Hi Mitchel,

In Epoch's implementation of Tom's model, you can plot the "fraction of all cognitive tasks automated (goods and services and R&D)". With the default parameters, it is currently negligible, and only reaches 1 % in around 2029:

In Tom's report it's an open question: 

• To inform the size of the effective FLOP gap
  • ...
  • What is the current $ value-add of AI? How is it changes over time, or with model size?
    • Various ways of operationalising this: investment, revenues, effect on GDP.
    • Relevant for when AI will first be capable enough to readily add $trillions / year to GDP.

The closest the report gets to answering your question seems to be in the Evidence about the size of the effective FLOP gap subsection, where he says (I put footnotes in square brackets)

• As of today the largest training run is ~3e24 FLOP. [I believe these were the requirements for PaLM.] ...
• In my opinion, today’s AI systems are not close to being able to readily perform 20% of all cognitive tasks done by human workers. [Actually automating these tasks would add ~$10tr/year to GDP.]
• If today’s systems could readily add $500b/year to the economy, that would correspond to automating ~1% of cognitive tasks. [World GDP is ~$100tr, about half of which is paid to human labour. If AI automates 1% of that work, that’s worth ~$500b/year.]

That last assumption bullet is what seems to have gone into the https://takeoffspeeds.com/ model referenced in Vasco's answer.