Category: Commentary

  • Why semiconductors and memory is a bubble

    Demand Side risk

    My issue with Nvidia is their reliance on a few massive hyperscalers. These hyperscalers are investing massively into a heavily depreciating asset; (4-6 years). When so many massive players are piling into the same sector, it creates a supply-demand effect that is favouring Nvidia massively. The question is, will they keep pilling into the data centers – or is supply being met to an extent that the ROIC is simply not strong enough given the high capex?

    People seem to forget that the chip industry is cyclical.

    AI has massively improved on the basis of training the models on the whole internet, this was a breakthrough – expecting the same improvements or an exponential curve of endless improvements is optimistic the least. Currently the market might be overestimating the technological developments in the short term and underestimating in the long term.

    I am not saying Nvidia is a bad investment, but I am arguing that there is inherent supply-demand risk from their biggest customers, whom are currently fighting a race to the bottom. My theory is that compute will become so cheap, that hyperscalers have to decrease spending.

    There are also other issues regarding grid capacity, electricity and necessary natural resources.

    Supply side risk

    When demand outstrips supply, you increase supply – there is a few years delay in the chip industry, due to long manufacturing times … when supply finally hit the market, demand has often normalised

    In this case the hyperscalers are rushing in to ensure they are not losing the ai race – this is nothing new, the worst thing that could happen for a mag7 company is that their technology becomes a thing of the past, so they continuously make big investments and innovate – anyways, they are ordering a little extra to make sure they are not falling behind – at the same time, foundries are building a little extra to make sure they are not losing sales – eventually these two things are going to collide, leading to a bust before the next boom (and so the cycle continues)…

    Note: neither demand or supply is linear

    AI is overhyped

    This needs to be addressed with an abundance on AI slob.
    It hallucinates, it fails at basic tasks, it creates a narrative on assumptions.
    I have played around with it quite a bit – and honestly – its almost useless.
    Even kids are using AI for a synonym for trash. I am well aware this is not valueinvesting – but also 95% of the posts here are not – its more like a growth at any price forum.

    Ever pulled data using AI only to double check it messed up the abbreviations? I have.
    Ever had it miss the latest annual report when making conclusions? I have.
    Ever had it create an obviously wrong narrative? I have.
    Ever had AI fail at basic math? I have.

    AI are yes sayers; if you present a bull thesis it will in many cases agree. I have prompted mine to disagree/evaluate both sides – but often I notice it will disagree with things that are obviously true. It does not think – It cannot evaluate moats. It reads a TAM and it assumes its true – it does not think.

    Lately it has come to the attention that Reddit is the most cited source from ChatGPT. Furthermore, programmers are again being an sought after resource; hinting at layoffs being overdone. Finally, AI expenditures are being scaled down in major firms, as costs are simply too high to justify.

    Always be careful with the most hyped stocks in the most hyped sectors

  • Sector returns OMXc25 and XETRA

    Sector returns OMXc25 and XETRA

    Sector Returns

    OMX C25 Denmark • MSCI Europe

    OMX C25 β€” All Constituents

    Daily returns
    Fetching market data…

    Live data from Yahoo Finance Β· Cached server-side to keep it fast.
    OMX C25: All 25 constituents on Nasdaq Copenhagen.
    C25 Sectors: Grouped by ICB sector with a representative stock as proxy.
    MSCI Europe: iShares MSCI Europe sector UCITS ETFs on XETRA.

  • The most dominant force of the AI boom

    Prediction 1. The ease of app development will be the most disruptive force of GAI.

    Models like Claude significantly lower the barriers to entry for software developmentβ€”by making it infinitely easier and faster to build.

    When everyone can program, a plethora of apps is bound to flood the market, causing massive disruption for easily replicable products. A model to access Bloomberg-like information and analysis? Done. A price-comparison tool like PriceRunner? Done. A dating app like Tinder? Done. A music streaming app like Spotify? Done. Health platforms, system testing, marketing dashboards… any platform? Done.

    Of course, this must be taken with a grain of salt. “Moats” such as network effects and proprietary data are becoming increasingly vital. You can’t easily copy Bloomberg’s extensive data network and APIs or Spotify’s music licenses. But the technical barrier to building the software itself? That is certainly gone.

    Ultimately, this force will drastically increase competition. Because the product itself is highly replicable, the battleground shifts. Strong marketing strategies, brand awareness, and user trust are no longer just nice-to-havesβ€”they are becoming increasingly vital for software firms in the AI era.

    This is already reflected in saas/software consultancy companies, which increasingly are selling products instead of hour.

