“The most striking finding is that world electricity energy efficiency (measured as overall primary-to-useful exergy efficiency) has stalled, rising dramatically from 2% in 1900 to 15% in 1960, and remaining nearly stable for the last 50 years, only reaching 17% by 2017.”
Why? Power generation got very efficient from 1900 to 1960 but we started to use the electricity in uses that aren’t efficient (mainly switching use to heat and cool buildings).
“If you look at Mr. Peltz’s track record, this guy used to drive trucks for his father’s food company.You might know that cold drink called Snapple. Nelson Peltz and his colleagues bought that brand after it had been run into the ground by Quaker Oats from $1 billion brand to 300 million brand. Mr. Peltz bought it around 98, 99. Within three years, they turned it around, made it a $1 billion brand again, and sold it off to Cadbury. These are folks who have real experience. They’re not just activist investors. They’ve done this. They have gotten their hands dirty.”
In the spirit of Feynman this superb blog post, by none other than Stephen Wolfram, gives a lucid explanation of what is going on under the hood of the latest tech phenomenon.
The short answer is “it’s maths”.
“But in the end, the remarkable thing is that all these operations—individually as simple as they are—can somehow together manage to do such a good “human-like” job of generating text. It has to be emphasized again that (at least so far as we know) there’s no “ultimate theoretical reason” why anything like this should work. And in fact, as we’ll discuss, I think we have to view this as a—potentially surprising—scientific discovery: that somehow in a neural net like ChatGPT’s it’s possible to capture the essence of what human brains manage to do in generating language.”
Recent academic work has been finding that expert networks have idiosyncratic value for investors.
After analysing 15,000 transcripts they find that these calls give incremental information, especially on technology, product, and operational topics relevant to firms, and tend to be a good tool to understand complicated negative developments.
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Imagine if foundations/grants would only pay for projects that had already succeeded at making an impact, leaving “venture” to take the risk on which projects actually would succeed by buying shares in them.
This idea, reverse engineering how capital markets operate, is discussed at length here in a very practical way.
“Nationally, across all industries, hiring decreased 6.5% in February compared to January. This is the largest month-over-month decrease we’ve seen since April 2020, though we don’t expect declines of this magnitude to occur on a regular basis going forward. Year-over-year hiring decreased 27.9% – and hiring has now declined for 10 consecutive months“.
The argument that AI is unlikely to be a winner for the middle-ground companies.
Why? “was a feature not a product” – in other words value will either accrue to core AI platforms (e.g. Open AI) or to incumbent software tools with distribution who will just add AI features.
“Adobe will own the AI-based image editing market Office & Google Docs will own the AI-based writing market Salesforce will be the best AI-enabled CRM Shopify the best AI optimization and customer support Zoom the best AI meeting summaries … all with a few API calls“
