I didn’t find the stock idea I expected.

I was rewarded with 3 realisations instead.

1. Stocks need ‘communities’

2. Dividends might be better than buybacks

3. All roads lead to BlackRock, and maybe we should buy it.

Let me explain.

Two weeks ago I joined a London ideas dinner and was blown away by all the great ideas. We enjoyed great food, wine and war stories of undiscovered stocks from the past which had 5x’d. At the end of the dinner we were discussing what might be ‘the next thing’.

Which beaten up sector, or stock could be the next AST SpaceMobile?

In the end we came up with this former darling which has completely fallen from grace and is now massively hated.

Payment companies: Paypal, Fiserv, Remitly, Adyen, Global Payments, Western Union, Fidelity Information Services

So this week I spent some time looking at their results and presentations. In the end Global Payments (value play) and Remitly (still growing) looked pretty good. They will probably do well. Capital was at the dinner and is the real expert on them.

But I struggled to get excited.

I was thinking of that Buffet quote if you only had 10 stock investments you could make in your life, would this be one of them?

Not really.

It caused me to reflect on why I couldn’t get myself excited about them. Because the management teams were saying all the things you are supposed to say.

And here is what I came up with:

• The whole thing sounded ‘corporaty’, all about sales channels, routes to market, returning capital, and nothing really about innovation. Nothing interesting.

• No cool founder/leader. If the CEO is great, I’ll kind of invest in anything.

• No macro variable which was going to change everything, like interest rates for banks, or the oil price for energy stocks. The one possibility is that the upcoming inflation surge boosts the payment volume growth temporarily from 1-3% to 3-5%.

And playing 3D chess, I was worried the rest of the US retail army wasn’t going to care about this financial sub-sector either.

Which made me reflect on what things the US retail army cares about. And you realise attention is a scarce thing and you need a dedicated constituency of investors who still care about a sector even after it falls out of favour. Tech has its Silicon Valley groupies. Mining stocks have the Vancouver Mafia. The energy sector has Texas. Cryto has its hodlers. There is even a community of investors who like investing in shipping stocks. For international stocks in smaller markets the dedicated constituency is the domestic investor base.

But who cares about mid-cap payment companies fighting for their life in a market like the US when you have stocks like Nvidia? You realise that in a market with so many choices, and the best companies in the world, payment companies fall through the cracks.

And if the US retail trader isn’t going to care about struggling payments company, then who else will?

Maybe a smart ‘value investor’ at a mutual fund? But then you realise she is getting redeemed in favour of an index ETF and doesn’t have any money to invest.

Maybe a cunning PE fund will swoop in an take these undervalued gems private. Then you realise the PE funds own a whole portfolio of similar companies and rather than buy more, are struggling to get liquidity so they can return money to their investors.

And this is where I came to my three realisations.

Realisation number #1: Stocks need to build ‘communities’: The active management community in public markets is under a lot of pressure. Over the last ten years $3 trillion of funds has exited active managers to buy index ETF’s.

Which means companies can’t assume there is some smart analyst at a big fund co with lots of screens and tools who is going to do the work, figure out their company is a great opportunity and buy hundreds of millions of $’s of their stock for the long-term. They can’t trust the markets will automatically be efficient. What is replacing the active manager is a thriving retail trading community. But their attention span is fickle.

So there is this sad state of affairs where there is an attention span cliff. When stocks fall out of the narrow band of investor interest (high growth, innovation, high-vol) they quickly become worthless because there aren’t any active investors to save them. It’s why there is such a wide dispersion in valuations. The 25 year average P/E multiple spread between the 20th and 80th percentiles is 11.8x. Currently, it’s 15x. You are either interesting, or you are not.

Which means in this new normal companies have to build up their retail investor base. It means they have to go to smaller conferences, meet smaller investors, use social media, go on podcasts, etc to build a retail following. The crypto world has learned this.

Realisation number 2 is that when stocks become ‘no-growth value traps’ maybe they should pay out the cash as dividends instead of buying back shares.

If you are a dying star, that’s fine, but give us the cash so we can milk it and use the money to reinvest in something else, like Buffet did with Berkshire Hathaway. Take Paypal. They say they are going to return $6bn in share buybacks when the market cap is $45 billion. Which sounds great, but what if they are just shovelling more cash into the furnace? What if the stock price still goes lower despite all the buybacks because they are losing market share and payment volumes aren’t growing? If Paypal paid $6bn out as dividends at least we would have the cash.

The second benefit for a company from paying dividends is that it shows up on dividend screens. Lots of investors screen for stocks for dividend yields. And a company with a high enough dividend yield might even get picked up by a dividend ETF. There is a home for stocks with double digit dividend yields, but not for stocks which aren’t growing and have dividend yields of 1%

The market structure has changed.

What you realise is the market structure has changed. It used to be that financial advisors allocated money to the institutional active fund managers who had good access to the companies, were relatively long term and had a value bias. The analysts at the active fund managers would build their spreadsheets and identify which company in the sector had the best ROIC, and pick that one, even if it wasn’t the most liked. They would never buy a stock like Tesla for example. Meanwhile, the financial advisor would sit back and leave the stock picking and tough decisions to the active manager.

But with the rise of ETF’s for every possible sector, country, index and theme that structure has changed. The investment decision shifted back to the advisor. The advisor now became an asset allocator. And ETF’s are the lego blocks they use to build their client portfolios.

The financial advisor doesn’t care about some one-off aircraft part company in Wisconsin which is cheap. Or, a cheap payment company. The advisor cares about sector and country beta. They need to make sure they don’t miss the hot ‘themes’. They can’t be the advisor who missed ‘EM’ or ‘Korea’ or ‘Semis’. It’s why so many financial advisors read YWR.

This is how ETF’s changed the game. We went from stock picking to theme picking.

And it’s why the more I looked at the dying payment companies and the appreciated the challenges they would have in attracting investors, the more interested I became in BlackRock. Because the death of the payment company share prices is directly related to the success of BlackRock ETF’s.

If you aren’t big, aren’t liquid, aren’t growing, and aren’t in the ETF’s, and there isn’t some retail community which cares about you, you don’t exist.

Realisation #3: If BlackRock ETF’s are a Death Star taking over the market, shouldn’t we own the Death Star?

Is the big takeaway from analysing the payment companies, that we should be buying BlackRock?

You thought we were going to buy the small, edgy undervalued stock with 5x upside? Nope. Fake Left. Go Right.

We are buying the Death Star instead.

Bam!

Could BlackRock be on the verge of an earnings acceleration and a move to $1,700/share by 2030?

• Platform as a Service

• The NYCERS Insight

• More Legs to the ETF Story

• YWR EPS Estimates and Earnings Model

• Risks

Platform as a Service

The thing about great entrepreneurs like Larry Fink, and why you want to invest in them, is they never stop thinking about the next thing. They are always moving the goal posts. So while the rest of the asset manager community is finally getting around to launching their own ETF’s, Larry is turning BlackRock into a platform which they call 1BLK.

Read more

So I came up with 393. I hope that’s OK.

