AI capability and the frontier
What the models can currently do, how fast that is moving, and which milestones are worth taking seriously.
Research sweeps
2026-07-28 · deep
AI 2027 Reality Check, Deep
AI 2027 scenario tracking from the report's April 2025 publication through to late July 2026: whether the OpenAI sandbox escape and Hugging Face compromise, the US export-control suspension of Claude Fable 5 and Mythos 5, state and criminal use of AI in cyber operations, and Chinese open-weight model capability reproduce the scenario's predicted sequence of loss of control, cyber capability and state intervention; whether the capability curve has plateaued or merely re-based; and which of the scenario's remaining failure conditions have or have not materialised
GPT-5.6-sol- financial
- frontier
- academic
- +3
2026-04-08 · deep
AI 2027 Milestone Tracker
AI 2027 report milestone tracking (January 2025–present): which predicted capabilities have shipped across Anthropic, OpenAI, Google DeepMind, Meta, xAI, and major enterprise adopters; what remains unshipped or contradicted; and what near-term signals suggest for agentic AI, safety frameworks, autonomy, and deployment timelines
Claude Opus 4.8- financial
- frontier
- academic
- +2
Explainers
- Research Explainer · Jumper (2021)
AlphaFold predicts protein structures with near-experimental accuracy, but sequence alone still sets the boundary
AlphaFold combined evolutionary information with explicit geometric reasoning to outperform every other method at CASP14. Its predictions are fast and unusually accurate, provided the sequence carries enough evidence about the structure.
- Research Explainer · Litowitz (2026)
The token economy has a finite energy budget, but the real bottleneck is knowing which questions to ask
Litowitz, Polson and Sokolov treat the AI token as a physical quantity with measurable thermodynamic cost, then build a MacKay-style balance sheet showing that projected 2028 US infrastructure could supply 225,000 tokens per person per day, over 1,000× current usage. The binding constraint, they argue, is not compute but the human capacity to formulate questions worth answering.
- Research Explainer · Zandieh (2025)
A random rotation turns vector quantization into a solved problem, within a factor of 2.7 of perfection
TurboQuant achieves near-optimal distortion for both MSE and inner product metrics across all bit-widths, requires zero preprocessing, and matches full-precision LLM quality at 3.5 bits per channel.
- Research Explainer · Wu, Sun, Li, Welleck & Yang (2025)
Smaller models with smarter inference beat bigger models, and it's not even close
A 7-billion-parameter model paired with a novel tree search algorithm consistently outperforms a model five times its size on maths benchmarks, using half the compute. The trick is spending your budget on thinking harder, not on being bigger.
- Research Explainer · Wang et al. (2025)
Letting AI models "think longer" hits a wall, but the math tells you exactly where that wall is
A unified probabilistic model shows that both parallel sampling and sequential rethinking strategies for large reasoning models converge to the same saturation formula, letting you calculate the exact point where more compute stops helping.
- Research Explainer · Song, Han & Goodman (2026)
LLMs ace reasoning benchmarks, but they keep failing in ways that should be embarrassingly easy
The first comprehensive survey of LLM reasoning failures catalogues every known way these models break down, from reversing simple facts to misjudging whether a house fits inside a light bulb, and maps the root causes to a two-axis taxonomy of reasoning type versus failure type.
- Research Explainer · Polo, Somerstep, Choshen, Sun & Yurochkin (2025)
LLM benchmarks are correlated for a reason, and exploiting that lets you predict performance without training the model
Sloth introduces skill-based scaling laws that treat benchmark scores as reflections of three latent abilities, predicting multi-benchmark performance across model families from a single small model per family.
- Research Explainer · Prucs, Csutora, Antal & Marosi (2025)
Reasoning models hit a compute ceiling, but sparse architectures keep climbing
A benchmark study of 19 open-source LLMs finds that Mixture-of-Experts models consistently dominate the accuracy-per-FLOP frontier, while all architectures eventually reach a task-dependent saturation point where more thinking time stops helping.
- Research Explainer · Mohsin et al. (2026)
Scaling LLMs hits five hard ceilings, and more parameters won't break through any of them
A 67-page theoretical synthesis proves that hallucination, context compression, reasoning collapse, retrieval fragility, and multimodal misalignment are mathematical inevitabilities, not engineering problems awaiting bigger budgets.
- Research Explainer · Chi et al. (2024)
LLMs look like causal reasoners, but they're mostly just remembering
When tested on fresh news articles they couldn't have seen during training, four leading language models showed dramatic accuracy drops on cause-and-effect questions, revealing that their apparent causal reasoning is largely a retrieval trick.
- Research Explainer · Zhang, Takeuchi, Kawahara et al. (2025)
General-purpose LLM benchmarks miss the mark, because domain-specific enterprise tasks reshuffle the leaderboard
A 27-benchmark evaluation across finance, legal, climate, and cybersecurity shows that the model topping generic tests rarely wins in specialised enterprise tasks, and smaller models routinely outperform larger ones in specific domains.