From root: Why should I simulate the real world?
Is black box AI an accelerator?
Black box AI may not be the answer to all problems, but it is clearly an enormous accelerator for developing world models and making them more performant (in both runtime and predictivity).
With the rise of agentic coding tools (e.g., Cursor, Claude Code or Antigravity) the acceleration in developing world models is obvious: developers can focus more on higher-level conceptual design while the AI tool fills in the programmatic details within minutes.
As for computational and predictive performance: these have already been well-established capabilities of black box AI models with enough compute capacity through bespoke simulation emulators (e.g., AlphaFold or WeatherNext).
References and further reading
Anthropic. n.d. “Claude Code Documentation: Overview.” Anthropic. https://code.claude.com/docs/en/overview.
Anysphere. n.d. “Cursor Agents.” Cursor. https://cursor.com/agents.
Google. n.d. “Google Antigravity.” Google. https://antigravity.google/.
Google DeepMind. n.d. “WeatherNext.” Google DeepMind. https://deepmind.google/science/weathernext/.
Hardwick, Robert J. 2026. “Why keep humans in the loop with world models?” umbralcalculations, November. https://umbralcalc.github.io/posts/why_keep_humans_in_the_loop_with_world_models.html.
Jumper, John et al. 2021. “Highly Accurate Protein Structure Prediction with AlphaFold.” Nature 596: 583–89. https://doi.org/10.1038/s41586-021-03819-2.
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