Oh, and I built a website for you with all the write ups.

But before I get to YDX let me explain the background, and how we got here.

In the beginning we created the global factor model dashboard on Retool so we could scan global estimate revisions for 10,000 stocks each month. It helps us find trends nobody is talking about.

This dashboard and the data became the basis for our monthly factor model trends analysis.

Then to help improve our entry points we created the estimate/price scatter on Tableau to find stocks where estimates were rising, but price hadn’t moved.

Then we created the QARV dashboard on Retool to scan for high quality stocks trading at relatively attractive valuations.

Then we added the automated AI Reports button to the Factor Model and QARV dashboards so if we saw something interesting with 1 click we could get a 5 page deep dive. These AI reports included an earnings call section where we used AI to analyse how the company earnings calls were evolving over the last 3 quarters. Not ‘summarise the call’, rather ‘tell me the top concerns in the Q&A and if management sentiment is improving’ over 3 quarters.

Next we moved on from quantitative screening to see if we could use AI to build qualitative screening frameworks. This is the fascinating new possibility with AI.

For example, we loved the book ‘Blue Ocean Strategy’ and wondered, can we use AI to analyse and score 2,000 stocks for Blue Ocean shifts? This inspired us to create the YWR Blue Ocean Dashboard.

Then we read ‘Own the Rule Breakers’ by David Gardner, which we also loved, and used AI to screen qualitatively for his top 6 attributes. We created the YWR Rule Breaker Dashboard so we could screen quantitatively for ‘Top Doggedness’ or ‘Valuation Skepticism’. Remember David loved when investors didn’t want to buy high growth, founder led companies in growing industries because they were ‘expensive’. So we used AI to screen for this skepticism. We could also use AI to screen for ‘corporate culture’ if we wanted. This is all new. Things we could never do with quantitive screening.

Then came ClawdBot and we wanted one too. We wanted our own custom investment AI connected to as many financial tools, API keys and capabilities as possible. So we created ‘Stevie’, our own AI investment analyst on Telegram with access to the YWR data, financial data, options data, its own internal read/write database, Coinbase Wallet, and its own Google Workspace account.

We also gave Stevie tasks to do overnight, like listen to earnings conference calls, so he had interesting snippets for us in the morning.

So what’s next? What’s the new, new?

YDX.

Let me explain.

What problem are we trying to solve?

Our challenge in asset management is always how to increase our perspective. We may have found a great idea, but is it the best idea? How does it compare to all the other stocks out there? How do we make sure we are not late to the trends?

That’s the challenge of the active manager.

Find good ideas + make sure they are better than the 1,999 other ideas out there. It’s 3D chess.

But how do you do that unless you cover every stock in the market?

Maybe AI can help. What if we can automate AI to analyse hundreds of stocks on its own?

What if we could get the AI to build financial models, listen to conference calls, create earnings forecasts and write an investment case on its own? And if this was possible, could we do it across hundreds of stocks?

Then if we had a research on hundreds of stocks in our SQL database could we query the investment cases and narratives to find the best ideas, or ‘see’ the market?

That’s what we created with YDX and it’s how we came up with the LNG Terminals idea in Qatar changes Everything.

The 5 step process

But first a learning from Stevie. Stevie has API access to Google Sheets, so YWR readers would often ask Stevie to build an earnings model on a stock idea. And what I would notice is these financial models were not very good. Stevie was trying to do too much at once. Stevie was trying to research the company, build a model and come up with forecasts all at once in under 10 minutes.

For YDX I decided to break the research up into automated steps.

Step 1: Pull 5 years of historical financial data from the SEC (for US stocks). Just pull the data. Nothing else.

Step 2. Build a historical financial model (balance sheet, income statement and cash flows) based on a template I gave it. Make sure numbers like Net Income and Total Assets tie to the underlying line items. Just arrange the data into a financial model.

Step 3. Write a research report on the company. Analyse the products and services. Listen to the conference call. Just write a report. Be objective. Don't tell me whether you like the company or not, or whether I should buy it. Save it in the SQL database.

Step 4. Using the financial history (Step 2) and the research report (Step 3), build 3 years of financial forecasts. Save it in the SQL database.

Step 5. Using your financial forecasts (Step 4) and research report (Step 3) write an investment case. Save it in the SQL database.

Work through this 5 step pipeline at night.

The result is a research pipeline running at night as Stevie chews through 584 stocks. Currently, 302 have completed Stage 5 (the investment case). We have also already analysed 91 US energy stocks as part of building out our US energy view. So Stevie has analysed and modelled 393 stocks so far.

The output from each step is stored in our cloud based SQL database (I use Neon).

YDX Website

Finally, I built the YDX website to access Stevie’s research. You can read Stevie’s latest reports in Views or search by stock.

It started as a theoretical exercise, but I have to say it has turned out really well. I enjoy Stevie’s write ups. They’re pretty good.

Remember this is an AI research experiment. Not financial advice!!

You’ll see I built the YDX architecture around the possibility to have multiple authors. Because why not create different AI’s with different personas to analyse stocks? Same as the sell side. Stevie’s reports are all quite optimistic. Maybe we need to create a cynic and have it write 584 reports too. So we have the Bull and the Bear on every stock.

Caution: I know you know this, but treat Stevie and his write ups as overly bullish analysis which is slightly prone to exaggeration to get your attention. Use the mosaic theory. Trust but verify and look at both sides. And they are not financial advice!

Narrative Visualisation

At the end of the day we are investing in stories.

And if we have a database of stories we can start to do some interesting AI analysis at the index or sector level. For example, we can vectorise these investment cases then run a Principal Component Analysis (PCA) to display them visually.

I built an example of this for Stevie’s research on the US Energy Sector with each stock hot linked to the write up.

The PCA visually shows where the investment cases are ‘basically the same thing’. On the chart below you see Chevron and Exxon are very close to each other, which makes sense. Then towards the top you see a cluster of triangles which are all refinery stocks. It tells you this cluster has a differentiated investment case from everything else, which is true. Another example, is that the star shaped cluster (oilfield services) is closer in similarity to Chevron than it is to Valero. Which makes sense.

Potential Next Steps?

Should we schedule Stevie to update these investment cases every 3 months, or after the next earnings report? Stevie would be like a sell side analysis updating his view after the results.

Should we create a Google Sheet to compare where Stevie’s EPS estimates differ from consensus?

Should we use this process to build bottom up views on indices and ETF’s?

The importance of being curious.

I wanted to share one final thought from working with AI on the US energy stocks for Qatar changes Everything, because it’s almost a contradiction.

I built the whole energy universe coverage with AI, but to some extent it didn’t help much. At the end I had 91 bull cases. They all seemed potentially interesting. Where do you start? Oil versus gas? Producers versus refiners? Water extraction? Pipelines?

In the end I use AI to explore a key seed idea I had.

