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Key source of material: https://medium.com/@dfinity/deai-shedding-light-on-ais-black-box-problem-34b29281b743

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• Removal of Trust

• Privacy, Censorship-Resistance, Tamperproof

• Transparency

• Digital Ownership

• Financial Rails

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It’s important to highlight the competitive advantage that blockchain has over the traditional electronic cash systems:

1. Transactions are final… there’s no reversing a transaction

2. Transactions can execute in a second or two (relative to the multi-day settlement in the traditional financial rails)

3. Transactions can execute for a fraction of a cent (relative to fees that are typically greater than 20 cents)

4. Transactions aren’t denominated on local currencies, making cross-border transactions more seamless

Don’t get me wrong… the current credit card-based system works pretty well for consumer purchases. I like the fact that I can challenge any fraudulent credit charges (although, one could argue why fraudulent charges is something we accept within the system). For the most part, purchasing online and in retail stores is pretty efficient with the current credit card system. But anyone who has needed to send money in one of the following ways can attest to the limitations of the current system:

• Sending money across borders can take a visit to a local bank and funds can take days to arrive

• Sending money from one account to another (for example, Coinbase to Robinhood) can take a week or more.

• Sending small amounts of money is challenging as fees tend to wipe out the benefit

• Reversibility in transactions can introduce trust assumptions that require extra inefficiency

So what does any of this have to do with AI? Well, below I’ll argue that it’s highly likely we will want AI conducting transactions on our behalf, and if that comes to fruition it’s highly likely that those transactions will need to:

• Be cheap to make it cost efficient to process thousands or millions of transactions a day

• Be global

• Be highly integrated into our various aspects of life

• Settle in seconds to make it possible for those funds to be used elsewhere

Blockchain is the obvious choice for AI when it comes to payments and financial rails.

So what type of reasons might AI perform transactions? That’s hard to predict today because we are still early in both AI and blockchain tech. Let me tell a story to illustrate this point. I’m old enough to remember the very early days of eBay. Back then it was inconceivable that anyone would use a credit card for payments online and most people assumed that all financial transactions would be offline. In the early days of eBay, a buyer would mail a physical check to the seller, which would take days to arrive and the seller would ship the item once the check cleared (many more days). Can you imagine doing that today? And, despite the early trepidation for online payments, today much of the global economic activity happens online. That’s where we are today with AI and blockchain… it’s hard to fully predict what AI may use financial rails for in the future, but we can take a few guesses.

• Service/goods payments - Imagine having an AI help you plan your next vacation (I did this recently). It makes sense that we might want the AI to then proceed to make all the purchases on our behalf, right?

• IP pass throughs - Depending on how courts rule in the coming years, various intellectual property that was used in AI training or prompting may be owed a royalty for their use. An efficient system would be for an AI service to automatically pay that royalty at the time of data ingestion or output. This could be controlled using royalty smart contracts.

• Pay-per-use AI - Many predict AIs will interact with each other, with highly-specialized AI. Those highly-specialized AI may want to be compensated, perhaps at a micro-transaction layer. Having AIs that can consume other AI services could justify creating financial rails between AI environments.

• Financial markets - It seems like a no-brainer that AI will be able to execute financial trades. These trades may require near-instant settlement for efficient capital and asset flows.

These are a few ways in which AI may use blockchain as a financial rail. I’d be interested in your ideas… drop them in the comments below. Next article I’ll explore how blockchain’s ability to provide digital ownership could be leveraged by AI apps.

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What unique features does blockchain have and how can those features be useful in AI?

And in answering that question, I came up with dozens of AI products that are only possible by leveraging blockchain technology. Some of these potential products would be clones of web2 products, but with a specific competitive advantage. And some of these potential products would be completely new product lines that don’t exist today.

Pause now and consider the above question for yourself. I think you’ll be amazed at what is possible. I’ll give my answer below.

Decentralized AI Leadership

There’s a lack of leadership within the decentralized AI space right now. There are many people building interesting infrastructure, tools and products, but very few who are focused on bringing attention to those people and products. This publication aims to correct that. I plan to highlight the incredible products that are either being built, or already built, and talk to the people behind those projects. My hope is that this publication can bring light to the amazing work these people are doing, and in doing so, bring awareness to the general public regarding DeAI.

Blockchain + AI

So what features of blockchain did you identify and how are those features useful in AI? Give your answers in the comments below. For me, here’s the blockchain features I believe are most relevant to AI:

• Cheap financial rails - the ability process microtransactions at a very small cost and with near instantaneous finality.

• Digital Ownership - the ability to assign ownership of any digital asset, including images, audio, video, text and code.

• Privacy, censorship-resistance, tamperproof - the ability to fully control access to digital assets and prevent unauthorized access or tampering.

• Transparency - the ability to execute immutable logs.

• Removal of trust assumption - the ability to democratize development and deployment of AI.

I have specific products in mind that could be built with those features, but I’ll save that content for future editions of this publication. In the meantime, if you want to hear more, check out the keynote speech:

Below is the chart I posted back in March 2022 that showed the amount of dissolving and locked ICP by the minimum date it could dissolve.

Changes In Dissolving Neurons

Much has changed since March of 2022. Some neuron owners have started to dissolve their previously-locked neurons, while others have decided to re-lock previously-dissolving neurons. New ICP has been staked (a lot of it, in fact). To keep this update focused, let’s look at the current state of dissolving and locked (aka non-dissolving) neurons separately and compare to the above chart from March 2022.

Below is how dissolving found neurons looks today.

Very surprisingly, there isn’t much change relative to the 2024+ dissolving picture in the chart from 2022. For the most part, I very similar amount of ICP is dissolving in 2024 and 2025 as was dissolving in those two years way back in 2022.

There are a few key differences between the chart from 2022 and today’s chart:

• March 2024 has an increase of 1.05M ICP dissolving.

• July 2024 has an increase of 1.18M ICP dissolving.

What would cause this lack of change? Quite frankly, it suggests that locked neurons in 2022 have not decided to dissolve in the past 18 months AND that newly staked ICP has been staked and set to dissolve. That is to say, of all the new ICP that was staked on the NNS in the past 18 months, very little of it is dissolving in the next few years. This is surprising because over 41M ICP has been added to the NNS since March of 2022. In fact, as the dashboard shows, over 28M ICP has been added to 7+ year neurons in that time. All of this to say, almost all of new ICP to the NNS is highly committed for a long period of time.

If you want to split hairs, I guess you could move the “official start” of Zone 3 from May 2024 to August 2024. <shrug emoji>

Change in Locked Neurons

High level, there hasn’t been much change in dissolving neurons in 2024 and 2025 relative to how it looked in 2022. That’s definitely not the case for locked neurons. Below is the picture for locked neurons as it stands today (with the x-axis being the earliest these neurons could dissolve if they started dissolving today).

There are two major changes in 2024/2025 locked neurons relative to the 2022 picture. First, across the board there is a reduction in the amount of locked neurons for each month, with most months seeing a decrease near 50%. Second, there are two exceptions: June 2024 and December 2024. These two exceptions represent neurons that are currently locked and have a 6-month or 1-year dissolve delay. Note - this does not mean that June and December 2024 will see massive dissolving neurons… these neurons are currently locked and I haven’t seen any trend that suggests their owners will start dissolving.

The two differences above can be explained in two ways:

1. Some neuron owners who were locked in 2022 with dissolve delays less than 4 years have increased their dissolve delays. This shows a strengthening commitment from those owners into the governance of the Internet Computer.

