenticate the data, their margin will be pressured.

4. The market grows as previously unprofitable borrowers due to verification costs become a profit center.

Crypto and zero-knowledge change lending economics. As verification costs evaporate, you can operate with leaner organizations, offer instant loans at better rates, and service underserved customer segments.

The grid is becoming more decentralized. Households and companies can generate and store energy with inexpensive investments, sometimes below the 5-figure mark.

This is good; it increases the renewable energy share in the overall mix and adds independent resources to adjust grid capacity. When most homes have batteries, it smoothens consumption and reduces peak loads.

The assumption is that grid operators are able to tap into this unused and flexible capacity. They need to 1) know available capacity and 2) control the energy resource.

The difficulty is that individual participants own different hardware, are spread geographically, and have varying grid connections.

The most elegant mechanism to address the issue is Virtual Power Plants (VPPs). They aggregate distributed energy resources like rooftop solar panels, home batteries, and electric vehicles into a single, controllable entity.

They offer 1) products for better usage control and 2) financial incentives, like reduced rates, cash payments for energy contributions, or credits towards future energy bills.

In practice, this is an offer available to users who own DERs. The typical contract includes financial rewards over time for giving control of resources during periods of instability, like summer afternoons from 4pm to 9pm.

For grid operators, these platforms provide real-time monitoring of capacity and availability, as well as the ability to dispatch energy resources as needed to balance grid load.

VPPs make money by selling energy and capacity.

VPPs sell aggregate resources to the wholesale or consumer energy market. They capitalize on their lower cost structure (no large-scale CAPEX required) and competitive prices. By orchestrating resources, they can store energy when prices are low and sell when they increase and arbitrage energy markets.

VPPs also sell capacity, i.e. the ability to provide power when needed by grid operators. These contracts involve giving access to pre-agreed amounts of resources on demand. The pricing structure is different because the contracts are paid upfront, and providers are compensated even if their services are unused.

In the next post, we’ll address the question of whether DePin can serve as the foundation for a more competitive VPP operator.

The grid is responsible for transporting electrons from where they are produced to where they are consumed.

Energy is produced and distributed close to instantly to where it’s needed. Due to the lack of large-scale storage solutions, it is produced in a continuously adjusting flow based on projected supply and demand. This process of adjusting supply to demand is what people mean by balancing the load.

This leads to two important grid properties:

1) It’s architected for peak load, i.e. that hot day when everyone turns their AC on, or when everyone is cooking dinner and charging their Tesla.

2) It’s premised on being a predictable and controllable system. Grid operators forecast demand, and adjust supply as needed.

Over the past few years, two important changes have occurred that jeopardise grid predictability: the shift towards renewable energy sources and electrification. While both trends are positive for the environment, they introduce volatility and unpredictability in a system premised on its consistency.

On the supply side, renewable energy sources increase volatility because their output can’t be controlled or predicted. You can’t crank up the sun’s intensity to boost a solar panel’s output, and it’s hard to predict weather and solar intensity. Weather forecasting is difficult.

This is different from traditional energy sources like gas, coal, or oil plants, which can be turned on and off.

Electrifying everything on the demand side creates unpredictability by increasing the peak load and adding variability. When cars go electric, you have to account for the fact that there are times when many people will charge them - that will create spikes in demand.

Increased volatility leads to wasted energy. Excess demand is more problematic because it leads to blackouts with consequences ranging from mild inconvenience to large-scale economic damage (e.g. in South Africa) or even fatal harm.

There are no easy solutions. Right now, utility providers use temporary fixes. They pay ‘capacity payments’ to generation facilities not to shut down, even if they are not producing power, in case they are needed. These, however, are mostly based on fossil fuels.

There are other alternatives, like batteries, and we’ll explore that in another post.

Bitcoin’s market cap is $1.24 trillion, but most of it is idle because there’s no easy way for holders to use their assets without giving up custody.

