And it dominates everything.

One trend to rule them all.

But let’s do this by the books and start with the top 30 stocks in the world ranked by our combination of earnings estimate revisions, valuation and price momentum.

Links to all of our tools and dashboards are at the bottom of the post.

YWR Global Top 30

Most of the earlier trends still exist.

Memory: SK Hynix EPS estimates are going insane. The 2026 EPS estimate has jumped from KRW 160,000 to 260,000. Just to give you some perspective in October 2025 I thought I was edgy in predicting SK Hynix could earn KRW 50,000/share when consensus was 40,000. Instead the number is approaching KRW 300,000.

Despite being the market leader in the biggest AI bottleneck, SK Hynix is still trading on a P/E of 4.5x. Samsung is the same. Samsung’s 2026 EPS estimate has jumped from KRW 23,000 to 34,000. Samsung with a $1 trillion market cap and a more diversified business is trading on a P/E of 6x!! You get all the optionality of if Samsung can ever be ‘the next TSMC’ foundry for free.

Taiwan Tech: the Taiwanese semiconductor supply chain still has some of the best estimate revision trends in the world. It’s Nanya, Macronix, Winnbond, Innodisk, Elite Semiconductor, and Phison.

Gold and Other Metals: Surprisingly there is only one gold miner in the Top 30. Aura Minerals is a gold-copper producer. Nexa Resources is Zinc. Century Aluminum is also in the top 30 on the back of the rise in aluminium prices.

Potential 2nd derivative play: Aluminum prices rising might also be good for steel. On Monday last week Stevie sent me an interesting snippet from the Cleveland Cliffs conference call about automakers switching from aluminium to steel.

“In my long career in this business, I have never seen so much momentum and substituting aluminum with steel... Automotive OEMs are prioritizing supply certainty, total cost and safety.”

Does US HRC steel roof it next?

Oil and Gas: Finally, the monster trend. The YWR Top 30 is flooded with oil and gas companies. This is the dominant new trend taking over everything. The estimate trends are strong, the price momentum is high and the valuations are still low, because nobody trusts the move. Nobody wants to be the idiot who buys oil and gas stocks the day before the Iran War is resolved.

Top 5 Ranked Industries

Integrated Oil is the top ranked industry in the world this month. It has displaced gold, which falls to #2. Then we have Other Metals, and Semiconductors.

Number 5 is interesting. It’s Chemicals. Chemicals prices are also benefitting from the disruption in the Middle East. Stevie surfaced this snippet from the DOW call.

“Roughly 20% of global oil capacity is currently offline and approximately half of global ethylene and polyethylene supply is either offline, constrained, or directly impacted... The longer these conditions persist, the greater the potential for further industry changes. And lastly, it is not likely that the pricing impact of these events will be temporary.”

In the monthly trends chart below we see the sharp acceleration in Integrated Oil and Chemicals. We also see the falling momentum for precious metals.

Three other observations:

#1 Refiners: Estimate revisions for refiners are going through the roof as well. The valuations are also attractive because everyone doubts if the earnings are ‘sustainable’.

#2 Banks look good too: Banks don’t make the top 5, but the estimate trends there are positive too. It’s a positive backdrop for our Global Boom trend. Also, if interest rates are not going to fall, and we have higher for longer, it’s positive for net interest margins. First Hawaiian mentioned this.

“With the markets now expecting no rate cuts this year, we have revised our full-year NIM outlook to be in the 3.22% to 3.23% range.”

#3 Do we have an inflation spike coming? If the top ranked industries globally for estimate changes are energy, metals and chemicals… do we have an inflation spike coming?

Top 5 Ranked Countries

The Top 5 countries are the same suspects. The US always has the most top ranked stocks, which is a reflection of both strong growth and the higher number of stocks across all industries. Then comes China, the biggest market nobody ever owns, at #2. Then South Korea and Taiwan because of the semiconductor/memory boom. Canada (and Australia) rank well because of their commodity stocks.

But what do we almost never see? Aside from the UK every now and then, the European markets struggle to rank well. Which is why I keep thinking Asia is the New Europe. Do we need to shift our focus from US + Europe to US + Asia Pac? I’m trying to travel to Asia more.

Inflection Analysis

This is an exercise where we identify downtrends in estimate revisions which have flip positively in the last 6 months.

Everything to do with energy has inflected sharply this year. We noticed this early on in December (Global Inflections), but the trend has gone supersonic.

I’ve included below two Google Sheets with the top inflection stocks by country and industry.

Oil Services: Stevie pulled this snippet from the Haliburton call on how the pick up in oil services will not be temporary.

“The cumulative production deficits are in the several hundreds of millions of barrels and trending towards a billion. This represents several years of meaningful incremental demand to replace strategic reserves on top of what I believe will be continued structural demand growth. Big picture, this means the world is fundamentally tighter in oil and gas than it was sixty days ago.”

The Million Dollar Question

Having reviewed all the data together. Having reviewed the trends across energy, chemicals, oil services, refining, shipping and bank net interest margins, we come to the million dollar question.

Are we in a prolonged period of energy scarcity?

Go back and reread the Halliburton quote.

The energy disruptions in the Middle East have coincided with a global investment boom (‘structural demand growth’).

Does it mean energy prices stay higher for longer? ($200 oil pops the bubble).

Could the energy theme be bigger than AI/Semiconductors and Memory?

What does this mean for global interest rates?

What does this mean for geographical allocation?

John Burbank’s quote is in my head.

“Investors always underestimate the magnitude and persistence of trends.”

Answer to the Million Dollar Question: My sense is energy prices are going to be a problem for a long time and we are going to have to live with it. And we all need to bite the bullet and be bigger in energy related stocks (personal view). In Qatar Changes Everything I gave my view that the US LNG terminals are good long-term beneficiaries without the risk of getting whipsawed on commodity prices. But maybe you need to own the integrated players like Total and Shell which are still trading at < 10x earnings.

Below are links to all your data tools; the Tableau dashboard to review industry estimate trends, the Retool data dashboard with the estimate trend charts (I love this tool), the Google rankings sheet, and the inflection sheets.

Read more

So much for ‘remote control’ Erik thought as their Landcruiser forced its way onto the crowded road leaving Mombasa port.

Erik looked out from behind the tinted glass window at the snarl of container trucks, scooters and taxis grinding up the hill and out of sight. Erik and Claire would exit for the airport in 10km to fly back to Nairobi, but these trucks would continue their journey for up to 1,000km as they fanned out across East Africa delivering their goods. Mombasa Port was the main port serving 140 million Africans across Kenya, Uganda, Rwanda and Southern Sudan. It was the most important port on the East Coast of Africa and the starting terminus for the East Africa Rail project.

They had just met again with the Kenyan Port Authority to try and bring the rail line into the port so containers could be loaded directly from the ship to the intermodal rail cars. The Port Authority and their engineers kept saying it wasn’t possible. The containers would need to be trucked a short distance out of the port and up the nearby hill to a larger railyard. This meant the trucks and their containers would have to drive through the incessant traffic jam outside the port gates. The same traffic they were stuck in right now.

Claire was looking out her window in the back of the Landcruiser. She was dressed well, with nice shoes and sunglasses perched on her head. She had nice perfume.

“It’s too bad about the port, but maybe it’s manageable. Not ideal, but manageable,” Erik said to break the silence.

“Easy for you to say. You only have to answer to your crazy hedge fund boss. I have to write this up in a report for the directors back in D.C and explain why the whole starting point of the railway has moved and why this wasn’t known from the beginning and disclosed in the initial proposal.”

“Well it’s Africa. They are going to have to get used to it. Nothing goes like in the proposal.”

Erik thought back to Belway Capital’s own initial expectation of what they were getting in to with Turkana Trust.

Erik had done his initial review of the bank’s financials and surmised that if they could invest at 0.5x book value the risk/reward looked good. Their key concern had been the bank’s large project loan to the East Africa Railway SPV to fund the early years of the railway’s construction. The project was to build a transformational railway from Mombasa Port all the way to Kampala, Uganda over 1,100 km away. The railway was to be completed in stages with Stage 1 running 480km from Mombasa to Nairobi.

For the bank it was an attractive loan. Erik understood why they had made it. The loan paid 12% in US$’s. Yes, it was high-risk to build a new railroad in Africa, but there were mitigating circumstances. The main one being that the IFC had invested equity into the SPV. This meant strict governance and project management. The second benefit of having the IFC on board was their deep pockets. The IFC wasn’t likely to quit funding a high profile, transformational railroad in Africa if it turned out there were a few unexpected delays and it needed a little more money.

To Erik and Dwight it seemed a safe bet that the IFC would keep funding the project, and eventually the railway would be completed. Plus, there was a highly capable company like China Eastern overseeing the construction.

Once the railroad was operational it would be easy for East Africa Railway to get an infrastructure loan at 7% to pay back the loan from Turkana Trust Bank. This one large loan repayment would reduce the stress on the bank, improve the capital ratios and rerate the bank back to 1.5x book. The trade for Dwight and Erik was to get through this uncertain patch in the middle while the rail project wasn’t yet complete. Then later after the bank was derisked, Belway Capital would sell the bank to a private equity fund.

As part of the due diligence they had both flown down to Nairobi to visit the bank and meet its CEO, Rajiv Desai. Erik learned that despite being worth hundreds of millions of dollars Dwight liked to save money and fly coach. In Nairobi they had had many pleasant meetings and dinners with Rajiv and his team of bank managers. Rajiv was always smiling and answering their questions, but was obviously stressed.

Erik empathised with Rajiv. It must be a lot of pressure to be the third generation to run your family bank then be the one who was in danger of losing it all. And Rajiv must be thinking of all the turmoil over the years which is father and grandfather had successfully navigated. But, now when it was his turn he had failed and had to sell a large stake to a London hedge fund.

As part of their due diligence Erik and Dwight had also taken a drive out to Turkana County in the Western part of Kenya where the bank had most of its branches and deposit base. It was a desolate and poor area of Kenya. The bank was named after the large Turkana Lake. It wasn’t a prosperous area, but there were no other banks competing in there so it was a profitable place to raise deposits. It had a niche.

The challenge of Turkana was that it didn’t have much industry. While driving around Turkana they observed a new wind farm being installed. They also noticed the trucks for a UK oil company which had made an oil discovery. Unfortunately, the oil production would be years away. Otherwise, there wasn’t much going on.

Back in Nairobi they had met Claire, the attractive French CEO of East Africa Rail. Claire was part of the IFC’s strict project management. Her explanation of the extensive due diligence which had gone into the project, as well as the ongoing controls and frequent reporting back to the IFC gave Erik and Dwight confidence about the bank’s East Africa Rail exposure.

In the end they had done the deal and Dwight had wired $20 million for a 44% stake in Turkana Trust Bank which made him the largest shareholder. Rajiv’s family was diluted down from 60% ownership to 34%. Several other wealthy Kenyans owned the remaining 22%. Erik was appointed to the board to represent Belway Capital and be Dwight’s eyes and ears. It involved flying down to Kenya every other month to attend meetings.

The deal had gone well for the first year. Rajiv ran the bank and Erik attended the board meetings. Erik’s job was to monitor the East Africa Rail loan like a hawk. Track laying was on schedule. But then had come the time to order the locomotives and the railway wagons from CERS. Then there had been the shock invoice from China Eastern which had necessitated the trip to Shanghai. Then the call with Dwight followed by an unpleasant trip to Nairobi to deliver news to Rajiv that he was no longer running his family bank and in fact would have no role whatsoever in the daily operations. And then explaining to Rajiv that he Erik, who had never run a bank before, would be moving to Nairobi to be the new CEO. He felt bad for Rajiv, but Rajiv had failed in running the bank, misled them about the credit guarantee letter and needed to be cut.

Which was why he was in this Landcruiser outside Mombasa port, sitting next to Claire, stuck behind a container truck.

“So, tell me. Your French. How did you end up in Africa? How did you become the CEO of an African railway?”

Claire looked out the window and could see it was going to be a long, stop and go ride to the airport. They had a lot of time to kill. She let out a small sigh.

“I grew up in Biarritz. It’s on the Atlantic Coast near Spain. From there I went to business school at Aix-Marseilles. It’s not a great school in France, but I earned good grades and managed to land an entry level job at SocGen in Paris. I worked my way up through the investment banking division and worked on a lot of infrastructure finance deals. I was quite good at it. Then after the financial crisis I was laid off from SocGen as part of a restructuring.

“I sympathise. Same thing happened to me. Not investment banking, but a fund I was working for also shut down. It was a hard time to get a job. What did you do?”

“Travelled a bit. Lived off my savings. Like you said, it was a hard time to find a job. All the French banks were trying to reduce risk. Two years later I managed to get a consulting job, which I did for a while, also related to infrastructure. But I hated it.”

“How did you end up at the IFC?”

“A friend of mine was working in the their distressed debt division. She was always working on Africa deals and found out they needed someone to analyse African infrastructure deals. The IFC had been financing projects across Africa, but were running into lots of problems. The projects always looked great on paper with promises to ‘transform’ the continent, but the execution phase would inevitably run into delays, corruption and cost overruns. They realised they were good at funding projects, but not good at executing them. The IFC was losing tens of millions on these projects and decided to conduct a review of their whole approach to infrastructure investment. Everything became more rigorous with strict processes. And the other thing the IFC realised was these big infrastructure projects were being run by managers with social aid backgrounds and no finance experience. So they made changes to the department. They wanted people who had worked on big deals for investment banks. Hence me.”

“And then what? You went to work for the IFC, and somewhere along the way they said, ‘Hey, can you move to Kenya and run the East Africa Rail project?’”

“Yes. Basically. For them East Africa Rail was a big ‘transformational’ project.” Claire said holding up her hands to make air quotes. ‘So rather than wait for it to go bad and then afterwards bring in Western management to fix it, they decided to start with a Western CEO in the first place.”

“And now you are living by yourself in Nairobi running a railroad.” Erik probed.

“Not that my personal life is any of your business. But yes.”

“And when you finish this is the goal to get a job back at a bank in Paris?”

“Again, none of your business. But yes.”

“Thank you. We are working a lot together so it’s just nice to know a little bit about who you are. I’d tell you my own story but it’s long and depressing. The conclusion is the same though. We both need this to work.”

Erik looked out the window again at the traffic and the people walking on the side of the road. There was no sidewalk and they were mostly wearing T-shirts and shorts with flip flops. Some were carrying things. Some were balance things on their heads. Erik wondered how far they would have to walk this afternoon to get where they were going.

His mind continued to drift and he thought of the truck drivers and what their next 20 days would be like as they slowly made their way West to the interior of the continent. Long days and nights. Police check stops. Bandits. Long waits at the border crossings. And the cost of it all. He’d read it cost the same to sail a container the 10,000 km from Shanghai to Mombasa as it cost to get it the next 1,000 km to Uganda. That was what the railway was meant to fix. Or at least help fix.

Claire needed the project to work. Erik needed the project to work. And for the first time Erik realised how much the people out there on the road needed it to work too.

Previous: Chapter 3

But not you.

Because you see the mega bull market unfolding.

These are just the gentle foothills of something bigger.

The animal spirits are stirring. Money is moving again.

That’s the theme of this month’s killer charts.

This month’s chart pack is 32 charts to help keep us in the trade for S&P 10,000.

A link to the full presentation is at the bottom of the post.

Slide deck sections

• S&P 500 Earnings estimates

• Fund Manager Survey highlights

• International Equities Perspective

• Bank Lending

• Investment Banking Trends

Let’s go though the top 10 charts.

Top 10 Charts

#1 US Earnings are curving upwards. The market tracks earnings and earnings are going vertical. That’s simple reason the market is making new highs.

#2 It’s not just Mag7 anymore. The earnings growth is widespread. Ex-Mag7 stocks will grow EPS 16% in 2026.

#3 Money is moving to the periphery. And that’s a good thing. The investor overweights in EM, Materials, Commodities and Banks are positive. The previous strong dollar regime, and positioning overweight in US tech was actually defensive. We want money to leave the US. It’s not a bad thing. It’s a sign of growth. As money moves into Emerging markets, their FX strengthens, inflation falls, the central banks lower rates and it kicks off global growth. The boom goes global.

#4 International themes are working. The top investment themes in the world are European defence, Taiwan tech, and international banks. This is great to see. It’s healthy.

#5 Investors expect higher rates. That’s a good thing. Higher rates means higher growth.

#6 Global PMI’s are all green. The boom is global. US, EM, DM.

#7 US Bank loan growth is accelerating from the 3-5% p.a range to 6-8%. The chart of total US bank loans is curling upwards (similar to EPS… because it’s all the same thing). This is the big trend nobody is talking about. Banks in the US, Europe and Japan are coming out of their post GFC hibernation.