2. Newly staked ICP tends to be staked in locked neurons of 6-months, 12-months and 7+ years. In fact, some back of the envelope calculations show that those three buckets make up almost all of the newly-staked behavior (with 8 year neurons being the plurality of newly staked ICP).

Conclusions

For the most part, you could argue the near-term ICP NNS picture has gotten slightly worse in some ways (extra dissolves in March and July, locked ICP at 6-months and 12 -months) and better in other ways (longer dissolve delays on locked neurons). But the bigger picture is this: that in 21 months since I last looked at this analysis, the picture hasn’t changed much at all. This means as we progressed from Zone 1 to Zone 2 (and progressed almost to Zone 3), the concept of those zones has remained consistent. And I think there’s reasons to believe the next time I look at this analysis in the future, it still won’t have changed much.

In terms of what it might mean for the the picture to remain the same as we move from Zone 2 to Zone 3 in 2024 and from Zone 3 to Zone 4 in 2025… I’ll let you draw your own conclusions.

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• Demand for ordinal inscriptions has kept the Bitcoin mempool full since April and make up the majority of Bitcoin transactions.

• Bitcoin L2s and sidechains are organizing and collaborating on a Bitcoin Builders Association and I’ve heard Bitcoin Unleashed will have over 500 developers in attendance.

• In private conversations, many large bitcoin businesses have signaled a need to support a larger bitcoin economy in order for their business models to work.

These are but a few data points that suggests that the next big story in crypto will be an evolution in the OG. But before we explore that story, let’s review where Bitcoin is at today.

What was Bitcoin Season 1?

If I’m being honest, and feel free to disagree, Bitcoin is the only true product-market fit that crypto has found with a mass audience. Bitcoin’s narratives as a store-of-value, fiat hedge and cheap, trustless cross-border payment system has attracted the attention of hundreds of millions of people, including major financial players and governments. Bitcoin is certainly the only blockchain project my mom could name (and I work for a blockchain project!) and in the minds of most of the world’s population crypto and blockchain are Bitcoin. Nothing in crypto comes even close to Bitcoin’s brand recognition. Season 1 of Bitcoin built this brand recognition and established the product-market fit.

Many people may not remember, but Bitcoin hasn’t always had a clear product-market fit and its early history shows a struggle between understanding what use case Bitcoin intended to disrupt first. Even at the inception there was a tension between the vision of Bitcoin as a currency in a “peer-to-peer electric cash system” (Bitcoin’s whitepaper) and the vision of Bitcoin as a hard asset in a world of increasingly soft assets (as demonstrated by “Chancellor at the brink of a second bailout for banks" in the Genesis block). However, those of us old enough remember the Cyprus “bail in” in early 2013 as a clear delineation point for when Bitcoin found a strong use case as a monetary asset outside the control of government officials. That event, and the success of Bitcoin relative to the success of Bitcoin Cash and Bitcoin SV after the block wars in 2017 sealed the direction for Bitcoin for Season 1.

It was precisely because Bitcoin chose a path of representing a store-of-value and a fiat/inflation hedge and not a medium of exchange (aka cash) in the 2010s and because world events have supported the need for a store-of-value and fiat/inflation hedge that Bitcoin has succeeded in Season 1 of its journey. It’s market position (or better said as market dominance) as a permission-less way to store value and as a hedge against long tail fiat and inflation risks has led to significant adoption and made Bitcoin an investable asset for even the biggest global financial players. This market dominance will only grow moving forward, as demonstrated by the flight to Bitcoin during the US regional banking crisis earlier this year.

What is Bitcoin Season 2?

If Bitcoin Season 1 can be categorized as Bitcoin finding its product-market fit as a store-of-value and fiat hedge, then what exactly is Bitcoin Season 2? Well, to understand that, you need to understand the history of all money. But before we begin, you need to understand that the true output of Bitcoin Season 1 is this: trust. Financial participants now have trust in Bitcoin on many different levels: (1) trust that the monetary policy won’t be changed, (2) trust that there is non-speculative demand for Bitcoin (aka, product-market fit) and (3) trust that external parties (mainly governments) can’t destroy that demand.

The history of money has gone from a bartering currency (3000+ years ago) to an official currency (1000+ years ago) to a paper currency (500+ years ago) to a fiat currency (more recent). During the Official Currency Era of money, central authorities would mint currency literally created out of materials of value. Usually this was silver or gold. The participants in this system had trust in the system because they could always melt down their currency into the object of value if the currency ever failed to serve its purpose. This era is typically referred to as the era of sound money because the currency literally made a sound when dropped. However, the Official Currency Era also had it drawbacks: participants could cheat by clipping parts of a coin (thus reducing that specific coin’s value) and assembling a new coin from the clippings and the government was limited in its ability to create new coins based on the availability of the underlying material, severely limiting the ability to grow their economy. These downsides produced inefficiencies in the economy. The Official Currency Era is very similar to Bitcoin Season 1 in that it produced hard money, but did so with drawbacks that led to market inefficiencies. In the case of Bitcoin, those efficiencies are seen in high transaction costs and low transaction throughput.

The drawbacks of the Official Currency Era led to the Paper Currency Era in which governments realized they could reduce the economic inefficiencies while at the same time maintain trust in their currency by issuing paper money that was redeemable for the material of value (for example, gold and silver). This allowed for far less inefficiency due to coin clipping and allowed the government a sense of control over the economic activity. The efficiencies inherent in paper money meant that currencies that “went paper” outperformed the metal-based currencies until practically the whole world adopted paper currency. In the same way that the Paper Currency Era was identifiable as a reduction in the inefficiencies created by the Official Currency Era, Bitcoin Season 2 will be identifiable by a reduction in the inefficiencies of Bitcoin Season 1. And in the same way that the world adopted paper currency because the efficiency of paper currency led to natural selection (those that adopted it succeeded over those that didn’t), Bitcoin Season 2 will be adopted because the people, organizations and businesses that adopt it will outperform those that do not.

So what exactly will be Bitcoin Season 2? It’ll be the flourishing of the paper currency equivalent of Bitcoin: layer 2s and sidechains. Bitcoin Season 2 will see a mass adoption of new tokens/coins that are pegged 1:1 to native Bitcoin… in the same way that paper currency (don’t confuse paper currency with fiat currency) could be exchanged 1:1 with the underlying material of value, new Bitcoin L2s and sidechains will be directly exchangeable for native Bitcoin, inheriting the trust of the Bitcoin layer. But more importantly, Bitcoin Season 2 will be a flourishing of economic activity built on top of the Bitcoin layer. This could be economic activity similar to the types created on Ethereum (DeFi, NFTs, GameFi, etc) or even a movement of activity off Ethereum and onto Bitcoin, however I predict the form that this economic activity takes will be quite different (that’s a topic for a different post).

There is an important distinction between Bitcoin Season 2 and the Paper Currency Era and that, as everything in money, has to do with trust. Paper currency required trust in two things that Bitcoin layer 2s and sidechains won’t require: trust in a central authority to hold the material of value in reserves and trust that the central authority will also permit the peg in / peg out. The Paper Currency Era ended when both of those trust assumptions were broken (one example is when Charles De Gaulle famously requested France’s gold reserves held by the US, resulting in Richard Nixon abolishing the gold standard for the US dollar). Bitcoin L2s and sidechains will not have these trust assumptions because the peg in/peg out will be controlled via immutable smart contracts and native Bitcoin reserves can be verified on the Bitcoin ledger.

Will there be a Bitcoin Season 3?

