AI, Actually

About AI, Actually

Tired of the AI hype? So are we. Welcome to AI, Actually: the podcast that cuts through the noise and gets real about how artificial intelligence can work for your business. In each episode, our resident AI and business transformation experts–along with occasional industry guests–hold a candid, jargon-free conversation on what it takes to get actual value from AI. Join us as we tackle topics like: the real difference between the latest LLM models, why generic AI can't make sense of your messy company data, how to get your GenAI use case off the ground, and what the rise of AI agents means for your business. This is your practical playbook for putting AI to work. No PhD required. AI, Actually is produced by AnswerRocket. Since 2013, our enterprise AI solutions have helped Fortune 500 companies achieve measurable results through their AI transformations. This podcast is where we share what we’ve learned.

Publisher

AnswerRocket

Category

business · technology

Language

en

Explicit

No

First episode

25 Aug 2025

Latest episode

29 Sept 2026

Latest episodes

26 episodes in the feed.

1. 

   29 Sept 2026

   Multiplayer AI, Harnesses, and Why Pacing the Frontier Won't Change Your Roadmap

   Your AI agents work great for you. Getting your team to use the same AI agents is a completely different problem, and it's one most companies trying to scale AI haven't solved yet. This episode breaks down why single-player AI tools like Claude Code and Codex don't automatically scale into team-wide workflows, and why the fix isn't a smarter model. What's actually missing is better infrastructure for humans and AI agents to collaborate, and that's where AI harnesses come in. This episode of AI, Actually looks at what an AI harness actually is, the coordination layer that turns a raw model into a reliable business tool, and when it's worth building a custom one instead of using an out-of-the-box harness from a foundation model provider. The team also covers the vendor lock-in risk built into that choice, gives a candid read on the "pause AI" debate sparked by Dario Amodei's blog post, and reacts to JEV, a new fast AI classifier model already replacing multi-step LLM chains in production. You'll learn: Why scaling AI agents across a team is an infrastructure problem, not a smarter-model problem What an AI harness is, and why you're always using one whether you realize it or not How to decide when to build a custom AI harness versus using a foundation model provider's default The real business risk in the AI regulation debate: vendor concentration, not runaway AI A first look at JEV, the fast, cheap AI classifier already replacing multi-step LLM workflows This episode is designed for: business and technology leaders scaling AI agents across their teams, anyone evaluating build vs. buy for AI infrastructure, and leaders trying to separate real AI regulation risk from the headlines. https://answerrocket.com/ (https://answerrocket.com/) Follow the Gang Stew Chisam, Operating Partner, StellarIQ - https://www.linkedin.com/in/stewart-chisam-7242543/ (https://www.linkedin.com/in/stewart-chisam-7242543/) Mike Finley, Co-Founder, AnswerRocket & StellarIQ  - https://www.linkedin.com/in/mikefinley/ (https://www.linkedin.com/in/mikefinley/) Pete Reilly, Co-Founder, AnswerRocket & StellarIQ - https://www.linkedin.com/in/petereilly (https://www.linkedin.com/in/petereilly) Alon Goren, AnswerRocket, CEO - https://www.linkedin.com/in/alon-goren-87889681/ (https://www.linkedin.com/in/alon-goren-87889681/) Chapters: 00:00     Introduction to AI evolution and key topics 02:00     Single-player vs. multi-player AI: limitations & collaboration 04:37     Workspace infrastructure for AI collaboration 06:30     Challenges in sharing AI agents and scaling 15:41     What AI harnesses do 18:20     Why harnesses make AI reliable 25:03     Building an AI strategy 26:46     Should businesses pause AI adoption? 33:25     Fast AI classification 40:25     How businesses should move forward #AIAgents #EnterpriseAI #AgenticAI #AIStrategy #AIHarness #AIRegulation #ArtificialIntelligence

2. 