The seed idea, which I knew was differentiated, which led me to focus on the LNG terminals as the key investment idea, originated from watching the Total Investor Day back in September 2025 and remembering the importance Patrick Pouyanne put on the new supply coming from the Qatar LNG projects. I remember how he said it and that he mentioned it again later in the Q&A. It stuck in my head. It was key to Patrick’s whole view on global LNG. Then when I saw the damage to the Qatar LNG from Iran, the idea clicked… this was going to change everything about global LNG.

Then I used the AI research universe to search for the best way to play it. The process worked well, because Stevie had already done background research on the entire sector.

So we can have all this AI analysis and coverage, but it helps to have a creative, original idea. The human still needs to connect the dots.

And the contradiction I have is that the connection came from sitting for an hour, potentially ‘wasting my time’ listening to Patrick Pouyanne. I had no idea if he would say anything interesting, but I take time to listen to Patrick because I like how he talks, and I alway often get good insights from him. Jensen Huang, Elon Musk, Jeffrey Sprecher, Gary Nagle, Andrea Orcel are all the same. It reminds me of that advice from when I was at my liberal arts college where we were encouraged to pick courses based on the teacher not the topic.

What I’m trying to say is I think we still need to go down rabbit holes, and ‘waste time’. We need to be curious. In fact that’s our key job. And we need to look out for great teachers and tune into that. Because that’s where the gold is.

AI can help with exploring rabbit holes and maybe make the process quicker, but you still have to take time to read things and make those intuitive, creative connections which AI doesn’t because our brains work differently (Primal Intelligence)

But then when we do have an insight AI can help us run with it quicker.

And if you are building out AI research for your firm and want to chat about it contact me at erik@ywr.world.

Have a good rest of the week!

Sorry, again for the 393 stock ideas.

Erik

• I would extend NVDA/TSMC/ASML call to memory (SK Hynix, Samsung).

• what is saying about the internal fabric of how companies manage their AI’s, the prompts ,the tasks, the datasets, the ‘Master Prompt’, is 100% right on the money and firms are only starting to realise this.

Now over to Pancras.

Read more

We learn how Andrew organises a team of AI agents to build this complex game while he takes care of his new born baby and his day job in cloud dev ops.

We also learn from Andrew the key to thrive, not just survive in the AI world ahead.

Chain Street (Part 1) - The Opportunity
Chain Street (Part 2) - The Challenges

What are we even doing here?

I am writing Part 3 after a massive correction in crypto prices. Phone ringing off the hook. Fundamentals up and prices down. Fear, uncertainty and doubt all over my social media timeline.

When I need to remind myself of why I do what I do, I always come back to this Erik Voorhees Video. If you are confused about what crypto is, what is being built and why it matters so much, take 30 mins to watch this speech.

The History of Open vs Closed

The battle between open and closed systems is ancient. Throughout history, profiteers have attempted to curate and capture the future, while open design advocates have sought to standardise and remove friction for all. The pattern repeats across centuries, with outcomes favouring openness.

One of the earliest examples is the Silk Road, a legendary network connecting Europe, the Middle East, and Asia. Owned by no one, it routed around the Roman Empire’s controlled network of roads and enabled an explosion of trade between distant lands. Roman roads existed to serve Rome’s military deployment and tax-collection purposes and were constrained by centralised control. The Silk Road, on the other hand, served everyone’s purposes. This is the fundamental beauty of open networks, by removing the frictions and hidden extraction of an owner, they make our world smaller and our economies bigger.

The pattern continued into the industrial age. Prior to 1886, there was no agreed upon standard gauge (the distance between the two rails of track), so where one track ended and another began, there was enormous friction. For the private railway companies, this was a feature, not a bug. If you controlled the only railway serving a region, forcing cargo transfers at your borders meant you could charge monopoly rates for that service. Furthermore, the incompatibility protected you from competition as rivals couldn’t simply extend their tracks into your territory.

But in 1886, North American railways finally agreed on a standard gauge. Standardisation transformed a collection of isolated walled gardens into a continental transportation system that unleashed economic value that benefited everyone, including the railway companies.

The shipping container revolution of the 1960s followed a similar path. Before Malcolm McLean’s stroke of genius, global shipping was a chaotic mess of different packaging, handling procedures and loading methods. Different ports, ships and cargo types were incompatible. McLean introduced a standard shipping interface, the universal shipping container. I was blown away when learning about this small but profound example of standardisation’s power. After its introduction, shipping costs collapsed by over 90%, and it directly enabled the global supply chains we have today.

This pattern has only accelerated with the rise of computers. In the 1980’s, personal computers were much like railways before standardisation, every PC manufacturer had their own closed design. Apple made computers that only ran Apple software. Commodore made computers that only ran Commodore’s software. Buying these PC’s locked you into the manufacturer’s ecosystem forever.

Then in 1981, IBM did something unexpected, they published the technical specifications of their computers and used off-the-shelf parts. This meant that companies could build clone PCs that were “IBM compatible”. This gave rise to companies like Compaq, Dell and dozens of others who made hardware that could all run the same software. The combination of Microsoft Windows (software) and Intel processors (hardware) became known as Wintel.

This ignited a Cambrian explosion in software. Software developers could now write one program and sell it to everyone and anyone with a Wintel PC, whether they bought it from IBM, Compaq, Dell or a no name brand. The market for software all of a sudden became enormous. More software made PCs more useful, which led more people to buy them, which in turn led more developers to write software. This virtuous cycle fed on itself, crushing closed systems.

Better technology didn’t matter in the case of PC’s. Apple’s Macintosh was easier to use and more elegant, but Apple’s stranglehold on its ecosystem meant it couldn’t grow as fast as the open Wintel ecosystem. By the mid-1990s, Wintel PCs had over 90% market share, while Mac was relegated to designers and schools.

Linux pushed openness even further. While Wintel had opened up hardware, Windows itself remained proprietary, closed-source, paid-licensing, and controlled by Microsoft. Linux went all the way by being completely open source, free to use, free to modify, and free to distribute. Despite being “free” in every sense, Linux now powers the majority of servers, all Android phones, and increasingly the critical infrastructure of global commerce. Microsoft’s Windows dominated desktops but lost the war for servers, mobile devices, and embedded systems. Even with Microsoft’s massive resources, a swarm of thousands of developers improving a shared codebase eventually won out.

The last battle between open and closed belongs to the internet. Perhaps the most powerful validation of open systems in human history. To put the open internet’s success into perspective, the Global Web Index (GWI) estimates that the average person will spend almost a third of their life online.

The internet’s only competitor is that of sleep.

The internet is a collection of open protocols such as TCP/IP, HTTP and SMTP. Early alternatives to the open standard, like CompuServe, AOL, and France’s Minitel, believed they could control access and curate experiences. But the internet, like today’s blockchain ecosystems, offered chaos and freedom. In less than three decades, the open internet has grown from a research project into a substrate of modern civilisation.

Why Does Open Win?

The victory of open systems isn’t random or ideological. Like gravity, openness wins because of fundamental advantages that compound over time.