The arc of Bitcoin is following the arc of money history, albeit in a much smaller timeframe. Money history is currently in the Fiat Era, where most currencies are not backed by anything except the trust in a central authority. To be fair, because the Fiat Era is so new, history may end up proving that this was not an era after all, but rather was an experimental blip. This would be true if world currencies ended up going back to a gold (or other precious metal) standard in order to build trust back into the currency. Many economists and gold bugs believe this is the natural outcome of the fiat experiment. So your belief in whether or not there will be a Bitcoin Season 3 will be heavily tied into your belief into whether the world will return to the Paper Currency Era.

However, one thing I am confident about is that as the Bitcoin Season 2 era plays out and Bitcoin L2s and sidechains proliferate, many will try a “fiat approach” to L2s and sidechains. This approach will be identifiable by a move away from the 1:1 peg back to native Bitcoin. It’s hard to imagine this ever being adopted from today’s framework, however 200 years ago it would have been hard to imagine a currency not being backed by anything other than trust in the central government. For sure, people and companies will try to create a Bitcoin Season 3, however it’ll be the market that decides whether those attempts succeed.

What to expect in Bitcoin Season 2?

Bitcoin Season 2 will lead to an exciting and innovative time in money history. A clear line can be drawn between the Paper Currency Era and the Age of Exploration and Global Trade. In the same way, Bitcoin Season 2 will kick off not only vast economic activity built on top of the Bitcoin layer, but also innovative uses for money that the world has never seen. Future articles from me will focus on what this innovation might look like, as well as what to look for in Bitcoin L2s and sidechains that could lead to their success or demise (hint: it comes down to trust). Follow along if you are interested in exploring this topic more. I also recommend subscribing to the Very Good Bitcoin Club to watch Season 2 play out before your eyes.

Cheers to Bitcoin Season 2!

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First, let’s clear up what I mean by “AI components”. Essentially, an AI component is any of the building blocks that make up an AI model, whether in the development stage or in the deployment stage. AI components vary drastically based on the process used to develop the AI model, so let’s look at one type of AI model: an expert system. An “expert system” is comprised of four main components: (1) a user interface for the consumer to access the system, (2) an inference system that acts like the “brain” of the model, (3) a knowledge base which is comprised of structured and unstructured data and (4) a rules base that consists of simple or complex human knowledge rules which serves as a baseline of understanding for the model. AI model developers combine the above 4 components to create an expert system, which is an AI intended to do one thing very, very well (for example, evaluate x-rays for cancer).

So which of the 4 components can be placed on the Internet Computer? Quite frankly, 3 of them, however with limitations to one of them. The user interface and rules base can be easily hosted on the Internet Computer due to their storage and computational requirements. Many apps on the Internet Computer already store their user interface and large code bases on the IC. The knowledge base of a complex expert system may require TBs of data, which is most likely outside the capabilities of the Internet Computer and would be better stored in web2 tech. However, simple data sets (on the order of magnitude of GBs) can be stored on the Internet Computer. Inference engines typically require large amounts of data stored in RAM, and will typically be outside the current design of the Internet Computer. So for an expert system, the user interface and rules base can be easily deployed to the Internet Computer, the knowledge base can be deployed for simpler models and the inference engine will most likely not be feasible in the current design (however, much work is currently being done to enable inference engine deployment on the IC).

Alright, back to why companies should be interested in democratic governance of these AI components. I can think of two clear use cases:

1. Industry-wide AI: It seems reasonable for every industry to have multiple industry-specific AI to help consumers acquire information, to evaluate safety across the industry, to track supply issues, and for a million other reasons. These industry-wide AI systems would acquire data from each industry participant and would produce an AI product that, in theory, should benefit each participant in the industry. However, whoever controls the code, controls the AI and therefore trust could be an impediment to launching an industry-wide AI… for example, why would one company want to participate if their competitor could sway the AI towards an unfair advantage? The only way to solve this is to put some or most of the AI components into a system in which each of the participants have some form of control over the code. This would be one implementation of democratic governance over AI components; in this case, the democracy is solely comprised of participants in an industry.

2. Crowdsourced AI: Crowdsourced AI has significant opportunity to unite a diverse group of people and resources towards building and deploying an AI model. This group of people could combine diverse data sets to ensure a fair, balanced and non-biased AI model, could funnel financial or technical resources to a specific problem and/or could evenly distribute the gains created by the AI model. Due to the complexity of AI models and the requirement of access to massive amounts of (sometimes proprietary) data, crowdsourced AI may be the only alternative to Big Tech AI. A democratic governance system over the AI components is one way to ensure the AI project is protected against malicious behavior, provide participants control over the project and create incentive structures that can fairly compensate for participation in the project.

Both of these use cases resolve the trust issue associated with “whoever controls the code, controls the AI” and, in addition, they open up opportunity for global collaboration. Here are a few hypothetical examples of how this could play out:

• The pharma industry wants to create an AI to advise doctors on best course of action when a patient shows signs of an adverse event to a medical treatment. Each participating pharma company supplies data related to their drugs, symptoms of adverse events and corrective actions taken by the doctor (training data). This data can live in pharma companies’ legacy web2 tech stack, however the master file for which data sets to use for training, testing or validating is stored on the Internet Computer. New data can only be added to this list (or old data removed) based on a majority vote of all participants, preventing malicious intent from one participant and preventing one pharma company from supplying data that might lead to AI outputs more favorable to their drugs relative to their competitor’s drugs.

• A developer is interested in creating an open source app that helps gardeners identify how to improve their plants livelihood by providing a diagnostic tool leveraging photos of the plants. The developer creates an AI project in which anyone can upload a photo of their own plants with a diagnosis of what was wrong and what corrective action they took (training data). These uploads can be stored on the IC and a governing body can determine whether the upload meets the data quality expectations and whether it should be included in the model. The governing body is located in geographically disperse locations to ensure the data set being built isn’t biased. In addition, the governing body can control a text file that contains the rule base. This allows global participation in the AI project.

• Let’s say the above developer wants to turn this AI into a sellable product, but needs to raise capital. She could raise funds through a crowdfunding site (like Funded.app) and give voting rights over the AI model, the uploads and the rules base directly (and cryptographically) to the funders of the project. This would align the funder’s interests with the developers. In addition, since the training data is on the Internet Computer, the developer could set up an incentive model that rewards the persons providing the training data uploads with a share of the revenue generated by the model.

These are just a few examples I can think of off the top of my head, however the surface area for innovation is quite large. History has always arced towards decentralization of control and the concept of putting AI components on a blockchain is part of that evolution. The main question is: who will come build out that innovation on the Internet Computer?

One final note, I’ve intentionally called these “democratic governance bodies” and not DAOs in order to appeal to the non-web3 readers. However, everything above can be boiled down to DAO-controlled AI components. I have a few more thoughts on DAO-controlled AI, but I’ll save it for a later episode. In addition, later episodes of this series will focus on digital ownership and the monetization of AI components like data sets. Until then, happy reading.

Kyle

The fact is, the Internet Computer has already provided a solution to this problem and it is possible for online products to turn some or all of their product management over to their users or another trusted body of people. If Elon wanted to take Mark Cuban’s up on his challenge and fully decentralize the X feed algorithm, it would be as simple as loading the algorithm’s code into a canister on the Internet Computer and turning over it’s control to a DAO. As the tech stands today, this is not only possible, but almost easy.

One key aspect of the above decentralization for X’s feed algorithm is that it is not necessary to move all of X’s infrastructure to the Internet Computer for this to work. X could move over the feed algorithm, but keep the remaining applications living on centralized servers and this arrangement would probably satisfy both X and its users so long as X and its users can satisfactorily verify that it is using the IC-hosted algorithm without alteration.

