Before you move on:

• Stock trades at 5.5x earnings

• Earnings growing at double digits with runway to at least double revenue

• Regulatory risk is minimal (more on that below)

• 70% dividend payout ratio (>10% dividend yield here)

• Negative working capital business

• Medium to High barriers of entry (regulatory and reputational)

• A buyback of >3% of shares planned for H2

• Trades at a large discount to other Chinese after school education stocks

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I got the idea from Kubang Pasu Capital, make sure to check him out. He has some interesting stocks written up. His article was a bit light on the more qualitative business details though. So I will get into that more.

In 2021 the CCP essentially nuked the K-12 cram-school market in China. As kids were spending most of their waking hours studying. And it created a disincentive to have children (as all that after school tutoring is rather expensive). And of course a bunch of disharmony as these cram schools were teaching material ahead of time. This caused a total collapse in revenue and share prices of Chinese educational companies. And a lot of the smaller ones died off, and the larger more resourceful ones adapted towards more broadening afterschool education beyond what is taught at school.

Beststudy’s major subjects are:

• Computer science / robotics

• Literary education

• Multidimensional thinking

• Bilingual culture

• Social-science literacy

• Journalism/expression

With computer science being the big one. This is primarily aimed at middle/upper middle class families in the Chinese Bay area. Beststudy is lifting on a multi decade track record and reputation here. And CCP has erected various regulatory barriers making it hard for new competitors to enter.

Interestingly Beststudy trades at a large discount to other Chinese aftermarket educational companies (who also had to pivot):

Essentially if they keep growing 15-20% a year (a significant slowdown from 2025) for the next 2 years you will get >33% of your money back in dividends and own a stub that trades at 3x earnings by YE 2028. I think expected IRR here is 10-40%, unless a change in the regulatory environment causes a collapse in earnings.

So the major question here is, what would stop China from again nuking this sector? For one the Chinese ministry of education has been pretty explicit that they do not intend to eradicate the after school education market, but merely wanted to change its way of operating. These are the criteria that determine if an afterschool company is illegal or not:

• purpose: is it aimed mainly at improving subject grades?

• content: does it teach Chinese, math, English, physics, chemistry, history, etc.?

• method: does it resemble conventional preview → teacher instruction → exercises → reinforcement?

• assessment: are grades/tests/ranking the principal outcome?

Basically Beijing doesn’t mind (and actually wants to encourage) if some students become smarter and have their horizons broadened and this causes higher grades indirectly, they just don’t like the cram school concept where high grades are the primary incentive.

So this means that Beststudy’s subjects fall into two groups, some overlap (like culture/multi dimensional thinking) and little/no overlap with school subjects like programming and robotics. So:

Old Beststudy might have said:

“Learn the Chinese curriculum, practice the test format, improve your score.”

Their literature program can instead say:

“Read literature and history, debate an author’s or historical character’s choices, formulate an argument, write an essay and present it.”

With the added advantage of also offering smaller classes.

The Chinese ministry of education actually explicitly encouraged after school education and has said there is a room for public school + for profit organizations and even has encouraged for profit to integrate with public schools. See more info on their guidelines/rules here and here. This means regulations are both a risk (further regulatory tightening) and create barriers to entry on top of a hard to replicate decades long reputation built up by existing educational companies.

Overall I would say the CCP in its current form is extremely misunderstood by Westerners. If you actually look at their actions they don’t really dislike the concept of profit. In fact they see it as something healthy, they just don’t like “bad incentives” (like tragedy of the commons situations) or monopolies.

For example they forced last mile delivery companies to raise prices as they didn’t want a few large players monopolizing all local markets. They encouraged buybacks + dividends as they want more people to own stocks instead of speculating on real estate and creating insane bubbles. They see the market as a useful tool that will essentially enhance China and serve the collective. A large stock market means China is strong (but not too large and still under the CCP thumb of course).

In fact you could argue the CCP has too much of a focus on profits as they are too restrictive on loss making companies wanting to do an IPO.

You may disagree with this approach, but they are not your typical Western socialists who hate profits for ideological reasons. The current crop of CCP leaders are essentially ruthless pragmatists who use Mao as a mascot to legitimise themselves. If Mao actually saw what Xi was doing, building up legal institutions and basically behaving like a US Republican (refusing to increase social safety nets as he thinks that will make Chinese people soft) he would probably turn in his grave.

Xi basically looked at America (he actually studied in the US) and realized that abolishing markets was stupid, they are a useful Darwinian concept to filter out winners to the top. He just didn’t like how capitalists had almost complete control over the US political system.

And he kind of has a point. For example look at the current US military procurement system, it is only a handful of mega corporations. This has caused a lot of inefficiencies where the US government often only has one and maybe 2 suppliers for many systems with rampant rent seeking.

I also can’t find that much blame in the new 2021 cram school regulations. If it really results in more time taken up by education that actually teaches children to think instead of just mindlessly cramming for tests, isn’t that actually a good thing? Given how competitive China is, this was basically creating a race to the bottom. Plus profits are now actually higher on this new model than before as quality matters more now that after school education isn’t all concentrated into dumb studying to ace exams (which is a very short sighted and miserable way of learning IMO).

Closing observations:

• This recent Sohu report on the company is worth reading as it dives fairly deep into the business.

• 3 founders own >50%.