Network Effects and Permissionless Innovation

When a system is open, anyone can build on it without asking for permission. Openness unleashes human ingenuity, manifesting as innovation on a scale no centralised organisation can replicate. The internet has millions of developers building applications, services and businesses without needing approval from any central authority. Compare this to AOL and CompuServe, where new features required corporate planning, approvals and implementation.

How can an organised, closed system attempting to curate the future compete, while its open competitor innovates at exponential rates? Over time, the linear progression of the closed and the exponential progression of the open creates a gap that can never be closed.

Network effects amplify this, we see this in the victory of Linux and the open internet. Each participant in an open system increases its value for all existing participants. Each new Linux developer made Linux more antifragile. Each new website made the internet more valuable. Closed systems, on the other hand, are so focused on capturing enough value to justify their control that it eventually leads to limits on participation. Herein lies the key difference between the two, closed optimises for extraction, while open optimises for growth.

It was never a fair fight to begin with.

Efficiency Wins Short Term, Resilience Wins Long Term.

Closed systems often appear more efficient and convenient initially. They are optimised, coordinated and free from the chaos of competing standards. The Roman roads at the time were engineering marvels, far superior to the rough tracks of the Silk Road. But that efficiency came with fragility. When Rome fell, the network of roads deteriorated. The Silk Road, on the other hand, endured for centuries because it lacked a central dependency, it belonged to everyone and no one.

In modern technology, this principle of resilience beating out efficiency is demonstrated repeatedly. The smooth user experience of Microsoft’s tightly integrated Windows also meant that a security flaw in a single component could compromise the entire system. On the other hand, Linux’s modular antifragile design meant that problems could be isolated and fixed without the risk of cascading failures.

Resilience in open systems extends beyond mere technical robustness, open systems are politically resilient because they don’t depend on a single entity’s survival or good behaviour. Governments can’t easily censor truly open systems. Companies can’t easily co-opt them. Special interests can’t easily capture them. The right feature to optimise for is resilience, efficiency comes later. It’s this emphasis on resilience that makes open systems grow stronger in the face of stressors.

Sovereignty and Self Determination

We are in such an interesting moment in history. Born too late to explore and conquer new lands and too early to explore space, I guess I will have to settle for playing a small role fixing the financial system.

Humans have an innate desire for sovereignty and self-determination, and in this desire is the deepest reason open systems tend to prevail. People and organisations ultimately prefer control over convenience. When the convenience gap is enormous, they will tolerate closed systems, but as open alternatives mature, sovereignty considerations dominate. It’s why open-source software has captured vast territory despite often being initially inferior to proprietary alternatives.

Organisations particularly value this sovereignty. Minimising external dependencies is just good business. No company wants its critical infrastructure dependent on a competitor’s goodwill or priorities. No country wants its digital infrastructure controlled by foreign corporations.

Open systems offer an exit option, if the current stewards fail, the community can fork and continue. Closed systems offer no such guarantee.

Why Decentralised Blockchains will Win.

Read more

Investment research and the business of providing financial information are in the midst of AI driven disruption (The End of the Seat).

But where are we going with this?

Do 1,000 terminals get replaced with an API used by 1 Claude Agent?

How does the industry price for this?

Where is the Blue Ocean?

Pancras has a solution for the industry.

But it requires eating their own children.

Another PERS View guest post from .

• The Apple Paradox: A Study in Self-Preservation

• Embedded Infrastructure and the Biological Tax

• The Disruptor Coalition and the Headless Feed

• Claude Cowork and the Decoupling of Intelligence

• The Microsoft Precedent: Eating Your Own Children

• The Bloomberg Gambit: YouTube, Amazon, or Google?

• The Migration to Cognitive Arbitrage

• Solving the Innovator’s Dilemma

One sometimes wonders whether the grandest structural shifts in markets truly arrive with the fanfare of a technological breakthrough, or if they are, in fact, the quiet consequence of a collective refusal. It is perhaps the refusal of an incumbent to dismantle their own fortress that creates the very vacuum a disruptor requires to breathe. We are currently witnessing what is an instructive case study of the digital age: the moment Apple, the master of disruption, allowed itself to be disrupted because it fell in love with the unit economics of a dying paradigm. It is my contention that the financial information industry is now perched upon a similar precipice, caught in the classic tension between the high-margin comforts of the annual seat fee and the technical inevitability of the agentic workforce. To innovate is to risk one’s legacy; yet, the question lingers: is the failure to innovate anything other than a guarantee of obsolescence?

We appear to be moving from an era defined by the scarcity of the golden copy to one defined by the abundance of cognitive arbitrage, a transition that demands a fundamental re-evaluation of what constitutes a data moat. In an age where the cost of processing information is collapsing towards zero, the value of the “screen” as a mediating interface is being interrogated by a new species of digital worker. This note serves as a roadmap through the shifting architecture of financial intelligence. I shall begin by dissecting the historical collapse of the iTunes hegemony—not as a simple failure of vision, but as a crisis of incentive—to understand how the cannibalisation trap functions within a dominant ecosystem. From this post-mortem, I will pivot to the current infrastructure coalition, questioning whether the embedded defences of Bloomberg, LSEG, and S&P Visible Alpha represent a sustainable moat or merely a biological tax that is increasingly at odds with the returns of enterprise AI.

I will then examine the disruptor coalition—the FactSet-Anthropic alliance—to interrogate whether the Model Context Protocol (MCP) and consumption-based pricing are truly the Spotify solution for data, or perhaps a different kind of commodity trap. The arrival of Claude Cowork in January 2026 provides the catalyst for what I term seat destruction, a phenomenon that forces us to consider the digital worker not as a tool, but as a colleague capable of broad-scale integration.

Finally, I will construct the argument for the Bloomberg Gambit, drawing a parallel to Microsoft’s successful cloud pivot to illustrate how a legacy giant might survive by eating its own children. I believe Bloomberg possesses a unique strategic window to transcend its role as a data vendor and become the unassailable, sovereign platform for financial data exchange—a Google-like reference point that integrates the high-end research of the world’s most acute analysts with the independent expertise of Substack and the Superme.ai model. This journey leads us from a world of information arbitrage to the eventual resolution of the dilemma through the creation of a sovereign index of settled, licensed cognition.

The Apple Paradox: A Study in Self-Preservation

To understand the threat to those in the infrastructure coalition like Bloomberg, LSEG, or S&P, one must first deconstruct the collapse of the iTunes hegemony. By 2010, Apple possessed absolute dominance; the iTunes Store commanded 70% of the digital music market, backed by the hardware lock-in of the iPod and a mountain of consumer credit cards. Apple was the apex predator of the music industry, having successfully transitioned the world from physical CDs to digital downloads. They held the embedded infrastructure—the hardware, the payment rails, and the customer relationships. Yet, they lost the future of music to a Swedish startup with a fraction of their capital and zero hardware presence. Why did the master of the ecosystem fail to see the shift in the very ground they stood upon?

