Description

In this episode of Inference Time Tactics, Cooper and Byron break down NeuroMetric's Thinking Algorithm Leaderboard and what it reveals about building production-ready AI agents. They share why prompt engineering with a single model won't cut it for enterprise use cases, explore the impact of inference-time compute strategies, and discuss what they learned from testing 10 models across real CRM tasks—from surprising token inefficiency to catastrophic failures in SQL generation.

We talked about:

• Why NeuroMetric built the first leaderboard combining models with inference-time compute strategies. 
• How Salesforce's CRMArena-Pro reflects real multi-step business tasks better than pure reasoning benchmarks. 
• The jagged frontier: no single model or technique dominates across all tasks. 
• Why GPT 20B was surprisingly token inefficient—twice as slow as GPT 120B for similar accuracy. 
• How GPT-5 nano's conversational style broke SQL generation tasks completely. 
• Trading accuracy for speed: two-model ensembles versus five, and saving 20+ seconds per task. 
• Throughput constraints as a hidden bottleneck when scaling to production volumes. 
• Future directions: LLM-guided search, task clustering, and compression to specialized small models.

Resources Mentioned:

CRMArena-Pro from Saleforce:

https://www.salesforce.com/blog/crmarena-pro/

Thinking Algorithm Leaderboard: 

https://leaderboard.neurometric.ai/ 

Connect with Neurometric:
Website: https://www.neurometric.ai/ 

Substack: https://neurometric.substack.com/ 

X: https://x.com/neurometric/ 

Bluesky: https://bsky.app/profile/neurometric.bsky.social

Hosts:

Calvin Cooper

https://x.com/cooper_nyc_ 

https://www.linkedin.com/in/coopernyc

Guest/s:

Byron Galbraith

https://x.com/bgalbraith 

https://www.linkedin.com/in/byrongalbraith