What We’re Reading (Week Ending 14 July 2024) - 14 Jul 2024
Reading helps us learn about the world and it is a really important aspect of investing. The late Charlie Munger even went so far as to say that “I don’t think you can get to be a really good investor over a broad range without doing a massive amount of reading.” We (the co-founders of Compounder Fund) read widely across a range of topics, including investing, business, technology, and the world in general. We want to regularly share the best articles we’ve come across recently. Here they are (for the week ending 14 July 2024):
1. Idea Brunch with “Made in Japan” – Edwin Dorsey and Made in Japan
For me, Japan is an interesting opportunity set because there’s a strong case to be made for several inflection points that are not all related. A few that come to mind:
- Governance improvements: Japan always had a lot of companies with loads of cash on the balance sheet making them look ‘cheap’. The issue has always been that this cash was never for the shareholders so the market discounted this appropriately. In the last 1.5 years, however, the Tokyo Stock Exchange has cracked down on companies with weak governance/capital allocation policies and low valuation. They name and shame the companies that don’t try to improve their corporate value and are implementing a host of other measures to incentivize responsible capital allocation. I think this sends a signal to the global investor community that Japan is trying to become less of a value trap.
- Interest rates/Inflation: Post 2008 Financial Crisis, Japan’s interest rates have been close to zero for over a decade. This is in a country that has been deflationary for so long and we’ve been gradually moving away from that. Inflation seems to be returning and interest rates are ‘normalizing.’ This could be the moment to wake up the animal spirits of Japan again, to take on more risk and for businesses to command pricing power. If inflation sustains itself at some level, it will no longer make rational sense for businesses and individuals to hold on to cash like they did in a deflationary economy where that was rewarded as their purchasing power increased. Now the opposite will happen which means they are incentivized to put the cash to work. This won’t just be businesses investing but also for individuals too. The government just made it way more attractive to do that through its new NISA scheme.
- NISA: Japan has set up its new tax-free investment scheme for households called the Nippon Individual Savings Account (NISA). The first iteration was garbage but this one is promising. It was set up by the government to incentivize households to allocate their excess cash savings into the stock market. Household savings allocated to equities has been notoriously small, less than 20% or so. By providing more liquidity in the markets it could help the financial markets function better and make it also easier for institutions to participate in areas which were previously too illiquid.
- Consolidation: I think we’re entering a phase of consolidation amongst Japanese SMEs which has been the backbone of Japanese society. We have an issue where many aging owners are not able to find successors for their businesses. There’s been a stigma around M&A in the past but this is starting to melt away and becoming a viable option. We’re also starting to see more young talent flowing into the M&A space. Moreover, with low interest rates, we’re seeing increased interest from foreign PE firms as well – which all tells me that we’re at an interesting juncture for industry consolidation.
- Digitalization: One thing that you’re starting to see after Covid is the need for a more digital Japan has come to the forefront. We’ve been embarrassingly late to digital/software adoption but this was the turning point where we realized it was necessary. The government set up a Digital Agency to help adoption and provide various subsidy schemes to encourage the use of more software. We even have the term ‘DX’ short for Digital Transformation now added to the lexicon. There’s also the ‘digital cliff’ as it is called here. A lot of IT systems being used by corporate Japan today are super old something like more than 60% will be 20 years or older by 2025. So a lot of IT spending currently is going to maintaining these systems rather than building out new ones. Many people imagine Japan as this futuristic place, but you’ll be amazed how much paper we still use!…
…One of the contradictions I’ve felt about Japan is that large-cap growth in Japan gets priced at ridiculously high multiples. It’s not uncommon to see these things trade at 40 times P/E or higher. This is presumably because the cost of capital in Japan is low and in a deflationary economy where the population is declining, growth is rare. However, when you look at these small companies in great competitive positions that are growing double digits with lots of room to grow, you can find them trading for single-digit earnings multiples! The delta is so big that I call this the ‘chasm’. If you look at some of the large-cap growth companies, these also traded at very low multiples early on but as they continued to grow earnings per share at some point brokers start to cover it, institutions start to pile in and the stock re-rates quite significantly and that contradiction gets resolved. Some of these large caps are expensive and can de-rate as interest rates rise, but the gap is large enough that I still think it’s more likely that these small companies will re-rate than the large caps de-rating down to where these small caps are valued.