Perhaps the hesitation was not a failure of vision, but an inevitable consequence of an incentive structure that rewarded the preservation of high-margin hardware at the expense of an emerging, low-margin utility. Apple’s model was built on the transactional unit: the USD 0.99 download. This high-margin revenue stream was more than just a line item; it was the fuel for a hardware ecosystem where the iPod functioned as a high-margin dongle for the iTunes Store. As Steve Jobs famously argued in 2003, the subscription model of buying music was bankrupt because consumers wanted to own, not rent. He believed that owning a song represented a permanent emotional connection, while a subscription was a fleeting utility.

A sceptic might suggest: was this a genuine philosophical conviction, or was it motivated reasoning designed to protect the unit economics of the past? To launch a streaming service would have meant actively cannibalising a lucrative download business—trading a customer who spent USD 200 a year on individual tracks for one who spent USD 120 a year on all-you-can-eat access. Furthermore, one must consider the internal pressures of record label negotiations. Apple had spent years convincing labels that the USD 0.99 track was the only way to save them from piracy. To pivot to streaming meant telling those same labels that their price floor was about to vanish. It meant telling the board that the hardware margins of the iPod, which relied on proprietary library lock-in, were no longer the primary moat. Apple chose the comfort of the unit; Spotify chose the sovereignty of the index.

Apple’s error, if we are to call it that, was to mistake the medium for the message. They believed they were in the business of selling digital files, when they were actually in the business of managing attention. By insisting on the download, Apple forced the user to act as a librarian—managing storage, syncing devices, and making discrete purchasing decisions for every artistic unit. This created a high-friction experience that was manageable only as long as no alternative existed. Apple missed the fundamental shift in consumer psychology: the migration from ownership to access. Spotify viewed music as a utility to be streamed. Whilst Jobs believed that playlists and libraries were the product, he failed to see that in a world of digital abundance, the value shifts from the item itself to the index of items. The discovery of music—the algorithm that finds your next favourite song—became more valuable than the file containing the song itself.

Spotify became the iPod killer—not because it was a better piece of hardware, but because it made the hardware irrelevant. It was a low-friction technological wedge that turned the iPod into a redundant box of stored files. The iPod killer wasn’t a device; it was a protocol. It was the shift from local storage to cloud-based stream. While Apple guarded its walled garden, Spotify built a bridge to the entire world, commoditising the very thing Apple sought to preciousise. By the time Apple capitulated and launched Apple Music in 2015, they were forced into a defensive fast follower position, burdened by the legacy UI of a previous era. They had preserved the download for five years too long, and in doing so, they lost the platform. They were no longer the ones defining the experience; they were merely trying to match it. It is a choice between guarding the unit of the past and owning the platform of the future; a choice that the current infrastructure coalition is now being forced to make.

The Embedded Infrastructure and the Biological Tax

We now see an identical architecture of displacement forming in the financial data markets. For forty years, the industry has been ruled by the infrastructure coalition. Their bedrock is the annual seat fee, a pricing architecture predicated on a 1:1 ratio between a human operator and a screen. This model is the financial equivalent of the USD 0.99 download. It assumes that value is inextricably tied to human cognitive bandwidth—the ability of a trader to navigate an interface and process information via the optic nerve. It is a world of information arbitrage, where the vendor’s role is to deliver the golden copy of data to a human biological brain. But is the human brain still the primary consumer?

The biological tax is the premium paid for the friction of human mediation. Like the iTunes download, the annual seat fee is a unit of scarcity linked to a biological constraint. LSEG (Refinitiv) and niche providers like S&P Visible Alpha are currently attempting to contain the agentic disruption through walled gardens and non-display restrictions. These are not merely technical features; they are legal fences designed to protect the unit economics of the human analyst. LSEG’s strategy, for instance, requires a user to hold both a Workspace licence and a Microsoft Copilot licence to access AI tools. As I wrote previously in The Agentic Dilemma, this is the equivalent of Apple saying: “You can stream music, but only if you pay for the human licence first.”

One wonders whether non-display restrictions are a defensible moat or a regulatory target in waiting. These terms essentially prohibit an algorithm from seeing or reasoning with the data unless a human eye is present. It is a literal tax on machine intelligence. In an era where McKinsey suggests that AI could reduce an asset manager’s cost base by 25% to 40%, these restrictions act as a drag on the very productivity gains the industry requires. The incumbent’s trap is the belief that their embedded infrastructure—the thousands of terminals installed in bank basements—is an unassailable advantage. Apple thought the same of the iPod. But embedded infrastructure is a weight as much as a weapon; it creates a sentimental attachment to the hardware interface at the exact moment the market is moving to a headless protocol.

These platform players are betting that the terminal is a mandatory heads-up display for human intelligence. But as Azeem Azhar argues, any model that relies on biological limits is fundamentally fragile in an exponential age. If the biological seat is the past, then the unit of value must shift from the screen to the stream. Yet, the question lingers: can the incumbents actually afford to let go? If the vast majority of your revenue depends on the biological tax, can you truly embrace a headless future without inducing a corporate heart attack? To dismantle the fortress while the siege is underway requires a degree of strategic courage that is rarely found in public company boardrooms. It is a transition already being engineered by those with no legacy to protect, who view the terminal not as a fortress, but as a bottleneck.

There is one listed financial information provider making the pivot and starting to get it right.

The Disruptor Coalition and the Headless Feed

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Jason addresses head on the big elephants in the room around hyper competition, token governance, token economics and privacy.

This is part two of a three part series on Chain Street.

I encourage you to start with Part 1 which you can find here.

• The Shift from Counterparty Risk to Technical Risk

• Why the Hyper Competition of Crypto is Good

• Token Holders vs Equity Holders

• ZK Privacy and why we need Blockchain Identity

The Shift from Counterparty Risk to Technical Risk

​Legacy finance relies heavily on trusted intermediaries such as banks, brokers, custodians, and clearinghouses to facilitate transactions and enforce agreements. This introduces counterparty risk to everything we do with our money, the danger that one party defaults on its obligations, whether due to insolvency, fraud or mismanagement. Enforcement depends on social processes like legal contracts, regulatory oversight, and human-led dispute resolution, which can be slow, costly, and prone to bias or corruption.

​Blockchain technology, which we described as Trustware in Chain Street Part 1, fundamentally reorients this dynamic by replacing human intermediaries and social enforcement mechanisms with cryptography, economic incentives and consensus protocols. DeFi protocols like lending platforms (e.g. Aave) or decentralised exchanges (e.g. Uniswap), let users interact directly with smart contracts (self-executing code on blockchains like Ethereum. Trust in these interactions is minimised in that no single entity holds custody, and outcomes are determined deterministically through game-theoretic incentives (e.g. staking penalties for misbehaviour), and network consensus (e.g. proof of stake validators agreeing on transaction validity and guaranteeing finality). These new rails eliminate counterparty risk, as it allows us to transact without the need to trust an intermediary and the social processes holding that intermediary to account.

​Blockchains essentially transform counterparty risk into technical risk, which is not risk-free. Once deployed, smart contracts are immutable, meaning bugs and vulnerabilities can lead to irreversible losses. This is another important distinction. In a blockchain world, we don’t have recourse when something goes wrong, you can’t call up Vitalik Buterin and say, “Hey man, I messed up with that last payment and sent it to the wrong person. Can you roll it back for me?”