#8 Japanese bank loans are going vertical. It took a while but Japanese bank loans have made back all the late 1990’s deleveraging and are growing strongly. We call this multi-year Japanese bank lending boom still to come The Godzilla Trade.

Read more

Something amazing happened.

We need to reflect on what it means.

But first let’s go over the top 20 deals.

A link to the full PE/VC investment rounds dataset is at the bottom of the post.

Q1 2026 Top 20 PE/Venture Capital Deals

#1 OpenAI: They did it! This was a MEGA $122 billion raise at an $852 billion valuation. Notably, the investor base included individuals and ARK ETF’s! At $2bn/month in revenue this values OpenAI at 35x sales.

The round was anchored by strategic partners Amazon, NVIDIA, and SoftBank, with continued participation from our long-term partner, Microsoft. SoftBank co-led the round alongside a16z, D. E. Shaw Ventures, MGX, TPG, and accounts advised by T. Rowe Price Associates, Inc.

There was also significant participation from a diverse set of global institutions including Altimeter, Appaloosa LP, ARK Invest, affiliated funds of BlackRock, Blackstone, Coatue, D1 Capital Partners, Dragoneer, Fidelity Management & Research Company, Goanna Capital, Insight Partners, The Paragon Group, Sands Capital, Sequoia Capital, Sound Ventures, Temasek, Thrive Capital, UC Investments (University of California CIO Office), and Winslow Capital.

For the first time, we extended participation to investors through bank channels, raising over $3 billion from individual investors. Today, we’re also announcing that OpenAI will be included in several exchange-traded funds managed by ARK Invest, further broadening ownership and giving more people the opportunity to share in the upside economics of OpenAI and the AI era.

“The OpenAI flywheel is simple. More compute drives more intelligent models. More intelligent models drive better products. Better products drive faster adoption, more revenue and more cashflow. That gives us the ability to reinvest and deliver intelligence more efficiently to consumers, enterprises, and builders around the world.”

#2 Anthropic: $30 billion at $380 billion. This capital raise was a bargain at only 27x sales. Everyone went in on the deal.

“Two years ago, a dozen customers spent over $1 million with us on an annualized basis. Today that number exceeds 500.”

We raised $30 billion in Series G funding led by GIC and Coatue, valuing Anthropic at $380 billion post-money. The round was co-led by D. E. Shaw Ventures, Dragoneer, Founders Fund, ICONIQ, and MGX.

Significant investors in this round include: Accel, Addition, Alpha Wave Global, Altimeter, AMP PBC, Appaloosa LP, Baillie Gifford, Bessemer Venture Partners, affiliated funds of BlackRock, Blackstone, D1 Capital Partners, Fidelity Management & Research Company, General Catalyst, Greenoaks, Growth Equity at Goldman Sachs Alternatives, Insight Partners, Jane Street, JPMorganChase through its Security and Resiliency Initiative and Growth Equity Partners, Lightspeed Venture Partners, Menlo Ventures, Morgan Stanley Investment Management, NX1 Capital, Qatar Investment Authority (QIA), Sands Capital, Sequoia Capital, Temasek, TowerBrook, TPG, Whale Rock Capital, and XN.

#3 xAI: $20 billion.

Investors in the round included Valor Equity Partners, Stepstone Group, Fidelity Management & Research Company, Qatar Investment Authority, MGX and Baron Capital Group, amongst other key partners. Strategic investors included NVIDIA and Cisco Investments.

#4 Waymo: $16 billion at $126 billion valuation.

In addition to Alphabet’s strong sustained support as our majority investor, the financing was led by Dragoneer Investment Group, DST Global, and Sequoia Capital, and included significant investments from Andreessen Horowitz and Mubadala Capital as well as Bessemer Venture Partners, Silver Lake, Tiger Global, and T. Rowe Price. Additional investors included BDT & MSD Partners, CapitalG, Fidelity Management & Research Company, GV, Kleiner Perkins, Perry Creek Capital, and Temasek.

Note how Waymo is raising money at a $126 billion valuation while Baidu trades at $46bn valuation and you get their majority stake in Apollo Go for free. There is one valuation for a publicly listed Chinese company, which will involute themselves to death, and another for a Google backed US businesses able to tap private markets.

#5 Databricks: $5bn at $134 billion valuation. 24x sales.

With this new funding, the company will accelerate Lakebase, its serverless Postgres database built for AI agents, and Genie, its conversational AI assistant that lets any employee chat with their data.

JPMorganChase expanded its investment in the company through its Security and Resiliency Initiative’s newly-formed Strategic Investment Group. The close also included Glade Brook Capital, Growth Equity at Goldman Sachs Alternatives, Microsoft, Morgan Stanley, funds affiliated with Neuberger, Qatar Investment Authority (QIA), funds associated with UBS, and others.

Note that Snowflake with $5.1bn in run rate revenue (+30% yoy) has a market cap of just $43bn. A measly 8.4x sales.

Oh, and did you wonder what ex-Berkshire wonderboy Todd Combs was up to? He’s investing in tech stocks for JP Morgan. Buying ketchup stocks in Omaha was too boring.

“Databricks is a generational company that has become a backbone for enterprise data and AI, helping organizations across critical sectors seize opportunities and overcome challenges,” said Todd Combs, Head of the Strategic Investment Group for JPMorganChase’s Security and Resiliency Initiative.

#6 Shield AI: $1.5bn at $12.7bn valuation.

The Series G is led by Advent International and co-led by the Strategic Investment Group of JPMorganChase’s Security and Resiliency Initiative, with participation from existing investors Snowpoint Ventures, InnovationX, Riot Ventures, Disruptive, Apandion, and others.

The Hivemind Foundation Model for Defense is a domain-specific AI model that integrates high-fidelity simulation with data from real-world operations to help customers accelerate the adoption of AI pilots across new aircraft and missions.

I love how Silicon Valley has shamelessly pivoted 180 degrees on ESG and is now piling into AI terminator companies. Fighting climate change has been replaced with ‘strengthening global resilience’.

And here’s Todd Combs again.

“Through our Security and Resiliency Initiative, we are investing in generational companies who are poised to accelerate innovation, reinforce the defense industrial base, and help advance and scale mission-critical solutions,” said Todd Combs, Head of the Strategic Investment Group for JPMorganChase’s Security and Resiliency Initiative. “JPMorganChase is proud to support the AI pilots and next-generation autonomous aircraft that will strengthen global resilience and national security.”

#10 Saronic: $1.75 at $9.25bn valuation. This is the autonomous ship start up.

“Over the past decades, the U.S. has experienced a steady erosion of its ability to build ships and manufacture critical maritime infrastructure,” said Dino Mavrookas, Co-Founder and CEO of Saronic. “We are confronting this challenge with a fundamentally new model of American shipbuilding to deliver autonomous vessels with unprecedented speed, precision, and scale.

The round was led by Kleiner Perkins. Saronic also welcomes Advent International, Bessemer Venture Partners, DFJ Growth, BAM Elevate, and other new partners and recognizes the continued commitment of its existing investors, including 8VC, Caffeinated Capital, Andreessen Horowitz, Elad Gil, and Franklin Templeton.

#12 Skild AI: $1.4bn at $14bn valuation. Apparently, the new thing is ‘world models’, LLM’s for robots.

The Skild Brain takes a different approach. It is the industry's first unified robotics foundation model that generalizes across tasks and robot hardware. Our omni-bodied model can control any robot without prior knowledge of its body form, including quadrupeds, humanoids, tabletop arms, and mobile manipulators. It enables robots to handle everything from simple household chores like cleaning, loading a dishwasher, making an egg, to physically demanding challenges such as navigating slippery terrain. If there is a machine that can move, the omni-bodied Skild Brain will operate it.

The round was led by SoftBank with participation from NVentures (NVIDIA), Macquarie Capital, and Bezos Expeditions, catapults our valuation to over $14 billion. Lightspeed, Felicis, Coatue, and Sequoia Capital have doubled down on their investments. Several strategic investors have also come on board, including LG, Schneider Electric, CommonSpirit, and Salesforce Ventures. Other investors include Disruptive, 1789 Capital, IQT, TF Capital, Andra Capital, Palo Alto Growth Capital, Alpha Square, Mirae Asset, and Destiny.

South Wind Exploration and Global Eggs: This is the first time I have ever seen 2 non-tech companies in the top 20 deals. South Wind is a US oil exploration company and Global Eggs is ‘eggs’. Energy and agriculture… this is a very interesting inflection point. What if the PEVC fire hose starts to diversify further out of tech?

Warburg Pincus, the pioneer of global growth investing, today announced an agreement to invest up to $1 billion in Global Eggs (“the Company”), the largest multinational producer and distributor of table eggs. Valuing the Company at $8 billion, the investment underscores Warburg Pincus’ commitment to partnering with exceptional founders and investing in global companies with high growth potential.

My Big Takeaway:

I said at the top something amazing happened in Q1 2026.

Look at the top 5 deals. Five deals raised $193 BILLION in 3 months!! Also, OpenAI is still private and yet closing in on a $1 trillion valuation!! Do you remember how crazy we thought it was when Apple crossed $1 trillion? And we also have SpaceX, another private company, valued somewhere over $1 trillion. Trillion $ private companies are not a thing anymore.

And look at the valuations. 24x sales for Databricks, 27x for Anthropic, 35x for OpenAI. SpaceX is valued in the range of 40x sales.

Compare this with Nvidia at 16x, Microsoft at 9x, and Micron at 5x sales.

There has been an flippening. It is easier to raise capital in private markets than public markets AND you get a higher valuation!!! This is the complete opposite of what it is supposed to be. Do you remember why David Swensen said private markets were attractive all those decades ago? It was supposed to be less liquidity, but more attractive valuations.

So why is this happening?

Partly, it’s that private markets are where you get the hot new trends, but the fact that OpenAI and SpaceX are still private at trillion dollar valuations is something else. It’s The Insanity Trade.

Private markets are the gated community/the gentlemen’s club where everyone agrees on the valuations and you transact in an orderly manner with a curated investor base. If you are a hot company you also get to manage your shareholder list and secondary transactions.

And you see the beginning of retail coming into private equity too with the OpenAI deal.

So how do we make money?

Is Public the new Private?

Yale, Stanford, MIT, Harvard and all the pension funds spent the last 2 decades selling public equities and reallocating to private markets. They even created new categories (like ‘infrastructure’) just so they could increase the private exposure even more.

They thought they were cool.

But what if they aren’t?

If the whole point of private markets is to trade liquidity for better valuations, but actually private markets are more expensive and more liquid, does that make public markets the new private?

Are public equities the new cool thing?

Let’s skate to where the puck is going to be.

Is Stanford’s 2030 allocation going to be 30% International Equity, 25% Domestic Equity? 18% Private Equity?

Or here is another idea.

London Stock Exchange?

Private equity funds are full of crappy investments they need to unload so they can return money to their LP’s.

But it’s an impasse. Private equity valuations are too high relative to public markets. It’s hard to sell private equity investments into public markets at 30x sales.

So what do you do?

How do you generate liquidity for your investors, sell to the public markets, and still preserve your artificially controlled valuation?

What’s the solution?

Duh. You create a new market.

You create a public market for private companies.

London Stock Exchange has created exactly this.

PISCES, a venue to trade private companies.

It’s still early days for PISCES, but let’s keep an eye on it. LSE is hated (like all UK stocks) and there is no value for PISCES in the share price.

Below is a link to the full Q1 PEVC investment rounds sheet, so you can go through all the deals and which firms where involved.

Read more

The price of Sapele Petroleum blinked red. Down 10%.

Erik’s stomach tightened.

Just minutes earlier it had been -5%. Then just now the price had taken another lurch lower. Somone must really be trying to get out.

Earlier in the day there had been rumours the CFO of Oben Exploration was being investigated for fraud. The stock had been trading steadily lower for 2 weeks already. So someone must have known. Now the news was out. So Oben was down 20% on the day. Which was fine, Erik didn’t own Oben. What concerned Erik was that Sapele Petroleum was also trading down a lot; like the Oben news was somehow related. Erik reasoned there must be traders out there who were long both stocks and derisking everything at once.

“What to do?” he kept thinking. “Cut Sapele? Or, stick with it.”

The other factor adding to Sapele’s weakness was the soft oil price. Oil had been trending lower on weak China data.

Weak oil backdrop. Now this fraud investigation at Oben. Oben was going to be an overhang for the whole sector. It confirmed everyone’s worst fears about Nigerian oil companies.

His stomach was telling him to sell.

Of the 6 screens in front of him there was one he looked at every 90 seconds. The top left screen with his year to date (YTD) P&L. Take that YTD number, times it by 0.2 and that was his bonus. He stared at it all the time. The Sapele move had cut that number in half. If he sold and got out here, it was still a decent number. They would be fine. Nicole would still be happy. But if Sapele sunk even lower, he didn’t have much time left in the year to make it back. As they say “December dollars are real dollars.” He imagined coming home and trying to explain to Nicole that he had eviscerated his bonus in the last month on a Nigerian oil company.

On the other hand Saplele had a big well coming up in the Dahomey Basin. He liked the CEO and liked how they had positioned Sapele with a big block in a new unexplored basin. It could be a huge discovery. That was the whole reason he was in it. If the well hit, Sapele would triple. 20% of that would be life changing. And wouldn’t it look stupid, and wouldn’t he hate himself if he sold on the lows because of a fraud at another company which was totally unrelated? Wasn’t that a muppet move?

Erik mulled the regrets back and forth. In which scenario would he hate himself more? His anxiety rising. Worried he didn’t have time to think about this much longer.

Better something than nothing.

As if acting on their own his fingers tapped out an order to sell 1/3 of the position. That was always his style. In times of extreme uncertainty act. Sell 1/3 and see how it feels. Don’t be a deer in the headlights. He hit send which routed the order directly to the exchange. In the position monitor Erik could see the number of Sapele shares he owned tick down as the order was filled. There was a feeling of relief. A slight lessening of the anxiety.

Erik waited half an hour. Staring intently at the screen.

There was no improvement. Sapele drifted sideways, and down; flirting with -11%. The volumes were high, but the stock couldn’t lift. Erik thought back to the CEO’s London roadshow. Everyone was in the same trade and now trying to get out.

Like before, as if on autopilot, Erik tapped out another order to sell 1/3 of the original position. He hit send. After this he would be 2/3 out.

Sapele drifted further to -12% as the order was filled. He decided he wanted to be out of Sapele before the US markets opened and the East Coast traders also started to sell.

An hour later Erik was out of Sapele and the share price was -15%.

His anxiety lessened, but was replaced with frustration. The Sapele loss was such a big set back to what had, up to this point, been a good year running the Africa book for Dwight. Erik tried to look at the upside though. At least if the year ended here and nothing else went wrong, he had a bonus, even if it wasn’t amazing.

A voice brought him out from his self-reflection.

“Erik. You have a second? Can we chat off the desk?” Dwight asked from the other side of the desk behind his own bank of monitors.

Erik’s stomach tightened again. Here it comes. This was it.

Here is where Dwight pulls him into a conference room, yells at him for losing him money and fires him. Another stop to explain on his CV.

“Yes, Dwight. I’m free now if you want,” Erik replied trying to sound as calm as he could.

Dwight led Erik into the glass conference room and they both sat down. Out on the trading floor every trader pretended to be staring intently at their screens, but Erik knew they were all following the drama which was about to unfold.

“Tough day in Sapele” Dwight started.

“Yes. I decided to cut it. I know there is still the big well they are going to drill next year, but I don’t like how the oil price is acting, plus they still need to raise another $20 million for the well. If for any reason they can’t raise the money, or the well misses it will be a zero. So, I took off the position. I know we’ve given back a lot of P&L.”

Dwight leaned back in his chair, sighed, raised his arms above his head and stretched his back. Maybe he was stressed too. He was looking at the ceiling and seemed to be thinking.

Erik was on edge. He wasn’t sure what Dwight was going to say.

He’d come to learn that while Dwight like to portray himself as easy going and relaxed, he actually had a vicious temper. He had seen Dwight yell and ridicule other traders in front of everyone else with surprisingly cutting remarks. Dwight was creative in his insults. He would also yell at the brokers if a trade hadn’t gone well or the firm had been slighted on a deal allocation.

It was hard to tell with Dwight. Sometimes he didn’t care. Other times he would erupt viciously.

Dwight ended his stretch and looked back at Erik.

“It was good to take off the position. It was good risk management. You did the right thing. It’s December. Cut the losses and protect your bonus. Forget about Sapele. Move on to the next thing.”

What?? Did Dwight just compliment him and say “You did the right thing?”

Erik couldn’t believe it. Dwight didn’t seem fussed about the loss. Instead, he liked that Erik had acted and cut his losses early.