What does this have to do with AI? Well, AI has a similar trust issue where consumers are naturally weary about the “black box” aspect of AI. Solving this weariness is the same as solving the trust issue Mark Cuban raises about the X feed algorithm. Moving some or all of the components of an AI model development and/or deployment to the Internet Computer and allowing democratic governance over that component(s) would go a long way to resolving consumer weariness.

This has two direct implications: (1) solving trust issues between AI deployment and its intended consumers (discussed more in a later article) and (2) solving the “alignment problem” (discussed in the remainder of this article).

The alignment problem centers around aligning a super-intelligent AI with human values to ensure that the super-intelligence is always operating in a manner that is beneficial for humans. Basically, how do we stop the machines from taking over the world? However, human values are extremely diverse, so it begs the question: whose human values are we aligning super-intelligence to? It’s not hard to see that aligning super-intelligence to one set of human values would be to the benefit of a small set of humans at the expense of the majority of humans. Consider the different value sets based on business interests, geography, religious and political factors and how they have impacted human history.

The solution to “whose values?” is to use a democratic process in the alignment of super-intelligence to human values. As discussed above, this is possible with today’s technology. If the AI model inputs, controls and outputs are placed in a canister on the Internet Computer then it is possible to introduce a democratic process for how they are governed. This ensures (assuming the democratic body is diverse) that the model is aligned to the human values of a diverse set of human values. It also removes the need for a “benevolent” gatekeeper… a person who has true control of the code and therefore can overturn the democratic decisions.

These are exciting times. Democratic control over AI inputs, outputs and processes is an area worthy of academic resource. However, companies have little incentive to worry about the alignment problem; in fact, they are incentivized to align AI towards their own unique values. However, I do believe there’s a robust use case in which businesses will want to allow democratic control over their AI products. That will be the topic of a future article.

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While it’s likely that other layer-1 chains will be able to host AI at some point, as far as I know the Internet Computer is the only blockchain capable of playing a role in AI as it stands today, so I will use “blockchain” and “Internet Computer” interchangeably throughout this series. It’s also important to state that this series is not meant to be a discussion of theoretical use cases between these two technologies, but rather specific use cases that are possible with the technology as it exists today.

Value Proposition

This series will focus on 4 major unique selling points that blockchain brings AI: security, digital ownership, decentralized control and transparency.

Security - The ability for people and companies to secure their data and model development without the fear of data leaking, privacy incursion or system hacks.

Digital Ownership - The ability for people and companies to cryptographically prove ownership over any AI component (data, model, code) and the ability to easily transact that ownership and define licensing agreements within the digital product itself.

Decentralization - The ability for people and companies to remove trust assumptions and risks associated with AI model development and deployment.

Transparency - The ability to build trust with AI model consumers by giving them insight, and possibly control, into the model inputs, processes and outputs.

These four value drivers for blockchain will comprise most of the use cases between blockchain and AI in my opinion.

Team Size

While web3 has been mostly led by solopreneurs and small developer teams, followed by later adoption by larger companies and organizations, I believe the reverse will happen for AI and Blockchain. During the Ai4 conference, I heard many AI companies speak on their pain points regarding the four unique selling points above. Security and transparency were particularly common concerns for Ai4 speakers. For that reason, I believe the revolution of AI to the blockchain will happen with existing AI service and software providers/companies moving some of their product’s infrastructure to a blockchain. It will be enterprises who first explore the space of AI on blockchain and who will first prove out the use cases and find product-market fit. From there I predict smaller developer teams will carve out niche markets in this space.

I have much, much more to say on this topic and things are evolving fast. Be sure to subscribe to remain in the loop.

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I’d love to turn this into a conversation, so let me know your feedback on this post on Twitter/X (@kylelangham).

High Conviction

1. The SEC’s crypto crackdown is a nothingburger - Crypto has become political in the US and now falls across political party lines with Republicans being pro-crypto and Democrats being anti-crypto. Nowhere is this more evident than within the SEC, which has aggressively gone after crypto in both public statements and legal action. I believe this will reverse course and ultimately the SEC will embrace smart crypto regulation (with a few more bumps in the road along the way). I believe this because of two trends: (1) the US courts have routinely sided with crypto and recently have struck down the SEC’s “govern through ambiguity” approach and (2) money talks in US politics and crypto is starting to fund POTUS campaigns in 2024. Currently, the #2 Democrat (RFK Jr.) and #4 Republican (Vivek Ramaswamy) in recent polls have taken pro-crypto stances, with RFK Jr. going so far as suggesting the US dollar should be partially-backed by Bitcoin. The tides are changing in the US and our darkest regulatory days are behind us.

2. Bitcoin and Ethereum are still kings - If 2023 has done anything, it’s proven out the “Bitcoin as a store of value” narrative. Between bank failures in the US, high inflation in countries like Argentina and the UK and a general lack of trust in authorities throughout the world, Bitcoin continues to build the case that it is an investable asset (buoyed by BlackRock’s recent ETF application). Bitcoin’s dominance (the percent of Bitcoin’s market cap relative to all of crypto’s) is at 50%, it’s highest level since early 2021.

   Ethereum, on the other hand, continues to show that network effects matter A LOT. While other L1 projects have outpaced Ethereum on the technology front, these projects (like Internet Computer and Polygon) have chosen to extend Ethereum’s capabilities, not compete with them. These strategic decisions are a clear indication that Ethereum’s network effects are insurmountable for the time being.

3. Crypto is still looking for it’s product-market fit, and that’s OK - Let’s be honest, other than Bitcoin’s “store of value” use case, crypto has yet to find a product-market fit. There’s a lot of interesting and exciting things going on in DeFi, privacy, digital ownership, etc., but nothing that is even close to mass adoption. And that’s OK. Few people remember that in 1999 (DotCom bubble) the internet hadn’t found a product-market fit either. Anecdotally, I remember my uncle telling me in 1998 “Other than email, the internet is just a toy”. It wasn’t until around 2005 until the internet found true product-market fit (social media, gig economy, cloud computing, ect). A key component of that was hardware and infrastructure (smart phones, cable modems, 3G cell data) advancements. Crypto is still looking for those use cases and has time to find them. As long as the industry continues to experiment with software products and build out its infrastructure and hardware, product-market fit will come.

High-Medium Conviction

4. Crypto is in the beginning of The Great Consolidation - All new industries go through major consolidation periods. It’s a natural outcome of market efficiency and typically happens in waves. I believe we are in one of those waves for crypto and will see the number of crypto projects vastly decrease as resources (developer time, funding) consolidate. Not many people are talking about this now, but I believe they will be soon as projects begin to fail. So how do you know who will survive the consolidation? Here’s three questions you should ask of your favorite project: (1) does it expand the functionality of Bitcoin and/or Ethereum (see #2 above)?, (2) does it serve a niche market and have a clear fit with that market?, (3) is the project currently attracting more developers and/or users? If you can’t answer yes to at least one of those questions, it’s probably not gonna make it.

5. Developer Stats are worrisome - Almost all reliable sources confirm that developer counts within crypto are down over the past year. According the Electric Capital (https://www.developerreport.com/), the gold standard for developer stats, the number of developers building open-sourced crypto software peaked in the summer of 2022. The number of full-time developers is down 17% and the number of part-time developers is down 23%. These are by far the largest declines in crypto developer counts since its inception in 2009. Developer counts, in my opinion, are a leading indicator to the adoption and innovation within crypto, so these declines are worrisome, particularly if the trend doesn’t reverse over the next year. The silver lining - developer counts are still twice the levels seen after the 2018 bull market.

