AI monthly: The world of the unknown

Reality check: The 500 billion-dollar AI investment gap

Sequoia, ranked number one in all global venture funds by Dealroom, has concluded in its analysis on the gap between AI sales expectations and AI actual sales growth that the 125 billion dollar gap is now set to become a 500 billion dollar gap.

The problem with developments in AI is that today's state of the art technology quickly becomes obsolete

So if, for example, Nvidia sells 50 billion dollars in run-rate GPU revenue, running a data centre costs 100 billion dollars. Adding the end users’ margin of 50% implies that for the GPU investment, a 200 billion dollar revenue is needed to pay back the upfront capital investment, estimated as of September 2023. Even if the big tech companies and other AI-related companies were able to generate a part of these expenses, we're currently some way off from closing the upfront investment gap – resulting in a 500 billion dollar gap, according to David Cahn from Sequoia.

Progress in AI models: The ever-changing world of AI

Indeed, the pace of advancements in AI technology is incredible. Another milestone was recently met by the company Meta, which released pre-trained models with a multi-token approach. But what makes them so revolutionary?

Currently available large language models (LLM) learn tokens one at a time, i.e., one unit of text that is used to represent a word or symbol such as dots or spaces. A multi-token model, on the other hand, can directly understand and predict an entire sentence. A good example to illustrate this is an orchestra. Each instrument plays beautifully on its own (current standard LLM), but what is really fascinating is the interplay of all the instruments together (a multi-token LLM).

But of course, it's not just limited to sentences. Complex formulas can also be obtained in seconds, for example. And what are the benefits? Faster and more efficient results that require less computing power. The models currently available can predict four tokens simultaneously, performing some 15% better than comparable LLMs and generating output three times faster.
 

AI’s effect on monetary policy

AI will not only affect the labour market or productivity, but also monetary policy by affecting inflation, monetary policy transmission and the natural rate of interest as Piero Cipollone, member of the Executive Board of the European Central Bank, explained in a speech:
 

1. Inflation

Downward pressure

• Reduced risk of labour shortages and downward pressure on unit labour cost growth if the net effect of AI is substituting labour and increasing productivity.
• Decline in energy prices through enhanced grid management and more efficient energy consumption on the supply side, better tools for price comparison on the demand side.

Upward pressure

• Increase in energy prices due to higher global demand for energy due to the computing power required
• Discriminatory pricing by facilitating the real-time analysis of consumer demand and price elasticities
   

2. Monetary policy transmission

If AI leads to an increase in intermediation outside the banking sector, monetary policy transmission could be faster as non-banks react more strongly to monetary policy measures affecting longer-term interest rates, such as asset purchases. They have a higher credit, liquidity and duration risk compared to the banking sector, which affects people's marginal propensity to consume and their access to credit.
 

3. The natural rate of interest

Downward pressure

• Labour displacement and rising income inequality leads to an increase in precautionary savings and a subsequent boost to the supply of loanable funds

Upward pressure

• Productivity and output boost lead to higher demand for capital investment and expansion of production capabilities

However, it is not yet clear which effects will dominate. In general, much is still unclear in the world of AI. But that is also what makes it so fascinating. 

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