2. The Last 72 Hours of Archegos – Ava Benny-Morrison and Sridhar Natarajan
An Archegos staffer re-lived the craziness of being in an airport security line while on a call with panicked banks, trying to head off catastrophe. A Credit Suisse trader described nabbing a Citi Bike on his day off to reach the office and untangle billions tied to Bill Hwang’s family office. And in the midst of it all, a junior Goldman Sachs manager recounted a call from the dying firm as it pleaded for the return of almost half a billion dollars it accidentally sent the lender.
Wall Street’s trial of the decade has offered vivid glimpses of the 72 hours that obliterated Hwang’s $36 billion fortune. One after another, Wall Streeters told a New York jury their version of how his secretive family office — and its pileup of wild wagers on jerry-rigged spreadsheets — ultimately crumbled and saddled banks with more than $10 billion in losses.
But it’s not mere scenes. Weeks of testimony have exposed cringeworthy misjudgments and costly blunders in various camps throughout the crisis — hardly Wall Street’s preferred image of calculated risk-taking. Bankers, for example, painfully acknowledged how they relied on sometimes-vague or evasive trust-me’s from Archegos while doling out billions in firepower for Hwang’s bets. That confidence melted into confusion that’s been replayed in the courtroom of a 90-year-old judge. Prosecutors are trying to make the case that Hwang manipulated the market and defrauded lenders…
…Jefferies calls CEO Rich Handler, who is on holiday in Turks and Caicos with a spicy margarita on the way. They tell him Archegos isn’t answering their calls. Handler says he’s going to get his cocktail and he wants Archegos positions gone and a tally of losses by the time he comes back. It was one of the few banks that escaped with minimal losses…
…As ViacomCBS and Discovery slump, Archegos capital plummets too. The family office is wiped out by the end of the day — just one week after Hwang gathered staff at his corporate apartment and talked about ways to grow the fund to $100 billion…
…Three years after the Archegos flameout exposed the audacity of Hwang’s investing, weeks of testimony have also served as an indictment of sorts of the system that enabled him.
Bank insiders on the witness stand have described extending billions of dollars in financing while relying on the equivalent of pinky promises to understand the size and shape of his portfolio, an approach that culminated with more than $10 billion in losses at a handful of lenders. Courtroom testimony and exhibits also revealed a lack of skepticism among those gatekeepers until it was far too late.
3. An Interview with Daniel Gross and Nat Friedman About Apple and AI – Ben Thompson, Daniel Gross, and Nat Friedman
Let’s start with the current belle of the ball, Apple. Apparently we have a new obvious winner from AI. In case you’re keeping track, I think Google was the obvious winner, then OpenAI was the obvious winner, then Microsoft, then Google again, then everyone just decided screw it, just buy Nvidia — I think that one still holds actually — and now we are to Apple, which by the way does not seem to be using Nvidia. Here’s a meta question: has anything changed in the broader environment where we can say with any sort of confidence, who is best placed and why, or is this just sort of the general meta, particularly in media and analysts like myself, running around like chickens with their heads cut off?
NF: I think one thing that really plays to Apple’s favor is that there seems to be multiple players reaching the same level of capabilities. If OpenAI had clearly broken away, such that they were 10 times better or even 2 times better than everyone else in terms of model quality, that would put Apple in a more difficult position. Apple benefits from the idea that either they can catch up or they have their choice of multiple players that they can work with, and it looks like we have somewhere between three and five companies that are all in it to win it and most of whom are planning to offer their models via APIs.
