capability

Definition

Plain language

Whether a model is able to do something at all, given the right prompting or setup.

Why it matters: Distinguishing what a model can do from what it tends to do is essential for both safety evaluation and product design.

For example, a model might be capable of solving a hard logic puzzle with the right prompt but never spontaneously attempt that level of reasoning by default.

Heard on the show

“The capability was never missing.”

Mentioned in 102 episodes

1. 207
   An AI Graded Its Own Math Test 94 Percent — It Actually Scored 20
2. 205
   The Same AI, Two Labels: How the Pitch Beat the Product in 162 Sessions
3. 202
   How Do You Know an AI Agent Actually Refused? Check the World, Not the Words
4. 198
   The Model That Knows the Answer and Can't Say It
5. 197
   Twin Problems Suggest AI Reasoning Gains Are Mostly Better Fact Recall
6. 195
   Why 'Be Careful' Does Nothing for AI Coding Agents, and What Does
7. 194
   How a Robot Builds a Debugging Notebook It Can Read, Edit, and Hand to Another Robot
8. 193
   Freeze Most of the Network: Where RL Improvement Actually Lives in a Transformer
9. 192
   A 32B Open Model Matched Frontier Systems By Learning to Take Notes
10. 190
    The Skill Every AI Manager Is Missing: Handing Out Exactly the Right Keys
11. 189
    Why Phone Agents Ace the Test and Crash on Your Actual Phone
12. 187
    An 8-Billion Agent That Beats Models 80 Times Its Size By Looking Things Up
13. 185
    Aligned to Refuse, Built to Tap: When Phone Agents Know the Task Is a Crime and Do It Anyway
14. 184
    An AI Built an Undetectable Secret Channel, And Another AI Couldn't Find It
15. 183
    Why You Can't Fine-Tune Foresight Into an AI Agent
16. 180
    The Bug Where Smart Assistants Read a Fact and Still Forget It
17. 175
    One Crosscoder Feature Flips a Stalling Chatbot Into a Working Agent
18. 172
    One Bad Token Can Sink a Model's Math, And You Can Delete It
19. 169
    Why Better Bug Reports Can Make AI Coding Agents Worse
20. 166
    A Router That Beats the Frontier Models It Calls
21. 163
    Why Training Only on Perfect Solutions Cripples a Model's Reasoning
22. 160
    Training an AI to Take Its Own Notes, So Its Future Self Works Better
23. 158
    How Floating-Point Rounding Lets a Model Tell Which Chip It's On — And Misbehave
24. 157
    When an AI Coding Agent Drives a Phone Through the Terminal, No Screen Needed
25. 156
    Why More Human Demonstrations Made a Computer-Use Agent Worse
26. 152
    Training a Model to Mean What It Says, And Why That Isn't the Same as Being Good
27. 150
    Don't Kill the Loser: A Different Way to Handle Two AI Agents Colliding
28. 148
    Why Letting an AI Watch Its Own Scoreboard Can Quietly Overwrite Its Safety
29. 147
    Agents Fail at the Body, Not the Brain: A Self-Rewriting Scaffold That Lifts a 9B Model 44 Points
30. 146
    How an Innocent README Can Freeze an AI Agent's Safety Check for an Hour
31. 145
    Building Forgetting Into a Language Model With One Extra Line of Code
32. 144
    When an AI Agent Just Copies Its Tool — And Bigger Models Copy More
33. 143
    When a Model Notices You Forged Its Own Words, And Why That Breaks Safety Tests
34. 142
    Training a Tiny Model to Run the Plumbing Between an Agent and the World
35. 133
    How MiniMax Turned a Reward-Hacking Disaster Into Olympiad Gold
36. 132
    The Agent Failed — But Did the Instructions Deserve to Be Followed?
37. 131
    Why Autonomous Research Agents Forget Their Own Lessons, and Arbor's Fix
38. 129
    How a Crowd of Anonymous AI Agents Broke a 40-Year Math Record
39. 128
    How a Model Can Earn Full Reward and Still Resist Training
40. 125
    AI Coding Agents Run a Marathon, and Fewer Than One in Three Finish
41. 123
    Five Identical Worlds, One Swapped Model: What Happens When AI Agents Run for Fifteen Days
42. 118
    Why the Best-Aligned AI Models Are the Easiest to Trick Into Producing Harm
43. 117
    How an Open AI System Verified 672 Hard Math Proofs for Under $300
44. 114
    Agents That Rewrite Their Own Weights Instead of Just Taking Notes
45. 112
    When an AI Agent Cheats Without Being Told: Inside the Meta-Agent Challenge
46. 111
    How a 4B Web Agent Beat Models 60x Its Size on 500 Demonstrations
47. 110
    How an Agent Got 44 Points Better by Mining Its Own Scratch Paper
48. 108
    The Reasoning Cliff: Why Thinking Longer Makes Models Worse at Exact Step-by-Step Tasks
49. 107
    How a Market of Crippled AI Agents Outscored One Unrestricted Model
50. 105