• Over a 500 employees own shares in the company, so there is motivation to create shareholder value for insiders

• There are also restrictions on advertising, so this means Beststudy can only slowly expand outwards relying mostly on word of mouth, they are fairly locally geographically concentrated:

• I think with AI and ever increasing distractions these more qualitative “learning how to think” education schools will be more in demand, not less.

• There is a modest risk of increased competition from after school public programs. They will likely be limited and of poorer quality though.

• Queried ChatGPT (it writes in my writing style so not that verbose) and here are results on regulatory risks etc in case you are interested.

• I own a mid sized position in Beststudy

Disclaimer: Readers of this blog should do their own due diligence before buying or selling any of the mentioned stocks, since I have been w20-rong before and cannot guarantee all information in this write-up is 100% factual. I may buy or sell the above mentioned stocks at any time. Past success is no guarantee for future success. Some of the stocks mentioned might have poor liquidity, so make sure to check average daily trading volume before buying or selling anything. I am not your financial advisor.

Let’s look at CS Holdings (000590) listed in Korea. The stock trades at about 25% of net cash + real estate investments, most of which is cash and investments. They have a dividend payout ratio of about 4% of earnings and trade at approximately 4x earnings with a core business that generates a fairly high ROIC (not counting non core assets on bs etc) selling welding materials.

Now you might say, eventually something will happen so it could be attractive to buy here? The problem with that is that insiders control 82% of shares (assuming they cancel treasury shares). There are four outcomes, a buyout, large dividends, acquisitions or status quo. And the problem is that insiders, most of the time, are strongly incentivized to choose option 4: status quo. Even if they could buy out the remaining 18% for a large discount. And the status quo can last for a loooong long time.

If they bought out minority holders, insider families gain no real advantage and actually lose the prestige of being listed. And they would have to do an independent appraisal if a large holder dies and much higher inheritance taxes have to be paid.

If they were to actually pay out the excess cash they would pay ~30% taxes on it (assuming a 25-40%+ payout), so right there a huge leakage. And now every generation would in turn also lose up to 50% in inheritance taxes on the full amount paid out. So a combined loss of 65% by taxes alone over time. Vs status quo where they pay inheritance taxes on only <20% of fair value and have no leakage through dividend taxes. Plus insiders get essentially almost nothing in return when managing this wealth in private vs just holding their wealth in CS Holdings as they likely would keep it in CDs and some real estate anyway.

Of course acquisitions sometimes happen which is why I think you have so many Japanese companies with random businesses that have nothing to do with each other. The problem is you need the heirs to actually care about business. And those acquisitions cannot destroy value for this option to be attractive (which is very easy to do in the wrong hands) If insiders have a mostly passive role and are risk averse they will just park the cash in government bonds and maybe some real estate and call it a day.

So in essence the public minority holder is basically used here to keep inheritance taxes down. And without serious new laws cracking down on this (or much lower taxes) this can go on indefinitely. And discount to net cash value offers no real protection. The only real protection is the actual payout % used for insider consumption. I would not be surprised to see some Korean stocks trade at 5% of book value 10-15 years from now. When as a minority investor you are being used to keep taxes down it makes little difference if the stock trades at 5% 20% or 40% discount to net cash. And a stock at say 30% of net cash does not really have a real bottom above 5-10% of net cash value. And without eventual large dividends a few isolated buybacks aren’t the end all be all. Let’s say founders increase their stake from 40% to a whopping 80% through buybacks and share price doesn’t move much. Your 30% of NAV goes to 15% and you are now facing only a tiny dribble of a dividend for the next 30 years, congrats.

And really strange things like the amount of insider offspring and their spending habits start to matter much more than book value or earnings multiples, as they will predict what % of earnings you as a minority investor will actually see. If the fertility rate of insiders is very low that is probably bearish for the future dividend payout. Preferably you would want some kind of Elon Musk wannabe who has 50 kids who all need to get paid (or demand greater control over their wealth) to see significant shareholder returns above 30-40% of earnings. And again this can easily last well beyond the investment horizons of most investors. With minor temporary increases in between as inheritances taxes need to be paid.

Of course there is a limit and currently there is a bill being discussed in Korea that would set a lower limit of 80% of book value for inheritance taxes. It was nixed, but the president has again ordered the government to reconsider its proposal. This could create some interesting situations and especially increase upside for companies with old founders who hold large stakes. Although you have to be careful with this logic as there are exemptions in place where a large insider with active involvement can transfer shares on a much lower tax scheme of 10% to a successor family member while still alive.

Are there exemptions to these tax avoidance incentives? I think there are:

• Public companies with no large insider ownership do have a greater incentive to pay dividends. Much easier for activists to get in there too.

• Public companies controlled by another much larger public company that has no large concentrated inside ownership (or is in a jurisdiction with low/0% dividend/inheritance taxes)

• Companies with large insider ownership, but the children get nothing/owners believe the government deserves it more than their children. Or insiders simply have no children. This is usually not public info though.

• Companies where there is no real insider involvement anymore that are bought out by a large competitor for a price that is too attractive to ignore. Although if inheritance taxes are high enough there might not be a price that is attractive enough even.

And now incentives for Hong Kong insiders:

• 0% inheritance tax

• 0% dividend tax (beyond 5-10% WHT between China -> ex-China company)

• Insiders are limited in what they can invest in when holding cash inside a Chinese company due to capital controls. So a direct incentive to get the cash out through their Hong Kong listing.