    Prediction 2. The cost of GAI as an everyday tool, will significantly increase

    Most likely using the best AI tools will be significantly more expensive than today – and will be consolidated between a few big players. Custom AIs will still play a role. As happens naturally with these highly competetive industries, is that they fight for marketshare – and after a while this fight turns into consolidation – which in turns decreases competition and in turn increases profitability – by increasing sale prices.

  • When Flawed Science Makes it into Top Marketing Journals

    The Journal of Marketing, published by the American Marketing Association (AMA), is arguably the most prestigious and influential journal in the entire field of marketing. If a paper is published here, it has survived one of the most brutal peer-review processes in academia.

    But even so, obviously flawed articles make it through. In this paper I review: β€œBMW is powerful, Beemer is not: Nickname Branding Impairs Brand Performance” by Zhang, Ye and Thomson (2025).

    The text tries to prove that when a brand uses a nickname created by consumers, it signals a “loss of authority” or a “submission” to the audience. This argument relies on the Speech Act Theory, where the authors argue that “naming” is a privilege of the powerful. Adopting a nickname is thus a sign of weakness. The text aims to prove that this perceived weakness (low brand power) is the exact reason why consumers stop buying. What they find though, is that nickname branding is unsurprisingly affected by many other criteria, such as (in my own words) the perceived intention of the advertisement and the perceived conformity of the organization.

    But this paper falls short on several validity criteria. The remaining bit will argue why the text falls short on ecological and construct validity on the basis of a select few experiments; but these select few experiments represent an issue across the majority of the paper.

    In Study 3a, the Wally vs. Walmart study, the respondents were asked which brand they found most β€œpowerless, weak or powerful, strong” as an attempt to examine “brand power.” They then ran a mediation analysis with brand power as the mediator and purchase intention as the dependent variable. A potential issue is the fact that Wally sounds weak or powerless, while Walmart sounds more powerful. The issue here is that Wally might just score lower and affect purchase intention simply because it sounds silly. After all, Wally is a famous children’s cartoon book and Walmart is a powerful conglomerateβ€”associations the respondents are likely to have, and associations that might affect purchase intention.

    Study 5: Admittedly, in Study 5 the authors attempted to examine if this effect exists in fictitious competent vs. warm brandsβ€”essentially ruling out the case that it’s the associations customers have with Walmart and Wally. To represent competent/warm brands, they made up a fictitious law firm and charity called Atlantic Eagle, explained to the respondents that consumers named this brand “Birdie,” and stated that the firms were using the nickname Birdie in their advertising. The issue is that Atlantic Eagle is an apex predator and a birdie is synonymous with a small, unthreatening bird. Respondents might find that a law firm calling itself Birdie is hurt by the name itself, and not by the effect of submitting power to the customer.

    This raises a question: why not test for the strength of the brand names?

    In Study 4, the authors attempt to rule out competing mechanisms. They claim that because the Consumer-Brand Relationship scores did not shift after seeing the BMW ad, nicknames do not strengthen the consumer’s connection to the brand or act as an improvement to relationships. However, they cannot confidently rule out this mechanism because their experiment lacks ecological validity. They tried to measure fluctuations in a deep, longitudinal relationship using a single, 5-second exposure to a mock X post. True Consumer-Brand Relationships are an ecosystem built over time. They cannot declare that a marketing tactic fails to strengthen brand relationships when their experimental designβ€”a single ad experimentβ€”is most likely fundamentally incapable of shifting this metric in the first place.

    They ran an experiment with an ad using the nickname and one with the real name, with the intention of testing Brand Power, Consumer-Brand Relationship, Brand Familiarity, and Brand Performance. They found that brand familiarity and customer brand relationships did not change after the experiment, but brand power and brand performance did. Therefore they conclude that it must be Brand Power that affects Brand Performance, with brand familiarity and customer brand relationships having little to no effect. This is obviously wrong, and contrary to existing researchβ€”which they, based on this experiment, conclude is wrong.

    Consumer-Brand Relationships develop over time and include a broader ecosystem of previous product experiences, word of mouth, and so on.

    Therefore, expecting a single ad to change this customer-brand relationship, just because the brand is using a nickname, is … I am out of words.

    To finalize, while the methodology is obviously flawed and to a certain extent smells like “design bias” (trying to prove a point using sketchy methodologyβ€”in this case: garbage in, garbage out), the text does raise some relevant questions. Does the namer matter? For example, if the third party is not just ‘the public’ but a person of superlative status, such as royalty or a celebrity, does this affect the ‘power submitted’?

