AI, Actually

Every week, someone declares that vibe coding will kill software development. Every week, they're wrong — but not for the reasons most people think.

The real story isn't about job loss. It's about an explosion in software creation that most business leaders aren't prepared for.

In this episode of AI, Actually, the crew breaks down what vibe coding actually means for enterprise software, why the "death of engineering" narrative misses the point, and what executives need to understand and do, before their competitors get there first.

You'll learn:

• Why Jevons Paradox predicts we'll have more software than ever, not less
• The "compound engineering" principle that separates durable AI-built code from slop
• Why every executive needs to personally try vibe coding, not just read about it
• What Jack Dorsey's layoffs at Block, parent company of Square, actually signal for enterprise workforce strategy?
• Agency vs. intelligence: the new career currency in an AI-powered world
• The socioeconomic upside of AI democratization that almost nobody is talking about

This episode is designed for: CTOs, engineering leaders, Chief Data Officers, innovation executives, and enterprise decision-makers navigating AI transformation and workforce strategy.

Learn more about AnswerRocket's enterprise AI solutions: https://answerrocket.com/accelerators/

Speakers

• Pete Reilly, COO, AnswerRocket | https://www.linkedin.com/in/petereilly
• Mike Finley, CTO, AnswerRocket | https://www.linkedin.com/in/mikefinley/ 
• Shanti Greene, AnswerRocket | https://www.linkedin.com/in/shantigreene/ 
• Stew Chisam, Operating Partner, StellarIQ | https://www.linkedin.com/in/stewart-chisam-7242543/

Chapters
00:00     The Evolution of Software Development
05:20     The Future of Custom Software
10:53     Agent-Friendly Interfaces and Integration
16:58     The Importance of Curiosity in Software Development
26:07     Re-architecting for Modern Standards
31:04     The Impact of AI on Traditional Jobs
38:11     Navigating Job Losses and Transitioning in the Workforce

#VibeCoding #EnterpriseAI #FutureOfWork #SoftwareDevelopment #AIStrategy #WorkforceDisruption #GenerativeAI

Enterprise AI adoption is accelerating — but most organizations are still stuck debating tools like ChatGPT while a smaller group is deploying AI agents that operate like digital colleagues.

The gap between experimentation and operationalization is widening.

In this episode of AI, Actually, leaders from AnswerRocket break down what’s really happening on the front lines of enterprise AI adoption — and share a practical framework for moving from pilots to measurable business impact.

You’ll learn:

• Why the shift from AI “co-pilot” to AI “colleague” is reshaping enterprise workflows
• The leadership mindset separating fast movers from stalled organizations
• A crawl-walk-run framework for AI adoption at the individual, team, and enterprise level
• How to measure AI ROI (including the adoption J-curve most leaders underestimate)
• Why there’s no such thing as an “AI project” — only business problems enabled by AI

This episode is designed for:
Chief Data Officers, Heads of Analytics, AI leaders, innovation executives, and enterprise decision-makers navigating AI transformation.

Learn more about enterprise AI strategy and operationalization:

https://answerrocket.com/

• Stew Chisam, Operating Partner, StellarIQ - https://www.linkedin.com/in/stewart-chisam-7242543/ 

• Jim Johnson, Managing Partner, AnswerRocket | https://www.linkedin.com/in/jim-johnson-bb82451/
• Pete Reilly, COO, AnswerRocket - https://www.linkedin.com/in/petereilly 
• Andy Sweet, VP Enterprise AI Solutions, AnswerRocket - https://www.linkedin.com/in/andrewdsweet/ 

#EnterpriseAI #AIAdoption #AIROI #AIAgents #AIStrategy #DigitalTransformation #GenerativeAI

Artificial intelligence has crossed a major threshold. AI is no longer confined to answering questions in a chat window. It's sending emails, scheduling meetings, moving data between systems, and operating with its own credentials and access. This shift from suggestion to action is forcing every enterprise leader to rethink their AI strategy.

