🔮 The Sunday edition #504: Pricing intelligence; the quantum dragon; investor democracy; geopolitical dominoes; Norway’s moves; diamond batteries & spiderweb math++

An insider’s guide to AI and exponential technologies

Hi, it’s Azeem. Today’s issue explores how we value and price intelligence in an AI-driven world. Meanwhile, Google had a quantum breakthrough. Is it a pivotal moment? Dominoes after Syria. And can democracy improve companies? Let’s dive in!

Ideas of the week

How do you price intelligence? 

ChatGPT Pro’s $200/month access fee and Devin’s $500/ month fee for the AI coding agent are steep for a solo tinkerer, but it’s a routine business expense if the payoff is higher productivity and profit. I spoke about this with

Ethan Mollick

just a few days ago and his throughline is similar:

In a world of ubiquitous AI, where it turns out ChatGPT rather than the API is a large part of where the use is coming from, there is not enough value captured for $20 compared to how much value these systems create. This is a signal that this is a different kind of system. [… ] Competition will come very quickly for that $20 price point. It is a signal that higher value AI systems that might be more expensive to run are coming. But I don’t know how long you maintain that advantage. So it’s another way of pricing this. If we say AGI is coming, paying $500,000 for AGI access, it turns out to be a really good deal until the price drops. 

Competition is on for the $20 price point. Google’s new Deep Research feature feels revolutionary for that money – I had it scour through more than 70 sources to put together a 3,441-word report. I wrote yesterday:

The service offers what might be called the industrialisation of basic research: not quite artisanal quality, perhaps, but reliably adequate and available at a fraction of the traditional cost in time and money. For many, including me, that trade-off will prove irresistible. And in these exponential days, we can be sure the quality will only improve.

Box CEO and co-founder Aaron Levie shared an interesting rumination on AI agent pricing. One option is to treat AI “workers” like human labour, billing by the hour or task. Another is to charge by outcome: a legal brief produced, a research insight delivered, etc. (OpenAI CFO, Sarah Friar, revealed that they may charge customers based on the derived value.) A third approach ties cost to underlying expenses, like computing time. Or a fourth model might offer a flat monthly subscription, allowing organisations to squeeze unlimited gains from a single fee. Each approach has downstream effects. Pricing based on underlying costs, for instance, might make the agents cheaper – and thus, more broadly deployed. Pricing based on value, meanwhile, could spur the development of smarter agents, but with fewer of them running at scale.

See also: 

• The world’s top AI labs are no longer content just to pile on parameters and burn through hardware. They are pursuing more nuanced approaches: reasoning architectures that solve complex problems step-by-step, synthetic data pipelines that sharpen a model’s skills and advanced reinforcement learning methods that refine the behavior. 

• Ilya Sutskever spoke at NeurIPS about the next frontier of AI and his expectations of reasoning systems and agentic AI. Recommend you listen/watch.

• OpenAI continues to deliver: Video, ChatGPT, Apple Intelligence, Canvas and Sora. But Google delivered a new AI model, Gemini 2.0, the third best model (behind o1 and o1 mini)¹.

• Allison Pugh, a sociologist at Johns Hopkins University, penned an article in Wired arguing that AI could aid sectors where human providers are limited or unavailable – and that this could deepen elitism.

Tickling the quantum dragon’s tail