    While the methodology is flawed, it could be relevant to properly investigate whether nickname branding is more powerful for brands with warm personalities or those emphasizing community values. Based on the paper, I have certain questions:

    Is nickname branding more successful if the name has positive associations? To what extent does the original perception of the brand nickname matter? And does nickname branding work better for people who actually use the brand nickname?

    Finaly, this paper passed the most brutal peer-review process in marketing, yet it bases its core conclusions on severe confounding variables and low ecological validity.

    Gemini 3.0 Has been used “as a peer reviewer” and to improve readability.

  • Why investment analysts fail to outperform – A step by step guide to institutional underperformance

    Why investment analysts fail to outperform – A step by step guide to institutional underperformance.

    Initial comment:

    In Berkshire Hathaways annual meetings Charlie Munger referred to a concept called inversion – rather than asking, “How do I achieve success?” he would ask, “What are all the things that would guarantee failure?” His strategy was simply to avoid these mistakes.

    Keeping this in mind – the following text aim to explain how and why investment analyst underperform – it does not directly answer the mistakes the average investor makes, because the text assumes an investment funds perspective. In hindsight, this would have been a more relevant article.

    A step by step guide to institutional underperformance.

    Step 1. Become institutionalised

    Rely on flawed models like GGM, CAPM or APT – while forgetting business fundamentals. Alternatively – predict the macroeconomic environment down to the decimal, but still importantly always forget business fundamentals.

    Step 2: Nepotism is key

    Skills or track records – does not matter – what matters is that your rich family can reference you. Alternatively, get referenced by a friend. If neither is an option, you ought to get lucky – because there are finance bros and macro economist with better grades than you.

    Step 3: Become complacent

    You have now gotten your job. Here it is important – stop improving. Rely on fellow analysts predictions – and speedrun your due diligence. After all – your worldview is correct and your holistic godlike predictions must outperform. This leads us to the next step.

    Step 4: You are not biased or flawed in any way

    4.1 We have already established your godlike presence.
    (The Dunning-Kruger Effect).

    4.2 Always ignore contradictory evidence
    (Confirmation Bias).

    4.3 You have made your decision, do not change your opinion (Anchoring Bias).

    4.4 You may have lost money and time researching – I REPEAT – DO NOT CHANGE YOUR MIND (Sunk Cost Fallacy).

    4.5 Your fellow investment analysts price target are way above your initial assumptions – of course you are wrong – change your assumptions to match the almighty group (Group Bias).

    If you really want to generate alpha underperformance – there are loads of other biases to rely on, such as: Availability Heuristic, Hindsight Bias, Negativity Bias, Halo Effect: Automation Bias and historical/representation Bias.

    Step 5: Fees

    Just – always take high fees. This is an almost guaranteed way to underperform (most of your coworkers actually outperform before fees – you can do better!). Also, remember, high fees = high skills.

    Step 6: Diworseify

    Never let a high-conviction idea ruin a perfectly mediocre portfolio. Once you find a great investment, immediately dilute it with 50 terrible ones to “manage risk.” After all, if you drastically underperform the index, you get fired. You are even obligated to by law.

    Final comment

    Not all investment analysts seek to outperform the market – investing is not always about making the maximum amount of money – but in many cases its about preserving wealth.

    A point I think is sadly overlooked in these “randomly throwing darts and outperform investment funds” articles.

    Also keep in mind – everyone will make some of these “mistakes” – and no step will alone lead to underperformance. It is the sum of these steps, that likely – by average – lead to underperformance.

  • Watchlist

    Watchlist tier 1:

    • Keyence
      • Leadership position in scanners and sensors for automation.
    • Netflix
      • Leadership position in streaming.
    • Taiwan Semiconductor Manufactoring Company
      • Leadership position in manufactoring of semiconductors.
    • ASML
      • Leadership position in photolithography systems.
    • Bandai Namco
      • Leadership position in leveraging anime franchises.
    • Veolia Environnement
      • Leadership position in ecological transformation (#2 in waste management, #1 in water technologies).
    • Match Group
      • Leadership position in online dating
    • Duolingo
      • Leadership position in language learning

    Watchlist tier 2:

    • Advanced Micro Devices & Nvidia
      • Leadership positions in designing computer chips.
    • Iberdrola
      • Strong performance in clean energy. Stable and predictable earnings.
    • Magnum Ice Cream Company
      • A strong brand.
    • Saint Gobain
    • Baltic Classified Group
    • Linde
    • Tomra
    • Atlas Copco
    • Krafton & NetEase
      • Both cheap on PE with historically strong execution