In this episode, Pete Reilly sits down with Shanti Greene, Jim Johnson, and Stew Chisam to examine three developments reshaping how businesses approach AI. From the OpenClaw experiment that briefly turned the internet into a Skynet-like scenario, to Anthropic's Cowork tool bringing agent capabilities to non-technical workers, to the fundamental question of whether enterprises should build their own software instead of buying it—this conversation tackles the practical implications of AI that actually does things.

Topics covered:

✅ How OpenClaw (formerly ClawdBot/MoltBot) represents the future of AI agents with their own identity, proactive behavior, and inter-agent communication

✅ Why enterprise leaders need to start thinking about "onboarding non-human employees" with email addresses, Slack accounts, and human supervisors

✅ Claude Cowork's approach to bringing agent capabilities to business users through local file access and workflow automation

✅ The shifting build-vs-buy decision as AI makes custom software development dramatically faster and cheaper

✅ Whether traditional SaaS companies like Salesforce can survive in an AI-first world

Follow the Gang:

• Stew Chisam, Operating Partner, StellarIQ - https://www.linkedin.com/in/stewart-chisam-7242543/ 
• Jim Johnson, Managing Partner, AnswerRocket | https://www.linkedin.com/in/jim-johnson-bb82451/
• Pete Reilly, COO, AnswerRocket - https://www.linkedin.com/in/petereilly 
• Shanti Greene, Head of Data Science and AI Innovation, AnswerRocket - https://www.linkedin.com/in/shantigreene/ 

Chapters:

00:00 Introduction: The Evolution of AI in Enterprises

01:20 OpenClaw: The Next Iteration of AI Assistant

04:18 Implications of AI Agents in the Workplace

10:01 Understanding the Difference: Traditional AI vs. OpenClaw

14:48 Navigating Security and Accountability in AI

21:29 The Future of Non-Human Employees in Enterprises

26:42 Exploring Claude Cowork: A Shift in Focus

39:29 The Build vs. Buy Debate in CRM Development

#AIAgents #AutonomousAI #OpenClaw #ClaudeCowork #EnterpriseAIStrategy #AIWorkflows #AgenticOperations #BuildVsBuy #SaaSDisruption #AIEmployeeOnboarding

What happens when AI coding agents enter the development process? AnswerRocket's team built a production-ready CRM in just four weeks, showing what's possible with next-generation software development.

In this episode, Pete Reilly, Alon Goren, Mike Finley, and Andy Sweet pull back the curtain on what modern AI development actually looks like in practice. Using a custom CRM project as their case study, they demonstrate how coding agents, contextual design, and new architectural patterns are fundamentally rewriting the build versus buy equation for enterprise software.

This isn't theory—it's a live demonstration of software that would have taken 10x longer to build just six months ago, with insights on what this means for development teams, IT strategy, and the future of business software.

Topics covered:

• How AI coding agents reduced development time by 10x for production-grade software
• Why context is now more valuable than data entry in modern CRM design
• The shift from "system of record" to "sales assistant" in CRM philosophy
• How playbooks and workflows enable AI to follow company-specific processes
• Why the build vs. buy decision is becoming "build with composable blocks"
• What "vibe coding" means and why offshore development strategies need rethinking
• How maintenance changes when AI agents can read source code directly

Follow the Gang

• Mike Finley, CTO, AnswerRocket  - https://www.linkedin.com/in/mikefinley/ 
• Pete Reilly, COO, AnswerRocket - https://www.linkedin.com/in/petereilly 
• Andy Sweet, VP Enterprise AI Solutions, AnswerRocket - https://www.linkedin.com/in/andrewdsweet/ 
• Alon Goren, AnswerRocket, CEO - https://www.linkedin.com/in/alon-goren-87889681/ 
  