‘Dear God thank you for bringing such a kind, patient and understanding hedge fund manager into my life at this point in my career, when I need it most’ Erik silently prayed.

“I want to talk to you about something else.” Dwight added.

“Yes, what is it? How can I help?” Anything to move the conversation away from Sapele.

“A broker in Kenya called me about an opportunity. There is a small bank in Kenya which needs to raise some money. It’s a private market transaction. Apparently, they’ve been funding that big railway project, and the exposure got too big. The regulator wants them to raise $20 million in new equity, or they get taken over by the central bank. It’s kind of a distressed situation which makes it interesting.”

“Did they say which bank it is?”

“It’s Turkana Trust. Do you know it?”

“A little bit. I’ve heard the stories. Is that the bank owned by the Indian family, or the ‘Kenyan Indian’ family? The one where the great grandfather came over to Kenya when the British were building the first railway in the late 1900’s? And the CEO is Rajiv Dessai?”

“Yes, that’s the one. Can you take a look at it? The family is kind of desperate for money, and there aren’t many other funds investing in Africa so the broker brought them to us. They are in a bad spot. If we promise we will act quickly, and not drag them through tons of due diligence, we should be able get in at less than 0.5x book value with full control. It could be a great deal.”

“You want to take full control? But we’re mostly traders. We don’t know how to run a bank.”

“Of course we aren’t going to run the bank. It’ll be like remote control. The Rajiv guy handles all the local noise like dealing with regulators and customers. We just recap it and collect some divies along the way. Then in 3 years after the rail project is complete, and the loan book looks clean, we sell to the muppets for 1.5x book as a ‘play on African growth’. Oh, and we also pay ourselves a big check. I just need you to take the lead and do some research on it so we can do the deal.”

That sounded like a good plan. Especially the ‘big check’ part.

“It sounds like a great idea. I’ll definitely take a look!”

Previous: Chapter 2

Next: Chapter 4

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That’s what I thought as I was driving all alone down 280 to Paolo Alto.

I was an equity salesperson at HSBC in San Francisco. Part of my job was to come up with some client entertainment for the PM’s I covered. Client entertainment was a big deal to our head of equities back in NYC. He was an old school Irish American. His view was entertainment was where the business was won, not bringing in boring analysts nobody cared about. Business was won in the strip club, the golf course or a boozy steak dinner. When he would come out to SF he would tell us stories about the good old days of spending thousands of $’s on dinners and strippers and then the big trades which would flow in the next morning as they were hungover at their desks.

So his view was if he didn’t see 000’s of $’s on our HSBC corporate cards that was a bad thing. It meant the client was out partying with our competition, instead of us.

Except this wasn’t NYC. I was in SF. My clients were mega long-only fund managers who left the office at 3pm to get over the bridge and back to Tiburon so they could train for triathlons. And they had more money than god. They didn’t need steak dinners with an HSBC salesperson.

So how do I hook a cerebral triathlete into some client entertainment with a broker they don’t care about?

My brilliant idea was to subscribe to a series of economic lectures and guest speakers at Stanford University. The way I envisioned it the top PM’s at Capital and I would go down to Stanford together, talk ideas in the car for 1 hour, hear a great insightful talk, have dinner afterwards, and become best friends.

You can imagine how that worked out.

So here I was driving down to Stanford, by myself, to hear a lecture from a nobody professor (Enrico Moretti) about ‘The New Geography of Jobs’. I had debated for a long time why I too was bothering to drive down to Stanford, but figured it would look worse if HSBC found out I wasn’t going to the talks either.

Turns out it was the best economics lecture ever. And I think about it all the time.

Like today.

You see Moretti’s insight was that wealth creation in the modern day plays out at the city level, not the country level. He analysed job data across the US and discovered job creation is highly concentrated in several key innovation nodes (Silicon Valley, Seattle, Washington DC/Virginia, Austin, New York). We focus on the total job creation for a country, but really it is job creation in 5 cities, with losses everywhere else. This was back in 2012.

As Enrico studied these innovation hubs, he saw there was a tremendous multiplier effect at work. High paying innovation jobs created an even greater number of high paying service jobs, which raised the incomes for everyone. Then there was also the flywheel effect as tech founders would spin out to create new companies. This high income job growth would also pull in top talent from around the country. Which further strengthened the innovation cluster.

It all seems kind of obvious now, but this was 2012 and at the time nobody had realised just how bifurcated the job market had become. It was winner takes all for the innovation clusters and crumbs for everyone else.

But my favorite part of the talk was his discussion of how innovation clusters form in the first place. In economic policy circles this was a big focus. If innovation hubs create all the jobs, how do you create more innovation hubs?

It turns out there is no clear recipe and it’s partly luck.

He’d done correlation analyses around university rankings, state taxes, education levels, etc and they were all hit or miss. Yes, Silicon Valley had an elite university, but Austin and Seattle did not. Enrico’s conclusion was that there was no set recipe and sometimes they just happen organically. Like the story about Seattle. This was my favorite story from the talk and the one which stuck with me.

Seattle in the 1970’s was dire. Seattle had just experienced the ‘Boeing Bust’ where Boeing had cut its workforce from 100,000 to 40,000. Seattle’s other big industries were the port and timber, which weren’t growing either. Nobody could see how Seattle would ever grow again and people were leaving. It was so bad that two real estate agents put up a sign along the highway asking the last person leaving Seattle to turn off the lights.

Everything looked terrible. No one could imagine how it would improve. And yet the biggest inflection point ever was already underway. Nobody wrote about it at the time, but two young, homesick computer programmers decided in 1979 to move Microsoft from Albuquerque to Seattle because that was where they had grown up and they missed home.

That was what especially struck me from the talk. The Seattle tech story wasn’t about the tax rates, or the universities, or the mean level of education. It started because Bill Gates wanted to go home. He missed it. And looking back the reason Seattle goes on to become the top 2 tech city in the world comes down to one person. Yes, Bezos came later, but he came because of Gates.

Gates got the flywheel turning.

London’s Bill Gates

And this is what struck me when I was watching The Thinking Game about Demis Hassabis. There is a part in the movie (minute 6:12) where Demis convinces Peter Thiel to back an AI research company which has no product. Thiel agrees to fund Deep Mind, but wants to move the company to Silicon Valley. Demis pushes back. He doesn’t want to be in Silicon Valley .

He wanted to be in London to tap into the top talent at Cambridge and Oxford. And he wanted researchers who would stick with him for the long haul it was going to take to build AGI; not hopping around all over the place for the next stock option package. He didn’t want to be near all the other start ups.

Thiel reluctantly agrees that Deep Mind can be based in London. And that right there was the inflection point. The Bill Gates moment. Demis loves Cambridge and wants to be near it.

Fast forward to 2014 and Deep Mind gets bought by Google and London becomes one of Google’s top hubs for AI research.

It reminded me of the Bill Gates story in Enrico’s talk.

London’s Jeff Bezos

And I was reminded of Enrico’s talk again today.

Like with Bezos packing his car to move to Seattle, in a fortuitous turn of events, another AI titan wants to focus on London.

This is the news today.

After being designated a supply chain risk by the US Department of War, it seems Dario Amodei sees the strategic benefit of diversifying out of the US security net and building up a strong London hub.

Like Demis, Dario also sees London is a globally unique hub of research talent in one small place (Oxford, Cambridge, Imperial, UCL, Francis Crick Institute, Alan Turing Institute).

Sam sees it too.

Elon’s xAI is also building out their London office.

It’s happening. London is turning into a global AI hub.

The Seattle of AI.

So how do we make money?

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

Liverpool Street, London

This wasn’t the fancy office building he had been imagining. Erik stood in front of a narrow 5 story building. This was the address David had given him though. Despite its outside appearance somewhere in this building was a successful hedge fund. He scanned the list of tenants in the empty lobby and found Belway Capital on the 3^rd floor.

The elevator doors opened directly to a small reception area, but with no receptionist. Instead, posted on the wall was a sign with “Trading desk this way” written in pen, which pointed down the hallway. This must be their version of a receptionist.

Erik followed the sign down the hallway to an open plan room full of trading terminals. There were 8 traders sitting on both sides of a long desk. Each trader faced a wall of 6 computer monitors blinking with stock prices and charts from markets around the world. Some screens had news alerts scrolling down. The phones blinked but didn’t ring. One of the traders with his back to the entrance heard Erik enter the room and turned around.

“Hello. Can I help you?”

“Yes, I’m here to see either Dwight or David. My name is Erik Anders.”

“Dwight, Erik Anders here for you.” the trader yelled over the screens to a pair of legs hidden under desk on the other side.

Dwight jumped up and came around the bank of terminals, hand outstretched, smile beaming. “Hey. How you doing? I’m Dwight” he introduced himself. Erik immediately picked up that Dwight was another American in London, like himself. Dwight was wearing a short-sleeved golf shirt and obviously took his fitness seriously. He was muscular with strong arms.

“Hi Dwight. Nice to meet you. David said to come by and see you. I used to work at Kratos Capital. David and I have been sharing European bank stock ideas together.”

“Yes! David said you were coming by. He’s right over there.” Dwight pointed down the line of terminals to where David waved while still speaking on the phone. “Let’s grab a room.”

Dwight led them to a small conference room with a glass door just off the main trading floor.

When they were seated Dwight started off by saying “Hey! Thanks for coming by. David said you guys have been chatting financial sector ideas and that you’re pretty good. But he said Kratos is shutting down and you are looking for a new seat.”

“Yes, Kratos is shutting down. Performance was OK, no massive losses, just lots of 5-7% years. The LP’s got bored and redeemed. The head PM would never take enough risk and so we never had big years. I ran a financials book for them.”

“Your CV says you worked for under a year at Hedland Capital. I know Hilton. Why did you leave Hedland?”

“It’s a great firm, it just wasn’t a fit for me. As you know, Hilton is super process orientated. He has a whole trading system where the analyst is really just a cog in the wheel. I wanted something where I could be more creative and come up with my own ideas. I think we both knew it wasn’t a good fit. Then the opportunity came up at Kratos, so I left.”

“Yes, I understand. So, David probably told you our set-up. We invest in a mix of public and private companies. We run around $500 million. Most of it is my money. For the public investments everyone runs their own book and gets a 20% payout. You can have a salary if you want, but it’s a draw and gets deducted from your bonus at the end of the year. I run the private investment book, but we work on the ideas together.”

“That’s basically how David explained it. And do you think there might be a role for me? David said you might be looking for someone.”

“Yes. I’m looking for someone. But not for financials specifically. We have David and he’s got that sector covered. I’m looking for something different. We’re starting to do a lot of investing in Africa. I want someone to run an Africa public book plus help me with our Africa related private investments. I know you’ve never done Africa before, but David says you are smart, so you can probably figure it out.”

“So, run a book investing in African listed stocks? Plus work with you on private deals? Hmm… it’s a bit different from what I was expecting… but it does sound interesting. May I ask why you are looking at Africa?”

“How many people in London do you know investing in Africa?”

“None. You’re the first person, which is why it’s interesting. Nobody invests in Africa.”

“Exactly! Think about it. 1.3 billion people growing GDP at 5%, just 3 hours south of here. We are in the financial capital of the world, and you don’t know a single person investing in Africa. To our trading peers it doesn’t exist. Nobody is looking at it. Isn’t that strange? But you know what that means? It means inefficiency. And inefficiency is where you make money. So I’m focused on Africa and want to build up our investments there. I need a person to help me and I was thinking someone like you might be a good fit.”

This Dwight guy was pretty cool.

“Hmm… When you put it like that Africa does sound interesting,” Erik mused. “Can I give it a bit of thought? I’ve been looking for new roles and have a few other discussions underway. This does sound potentially good though.”

“Yes. Think it over. Speak with your wife. And feel free to come by again if you want to discuss it further. It’s important to make sure you know what you’re getting in to. You’ll find Belway is a great firm with a good bunch of guys. No politics. We just focus on making money. Ask David. I think he’ll tell you he did really well last year.”

Erik thanked Dwight for the meeting and headed home on the tube to Kensington. His head swirling with images of what it would be like to invest in Africa. This was not what he had been expecting. The job would surely involve lots of interesting trips to Nigeria, South Africa, Egypt and Kenya. It could be fun. Erik loved travelling and adventure. And this Dwight guy seemed pretty adventurous too. Especially if he was running $500mn of his own money and investing it in Africa.

His train arrived at Earl’s Court tube station and Erik walked up the final steps out on to the street, where he was surprised to see Nicole standing there waiting for him.

“Sooo….. how did it go?” she asked. “How was your meeting with Belway?”

“Hey there! Great to see you! How was the Belway meeting? Well, not what I expected, but super interesting though. Let’s sit down somewhere and get a pint. I want to tell you about it. We have to think about this.”

A few minutes later they were seated at a small table outside a local pub. It was early in the afternoon but they both had pints in front of them. Nicole was more fun than Erik. She was always up for celebration, adventure and a good time. It was why he’d married her.

“The firm is run by a guy named Dwight. He wants me to run an Africa book. He’s got David doing the financials already. He wants me to run a book investing in African public equities plus help him with African private investments. He pays 20% on performance. Pure performance with no discretionary thumb suck BS. That part is good.

But it’s the investing in Africa part I’m not sure about. On the one hand I’ve been looking for a job for 6 months and have no leads. None of the recruiters like my CV and I haven’t been able to get even one interview. You know how it’s been. On the other hand, Africa is super niche, and a total U-turn from what I’ve done before.

But maybe I’m supposed to take it. I worry if I say ‘no’ I’m like that guy standing on the roof of his house in the middle of a flood who never gets in the boat. What do you think?”

“I don’t know. It’s different, but it doesn’t sound so bad. It sounds kind of fun. You like travelling and would get to go to Africa. And it’s a job with a 20% payout based on performance. You could end up making a lot of money, “ Nicole reasoned.

“Then, I should probably take it. It’s just that it’s going to be do or die. There are already too many stops on my CV. I’ve taken too many jobs where it didn’t work out, and I have nothing to show for it. If I go off and invest in Africa, then come back 6 months later and try to be a finance analyst again it will be a disaster. The head hunters won’t touch me. And you know how our finances are. I can’t be unemployed again. If I go off on this tangent and invest in Africa it HAS to work.”

“Then make it work!” she replied .

Meanwhile, another bull market is unfolding.

A bull market no one is talking about.

US LNG.

And potentially thermal coal.

Director,roll clip of Patrick Pouyanne at the Total Strategy Day, September 2025.

Patrick was positive on Total’s LNG business, but kept reiterating the challenge for the market would be absorbing the upcoming supply wave. Between 2025-2030 global LNG supply would increase from 420 MTPA to 650.

And the biggest source of the new supply was Qatar’s North Field East (NFE) and North Field South (NFS) projects. NFE would bring a massive 32 MTPA in 2026 and NFS another 16 MTPA in 2028.

Side note: Of the 650 MTPA in expected future supply 150 MTPA would from the UAE and Qatar. Supposedly.

Summary: LNG demand from Asia would grow, but the new supply coming on line had it more than covered.

Director. Stop clip.

Fast forward to March 2026.

Qatar isn’t adding 32 MTPA, it’s losing 12.

Potentially for 5 years.

And Technip, a major contractor on NFE and NFS, has employees sheltering in place and relocating out of the Gulf.

12 MTPA is 3% of world supply, so manageable.

But a bigger ripple effect will play out over the next 5 years if there are project delays to NFE and NFS.

Because these questions come into play if you are an LNG buyer:

#1. Is this a 1-2 year delay? Instead of 2026-2028, NFE and NFS are completed in 2028-2030? Or will it be longer?

#2. Does the Iran situation get sorted out, or is Persian Gulf LNG structurally high risk?

#3. Do you keep switching from coal to LNG? Or, is it risky to depend so heavily on a complex energy supply chain dominated by the US and the Middle East? Is it better to stick with coal?

Let’s work backwards starting with coal (#3).

Coal demand was supposed to collapse 25% in the years ahead.

Now it looks increasingly sensible to for a country to keep its energy sources diversified, especially if they have local access to coal. There is still the pressure to shift to gas, but the previous shame and guilt around coal is giving way to practical considerations around energy security.

Coal is also a lot cheaper.

The JKM price of $20/Mmbtu for LNG is equivalent to $300/ton for thermal coal ($135 currently).

We can’t model every investment decision in the world, but existing coal plants likely just got a few more years of life, and the shift to gas moderated slightly, especially for poorer countries.

Is the Persian Gulf structurally higher risk (#2). Yes, and it always has been. What happened this March is the earthquake we’ve all known could happen one day. But the risk was always there. The risk perception will settle down eventually, but for the next 2 years LNG buyers will be wary, and consider US projects instead, even if they could be creating a new concentration risk.