Medium Conviction

6. The 4-Year Crypto Cycle has one last cycle in it, but it will be different - Much has been said about the crypto 4-year boom-and-bust cycle. This cycle is timed to the bitcoin halving cycle, with a massive bull run typically starting ~6 months after the halving event. I predict that the cycle has only one more iteration left, corresponding to next April’s halving. The 4-year cycle will become obsolete due to three things: (1) the awareness of the 4-year cycle is universal and thus has minimal chance to surprise to the upside, (2) the impact of Bitcoin’s halving relative to its supply diminishes and (3) the crypto industry matures. I anticipate that the market will front-run next April’s halving and that the majority of the gains of the next bull market will be realized prior to this time next year. From there, the crypto market will become far less volatile and the triple digit percent annual gains and >50% losses will become a thing of the past. Instead, over the next 5 years it will be much more important to invest in the right project or company (see #4 above) than it will be to time the market.

7. BTC-denominated stablecoins will become the preferred currency of the internet - All the talk about Bitcoin becoming legal tender is a bit ridiculous when you consider it hasn’t even made a dent as the preferred currency of its home turf - the internet. There are two big reasons for that: (1) the user experience for bitcoin online is horrendous and (2) there still isn’t a great solution for scaling bitcoin. That’s beginning to change as builders are beginning to flock to sister chains and L2s of Bitcoin, primarily due to the awareness of “building on Bitcoin” created by Ordinals and the potential to be early on a multi-trillion dollar shift in online behavior. These projects will produce highly secure tokens that are 1:1 redeemable to Bitcoin through direct integration with the BTC network; these are de facto BTC-denominated stablecoins and they will open the door to much better user experiences with using Bitcoin as a currency online. ckBTC is one such token already released; sBTC is another token slated for Q4 of this year. In addition, these stablecoins will unlock incredible innovation as programmable money and, I predict, will usher in a new way in which internet users think about money online, well beyond the current status quo of simply buying services and products online.

What is crazy is we haven’t evolved that payment system since 2000. Literally, the same mechanism is still driving almost all of the economic activity online. Bitcoin needs to change that.

The Issue with Credit-based Transactions

While innovative in 2000, we have become complacent with the user experience of online credit card transactions and rarely do we recognize the downsides of the current system, including:

• It is inherently insecure.

• Credit-based systems encourage debt consumption

• Most of the world’s population still does not have access to credit

• Cross-border transactions encounter costs of exchanging between fiats

• Credit card transaction fees are relatively high

Bitcoin solves many of these issues, however the user experience of Bitcoin purchases online is more like pre-Paypal eBay than post-Paypal eBay. It’s embarrassing to think that, as we pursue legal tender status in the real world, Bitcoin has made almost no progress in adoption within its digital home turf. In short, making Bitcoin the preferred currency of the internet is the path of least resistance to making Bitcoin legal tender in the real world. The problem, however, is the user experience of paying with Bitcoin.

Bitcoin as the Solution

Many have tried to solve the user experience problem with transacting in Bitcoin online, but every solution to date has failed either because it required too many sacrifices to core Bitcoin principles (like security and decentralization), was not scalable, or, quite frankly, still sucked from a user experience perspective. For Bitcoin to become the internet’s currency, transacting in Bitcoin needs to be at parity with transacting with fiat.

Luckily, the Internet Computer’s native integration with Bitcoin makes it possible to transact in Bitcoin not just at parity with fiat, but far exceeding it. It is now possible to create user experiences with Bitcoin that far exceed the current user experience of using credit online. The ICP <> BTC integration, which went live late last year, provides the first scalable web solution for serving the global online economy, and since Internet Computer dapps can be fully on-chain (including front ends), this user experience comes without the standard trade-offs on security and decentralization.

It’s time for Bitcoin to reach a billion monthly users. It’s time for Bitcoin to become the internet’s currency. The tech is now ready with the Internet Computer. It’s up to web developers and Bitcoin supporters to make it happen. I look forward to watching Bitcoin become the preferred currency online.

Bitcoin’s price is extremely volatile, but its unique supply mechanics make it possible to understand that volatility from a supply-demand perspective, and thus understand price movements.

This article will focus on supply-side mechanics, but later articles will focus on demand-side mechanics.

Note from Author

Thank you for reading this post. I have two goals for this series: (1) educate on a simple way to understand Bitcoin’s current price and future price movements and, (2) to reach 10,000 subscribers to this Substack. If you are interested, please help out by Subscribing and sharing this post with your Bitcoin network.

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Supply-Demand-Price

The “Law” of Supply and Demand states that price is determined by the relationship between supply and demand of a good, or in mathematical terms, price is a function of supply and demand. However, analyzing the pricing of goods based on supply and demand is complicated by two factors: (1) supply and demand are often dependent variables (ie, events that affect one also tends to affect the other) and (2) the function can change over time based on a myriad of external factors.

Bitcoin is unique to most other goods because its supply is fixed, knowable and predictable based on its code. In addition, now that Bitcoin is a tradable good with enough liquidity that its price can be meaningful, it’s possible to have insight into the supply-demand-price (SDP) function for Bitcoin. Understanding the SDP function provides incredible insight into questions such as: “What is causing Bitcoin’s price movement?” and “What might drive Bitcoin’s price movement next year?”.

PlanB’s stock-to-flow model takes advantage of the fixed supply mechanism to understand past price performance and predict future price movements. I will use PlanB’s stock-to-flow model (s2f) as a substitute for supply-side mechanics in this article (due to the ubiquitous understanding of the model within the community and because it generally captures the supply mechanism, although not price, well), however I may challenge this in future articles and propose additional models.

What Does Supply Tell Us About BTC’s Price?

There are times in Bitcoin’s history where supply mechanics explain almost 100% of Bitcoin’s price movement.

Here is a chart of Bitcoin’s price correlation with the s2f model. Notice the long periods of extremely high price correlation (the purple box shows correlations greater than 0.75)

There are two areas of interest on this chart:

1. The Green Dot - This was a period of relative sideways action in Bitcoin’s price from January - March 2019 and corresponding mid-cycle sideways action in the s2f model. With the exception of the failure of Quadriga Fintech, this was a period with little news and events for Bitcoin.

2. The Blue Dot - This period started 3 months after the 2020 halving and lasted from mid-August 2020 to mid-January 2021. This period corresponded to a rapid increase in both Bitcoin’s price and the s2f model.

I’m excluding prior periods of high correlation due to the fact that the s2f model was created in early 2019 and was statistically fitted to price data prior to 2019. However, it is worth noting that Bitcoin halvings seem to produce periods of high correlation between the Bitcoin price and supply mechanisms.

A quick note - high correlation between s2f and Bitcoin’s price does not mean that s2f is accurately predicting Bitcoin’s price, but rather the two are moving consistently in the same direction. A correlation analysis removes the price prediction

SDP Theory

I want to postulate a theory that I will explore in later articles, it goes like this:

Supply-side mechanics will drive Bitcoin’s price around Bitcoin halvings.

What does this mean? Well, under this theory you can expect a period where Bitcoin’s price will rise after each Bitcoin halving because the price will be highly correlated with the stock-to-flow. I believe this will occur both because of an actual supply “shock” (less Bitcoin will be minted after each halving) and because the narrative around Bitcoin will focus on that supply shock and thus market participants will be more supply-side-minded.