While audits, formal verification and bug bounties mitigate some of these issues, they cannot eliminate them entirely. Code afterall, is written by humans and unforeseen risks exist. This is an obvious barrier to adoption, as legacy finance relies on safeguards such as recoverable human processes and recourse in the event that something doesn’t go as planned.

Persistent Exploits with Declining Relative Impact

Technical risks remain a persistent threat in crypto. According to Chainanalysis and TRM Labs, in 2025 alone, total losses from hacks, exploits, and theft ranged from $2.7 billion to $3.4 billion. Interestingly, the majority of losses were driven by centralised exchange breaches ($1.5 billion Bybit hack) and social engineering rather than pure DeFi smart contract vulnerabilities. Even as Total Value Locked (TVL) has rebounded significantly, DeFi-specific losses were notably suppressed compared to prior cycles.

​As of January 2026, global DeFi TVL stands at around $100-130 billion. Exploits resulting from smart contract bugs accounted for a smaller share of losses in 2025, while most exploits occurred via offchain vectors such as private key compromises and phishing attacks. Smart contracts are evolving and maturing to risks. In earlier years saw exploits scale with TVL growth, but improved tooling, better formal verification, timelocks and multi-signature wallets have broken this relationship.

As a percentage of TVL, exploit losses have trended downward over time. While absolute figures remain high due to larger ecosystems, relative risk (losses/TVL) is lower than in DeFi’s early days.

One of the beautiful features of blockchains open source nature is that many eyes can tame complexity, and risks are constantly being identified and resolved. However, it’s worth acknowledging that, for some, technical risks remain a deal-breaker. Whether it’s the bearer nature of cryptoassets or outsourcing the management of those assets to smart contracts, these risks are scary and require vigilance.

This gives us two paths forward: we either throw our toys out of the cot at an emerging technology with enormous potential because there are risks, or we keep working toward making wallets, custody and DeFi safer. As an optimist, I believe we have the human intelligence and ingenuity to lower technical risks to parity with counterparty risks, and eventually reduce them to the point where interacting with a counterparty no longer makes sense.

Why the Hyper Competition of Crypto is Good.

Banks, payment networks, and custodians operate closed, permissioned systems protected by regulation, licensing requirements, and decades-old trusted relationships. A startup cannot simply “plug in” to Fedwire; it needs approvals, capital reserves, compliance infrastructure, and political connections that take years (if ever) to secure. Gatekeeping and high barriers to entry are a consequence of trust maximised legacy finance. For users, switching costs are punishing. Changing banks or brokers means time and money spent opening new accounts, setting up direct debits, credit history checks and KYC paperwork. The result is clients locked into entrenched monopolies or oligopolies that extract rents far above what would be possible in a more competitive environment. These problems are amplified in the developing world.

Crypto turns this model upside down. Core DeFi protocols are open source, composable and permissionless. Anyone can fork Uniswap or Aave in an afternoon and offer the same functionality with a few tweaks. Users face near-zero friction (assuming they stay on one chain), a wallet connect, a couple of clicks, and they can be routed to the best price via a decentralised exchange aggregator. This is hypercompetition, far more intense than anything in traditional software, let alone finance.

For users, the outcome is unambiguously superior. Capital can flow to the most efficient venue in real time, fees compress quickly, and low barriers to entry spur constant innovation. New capabilities pop up regularly because no one needs permission to build on Ethereum. This mirrors the early internet’s triumph over corporate intranets as open protocols eat closed gardens because developers and liquidity naturally gravitate to where friction is lowest.

Investors, however, face a challenging environment. Moats are shallow and temporary. Clever token incentive programs can cause liquidity to migrate from venue to venue. Valuation remains difficult and feels speculative because sustainable technical competitive advantages are rare in a world where code can be copied, and users are mercenary.

Surprisingly, even in the face of hyper competition, winner-take-most dynamics still emerge. Uniswap, for example, has been forked thousands of times (Sushiswap, Pancakeswap, Quickswap, Aerodrome) but still commands 40%+ of Ethereum DEX volumes.

​

The same is true for Aave’s core lending logic, which has been replicated and tinkered with endlessly, yet it continues to command 50%+ of active loans.

It seems evident that network effects can take hold in DeFi, especially around liquidity and integrations. The best DeFi protocols quickly become infrastructure that developers build on top of rather than replace.

Token Holders vs Equity Holders

​The reality is that tokens are new, unique assets we have never seen before. Tokens can encapsulate multiple functionalities (Service provision, Governance, Value distribution, Membership, etc). I discussed this in the Art of Crypto Economics. Our friends at Just Cryptoeconomics have a brilliant dashboard that displays these functionalities for over 50 cryptoassets. This innovation is simply incompatible with existing securities laws. Ideally, 10 years ago, regulators and industry participants would have sat down together and had a conversation to lay out the rules of the road, this isn’t how things played out.

The situation reached a climax during the Gensler era at the SEC (2021-2025). Gensler led an openly hostile regulatory environment for US-based crypto projects, forcing a stark separation between equity holders (typically in entities such as Uniswap Labs or Aave Labs) and token holders. The fear of tokens being classified as securities led teams to structure protocols in such a way that minimal value generated by the protocol would accrue to tokenholders, while equity captured upside from valuable IP, siphoning of fees and side hustles. Bad incentives bred bad outcomes.

Venture funds and Founders of DeFi protocols reaped the benefits of these poor incentives through salaries, advisory contracts and various forms of offchain revenues. Leaving many DeFi tokens as speculative bets without claims on cashflows. These claims don’t need to be legal in nature, they can be programmatic (either via a dividend or buyback) or captured and allocated via governance of the protocol, often a decentralised autonomous organisation (DAO).

Addressing this requires regulatory clarity for the crypto industry and its builders. The CLARITY Act will be a good first step in cleaning up token holder rights. Companies like Blockworks are also doing great work with their minimum disclosures framework and investor relations platform, something I think is integral for crypto to step into adulthood. There is no reason why DeFi protocols should not be able to follow similar value accrual methodologies as companies to maximise token holder value. They provide a service, charge a fee for that service, build up residual interest, and have governance structures to decide how that residual value is allocated. But incentives are key, and the separation between development labs and the DeFi protocol itself needs to be more clearly defined and aligned.

Why the token crash is at an inflection point.

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We’ll go over:

• how AI workflows look different than Microsoft 365

• the new concept of analysing stocks based on frameworks

• importance of SQL databases

• using multiple AI models together

• what AI work looks like

• Amazon’s Bedrock platform

Hopefully this …

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One sometimes forgets that the grandest structural shifts in capital markets rarely arrive with the fanfare of a crashing bell or a sudden regulatory decree. They arrive, instead, in the quiet migration of value from one substrate to another. For the better part of the last forty years, the operational logic of global finance has been defined by the paradigm of Information Arbitrage—the ability of market participants to acquire, transmit, and act upon market data marginally faster or more comprehensively than their competitors. In this era, the dominant strategy for the providers of financial intelligence was the establishment of the “Golden Copy” of truth, anchoring value in closed, secure, and highly networked hardware-software ecosystems. The business model was elegant in its simplicity, ruthless in its efficacy, and grounded in a biological constraint: a per-user licensing fee that inextricably linked human capital to data access.

