How does ChatGPT work?

  • 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.

Expert Network Calls

  • Snippet is once again going to be sponsored by Stream by AlphaSense.
  • 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.
  • Sign up for a free two-week trial of Stream’s 25k+ database here.

Sector Weights

  • Fintech is getting more recognition in the new GICS changes.
  • As of March 17th, 11 S&P stocks will be reclassed out of IT and eight of them will land in Financials, into a new sub-industry focussed on payments.
  • This will raise the Financials weight to 14% from 11% – though clearly reducing the weight of banks within that, at an interesting point in time.
  • Source.

Impact Markets

  • 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.

Sources of Funds

  • Businesses have seen sources of funds (profits and credit) start to fall according to this Bridgewater analysis.
  • “As you would expect, when this happens, they cut back first on buybacks and M&A, then on capex, and last of all hiring.”
  • Once the latter happens consumer spending suffers and contraction ensues.
  • Though they do point out that earnings have yet to fall the required amount for this to start properly.

LinkedIn Workforce Data

  • The social network and it’s 199m members is a rich source of data.
  • Here is their latest workforce report.
  • 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“.

AI Winners

  • 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

Challenger Banks Continue to Win?

  • 900 days ago this site ran a series of article comparing challenger banks and incumbents in the UK on how good their user experience was (we covered it here).
  • Today, there is an update, and the picture hasn’t changed.
  • This chart for example shows the time between app updates – a measure of how quickly improvements are coming to customers.
  • On average, the challenger banks deploy updates 4.6x more frequently than the incumbents“.
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