Chapters

00:00 Intro: Demo to AI in Software Development

02:10 Understanding Customer Interactions and CRM Needs

04:39 Reimagining CRMs in the Age of AI

06:00 Demo Walkthrough of the CRM

20:02 What's Possible Now with AI-Assisted Development

21:25 Designing an AI-Compatible Stack

27:32 Flipping the Build vs. Buy Dilemma

32:59 AI's Impact on Offshore Development

35:57 The Future of Business and Software Customization

39:23 Maintaining AI-Assisted Software Solutions

#AIDevelopment #CodingAgents #CRMCustomization #VibeCoding #BuildVsBuy #EnterpriseSoftware #AIAgents #SalesAutomation #SoftwareDevelopmentLifecycle #ClaudeCode #AgenticDevelopment #ContextDrivenDesign

As enterprises deploy AI agents into production, a new operational challenge emerges: how do you monitor and maintain systems that don't fail with error codes, but instead drift subtly away from expected performance? In this episode, the AI, Actually crew tackles the emerging discipline of AgentOps—the practice of keeping AI agents performing at peak business value over time.

The discussion cuts through the hype around "self-learning" and "automated" AI to reveal the hard truth: agentic systems require continuous human oversight, just like human employees do. From probabilistic model behavior to reasoning model complexity, the team explores why traditional IT monitoring approaches fall short and why businesses need to rethink who owns these digital workers.

Topics covered:

• Why AgentOps is fundamentally different from traditional DevOps and LiveOps
• The three levels of agent complexity and three types of drift that can derail performance
• Why traditional IT support models don't work for goal-driven agentic systems
• The organizational challenge of bringing together business knowledge, AI expertise, and technical skills
• Why there's no "blue screen of death" for agent failures—and what that means for monitoring

Follow the Gang:

• Nicole Kosky, Senior Director of Services, AnswerRocket | https://www.linkedin.com/in/nicole-kosky-5b9a3b6/
• Joey Gaspierik, Director of Enterprise Sales, AnswerRocket | https://www.linkedin.com/in/joey-gaspierik-4a613642/
• Stew Chisam, Operating Partner, StellarIQ - https://www.linkedin.com/in/stewart-chisam-7242543/ 
• Jim Johnson, Managing Partner, AnswerRocket | https://www.linkedin.com/in/jim-johnson-bb82451/

Chapters:

00:00     Introduction to AgentOps

02:38     Defining Agentic Operations

09:30     The Role of Human Oversight

11:01     Understanding Performance Degradation

17:27     The Complexity of Monitoring Agents

26:07     Organizational Challenges in AgentOps

31:00     The Future of Agentic Operations

35:26     What's An Agent?

Hashtags: #AIImplementation #EnterpriseAIAdoption #OrganizationalChange #AILiteracy #GeneralistEngineers #LastMileProblem #VibeCoding #AIROI #RevenueGeneration #OpenAIWhitePaper

Keywords: Agent Ops, AI agents, enterprise AI, LLM monitoring, model drift, probabilistic systems, agentic AI, AI operations, AI governance, AI deployment, DevOps, LiveOps, reasoning models, AI scalability, digital workers

The old IT playbook is officially dead. Quarterly release cycles, endless approval committees, and throwing requirements over the wall? None of that works when AI models evolve every few weeks. In this episode, Pete Reilly sits down with Jim Johnson, Alon Goren, and Shanti Greene to unpack OpenAI's new white paper, "From Experiments to Deployments: A Practical Path to Scaling AI," and share what they're seeing on the ground with enterprise clients.

This isn't theory—it's a frontline report from teams who are helping Fortune 500 companies navigate the messy reality of AI adoption. The conversation tackles the organizational upheaval required to move fast, the "last mile problem" that kills so many AI projects, and why the future belongs to curious generalists who can bridge business and technology. They also explore how AI is shifting ROI conversations from cost savings to revenue generation, and why vibe-coding a working prototype in hours is now entirely possible (with some important caveats about technical debt).