However, we now stand at the precipice of a transformation that renders this previous regime quaint, if not obsolete. We are witnessing the transition from Information Arbitrage to Cognitive Arbitrage. In this emerging epoch, the scarcity is no longer the data itself, nor the speed of its transmission—commodities that have been arbitraged into abundance by fibre optics and microwave towers. The new scarcity is synthesis. Value is no longer generated solely by possessing the data first, but by the ability to metabolise vast troves of unstructured information—regulatory filings, earnings call transcripts, geopolitical noise, and macroeconomic sentiment—into actionable insights at a scale and velocity that defies biological limits.

This shift is driven by the deployment of autonomous Agentic AI—software entities capable not merely of answering questions, but of planning, reasoning, using tools, and executing complex workflows without continuous human intervention. This presents a fundamental strategic tension—the Agentic Dilemma—for financial data providers. They must navigate a critical, existential inquiry: to what extent should they allow clients to run their own autonomous agents through proprietary data sets?

This analysis is the natural continuation of the inquiry begun in my previous notes. In Capital in the Age of Enterprise AI, I argued that the bottleneck for adoption was not technological but institutional, contrasting the sclerosis of the NHS with the compelled transformation at Norges Bank. In AI Sovereignty, I warned that these systems act not as neutral tools but as “Executive Centres,” quietly reordering institutional reasoning. And in The Marlboro Liability, I outlined the unbooked legal risk that underpins the entire sector.

This essay weaves these threads into a single strategic fabric. I will begin by deconstructing the economic physics of the “Agentic Shift” and the threat of “Seat Destruction.” We will then examine the strategic debate of “Allow vs. Disallow,” weighing the risks of commoditisation against the perils of irrelevance. I will explore the emerging architecture of the “Walled Garden” as a defensive moat, drawing on the strategies of institutions like Bridgewater and Norges Bank. I will then turn to the governance of these new minds via the “Master Prompt,” before confronting the systemic legal risk of the “Marlboro Liability.” Finally, I will propose the “Spotify Solution” not just as a data licensing model, but as a gateway to a new form of “Trusted Advisor Network”—exemplified by concepts like Superme.ai—where the true alpha of the future resides.

The Death of Information Arbitrage

To fully appreciate the gravity of the current moment, one must first deconstruct the economic physics of the traditional financial workspace and contrast it with the emerging economics of the agentic workforce. The commercial bedrock of the financial data industry has long been the “per-seat subscription,” a pricing architecture predicated on a strict 1:1 ratio between a human operator and a screen. The glass terminal, in this view, is a “heads-up display” for a human trader or analyst. The value proposition is inextricably tied to the human’s ability to navigate the interface, consume information via the optic nerve, process it within the biological brain, and execute a decision. This model assumes, quite logically for the 20th century, that the rate of information consumption is capped by human cognitive bandwidth.

The emergence of autonomous agents introduces a “multiplier effect” that threatens to decouple revenue from utility. An AI agent is not merely a faster user; it is a hyper-efficient, non-biological worker capable of performing the information gathering, synthesis, and preliminary analysis work of ten, twenty, or even a hundred junior analysts. If a hedge fund can utilise a single data feed to power an internal AI agent that serves synthesized insights to fifty portfolio managers, the fundamental mathematics of the seat license disintegrates.

Consider the workflow of an earnings season. Traditionally, a large asset manager might employ a phalanx of junior analysts, each with a license, to listen to calls, read transcripts, and write summary notes. In an agentic world, a single “Earnings Agent” can ingest the audio and text feeds, process thousands of companies simultaneously, and distribute structured, context-aware summaries to the entire firm. If the data provider allows this agent to run on a standard desktop license, they effectively enable a form of arbitrage where one license serves the entire organisation, leading to massive revenue cannibalisation.

The threat is not just theoretical; it is already manifesting in the phenomenon of “Seat Destruction.” As I detailed in Capital in the Age of Enterprise AI, Norges Bank Investment Management (NBIM) has reported saving hundreds of thousands of hours annually—213,000 hours to be precise—through agentic automation. In a per-seat model, “saving hours” is equivalent to “destroying revenue” for the vendor. If a client can do more with less, they will inevitably buy fewer licenses.

Market participants are moving beyond simple speed to Cognitive Arbitrage, defined as the ability to extract signal from noise in unstructured data. Historically, quants focused on structured numerical data (price, volume). The current AI wave focuses on qualitative data (text, sentiment, tone, legal nuance). Agents can read a regulatory filing and understand the context of a footnote change relative to five years of history, effectively performing “cognitive time travel.”

Incumbent vendors possess the world’s most valuable corpus of this unstructured data. The strategic question is whether they sell access to the corpus (allowing clients to build their own cognitive engines) or sell the cognition itself (forcing clients to use the vendor’s tools). If a vendor allows clients to run their own agents freely, the firm risks becoming a “dumb pipe”—a mere utility provider of raw tokens to sophisticated client models. If they disallow it, they risk irrelevance as clients seek data from alternative providers who enable this cognitive workflow.

The Strategic Trap: Allow or Disallow?

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The last time we talked about AI and equity investing was AppLovin in April. The stock has roughly doubled since then, largely on the strength of its internal use of AI.

Everyone already knows that Nvidia and Google are going to m…

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Part one of a three part guest post from .
Part 1. Lays the foundation and explores the opportunity of Chain Street.

Part 2. Explores the nuances, business models and challenges of investing in the DeFi protocols that make up Chain Street.

Part 3. Explores maturing and emerging ideas on Chain Street.

Chain Street is the financial centre of cyberspace. Unlike its physical counterparts, Chain Street has no fixed address. It thrives wherever code executes, on laptops in Lagos or servers in Seoul. Chain Street is already transforming finance, delivering unprecedented transparency, efficiency, and accessibility to services once shrouded in opacity and gatekept by intermediaries.

Chain Street is a collection of Decentralised Finance (DeFi) protocols.

I am Bullish on The DeFi Protocols of Chain Street.

I like DeFi for the same reasons investors love software companies.

Software collapsed the cost of information, communication and services. The success of software businesses, in my view, comes down to one quality: low marginal cost. Unlike their meatspace or hardware-based competitors, they could achieve almost 100% profit margins on new sales and recycle those profits into product innovation and improvement.

Every time someone downloads Excel, it costs Microsoft nothing. Every time someone listens to a song, it costs Spotify nothing. Every time someone requests a taxi, it costs Uber nothing.

Low marginal cost is the secret behind the engines of infinite growth in software businesses. This same dynamic exists with DeFi protocols on Chain Street.

