Inference Time Tactics

By NeuroMetric AI

A podcast exploring the emerging field of inference-time compute—the next frontier in AI performance. Hosted by the Neurometric team, we unpack how models reason, make decisions, and perform at runtim... more

· Technology

Download on the App Store

Download on the App Store

Get it on Google Play

FAQs about Inference Time Tactics:

How many episodes does Inference Time Tactics have?

The podcast currently has 9 episodes available.

Inference Time Tactics episodes:

November 05, 2025Benchmarking Generalization: How AI Learns Beyond Training Data
In this episode of Inference Time Tactics, Rob and Cooper from Neurometric sit down with Yash Sharma, an AI researcher whose work is reshaping how we understand model generalization. Yash recently completed his PhD at the Max Planck Institute for Intelligent Systems and has held research roles at Google Brain, Meta AI, Amazon, Borealis AI, and IBM Research. His studies on compositional generalization, adversarial robustness, and long-tail benchmarks reveal when and why models succeed—or fail—at reasoning beyond their training data.
If you’re designing inference-time systems, building agents that need reliability, or just want to understand what “generalization” actually means in practice, this conversation bridges deep theory with actionable insight—clear, technical, and strategically grounded.
Key Topics
What it really means for AI systems to generalize beyond their training data
Why large language models still fail in novel or unpredictable scenarios
How inference-time compute can both amplify and reveal generalization limits
What these limits mean for building reliable, agentic AI systems
How to benchmark generalization in real-world settings
Yash’s “Let It Wag!” benchmark for testing long-tail and under-represented concepts
Why genuine scientific breakthroughs (like curing cancer) require more than scaling test-time compute

Connect with Yash Sharma:
Yash Sharma
Let It Wag! Benchmark
Paper: Pretraining Frequency Predicts Compositional Generalization of CLIP (NeurIPS 2024 Workshop)
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

Rob May
https://x.com/robmay
https://www.linkedin.com/in/robmay

Calvin Cooper
https://x.com/cooper_nyc_
https://www.linkedin.com/in/coopernyc
...more
37min
October 03, 2025Solving the Cold Start Problem in AI Inference
In this episode of Inference Time Tactics, Rob, Cooper, and Byron sit down with Prashanth Velidandi, co-founder of InferX, to explore how serverless inference is tackling the AI “cold start problem.” They dig into why 90% of the model lifecycle happens at inference—not training—and how cold starts and idle GPUs are crippling efficiency. Prashanth explains InferX’s snapshot technology, what it takes to deliver sub-second cold starts, and why inference infrastructure—not just models—will define the next era of AI.

We talked about:
Why inference represents 90% of the model lifecycle, compared to the training focus most of the industry has.
How cold starts and idle GPUs create massive inefficiencies in AI infrastructure.
InferX’s snapshot technology that enables sub-second model loading and higher GPU utilization.
The challenges of explaining and selling deeply technical infrastructure to the market.
Why enterprises care about inference efficiency, cost, and reliability more than model size.
How serverless inference abstracts away infrastructure complexity for developers.
The coming explosion of multi-agent systems and billions of specialized models.
Why sustainable innovation in AI will come from inference infrastructure.

Connect with InferX
Prashanth Velidandi
https://inferx.net
https://x.com/pmv_inferx
https://www.linkedin.com/in/prashanth-velidandi-98629b115

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

Rob May
https://x.com/robmay
https://www.linkedin.com/in/robmay

Calvin Cooper
https://x.com/cooper_nyc_
https://www.linkedin.com/in/coopernyc

Byron Galbraith
https://x.com/bgalbraith
https://www.linkedin.com/in/byrongalbraith
...more
35min
September 30, 2025From MIT Decoding Research to Today’s Inference Tradeoffs
Check out the latest episode of Inference Time Tactics. Our guest is Pawan Deshpande, founder, product leader, and angel investor in companies like Anthropic and Toast, with roles at Google, Scale AI and Domino Data Lab.

