Research Publication – Artificial Intelligence – Bubble Economics – Radio Free Mobile

November 8th 2023: RFM deepens its coverage of generative AI with the publication of Artificial Intelligence – Bubble Economics. 

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The bubble remains inflated but there are signs that generative AI is not taking over as had been forecast. The economics of AI remain very good even if prices fall by 92% but this means the industry would be 1/12^th of the size previously envisaged. This combined with ongoing problems with AI’s reliability and veracity is likely to cause the bubble to pop and reset expectations to reality.

• AI Bubble: There is very little doubt that AI is currently in a bubble as super-intelligent machines are as far off today as they were 10 years ago. However, generative AI does offer genuine improvements in abilities that have a large number of revenue-generating use cases.
• Usage & search: The first sign is that usage of ChatGPT has stopped growing and it is having no impact on the search market. This is not the trajectory of a revolution and disruption.
• AI economics: $20 per user/month is the current benchmark and at this price, a lot of money will be made. However, RFM thinks that the ever-increasing number of models will mean that the price becomes something closer to $20 per user/year, a decline of 92%. At this price monetisation via advertising becomes a realistic option and many may choose this route.
• The pin: Price erosion of 92% is likely to collapse the business plans of most generative AI start-ups meaning that they will need to raise more money after having missed their targets. This will trigger down rounds and falling valuations causing the bubble to deflate. This will look very similar to what happened with autonomous driving and bike sharing.
• Inference at the edge: is a no-brainer in RFM’s opinion as the economics are far better for the service provider. This is because the service provider does not have to support inference as it is executed on the user’s device.
• Privacy and security: inference at the edge also offers better security and privacy options which are likely to become crucial in the provision of LLM-powered services.
• Customisation: Running models on devices also raises the possibility of substantial customisation where a model on the user’s device is finely tuned to that user’s requirements and preferences.
• Black box & entanglement: are old problems with deep learning and are becoming real issues with generative AI. This is because no one really knows how generative AI does what it does or can promise that it won’t go crazy. This causes real problems when regulators are demanding verifiability and guarantees that models won’t go off the rails.
• Deflation: There is a large revenue opportunity to be had but expectations and valuations need to be adjusted downwards as super-intelligent AI looks to be as far away now as it was 10 years ago.