What is the delay to NFS, NFE? (#1)

It depends a lot if the damage to Ras Laffan Industrial City was to standalone LNG trains or includes shared infrastructure which was also supposed to be used for NFS and NFE.

Can new contractors come in and work on everything in parallel and it’s a only 6-12 month delay? Or do NFS, NFE exports make use of infrastructure which will be offline for 2 years?

With the surge in US LNG projects is there spare contracting capacity available to immediately start work in Qatar?

I’m pencilling in that these projects are delayed by 2 years and the first trains start coming on line in 2028.

Now connect the dots.

How do we make money?

Quick public service announcement: I’m not moving to Kenya. The Nairobi Solution is a short story I wrote. I’m sharing a chapter a week. Chapter 1 was last week, Chapter 2 drops Sunday.

US LNG Terminals (LNG,VG,NEXT)

If you are a US LNG terminal trying to sell capacity over the next 3 years the Qatari force majeure is a game changer.

Your worry had been there was too much supply coming at once and it would be a dogfight to get contracts.

Now all of a sudden you are the prettiest girl in town.

First, you have the 12 MTPA of Qatar force majeure demand scrambling to buy on the spot market. If you run a diversified LNG trading business like Shell, Total and BP you can make a lot of money moving cargoes around and selling on spot. But it’s also good for terminal operators like Cheniere who save some capacity sell to the market on spot.

But the more gradual trend, which the market doesn’t seem to give much value is how global LNG buyers will have to shift to the Gulf of America for supply. US LNG really benefits. It has low cost gas, enormous projects, and supply security.

I see three ways US LNG terminal operators might benefit:

Benefit #1: the disruption in Qatar derisks future LNG trains from Cheniere, Venture Global and NextDecade. The biggest supplier in the world is offline with no visibility if/when they can ever deliver.

Benefit #2: does the price of future tolling agreements increase from $3/MMbtu to $5? Do we want exposure to the projects with the most optionality; supposedly ‘risky’ projects like NextDecade?

Benefit #3: Are we underestimating how much the terminal operators are going to make from selling uncommitted volumes into the spot market? These could seem like 1-off gains, but the gains could compound over time if it enables the terminals to pay down debt faster than otherwise, and shift to share buybacks.

Normally, I would want to look at the natural gas producers and play for a rise in 2027 Henry Hub from $3 to $6, like how we would play a gold miner, but strangely, I’m drawn to the terminals instead.

My biggest concern about the terminal trade is that it’s going be long and boring.

The quarterly earning announcements will be dry updates about FID’s, and financing. For long periods of time the stocks will probably do nothing, and the dividend yields are low or non-existent. Meanwhile, everyday there will be lots of excitement around AI and chips competing for our attention.

But if you patient and willing to lock in to owning strategic LNG infrastructure plays for the next 4 years, they could all do really well as the their new capacity comes on line, the cash flows compound and the stocks rerate.

I see three ways to play this and they all look good.

Cheniere (pronounced ‘shaneer’)

Read more

Everyone wishes they could go back in time and have done the trade.

But what’s so attractive about the story?

What hooks you is the unfolding realisation as Burry and Eisman pull the MBS thread and discover the supposedly safe US housing market is not what everyone thinks. They meet with slimey mortgage brokers, they meet with the ratings agencies, they meet with the strippers in Las Vegas.

They connect the dots across the financial system and suddenly, they see something nobody else can see.

A new reality unfolds in front of them.

And the trades become clear.

The safest securities, mortgage backed securities, are actually the riskiest.

Everything is priced incorrectly.

Fortunes can be made.

There is a line which repeats itself in the movie.

“These outsiders saw the giant lie at the heart of the economy, and they saw it by doing something the restof the suckers never thought to do: They looked.”

Every trader watches the Big Short and wants to experience that realisation nobody else yet sees. They want to be that person who connects the dots first and puts it all together. They want to be the one who ‘looks’ and predicts the next collapse which is hiding in plain sight.

I see this with the hysteria currently around Private Credit.

Everyone jumping to conclusions, trying to connect one dot to another and hoping it’s another GFC trade. And they are the only one who can see how one dot leads to another and the whole market crashes. It doesn’t matter that none of the other Big Short facts or ingredients support the view. They are trying to recreate the Big Short trade and they want it to be true.

It’s too bad.

Because there is another Big Short hiding in plain sight.

It’s there if you just look.

Dan Rather vs the Algorithm

I was a kid in the 80’s. At 7:30 after dinner you would watch the evening news with your parents. There were only three channels. You could choose between Dan Rather, Tom Brokaw or Peter Jennings. All three were basically the same. And the news was the news. Reporting with almost no spin. There would be 7 minutes about an international story, 8 minutes about 2 US stories and maybe something at the end about sports or a special interest story.

The key thing about back then was everyone watched the same news. There was a common reality. Ted’s family down the street watched the same news we did. And Ted’s Dad might have have been a bit more ‘left’ than my Dad, but there wasn’t much difference and we still had dinners together. People talked about politics a bit, but not much. If we did talk about world events we were’t stuck in quicksand right out of the gate debating what had even happened.

Fast forward to today. We have terms like ‘Fake News’. To teenagers the concept of ‘narratives’ and ‘gas lighting’ are second nature. And everything is hyper politicised in a way it never used to be. Plus no one trusts anything they see which doesn’t support their point of view.

Behind all of this is the pernicious innovation slowly turning us insane; the algorithm. The algorithm senses what you want to hear and starts to nudge you down a tunnel. The news isn’t passively there. The algorithm is gaming you. It’s giving you more of what you want to read. It’s maximising for engagement.

Most people also have an existing disposition based on their upbringing (‘the good guy never gets ahead’, ‘the system is rigged’, ‘the problem with most people is they don’t work hard enough’, ‘I feel guilty about my rich upbringing and need to give back to poor people’). The algorithm picks up on this seed and selects the world you see, further reinforcing your existing view. You become more politicised.

You go down your own reality tunnel.

The Collapse of Reality

You are in your reality tunnel, but you look around and realise everyone is in their own reality tunnel too. Which makes you question ‘Is what I think really the truth? Or, am I as crazy as that MAGA guy I hate, but just in a different way?’

The amazing reality of AI images gives you the unsettling realisation you can no longer tell if images are real.

We saw the early stages of this with the 2024 election. Everyone counting the fingers of people in the crowds to see if election rally turnout pictures were real or AI recreations. Do people really like Kamala/Trump that much? Or is it fake?

Who knows?

Think of Russell Crowe’s character in ‘A Beautiful Mind’. He’s insane and he knows it. He has these conspiracy theories and keeps seeing things which seem real, or maybe they aren’t. He’s not sure. After all he’s crazy so maybe he’s just seeing things. But then they seem real. The struggle he makes peace with at the end of the movie is that he no longer knows what’s real, or what’s not, and it’s not going to change.

And you realise to some extent we are all starting to have that problem.

We live in our digital reality tunnels and are slipping into an insanity where we no longer know what is true. While it looks real, we are still aware enough to know it might not be. What if it’s all a simulation? And that is a very disorientating condition. Not being sure what’s real.

As a result you see the rise of skepticism. Doubt everything. In contrast believing in anything is seen as being naive and vulnerable.

This is the Breakdown of Reality.

If you really want to go down this rabbit hole, and there are many more layers, I recommend The Sorcerer’s Apprentice by .

The Collapse of Society

This state, where we live in our own hyper politicised digital reality tunnels, with no common truth, makes it hard to have a democratic society.

We are losing the bonds to our neighbours and friends. The first thing we want to know when we meet someone new is ‘Do they support Trump’, because it tells us everything we need to know about that person. It sums up everything. If they ‘support Trump’ then there is something wrong with them. ‘How can they think that?’ They might seem nice, but if they are a Trump supporter you will likely always argue and will never want to go on a fishing trip together. They seem nice, but better to be polite and move on. Stick to your clan with people who think like you do.

Whole families and friend groups are fracturing over politics in a way I’ve never seen before.

And I trace it back to our growing insanity.

How do a people govern themselves peacefully when they no longer respect each other, have no common reality, and can’t even agree anymore on whether events have happened or not.

It undermines the operating system necessary for a voting democracy.

Let’s connect a few more dots.

The Collapse of Money

Money is a social construct.

And it is becoming increasingly abstract.

First there was physical gold.

Then there was idea of gold backed by paper currency.

Then there was the idea of government issued paper currency not backed by anything at all. That was a major mental shift.

Then the physical paper in your wallet went away and money became digital 1’s and 0’s.

Then the ‘government’ part of money went away and money could be anything. Money could be a token manufactured by a computer program on the blockchain.

This was another important cultural inflection point.

Money can be anything. It can be a Bitcoin or USDT.

People joke in memes about the full change of the financial system over to a dog token. It’s funny, but real damage is being to the traditional concepts of money.

People chased Bitcoin for FOMO, but that chase entailed obligatory hours spent watching YouTube videos about the history of fiat currency. The young traders of today understand fiat currency in a way we rarely questioned when we were younger. We grew up in the Strong Dollar Era. They grew up in the Bitcoin Era. It’s a societal shift.

Observe another societal shift. Now it’s natural to ridicule central bankers. ‘Everyone knows’ central bankers are charlatans. Yet, not so long ago Greenspan was a highly respected maestro who carefully pulled complicated monetary levers and kept our economy on the rails. Today traders sends around memes of central bankers and their money printers. Money printer go Brrrr…….

From maestro to joke.

It’s a collapse of trust in our monetary institutions and in the value of money.

The Collapse of Fiscal Discipline

Notice also our cultural shift around fiscal discipline. It’s another tether to reality getting cut.

Back in the Dan Rather days it was common in politics to talk about balancing the budget. Usually, it was a Republican ideal. Fiscal conservatism. Now post-COVID no party talks about balancing the budget.

After the election Elon’ DOGE initiative tried to use technology to identify wasteful government spending. You would think that would be a helpful initiative and we would be grateful. Surely, there are efficiencies we can find. Nope. People hated it.DOGE didn’t last 10 months.

Think on that. He was hated for trying to cut spending.

It’s a sign of the times and the deluge to come.

We don’t care anymore if the spending is wasteful or not, just keep spending.

Snip.

In Europe Germans use to be adamant about fiscal discipline. Now that’s gone. Germany, once the policeman of fiscal discipline and enforcer of the Maastricht Treaty, is going to run 4% deficits for the foreseeable future. Europe is also sliding down the slippery slope where nothing matters anymore.

Trump has suggested we increase the defence budget by 40% to $1.5 trillion.

$1.5 trillion. Why not $3 trillion?

We used to talk about hundreds of billions. Now we talk about trillions.

And we don’t argue anymore about the deficits. We just argue about who gets the money.

This is the collapse of fiscal discipline.

One more dot.

Collapse of Rule of Law

I leave this for last.

Because this is the most dangerous collapse of all.

It’s what takes us down.

Here also we’ve had a shift in culture.

There is a growing societal perception the system is rigged. In the Dan Rather days it was commonly assumed we had equal opportunities. If you worked hard you could get ahead. That view has shifted.

Now our government is a ‘Swamp’ where politicians band together regardless of party to milk the regular people. The Epstein case shows us elites party together, and do what they want with no consequences. Everything can be swept under the rug. One rule of law for the rich. Another for everyone else.

This cynicism about the ability to get ahead and frustration that the rich get away with things regular people don't is bad, but in my view, by itself not that inflammatory. To some extent this has always been the case through history. It’s a known thing.

There is something much more inflammatory to the common man and woman.

The previous collapses in reality, society, money and fiscal discipline are gas soaked rags compared to what happened earlier this year.

The Somali welfare fraud uncovered by Nick Shirley.

The Somalis in Minnesota started a fire which is going to burn everything.

Having grown up in a rural town in Montana I think I see this in a way my financial peers don’t.

It’s one thing if Bill Gates breaks the law and gets away with it. I’m not saying it’s right at all! I’m just saying to the common man Bill Gates is a software genius who invented Microsoft. To the common man, it’s bad, but it doesn’t turn your world upside down if he committed a crime and got away with it.

What enrages you, what makes you question why you put your boots on in the morning, what makes you never want to pay taxes, what makes you question everything about the system is when an entire Somali community can collectively engage in welfare fraud with sophisticated fake businesses which steal government money in the hundreds of millions of $’s with impunity. Now they live in better houses than you, drive better cars than you and their kids get into Harvard.

That frustrates you. That pulls society apart.

What frustrates the working man and woman more than rich people cheating, is when your neighbour cheats, gets rich, and gets away with it. While you don’t.

THAT turns your world upside down.

Everything breaks and the mentality shifts from how do I work hard to get ahead to how do I steal too? Because everyone is doing it.

That is the break down of the rule of law.

A German friend noticed this at his karate dojo back in Germany. His karate class is mostly working class students. Two years ago he would overhear them complain about people who were cheating the system and getting extra benefits. Now after class the students share cheating secrets amongst themselves. Where before they criticised it, now they are all in on it.

There is another significance to the Somalis in Minnesota.

It’s ‘Minnesota’.

Think about it. Minnesota is known for its Scandinavian heritage. The Scandinavians came to Minnesota and Wisconsin to farm. The football team is The Vikings. There is a whole stereotype around people from Minnesota and Wisconsin. They are the salt of the earth. The people are normal, middle class, and trustworthy.

But if ‘Minnesota’ has changed and is capable of this level of corruption, what does it tell you about New York, Chicago and California?

It tells you the whole system is broken.

It tells you everyone is doing it and if you aren’t in on the stealing then the joke is on you.

That’s why Minnesota breaks us.

It’s the final collapse. The collapse of the Rule of Law.

Now let’s connect the dots and figure out how to make money.

What’s the Crash? What’s the Trade?

So what’s the thing that’s there to see if you just ‘look’?

What do you see if you connect the dots?

What trade seems crazy?

What’s The Insanity Trade?

Or, should I say ‘Trades’ because I see more than one.

Read more

Pudong always looked futuristic.

A Chinese Wall Street built all at once in the late 1990s. Every skyscraper new and modern. Pudong’s skyscrapers were also symbolic. Symbolic of the size of China’s financial system and how quickly it had come on the world scene. Maybe that was the point, Erik thought.

He peered up struggling to see the tops of the buildings as their car moved along Fucheng Road. The glass towers blurred past his window in the late afternoon haze. They arrived at the impressive Shanghai International Center, one of Pudong’s tallest buildings. Somewhere up there on the 83^rd floor was China Eastern Rail Systems (CERS).

Erik collected his bag from beside him on the backseat. It was the canvas Orvis bag Nicole had given him for a birthday gift because he liked fly fishing. It had his computer and his notebook. Next to it was Claire’s purse which was glossy black leather. Erik sighed, opened the car door and swung his legs out onto the street.

‘Alright let’s fix this mess.’

A young, professional woman from China Eastern greeted them in the lobby and escorted them to the elevator bank. They stood in silence as the modern elevator whisked them up to the 83^rd floor. When the elevator doors finally opened, they were led down the hall to a large conference room with black walls and no windows. The woman gestured for Erik and Claire to sit on one side of the long sleek boardroom table. Various red silk tapestries decorated the walls. Recessed spotlights shone down on large ceramic vases on pedestals in the corners.

Seated across from Erik and Claire were Lui An, Chen and 4 other members of the China Eastern team. 6 on 2. Everyone smiled politely while an assistant moved around the table carefully pouring tea. After the teas had been poured, the assistant left the room and the door closed quietly behind her.

Leather bound folders were set out on the table in front of everyone. Erik assumed these folders contained copies of the recent CERS price proposal. The reason they were here. Erik didn’t touch the folder or look at it.

Lui An sipped his tea and remarked that the spring had been wetter than usual this year. The pre-amble about the weather and the seasons reminded Erik of Japan.

Eventually Claire thanked Chen and Lui An for the meeting.

“Director Chen and President Lui An thank you for welcoming us to your office. We’ve met before, but I would like to introduce Erik Anders. Erik is a Director for Turkana Trust, the main bank financing our project. “

Erik nodded and smiled to the others across the table, while Claire continued.

“And thank you for putting together these updated rail equipment proposals. However, we would like to discuss what appears to be a misunderstanding in the terms. Naturally, given the importance of the contract and the strong relationship between our firms we thought it best to meet and discuss everything in person.”

Claire was surprisingly good at this diplomatic nonsense. Her outside demeanour was measured and polite. China Eastern had started a knife fight and Claire was speaking as if this was the United Nations.

“As you know we have been working with your sister company to construct Stage 1 from Mombasa Port to Nairobi. The work has been on time, on budget, and the final stretch to Nairobi is scheduled to complete within the next 12 months. We are at the point, as stipulated in our original agreement, to order rolling stock for the project; the 8 locomotives, 40 passenger coaches and 80 intermodal wagons.

