🔮 Exponential View #547: When AI invests $10k. Atlas unleashed. Anticipation, cheap robots & bionic eyes++

A weekly briefing on AI and exponential technologies

Atlas holding the celestial sphere, “Farnese Atlas”, 2nd c. CE Roman marble sculpture

Good morning from London!

In today’s briefing:

• Why simpler AI may be the next frontier in AI capability

• The rise of anticipation markets and new ways to price the future

• One AI model invests better than all the rest

• Bionic eyes are here…

Let’s go!

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Simpler, smarter AI

Today’s frontier models have gorged on internet’s noise. They are brilliant mimics with blurry reasoning, as argues OpenAI founding member Andrej Karpathy. The problem is that true reliability can’t come from these feats of memory but from deeper understanding. Future AI systems will need this.

Andrej proposes an austere remedy– reduce memorisation, preserve the reasoning machinery and pull in facts only when needed. He pictures a “cognitive core” at the 1-billion-parameter scale that plans, decomposes problems and queries knowledge. It is a librarian, not a library.

Philosopher Toby Ord points out that the very approach that’s given us the surprising capabilities of “reasoning models” like o1 is reaching its own limits. These systems extract gains from post-training reinforcement learning (refining answers through trial-and-error) and extended inference-time reasoning. Compute is paid per query, not once during pre-training. Ord estimates that this burns 1,000 to 1,000,000 times more compute per insight than traditional training. Returns shrink faster to get to the next milestone. Even OpenAI’s o1 reasoning model improves only when it’s given more RL cycles and longer “thinking time,” which raises the cost per task.

How should we make sense of this? Technological progress advances through overlapping S-curves and rarely follows a smooth exponential. Both Ord and Karpathy are pointing to a similar direction of less brute memorization, more search and recursion. Less unlimited inference and more careful allocation of reasoning budgets. Away from monolithic models and toward tool-using, modular agents.

As the cost of using AI systems (rather than training them) becomes dominant, pricing will shift to usage-based models. Firms that deploy AI will precision will be rewarded. And as a result we could see a broad, rapid seep of AI into many corners of the economy, rather than a sudden leap in GDP.

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The weight of the Web

OpenAI has entered the browser arena with its own browser Atlas. We argued a few times in the past that…

[t]he company that owns the browser owns the user session, valuable behavioral data and the ability to steer the revenue funnel. Whoever captures the front door to the web gets to watch, and eventually automate, everything we do online.