The first corollary of this theory is:

Bitcoin’s price will be driven by demand-side mechanics (aka narratives) during periods between halvings.

While supply mechanics will be the focus of market participants around the time of each Bitcoin halving due to the size of the supply shock relative to the demand mechanisms, Bitcoin’s price during the period between halvings will be driven mostly by demand mechanisms.

The second corollary of this theory is:

In the absence of demand narratives, the Bitcoin price will be correlated with the s2f model, which suggest sideways price action between halvings.

The s2f model is *mostly* sideways between halving events. During periods in which there is no strong demand narrative Bitcoin’s price will also be mostly sideways.

The third (and final) corollary is:

Given that the size of the supply shock of each halving drops with each halving, over time the supply mechanics of Bitcoin will no longer have any impact on price and demand will drive 100% of the Bitcoin price movement.

SDP Implications

The fact that halvings lead to price increases isn’t news to anyone. However, if you are willing to accept the above SDP theory then there is an interesting implication: periods between Bitcoin halving events can be best understood through the lens of demand mechanisms. That means that price action should be heavily correlated with certain demand metrics for Bitcoin, like exchange flows, wallet behavior, hodler behavior, etc., and the metrics that are most driving that price behavior can explain the narrative that is driving price. The SDP theory enables a deeper understanding of what is driving demand to and from Bitcoin. This will be the focus of my future articles in this series.

If you want to learn more about how demand narratives drive Bitcoin price (supported by data, of course!) then be sure to subscribe and share this article.

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Bitcoin’s Stock-to-Flow model should not be used to predict Bitcoin’s exact future price, but rather should be used to understand Bitcoin’s current price action and what narratives are truly driving demand. There’s incredible value in combining S2F with demand-side analysis to understand current price action and trends.

Stock-To-Flow

I’ve always been a fan of Bitcoin’s tokenomic model that hardcodes a future supply curve because if you fix supply then analyzing a supply-demand curve becomes all the more easy. In the same vein, I’ve also always been fascinated with Bitcoin’s stock-to-flow (S2F) model, popularized by pseudonymous analyst PlanB (https://planbtc.com/) because it attempts to take advantage of the simple supply-demand mechanisms (and their implied scarcity) in order to predict future price movements. PlanB’s model gained significant notoriety when it (more or less) predicted the average Bitcoin price of the 2019/2020 bear market and the subsequent rise in price in 2021. In recent months, the model has fallen out of favor due to the bear market, which many consider to invalidate the model.

Model Criticism

It’s not my intent to explain the model in this article (reference PlanB’s website above to learn more about it) but rather to use it as a starting point for a more meaningful analysis. I believe the S2F is one piece to a larger analysis that allows Bitcoin holders to understand the underlying demand for Bitcoin and whether the current price accurately represents that demand. Prior to that, I want to address the two main criticisms towards the model, because they dovetail well into my methodology.

Criticism 1 - The model relies too heavily on past price performance and poor statistical methodology. This criticism is especially true considering the S2F models were based heavily on early bitcoin price data, which suffered from more inefficient market conditions and thus is probably less reliable. This criticism is also bolstered because PlanB updated his statistical model later to show two more bullish models… the initial model had a price prediction of ~$55k for this time period, whereas the second model (which, in my opinion, had an inferior statistical approach) had a price prediction of $100k for this time period. A third model (S2FX) was based on other assets like silver and gold and predicted a current price of $288k.

Criticism 2 - The model doesn’t take into account current market conditions or demand drivers. The model assumes constant demand that outpaces the scarcity of supply, however its clear that black swan events (like FTX) and market conditions play a large role in demand and greatly impact the price of Bitcoin.

A Different Approach

The S2F has a lot of merit and value (that’s why it has remained popular for 4 years) however the criticisms above are accurate. But the criticisms above also reflect something else: the application of S2F (to predict future Bitcoin price) is the wrong application. I believe the true value of S2F is in a different application… and this new application is both more accurate and more meaningful if you own Bitcoin.

To understand this approach, you need to understand the Bayesian concept of “priors”. This is an intuitive concept, one you certainly already use every day but probably never stopped to think about. A prior is your default assumed probability of one event occurring just before another event occurs. For example, let’s assume your favorite sports team is playing their rival in three weeks and you assume your team has a 50% chance of beating its rival. However, between now and the rivalry game, your team plays a weaker team and loses badly. After that embarrassing loss, you now think your team only has a 25% chance to beat the rival. In this case, the 50% probability of beating the rival before the embarrassing loss is your prior.

Priors are useful concepts for understanding the impact of a specific event occurring. In the example above, if someone were to ask you what the impact of the embarrassing loss was on your impressions for the rivalry game, you could quantify the impact by saying it decreased your team’s odds in half. From a statistical point-of-view, priors are extremely useful for understanding the current situation and judging the effects of changes on that situation.

This is a really long-winded way of saying this important point: S2F should be thought of as one set of priors in the supply-demand-price dynamics of Bitcoin. When S2F is used in this context, as opposed to predicting future price action, it unlocks multiple analyses that allow you to statistically answer such questions as:

• Did the recent bank runs / failures change the demand drivers for Bitcoin? Are people buying Bitcoin to “get off the traditional banking system”? Is the price rally that has occurred recently sustainable from the context of the narrative?

• Is the major driver of Bitcoin demand the “digital gold” narrative or the “payments” narrative (for example, LN)?

• How might additional central bank rate hikes or cuts affect Bitcoin demand and therefore price?

There’s additional sets of priors that I think are important and additional questions these priors can help answer, but I’ll get to those in a later post.

Supply-Demand Series

I will be posting multiple articles to help flesh out this approach to understanding Bitcoin’s supply-demand curve and hopefully will end up in a place of providing a model for understanding Bitcoin’s current price and whether it is likely to increase or decrease at any given time. S2F will be one component of that approach, however I will also need to develop additional ways to understand demand drivers and external shocks in order to establish answers to the questions above (and additional questions). If you are interested in learning more about approaching Bitcoin’s supply-demand-price dynamics from the Bayesian context of priors, and how it might adjust your thinking about Bitcoin, subscribe to this Substack and share it with your colleagues.

In the end, we all want to make it, Kyle

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The chart above is the federal funds rate projections from the FOMC’s December 15th, 2021 release, the infamous “dot plots”. It shows that the US Fed governors at the end of 2021 expected the Fed fund rates to end 2022 somewhere around 0.75% and for rates to top off in 2024 around 3%. Not a single FOMC participant expected rates to get above even 2.25% in 2022. In actuality, rates finished 2022 at 4.25-4.5% and currently sits as high as 4.75%. In short, even when predicting just 12 months into the future, the every single US Fed leader significantly underestimated the speed in which they would need to raise rates.

Therein lies the root cause of the current financial crisis. Banks made risk decision in 2021 that modeled similar rate hike speeds forecasted by the Fed’s dot plots. When the Fed raised rates faster than expected (the fastest rate of increases in history), all of a sudden the banks’ risk models turned upside-down. It’s easy to forgive the bankers for believing in the Fed’s dot-plot projections; after all, the people making those dot-plot projections are the exact same people who make the decisions whether to raise rates and by how much. When choosing a projection to base your models on, it’s usually wise to trust the projection made by the people who have the most control over whether the projection comes true…

If you think this is an anti-FOMC article, think again. I’m not an armchair economist who thinks he can do better than the professionals. In fact, I think the Federal Reserve gets it right most of the time. It’s not easy to predict the future, especially with something as complicated as the economy, and I do believe the professionals at the Federal Reserve are probably the most competent people in the world at understanding where the economy is and where it might go. The problem, in my opinion, is that even these competent people get it wrong sometimes. And when the Federal Reserve gets it wrong, as they did in 2021, things go really bad.