   8 Sept 2026

   Why AI Agent Pricing Should Be Value-Driven

   Most AI is still billed like a utility. You pay for usage and hope it adds up to something worth the spend. That model breaks down once an agent starts finishing entire jobs instead of just responding to prompts. The real story: pricing has to be tied to what the agent actually delivers, not how much of it ran. On this episode of AI, Actually, Jim Johnson, Andy Sweet, Nicole Kosky, and Ada Gil break down why AI agent pricing needs to shift toward value, what that actually requires you to define first, and why most companies aren't set up to measure it yet. You'll learn: Why usage-based pricing breaks down once agents start delivering finished work instead of just activity How to define an "atomic unit of output" so you know what you're actually paying for Why acceptance criteria have to be defined before you build, not after Why you can't let an agent evaluate its own output, and why unchecked human evaluation is risky too How adoption, not just accuracy, determines whether you're getting the value you're paying for Why model cost should flex by task, not default to the most expensive option every time This episode is designed for enterprise leaders evaluating AI agent vendors, building pricing models for AI initiatives, or making the internal case for how AI spend should be measured and justified. Learn more about AnswerRocket's enterprise AI solutions: https://answerrocket.com/ (https://answerrocket.com/) Speakers: Andy Sweet, VP Enterprise AI Solutions, AnswerRocket - https://www.linkedin.com/in/andrewdsweet/ (https://www.linkedin.com/in/andrewdsweet/) Jim Johnson, President, AnswerRocket - (https://www.linkedin.com/in/jameswjohnson/)https://www.linkedin.com/in/jim-johnson-bb82451/ (https://www.linkedin.com/in/jim-johnson-bb82451/) Ada Gil, Director, AI Business Transformation - Practice Lead - https://www.linkedin.com/in/ada-gil-84837648/ (https://www.linkedin.com/in/ada-gil-84837648/) Nicole Kosky, Senior Director of Services, AnswerRocket | (https://www.linkedin.com/in/petereilly/)https://www.linkedin.com/in/nicole-kosky-5b9a3b6/ (https://www.linkedin.com/in/nicole-kosky-5b9a3b6/) YouTube Chapters: 00:00     Introduction to AI's ROI and Business Impact 05:13     From AI Tools to Agentic Workflows 08:29     Treating Agents Like Interns 10:44     Building Trust and Measuring Good Enough 13:31     Adoption, Pricing, and Business Value 20:29     Scaling AI Value Through Better Workflow Design #AIAgentPricing #ValueBasedPricing #EnterpriseAI #AIActually #AgenticAI #AIROI #AIStrategy

3. 

   25 Aug 2026

   Token Management, Spend, and How to Actually Bring AI Costs Down

   Managing AI costs has become increasingly difficult. Uber’s CTO made headlines when he shared the company had burned through its entire 2026 AI budget in just 4 months. That's the problem this episode tackles head on. Token prices are falling for a given level of intelligence, but most companies' actual AI spend keeps climbing, because the bar for "good enough" intelligence keeps moving too. Nobody has a clean way to forecast it yet. In this episode of AI, Actually, Jim Johnson sits down with Shanti Greene, Stew Chisam, and first-time guest Jake Barger to break down what's actually driving enterprise AI costs and what to do about it. The conversation moves from the economics of model pricing into a real case study: a 33x cost reduction on a production agent, achieved in about two weeks. You'll get insight into: Why token prices dropping doesn't mean your AI bill is dropping How model routing, caching, and context management quietly drive cost The step-by-step process behind a 33x cost reduction on a live enterprise agent When open weight and self-hosted models actually make sense, and when they don't Practical takeaways for building a repeatable cost optimization process Follow the Gang: Jim Johnson, Managing Partner, AnswerRocket - https://www.linkedin.com/in/jim-johnson-bb82451/ (https://www.linkedin.com/in/jim-johnson-bb82451/) Shanti Greene, Head of Data Science and AI Innovation, AnswerRocket - linkedin.com/in/shantigreene/ (http://linkedin.com/in/shantigreene/) Stew Chisam, Operating Partner, StellarIQ - https://www.linkedin.com/in/stewart-chisam-7242543/ (https://www.linkedin.com/in/stewart-chisam-7242543/) Jake Barger, Director of AI & Machine Learning, AnswerRocket | linkedin.com/in/jacob-barger-30b1ab1b9/ (http://linkedin.com/in/jacob-barger-30b1ab1b9/) Chapters 00:00     Introduction and panel overview 02:44     Token Economics: Why Costs Stay High 07:34     Model Routing, Caching & Task Fit 12:22     Continuous Optimization for AI Agents 16:51     The 33x Cost-Reduction Case Study 23:36     Context Management & Smart Escalation 29:28     Open-Weight and Self-Hosted Models 37:22     Enterprise Reliability and Deployment Tradeoffs 44:26     Closing Takeaways: Managing AI Spend #TokenManagement #AICostOptimization #LLMTokenSpend #ModelRouting #AgenticAICosts #OpenWeightModels #SelfHostedLLM #KVCacheOptimization #EnterpriseAIROI #AIInfrastructure

Who has been a guest on AI, Actually

Names identified in recent episode analyses. Showing up to five guests.

• Nicole Kosky
• Michelle Hamilton
• Evan Gatch
• Andy Sweet
• Shanti Greene

Reach and audience

Public platform figures. Ratings count people who left a rating, not total listeners.

Apple Podcasts (US)

5.0 / 5

7 ratings

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Guest appearances

Books guests

Based on episode analysis; this does not confirm that the show is currently accepting guests.

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