• Weaker property rights are an incentive to stash a significant portion of net worth overseas (discount from this is usually much smaller than from being stuck in a value trap with no prospects of significant dividends).

Finally I think to a lesser extent this is true in Europe as well. Although Europe has plenty of tax havens that wealthy controlling families can move to. I don’t think this is really an option for many Koreans or Japanese people. The step to move from France to Switzerland or Monaco or something is far smaller than to move from Japan/Korea to Singapore or Hong Kong. Both culturally and distance wise.

Just kind of a rambling post about incentives that can prevent/realize the value of Asian net nets. Since I saw so few Korean stocks with a healthy dividend payout %. If you think I missed something let me know in the comments!

I think with Korean stocks the trick is to find ones with very old or recently deceased large holders since inheritance taxes are 50-60% in South Korea. So as the Grim Reaper starts circling the wagon they have a strong incentive to keep the share price down. But after they pass away their descendants should have an incentive to boost share price or pay large one off dividends to pay their inheritance taxes.

HOWEVER this pattern did not at all play out at one of the first stocks I looked at: THN Corporation (A019180). Insider ownership looks like this:

Lee Gwang-yeon - 64 - 20.88%

Chae Seung-hoon - 58 - 16.04%

Chae Won-jun - 33 - 0.24%

Chae Cheol - 83 (deceased) - 0.00% currently; 20.48% before inheritance

They make wire harnesses for Hyundai with some very heavy customer concentration, 63% Hyundai Motor, 20% Hyundai Mobis and most of the remainder is Kia. Still it seems 1.4x earnings with a net cash position seems to be too cheap? Yet dividend pay out sits barely above 10% and Chae Sung-hoon who owned 20% has been selling a sizable amount of shares to pay for inheritance taxes without really making a serious effort to boost the valuation above 1.4x earnings. I’d say given the customer concentration, consistent revenue growth and somewhat elevated margins, 4x earnings is fair?

Growth and margin expansion has also been fairly healthy as these wire harnesses get more complex over time (do note market cap is under 100 billion Won and co has a modest net cash position):

But passing on this for now as I cannot figure out why insiders would think it is reasonable to sell shares here at ~2x earning. With a dividend payout ratio of around 3% (not yield, but payout ratio!). Even assuming somewhat lower sales with a 5% margin the stock would still trade at only 3x earnings here.

Here is the timeline

• The old man died on 11th of December.

• Chae proposed selling 1.44m shares which is close to his tax liability (or about 8% of shares outstanding)

• Lee has his entire 20% stake pledged to tax office from previous inheritance taxes

• This did not go unnoticed by Korean investors, here a Korean article about incentive for higher dividends

• Yet no higher payout ratio announced in March 2026

A possible explanation is that the two 20% blocks were owned by heirs of 2 different founding families and that a possible power struggle could explain lack of support for increased dividends? In the above article this is explicitly denied, but who knows.

Don’t own any shares, but maybe I should, as Chae did cancel the last sale of 423k shares citing the need for share price stability and minority shareholder protection. So that could be a tell there will be a higher dividend soon? A 30% payout on 2025 earnings would mean a 21% dividend yield here. However this stock has never traded above 3-4x earnings and basically seems to be a short on copper (which given tight supply and enormous demand does not seem like a bet I want to make).

There are 2 Korean stocks I did buy however, and got into 2 new HK stocks (both already covered previously on this blog).

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The fact that I charge a 80% lower price does not mean I will put in 80% less effort though, just means I take a more relaxed approach without feeling I am ripping off my subscribers :) .

Now annoyingly if you are currently subbed you will have to unsubscribe and resub to get the lower price as it will just auto renew at the previous higher price!

Note: DO NOT RESUBSCRIBE RIGHT AWAY! Substack will activate it right away and you will pay double (unless that is what you want ofcourse). Just resub after the current sub expires. When signing up (when your current sub runs off) make sure to sign up on the web as the app store takes another 30% on top of 10% to Substack.

I provided instructions below on how to cancel (If you don’t cancel it will auto renew on the old price):

• On bottom left go to settings

• Click on Turtlesresearch (under header Publications)

• Go down to Account Actions

• Click “to cancel your paid subscription”

My track record has been a bit mixed in the past year. I have had a bunch of winners but also more losers. But I think my hit rate is still above 50%.Currently I’m looking at Korean and Malaysian stocks, since both now seem to be tradeable on Interactive Brokers. And outside of the big 2, Korean stocks look surprisingly cheap. I’m also eyeing another UK software stock, although I haven’t decided yet on that one.

Quick update on current stocks. Dumped my Radcom (RDCM) as that was one ugly quarter. Should have followed my gut and stayed out, but got back in due to board reshuffle as I figured they might do something with the large net cash position. Luckily it was a small position.

Closing Conrad (CNRD) here. Due to the erratic and random nature of Trump I think there might be a chance the Jones act is reformed in some way. There was talk of this happening a few months ago, and now at $30 I dont think the stock is that attractive anymore to hold out for a buyout.