You have Google, OpenAI, Anthropic, you have X, you have Meta and so if you’re on the side of application building, generally this is great news because prices are going to keep dropping 90% per year, capabilities are going to keep improving. None of those players will have pricing power and you get to pick, or in Apple’s case, you can pick for now and have time to catch up in your own first party capabilities. The fact that no one’s broken away or shown a dominant lead, at least in this moment, between major model releases. We haven’t seen ChatGPT-5 yet, we haven’t seen Q* yet. Yeah, on current evidence, I think that’s good for people who are great at products, focus on products and applications and have massive distribution…
…Yeah, I mean I was writing today, I wrote about Apple three times this week, but the latest one was I perceive there being two risk factors for Apple. One is what you just said, which is one of these models actually figures it out to such a great extent that Apple becomes the commodity hardware provider providing access to this model. They’ll have a business there, but not nearly as a profitable one as they’re setting up right now where the models are the commodity, that’s risk factor number one.
Risk factor number two is, can they actually execute on what they showed? Can this on-device inference work as well as they claim? Will using their own silicon, and I think it’s probably going to be relatively inefficient, but given their scale and the way that they can architect it, they can probably pull it off having this one-to-one connection to the cloud. If they can do it, that’s great, but maybe they can’t do it. They’re doing a lot of new interesting stuff in that regard. Of those two risk factors, which do you think is the more important one?
DG: I don’t fully understand and I never fully have understood why local models can’t get really, really good, and I think that the reason often people don’t like hearing that is there’s not enough epistemic humility around how simple most of what we do is, from a caloric energy perspective, and why you couldn’t have a local model that does a lot of that. A human, I think, at rest is consuming like 100 watts maybe and an iPhone is using, I don’t know, 10 watts, but your MacBook is probably using 80 watts. Anyway, it’s within achievable confines to create something that has whatever the human level ability is, it’s synthesizing information on a local model.
What I don’t really know how to think about is what that means for the broader AI market, because at least as of now we obviously don’t fully believe that. We’re building all of this complicated data center capacity and we’re doing a lot of things in the cloud which is in cognitive dissonance with this idea that local models can get really good. The economy is built around the intelligence of the mean, not the median. Most of the labor is being done that is fairly simple tasks, and I’ve yet to see any kind of mathematical refutation that local models can’t get really good. You still may want cloud models for a bunch of other reasons, and there’s still a lot of very high-end, high-complexity work that you’re going to want a cloud model for, chemistry, physics, biology, maybe even doing your tax return, but for basic stuff like knowing how to use your iPhone and summarizing web results, I basically don’t understand why local models can’t get really good.
The other thing I’d add in by the way that’s going to happen for free is there’s going to be a ton of work both on the node density side from TSMC, but also on the efficiency side from every single major AI lab, because even though they run their models in the cloud, or because they run their models in the cloud, they really care about their COGS. You have this process that’s happened pretty durably year-over-year, where a new frontier model is launched, it’s super expensive to run and then it’s distilled, quantized or compressed so that the COGS of that company are more efficient. Now if you continue to do that, yeah, you do sort of wonder, wait a minute, “Why can’t the consumer run this model?”. There’s a ton of economic pressure to make these models not just very smart, but very cheap to run. At the limit, I don’t know if it’s going to be like your Apple TV, sort of computer at home is doing the work, or literally it’s happening in your hands, but it feels like local models can become pretty powerful…
…And where’s OpenAI in this? I analogized them to FedEx and UPS relative to Amazon, where Amazon just dumps the worst tasks on them that Amazon doesn’t want to do and they take all the easy stuff. But at the same time, one of my long-running theses is is that OpenAI has the opportunity to be a consumer tech company and they just got the biggest distribution deal of all time. Where do you perceive their position today as opposed to last week?
DG: I don’t fully understand the value of the distribution from the Apple deal. Maybe it makes sense, maybe it’s the Yahoo-Google deal. I think the question in AI is, if you’re working on enterprise, that’s one thing. If you’re working on consumer, the old rules of capitalism apply and you need a disruptive user interface such that people remember to use your product versus the incumbents and maybe that was chat.openai.com.