The Solution

It’s 2023, 94 years after the Great Depression, and we are still suffering from bank runs, albeit less often and less severely. This seems ridiculous as bank runs should be a thing of the past. What do we do though to improve? I’ve seen calls that we need more regulation of the banking industry and I’ve seen arguments that regulation is at the heart of the problem. Both arguments seem logical. And both arguments miss a central point… we are probably near the point of maximum optimization of the current system. The Federal Reserve was created in 1913 and its current structure took shape in the wake of the Great Depression; how likely is it that the current structure can be vastly improved upon, given that we’ve had almost a century to optimize it? It seems, at least to me, that occasional bank runs and the need for a central authority to backstop them, is just an outcome of the current system that will always persist.

Instead we should be recognizing the need for a new system, one that can run parallel to the current centralized system, and one that provides participants in the economy a choice in which system they use for their monetary decisions. Competition is at the root of all that is good about free markets and it’s about time we had competing monetary systems. It’s about time that two systems existed, each holding the other accountable through the risk of capital flight from one to the other.

Obviously, the parallel system I am talking about is blockchain-backed cryptocurrencies that focus on decentralization and premission-less participation. It would be a system built with no central authority, no possibility of bailouts and one in which moral hazard is eliminated. Think Bitcoin + DeFi, but don’t limit yourself to today’s technology and instead think about a system designed from the preferences of the system’s participants in a free market and one in which there is no need or want for centralized planning.

This is not a rally cry to “burn down the current system”, but rather a rally cry to allow competing systems that operate on equal footing. Let the market participants decide which monetary system they prefer, based on their self-interests, and captured in the decisions they make with their capital. Isn’t that type of competition what makes free markets so dynamic and efficient?

A Starting Point

How can our elected officials facilitate the movement to a duopoly monetary system? Quite frankly, ever since the day that Bitcoin went live, the market has been already moving in that direction. Year after year, market participants have moved capital to the decentralized monetary system. The key would be for federal agencies to ensure the blockchain monetary system can compete on equal footing as the current centralized system. Surprisingly, this means smart regulation, not no regulation. Blockchain is currently operating at a massive disadvantage because of regulatory uncertainty. Our elected officials could take these two important first steps:

1. Write regulations that allow efficient and smooth on-ramps between the two monetary systems. In order for the two systems to compete the touchpoints between them must be as frictionless as possible.

2. Update the definition of a security to work in a digital world. The current definition was written at a before computers and its about time to update it so the industry can accurately predict what cryptocurrencies will be considered a security.

The way out today’s bank runs may be bailouts to prevent the possible contagion that might stem from bank failures. However, if we want to live in a world in which bank runs and bailouts are not an issue, then we need to significantly rethink our approach to monetary systems, not with marginal changes to regulation of the current system, but with a thriving marketplace of monetary system options. Let the market decide the optimum monetary system, not central authorities.

#WeNeedCompetingMoney

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This is a wonderful accomplishment for the engineers at OpenChat and DFINITY and its success is captured in the fact that the decentralization sale met its maximum goal of 1 million ICP ($5,500,000) for 25% of token supply in less than 6 hours(!). It proves that even in this bear market there is demand from app users to participate in decentralization sales. Additional apps built on the Internet Computer will no doubt be preparing themselves for their own decentralization sales.

The Great Social Experiment

For me, I’m mostly interested in what I believe will be an incredible social and “business” experiment, namely what happens when the users of a product are also the owners of the product. I’ve heard of employee-owned companies before, but I’ve never heard of an app in which its users have the option to participate in governance over every aspect of the application, including all code changes, the product roadmap and control over the treasury. I’m interested in this because it seems to me to be the perfect marketing tool… it has the potential to harness the power of word-of-mouth marketing and put it on steroids', perfectly aligning the interests of the application’s users and its controllers, because they are close to being one-in-the-same.

Where I live it seems like every 10th car I see on the streets is a Tesla. It doesn’t shock me that Tesla became the highest valued car manufacturer in a matter of a decade, but it does shock me that they did so with almost no advertising. They also had to re-invent the distribution model, foregoing the salesperson at the dealership and instead needing to educate its potential customers through an online presence only. If you had asked anyone in 2013 whether a new car manufacturer could capture any market share without a robust dealership network and with almost no advertising budget, they would have probably given the most confident “no” they had ever given. Yet here we are with Tesla and its all thanks to word-of-mouth marketing.

Tesla tapped into word-of-mouth marketing in a way that almost no company has before. They built incredible cars that caught the attention of other drivers on the road and were so feature-rich that their customer’s loved to brag about them. The nature result was selling a car was also putting the equivalent of a Telsa salesperson on the road. I believe OpenChat, and the dapps that follow in its footsteps, have similar potential in that their users are also highly incentivized to evangelicalize and market their applications as the greater the number of users of a dapp, the greater utility the dapp’s token has. OpenChat, as a communication tool which naturally has network effects, seems particularly well-suited for this type of potential growth.

And this isn’t even considering the fact that the users of these dapps are also now the product managers… what better way to align product design and roadmap with user desires than making the users the product managers?

I know I’ll be watching the progress of the OpenChat DAO, and those that follow in their footstep, to see if perhaps we are on the eve of a complete rethink of how “businesses” operate.

Let me know your thoughts below.

Kyle

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However, I think this is the wrong way to think about crypto.  But before I explain that, I want to ask you a question… what is the most innovative thing you’ve experienced online since 2015?

Here’s how I think about the evolution of the internet since 1995:

1995 - 2005: this decade saw incredible innovation on the internet including search (Yahoo, then Google), music (Napster, then Apple), e-commerce (eBay, then Amazon), payment (PayPal) and chat.

2005 - 2015: this decade saw the rise of mobile internet (smart phones), social media, SaaS-everything (redefining corporate IT), “Uber”ization of all services and financialization.

What innovation have we had since 2015?  ChatGPT/AI is certainly one channel of innovation, but how many other examples come to mind?  Compare how much innovation has occurred online since 2015 with the time periods prior.  I think we can agree that internet innovation has slowed dramatically and it could be that we are reaching the end of the internet innovation cycle, with perhaps only a few examples to the contrary.

It’s natural for a technology (and the industries it supports) to mature and slow down its innovation.  However, are we ready for the internet to slow its innovation?  Think about this, is this article the first time you’ve thought about the internet innovation cycle coming to an end?

That, to me, is the problem that crypto solves.  Blockchain technology provides an entirely new avenue of technological exploration and product development.  While self-sovereignty, digital ownership, low transaction costs, privacy and security are core tenets of crypto, we need to stop thinking about them as The Features and instead start thinking about them as building blocks for innovative products.  That fact is, blockchain enables products that were not possible even a few years ago and it’s going to take time for entrepreneurs and dreamers to think of these new capabilities and test them in the market.  NFTs, DeFi and meme coins are not the finished state of crypto, but rather are the first attempts at innovation using blockchain technology.  The next step is to innovate beyond these products to introduce new ideas to the average internet user that are only possible because of the core tenets of self-sovereignty, digital ownership, low transaction costs, privacy and security.  Blockchain is currently like the pre-1995 internet, where websites were basically just online versions of things that existed prior.  It’s time to innovate beyond that.

It’s worth pointing out that the technology stack of crypto has, up until recently, prevented a lot of innovation because it was incapable of serving a user experience that could come close to the expectations of average consumers.  It’s very difficult to find product-market fit when the user experience is poor and thus it’s been very difficult to collect market feedback and evolve.  That is until the Internet Computer launched.