Faced with that choice, most abide by the rule ‘not your key, not your coin’. But not all do. Blockfi and Celsius, two centralised lender services, managed respectively $10b and $3b, offering 5-7% on Bitcoin before collapsing in the FTX aftermath.

It’s a cautionary tale of the worst, but also a market signal that there is demand for productive uses of bitcoin. This has led to the creation of an ecosystem of 90+ blockchains built on top of the main bitcoin blockchain, offering ways to make it productive, while trying to reduce the risks and maintaining self-custody.

It’s a worthwhile effort, but most are recreating what already exists on Ethereum, with a technology that’s worse.

Market economies thrive on specialisation. Ethereum specialises precisely on what’s needed for a successful decentralised finance ecosystem. Bitcoin focuses on making its asset a type of asset that is resistant to inflation and censorship. Bitcoin for storage, Ethereum for usage.

The point is, for most financial products using Bitcoin, you can build a faster to market, more scalable, and easier-to-use version on Ethereum rather than on a Bitcoin L2. Ethereum is the best place to build bitcoin defi because it has the most primitives, assets, and liquidity.

The major DeFi building blocks and primitives are already on Ethereum. You can create a trading pair with bitcoin on Uniswap. The simplest method to create a money market using Bitcoin as collateral is to create a market on Morpho, a decentralized lending platform, using any asset as borrowed and your preferred oracle.

As a consequence, Ethereum hosts the most liquid DeFi ecosystem. Technical fitness is just one factor for the success of a financial rails. Liquidity is more crucial.

Traditional financial rails demonstrate the inertia of liquidity impeding technological change. Players who innovate too much move away from the liquidity flows and isolate themselves. Indeed, most innovation in finance over the past decade has been in improving frontends and customer service.

With over $50b in TVL in Ethereum L1, the Bitcoin ecosystem needs to 10x - twice - before catching up. This is while dealing with a slower rate of development due to the protocol not being designed for the use case, and not particularly developer-friendly.

On scalability, it is not an issue if you’re building directly on Bitcoin, but for most intents and purposes, it’s not possible because Bitcoin script is just not expressive enough and the chain not meant for high throughput. The alternative is building on one of the numerous L2s, but since the ecosystem is early, it’s a gamble on their longevity.

Going back to the first line, my expectation is for this ratio to widen. For this to happen, the main thing needed is a low-friction, high security option to bridge assets from Bitcoin to Ethereum.

The current offering isn’t good enough because it’s too centralised and managed by institutions reminiscent of the past cycle’s centralised lenders.

With all the DeFi building blocks ready to use Bitcoin as an asset & collateral, what’s needed is a mechanism that allows bitcoins to be moved to Ethereum with low trust assumptions. One key element is retaining self-custody of the asset. Another is the segregation of risks from one user to the other. The third element will be the smooth bridging to Ethereum.

There is progress in the space, with products like dlcBTC and Hemi, but I expect more in the coming months.

Reach out if you’re building in the space!

To make sense of new technology, you need to understand the costs they decrease, which leads to understanding the new design space it opens.

For instance, the internet brings marginal costs to zero. It costs nothing for Google to serve one more user, and it makes no difference to an author whether their blog is read by 27 people or a billion. The internet is infinitely scalable on the backend. This property upended industries, created products we use daily, and generated billions in shareholder value.

The key to creating new internet businesses was to capitalise on the absence of marginal costs. Media is a prime example. Before the internet, media companies had non-negligible marginal costs for each additional customer, involving printing and delivery. Within these parameters, your advantage lies in local implementation, and printing/distribution economies of scale, more so than the content itself.

On the other hand, online media like blogging has zero marginal costs. An author can become a leading voice in a niche and attract readers worldwide. The absence of marginal costs leads to both a radically changed cost structure and a massively expanded market. One-man businesses making millions in revenue like Stratechery or Byrne Hobart’s The Diff are great examples of ventures that were previously impossible.