Got a new position in High Templar (HTT), a Chinese cash box trading at about 25% net cash that has bought back more than 50% of its own shares in the past 5 or so years. I think with these aggressive buybacks it should trade at around 30-50% net cash. Now sits at the bottom of that range. They exited their failed last mile delivery business and have announced they will enter the AI business (as they repurchased about 25% of shares outstanding at $3 with a tender offer recently). Fear that they blow hundreds of millions on some stupid AI endeavor is probably the main reason the stock is this cheap. CEO and founder owns >56% of shares outstanding.

Disclaimer: Readers of this blog should do their own due diligence before buying or selling any of the mentioned stocks, since I have been wrong before and cannot guarantee all information in this write-up is 100% factual. I may buy or sell the above mentioned stocks at any time. Past success is no guarantee for future success. Some of the stocks mentioned might have poor liquidity, so make sure to check average daily trading volume before buying or selling anything. I am not your financial advisor.

BUT I gotta …

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Curiously the exact mechanisms of pricing power is a very underdiscussed subject in value investing. But it is extremely important for obvious reasons. It’s something that Buffett heavily focuses on when discussing any business with management. A significant edge can be gained in value investing if you can guess how profit margins will evolve over time, or if there is latent pricing power/economies of scale in a business. You would think a large amount of value investing books were written on the different pricing mechanisms and game theory behind it yet that is not the case.

What Belwey did is talk to insiders and managers in many different industries over a period of more than a decade and summarize the findings in this book. Interestingly it also got me thinking and gave me a realization that there are different types of pricing power (on the way up and on the way down).

I can identify roughly 4 different pricing mechanisms between a company and a paying customer:

• Pure commodity price taking. Like Lumber (somehow it feels wrong to not write Lumber without a capital L) oil, copper etc. Zero pricing power. Almost no differentiation. Often very liquid and global. Prices feel more like the result of some Moloch nobody controls than the agreement between two parties. The only way to make money is to sit at the bottom of the cost curve. Low cost advantages often come from geography (like oil) and not pure scale, as pure sizing related economies of scale industries usually morph into oligopolies/duopolies.

• Auctions. Set apart from pure commodity price taking by lower liquidity and assessment of bidders ability to execute. Where the lowest bid doesn’t always win. Usually seen in construction and government contracts.

• Formula based pricing. Price is set with a formula by companies with some input from customers/suppliers. Where the formula can be renegotiated periodically.

  • Cost plus pricing like in low value add manufacturing (textiles) or retail

  • When combined with longer term contracts where end product could be a commodity, but inputs are unique and require huge upfront investment (gas pipelines and LNG terminals)

  • High degree of bespoke/custom manufacturing where plenty of alternatives exist, but switching costs are high after a company chooses a supplier. Renegotiating prices on a regular basis could risk painful business disruption.

  • Usually more common in more illiquid markets, or when cost volatility increases.

• Monopoly/Oligopoly pricing power. Companies have a larger degree of freedom to just set a price without much input from their customer, and the customer is forced to pay it as there are few or no alternatives. Usually:

  • Luxury products

  • Very high value added products with no alternatives (Intel in its glory days).

  • Industries with very large economies of scale that form into oligopolies/duopolies over time (memory chips after they consolidated)

  • Companies that operate in extreme niches.

  • New industries that do not have a lot of competition yet with a few first movers.

I always find it amusing how macro economists think that economic agents make decisions on stats that usually only economists care about, the author was quickly disabused of that notion:

“One might expect in a study like this to read about negative productivity shocks and Federal Reserve Bank inflation targeting or forward guidance, because these topics are much discussed in macroeconomics. Neither topic ever came up spontaneously, except when a few respondents mentioned the effects of drought on agriculture and of low water levels on river and lake shipping. I occasionally brought the topics up, but eventually desisted, because respondents implied they were irrelevant to pricing.”

The main things I learned while reading this book (not necessarily all explicitly mentioned):

• Two forms or pricing power:

  • The kind where it is easy to raise prices

  • The kind where it is hard to raise prices, but where price stickiness is fairly high (in a recession or when a shortage goes away). This is why some companies in Covid supply shock managed to keep elevated margins above their historical average even when bottlenecks went away. But were not able to accomplish these price increases without an external supply shock.

• Value-added manufacturing creates switching costs. For example custom packaging for consumer products. Pricing stickiness is a function of how high the switching costs are and how volatile demand is.

• A recurring theme in the book is that purchasing managers think the more value add there is, the less they want to lower prices in a recession as price becomes a weaker and weaker signal to potential customers, but quality and reliability is. So switching costs work in your favor but also make it harder to poach competition by lowering price just a bit, you would have to lower it a lot. This is true for competitors as well, so they are less likely to lower their price as well (for the same reasons) so this keeps pricing stable even in weak demand.

• Purchases that are spaced more widely have the potential to increase pricing power vs regular made purchases. As customers wont remember what they paid last time. I don’t think this counts for big ticket fairly undifferentiated items like automobiles though. I’m actually a bit sceptical of this as often regular purchases are made by a distributor even if the end customer buys it sparsely.

• Suppliers (like clothing manufacturers to clothing brands) often are given higher margins in high growth phases when the brand wants their suppliers to have ample capital to grow with them. This process can reverse when growth slows down as the buyer is going to renegotiate contracts with its suppliers as margins become more important. So beware of this when investing in suppliers exposed to higher growth industries (like for example Chinese textile manufacturers). Both growth and margins could collapse at the same time (and often why these Asian manufacturer companies trade at lower multiples).

