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Tibo Sottiaux, head of ChatGPT and Codex at OpenAI, predicts that agents will handle most internet actions soon, the model picker will disappear, and people should build assuming models will be 10x better in a year. He also argues that complex agent architectures are a passing phase and that taste—understanding users and knowing what good looks like—will matter more than technical skills like typing speed.
- Agents will soon take most actions on the internet, forcing every product team to decide whether to build for them; Notion's MCP server launch caused system strain and forced economic rethinking.
- Models will improve at "incredible" rates in speed and cost over the next year, so builders should design as if today's capabilities are 10x worse than what's coming, not optimize for the present.
- Complex agent loops and graph architectures are a temporary solution—as models get better, larger single agents can handle what previously required teams, making intricate setups obsolete.
- The OpenAI plugin ecosystem could be a major opportunity: plugins get revenue share when users spend tokens, and ChatGPT recommends them to 1.2 billion users based on retention and quality.
QuickReset AI tracks OpenAI's public Codex usage resets by monitoring Tibo's announcements and historical patterns, letting you see when resets happen and get email alerts before your allowance refreshes. It separates confirmed schedules, public signals, and historical forecasts so you can plan around rate limit resets without guessing.
- Tracks 26 weeks of Codex reset history with source-linked announcements from @tibo, showing direct resets (immediate refills) and banked resets (saved credits) separately
- Generates a daily forecast based on recent reset intervals and public signals, currently showing 35% chance for the next reset
- Sends email alerts when resets approach so you can use remaining allowance before limits refresh
- Only reports public bonus events and cannot trigger, grant, or sell resets — forecasts are estimates, not guarantees
Google introduced TimesFM-3, a 330-million-parameter time series model that forecasts multiple related data streams simultaneously using a single forward pass. It outperforms competing foundation models on three major benchmarks by incorporating both historical and future-known information (like promotions or weather) to improve prediction accuracy.
- TimesFM-3 handles multivariate forecasting natively—predicting multiple related time series at once while capturing dependencies between them, unlike previous versions limited to single series.
- The model generates entire forecast horizons in one pass using "contiguous patch masking" instead of iterative generation, reducing latency and error accumulation.
- It supports three data types: multiple targets, past-only features, and known-future covariates (like scheduled promotions), letting it incorporate planned events that univariate models miss entirely.
- On Gift-Eval, FEV-Bench, and Time benchmarks, TimesFM-3 ranked first among foundation models for both point and probabilistic forecasting accuracy.
The article argues that “AGI” has become a fuzzy umbrella term with conflicting definitions, making it useless for tracking AI progress or predicting major shifts. It urges us to drop AGI in favor of clear, concrete milestones—like fully automated AI R&D, human-level adaptability, or self-sufficient AI—to ground discussions and forecasts.
- "AGI" now spans wildly different thresholds, from matching human test performance to running civilization unaided, making claims like "AGI arrived in early 2025" vs "it's a decade off" talk past each other
- Uneven, jumpy progress across tasks means definitional gaps will keep widening rather than converging
- Concrete milestones (fully automated AI R&D, human-level adaptability, self-sufficient AI, machine consciousness) or vivid benchmarks (10x annual energy growth, interstellar probes) are proposed as replacements for fuzzy labels like AGI or superintelligence
- Swapping vague umbrella terms for specific milestones would make safety, regulation, and timeline discussions more reliable