- Tokenmaxxing, initially seen as wasteful, may have been a deliberate strategy by executives to overcome resistance to AI adoption within organizations.
- The concept of 'compounding correctness' suggests that increased token usage can lead to better outcomes, potentially reviving tokenmaxxing incentives.
- In cybersecurity, models like Anthropic's Mythos show that spending more tokens can enhance security by outspending attackers on exploit discovery.
- The rise of 'loops' in agentic AI allows for continuous, unsupervised operation, further driving token consumption.
- Open models like GLM 5.2 offer cost advantages over frontier models, making token-intensive processes more economically viable.
- The 'software factory' or 'dark factory' vision envisions fully automated coding pipelines, potentially justifying high daily token spends.
- Government regulation, such as controlled access to models like Mythos, is influencing AI deployment and creating opaque winner-picking dynamics.
- AI speed improvements, like OpenAI's Jalapeño chip, could shift workflows from async to more synchronous, real-time operations.