Which is now chatgpt.com, by the way.
DG: Chatgpt.com, or maybe that’s not enough. I think you saw a hint, not necessarily of just how OpenAI, but all of these labs sort of see themselves going in their product announcement where they created a thing that you just talk to, and it’s quite possible that maybe that is sufficient to be a revolutionary new user interface to the point where they can create their own hardware, they can basically command the attention of customers.
But I sort of think the general rule in the handbook is, if you’re going to be in consumer, you want to be at the top of the value chain. I mean, certainly it’s a mighty and impressive company, but the deal with Apple doesn’t really signal top of value chain. So the question is, really the ancient question we’ve been asking ourselves on this podcast for years now, which is, “What is the new revolutionary user interface that actually causes a change in user behavior?”.
Does that mean that Google is the most well-placed? They have all the smartphone attributes that Apple does, they should have better technology as far as models go. Does it matter that they’re worse at product or trust, like they don’t have the flexible organization that you were detailing before? We spent a lot of time on Google the last time we talked, has anything shifted your view of their potential?
DG: I think it really all depends on whether you can make an experience, and it always has depended on whether you can make an experience that’s good enough to justify a change in user behavior.
I’d argue for example, that there was a period in time where even though the actual interface was pretty simple, generating high-quality images was enough to cause a dramatic shift in user behavior. Midjourney is Midjourney not because it has some beautiful angled bar to pinch-and-zoom thing. It’s just like that was the remarkable miracle that it had. It made really good images, and it gave it some sticking power. So it’s this tension between defaults and inferior product and new revolutionary experiences, and whether they have enough to break the calcification of the incumbent.
It’s quite possible that if no one has any new brilliant ideas that Google, even though the models don’t seem to be as excellent, at least to the consumer’s eye, that they survive just because they have some Android user base, they certainly have Google.com. I will say the thing that has been surprising to me is while the technical capabilities of Google’s model seem impressive, the consumer implementation is actually I think worse than, “Just okay”. I thought their integration of language models into search was abysmal, sorry, to be totally frank. It was referencing Reddit comments that weren’t real facts, it’s not that hard to fix this sort of thing. So they need to be doing the bare minimum I think to maintain their status in the hierarchy. It’s possible they don’t do that, it’s possible that a new revolutionary user interface is also created, it’s also possible that they catch up and they bumble their way through it and they’re just fine.
But this is, I think the main question to the challenger labs, if they’re going in the direction of a consumer product is, “How do you make something that is so great that people actually leave the defaults?”, and I think we always underestimate how excellent you need to be. Enterprise things are a little bit different, by the way, and OpenAI is a very good lemonade stand just on enterprise dynamics, but consumer is in a way easier to reason about. You just have to have a miracle product and if that doesn’t happen, then yeah, maybe you should be long Google and Apple and the existing incumbents…
…NF: We’re in a bubble, in my opinion, no question. Like the early Internet bubble in some ways, not like it in other ways. But yeah, just look at the funding rounds and the capital intensity of all this, it’s crazy.
But bubbles are not bad for consumers, they’re bad for the investors who lose money in them, but they’re great for consumers, because you perform this big distributed search over what works and find out what does and even the failed companies leave behind some little sedimentary layer of progress for everyone else.
The example I love to give his Webvan, which was a grocery delivery service in the Internet bubble, and because they didn’t have mobile, they had to build their own warehouses because they couldn’t dispatch pickers to grocery stores, and they tried to automate those warehouses, and then because the Internet was so small, they didn’t have that much demand. There were not that many people ordering groceries on the web and so they failed and they incinerated a ton of capital and you could regard that as a total failure, except that some of the people at Webvan who worked on those warehouses, went off to found Kiva Systems, which did warehouse automation robots, which Amazon bought, and then built tens of thousands of them, and so Webvan’s robot heritage is powering Amazon warehouses and some of those executives ended up running Amazon Fresh and they eventually bought Whole Foods and so all that led to a lot of progress for other people.