Topics covered:

• Why IT and business teams must close the collaboration gap to succeed with AI
• The "last mile problem": getting from 80% complete to actually useful
• How generalists with medium depth are becoming more valuable than deep specialists
• Building an AI-literate workforce through curiosity and continuous learning
• Shifting from cost-savings ROI to revenue-generating AI products
• The reality of vibe-coding: prototypes in hours, but engineering rigor still matters

Follow the Gang:

• Pete Reilly, COO, AnswerRocket - https://www.linkedin.com/in/petereilly 
• Shanti Greene, Head of Data Science and AI Innovation, AnswerRocket - https://www.linkedin.com/in/shantigreene/ 
• Alon Goren, Founder, AnswerRocket | https://www.linkedin.com/in/alon-goren-87889681/
• Jim Johnson, Managing Partner, AnswerRocket | https://www.linkedin.com/in/jim-johnson-bb82451/

Chapters:

00:00     Introduction to AI Deployment Challenges

02:52     The Shift from IT to Business Collaboration

05:41     The Role of Generalists in AI Integration

08:42     Redefining AI Projects and Business Goals

11:35     Accelerating Development Timelines with AI

14:47     Building an AI Literate Workforce

17:45     The Changing Landscape of ROI in AI

20:58     Final Thoughts on Embracing AI

#AIImplementation #EnterpriseAIAdoption #OrganizationalChange #AILiteracy #GeneralistEngineers #LastMileProblem #VibeCoding #AIROI #RevenueGeneration #OpenAIWhitePaper

Keywords: AI implementation, enterprise AI adoption, organizational change, AI literacy, generalist engineers, last mile problem, vibe coding, AI ROI, revenue generation, OpenAI white paper

The race for AI dominance just shifted. Google's Gemini 3 launch was a coordinated ecosystem play that could reshape how enterprises think about AI infrastructure. In this episode, Pete Reilly sits down with Andy Sweet, Shanti Greene, and Stew Chisam to dissect what Gemini 3 really means for enterprise adoption, where the technology is genuinely improving, and where marketing hype obscures practical limitations.

The conversation moves beyond surface-level benchmarks to tackle the uncomfortable reality facing IT leaders: foundation models are converging on performance, but the real competitive advantage lies in how you architect solutions on top of them. The team explores Google's commanding lead in multimodal capabilities, the strategic implications of vendor ecosystems, and why enterprises betting everything on a single provider might be making a costly mistake. Then they close with their boldest predictions for 2026, from content exhaustion and the death of infographics to agents working autonomously longer than your employees.

What You'll Learn:

• How Gemini 3's pre-training approach signals continued model improvements and what that means for the scaling law debate
• Why Google's multimodal dominance (backed by YouTube, Google Photos, and Drive) creates a moat that competitors can't easily replicate
• The critical difference between general intelligence benchmarks and enterprise intelligence that understands your business context
• Why the "stateless" problem keeps plaguing AI solutions and how memory scaffolding becomes essential for business applications
• Predictions for 2026: content exhaustion, shadow IT proliferation, and the moment enterprises realize there's no easy button

Follow the Gang:

• Shanti Greene, Head of Data Science and AI Innovation, AnswerRocket - https://www.linkedin.com/in/shantigreene/ 
• Pete Reilly, COO, AnswerRocket - https://www.linkedin.com/in/petereilly 
• Andy Sweet, VP Enterprise AI Solutions, AnswerRocket - https://www.linkedin.com/in/andrewdsweet/ 
• Stew Chisam, Operating Partner, StellarIQ - https://www.linkedin.com/in/stewart-chisam-7242543/ 

Chapters:
00:00     Introduction to Gemini 3 and Episode Overview
01:45     Gemini 3's Long-Term Planning Capabilities
04:17     Are LLMs Becoming Commoditized Primitives?
08:04     Model Specialization and Jagged Edges
11:15     Why Multi-Vendor Strategy Matters for Enterprises
13:29     OS/2 vs Windows: Best Doesn't Always Win
15:10     The Scaling Law Debate and Pre-Training Improvements
16:12     Enterprise Intelligence vs General AGI
22:41     How Enterprises Should Think About Gemini 3
26:44     Bold Predictions for 2026
28:08     Content Exhaustion and the Infographic Problem
33:06     Agents as Autonomous Team Members