Building trust online is challenging. Trust is based on understanding, and physical interactions provide more signals than online. These signals are also easier to verify.

Claims are easier to verify in person because of the additional context. This allows for a quick assessment of trustworthiness by evaluating the coherence of the claims taken as a whole. Age, jobs, income, and sports performance can be easily referenced in person, but not online.

My mental model for where zero-knowledge proofs (ZKPs) make sense is in online services with high costs of verification i.e. that require personal information, where individuals are likely to lie. For example, dating apps where people lie about gender, age, salary, or height.

If you wanted to do this in a dating app right now, you would need to use a third-party verification provider, essentially asking your users to KYC, which is not only a catastrophe for conversion, but also a significant additional cost per user. By contrast, very simple ZK products like proof of passport or zkpassport, can verify a use age with a quick passport chip scan, without revealing any other information, and without having to rely on a costly trusted third-party.

There are many such cases where information is valuable but the cost of verification narrows the design space. ZK makes sense for these applications where you want to keep private information hidden but verified.

I always enjoy reading him because his writing leads to action. It's a good segue into the book's main argument: human beings operate mainly on beliefs, and the real value of a belief isn't its truth, but its ability to prompt action in a given situation.

The first part of the book deconstructs the idea of truth in our words or beliefs.

Most of what we say or believe are interpretations of events and facts rather than truths, which are universally, objectively, and necessarily valid.

When we say 'this is too expensive' or 'this country is mismanaged', it feels true to us, but it's a perspective. Hard facts are boring by themselves - what's interesting and what people bond over are interpretations.

Facts can be deceiving, as they can be presented in a way that promotes a certain perspective. Reporters at the same event can paint different realities by choosing which elements to include in their reporting. They often provide an angle, rather than the whole truth.

This is without accounting for our documented inaccuracies and bias. Our brains are designed to find causality and explanations even when there are none.

Similarly, our memories are distorted: we misremember even the most important events because we color everything through our interpretations and bend facts to fit them.

What do we do with that? One path is to train the mind to be right, become a better thinker.

The other is to take advantage of our beliefs and shift the perspective: instead of seeking truth, look for beliefs with good outcomes. Those most likely to make you take action in the right direction.

In bowling, the ball often leans on one side and goes off target. To correct the bias, aim in the other direction to compensate. The mind is similar - it strays in predictable ways and beliefs can course correct.

If you underestimate time things take, double your estimate. If you procrastinate, set fake deadlines. The new thought isn't meant to be correct - it's a correcting mechanism for the previous, equally incorrect, but less useful thought. Choose beliefs that correct your bias.

Pick beliefs for the actions they create. You can also work backwards - what belief leads to the action you need to take now?

Reframing is generating different perspectives on a situation and choosing the most empowering one. It's hard to control feelings, but easier to control thoughts, which largely influence emotions.

The first method is to move past your initial thought. Something happens, a thought offers an interpretation, and you react. Now imagine adding a step: consider other ways to look at the event and pick one that feels empowering.

A second tool for controlling your thoughts is asking questions. Thoughts are answers. Control them by changing your question. Asking better questions leads to better thoughts. There's a great book about this: Change your questions, change your life.

Example questions:

• How can I use my age to my advantage?

• What's great about this?

• How can I use this to my advantage?

Keep the first principle in mind: Your initial response to these questions may be discouraging. Push past the first answer.

Useful perspectives are direct, energising, self-reliant, balancing, lasting, and long-term.

Success would be an application attracting non-Farcaster users without them realizing they now have a Farcaster profile. Many applications use Farcaster login to access quality daily active users. However, the real proof would be an application with a broader or different user base choosing Farcaster as their user datastore.

Creating an account on such an app would generate an FID in the background, without requiring a Warpcast account first (Supercast already offers this - try their account creation flow to see the future). The app would then choose how much of their social graph to outsource to Farcaster and what to keep in-house.