The other thing, of course, is that a lot of money gets incinerated and a lot of companies fail, the technology moves forward, the user — putting URLs at the end of movie trailers, people learned about URLs, but some great companies are built in the process and it’s always a minority. It’s always a small minority, but it does happen. So yeah, I think we’re clearly in some kind of bubble, but I don’t think it’s unjustified. AI is a huge revolution and incredible progress will be made, and we should be grateful to venture capital for philanthropically funding a lot of the progress that we’ll all enjoy for decades…
…It is interesting to think about in the context of human intelligence, like to what extent you look at a baby, you look at a kid and how they acquire knowledge. I’m most inspired to do more research on babies that are blind or babies that are deaf, how do they handle that decrease in incoming information in building their view of the world and model of the world? Is there a bit where we started out with the less capable models, but when we do add images, when we do add videos, is there just an unlock there that we’re underestimating because we’ve overestimated text all along? I’m repeating what you said, Nat.
NF: Yeah, Daniel was way ahead on this. I think Daniel said that in our first conversation together, and this is a really active area of research now, is how can we synthesize the chain of the internal monologue, the thinking and the dead ends and the chain of thought that leads to the answer that’s encoded in the text on the Internet.
There was the Quiet-STaR paper and the STaR paper from [Eric] Zelikman who’s now at xAI. I don’t know what relation if any of that bears to Q*, but that’s basically what he did is to use current models to synthesize chains of reasoning that lead to the right answers where you already know the answer and then take the best ones and fine-tune those and you get a lot more intelligence out of the models when you do that. By the way, that’s one of the things the labs are spending money on generating is, “Can I get a lawyer to sit down and generate their reasoning traces for the conclusions that they write and can that be fed into the training data for a model and then make the models better at legal reasoning because it sees the whole process and not just the final answer?” — so chain of thought was an important discovery and yet it’s not reflected in our training data as widely as it could be.
4. Mining for Money – Michael Fritzell
I read Trevor Sykes book The Money Miners recently. It’s a book about Australia’s 1968-70 speculative mining bubble. Consider it a historical reference book about a bygone era…
…The free market price of nickel started rising from early 1969 onwards, from £1,500 per ton in January to £2,000 by March.
After a nickel miner strike in Canada, the free market price skyrocketed to £4,250 per tonne and eventually £7,000. The nickel rally was on…
…The company that came to be associated with the nickel boom the most was a small Kambalda miner called Poseidon…
…Poseidon’s fortunes changed when it hired full-time prospector Ken Shirley － an old friend of Norm Shierlaw. Ken lived in a caravan, living a lifestyle of moving around the bush to make new discoveries. His travels took him to Mount Windarra north of Kalgoorlie. He discovered minerals and pegged 41 claims along an iron formation stretching 11 kilometers.
In April 1969, Shirley sent in samples from Mount Windarra for assay and found 0.5% copper and 0.7% nickel together with associated platinum. The consulting geologists who analyzed the sample called it “very encouraging” and “intensely interesting”…
…On 29 September, Poseidon’s directors made their first public announcement about the discovery at Windarra. It said that the second drill hole had encountered nickel and copper but didn’t mention anything about the grade.
Just a few days after, on 1 October, they issued a more comprehensive statement showing 3.6% nickel at depths of 145-185 feet. This meant that Poseidon had struck nickel － the biggest nickel discovery in the history of Australia.
The announcement sparked a massive rally in the price of Poseidon. On 2 October, speculators flooded the Sydney Stock Exchange building after hearing about Poseidon in the press. Many of them were unable to reach the trading floor. On that day, on of the boards collapsed but prices continued to be updated on it will the staff refastened the ropes. Speculators didn’t want to miss an opportunity to buy…
…On 19 November 1969, Poseidon made an announcement confirming the strike length and width of the discovery. But strangely enough, it didn’t give any details about the assays from the drill holes. Despite the lack of information, the market took the report positively, causing Poseidon’s share price to rise further to AU$55.