#Gemini3 #EnterpriseAIArchitecture #MultimodalAI #AIAgents #VendorLockIn #SemanticLayer #ScalingLaws #PreTrainingCompute #EnterpriseIntelligence #AIPredictions2026

Keywords: Gemini 3, enterprise AI architecture, multimodal AI, AI agents, vendor lock-in, semantic layer, scaling laws, pre-training compute, enterprise intelligence, AI predictions 2026

Are most AI projects really failing, or are we just too early to judge? In this episode, we cut through the headlines to talk about what's actually happening with enterprise AI adoption. Our team tackles the disconnect between AI hype and business reality, exploring why the "easy button" mentality is holding companies back and what it actually takes to succeed.

We discuss why treating AI like magic instead of a project dooms initiatives from the start, how mid-market companies have a unique advantage to leapfrog their larger competitors, and why someone needs to supervise your digital employees. This is a practical conversation about bridging the gap between IT departments and business aspirations, with real talk about what works and what doesn't.

What You'll Learn:

• Why "AI failure" statistics miss the point about early adoption
• The three critical skills needed for successful AI implementation
• How to identify which use cases deserve an LLM versus deterministic code
• What "agentic operations" means and why it's not set-it-and-forget-it
• Why mid-market companies can leapfrog enterprise competitors right now
• How to bridge the dangerous gap between IT and business expectations

Follow the Gang:

Jim Johnson, Managing Partner, AnswerRocket | https://www.linkedin.com/in/jim-johnson-bb82451/

Nicole Kosky, Senior Director of Services, AnswerRocket | https://www.linkedin.com/in/nicole-kosky-5b9a3b6/
Joey Gaspierik, Director of Enterprise Sales, AnswerRocket | https://www.linkedin.com/in/joey-gaspierik-4a613642/
Shanti Greene, Head of Data Science and AI Innovation, AnswerRocket |

Chapters:
00:00     Introduction to AI Engagements
01:23     The Reality of AI Success and Failure
07:37     Deterministic vs Non-Deterministic Systems
11:12     Understanding Variation in AI Answers
13:35     Predictors of Success in AI Projects
16:41     Agent Operations and Ongoing Management
19:19     The Role of Senior Stakeholders in ROI
21:39     The Real Work To Do in Agent Operations
24:34     Putting Solutions Into Production
29:55     The Mid-Market AI Advantage
32:32     Closing: Recommendations for AI Success

#EnterpriseAIAdoption #AIProjectFailure #AgenticOperations #AIROI #GenerativeAIImplementation #LLMUseCases #MidMarketAIAdvantage #BusinessSponsorship #AISkillsGap #DeterministicVsNonDeterministicSystems

Keywords: enterprise AI adoption, AI project failure, agentic operations, AI ROI, generative AI implementation, LLM use cases, mid-market AI advantage, business sponsorship, AI skills gap, deterministic vs non-deterministic systems

Andrej Karpathy just dropped a reality check: 2025 isn't the "year of the agent,” it's the decade of the agent. But does that mean enterprises should hit pause on their AI initiatives? Not even close.

In this episode, the AI, Actually crew tackles the gap between AI hype and enterprise reality. Pete Reilly, Alon Goren, Mike Finley, and Jim Johnson break down why current LLMs aren't perfect, why that doesn't matter for ROI, and how smart companies are capturing value right now versus waiting ten years. From coding agents that ship features overnight to the organizational challenges of keeping AI solutions on the rails, this is your practical guide to navigating the AI landscape today.