But why would others want to outsource their graph to Farcaster?

The main benefits I see:

• Faster shipping: Using the protocol saves time that would have been spent creating a backend for users and relationships. You can focus on app-level iteration and quickly find a resonating proposition (similar to Warpcast's approach with direct casts and channels).

• Emerging behaviors: For early-stage companies, being at the technology and ideas frontier matters. The Farcaster team and ecosystem are arguably the closest thing we have to a frontier in crypto currently.

• Upside potential: Using Farcaster reduces friction for existing users, potentially benefiting from easier discovery and onboarding if large user inflows occur to Farcaster through Warpcast or any other successful apps using the protocol.

There are some tradeoffs, though:

• Restricted design freedom: Not fully controlling the backend might limit product design. However, the protocol is not very opinionated, allowing much logic to be implemented at the app level, and constraints are known to foster creativity.

• Protocol risk: As with any platform, you face execution and timing risks. While I'm not overly concerned about these for Farcaster given the team, I'd advise against using the protocol if you a) have important features dependent on future protocol changes, or b) rely on the protocol not building features unique to your product.

I think that for most applications building consumer products it makes a lot of sense to do it. I think the benefits outweighs the cons for most. I like how the Merkle Factory team has been relentlessly focusing on building a great consumer experience - my hope is that they keep making the protocol a great place for others to do so, because I'm of the opinion it's the fastest way for the ecosystem to grow and keep being the frontier.

Arthur's macro methodology amounts to finding hidden or misunderstood mechanisms for printing dollars. He assumes Bitcoin is a leading indicator of global liquidity. An example of this modus operandi is his piece on the Bank Term Funding Program (BTFP) in March 2023.

In this recent piece, his thesis is that the Japanese yen to U.S. dollar exchange rate (USDJPY) is the most important macro indicator for crypto. His reasoning is that the most likely liquidity injection mechanism is a potential joint initiative between the U.S. Fed and the Japanese Ministry of Finance (MOF), and that USDJPY is the clearest metric to monitor the start of this program.

Why would Japan and the US enter an agreement? Arthur believes the yen’s situation is a problem for Japan, the US, and China. Of these three, China is feeling the strongest pain, and he argues they have the leverage to pressure the US into resolving the yen crisis.

The weak yen makes China less competitive than Japan in exporting important goods like cars. This is problematic because China heavily relies on exports to escape its deflationary spiral. It also needs to print more money through credit. However, a deflationary economy makes it difficult and they can't print money due to potential capital flight, higher energy and food import prices, and ultimately possible social disorder.

China has political leverage on the US government fighting dearly for re-election. They can pressure on a sensitive issue: manufacturing jobs. By manipulating the dollar-yuan exchange rate, they can make it more or less competitive for US companies to manufacture abroad. In a tense electoral year where manufacturing-heavy states are crucial constituencies, the USG likely can’t ignore that threat.

As a result, Arthur believes China can pressure the US to instruct Japan to strengthen the yen. This raises the question, why is Japan so susceptible to influence?

Japan is also in a predicament: rates are too low, leading to capital flights to USD, but they cannot raise rates without destroying the value of Japanese Government Bonds (JGB), which the Bank of Japan (BOJ) holds the largest share of. If Japan were to match USTs yields, it would result in a $1.05 trillion mark-to-market loss for a bank that has $32.25 billion of equity capital (he estimates).

In turn, this also presents a challenge for the U.S. If Japan were to raise rates, it would compel domestic capital to sell USTs to raise dollars and buy JGBs. It's not in the US interest to see trillions of USTs and US equities being forcibly sold, so BOJ rate hikes are not a viable option.

So the yen situation is a headache for everyone, and rate hikes are out of the equation - what then is a good solution?

Arthur expects an easy solution with an unlimited dollar-yen currency swap between the Fed and the BOJ.

