AI and the dot-com lesson
AI may transform the economy. The return on an AI investment is a separate question. That distinction is the most useful lesson I take from the dot-com era.
The notebook
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34 notes
AI may transform the economy. The return on an AI investment is a separate question. That distinction is the most useful lesson I take from the dot-com era.
AI makes it easier to produce a convincing answer. Checking whether that answer deserves our confidence still takes work.
Behind every AI model is physical infrastructure. Electricity, cooling and grid connections help explain why this story reaches far beyond chipmakers.
A property brochure sells a possibility. The decision becomes clearer when the costs, rental assumptions and exit options sit on the same page.
An IPO can attract attention long before its shares trade. I prefer to separate that excitement from what the business is worth.
A purchase split into four payments is still one purchase. The challenge is seeing all those smaller commitments together.
Buying a crypto-related share means owning a company. Its costs, funding and management decisions come with the exposure.
Solana is both a technology story and a token story. Understanding the connection starts with keeping those two ideas distinct.
An NFT tells us something about a token. To understand its value, we also need to know the rights, the service and the potential buyer behind it.
With Phoenix Group, the questions begin with the business behind the crypto exposure: what it earns, what it spends and how it funds growth.
AI can be an interesting company story. For Presight, I want to connect that story to contracts, collections and the cash left in the business.
Oil, interest rates and currencies connect the markets I follow. The same change can help one business and put pressure on another.
Nvidia’s journey connects gaming hardware, developer tools and AI infrastructure. It also raises a familiar investing question: how much future success is already expected?
Living and working in the UAE, I’m interested in where AI makes everyday tasks genuinely more useful—and where a person still needs to check the result.
Air taxis are an exciting transport idea. Turning that idea into a dependable everyday service involves much more than an aircraft.
Trade, borrowing, reserves and savings are different uses of a currency. A headline about the dollar is clearer when we identify which one is changing.
A fund name gives only a first impression. The holdings, costs and overlap with other investments reveal much more.
Before asking where gold might go next, I find it useful to ask why someone wants to hold it—and in what form.
US markets offer a wide range of businesses to follow. My starting point is what a company does, who pays it and what the share price assumes.
ADX and DFM bring together businesses with very different drivers. Local familiarity helps, but the disclosures still deserve a careful read.
India’s growth creates plenty of compelling business stories. Understanding an individual share still means looking at cash, competition and the price.
Bitcoin’s price attracts attention. Its design, custody arrangements and risks deserve just as much of it.
Ethereum supports a network of applications. How that activity connects to demand for ETH is the more interesting question for an investor.
Polkadot’s ambition is to connect blockchains. The next questions concern adoption, token use and evidence that the network solves a real problem.
Bitcoin, stablecoins and smart-contract networks sit under the same broad label. They serve different purposes and carry different risks.
One company’s AI spending becomes another company’s revenue. Following that money helps separate a broad theme from an individual business.
An AI reference in a company presentation can mean several things. I want to understand what customers actually buy and how the economics change.
A private loan may have no daily market price. That can make its reported value look steady without making the underlying risk small.
Gold rarely moves for one reason alone. Currencies, rates and different sources of demand give a more useful starting point than a single dramatic explanation.
Quantum computing invites big expectations. I’m interested in the distance between a technical milestone, a useful application and a sustainable business.
IonQ is easier to assess through its own milestones and financial statements than through comparisons with a past stock-market winner.
The AI story has brought new attention to memory. Micron still needs to be understood through product mix, manufacturing costs and the supply cycle.
A growing server market can create opportunity. For Super Micro, revenue, margins and working capital need to be read together.
A battery supply chain is a series of businesses, not one commodity trade. Saudi Arabia’s industrial ambitions are more useful to discuss through those separate stages.
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