Hosts Rob May & Calvin Cooper sit down with Pawan to cover:
Early MIT NLP research applied to today’s inference-time tradeoffs
How to evaluate enterprise agents in practice
Training data plus inference filtering in real deployments
Open source adoption realities in the enterprise
Where durable value lives in the stack
Connect with Pawan Deshpande
Website: https://pawandeshpande.com/

Academic / Research Works & Thesis
Decoding Algorithms for Complex Natural Language Tasks (MIT thesis, 2007)
Randomized Decoding for Selection-and-Ordering Problems

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:
Rob May
https://x.com/robmay
https://www.linkedin.com/in/robmay

Calvin Cooper
https://x.com/cooper_nyc_
https://www.linkedin.com/in/coopernyc
...more
31min
September 22, 2025Drag, Drop, and Deploy: Rethinking How We Build AI Systems
In this episode of Inference Time Tactics, Rob, Cooper, Byron, and Dave share product updates for Neurometric’s Inference Time Compute Studio and what they reveal about the shift from single models to full AI systems. They discuss why wiring models together at scale is so challenging, how a drag-and-drop interface can make experimenting with inference strategies easier, and why open source, benchmarking, and community feedback are key to building the next generation of composable AI systems.

We talked about:
Why AI is shifting from single models to full systems and what that means for builders.
The challenges of wiring multiple models together at scale and running them in production.
How Neurometric’s drag-and-drop interface simplifies testing inference strategies without code.
Why open-source models are becoming increasingly competitive with commercial solutions.
The lack of standardization in AI stacks and why the industry still feels like the “early web” era.
How inference-time compute can balance performance, cost, and latency across different tasks.
Why benchmarks alone are insufficient and how domain-specific evaluations can fill the gap.
The role of community feedback in shaping priorities for benchmarks and new primitives.

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:
Rob May
https://x.com/robmay
https://www.linkedin.com/in/robmay

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

Dave Rauchwerk
https://x.com/elevenarms
https://www.linkedin.com/in/dave-rauchwerk-0ba82822
...more
21min
September 08, 2025Beyond Vibe Testing: Smarter Eval for Agentic AI
In this episode of Inference Time Tactics, Rob, Cooper, and Byron explore Salesforce’s CRMArena-Pro benchmark and what it reveals about the limits of enterprise AI agents. They share why benchmark scores often fail in production, how inference-time tactics like best-of-N can improve reliability, and what NeuroMetric is building to make eval easier—from an ITC Test Engine to a drag-and-drop interface for rapid visualization and experimentation.

We talked about:
Why Salesforce’s CRMArena-Pro benchmark highlights the gap between lab benchmarks and real-world agent reliability.
How leading models perform inconsistently across single-turn and multi-turn enterprise tasks.
Why benchmark scores are weak predictors of operational success in production.
The role of inference-time tactics in reducing variance and improving stability.
NeuroMetric’s new platform: ITC Test Engine and drag-and-drop interface for experimentation.
Challenges in building agentic systems, from database integration to managing multi-prompt complexity.
Why large language models’ stochastic nature conflicts with business demands for reliability.
Latency, cost, and rate limits as major bottlenecks in scaling agentic workflows.
The limits of “vibe testing” and why rigorous evaluation frameworks are essential.
How Google’s Stacks tool speeds up evaluation with LLM-as-judge, and why it still falls short for enterprise needs.

Resources Mentioned:
CRMArena-Pro from Saleforce:
https://www.salesforce.com/blog/crmarena-pro/

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:
Rob May
https://x.com/robmay
https://www.linkedin.com/in/robmay

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
...more
23min
August 30, 2025GPT-5, The $100B Gap, and The Economics of Inference
In this episode of Inference Time Tactics, Rob and Cooper unpack the launch of GPT 5.0 and what OpenAI’s new routing layer signals about the shifting AI landscape. They explore the tradeoffs of cost, latency, and accuracy, zoom out to programmable inference in an agent-driven world, and track the ripple effects on chips, data centers, and energy use.

We talked about:
Why GPT 5.0’s launch felt more like refinement than a revolution in AI progress.
How OpenAI’s new routing layer reframes the race around inference control.
The tradeoffs routing enables between cost, latency, and accuracy across models.
Why the “one model to rule them all” view is giving way to multi-model orchestration.
The strategic role of programmable inference in an agent-driven world.
How router companies are becoming a strategic layer in the AI technology stack.
The impact of inference compute on chips, accelerators, and data center design.
Why energy use at scale is driving a push for more efficient AI systems.
Why inference optimization may be the next big competitive edge.