Say Japan needs $1 trillion to strengthen the yen from 161 to 100. The Fed would swap that amount for the equivalent in Yen, in a transaction where the Fed prints dollars and the BOJ prints yen, costing them nothing. The dollars leave the BOJ balance sheet to buy yen in the open market, but the yen on the Fed balance sheet essentially leaves the system because the Fed has no use for yen.

The net result is that the dollar weakens as its supply has increased relative to other currencies. The yen appreciates from the BOJ's purchasing yen on open markets and the decreased effective overall supply (think of a % of the circulating supply as locked in the Fed balance sheet)

Arthur believes this is the most likely scenario because it benefits everyone:

• China can print more yuan without affecting CNYUSD rates and battle deflation. The yuan becomes weaker relative to the yen, restoring their export competitiveness.

• The U.S. eases China pressure. They also experience asset appreciation due to a weaker dollar, benefiting markets and taxes.

• Japan's domestic inflation declines, but with lower export competitiveness as a consequence.

But more importantly, if crypto indeed reacts to market liquidity, we're going higher.

The core indicator to monitor this thesis is the USDJPY . Arthur expects a point when JPY is too weak and the program rolls in (around 200, expected in fall). Once it rolls in, it would be visible on a website that tracks USD liquidity swaps. Anything in the billions is significant.

Two other things that could disprove this thesis:

• A substantial rate hike by the BOJ (>2%)

• China devalues yuan or pegs it to gold

And that's why USDJPY is important: it's a liquidity printing mechanism, and when the swap policy comes into play, Bitcoin ETFs are likely a great buy to hedge against fiat debasement.

The ability to curate content is the source of social networks' economic value. Controlling the feed ensures that 1) users see engaging content and 2) paid messages (like ads) are effectively integrated into the content consumption, and targeting the right users.

Farcaster channels inherit the power to recommend and moderate content. The latest experiment by Farcaster allows channel owners to select, display, and order content in the channel feed. The update aims to provide flexibility, allowing different channels to implement unique moderation/recommendation mechanisms.

The main consequence of this design choice will be experimentation from channels on a) different moderation/recommendation styles and b) monetisation. Focusing on (b), channel owners can now create economic mechanisms to determine what content appears and in what order. A simple example would be an openrank-like mechanism for the top cast in a channel, where third-parties can bid to display an add or a message in a high-traffic area of Farcaster.

An important caveat is that this depends on the major client (currently Warpcast) displaying content as the channel owner intends, respecting channel sovereignty.

If that holds true, I expect channels to become a valuable asset, and one of the most valuable activities on Farcaster to grow engaging and large channels.

Farcaster is running an experiment in moderation that is extraordinary in two regards:

• First, they delegate the decision-making power at the channel level (community discussion forums, similar to subreddits), and let any channel owner recommend content their own way.

• Second, the core mechanism is to empower moderators to elevate good content, instead of censoring bad content.

The core mechanism is a binary system where channel moderators or bots flag quality content. Anyone can post in a channel's, but the main feed the users see by default is composed of posts selected by the moderators. The algorithm for good content can be simple rules enforced by a bot like Automod ('poster has a power badge'), complex classification models predicting engagement, human curation, or a combination.

The main benefit of this system is giving developers maximum flexibility. Channel owners or app developers can plug their own moderation/filtering algorithms. Some channels will be human curated, others will use X-like algorithm for engagement, and some might use Reddit-like upvotes. Second, third-party developers can consume that data to inform their own recommendation/moderation engine.

I look forward to seeing many moderation & recommendation experiments. Channels and their interfaces/clients are products with a job to be done, and recommending the right content is crucial. Product support channels (like /neynar or /supercast) might benefit from upvote-based systems, while personal channels (shameless plug for /luc) rely on individual curation. Flexibility allows different channels with varied content to use suitable content surfacing mechanisms.

