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8 Predictions for the Era of Continual Learning
- AI systems will require continual learning to match human job competence, necessitating regulatory shifts from single pre-deployment checks to monthly or quarterly risk inspections.
- Locking in current safety regimes is risky as technology evolves rapidly, with unknown capabilities expected within one to ten years.
- Technical alignment strategies must adapt to constant weight updates to prevent jailbreaks, deceptive personas, and malicious backdoors during session-to-session improvements.
- Preventing user-injected backdoors and consolidating learnings across users will become critical challenges as models learn from diverse experiences.
- Increased diversity in AI minds is expected to avoid market "mode collapse," potentially leading to a more interesting world than the current landscape of fewer than five prominent models.
- Competitive dynamics will accelerate as deploying smart models earlier provides immediate real-world experience, forcing labs to prioritize rapid deployment to avoid falling behind.
- Maintaining a gap between internal and external deployment will become impossible, as competitors leveraging real-world experience will quickly surpass static models.
- Monetization models will shift toward recurring revenue streams, with high profit margins driven by switching costs that make replacing AI employees equivalent to retraining new interns from scratch.
- Enterprises may face a choice between locking into providers for continuous improvement or losing that competitive edge, while labs might restrict access to top models or subsidize users to allow training on session data.
- Technical approaches will evolve to support individual user weights and model forks, with solutions for pulling different weight forks into main models expected eventually.
- Inference economics will favor large organizations through batching, with optimal batch sizes for sparse models estimated at over 2,400 concurrent sequences to avoid two orders of magnitude efficiency loss for individual users.
- Large companies will efficiently serve personalized weight forks, whereas individual users may suffer significantly worse efficiency with batch sizes of one.
- AI training already demonstrates large economies of scale where lab revenues grow faster than compute usage, a trend expected to extend to inference batching.
- While specific changes like Keras and Styx usage are anticipated, the most significant future developments remain difficult to anticipate in advance.