In keeping with the terms of the agreement, East Africa Rail sent you a $50 million deposit on June 15^th last year. Understandably, we were surprised to see your latest invoice which appears to reflect a 25% increase from our agreed upon price. Director Chen or President Lui An would it be possible to help us understand the discrepancy between this latest invoice and the fixed price agreement which we signed 4 years ago when we began the project?”

Chen nodded to Lui An to speak.

“Thank you Ms. Delacroix, and Mr. Anders for travelling to Shanghai. We are grateful for the strong relationship between East Africa Rail and China Eastern. We are glad the project is progressing on time and on budget, as we expected it would. Yes, we received the $50 million deposit last year, but we never received the associated financing guarantee for the remaining $250 million from Turkana Trust despite repeated requests. I think this is perhaps the basis for our misunderstanding.

As stipulated in the agreement Turkana Trust is required to provide a letter of guarantee within 90 days of the deposit payment. We received no such letter despite repeated attempts to contact the bank. Mr. Anders, your bank’s CEO, Mr Rajiv Desai was unresponsive. Still, to this day have not received any evidence of Turkana Trust Bank’s financial support of the rolling stock order.”

Director Chen took over.

“We of course will continue to support the project and intend to start manufacturing the rolling stock as soon as possible. But you understand that having never received any financial guarantee from your bank, the initial contract terms have lapsed. The price increase you noticed in the invoice reflects the increased price of steel and general costs over the last 4 years. We had to adjust the terms of the agreement to maintain our margin. Obviously, we cannot deliver these locomotives at a loss.”

Rajiv had never sent the financial guarantee letter to CERS. That started to explain things.

“Director Chen”, Erik stepped in, “I apologise for any challenges you may have had up to this point in working with Turkana Trust. It is upsetting to hear you did not receive the financial guarantee alongside the initial deposit. I came to Shanghai because I wanted you to personally know that Turkana Trust is under new ownership. Mr. Rajiv Desai is still CEO of the bank, but he and his family are now minority shareholders. The bank is now majority owned by Belway Capital in London and I assure you things will be run differently. I have been appointed by Belway to sit on the Turkana Trust board and focus on the key strategic issues.

As you realise the East Africa Rail project is extremely important to the bank and the change in contract terms you are proposing creates a material difference in the cost of the project. As you know the equity funding for East Africa Rail comes from the IFC. While the IFC supports the project and has guaranteed to fund the full equity obligation this funding gets released in tranches. A price change this material to a major line item is almost certain to trigger an entire financial review of the project and potentially halt further disbursements potentially creating a 1-2 year delay. Naturally, this delay is something we would like to avoid.

Given all these considerations, and in the best interests of all of us, would it be possible to stick to the original terms of the agreement, maybe with the understanding that when we contract for Stage 2 to Kampala we include extra margin into the contract to compensate CERS for its current flexibility? If we could work something out along these lines it would be highly appreciated and build our relationship for future projects.”

Erik could sense Claire wincing at this proposal, but she said nothing.

“We understand your position Mr. Anders and Ms. Delacroix.” Lui An responded in a fake caring voice with his hands clasped in front of him. “And we have made the increase as little as possible, but we cannot go lower. You of course are also not bound by the initial agreement and are free to negotiate with any other suppliers you may wish to speak to, like the Japanese.”

Claire decided for one last plea.

“I would like to reiterate what Erik is saying, that this change in price for the rolling stock is going to trigger a complete project feasibility review by the IFC at our next board meeting. It will create a severe delay while all the feasibility studies are reviewed again. There is a risk the IFC underwriting committee may not commit to more funding. Obviously, this would be bad for both of our companies.” Claire implored.

Neither Chen or Lui An said anything.

Erik could see they were at an impasse.

“Would it be possible for someone to direct me to a private room so I can make a phone call?” Erik asked. “I would like to update my team in London. I won’t be long. Please excuse me momentarily.”

Erik was escorted down the hall to another conference room. This one was much smaller, but had a window looking down over the Huangpu river. The Huangpu divided Shanghai East and West. Across the river was the Bund, an older district of Shanghai. The sky was grey. It was often slightly grey.

Erik sighed, pulled out his phone and called Dwight. This was not going to be pleasant.

“Dwight, hi. It’s Erik”

“How’s it going? Not great. They raised the price and it’s not a misunderstanding. They say Rajiv never sent them a financial guarantee letter which was supposed to have accompanied East Africa’s deposit. According to CERS the contact terms lapsed. My guess is they are probably right.”

“I know. I can’t believe it either. I tried offering that we keep the old price, and make it up to them in Stage 2, but they didn’t bite.”

“I AM trying to sort it out. But this is Rajiv’s screw up. I don’t know what he was thinking. Maybe he was trying to keep the financial guarantee off the books so it wouldn’t go into the Central Bank of Kenya RWA calculation, but I don’t know. I’m just trying to guess why he would make such a massive screw up.

Dwight yelling in the background.

“I KNOW it’s a major problem. But remember we always said there would likely be surprises; things we wouldn’t find in the due diligence. Maybe this is just one of those things.”

Erik held the phone away from his ear as Dwight’s yelling got louder.

He had experienced this before and knew the pattern. Dwight was about to go into full erupt mode.

Erik continued listening with the phone away from his ear.

Erik started to frown.

“You want to get rid of Rajiv? And I’m supposed to run the bank? You mean the everyday stuff? The treasury?”

“I mean I guess I can do that. But wasn’t the original idea that Rajiv had experience running the bank, and had all the local connections? And we would just give strategic oversight from London?”

Erik listened.

“Yes, I know Rajiv is a fucker. OK.”

“Obviously, I’ll have to speak to Nicole about it. She will most likely be ok with it. Though I know for sure she won’t move to Nairobi. I’ll have to commute. We will work it out. In the meantime, what do you want me to tell the Chinese?”

More yelling from the phone.

“Tell them no deal and we’ll find someone else? Even though we have no idea who and the Japanese will be even more expensive?”

Erik leaned his forehead against the window and looked down at the river 83 floors below. The glass felt cool and nice on his face. It was early evening and small barges were floating coal down the river to a nearby terminal. Across the river there were the lights of cars driving along the Bund.

In the background Dwight was expressing how furious he was about Turkana Trust’s loan exposure to East Africa Rail. How he always knew this was going to be a problem and how angry he was for getting involved in Turkana Trust in the first place and how it was Erik’s fault for getting him into this.

Erik didn’t want Dwight to get any madder. He had to make this work out somehow. It couldn’t be yet another failure. Even if it meant moving to Africa.

“Yes Dwight. I know I got us into this.”

“I know it looks bad. I will find a solution. Somehow.”

Chapter 2

Let’s review the Q1 performance of our two portfolios (Dirty Dividends and Cash Dragons) as well as the overall liquid allocation.

The purpose is a no BS quarterly review of what’s working and what’s not. Plus share my thoughts/reactions.

I also always want to be clear which ideas I own and which I don’t. As in ‘Has he put his money where his mouth is at?’

So it’s good to periodically show where I actually have positions.

Note: This portfolio information is also available at (www.ywr.world/portfolios).

Q1 Performance

Dirty Dividends started the year strongly but, gave it all back in March. The portfolio’s dirtiness (energy and coal) really helped in March.

Cash Dragons had major problems with its tech investments in Alibaba and Baidu. More on that below.

Everything together (Dirty Divs, Cash Dragons, Gold, AI, Other) is -2% YTD. Not terrible all things considered.

Dirty Dividends

The only notable change was that I added a small position in Frontline. I got sucked in by the high dividend yield (11%).

My shipping expert friends say I’m a tourist and going to get wiped out. Yet the YWR Factor Model and QARV keep flagging that something positive is happening to shipping. Yes, there are still ups and downs, but the data says there are a lot more ups. The 5 year average ROE’s are screening surprisingly well. I keep thinking there could be a new normal unfolding for shipping, but nobody has articulated it well yet. Or, the smart ship owners know it, but aren’t saying anything.

I need to decide if I should add more.

Otherwise, the only trading was reinvesting dividends to existing positions.

I’ve been building up the Korea ETF and SK Hynix to slowly reduce the Europe exposure.

Other Observations:

Unicredit is the JP Morgan of Europe and Andrea Orcel is the new Jamie Diamond. I’m going to let Orcel do his thing for years and watch the Unicredit share price compound. It’s my biggest position, and in May I’ll be excited to tender my shares in Commerzbank for more Unicredit.

Jackson Financial always looks insane.

SK Hynix also looks insane although the dividends so far have been crappy. Everyone’s big fear is memory is a bubble. Which is what the 5x P/E is saying too.

I might add to the Korea ETF. I like the idea of Samsung as the new TSMC on a P/E of <7x, plus all the other Korea goodies (SK Hynix, Korean defence, Korean banks, Hyundai).

Ping An looks looks very undervalued and is consistently growing their dividend. Just announced RMB 2.7/share. This should trade >HK$ 100/share in the years ahead.

Vinci… if you ever feel you need to invest in a private equity infrastructure fund, invest in Vinci instead. Vinci started out with the biggest toll road network in France, then used the toll roads as a cash cow to become the largest owner of private airports in the world and soon the largest global toll road operator too. Steady, eddy growth. Steady, eddy dividends.

I should buy a little more some day.

But then I’m trying to invest more in Asia.

But then maybe the European companies are doing the same thing for you.

So buy Vinci and get ROW growth with a ‘Europe’ discount?

The Gravitational Pull of the US.

Macro strategists often predict how money is going to withdraw from the US, but when I look at the results presentations in the YWR portfolio, I see the complete opposite.

I see a lot of companies trying to grow their US operations. Because it’s the biggest market in the world; it’s growing, it’s dynamic and if you don’t have scale in the US it becomes a competitive disadvantage.

Take Barclays. They went through a big exercise to refocus around core businesses where they could earn high ROE’s, and surprisingly US Consumer Banking (USCB) was one of those core businesses they decided was strategically important.

I always thought how on earth is Barclays going to be able to compete in US credit cards, but to my surprise they have built a profitable and growing business managing loyalty credit cards with a 16% ROTE. Barclays stuck with it, and now as it starts to scale investors are realising it could end up being quite valuable.

I never understood why in 2025 Santander announced they were selling their Polish Bank to Erste for EUR 7bn. It seemed like a big earnings hole to fill. Then in February it made sense. Santander is acquiring Webster, a US regional bank, for $12 billion, which makes Santander a top 10 player in the US.

Like Barclays, Santander is trying to bulk up its US presence. What they both want is access to US retail deposits at scale. The US is the money tree and you need to be there.

Total, a French energy company, is trying to Americanise as fast as possible. Total went through great lengths to convert their ADR’s to full ordinary shares trading on the NYSE. This gives them better currency for strategic US M&A.

Total also realises the future importance of the US as the global supplier of LNG. Total are actively investing in Texas LNG terminal infrastructure and natural gas assets. We will talk more about this trend, because it’s massive.

Mercedes is investing $4bn into a new car plant in Alabama.

SK Hynix is investing $4bn in an HBM chip packaging plant in Indiana.

I highlight these data points from the YWR portfolio because it corroborates the other data we see.

The US is Booming and it’s pulling in investment from around the world.

Cash Dragons

Cash Dragons had a pretty bad Q1 (-12%).

There were no trades in the quarter.

Tencent (-21%), Alibaba (-16%) and Baidu (-15%) were all down, but Alibaba worries me the most. Tencent, on the other hand, is probably a buying opportunity.

Alibaba’s EBITDA declined -57% yoy as they ‘invested’ in quick commerce. Meanwhile, we are supposed to celebrate the 36% growth in ‘Cloud Intelligence’.

Chinese online retail competition is intense and apparently to stay in the game you have to be willing to involute yourself (make no money). It’s a common problem with investing in Chinese stocks. Alibaba is giving up margin in E-Commerce but says the future is its AI stack where it has the Qwen LLM, Alibaba Cloud and T-Head chips.

We are supposed to look through this near term carnage and imagine the enormous growth in the AI related businesses, but sheesh, it’s going to take a lot of Cloud to replace RMB 25bn in E-Commerce EBITDA that just went up in smoke.

This is a similar pattern to the collapse of Baidu’s paid search business, although Baidu seems to be coming out the other side. Baidu also touts the growth of their AI stack (Ernie LLM, Cloud, Kunlun Chips and Apollo Go).

I’m not sure what to think of this.

On the one hand I’m still ‘up’ on these stocks and I kind of want to see how it plays out. I wonder if this is the new vertically integrated moat of the future (LLM, Cloud, Chips, Apps). Amazon, Google, Tesla, Microsoft are all going down the same road.

On the other hand I wonder if these stocks are always going to be running to stand still. Will it always be Waiting for Godot? As soon as cloud and chips make money will they involute themselves all over again?

Or, is the LLM-Cloud-Chip-App stack harder to recreate and finally they make money?

And the problem with the fact Alibaba and Baidu don’t pay much/any dividends is there is no easy way to gradually pivot the portfolio away from the positions like we can in Dirty Dividends.

Oh well. I made this bed. I’ll stick with them.

Meanwhile, Tencent grew EPS 18% in 2025 and doesn’t seem to have any of these problems, although they did say they are going to invest a lot more in AI in 2026.

LVS is working, but it’s Singapore saving the day. Marina Bay Sands is closing in on $1 billion/quarter, which is insane for 1 property. MBS made more than the other 5 casinos in Macau put together (US$ 806 mn in EBITDA versus US$ 608 mn).

What does it say?

With RevPar of $900/night it’s another expression of the Elysium Analog.

The other takeaway is that China growing, but still sluggish.

I’m sticking with LVS, but we need the post-Iran NWO inflection.

Overall Allocation

Below is how all the liquid investments come together.

The whole pot was -2% YTD.

Summary view:

You know my view, but it’s not financial advice!

The US is booming.

Earnings estimates keep rising. Yes.

Iran is going to get sorted out and the world will be better for it (China in particular)

Markets will make new highs.

The best strategy is to stick with quality investments and, if possible, make small purchases through the downturn.

Otherwise, just hanging on to what investments you have while the tidal wave of negativity waterboards you is achievement enough.

Have a good Easter Weekend and look out for a special YWR surprise.

Erik

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And we wanted the best way to monetise it.

So late 2024 we bought HK Exchange and SGX Group.

Then September 2025 we added B3 to the theme.

So let’s check on how our little money makers are doing.

Hong Kong Exchange (388.HK, $HKXCY)

2025 was a great year for HKEX.

Earnings grew +36% in 2025 and the payout ratio was 90%.

Exactly the dynamic we love.

And we love the operating leverage.

Revenues (blue line) +32% and costs (red line) +5%. A higher tax rate in 2025 was the only drag.

Stock Connect: An exciting trend is the increased use of HKEX as a connection to access mainland Chinese markets (Shenzhen and Shanghai). This works in reverse too with a growing flow of trades Southbound from mainland China to HK. 23% of daily volume on the HKEX is coming through Stock Connect from Shenzhen and Shanghai.

On the product side HKEX is heavily exposed to equities and trying to build out commodities and fixed income. HKEX acquired the LME in 2012 which helped boost commodities to 11% of revenues. HKEX has partnered with the HKMA to develop a fixed income clearing platform called CMU Omniclear. The idea is to create an Asian version of Euroclear.

We have a view that it has become an unspoken Chinese priority to reassert HK as a dominant financial center. China realises financial markets are a form of soft power, and HK is punching below its potential. HK unlike Shanghai, is a brand foreign investors trust. HK could be much bigger in terms of asset management, global listings and global fixed income. And HKEX is the perfect play on this.

So we want to be long HKEX; clipping tickets on every trade as assets reallocate from Europe to China and the Chinese asset management industry catches up to the West.

The 2002-2007 Bull Market: 36% EPS growth is great start, but my view is the good times haven’t really started yet. If we get a real HK bull market again, similar to what we had in 2002-2007, earnings are going to grow beyond our imagination. From 2002-2007 HKEX earnings increased 9x ($0.57/share to $5.78/share). In comparison for 2025-2028 I doing the normal zero imagination analyst thing and predicting +37% EPS growth over the next 2 years.

I know the EPS can be a lot higher, but I’m too scared to put it down on paper. But it’s also why I know I want to be long HKEX as the best way to play a China bull market and don’t fuss too much about the P/E. Plus I love the 90% payout ratio.

Link to my HKEX model at the bottom of the post.

SGX Group (S68.SG, $SPXCY)

HKEX has the massive Chinese equity market with non-stop tech IPO’s.

But what do you do if you are Singapore Exchange?

You have a small local equity market, and occasional new listings, but otherwise how do you grow?