Philosophically, I find the principle of elevating over censoring to be more appealing. No one envies the hired moderators who are subjected to violent and graphic content on X, Facebook, and other social platforms, like the characters in 'A Clockwork Orange.'

As the cost of content creation trends to zero with generative models, personal and positive curation increases in value. Farcaster channels have a great opportunity to be the place where the best content is created and surfaced.

The basic description of these transactions is: the creator shares a product and gets paid for the placement.

It’s a reasonable way to make money, but it narrows the scope of how creators can create and capture value. There are few well-connected nodes making substantial amounts.

Some interactions that users crave and can lead to sustainable businesses include:

• Direct interactions from followers to creators

• Inter-follower transactions

• Sub-community formation/transactions

Proxy Studio is an interesting place for experimentation on how decentralized social can monetize through various interaction types:

• Direct economic transactions - through a Hypersub subscription, people pay Alex directly for access to his group chat and newsletter.

• Shared capital and pooled information - in a many-to-many relationship, through Mercenary Capital, Proxy members surface investment opportunities and pool capital for collective dealflow/access.

• Ownership in community-generated initiatives - the chat is filled with idea people with a bias towards action. Initiatives launch independently but as a result of community interactions. They usually share economics with Proxy.

• Intra-community transactions - There is an increasing number of transactions between Proxy community members. For instance, a member with whitelist access might share it. Another is the secondary market for shares of Mercenary Capital or the Hypersub NFT. These are prime candidates for monetization.

The beauty of Farcaster is that communities like Proxy, or Degen form and become experimentation grounds. These emerging behaviours give us a glimpse into the future of the protocol and social as a whole.

Study Proxy.

The life of Pi

1. User intent: It starts with a user intent, they want to swap a token on an zk-rollup, prove something about their identity, execute a derivative trade, etc...

2. Proof request: The application executes the transaction, typically in a zero-knowledge virtual machine (zkVM), and requests a proof.

3. Proof generation: Generating a proof is compute intensive. Application developers can use their own prover, but they can also outsource the job to a third-party provers like Succinct Labs, Gevulot, Bonsai and others.

4. Verification: Once the proof is generated, it needs to be verified somewhere. Right now, this typically happens on a blockchain. The application runs a smart contract on a destination chain, and that smart contract job is to verify the proof.

5. Settlement: Once the proof is verified on-chain, it's settled. At this point, some other component of the application that lives on the destination chain would use the proof to update the state of the application.

A simple bridging example: I want to send 10 USDC from source chain (SC) to destination chain (DC). I lock the tokens in a bridge contract on SC, and the bridge app generates a proof offchain. The proof is verified by a verification contract on DC. When it is, another contract releases 10 USDC.

This simplified lifecycle is helpful to understand the value chain forming around zero-knowledge proofs.

The proof value chain

Similarly to block construction in Ethereum, an infrastructure stack and along with it value chain is forming with actors specialising at each step of the process.

zkVMs: First is an ecosystem of programming languages and developer platforms to generate proofs. Because proofs are complex cryptography tools, there's demand for simplifying developer experience. Developer platforms like RISC Zero or SP-1 are zkVMs: they make it easy for developers generate ZKPs for general computation, removing the need to care for low level intricacies of circuits. In the background, they are essentially compilers of arbitrary code to provable circuits.

Prover markets: Applications can generate their own proofs, but they ultimately aim to use decentralised networks of provers to be censorship resistant and ensure that the service can't go down (liveness). They can run the prover set themselves, like a blockchain does with its validator set, but that job is likely to be taken on by marketplaces of provers like Succinct Labs, Bonsai or Gevulot.

Proof aggregation: Proofs generated by the network needs to be verified. This is currently done by a contract on a layer 1 or layer 2 blockchain. But it's expensive. The cheapest kind of proofs (Groth16) cost $20-30 (assuming $3000 ETH and 30 Gwei) to prove on Ethereum. A STARK proof costs $180.