Topics covered:

• Why Karpathy's agent timeline doesn't mean you should wait to start
• Where current AI models excel (and struggle) in enterprise environments
• The hidden engineering work between impressive demos and production systems
• Narrow use cases delivering ROI today: customer touchpoints, developer productivity, and sales pipelines
• The trade-offs between vendor lock-in and rapid implementation
• Why "it's just prompts" misses the entire point
• The three skill sets required to keep AI solutions running in production

Follow the Gang:

• Pete Reilly, COO, AnswerRocket | https://www.linkedin.com/in/petereilly/
• Alon Goren, Founder, AnswerRocket | https://www.linkedin.com/in/alon-goren-87889681/
• Mike Finley, CTO, AnswerRocket | https://www.linkedin.com/in/mikefinley/
• Jim Johnson, Managing Partner, AnswerRocket | https://www.linkedin.com/in/jim-johnson-bb82451/

Chapters: 

00:00     Introduction to AI Actually Podcast
02:22     The Decade of the Agent
05:45     Understanding AI Agents in the Enterprise
09:47     Navigating AI Use Cases
13:39     Building Modular AI Solutions
17:01     Identifying Low-Hanging Fruit for AI Implementation
20:29     The Importance of AI in Competitive Landscapes
24:36     Organizational Readiness for AI
28:16     Closing the Gap for Real Value in AI
32:06     Final Thoughts and Advice

#AIAgents #EnterpriseAIImplementation #AndrejKarpathy #LLMLimitations #AIROI #CodingAgents #AIVendorLockIn #AIDeployment #AIOrganizationalChange #EnterpriseAIStrategy

Keywords: AI agents, enterprise AI implementation, Andrej Karpathy, LLM limitations, AI ROI, coding agents, AI vendor lock-in, AI deployment, AI organizational change, enterprise AI strategy

OpenAI's Dev Day dropped some major announcements this month, but is AgentKit really revolutionary or just another "me too!" moment? The AI, Actually crew shares their reactions to the latest OpenAI releases, and digs into how to successfully implement AI agents in the real world.

In this episode, Jim Johnson steps in as host alongside Mike Finley, with special guests Nicole Kosky (who leads AnswerRocket's AI Business Transformation Practice) and Reilly Carrolll (Senior AI Solutions Consultant). Together, they tackle the practical, nitty-gritty challenges of bringing agents to life for enterprise clients—from gathering requirements that users don't know they have, to managing the surprising differences between what stakeholders say they need versus what they actually ask once they're hands-on with an agent.

Topics covered:

• OpenAI's Dev Day announcements and what they actually mean for enterprises
• Why successful agent implementations should follow the Software Development Lifecycle (SDLC)
• The critical role of context in agent performance
• Why user questions change dramatically from requirements phase to hands-on testing
• The emerging discipline of "agentic operations" and why it's non-negotiable
• Starting small: the power of quick wins over trying to boil the ocean

Follow the Gang:

Jim Johnson, AnswerRocket, Managing Partner - https://www.linkedin.com/in/jim-johnson-bb82451/
Mike Finley, AnswerRocket, CTO - https://www.linkedin.com/in/mikefinley/ 

Reilly Carroll, AnswerRocket, Senior AI Solutions Consultant - https://www.linkedin.com/in/reilly-carroll/ 

Nicole Kosky, AnswerRocket, Senior Director of Services - https://www.linkedin.com/in/nicole-kosky-5b9a3b6/ 

Chapters:

00:00      Introduction and Guest Introductions

01:30      OpenAI Dev Day Announcements

05:25      Understanding AI Agents

09:05      Practical Implementation of AI Agents

11:11      Challenges in Client Engagement

14:17      Agent Development and User Experience

16:55      The Challenge of Capturing Agent Requirements

19:52      The Importance of Agentic Operations

22:40      Navigating the Future of AI Agents

29:09      Final Thoughts and Advice

Keywords: AI agents, OpenAI Dev Day, agent development, enterprise AI implementation, agentic operations, software development lifecycle, AI business transformation, agent context, LLM applications, practical AI strategy