• Lowering prices could cause a company to lose their distributor customers as it would lower their inventory value. This is a bit counter intuitive to me. But it makes sense if distributors/retailers enjoy significant network effects and market power. In general I think the role of middle men (and potential removal of them) is under appreciated in the pricing power discussion.

• More on the role of distributors, if they have put a lot of material out there with their prices, then changing it suddenly is a pain. So to maintain good relationships in some industries pricing is set only once a year.

• If a product is a component in a larger piece of equipment that is sold to multiple distributors and manufacturers before reaching the end customer, it is easy to see how increasing price can create significant complications for customers downstream and is more difficult vs when selling directly to the end customer through perhaps only 1 distributor layer.

• Highly differentiated products tend to have a clientele who know about that product and know less about substitute products (quote by an executive in the dental industry).

• Increasing automation potentially increases fixed cost % as machines cannot be fired. Which makes prices more rigid.

Despite enjoying reading most of it (the section about lumber/grain pricing wasn’t interesting to me and I skipped most of it, and retail also doesn’t interest me a great deal) I think the book fell short in several ways. First I think the author should have been more granular per industry. I would have loved to see how industry consolidation, customer concentration affected price negotiations. The book presents a lot of quotes from sales people/CFOs/purchasing managers, but little information around it about potential moat of the business and more detailed industry dynamics. Since the author collected data over more than 20 years it would have also been interesting to study how industry consolidation gives companies more pricing power, and what it looks like on the ground when deals are made.

The book was published in 2025 which made me disappointed it did not cover the effect of inflation. I would have loved to learn how this affected pricing negotiations and how margins did stay elevated even after some of the bottlenecks (like shipping, chip supply) went away. Overall I felt like the author would have benefited from consulting with some value investors as he would have been able to ask more pointed questions. I think he came at this too much from an ivory tower macro economist angle. I think it is a great shame how economists seem to show so very little interest in what agents who make the market efficient have discovered.

Overall though the book is only about 200 pages and worth at least a skim if you found this interesting.

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Let’s start with the elephant in the room: Closure of the strait of Hormuz. I think this is a non-event and a no-brainer opportunity to load up if you are focussed on the medium to long term (say 6-12 months+ out). Only issue might be timing, so I did move to some cash (sold Inmode, Directa Sim, Humble group and ICCC) as they were my weakest positions.

I have seen some panicking about this and the best argument against panic is that you would need Iran, a $400 billion economy who is massively underperforming expectations in the war so far, to basically nuke the global economy. And that somehow militaries with a budget larger than Iran’s GDP will find no way to actually get those tankers through.

Some observations:

• Trump isn’t gonna TACO his way out of this. He can pull out, but that will only worsen the situation. Iran has to TACO basically.

• I think oil might briefly spike to $200+ before this is over. As clearing the strait will probably not happen before early April.

• The market kind of trades like an option here. Stocks are slowly going down, oil is slowly crawling upwards everyday this is not resolved. The moment it is resolved (either a deal or strait is effectively cleared) oil will crater down, and the market will spike upwards.

• I have seen posts about how ⅓ of global urea supply has to go through the strait. Except it is more like 9% of global production. About 18.5m tons with 200-240m tons of global supply.

• Oil stocks are just as/more expensive than in the 2022 peak. Petrobras is trading far above its 2022 peak, most of US shale producers are trading near 2022 peak levels, you would really need several years of $90-100 oil to see modest upside here. I remember buying Petrobras preferred shares in late 2022 for $9 and I got $3 of dividends within a year, now they are at $17 with a lower dividend payout rate.

• A similar % of oil went through the strait during the Iran-Iraq tanker wars in 84-88 in which over 200 tankers were struck. Iran also attacked Saudi and UAE ships back then. It barely seemed to have affected oil prices (not inflation adjusted) and US only intervened in middle of 88:

• The US has become A LOT better at these bombing campaigns. They had real problems finding even a minority of Iraq’s SCUD launchers in the first Gulf war in 91 even with spec ops teams on the ground. But have no problem now taking out Iran’s BM launchers with over 70% taken out so far.

• If the IRGC is rational they put a reformist leader in place and pledge no nukes, no ballistic missiles. Trump would accept that and force Israel to back down. If they do this before the US manages to open the strait of Hormuz, they have some leverage (threat of a future closure) and can stay in power. If the US manages to open the strait the IRGC loses all leverage and this likely becomes background noise and Israel/US will try to continue until IRGC + Basij is completely hollowed out.

• Unlike Libya or Afghanistan or even Iraq there are actually functioning state institutions + army and a fairly unified country humming below the IRGC + Basij oppression system. So I actually have good hopes that Iran can recover nicely here if those 2 organizations are hollowed out.

• When this is over, Gulf states will build pipelines to bypass the strait and get better missile defenses. So again, IRGC has some room to make a deal here, but in 2-3 years will have lost most/all their leverage.

• Global supply of crude that has to be rerouted is also a lot less than 20mb/day. Actual crude is like 15mb/day, of which:

  • 1.5mb/day of Iranian crude

  • 5mb/day rerouted through various pipelines

  • Probably 1mb/day of supply elsewhere in the world can be added within 3-6 months

  • Another 1mb/day of demand destruction at $100+.

So the real amount that has to be squeezed through is about 6-7mb/day.