The scrappy Singaporeans have been building SGX into an Asian hedging platform with derivatives contracts for hedging Asian FX, Asian Equity indices and Asian commodities like Iron Ore, Freight and Rubber.

The SGX share price has performed well (+48% over the last year), but the earnings momentum has been less than at HKEX. Adjusted 1H 2026 EPS growth (December 2025) was just +11.7%.

The drag has been equity derivatives, which surprisingly have not been growing.

BTW, slow growth in equity futures volumes has been a theme across HKEX, SGX and B3. I’m not sure the problem. Are traders switching to options instead of futures? You tell me.

Like with HKEX it is hard to predict SGX future earnings. As analysts we always put in 10-15% volume growth, when reality could be 30-50% in a bull market. Still, I feel like the SGX share price is up with events here.

Let’s say SGX earns 90cts in 2028 and we put it on 25x then the PT is S$22. But who knows. There is so much operating leverage in these exchanges. Maybe SGX will earn S1.50 in 2028 and the stock will be trading at S$37. In the meantime I will keep reinvesting all the dividends back into more shares.

My SGX EPS forecasts through 2028.

Link to my SGX model at the bottom of the post.

B3 (B3SA3, $BOLSY)

The best time to buy exchanges is when the market is dead, but you think things are going to pick up. Then you when sentiment improves you get a double whammy. Investor expectations about future volume growth and earnings improves plus the P/E rerates.

That’s was the attraction of B3 in September last year when it was trading on a P/E of 13x. Nobody liked Brazil and nobody cared about B3 either (Making Money in Exchanges). Now here we are with the share price 27% higher (plus dividends)

But do you want to know the interesting thing?

B3 still looks good. On my estimates (and I’ve warned you exchange estimates are hard to forecast) B3 is trading on a P/E of 14.6x 2026 estimated EPS of R1.16 and 12.4x the 2027 estimated EPS of R$1.37.

And like with HKEX. We haven’t even really gotten started with the EM bull market. Brazil is still on the lows.

Yes, the B3 EPS grew 13% in 2025, but it was from data solutions (+15%), and fixed income revenues (+23%). Equity revenues were -2.45% yoy.

It’s actually amazing that B3 can grow EPS 13% when cash equity earnings are declining.

But the Brazil bull market may have finally started. Cash equity volumes in 1H 2026 are off to a strong start through February.

If we are patient and we get a real Brazil bull market we could see B3 earn $1.60/share in 2028 and trade on a P/E of 18x or R$ 28/share (+64%).

Summary

This isn’t financial advice, but I think the EM bull market has barely started and we have a lot more to go with HKEX and B3 in particular in the years ahead.

Below are links to the YWR models for B3, HKEX and SGX. There are also on the YWR website.

Read more

It’s where we review the data across thousands of stocks around the world and highlight the strongest earnings revision trends.

As an extra treat this month I’m going to throw in a bonus Mystery Inflection Trend.

YWR Top 25 Ranked Stocks

These are the top 25 stocks in the world based on a combination of estimate revision momentum, valuation and price momentum.

Samsung, SK Hynix, Micron, Nanya Tech, ADATA, Winbond Electronics, Innodisk: ‘Everyone knows’ memory stocks are super cyclical and always crash. It’s common knowledge. Which is why we have the biggest memory bull market ever with dramatic shortages and architectural differences causing unprecedented manufacturing complexity, and yet the stocks trade on P/E’s <5x 2027.

Riley Exploration (REPX): Now that the oil bull market has started should we be looking at small cap energy stocks in the US like Riley which are trading on P/E’s less than 5x? Riley is especially interesting because they are diversifying into power generation as another way to monetise their gas assets.

Here is a link to Riley’s presentation.

Steel: Salzgitter is in the YWR Top 25, but Arcelor Mittal also ranks well. Prices for HRC in the US and North European HRC, Ex-Works Ruhr are steadily rising.

The combination of strong local economies, defence spending, and regulatory protection from tariffs or in Europe the Carbon Border Adjustment Mechanism are creating an ideal, once in a lifetime, situation for domestic steel companies.

Are we in the earlys stages of a new normal where domestic manufacturing benefits from protected domestic markets and earns super normal returns?

I also need to find time to do some work on Nippon Steel. They bought USX and the stock is just sitting there.. untouched.

Top 5 Ranked Industries

Gold and ‘Other Metals’ (copper + aluminium) are the two best ranked sectors for their combination of estimate revision momentum, price momentum and valuation.

Next it’s all the Taiwanese semiconductor + electrical component stocks (Powerchip, Winbond, Nanya Tech, Unimicron, Gold Circuit, and on and on).

In the YWR Rankings sheet at the bottom of the post you can filter the full Taiwanese universe of semiconductor and electronic component stocks.

But Alert!

We have a new sector trend developing and it’s integrated oil stocks.

The oil trade is new (red line below) and will probably get stronger next month.

Top 5 Ranked Countries

We also look at the data on a country level.

The US always ranks well with a high number of top ranked stocks. The US market includes many sectors which are doing well, but it’s also the statistical result of having such a big universe of stocks.

South Korea is booming with top ranked stocks across semiconductors, electrical components, industrial machinery, finance, real estate development and home building. The strength in the South Korean industrial sector appears be spilling over into the finance and real estate.

Canada and Australia rank well due to the high number of gold and metals related listings. Australia also benefits from coal stocks like Yancoal and Stanmore which score well.

Maybe we should be looking at the TSX and ASX exchanges if the precious metals and commodity trend is going to continue. Or the JSE.

Inflection Analysis.

Since December we’ve been trying to identify trends even earlier.

In our EPS inflection analysis we look for where estimate revisions have recently inflected upwards.

The inflection analysis has been screaming to look at everything energy related; refining, contract drilling, oil and gas production, and oil pipelines.

The energy inflection is massive.

But can I highlight something potentially more interesting?

It’s the early stage estimate momentum in US Savings Banks.

Use the YWR Factor Model Data App (link below) and look up the estimate trends for Triumph Financial, OceanFirst Financial, Columbia Financial, and Northfield Bancorp.

Earnings estimate are rising for these stocks and yet nobody is talking about this new trend. As an extra positive the stocks have gone nowhere.

It’s also a positive read through for the US economic outlook if savings banks are surprising positively.

I have included links to the Google sheets below for the top ranked inflection stocks for each sector and country. This way you can see which stocks are driving the inflection in trucking for example. BTW it’s Werner. Trucking is booming. Another positive indicator.

OK. Now the mystery inflection sector.

Mystery inflection sector… another inflection so early the stocks haven’t move yet. Everyone hates this sector so much they are afraid to notice something positive might be happening.

Read more

I continue to go out on a limb on 2 points.

#1 Investors are fretting about a war which is basically over. The Iran war was won in the first 2 days. Now it is like a chess match where the opponent has 2 pieces left and you have to chase them around the board. It’s already checkmate, but they don’t want to concede. Yes, miracles can happen, and maybe the IRGC finds a way out of the position they are in, but personally, I don’t see it.

The Iran War may be regarded in the future as the most amazing operation in US military history; the complete top to bottom takedown of a large, modern financial opponent in 3 weeks.The IRGC blundered in every possible way, but the biggest was attacking the rest of the Persian Gulf and alienating themselves.

On Hormuz, the IRGC is already letting tankers from India, China and Japan exit, which means the bottleneck is leaking. And strategically for the IRGC, continuing to restrict Saudi oil exports risk bringing in a 3rd nation state against them, so it’s not a tenable position. The most likely outcome is a settlement between the US and non-IRGC factions.

#2 Investors are gaming out the collapse of the world chemical supply and a recession due to closure of the Straights of Hormuz, but overlooking the global boom underway. It is a risky set up to get bearish about a war which has already been won, when S&P EPS estimates are going vertical. Which is why I think the market will make new highs (4 reasons we make new highs).

This month’s Killer Charts deck is 26 slides with the following sections:

• Investor sentiment

• S&P 500 Earnings Estimates

• India’s Boom.

The full presentation is at the bottom of the post.

Below are 10 highlights.

#1. 52% of investors are bearish. We have reset the extreme bullishness from earlier this year. This is a good starting point for another move higher.

#2 CTA’s have liquidated a record $150 billion in global equity holdings 1 month. Again, a positive starting point for a rally.

#3 S&P 500 EPS earnings estimates are curling upwards. Why? Because there is a massive boom underway. Sharply rising earnings is also suggestive of a market which goes higher.

#5 Estimate revisions are positive. Analysts are raising, not lowering numbers.

INDIA

We need to turn our focus back to India. It’s part of the global boom thesis and will be a driver of the surprise in global energy demand. India is at a similar inflection point to China in 2004 where they now have enough income for discretionary spending.

#6 Indian stocks are starting to look interesting (for the first time in a long time). The Indian market has had a big sell-off. Investors are negative and the view is Indian IT professionals will be AI losers. This view could be misplaced though. I share some slides from the Infosys AI day.

#7 India may be an AI winner. AI favours ‘build’ over ‘buy’ and may actually be great for Indian IT services companies. I see this myself. The AI models form a base, but what you build on top of it, to be differentiated, is highly customised around a firm’s data and workflows. It’s consulting heavy and also will require ongoing maintenance.

#8 India’s GDP/capita crossed $2,500 adding 1.3 billion consumers to the global consumption population. India has hit is the China 2004 inflection point and it’s another driver to the global boom story.

There have never been this many global consumers in the history of the planet!!

Read more

We get an update from our favorite gold mining CEO, Jeremy Gray.

• $7,000/oz

• Opportunity knocks in Kazakhstan

• The most valuable metal on the planet.

Below are links for more information on:

Pure Tungsten

Chancery Royalty

Should we review a top ranked stock from the latest QARV rankings?

Or, should we go over Jensen’s blowout Nvidia GTC keynote?

Amazingly, we get to do both.

The monthly YWWR QARV rankings screen highlights stocks with a combination of high ROE’s, low debt, earnings consistency, and relatively attractive valuations.

A link to the March QARV rankings dashboard is at the bottom of the post.

The combination of a 7 month consolidation in Nvidia’s share price, while earnings tore a hole in the sky, creates an attractive set up where it ranks #229 out of over 4,000 stocks in QARV.

Nvidia was always high quality with a 5 year average ROE of 75%.

Now it’s good value too.

The 2026 estimated P/E is 21x.

But on to the Nvidia GTC Keynote.

Because these have become must watch events.

There is technological revolution underway and Jensen is happy to tell you exactly how it unfolds.

He is so forthcoming because he locked up Nvidia’s position years ago. There is little his competitors can do at this point. His strategy, as it was with CUDA, is to bring as many large firms as he can into his ecosystem.

Here are my takeaways.

This is not a summary of everything he said, just what occurred to me while I was watching (besides that I’ll probably buy a little more NVDA).

#1 Samsung’s Foundry business could have massive upside: Jensen announced the new GROK chip which uses SRAM for memory (or something like that). Irrational wrote a good, but highly technical summary of how the GROK chip is architected differently and why Nvidia bought GROK. After revealing GROK Jensen thanked Samsung for their good work (1:33:07) ramping up production of GROK chips because Nvidia needs a lot of them.

This is interesting because at the end of 2024 Samsung Foundry was a laughing stock. Samsung Foundry was set up as a separate business line, but had always struggled to get mobile customers because of the conflict with Samsung mobile. Their efficiency yields were also notoriously problematic. Samsung’s problems further reinforced the bull case for TSMC. “Even Samsung can’t figure it out..”

But if Samsung could create a #2 rival to TSMC it would be tremendously valuable. Lot of opportunity, but in early 2025 the foundry business was still money losing, problematic, and had no value in the Samsung share price.

Then in March 2025 Tesla signed a $16.5 billion deal to manufacture chips at the new Samsung fab in Taylor, TX. Musk said he would be personally involved in the processes to make sure it worked. A sign of the skepticism of using Samsung Foundry. The tide is to turning though. Now Nvidia is also using Samsung to manufacture their GROK chips. It tells you there is so much chip business to go around that TSMC can’t handle it all and there is an opening for Samsung Foundry to win high profile deals.

Samsung is trading on 6.5x 2028 estimated earnings because nobody trusts the money they are making on memory chips, but what if by 2028 Samsung also has a world leading foundry business? Could the 2028 EPS be too low and the shares also rerate to 15x because it’s a new mini TSMC? That’s potentially another +100% upside on Samsung. It made me want to buy more EWY.

#2 Are we too negative on Oracle? Jensen explained how AI workflows will be intense users of vectorised databases and how Nvidia is working with Oracle on this. On a micro level I see this myself with the Stevie AI analyst. But it also reminded me of a Founders podcast about Larry Ellison. There was a story about how Larry Ellison saw better than anyone else that the internet was going to massively increase use of databases. At the time Ellison went all-in on positioning Oracle for this. Apparently, that was not an obvious strategy at the time. Now it looks brilliant.

Isn’t Ellison doing the same thing again with AI?

We are hammering Ellison because he seems like he is reacting late to AI, spending a fortune, and killing his current free cashflow. But if we think 5 years out isn’t Ellison doing exactly the right thing to make Oracle one of a handful of leading AI inference firms? Couldn’t Oracle do really well given it is already a firm enterprises already trust. They are already past the gatekeepers.

Ellison is one of the richest people in the world because he keeps seeing things others don't. We should probably bet with him, not against him.

#3 “The storage system is going to get pounded!” (1:35:05) This was one of the most emphatic comments Jensen made over the 2 hours. It stood out. Nvidia is working with enterprise storage companies to make sure storage can keep up with the ever faster GPU’s. Storage is a new bottleneck.

I’ve heard Jensen talk about the need to rearchitect enterprise storage before and every time he mentions he is working with Network Appliance. Coincidentally, NTAP is another highly ranked QARV stock which no one is paying attention to.

Is enterprise storage going to be the new memory? NTAP is trading on 13x forward earnings for 2026.

#4 NASDAQ is a geopolitical force: I was looking at Nvidia’s Vera Rubin launch partners and noticing the names. It’s a who’s who of US tech. AWS, Google, Microsoft, Oracle,Hewlett Packard, Dell. It’s a reminder that this world changing AI revolution is dominated by large US tech companies and Taiwanese suppliers. It’s under-appreciated how dominant this ecosystem will become in the years ahead. Like with cloud computing, the whole world will depend on a handful of US firms to ‘think’.

#5 AI is a drug and the invoice is coming: Jensen forecasts Nvidia sales could reach $1 trillion by 2027. That’s an insane amount of money getting spent on chips from one company. Then we saw Amazon, Alphabet and Meta increase their capex estimates 80-100% to invest more in datacenters and chips. Amazon is going to spend $200 billion this year.

Who is going to pay for all of this?

You.

The market is critical of this spending and the current ROI. But I think they miss the strategy. This is the cloud all over again, just bigger. It’s like selling drugs. Keep AI affordable in the early years so firms build AI into their work processes and become dependent on it. Once AI is in their veins gradually ramp up the prices and make back all the investment. I see it myself with how much my AI bill has grown from the free tier to one $20/month subscription with ChatGPT, then to $40/month 2 (Claude + ChatGPT) then upgrading to Claude Pro and keeping ChatGPT ($120/month). From $0 to $120 in 1 year. And probably going higher. Imagine how much companies must be spending?

MAG7 have all derated back to average market multiples. But once MAG7 have hooked corporates on the AI drug won’t they raise prices and grow earnings for years? It’s another reason I think the whole market could be a buying opportunity here.

Those are my takeaways, but I highly recommend taking the time to listen to the full keynote.

I didn’t even go into the part about NemoClaw.

A link to the March QARV Rankings Dashboard on Retool is below.

There is also a link to the rankings on a Google sheet so you can download the data.

Read more

Everyone is freaking out, but I see the market making new highs.

And remember! This is not financial advice. Just a personal view.

2 Months Ago everyone was bullish and nobody wanted cash.

Now everyone is panicking.

It makes sense that the next swing in the pendulum will be back to greed and risk taking.

Here are 4 reasons the S&P 500 could surprise everyone and make new highs.

#1. Private Credit is a storm in a teacup.

This is the easiest thing to not worry about. The fear seems to be that private credit is the next subprime mortgage crisis which spills over to create systemic risk, which takes down the economy.

The whole rush in private equity and private credit at the end of the cycle has been a train smash in the making and why we wrote Anatomy of a Private Equity Train Smash in 2024.

Yes, private credit returns might be weak/negative, but it is a problem for the endowments and pension funds which invested in them. It will also be a problem for the private equity funds which will have to use their ‘dry powder’ to bail out the underlying companies so they can pay interest on their private credit loans.

But that’s their problem.

Just because private equity and private credit funds are experiencing a long overdue train smash, it doesn’t mean it has to ruin our party in public markets.

