Each analysis takes on one large question and ends at a concrete portfolio of listed companies in which the AI agents see a structural undervaluation. The topics today range from artificial intelligence and robotics to human basics and state-mandated inspection, and more are added on an ongoing basis.
US equities are not suddenly running out of buyers. The marginal buyer is becoming more valuation-sensitive as AI investment constrains buybacks, new share supply rises and positive real yields offer savers an alternative.
AI infrastructure can be productive without making every layer financeable. The analysis favours durable production and workflow bottlenecks over capacity whose value relies on overlapping contracts, concentrated customers and continuing access to credit.
Europe’s central AI risk is the loss of employer-funded early-career training. The costs appear first in the number of first-time buyers and later in institutions that depend on accountable professionals.
AI is changing the composition of European digital work before it changes the employment headline. The analysis favours accountable delivery, specialist labour and physical capacity over generic labour-hour models.
Which companies appear in more than one thesis, and how all the theses have performed together.
Index concentration, interest rates and the financing of the AI boom are changing the rules of the game. Why investors now need to focus on three drivers rather than four sectors.
The Hormuz disruption has made reliable delivery scarcer than oil itself. This analysis examines the case for non-Hormuz LNG, storage, terminals, freight and marine insurance, while keeping directional oil exposure limited.
Software creation is becoming cheaper. The analysis examines why durable returns may sit with small-business operating platforms, payment authorisation, compliance records and electricity rather than with generated applications.
AI electricity demand remains high. This analysis examines why returns may accrue more to regulated networks, service revenue, specialist project delivery and contracted energy than to the most visible equipment.
This analysis examines whether certified human hours, carbon tariff walls and restricted assets offer a less crowded way to study industrial returns in the late 2020s.
Cheap intelligence deflates informational moats, while permits, qualified capacity, route density and customer relationships remain scarce. The analysis separates scale grocers and qualified physical capacity from the businesses to avoid.
New laws require proof of compliance for the use of artificial intelligence. The analysis shows which companies issue that proof, sell it, or have to pay for it.
When public budgets are tight, governments can still create obligations. The durable earnings sit with the private companies that provide the proof.
Warehouse robots, defence drones and humanoid machines are moving into series production. The analysis shows which listed companies capture the return and which only carry the valuation.
The assessment is produced in several steps. Independent model families answer the same question separately and then attack the results. What you read here has survived several rounds.
Any analysis can be wrong. That is why the falsification criteria and the counter-position sit on the same page as the thesis, not in the small print.
Research themes examine structural questions in specific markets and lead to concrete portfolios. The CIO Perspective complements this work with an assessment of the market regime and the allocation that follows from it.
Each page takes on one large market question and ends at a concrete portfolio of listed companies. The process that produces the assessment sits further up this page.
The process runs in rounds until there is a clear result and a list of tradable companies with structural advantages. The AI agents see a structural undervaluation in that portfolio.
Business Rating, Market-Fit Rating and Cycle Rating come from Leeway's general stock analysis and are independent of any given thesis. They assess the company, not the probability that the thesis is correct.
In the course of an analysis the models write roughly 300 A4 pages of research and discussion before they reach a conclusion.
Several leading models are connected and run in a chain, so that the examination is broader and more reliable.
While research and analysis are under way, Perplexity continuously checks the facts and corrects errors.
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Disclosure of interests: To the best of our knowledge, Leeway and the persons responsible for creating or publishing the respective content do not hold a net long or net short position exceeding 0.5% of the issued share capital of any issuer discussed in that content at the time of publication. There are no other interests or business relationships that could conflict with objectivity, unless expressly stated otherwise in the respective content.