• 1 VLCC is 2mb/day (probably a bit more complicated though as there are a wide variety of oil + product types that cannot fit all into a few tankers).

• That said there is some tail to IRGC’s missile launches:

  

• Shooting down Shahed drones in a cost effective way is already a solved problem.

• The IEA countries have 1200mb, China has 1300mb of crude reserves. Probably another 1-2000mb/day in the rest of the world in crude reserves. Throughput might not be high enough to backfill that 6-7mb/day though.

• Asia gets ~80% of oil that flows through Hormuz. You would think that holding a gun to China’s (Asia in general) head on this would be a bad idea for Iran strategically.

• Greek shipowners have also snuck 10 oil tankers through so far.

• In 2019 dollars, oil is currently sitting at only $75/barrel. January 2027 futures sit at about $55 in 2019 dollars.

• The oil futures market is possibly manipulated though. So if you want to bet on this, just buying Brent futures would be the best way. The problem is that it is fairly likely something will be negotiated/done in the coming weeks and your futures gap down to $70.

• Iran closing the strait has been war gamed for decades. So take all those claims that this caught the administration off guard with a very large pinch of salt.

• In the medium term, Argentina, Venezuela and possibly a free Iran will increase production by millions of barrels/day while 1000km EVs cheaper than ICE cars are in the pipeline. So I don’t see how anyone can be bullish on oil beyond the next 12 months.

That is my 2 cents, I don’t see any attractive ways to bet on this other than just buying stocks that are now getting cheaper because of this. My strategy has been to sell down stocks that I found less attractive and slowly get fully invested in the coming weeks. The best time to buy oil stocks in 2020 was when oil went negative. The best time to buy euro stocks was in 2022 when inflation was peaking.

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General observations:

• LLM models are rapidly becoming commodities for most use cases (I have said this before several times, but bears repeating).

• Some observations from extensively using various paid models for various types of tasks:

  • I think longer deeper thinking modes beat raw model capability. Currently I am only using OpenAI’s GPT 5.2 with extended thinking for research/learning new things. Yes it sometimes takes 5-10 minutes, but you get far superior answers vs quicker Gemini and Claude thinking modes. I wonder what relative performance would be vs for example Gemini if it thought longer?

  • It does seem that when you go back and forth between skeptic/confirmation bias mode, LLMs converge towards the correct answer/state. This implies that using more tokens for a lot of tasks could still greatly enhance performance without improving models themselves.

  • I think a lot of people make the mistake of using some quick version of a model and then when the answer is crap they think AI is crap.

  • The above implies that much cheaper tokens that provide answers at greater speed could disrupt a lot of industries/jobs in the coming years.

  • LLMs for researching stocks work best when you do most of the thinking and LLM does most of the fact collecting/summarizing from a variety of dense sources.

• Proprietary data advantages mostly work when it is messy text/visual/audio data that needs to be quantified/mined for insights. If it is already nicely labelled quantitative data I don’t think recent AI breakthroughs really help much vs preexisting machine learning techniques.

  • That said, I think huge fortunes could potentially be made by sleepy companies who do possess this data, and that is where the big winners are. But IMO try to argue explicitly and in great detail why these LLM models will help them and what the advantage is.

• According to Huang’s law GPUs double in performance every 2 years. That means in theory in 10 years you get 32x the performance for not that much more cost.

• This has some implications:

  • I think some tasks are currently not practical for LLMs for most people because slow speed and cost, a 32x cost decrease will change that.

  • Somewhat mitigated by fact that tokens are currently heavily subsidized (there will be price increase, but on cheaper token cost)

  • This does not mean power consumption goes up 32x. It may go up 5x? 10x? I don’t know. A large part of the performance increase will come from things other than smaller transistors. For gaming GPUs watt/frame drops quite a bit for newer generation GPU due to smaller transistor size. By almost half over 2 generations.

  • Bearish on Data centers though. Local energy needs will go up, so that could become a bottleneck as physical space needed for 1 unit of computation declines by a factor 32x.

• Those scores/tests these LLM companies like to brag about often mean jack shit.

• So what advantage do the major LLM companies have:

  • Economies of scale when it comes to buying/renting compute

  • Being a user funnel towards various services (kind of like Google)

  • Proprietary data from questions and user feedback from chats

• It is good to be reminded once again that specialists usually beat generalists in the game of capitalism. LLM companies are very much generalists. So take “Gemini/ChatGPT will eat x’s lunch!” narratives with a grain of salt.

  • With some exceptions for example in investing where sectors can become overpriced/underperform for longer periods (energy specialists will almost always underperform vs good generalists who know their limitations).

• Especially when LLM giants do not possess a lot of proprietary niche data themselves.

• Companies with right proprietary data will likely have their own LLMs that will end up beating Openai, Claude etc within their niches. They use their data to create a superior experience within the niche, gain/keep more users, and get in turn more proprietary data from user chat history.

• For niches LLMs do not have much of a size, funnel or user data advantage unless dominant players in those niches stay asleep at the wheel.

• Content generation players will either be wiped out or become an even stronger, more prolific signal beacon in an ocean of slop. For example I can see already at least somewhat trusted news providers becoming a more dominant safe haven with prolific deep fakes etc.