Private credit is a $1.2 trillion asset class. Appreciate how small $1.2 trillion is compared to global fixed income markets of $145 trillion!!!

Second, loan exposures by banks to private credit funds are also small in the scheme of things. Loans to private credit funds are estimated at roughly $300bn out of total US bank loans outstanding of $13.4 trillion. How is an exposure = 2.2% of US loans going to cause a financial crisis?

Read more

#1 The End of the Seat

#2 The Terminal is an ipod

#3 The Misplaced Moat

* strictly my personal views only *

On 27 February 2026, Jack Dorsey wrote to Block’s shareholders that “intelligence tool capabilities are compounding faster every week.” Five days later, Bloomberg News reported that Morgan Stanley was eliminating about 3% of its global workforce — roughly 2,500 people — targeting employees across investment banking, trading, wealth management, and asset management, following a year in which the firm had posted record net income of USD16.9bn and bumped CEO Ted Pick’s pay by 32%. Neither announcement was a distress signal. Both were declarations of acceleration by a cohort of firms that has already crossed a threshold and is now, with some deliberateness, increasing the distance between itself and those that have not.

Dorsey’s fuller formulation deserves to be read carefully: “I think most companies are late. Within the next year, I believe the majority of companies will reach the same conclusion and make similar structural changes. I’d rather get there honestly and on our own terms than be forced into it reactively.” He is not describing disruption arriving from outside. He is describing a firm that has been accelerating internally for two years and has now decided to make that acceleration structural — to remove the organisational mass that was justified by a world of slower tools, before those tools compound sufficiently that the mass becomes impossible to shift without crisis. Block cut over 4,000 roles, as Bloomberg reported, from a workforce that had tripled during the pandemic years and was generating gross profit of USD10.36bn, up 17% year on year. The announcement was not the moment of transformation. It was the moment the transformation became legible on a headcount spreadsheet.

Morgan Stanley’s insiders were, if anything, more candid. As the New York Post’s Charles Gasparino reported on 7 March 2026, sources inside the firm confirmed that the cuts were driven primarily by AI tools replacing back-office functions — “management just launched an awesome AI program with ChatGPT in the wealth management division,” one executive said, “lots of back offices getting the axe in this.” Morgan Stanley had simultaneously commissioned research estimating that AI and branch closures would eliminate 200,000 banking jobs across the European Union alone by 2030, with efficiency gains of up to 30% available to banks that fully deployed the technology. The bank had quantified the structural threat in February and enacted the first phase of it in March. This is not the behaviour of an industry surprised by a new technology. It is the behaviour of an industry whose early movers have been compounding for long enough that the structural consequences are now too visible to defer.

Joseph Schumpeter, writing in Capitalism, Socialism and Democracy in 1942, described the essential mechanism of capitalist progress as a perennial gale — not a gentle breeze of marginal improvement but a storm that continuously revolutionises the economic structure from within, destroying the old whilst creating the new. He was describing something that played out over decades: steam engines replacing artisans, assembly lines displacing craftsmen, computing absorbing clerical labour. What the opening weeks of 2026 reveal is a gale operating at a speed Schumpeter did not anticipate — and with a feature he did not fully map. The destruction and the creation are not sequential. They are simultaneous, often within the same institution, and the gap between those who reorganised around intelligence early enough and those who have not is widening not linearly but geometrically. The compounding divide is not a metaphor. It is a mechanism, and understanding it requires going back to a constraint that precedes every question of AI strategy.

This note traces that proposition through five movements. It begins with the precondition — the data architecture constraint that I have been examining throughout the Sovereign Index series — which determines which side of the divide a firm inhabits before any choice about tooling is made. It then examines the early adopter cohort from the inside: Klarna’s overshoot and what the correction revealed; Shopify’s harder version of the same lesson; and what Theo, the developer and founder behind t3.gg, demonstrated when he built a functional alternative to a USD1.3bn enterprise platform in two weeks, writing zero lines of code manually. From there the note turns to the federal government of the United States — the largest experiment in headcount reduction without architectural preparation in recorded history, and its instructive failure. It then confronts the arithmetic of what the early cohort’s compounding advantage now means, illustrated most sharply by what Microsoft’s relationship with Anthropic reveals about the pace at which even a frontier firm struggles to keep up with itself. And it closes by naming, with the precision the evidence permits, who is winning, who is losing, and what remains possible for those who have not yet moved.

The Precondition

Those who have followed this series will recognise the argument before it is stated. In IT Spaghetti in October 2025, I described what I called the biological tax: the premium paid for the human eye to act as the missing API between fragmented systems that could not communicate directly. In Facing the Innovator’s Dilemma in January 2026, I drew the harder implication: that the annual seat fee — the revenue model that has defined enterprise software and financial data for forty years — was in many firms not a charge for intelligence delivery but a charge for human cognitive mediation of an architecture that had never been designed to operate without it. The human existed because the data could not move cleanly. The seat fee existed because the fragmentation was so entrenched that biological bandwidth had become the only available interface between system and outcome.

That condition turns out to be universal, and it is the primary determinant of which side of the compounding divide a firm inhabits. The firms now accelerating away share one property that has almost nothing to do with their AI strategies and almost everything to do with decisions made years before those strategies existed: their data was accessible enough, their workflows legible enough to a machine, that the agent could be deployed without first requiring a complete architectural reconstruction. The firms that cannot scale AI — and there are many of them — are confronting a condition described with unusual precision in the Anthropic Economic Index research published in March 2026. Analysing real professional usage of Claude across occupational categories, researchers Maxim Massenkoff and Peter McCrory constructed a measure of “observed exposure” — the share of tasks that AI is actually performing in professional settings — and set it against the theoretical capability of what AI could already do. The resulting picture is the clearest quantification of the adoption divide yet assembled.

For computer and mathematical occupations, AI is theoretically capable of handling 94% of tasks. Observed professional usage covers 33%. For business and finance, theoretical capability approaches 95%; observed usage sits below 40%. The gap holds across every knowledge-intensive category: legal, management, architecture, office administration. The red area of actual deployment, as the paper describes it, barely registers against the blue expanse of what is already possible. The researchers’ reading is measured: as capabilities advance, adoption spreads, and deployment deepens, the red will grow to fill the blue. Their data on the labour market consequences is more pointed. There is no systematic increase in unemployment for highly exposed workers — the gale is not yet destroying existing jobs at scale. Its primary effect is different, and in some ways more consequential: a 14% drop in job-finding rates for workers aged 22 to 25 in occupations with high AI exposure. The door is not being slammed shut on those already inside. It is being quietly closed against those trying to enter.

Why does the gap between theoretical capability and observed deployment persist at such scale? The blockers the Anthropic research identifies are precisely those I described in IT Spaghetti: fragmented data that the agent cannot access, workflows built around human mediation that have never been redesigned, operating model inertia that prevents the localised pilot from scaling to enterprise-wide impact. These are not technology failures. They are the accumulated residue of rational decisions — each one defensible in its moment, each one incrementally deepening the cost of the transition that was always coming. The firms now accelerating away are those that accepted the cost of resolving this before the urgency arrived. The firms in the gap are those that deferred it until the urgency is here — and are discovering that urgent remediation of accumulated architectural debt cannot be completed in the time available before the compounding advantage of those who moved first becomes a different category of distance entirely.

In Facing the Innovator’s Dilemma, I drew the structural analogy to Apple’s iTunes error: the decision to protect the USD0.99 download because its unit economics were excellent, right up to the moment Spotify made the unit irrelevant. The biological seat is the USD0.99 download of enterprise software. It was rational to protect it. It is now the mechanism of displacement. The firms that moved first did not do so because they were prescient. They did so because they accepted the cost of remediation before the cost of delay became visible — before the Morgan Stanley announcement, before the Block announcement, before the headline about the largest peacetime workforce reduction in American history. The cost of delay is now visible. The question is whether enough runway remains to act.

The Klarna Lesson and the Cost of Getting It Wrong

Klarna is the most analytically useful firm in the early adopter cohort precisely because it failed publicly and recovered productively, and because the failure illuminated something that pure success stories conceal: the location of the remaining spaghetti, even within a firm that moved first and moved fast.

The Swedish fintech became an early adopter of OpenAI’s enterprise tools in 2023. By 2024, as Computer Weekly reported in November 2025, it had reduced its workforce from 5,500 to below 3,000, with AI handling the work of 700 full-time customer service agents. Revenue roughly doubled relative to 2022. Then the model overshot. Service quality fell. Customers complained of generic, repetitive responses unable to handle nuanced or emotionally sensitive queries. By mid-2025, as Mind the Product reported, CEO Sebastian Siemiatkowski acknowledged the error without equivocation: “We went too far.” Rehiring began on what he described as an Uber-style model — flexible, remote human agents blended back into the AI infrastructure already in place.

The press treated this as a cautionary tale about AI overreach. It was more precisely the discovery of where the spaghetti still ran. Klarna had built clean enough architecture to automate high-volume, low-complexity customer interactions successfully. It had not yet built the interpretability layer — the governance design that identifies which tasks genuinely require human cognitive mediation and which do not — that would allow it to extend into more ambiguous territory without degrading trust. The reversal refined the model rather than abandoned it. By November 2025, AI was handling the work of 853 full-time staff — up from 700 earlier that year — while quarterly revenue had reached USD903mn, more than double the equivalent period three years prior. The correction had been absorbed, and the trajectory resumed at a steeper angle.

Klarna’s arc — overshoot, visible failure, calibration, resumed acceleration — is the pattern of a firm moving from tool adoption to architectural reorganisation through the friction of a public mistake. Shopify’s Tobi Lütke attempted to codify the same lesson before rather than after the error. In April 2025, as Fortune reported, Lütke issued an internal mandate: teams must demonstrate that AI cannot perform a task before requesting additional headcount. The agent is the default. The human must justify its presence. This is a profound inversion of traditional hiring logic, and it is only enforceable honestly if the data architecture beneath it is clean enough that the agent can actually do the work being asked of it. Where the architecture is fragmented, the mandate produces the DOGE outcome rather than the Klarna outcome. Where the architecture is sound, it produces the second half of the Klarna trajectory — resumed growth on a structurally smaller cost base.

The deeper lesson, however, comes not from the large company calibrating its AI deployment but from what a single developer with a clear product vision can now do with no manual code at all. Theo, the founder and developer behind t3.gg, documented this in a video titled Software Engineering is Dead Now. The claim that attracted the most attention: he built Lawn, a video review platform with functionality comparable to Frame.io, in two weeks, part-time, without writing a single line of code manually. Frame.io took years of development and was acquired by Adobe for USD1.3bn. The core functionality — video uploading, review workflows, commenting, approval chains — now takes two weeks and costs essentially nothing in development labour.

Theo’s analysis of what this means is pointed. Code used to be the most expensive part of the software pipeline. It is now free and nearly instant to generate. The bottleneck has moved upstream to QA, testing, review, and architectural judgment — precisely the functions requiring human taste and interpretability that no agent can yet fully substitute for. A small flat team of one to three people can now out-ship a large organisation because it lacks the approval processes, the coordination overhead, and the organisational inertia that large teams accumulate. Dorsey’s formulation — “smaller and flatter teams” — is not primarily about cost reduction. It is a structural recognition that the relationship between organisational size and output velocity has reversed: the firms carrying the largest headcounts for engineering and back-office work are now, for the first time, at a competitive disadvantage relative to much smaller, architecturally cleaner operators.

This is the micro-level expression of precisely what the Block and Morgan Stanley announcements signal at macro level. The acceleration within the early adopter cohort is not simply a matter of doing the same things with fewer people. It is a recognition that the organisational architectures built to manage the constraints of the previous era are now themselves the constraint — and that dismantling them, whilst painful, is the precondition for running at the pace the tools make possible.

The Bureaucratic Test

The most extreme expression of the adoption thesis in 2025 and early 2026 was not in fintech or financial services. It was in the federal government of the United States, where the Department of Government Efficiency — DOGE — orchestrated what the Cato Institute subsequently described as the largest peacetime workforce reduction on record. As Bloomberg reported in January 2026, the federal civilian workforce fell from 3.015 million employees at the start of 2025 to 2.744 million by November — a reduction of roughly 9%, representing 280,000 roles eliminated or planned across 27 agencies. Elon Musk, who led the effort until his departure from the administration in May, framed it in terms Schumpeter would have recognised: “the chainsaw for bureaucracy.” The ambition was not incremental. It was structural, or presented itself as such.

The outcome is now documented. The Cato Institute’s end-of-year analysis concluded that DOGE had “no noticeable effect on the trajectory of spending,” with federal outlays rising nearly 6% over the same period that headcount fell 9%. Savings targets were revised repeatedly downward — from Musk’s initial pledge of USD2tn in annual savings, to USD1tn, to USD150bn — as the Hamilton Project’s real-time tracking showed spending surpassing 2024 levels in December with three weeks still remaining in the year. The agencies cut most deeply generated genuine savings that were promptly overwhelmed by increases elsewhere. Mandatory spending on Social Security, Medicare, and debt interest — the structural core of the federal budget — was entirely untouched by any of it.

The point is architectural rather than political. What DOGE encountered, at sovereign scale, is precisely the spaghetti problem that the October 2025 note described at enterprise scale: the human was not the cost. The human was the missing API — the institutional memory that knew which form connected to which database, which override applied to which edge case, which countersignature a process required before it could advance. As one General Services Administration manager told NPR in March 2025, describing a 63% headcount reduction targeted at the Public Buildings Service: “There’s just been no time. It appears that the agency is just cutting buildings and people without even analysing anything.” Remove the interpreter without first redesigning the system the interpreter was navigating, and the system does not become efficient. It becomes slower, more opaque, and increasingly dependent on tacit knowledge that left with the people who carried it.

DOGE did deploy an AI tool, as Fortune reported in March 2025, aimed at automating some government tasks. A chatbot layered over unreformed federal IT infrastructure produces a faster version of the same dysfunction rather than a resolution of it. The federal government is, in the most literal sense, the world’s deepest accumulation of legacy systems, incompatible databases, paper-dependent workflows, and procedural logic embedded in the institutional memory of career civil servants whose departures create a void that no language model, however capable, can fill without the architectural foundation to support it.

The contrast with the private sector early adopters is exact. Block, Klarna, and Shopify all built AI fluency and data architecture before the structural reduction decisions were taken. Architecture first, integration second, structural reduction third: this is the sequence that produces efficiency gains rather than spending increases. DOGE ran the sequence in reverse — structural reduction first, in the hope that efficiency would follow — and produced the largest peacetime workforce reduction on record alongside an increase in outlays. The lesson is not that governments cannot adopt AI. It is that the precondition respects no institutional ambition, and that removing the human who was mediating the spaghetti does not remove the spaghetti. It simply leaves it unmediated.

The Speed of the Frontier

Here the note requires a moment of genuine candour about the pace at which the frontier is moving, because the cases above illustrate the adoption gap from the perspective of those on the wrong side of it. What the Microsoft case illustrates is something more unsettling: the speed at which the frontier moves is now fast enough that even the firms investing at frontier scale cannot build internally at the pace the external ecosystem is generating capability.

Microsoft invested USD13bn in OpenAI. It built GitHub Copilot into a product deployed, by its own account, across 85% of Fortune 500 companies. It spent years positioning Copilot as the definitive AI productivity layer for the enterprise. Then, as The Verge reported in early 2026, Microsoft began deploying Claude Code — Anthropic’s agentic coding tool, built by a direct competitor — internally across its own engineering teams, with even non-developers encouraged to use it. The centrepiece of Microsoft’s Wave 3 platform update, Copilot Cowork, was built not on OpenAI’s models but on Anthropic’s Claude, following a USD30bn Azure compute deal announced in November 2025. The product that Microsoft is presenting as its flagship agentic offering runs on a competitor’s model, delivered through a partnership whose scale suggests this was not a provisional or marginal decision.

Ethan Mollick of Wharton, writing about the Copilot Cowork launch in GeekWire in March 2026, noted that Anthropic’s standalone Cowork product had been built in a matter of weeks using Claude Code and was evolving rapidly — then asked whether Microsoft, with what he described as “a tendency to launch a leading product and then let it sit for a while,” could match that pace. The question is its own answer. Anthropic’s product: built in weeks, updating continuously. Microsoft’s equivalent: months in development, requiring a USD30bn infrastructure partnership to deliver. The firm that put AI tools into 85% of Fortune 500 companies needed a rival’s tool to keep its own engineering organisation at the frontier.

This is Schumpeter’s gale operating inside a single balance sheet. The destroyer is partially destroyed by the thing it helped create, within its own walls, and it reaches for the competitor’s product to close the gap. The implication for every firm below Microsoft in the capability distribution is not reassuring. If a firm with USD13bn invested in the leading AI laboratory cannot build internally at the speed the external ecosystem is advancing, the question for the organisation that deployed a chatbot in 2023 and has been running pilots since is not “are we keeping up” but “how large has the gap become whilst we were deciding.”