It's a key bottleneck for proof usage, so there is an emerging class of solutions centered on bringing the cost of verification down. The main approach is proof aggregation. The intuition is combining multiple proofs into one proof. That single proof can demonstrate the validity of all the original proofs together, and therefore split the costs on all the rolled-up proofs.

Proof settlement: The proofs, aggregated or not, need to be verified on-chain to be used in smart contracts. This is currently done on L1 and L2, but we're excited by the prospect of dedicated layers that focus on bringing verification costs down, and enable interoperability between proof-generating applications.

Application: The final part of that value chain is the application where the user transacted, and paid for the service. This is where the flow of money starts, and it flows forward in the value chain.

Where's the money at?

Multiple points of the value chain have network effects and defensibility.

The application layer owns the users, and thus the 'proof order flow'. That's a cornered resource, especially in crypto where there are only so many users.

Next down the chain are developer platforms. They benefit from some lock-in of the application developers who use them. That also gives them some power over the order flow, because whoever they integrate with will benefit from the stream of proofs they generate.

Proof markets have a strong network effect. Their job is to match proof request to service providers who can compute them. Higher demand leads to attracting more suppliers of compute resources for proofs, creating the typical marketplace virtuous cycle. The flywheel is compounded by further economies of scale, as volume means higher utilisation rates and thus lower costs. We expect centralisation around that layer, and already see fierce competition.

Proof verification is the more emergent layer, but it also benefits from strong network effects. Aggregation scales with volume: the more proofs you have to aggregate, the lower the costs and the latency (because ceteris paribus, assuming a fixed cost per proof, you complete the batches faster).

Settlement also scales with volume: if proofs are all settled in one place, that layer can become a canonical source of truth for proofs, and thus a trustless interoperability layer.

Rising and sinking in the stack

We're expecting the value chain to verticalise, and we're already seeing companies increasing their feature offering to that effect, starting at one layer and looking to integrate up and down the stack.

RISC Zero started out as a zkVM, to let developers generate proofs for Rust and C++ code. They then built Bonsai, a prover market to generate proofs on behalf of their users. It's a great synergy because they own the 'proof order flow' and can direct it their own marketplace. Compiling is not particularly differentiated or defensible, but as we've seen the prover market is a compelling value accrual layer.

Succinct Labs did the same move in the opposite direction. They started out as a prover market. They rapidly added a proof aggregation layer, and went up the stack by creating SP-1, an open source zkVM to compete with RISC Zero. I'm not privy to their decision making, but it makes sense to commoditise the zkVM layer to ensure that no single actor like Risc-0 owns the proof order flow and circumvents your marketplace. They are also going down the stack with a recently added aggregation layer, to further benefit from their 'proof order flow'.

Destination chains like Polygon with a lot of unused blockspace are also looking to go higher up the stack, starting with aggregation.

Fin

In conclusion, the zero-knowledge proof value chain is emerging, and already verticalising, with companies expanding their offerings to capture more of the value chain to offer more comprehensive solutions and potentially capture more value.

Prover markets an aggregation/settlement seem to be the layers with the strongest network effect and long-term defensibility. This is where we're seeing the most competition at the moment.

But it's still early and important questions like how value transfer mechanisms will work are still open.

If you're thinking about these topics, write me.

Good decisions are the result of a good process, and a good process is an attempt at accurately representing our current state of knowledge and information.

Here's a simple process: for any meaningful decision, I write down my reasoning for making this decision.

The goal is to become more intentional about why and how I do things.

My current structure is the following:

It's a forcing function to express the core assumptions I make, and scrutinise them.

Turning them into predictions is a good way to test them, and revisit the quality of my decisions later on.

Having that written down let you assert that your process for making the decision was right. The outcome may not be, but the process should be.

The feeling part is also important because it gives room for intuition to have a say. It might make me revisit my decision.

I apply this to all decisions: investments, how to spend your evening, purchases, trading, etc.