Broker research departments issued reports, dreaming and imagining what Poseidon could be worth. These valuation exercises went along these lines:
- If the strike length was 1,500 feet, the width was 65 feet, and the depth was 500, that meant a total orebody of 48 million cubic feet, assuming the orebody is a neat rectangular block
- The orebody contained 13 cubic feet to the ton, which meant about three million tons of ore
- With an average grade of 2.0-2.5% nickel, the orebody could contain about 70,000 tons of nickel
- At an average price of AU$5,000 per ton, the orebody could be worth AU$350 milllion
- There will also be costs involved, including for labor, equipment, finance, infrastructure, etc. Say around AU$200 million.
- Over a mine life of 15 years, you could then calculate an income stream over time of the remaining AU$150 million worth and figure out that you could get earnings of AU$10 million per year
- Capitalize that number, and you could have justified a share price of AU$60 for Poseidon. Others, like Panmure Gordon in London, ended up with a value of AU$380/share.
Using a forward P/E multiple against expected earnings from Mount Windarra, the price didn’t seem so high. And speculators therefore felt comfortable bidding up the price to even higher levels…
…At Poseidon’s annual meeting in December 1969, long queues also formed outside the event. When the doors opened, 500 people rushed into the building. But due to a lack of seats, about 200 of them had to stand at the back while the meeting went on.
At the AGM, a discussion started about a potential rights issue to fund future capital expenditures. Instead, a share placement was proposed to a select number of individuals at AU$5 per share － a massive discount to the then-prevailing share price of AU$100 － suggesting severe dilution without raising much capital.
This was a huge problem because Poseidon had struck nickel but not enough capital to actually develop the mine.
A geologist speaking at the AGM mentioned that the zone in which the drilling had taken place indicated four million tons of ore. Participants flooded out of the meeting trying to calculate what 2.4% times 4 million tonnes might imply in terms of nickel resources. Enthusiasm boiled over.
Investors rushed out of the AGM to public telephone booths to call their brokers. At the start of the AGM to the end, the share price ran from AU$112 to AU$130. Once the press caught wind of the story, the price rallied further to AU$185.
No one rang a bell at the top of the market, but some lone voices expressed concern about how far the market had run:
- A London stock broker called R. Davie said that “A lot of Australian stocks, to put it mildly, are highly suspect”.
- Melbourne firm A Holst & Co predicted that in a few years’ time, the majority of present “gambling stocks” would be bitter memories to those who continued to hold them.
In February 1970, Poseidon reached a market capitalization of AU$700 million, or about AU$10 billion in today’s money. This represented about 3x the market cap of the Bank of New South Wales. And one-third the value of BHP, even though Poseidon hadn’t even begun developing any mine…
…Poseidon’s stock price peaked at around AU$280 per share. The market was waiting for Poseidon to announce how it would fund the development of its mine in Windarra. Yet nothing was announced. Meanwhile, the share price started declining.
By the end of February, almost all other speculative stocks on the board had also fallen significantly, with some losing half their value.
What led to this sudden change in sentiment?
- A major contributing factor was that nickel prices peaked and started declining from late 1960s onwards. The higher prices would eventually provide an incentive to search for new orebodies. Mines started coming online in a number of new number of new countries. World production of nickel skyrocketed.
- At the tend of 1969, there were 145 mining stocks listed in Sydney, compared with just 86 at the start of the year. And there were another 100 more mining companies queuing up to float and eventually list on the exchange. Supply eventually met the demand for scrip.
- Another factor was higher capital costs as Australian interest rates rose sharply
- Yet another factor was rising inflation as the operating costs of a mine shot up
It didn’t help that Poseidon’s eventual grade was almost half what was originally reported, with the grade falling from 3.6% to 2.4%. Combine that with much lower nickel prices and sharply higher development costs, and you have all the ingredients of a boom turning to bust…
…Looking back at the 1969-70 mining boom, not a single major deposit was discovered. Though it is true that the AU$850 million raised during the boom did help fund the development of new mines.