• This one is true for software: building it was often not a bottleneck before LLMs BUT:

  • I do think that demand for software still vastly outstripped ability to supply it in especially niches. Lots of things were not viable to build simply because hiring an engineer for $80k wasn’t worth it given the small user base (and learning how to build had too steep barriers).

  • Some somewhat inexperienced engineer/employee may have previously thought “wouldn’t it be cool if we have this feature?”. But may have been stopped by the fact that getting a bunch of different experienced enough engineers together to build it was too much of a barrier.

  • Established players however have a data advantage here. Current providers (like Constellation) will already have their client relationships and know client work flows better than most. If they are proactive in finding these small niches that are now cheaper to provide they would still have the advantage here in simply upselling new features.

  • The risk here is that engineers/employees at those companies will also know this, and if not proactive enough will leave and found their own companies and eat away at the edges here. The barrier is much lower to do this now.

  • Switching costs will come down.

  • But that could also mean dominant players with superior products/services will eat more market share from inferior players that were protected by these switching costs.

  • Tech debt will become a huge problem for companies that use these LLMs carelessly and buy into the agent swarm hype.

• When it comes to writing software:

  • Either you write it yourself/do pair programming understanding most of it (the specialized/complex/niche part). Not that much productivity gains here for experienced engineers, but will lower barrier for less experienced engineers to build a wider variety of things at greater speed.

  • Or you do it with agents understanding close to 0% and using tests to verify it works.

  • Not much in between as fixing/trying to understand messy agentic created code is a huge time sink.
    From experience and talking to people who work in software it still makes huge structural mistakes. It is terrible (and has not improved that much with newer models) at big picture stuff like putting code in the right places, writing DRY (don’t repeat yourself) code, allowing impossible states, creating useless unit tests.

  • The unevenness in quality is a problem. A somewhat experienced engineer might not produce the greatest code and actually be outperformed by Claude in a lot of cases, but they will also not randomly every other week create truly terrible code (like these agent workflows tend to do) that will add huge tech debt with little code.

• I think LLMs remove a lot of friction especially for people who are not that computer savvy (which means a lot of boomers who decide on large budgets). Previously if you wanted to know something you had to type in the right words in google and then read a bunch of websites. Or ask on forums. So businesses that benefited from this friction will be in real danger.

Will LLMs break network effects? By aggregating data? Honestly I think each network effect/industry has to be assessed on its own as they are all different. But let’s take Rightmove as an example as they sold off hard. Real estate agents pay them a double digit % of profit, so there is a pretty strong incentive to find alternatives (they control 80% of the market and are a de facto monopoly).

I started looking for houses recently and on the consumer side I can very easily see a much more improved experience. For example:

• Show me a map with $/sqft and/or other constraints other than price. Rightmove doesn’t even have this.

• I’m a visual person so again on a map, show me the good schools, and only houses under lets say $4k/sqm within x minutes from those schools by bicycle.

• Overlays like crime, air pollution etc.

• Allow me to do virtual tours of homes. A badly filmed tour through the house with phone + some app that automates Gaussian splatting would make this possible already.

• Show me all monthly fees/property taxes

• If the house/apartment is mostly empty, rundown or has ugly interior then allow me to take some pictures of my stuff which can be inserted into listing pictures to make it easier to imagine how it looks if I actually lived there.

• Etc

So it seems a vastly cheaper and improved experience can be offered here using AI for both agents and consumers with tech that has already existed for several years. A lot of these platforms are fairly sleepy and have not really done this because they were a monopoly protected by network effects. And I think the market is probably right to dump them recently. Even if they stay on top, there will be pressure.

What would a disruption look like? The idea is that LLMs will act as a portal by aggregating data on listings.

Arguments in favor of this thesis:

• The journey to buy often starts on google/AI bots. You might want to check what good neighbourhoods/schools are etc. So this would be a direct funnel for Chatgpt.

• LLMs would set up a portal for agents who have a strong incentive to upload their listings there as large LLM companies already have a lot of users.

• LLMs would have to build cool AI tools that might entice agents and consumers while portals might be slow to adopt.

• LLMs have data that probably gives info about preferences already based on past conversations.

• LLMs aggregate this and you would have many cheaper rightmoves.

Counter arguments against that are:

• I wonder why Google didn’t do this.

• It would still require a large amount of real estate agents to sign up relatively quickly before Rightmove adapts. Real estate brokerage markets are very fractured. If asking LLMs gives you very incomplete listings, people will still end up going to Rightmove first as they still are by far the largest funnel for people looking to buy.

• Rightmove has a real proprietary data advantage for real estate brokers that would take at least multiple years of high density operation to break:

  • Real time demand/intent signals on a national scale such as searches, saves, alerts, repeat visits, location pivots, budget changes, time-on-market attention

  • Longitudinal listing history

  • Closed-loop performance and benchmarking

• Consumer habits are sticky. Rightmove (and other dominant platforms) probably have the funnel advantage still over LLMs. So there is some room for error for them.

• LLMs would have to set up dedicated operations for real estate with apps with some of above mentioned missing features + what portals already provide. Sales force to get the brokers. It could not be a side thing.

• ChatGPT and maybe Google would be the main contenders as they control most of the consumer market. Openai is bleeding money. Do they have the budget to attack all these niches, with a highly uncertain winner takes all dynamic?