DeFi protocols are software, more accurately, they are smart contracts that run on platforms like Ethereum and Solana. Beyond the upfront gas fee for deployment, along with minimal ongoing expenses like oracle data feeds and occasional upgrade costs, there are virtually no additional operating overheads to maintain their functionality. If implemented properly, no single person or organisation can unilaterally halt their operation.

Ethereum, for instance, will continue running this code at negligible cost to its creators as long as the network persists on distributed computers worldwide.

But surely the same is true for FinTech businesses like Revolut, Tyme Bank, and Monzo?

No. And let me explain the key difference.

The difference 99% of investors miss.

Why crypto is a $9 trillion opportunity.

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The race to $5,000 gold by Christmas 2025 is well underway. It won’t hit our target before Hanukkah on December 14th, but by the time we welcome the Chinese New Year on February 17th, $5,500 should be comfortably in the rear-view mirror.

Five weeks ago, just about every gold chartist turned bearish and started calling for $3,700. Once again, they got it …

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Over the years, I’ve pointed out that crypto bull markets tend to coincide with mass token generation events: the issuance of assets on blockchains. Mirroring how bull markets in traditional markets align with waves of IPOs, private funding rounds, and the launch of new businesses.

2017: The ICO Boom – Thousands of initial coin offerings flooded the market, fuelling speculative frenzy.

2020/2021: The Beginning of DeFi – Decentralised finance protocols exploded, promising high yields and finance without intermediaries.

2021: NFTs – Non-fungible tokens turned digital art and collectables into a multi-billion-dollar phenomenon.

Each of these mass token generation events sparked bull markets not just in prices, but in user adoption, developer activity, and ecosystem growth. While past cycles were fuelled by the thrill of groundbreaking native crypto tokens, many of those tokens were speculative by design and launched without robust business models. Leaving their prices and fundamentals vulnerable to the ups and downs of crypto’s overarching hype.

Crypto’s early days remind me of a classic South Park episode of the Underpants Gnomes. In it, gnomes run an elaborate underground scheme to collect underpants in pursuit of profits. When the boys uncover the Gnomes’ plan, they discover it’s comically incomplete.

The parallel in crypto’s early days is strikingly similar: Step one: launch a token loosely tied to a big idea; step two: figure it out; step three: profit.

The combination of zero-interest-rate policies in the developed world during 2020 and 2021, coupled with crypto’s enormous addressable market, turned many of these projects into funding magnets, drawing in billions of dollars. As interest rates normalised and central banks began tapering their balance sheets, a reckoning for crypto ensued.

This is why the prices of most crypto projects have endured prolonged drawdowns even as crypto wins on regulation, adoption, and institutional participation. Consider this snapshot of the top 50 cryptoassets listed on Binance at the height of the 2021 mania; most are now trading below the lows hit during the FTX collapse in late 2022.

Source: Luke Martin @VentureCoinist

Dot-crypto’s deflated asset bubble has soured sentiment among crypto natives, breeding nihilism and driving many into the escapist chaos of memecoin gambling. It echoes the thesis I laid out in “The Great Divergence“. Interest in crypto has fragmented into bullish institutions piling in with production capital and battle-weary crypto natives retreating from the fray.

This month, I’ll double down on that argument. We’ve reached the end of the “dot-crypto” era, defined as an insular, niche internet subculture obsessed with experimental tokens.

We are now in a post-”dot-crypto” era, where dot-crypto has become a misnomer, as every financial service comes onchain, but with that maturation come higher expectations for crypto to deliver on its promises of the future.

Big ideas are no longer good enough; speculative capital has dried up and is now chasing opportunities in AI, quantum computing, and nuclear. Like a teenager coming of age, these years will represent significant change as crypto enters adulthood.

I am bullish on crypto’s adulthood. Here is why:

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David thinks we are in the midst of a shift where industrials outperform software.

US industrial companies continue to defy the recession call. It is clear middle America is at the beginning stages of a major macro shift, while the software industry will continue to face challenges. The most simplistic …

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Is there something the Fed is missing?

And could the Fed be about to make a big mistake?

has a fantastic answer to that mystery.

It’s a new trend and change in economic structure which classical economists haven’t appreciated yet.

But I’m sharing it with you below.

BTW, I really like this Marvin’s work in . Super rigorous and academic, with excellent regression work, but also commercial and edgy. He’s a money maker.

Take it away Marvin!

OK! Thank you Erik!

The Atlanta Fed’s GDPNow estimate of third quarter GDP is running at a 3.9% annualized rate, which would imply 2.2% average growth this year and solid acceleration in economic activity. Incoming data strongly corroborate that acceleration: core durable goods orders, a leading indicator of business investment, bounded at a 10.2% annualized rate in the three months through July and retail sales grew at an 8% annualized clip in the three months to August. Inflation, too, has reaccelerated since its spring trough and, at 3.7%, is now running at nearly double the Fed’s target rate in the three months through August. Hence, one could be forgiven for being mystified by the Fed’s latest decision to cut interest rates, effectively unfurling more sail amid a powerful tailwind.

Yet, in a quite confused press conference, Fed Chairman, Jerome Powell justified the cut on “risk management” grounds given a significant slowing in employment growth in the last year (as revealed by recent revisions). The focus on payroll employment isn’t entirely misplaced, and no, I’m not referring to the Fed’s “dual mandate” to achieve full employment as well as price stability. When I was at the Fed it was commonly agreed that if you could have only one piece of data it would be the headline payroll growth from the Bureau of Labor Statistics’ Establishment Survey given its high quality signal. But rarely do nonfarm payrolls so contradict other similarly reliable indicators of the economy’s momentum.

The contradiction between slowing establishment employment growth and the acceleration in other indicators of real economic activity raises a fundamental question of whether the employment data reflect constrained labor supply, are mismeasured, or indicate a structural shift in the relationship between capital and labor in the US economy.

The answer to that question will determine the course of US growth, inflation, real wages, profits, and interest rates over at least the next year. And, it also determines whether the Fed has made compounding policy errors over the last year, or will be hailed as the heroes of tomorrow.

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As many of you know, I remain staunchly bullish on AI and the implications for the US. While many continue to highlight the risk of recession, I view this as the golden age of infrastructure. In the past I have called it the modern day US Marshall Plan. AI is the largest and most important investment cycle the US and the world has ever embarked on.

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Nvidia quick glance, Bull Market is still intact.

As always, every pundit lined up to state Nvidia earnings report was the only thing that mattered. Bears certainly can find what they are looking for, Bulls can hang their hat on the outlook, and the markets are flat. 

Below is a chart summarizing the qu…

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The reference post I mentioned is The Containerisation of Thought.

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Home Depot is the number one player in home supplies in the US, Lowes (LOW) is the second. Home Depot stock has gone nowhere for 5 years as the company continues to buy back stock. Over the last 10 years the fully diluted share count has gone from 1.3 billion to 993 million. The Fed is going to cut rates because they are too tight. The average interest …

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And as a token of our appreciation your email address has been credited with 5 credits at YWR Intelligence (www.ywr-intelligence.world).

No need to do anything. It’s already done.

So next time you want to get up to speed quickly on a …

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