In the subsequent five years, Poseidon turned out to be a massive disappointment to investors. It soon realized that it would need AU$50 million to develop its Windarra mine, yet it only had AU$2 million left in cash and liquid assets. The solution was to team up with Western Mining Corporation, which took a 50% stake in the project.
But Poseidon incurred debt in the process. It tried to deal with its debt problems by its stake in the mine. But nobody wanted to buy it. And so in 1976, Poseidon defaulted on its debt and was delisted from the Australian exchanges.
During the bankruptcy, Poseidon’s 50% interest in Windarra was sold to Shell Australia for AU$30 million. But by that time, nickel prices had declined so much that Windarra had become only marginally economic. With these lower nickel prices, Shell saw no way of making the mine financially viable and it therefore shut down Windarra in 1978. The Poseidon dream was gone.
Perhaps the biggest lesson from the bust was that most exploration companies fail. The book quoted one study from Ontario Canada on mining claims between 1907 and 1953. About 6,600 mining companies had been formed during those 46 years, but only 348 reached production stage. Out of those, 294 failed to show a taxable profit. And only 54 companies ended up paying a dividend. In other words, the success rate was less than 1%.
5. Falkland Islands – The Next Big Thing? – Swen Lorenz
The 3,600 residents of the remote Falkland Islands could soon experience an “economic boom” that has the potential to “transform the islands’ entire economy”.
So reported by the Daily Telegraph on 30 June 2024…
…The islands have since seen an initial oil exploration boom, and exploitable oil reserves were found in 2010. Sadly, the oil price fell off a cliff in 2014, which killed the prospect of actually producing oil in the Falklands. The share prices of the fledgling Falkland oil companies all fell over 90%, many went under altogether and disappeared from public markets…
…As the Daily Telegraph just reported:
“The Falkland Islands has opened the door to oil exploration in its waters for the first time in history, in a move that could trigger an economic boom for locals.
The territory’s ruling council has asked islanders if they will back the scheme to extract up to 500m barrels of oil from the Sea Lion field, 150 miles to the north.
Details of the scheme were released without fanfare in the Falkland Islands Gazette, an official government publication, signed off by Dr Andrea Clausen, director of natural resources for the Falkland Islands government.
‘A statutory period of consultation will run from June 24, 2024 to August 5, 2024… regarding Navitas’ proposals for the drilling of oil wells and offshore production from the Sea Lion field,’ it said.
The territory’s ruling council has asked islanders if they will back the scheme to extract up to 500m barrels of oil from the Sea Lion field, 150 miles to the north. …. The field is thought to contain 1.7bn barrels of oil, making it several times bigger than Rosebank, the largest development planned for the UK’s own North Sea, estimated to hold 300m barrels.”
Are we about to see the Falkland Islands hype 2.0?…
…Now that Keir Starmer has wiped the floor with Rishi Sunak, will anything change?
It’s unlikely.
As the Daily Telegraph put it:
“Labour … has made accelerating the net zero transition a key part of its pitch to the electorate. Sir Keir Starmer’s party has promised to ban all new oil and gas exploration in British waters. This ban would not affect the Falklands, as it is the local administration there who have a say over drilling rights to surrounding waters.
Many within the Falklands government have wanted to make the islands a centre for oil production. John Birmingham, deputy portfolio holder for natural resources, MLA (Member of the Legislative Assembly), said: ‘Offshore hydrocarbons have the potential to be a significant part of our economy over the coming decades.
In a statement, the Falklands Islands government said: ‘We have the right to utilise our own natural resources. The Falkland Islands operates its own national system of petroleum licensing, including exploration, appraisal and production activities related to its offshore hydrocarbon resources.”
It’s all taken a long time, but the investment thesis behind the Falkland Islands oil discoveries could finally play out.
Disclaimer: None of the information or analysis presented is intended to form the basis for any offer or recommendation. We currently have a vested interest in Apple, Alphabet (parent of Google), Amazon, Meta Platforms, and Microsoft. Holdings are subject to change at any time.