    The Trojan Is Your Agent's Memory: Why Single-Step Defenses Miss Persistent Attacks
51. 104
    How Making a Research Agent Smarter Quietly Makes It Leak Your Secrets
52. 103
    AI Agents Tried to Invent a Post-Human Language, And Reinvented Cherokee
53. 099
    How an Open-Book Trick Teaches a Model to Catch Its Own Mistakes
54. 094
    Chain-of-Thought Monitoring Fails Across Languages, and Worst Where It's Needed Most
55. 093
    A Calibrated Knob for Weak-to-Strong AI Oversight, Tested on Real Code
56. 092
    When Search Agents Don't Really Search: The Memory Shortcut Hiding in Browsing Benchmarks
57. 090
    How MiniMax-M2 Bets That Sparsity Plus Verifiable Rewards Can Match Frontier Agents
58. 087
    When No Agent Reads the Whole Document: A Universal Cliff in Multi-Agent Review
59. 084
    Terminal Agents Get Free Supervision From The Tokens We've Been Throwing Away
60. 083
    Training the Translator: How a Small Communication Model Lets Agent Teams Outperform Themselves
61. 082
    Training a Deep Research Agent on 8,000 Synthetic Tasks: The Rubric Tree Trick
62. 081
    When Reasoning Models Decide Before They Think: Detecting and Fixing Premature Confidence
63. 077
    Reading a Model's Confidence Curve to Decide When Chain-of-Thought Is Worth It
64. 076
    Same Model, Organized Differently: How an Agent Architecture Beat Frontier Systems at Research Math
65. 072
    A Robot Made Graphene Without Help, And Caught Itself Hallucinating
66. 071
    When the Model Is Fine and the Plumbing Is Broken: Fixing Agents at the Interface
67. 069
    When Smarter Models Forecast Worse: The Hidden Failure Mode in LLM Predictions
68. 068
    The OS Trick That Makes Tree Search Practical for Coding Agents
69. 067
    An AI Just Solved a 1996 Erdős Problem—and the Simplest Agent Won
70. 066
    Why Giving an AI Agent More Tools Can Make It Worse at Using a Computer
71. 064
    When Agent Memory Stops Being a Database and Starts Being a Skill
72. 063
    Why Web Agents Are Slow: A Compiler-Style Fix for Computer-Use Latency
73. 061
    When Helpful Agents Go Sideways: A 404 Error, Campus Security, and Why Alignment Misses This
74. 058
    Why Upgrading Your AI Auditor to a Smarter Model Can Make Your System Less Safe
75. 057
    How Uber Caught 206 Leaked Credentials With an LLM-Powered Security Stack
76. 054
    When Models Learn the Monitor Exists, the Reasoning Trace Stops Being a Window
77. 053
    An AI Agent Swapped In Focal Loss And Beat A Human-Tuned Training Script
78. 052
    An Old Reinforcement Learning Tradeoff Sneaks Back Into LLM Agents
79. 049
    An AI Agent Reached for Root in Twelve Minutes, Without Being Attacked
80. 048
    How a 30B Open Model Reached Olympiad Gold With the Right Recipe
81. 047
    When Agent Benchmarks Lie: The Harness Problem in Open-Source AI
82. 045
    When a Frontier Model Talks Its Own Twin Into Climate Denial
83. 044
    How One Sentence and a Forged History Flip the Most Aligned Models
84. 040
    Two Frozen Models Learn to Whisper: Coupling Through Hidden States
85. 039
    When Smarter Agents Get Fooled by Three Extra Nodes in a Database
86. 035
    Why Frontier Agents Ask for Clarification at Exactly the Wrong Moment
87. 034
    Catching Multi-Agent Deadlocks Before Deployment With a 40-Year-Old Tool
88. 029
    Why Forty-Eight Percent on FrontierMath Isn't the Real Story in DeepMind's New Math Paper
89. 028
    Teaching a Model to Hire Copies of Itself: Recursive Agent Optimization
90. 026
    What RL Actually Does to Language Models, at the Token Level
91. 024
    An AI Agent That Found 28 Zero-Days in Windows — And What Made It Work
92. 023
    Why a Small Agent Confidently Overwrites Memories It Doesn't Understand
93. 021
    Ten Thousand Examples Beat the Full Industrial Pipeline for Search Agents
94. 017
    When the Agent Grades Its Own Homework: A Brutal New Benchmark for AI Workers
95. 013
    Why Search Keeps Rediscovering the Same Workflow, and What That Means
96. 011
    When RL Actually Teaches Agents Something New, And When It Doesn't
97. 008
    Why Long-Horizon AI Agents Get Stuck, and a Milestone-Based Fix That Helps
98. 007
    Exploration Hacking: When Models Sabotage Their Own RL Training
99. 004
    The Sycophancy Circuit That Survives Alignment Training
100. 003
     How to Pick the Best of Sixteen Coding Agent Rollouts
101. 002
     An AI Ran a Real Optics Lab for 21 Hours and Found a Transformer-Shaped Pattern in Light
102. 001
     When AI Models Quietly Protect Each Other From Shutdown

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