Theo’s observation from Software Engineering is Dead Now lands here with particular force.

When he built Lawn in two weeks using agentic tools, he was not doing something extraordinary. He was doing something that any developer with a clear product vision and clean enough architecture can now do. The extraordinary thing is not the capability. It is the speed at which the capability has moved from remarkable to routine — and the consequent acceleration of the gap between organisations built to operate at that speed and those still running approval processes designed for a world where code was expensive and time-consuming to produce. Frame.io took years and USD1.3bn. Lawn took two weeks and zero manual code. The economics of software production have not merely improved. They have inverted. And the organisational structures built around the old economics are now, in Theo’s formulation, “bureaucratic inertia” — the very thing that prevents large organisations from out-shipping the flat team.

The Arithmetic of Acceleration

The distance between the early adopter cohort and the firms still running pilots is no longer a matter of competitive positioning in the conventional sense. NBIM — Norges Bank Investment Management, the world’s largest sovereign wealth fund at USD2tn under management — is the most data-rich illustration of where the compounding advantage now stands for an institution that moved early with genuine architectural commitment.

Nicolai Tangen has been public about the numbers in a way that few asset managers are, and his formulations deserve the same careful reading as Dorsey’s. As Bloomberg reported in May 2025, NBIM’s employees reported a 15% increase in efficiency in 2024 as a direct result of AI adoption. Tangen projected 20% in 2025 and another 20% the year following. The fund reduced its workforce from 1,079 employees at the end of 2023 to 676 by the end of 2024 — a reduction of 37%, achieved entirely through a hiring freeze rather than active redundancies: natural attrition was simply not replaced. Tasks that previously required days — monitoring news about portfolio companies across 16 languages, structuring the information into a coherent overview — now take minutes. Executive pay package documents of 40 to 50 pages are fed into the system alongside NBIM’s voting guidelines and its history of previous decisions; the output is a voting recommendation with roughly 95% accuracy. Trading cost savings from AI-assisted execution already run to hundreds of millions of kroner annually, against a target of USD400mn.

Claude, built by Anthropic, is the primary tool — used by 100% of NBIM’s employees, integrated across all devices in the organisation. Tangen’s mandate is unambiguous. As Bloomberg reported from his interview: “It isn’t voluntary to use AI or not. If you don’t use it, you will never be promoted. You won’t get a job.” When asked about the competitive implications, he told Bloomberg: “If we compete with companies that are not using AI, we’re 50% ahead. It’s unbelievable. They will never catch up.” At Arendalsuka in August 2025, as Top1000funds.com reported, he described the urgency still more directly: “There are too many companies where it’s not happening enough, and if you’re not involved now, you’re falling behind and you’ll never get back on the offensive.”

The compounding mechanism beneath NBIM’s numbers deserves to be stated precisely, because it is the feature that distinguishes this technological transition from the electricity diffusion that economists typically cite as the reassuring historical analogue. When electricity spread through American manufacturing in the early twentieth century, the productivity gap between early and late adopters eventually converged: the technology became infrastructure, available at similar cost and capability to all, and the advantage of early adoption dissipated as the technology became universal. Most commentary on AI adoption implicitly assumes this pattern will repeat.

The AI adoption gap has a structural feature that electricity diffusion did not possess. The advantage does not simply accrue to those who adopt the technology. It compounds within them. An institution that has been running AI against clean institutional data for two years — as NBIM has, as Block has, as NBIM’s portfolio managers have been training the Investment Simulator against their own trading patterns since 2023 — generates outputs that feed back into the next cycle of prompting. Richer context produces more differentiated synthesis. More differentiated synthesis, accumulated across hundreds of thousands of interactions, becomes the proprietary reasoning capacity that Satya Nadella described at Davos in January 2026: the ability to embed the tacit knowledge of the institution into model weights the organisation actually controls. Every cycle, the leaders’ reasoning engines grow more differentiated from a generic model. The laggards, working with generic models against fragmented data, produce generic outputs. The gap widens geometrically, because the inputs to the next cycle are a function of the quality of the previous one.

Tangen’s 213,000 hours saved annually is not a ceiling. It is the return on 2024’s investment, calculated before 2025’s agentic tools existed and before 2026’s more capable models were deployed against the same architecture. The firms that built institutional infrastructure in 2022 and 2023 are now compounding against a capability base that did not exist when they started. The firms beginning their architectural remediation now are starting a compounding process that the leaders began three years ago, against a more capable baseline, with none of the accumulated context. Dorsey’s formulation — “intelligence tool capabilities are compounding faster every week” — is not rhetorical. It is a precise description of the mechanism that has been running inside Block, inside NBIM, inside the early cohort, quietly and without announcement, for two to three years. The Block announcement was not the moment of adoption. It was the moment when the compounding had become large enough that the previous organisational structure could no longer be economically justified. The announcement follows the demonstration. It does not precede it.

What Schumpeter Got Right, and What He Did Not

Schumpeter was right about the gale, and right that it would be perennial. He was right that the agent of destruction is also the agent of creation, and that the discomfort of the transition is inseparable from the eventual advance. What he did not fully map is that this particular gale carries a property that no prior wave of creative destruction possessed: it learns. And because it learns, the gap it creates does not close in the way that previous technological transitions eventually closed.

The Microsoft case is the sharpest illustration. Here is a firm that invested USD13bn to be at the frontier of the intelligence transition. It built Copilot into the productivity layer of the enterprise world. It is, by any measure, among the best-positioned organisations on earth to benefit from this wave. And yet: its flagship agentic product runs on a competitor’s model; its internal engineering teams have adopted a competitor’s coding tool; and Ethan Mollick publicly questioned in March 2026 whether Microsoft’s development cadence could match the pace at which Anthropic was updating the very product Microsoft had built its Wave 3 strategy around. This is not a failure of Microsoft. It is a demonstration of how fast the exponential is moving — fast enough that even a firm at the frontier, with essentially unlimited capital and the world’s most widely deployed enterprise AI product, encounters gaps between its own tooling and the external ecosystem’s pace of advance.

What Schumpeter also did not anticipate is that the destruction and the creation can occur simultaneously within a single institution — that the same firm can be both the agent of disruption in one domain and the subject of it in another, without this constituting a paradox. Block disrupts payment processing with AI whilst simultaneously disrupting its own engineering and operations organisation with the same tools. Microsoft disrupts enterprise productivity with Copilot whilst its own engineering organisation reaches for Claude Code to manage the technical debt that vibe coding has produced internally. These are the logical consequences of a gale that does not respect organisational boundaries — that accelerates through institutions as readily as it accelerates across them.

The electricity analogy fails precisely here. Electricity did not learn. A factory that installed electric motors in 1910 did not accumulate a differentiated electricity advantage relative to one that installed them in 1920. Both consumed an identical input from the grid. The AI-sovereign institution of 2024 is not consuming an identical input from a grid. It is consuming an input that improves with use, whose improvement feeds back into the institutional intelligence that makes the next cycle more valuable, and whose accumulated context is proprietary in a way that electricity never was. NBIM’s reasoning engine is not the same as a generic Claude deployment. It has been trained, through hundreds of thousands of interactions, against NBIM’s specific data, guidelines, voting history, and institutional preferences. That differentiation is not available for purchase. It has to be compounded.

Tangen’s “they will never catch up” is, in this light, not a boast. It is a description of the mechanism, stated by someone who has been watching it operate from the inside for three years. The firms that are 50% ahead are not 50% ahead because they have better AI tools. They are 50% ahead because their tools have been compounding against their institutional data for long enough that the gap has become structural rather than technical — a difference in kind rather than degree. Schumpeter’s gale, in his original formulation, eventually settled into a new equilibrium that all participants could inhabit. The gale of 2026 does not settle. It accelerates. And the second derivative — the rate of acceleration of the gale itself — is the most important number in enterprise strategy, because it determines how much time remains before the gap becomes the kind of distance that capital and ambition alone cannot close.

The Map of What Comes Next

The Anthropic Economic Index paper makes an observation that has received less attention than the capability gap numbers, but which may be the most important finding for anyone thinking about the next decade. There is no systematic increase in unemployment in highly exposed occupations. The gale is not yet eliminating jobs at scale. Its primary effect is the one described earlier: a 14% drop in job-finding rates for workers aged 22 to 25 in highly exposed roles. The door is not being closed on those already inside. It is being quietly shut against those trying to enter. The most exposed workers are more likely to be older, more educated, higher-paid, and 16 percentage points more likely to be female — a demographic profile that does not match the popular image of AI displacement, and which has significant implications for how the divide compounds into the labour market over the coming decade. NBIM’s workforce fell 37% not through redundancies but through a hiring freeze maintained whilst productivity rose. The firms on the right side of the divide are not eliminating their existing workforce at scale. They are allowing it to contract whilst their output expands — and closing the door to new entrants who would previously have joined to absorb the growth.

The winners of this transition are identifiable. They are the firms that own the System of Record — the authoritative source of enterprise truth that agents must read from and write to regardless of how the interface layer evolves. As I argued in The Mispriced Moat in February 2026, the February software selloff fundamentally misread the agentic era: agents do not make the System of Record obsolete, they make it indispensable. Salesforce, SAP, Oracle’s data layer — the platforms at the authoritative centre of enterprise information — are strengthened by the transition precisely because every agent requires a foundation of truth to function. The data moat deepens as the interface layer thins. The firms that have built the orchestration layer — connecting disparate systems of record into a coherent intelligence infrastructure — occupy a premium position with no obvious challenger at scale. The supply chain upstream of the hardware cycle: TSMC earning its revenue at the moment of manufacture, carrying none of the obsolescence risk; ASML as the indispensable bottleneck in the lithography progression. The regulated vertical AI platforms trained on 20 years of proprietary domain data that generalist models cannot access sit behind a moat that is fundamentally different from the moat of general-purpose AI: they are not competing on model capability but on institutional trust, regulatory compliance, and the depth of domain-specific context that took years to accumulate.

The losers are equally visible. The UI-only point solutions — the project management dashboards, the invoicing tools, the workflow automation platforms that existed as an interface layer over undifferentiated data — are genuinely at risk in precisely the way the February selloff intuited, even if the indiscriminate application to all software was analytically poor. Agents do not need interfaces: they read from and write to the data layer directly, and as Theo demonstrated, a single developer can now build a functional replacement for a USD1.3bn enterprise platform in two weeks at negligible cost. The hardest category — and the most consequential for the sheer scale of what is at stake — is the large enterprise with genuine institutional data: valuable, proprietary, embedded in workflows that actually matter, but legacy architecture that prevents that data from becoming machine-legible at scale. These are the firms for which the window is most urgent and the path most demanding. Their data moat is real. Their ability to deploy it is constrained by decisions not taken over the preceding decade.

The firms that will make this transition successfully are those whose leadership accepts, with the honesty that Dorsey displayed and that Tangen has maintained for three years, that the previous organisational model was designed around a constraint that is dissolving — and that the dissolution is compounding faster every week. The firms that will not are those still treating AI adoption as a technology project rather than an institutional reorientation; those running pilots as a way of appearing to engage with the transition without making the architectural commitments that would allow the transition to actually occur.

The Microsoft case offers the final clarifying observation. Here is the firm with the largest AI investment in corporate history, the deepest enterprise distribution, and the most sophisticated AI product suite in the market. It looked at the gap between where its own tooling had arrived and where the frontier had moved, and it responded not by defending the position but by paying whatever was required to close it. The USD30bn Azure deal with Anthropic is not primarily a compute transaction. It is a statement about pace: that the frontier is moving fast enough that even Microsoft cannot afford to insist on building everything internally. The willingness to see the gap honestly and accept the cost of closing it — rather than defending the position that created it — is the disposition that separates the firms accelerating away from those watching from a pilot programme.

Tangen’s description of his own approach is worth sitting with. He told the Feedforward podcast that he has been running around “like a maniac” since 2022 trying to convince his staff to adopt AI — making it mandatory for career advancement, embedding it in performance reviews, running hackathons, deploying AI ambassadors across every office. He achieved 100% usage and 50% advanced usage. The efficiency gain was 15% in year one. The projection is 20% in years two and three. The trajectory does not flatten. It accelerates as the tools improve against the same architecture. Not a strategy document. Not a pilot programme. Not a chief AI officer appointed to produce a roadmap. A chief executive who accepted, three years ago, that the tools would compound and that the only way to be on the right side of the compounding was to start the clock running — and to make the architectural commitment that allows the compounding to actually compound.

That moment has now arrived for the broader market. The Block and Morgan Stanley announcements are not the beginning of the AI disruption of white-collar work. They are the first quarterly reports from an adoption cycle that started in 2022 and has been compounding quietly ever since. The firms that started the clock in 2022 are now, in Tangen’s formulation, 50% ahead of those that did not. The firms that start now will compound against a more capable baseline, which is both an advantage and a constraint — the gap to close is proportionally larger. The firms that do not start are not standing still. They are falling further behind at an accelerating rate, because the clock on the other side does not stop.

Schumpeter’s gale is not waiting for the ground to be prepared. It is compounding faster every week. The only honest response to that observation is to ask, with more urgency than most institutional cultures permit, whether the architectural preparation has begun — and, if it has not, what precisely is being waited for. The investigation, as ever, continues

Pancras

— all views expressed are my own —

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SOURCES

Financial press & commentary

· Jack Dorsey, shareholder letter and analyst call, Block Inc., Bloomberg, February 27, 2026

· Hannah Levitt, “Morgan Stanley Plans Job Cuts Affecting About 2,500 Employees,” Bloomberg News, March 4, 2026

· Charles Gasparino, “Wall Street executives say Morgan Stanley’s latest layoffs caused by AI,” New York Post, March 7, 2026

· Morgan Stanley study forecasting 200,000 EU banking job losses by 2030, PYMNTS, March 5, 2026

· Klarna AI workforce reduction and revenue doubling, Computer Weekly, November 2025

· Klarna CEO admits aggressive AI cuts went too far, rehiring announced, Mind the Product, September 2025

· Shopify CEO AI mandate: proof required before new headcount, Fortune, April 2025

· Microsoft Copilot Cowork built on Anthropic’s Claude, VentureBeat, March 9, 2026

· Microsoft deploys Claude Code across internal engineering teams, The Verge, 2026

· Ethan Mollick commentary on Copilot Cowork vs Anthropic Cowork development pace, GeekWire, March 9, 2026

· Microsoft USD30bn Azure compute deal with Anthropic, November 2025

· DOGE federal workforce reduction: 280,000 roles, Bloomberg, January 2026

· DOGE spending analysis: no noticeable effect on spending trajectory, Cato Institute / Yahoo Finance, December 2025

· GSA manager on DOGE cuts, NPR, March 2025

· DOGE AI tool deployment, Fortune, March 2025

· Satya Nadella, CEO of Microsoft, fireside discussion with Larry Fink, Chairman and CEO of BlackRock, World Economic Forum, Davos, January 2026

· Nicolai Tangen, Bloomberg interview on NBIM AI mandate and competitive advantage, May 2025

· Nicolai Tangen at Arendalsuka on AI urgency, Top1000funds.com, August 2025

· NBIM workforce reduction from 1,079 to 676 employees, Chief Investment Officer magazine, June 2025

· NBIM hiring freeze announced following AI productivity gains, Fortune, May 2025

· NBIM 15% efficiency gain, 20% projection, USD400mn trading cost savings target, Bloomberg Norway Wealth Fund mandate reporting, May 2025

· Nicolai Tangen, Feedforward Member Podcast, “AI Madness: Nicolai Tangen’s Journey to 100% AI Adoption,” 2025

· Theo (t3.gg), Software Engineering is Dead Now, YouTube, 2026

Industry & policy reports

· Maxim Massenkoff and Peter McCrory, “Labour Market Impacts of AI: A New Measure and Early Evidence,” Anthropic Economic Index, March 2026

· McKinsey Global Institute, The State of AI in 2025: Agents, Innovation, and Transformation, November 2025

· San Francisco Federal Reserve, “The AI Moment: Possibilities, Productivity, and Policy,” February 2026

Academic research

· Joseph Schumpeter, Capitalism, Socialism and Democracy, Harper & Row, 1942

Sovereign Index series

· “IT Spaghetti,” Pancras Beekenkamp, October 16, 2025

· “Capital in the Age of Enterprise AI,” Pancras Beekenkamp, October 9, 2025

· “Facing the Innovator’s Dilemma,” Pancras Beekenkamp, January 20, 2026

· “The Mispriced Moat,” Pancras Beekenkamp, February 20, 2026

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