The fact that the Internet Computer provides the first web-speed blockchain capable of serving crypto services alongside strong user experience gives me hope that blockchain technology may finally be ready for the masses.  Only on the Internet Computer can an application be built in which self-sovereignty, digital ownership, low transaction costs, privacy and security are not the features themselves, but are rather the building blocks of truly innovative features and solutions.  And only on the Internet Computer can an application truly be decentralized through a DAO, opening up an incredible world of possibilities for diversified brainstorming and roadmapping.

So is crypto a solution seeking a problem?  Yes and no.  And I think we’re finally moving more towards the no.  So stop thinking about self-sovereignty, digital ownership, low transaction costs, privacy and security as selling points and start building new, crazy things with these as the starting point.  

#Buidl on, Kyle

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This article is an exploration of how I believe leadership will change as a result of the spread of DAOs and what types of skills will be most important as society moves towards decentralized decision making.

Leadership in DAOs?

Does it strike you as odd that I believe leadership will still be important in a world in which decisions are made from the bottom-up?  Wasn’t the point of DAOs to remove the authority from a central and small group of people?  Let me explain.  Leadership, I believe, is a core feature of any human organization, whether it’s a centralized or decentralized organization.  DAOs will still have leaders, however leaders will be empowered only by the preference of DAO participants and any given DAO will have multiple, if not many, leaders at any given time.  What makes leadership in DAOs different than leadership in modern top-down organizations is these characteristics:

• How leaders are chosen - In a DAO, leaders will be determined by the DAO participants in a process called liquid democracy.  Essentially, leaders will be those who receive additional voting power within the DAO because other DAO participants trust those leaders with their vote.

• Scope of leadership - Leadership within DAOs will be more narrowed-focused, most likely around topics in which the leader has expertise.  This is in contrast to leadership today in which leaders are expected to be decision makers within a broad range of subjects (for example, a politician may be expected to vote on topics ranging from economic to foreign policy).

• How long leaders serve - In a liquid democracy, common in DAOs, participants are free to change who represents their vote at any given moment.  Therefore, leaders in a DAO can be replaced at any moment.

Types of Power

In 1959 social psychologists John French and Bertram Raven categorized power in six categories:

Legitimate - Power obtained from formal organization structure.

Reward - Power obtained from the ability to compensate others.

Expert - Power obtained through a person’s abilities and knowledge.

Referent - Power obtained through respect from others.

Coercive - Power obtained through the ability to punish others.

Informational - Power obtained from the ability to control information needed to accomplish something.

I believe some of the above types of power will become less important or even extinct as DAOs become more prevalent and some of the above types of power will become much more important.

Less Important in DAOs

It’s hard to see how a decentralized group of people will utilize legitimate power, when one of the features of a DAO is the lack of organizational structure.  It could be that legitimate power becomes extinct if DAOs become the de facto form of organization.  Likewise, DAOs tend to operate globally and online (although that doesn’t have to be the case) and it’s hard to imagine how coercive power could be utilized since there are only a few ways in which a person can be punished online.  In addition, the nature of DAOs align participants’ incentives such that it will often be the case that punishing one DAO participant will be punishing all DAO participants.  Finally, blockchains are typically public and auditable by design and therefore information will be more widely accessible.  Informational power will most likely decrease due to this, however DAO participants with specialized technical skills in data science or software engineering may still hold an advantage in their ability to process blockchain data.

More Important in DAOs

I believe in a DAO-empowered world, having influence on DAO proposal votes will be one of the main forms of power.  There are two ways in which that power could be realized: (1) through DAO participants choosing someone to represent their vote in liquid democracy or (2) through the ability to make online arguments that sway voting behavior.  The first form of power realization will place a heavy emphasis on expertise as liquid democracy allows the ability for specialization-based representation.  The second form of power realization will place heavy emphasis on referent power.

I believe that expert and referent power will blossom as DAOs become more prevalent, coercive and information power will decrease in relevance and legitimate power will become extinct.

Leadership Style

So how does the source of power effect leadership?  Kurt Lewin is often credited with branding the basic leadership styles as:

Autocratic Leadership - a focus on leader authority and subordinate compliance

Democratic Leadership - a focus on consensus and group buy-in

Laissez-faire Leadership - a focus on individual autonomy and empowerment

Transformational Leadership - a focus on a grand vision and excitement/engagement in that vision

If you want to be a leader in a decentralized organization, it’s clear autocratic leadership styles will not work as there is no legitimate power and no mechanisms to enforce compliance.  In fact, the purpose of DAOs is partly to eliminate autocracy and the central point of failure they create.  In a DAO-centric world, it would be wise to avoid autocratic leadership styles.

However, there does seem to be a place in DAOs for democratic leadership, laissez-faire leadership and transformational leadership.  Democratic leadership styles could thrive in DAOs due to their ability to lead others towards consensus, a skill that will aid in leading others towards specific proposal votes.  Laissez-faire leaders, who tend to focus more on the big picture than minute details, may play a significant role in guiding proposal crafting while also allowing specific details to be worked out by more vested individuals.  Transformational leadership in a DAO will perhaps be one of the bigger winners as the decentralization nature of DAOs will naturally place more emphasis on leaders who can articulate a vision that other DAO participants can support.

Skill Sets

It’s clear that the source of power within a DAO will be different than it is in today’s centralized world and that autocratic leadership styles (which still dominate in today’s business world) will become far less effective.  That leads to the final question, namely, what types of skills will thrive in a DAO organization structure?

It’s pretty clear what skills won’t thrive in a DAO.  When you think about the fact that authoritative power and autocratic leadership will both wane in a decentralized world, the skill sets useful to both will also wane in effectiveness.  Specifically, the skill of delegating work and tasks and the skill of self-reliance (also known as “going with your gut”) will be particularly inefficient in a DAO organization.

However, since the dominant forms of power in a DAO are referent and expert power sources, skills that lead to respect and trust (reference power) and domain expertise (expert power) will find increased importance.  These skills include:

• Good communication, particularly in online mediums

• Ability to organize thoughts and ideas into coherent stories and actionable steps

• Building consensus and goal-oriented teamwork

• Relationship building and collaboration

• Critical thinking, particularly towards new ideas

• Conflict management in an online setting

• Innovation and ideation

• Colleague motivation

Luckily, modern management theory has tended towards the above skill sets so much of that experience can be translated from the centralized world to the decentralized world.  However, as mentioned above, much of centralized leadership revolves around delegation and single-point decision making, so leaders in a centralized environment may still find it difficult to translate their skills in a decentralized environment.  The above skill sets also align well with leadership styles that will be most effective including:

• Democratic leadership - heavy emphasis on the ability for a group to reach consensus. conflict management and critical thinking

• Laissez-faire leadership - heavy emphasis on relationship building, collaboration and motivation

• Transformational leadership - heavy emphasis on organizing thoughts and ideas, innovation and communication

The arc of history has always bent towards increasing the population that has autonomy and self-determination.  DAOs are the next evolution of that arc and will be a significant change from previous ways humans organized.  As such, power dynamics will change and there will be a need for leadership styles to change.  The net result, though, is the inclusion of the entire world’s population into the decision-making process and a more equitable distribution of opportunity.  The individuals who figure this out quickest, and the DAOs the participate in, will be the real benefactors of the transition.

Thanks for reading!  If you’d like more opinion pieces and data-driven articles, subscribe below.  Happy reading,

Kyle

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