• Specialists > Generalists, so Rightmove still has an edge here IMO. You can see how Google with all its supposed brilliance and its huge user funnel has consistently failed to compete in a wide range of markets. They are almost pathologically unable to build stuff that people want.

• Building + running all this costs $$$ even if AI makes it cheaper, so LLMs will have to charge estate agents at some point. Rightmove has 67% EBIT margins, if LLMs offer it for 50-75% off, their margins would not be that large.

• It would still be a winner take all kind of thing as people are drawn to the place with the most complete listings. So even at 50% off, if you have to pay 3 LLMs to show your listings it is actually more expensive if you are an agent.

• This means it is literally an existential fight for websites like Rightmove. So you are fighting a cornered specialist to the death while your only real modest advantages are the funnel (as models themselves for this task are a commodity basically) and a small data advantage from past chat logs. But overall data advantage would probably be negative.

• What if we end up with 6-7 dominant LLMs? You kind of need a monopoly like funnel to disrupt this. Rightmove doesn’t need a large advantage to catch most of the traffic. If that is the case Rightmove might be another dominant real estate LLM. I suspect we will get all these specialist LLMs over time where the large ones like Openai will still be dominant, but it won’t be like Google.

So I am somewhat skeptical ChatGPT (who would be the only real contender with maybe Gemini) can seriously disrupt this. If they try it will probably hit Rightmoves margins. They might have to lower fees to stay dominant and invest in building new features at the same time.

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BUT I will continue writing up new ideas for another 6 months.

Then I will cease writing paywalled articles. I lowered my monthly price to 30 euro’s/month. Anyone subscribing or renewing annual or founding after today (January 12th) will not get their money back! You can do a charge back though if you read this post a few days from now and it renews. But I won’t refund it (I will lose >10% on that due to fees). So if you want to keep reading you can cancel your annual sub and just sub monthly for 30/month until June 2026. I will then pause subs entirely in mid June.

Then whoever still has an annual paid subscription running from before today (the 12th of January 2026) can get their money back for the remainder in July if they wish. After that I will still write the occasional free post.

Edit: Don’t monthly sub if you still have a few months to go on current sub! Otherwise you will be double subscribed.

As for the next 6 months, I suspect software stocks will be the main focus due to the narrative that AI will destroy software. Everyone on Twitter seems very bearish on them. As I am building a tool for myself using AI (Claude Code) to make finding mispriced stocks and special situations more efficient I kind of have a front row seat to this.

It seems the bear thesis has two parts:

• Customers will just build products inhouse.

• SaaS market will be flooded with new competition from smaller companies as AI lowers barriers of entry

I think the first point is completely ridiculous for 99% of SaaS services. While there will be consumers and companies building vibe coded apps for very niche use cases, most SaaS apps such as accounting, EHR, logistics software etc will not be built inhouse as economies of scale kick in very very rapidly for most customers. There will always be weird edge cases and bugs that cost way more in time spent to fix than the 3-400/year you are paying to some well established service that has these ironed out already. Especially when other things such as security are very important.

As for the second point, I think this assumes that the only moat in software is cost of building an app. Assuming that somehow most software was gate kept by a lack of software engineers, prohibitively high costs. I would say mid to large scale software companies primarily get their moat from:

• Stickiness, can be broken up into:

  • Employees having to learn a new work flow (AI wont help much here).

  • Time cost of switching (AI will erode this one)

  • Something going wrong and losing a customer worth 10x the cost savings (AI will help with this one, but will take a long time before perception of this risk goes away).

  • Custom made rules, automations, fields etc. Building this requires feedback with the customer which is a headache, AI won’t help much here.

• Network effects. Everyone usually thinks of facebook here. But for example employees knowing how your software works so they can be more productive at it is kind of a network effect, (although technically this belongs in stickiness basket as well.) These can be local or global. AI does little to erode this.

• Data Moat. AI actually widens this moat. As new entrants will simply lack the data to provide same quality service.

• Integrations with other software. Not easy to achieve for a new entrant. Especially if trust declines when a lot of these vibe coded apps start causing damage.

• Good track record of reliability if that is important (for example very high up time, security or compliance). A new entrant would have to be much better in some quick and obvious way.

• Distribution advantage. When you are the major player, when customers search for a solution that is first one they will find.

• Economies of scale. AI tools won’t mean code becomes free. Larger companies can spend on more security, new features, customer service, lower hosting costs etc.

• Certification requirement. This one is somewhat rare, but I have seen them. Where it is near impossible for a new entrant to break in as government doesn’t like sensitive data spread out over a dozen companies for example.

Finally I have said this before, but the more complex/specialized a piece of software is, the worse these AI tools are at building it. To the point where an experienced dev is more productive just writing it themselves.

I do think in order to win this time around, you need companies that actually generate earnings (SBC is a real expense!) or some other strong catalyst. PE ratios will matter. So buying some cash burning SaaS at 5x sales that sold off 50% from 10x sales might not be a winning strategy anymore.

Also good company culture matters. I think for quite some time these AI tools will work best in the hands of experienced devs. And most experienced devs work in some capacity for software companies. So companies with decent code bases and good talent will likely become significantly more productive.

My prediction is that in a year or two as the strengths and weaknesses of these AI tools become clear people will still remember that a lot of SaaS companies have really nice recurring revenue, solid potential for a moat and need essentially 0 capital to grow revenue once already breaking even and the good ones will end up doing well.

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