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:
Rob May
https://x.com/robmay
https://www.linkedin.com/in/robmay

Calvin Cooper
https://x.com/cooper_nyc_
https://www.linkedin.com/in/coopernyc
...more
26min
August 18, 2025When AI Overthinks: Lessons from the Illusion of Thinking Paper
In this episode of Inference Time Tactics, Rob, Cooper, and CTO Byron unpack Apple’s “Illusion of Thinking” paper—why it split the AI community, what it reveals about reasoning model limits, and how hidden thinking traces shape performance. They share insights from building an open-source tool to reproduce the study, explain why models loop, overthink, or stall, and outline what it will take to build more reliable reasoning systems for real-world use.

We talked about:
Why Apple’s Illusion of Thinking paper sparked heated debate in the AI community.
How reasoning models work, including hidden “thinking” phases and token budget limits.
Key findings on when reasoning improves results, when it degrades them, and where it stalls.
Reasons models loop, overthink, or abandon tasks.
Building an open-source tool to replicate the study and test local reasoning models.
What real-time reasoning traces reveal about model behavior and limits.
Challenges in scoring reasoning quality and treating “I don’t know” as a valid output.
Why reasoning models must be matched carefully to specific tasks.
The ongoing debate over scaling vs. new architectures for advancing reasoning.
Developing a benchmarking platform to help enterprises choose models for IP-sensitive applications.

Resources Mentioned:
Illusion of Thinking Paper
https://machinelearning.apple.com/research/illusion-of-thinking
Neurometric Illusion of Thinking Tool
https://github.com/NeurometricAI/illusion-of-thinking

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:
Rob May
https://x.com/robmay
https://www.linkedin.com/in/robmay

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
...more
24min
August 12, 2025The Strategic Trade Offs Behind Inference Time Compute Decisions
In this episode of Inference Time Tactics, Rob and Cooper dig into the strategic trade-offs driving a major shift in AI: why some enterprises start with closed models like OpenAI or Anthropic, then move to open-source stacks. The team breaks down the challenges of switching and how inference-time compute is becoming a competitive differentiator. They also unpack why pricing is shifting, how governance will evolve for this new layer, and what Rob learned from reviewing 250 research papers on reasoning algorithms.

We talked about:
Insights from reviewing 250 research papers on reasoning algorithms.
Why enterprises start with closed models like OpenAI or Anthropic before moving to open-source stacks.
Challenges of switching stacks, including model fragmentation, capability gaps, and hardware choices.
Cost-performance trade-offs when choosing inference architectures.
How inference-time configuration can become a competitive differentiator.
The role of pricing shifts and vendor lock-in in AI adoption.
Emerging governance considerations for inference workflows.
The growing variety and complexity of inference-time techniques..
Benchmarking challenges for multi-step and reasoning tasks.
Why the lack of best practices makes inference optimization harder to operationalize.

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:
Rob May
https://x.com/robmay
https://www.linkedin.com/in/robmay

Calvin Cooper
https://x.com/cooper_nyc_
https://www.linkedin.com/in/coopernyc Comment end
...more
20min
August 01, 2025Why Inference Time Compute Is the Future of AI
Welcome to the very first episode of Inference Time Tactics — the podcast for builders, researchers, and engineers pushing the limits of AI performance.
In this kickoff conversation, hosts Rob May and Cooper (co-founders of Neurometric AI) break down why inference time compute is emerging as the third scaling law of AI — and why it matters more than ever.
They unpack:
What “inference time compute” really means (and how it differs from training and fine-tuning)
Why reasoning algorithms like best-of-N, chain of thought, and beam search are reshaping performance
How recent research — and OpenAI’s 2024 reasoning model — sparked an explosion of interest
The challenge of reliability (“three nines” and beyond) in multi-step agent workflows
Why open-source models may win big, and where inference fits at the edge
This is a technical, tactical deep-dive — but without the heavy math. If you’re building the next generation of AI systems, or just want to understand where the field is really headed, this episode is your starting point.
🔗 Learn more at neurometric.ai
...more
22min

FAQs about Inference Time Tactics:

How many episodes does Inference Time Tactics have?

The podcast currently has 9 episodes available.