On February 2nd, a single plugin wiped nearly $800 billion off the enterprise software market. Wall Street is terrified that AI agents are about to eat the legal industry's lunch. But LexisNexis isn't scared—they're building the moat.

In this episode of Beyond the Pilot, Min Chen (Chief AI Officer, LexisNexis) reveals the sophisticated architecture they built to counter the "LLM wrapper" revolution. Moving beyond standard RAG, Min breaks down their move to "GraphRAG", their deployment of Agentic workflows (using Planner and Reflection agents), and why they created a proprietary "Usefulness Score" because standard accuracy metrics weren't good enough for lawyers.

AI Gets Real Here. No theory, just the execution roadmap for deploying AI in a zero-error environment.

In this episode, we cover:

The "Dangerous RAG" Problem: Why semantic search fails in professional domains (retrieving "relevant" but overruled cases) and how "Point of Law" knowledge graphs fix it.

The "Usefulness" Metric: The 8 sub-metrics LexisNexis uses (including Authority, Comprehensiveness, and Fluency) to grade AI quality.

Agentic ROI: How deploying a "Planner Agent" to break down complex questions increased answer usefulness by 20%.

The "Reflection Agent": Using a secondary agent to critique and refine drafts in real-time.

Hallucination Detection: Why you should never rely on an LLM to judge its own hallucinations (and the deterministic code they use instead).

⏱️ TIMESTAMPS

00:00 - Intro: The $800 Billion AI Threat to Legal Tech

02:18 - Min Chen’s Journey: From Feature Engineering to Chief AI Officer

05:55 - Why Standard RAG Fails in Law (and How GraphRAG Fixes It)

10:40 - "Accuracy" is a Vanity Metric: The 8-Point Usefulness Score

14:20 - The "Auto-Eval" Framework: Human-in-the-Loop at Scale

16:40 - The Secret Sauce: Don't Use LLMs to Detect Hallucinations

21:15 - Agentic AI: How "Planner Agents" Drove a 20% Gain

22:00 - The "Reflection Agent": Self-Critique Loops for Drafting

30:30 - Distillation: Balancing Cost, Speed, and Quality

32:45 - Min’s Advice: Don't Build the Product First (Build the Metrics)

Presented by Outshift by Cisco Outshift is Cisco’s emerging tech incubation engine and driver of Agentic AI, quantum, and next-gen infrastructure. Learn more at outshift.cisco.com.

About VentureBeat: VentureBeat equips enterprise technology leaders with the clearest, expert guidance on AI – and on the data and security foundations that turn it into working reality.

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While most of the world is still running GenAI pilots, Mastercard is running AI inference on 160 billion transactions a year—with a hard latency limit of 50 milliseconds per score.

In this episode of Beyond the Pilot, Johan Gerber (EVP of Security Solutions) and Chris Merz (SVP of Data Science) open the hood on one of the world's largest production AI systems: Decision Intelligence Pro. They reveal how they moved beyond legacy rules engines to build Recurrent Neural Networks (RNNs) that act as "inverse recommenders"—predicting legitimate behavior faster than the blink of an eye.

AI Gets Real Here. This isn't just about defense. Johan and Chris detail how they are taking the fight to criminals by leveraging Generative AI to engage scammers with "honeypots," expose mule accounts, and map fraud networks globally.

In this episode, we cover:

The 50ms Inference Challenge: How Mastercard optimized their RNNs to score transactions at a peak rate of 70,000 per second.

"Scamming the Scammers": How GenAI agents are being used to automate honeypot conversations and extract mule account data.

The "Inverse Recommender" Architecture: Why Mastercard treats fraud detection as a recommendation problem (predicting the next likely merchant).

Org Design for Scale: The "Data Science Engineering Requirements Document" (DSERD) Chris used to align four separate engineering teams.

The Hybrid Infrastructure: Why moving to Databricks and the cloud was necessary to cut innovation cycles from months to hours.

🚀 CHAPTERS

00:00 - Intro: 160 Billion Transactions & 50ms Decisions

02:08 - Thinking Like a Criminal: Johan’s Law Enforcement Background

06:22 - Org Design: Why AI is the "Middle Lane" of Engineering

11:00 - The Scale: 70k Transactions Per Second

15:47 - Decision Intelligence Pro: The "Inverse Recommender" RNN

23:00 - The "Lego Block" Strategy: Aligning Data Science & Engineering

33:00 - Infrastructure: Why Cloud/Databricks was Non-Negotiable

37:00 - GenAI Offensive: Threat Hunting & "Scamming the Scammers"

46:40 - "Honeypots" and Detecting Mule Accounts

52:00 - Advice for Technical Leaders: Talent & Prioritization

Presented by Outshift by Cisco Outshift is Cisco’s emerging tech incubation engine and driver of Agentic AI, quantum, and next-gen infrastructure. Learn more at outshift.cisco.com.

About VentureBeat: VentureBeat equips enterprise technology leaders with the clearest, expert guidance on AI – and on the data and security foundations that turn it into working reality.

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While the rest of the industry chases massive models, LinkedIn quietly achieved a major engineering breakthrough by going small.

In this episode of Beyond the Pilot, Erran Berger (VP of Product Engineering, LinkedIn) opens the "cookbook" on how they distilled massive 7B parameter models down to ultra-efficient 600M parameter "student" models—scaling AI to 1.2 billion users without breaking the bank.

AI Gets Real Here. This isn't theory. Erran details the exact architecture, the "Multi-Teacher" distillation process, and the organizational shift that forced Product Managers to write evals instead of specs.

In this episode, we cover:

The Distillation Pipeline: How to train a 7B "Teacher" and distill it to a 1.7B intermediate and 0.6B "Student" for production.

Synthetic Data Strategy: Using GPT-4 to generate the "Golden Dataset" for training.

Multi-Teacher Architecture: Why they separated "Product Policy" and "Click Prediction" into different teacher models to solve alignment issues.

10x Efficiency Hacks: Specific techniques (Pruning, Quantization, Context Compression) that slashed latency.

Org Design: Why the "Eval First" culture is the new requirement for AI engineering teams.

🚀 CHAPTERS

00:00 - Intro: LinkedIn's Massive "Small Model" Feat

04:00 - Why Commercial Models Failed at LinkedIn Scale

08:00 - The "Product Policy" Funnel & Synthetic Data Generation

12:00 - The Pipeline: 7B → 1.7B → 600M Parameters

19:00 - The "Multi-Teacher" Breakthrough (Relevance vs. Clicks)

23:00 - How They Achieved 10x Latency Reduction (Pruning/Compression)

31:00 - Changing the Culture: Why PMs Must Write Evals

35:00 - The "Bright Green Matrix": Measuring Success & Future Roadmap

Presented by Outshift by Cisco Outshift is Cisco’s emerging tech incubation engine and driver of Agentic AI, quantum, and next-gen infrastructure. Learn more at outshift.cisco.com.

About VentureBeat: VentureBeat equips enterprise technology leaders with the clearest, expert guidance on AI – and on the data and security foundations that turn it into working reality.

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#EnterpriseAI #LLMDistillation #LinkedInEngineering #SmallLanguageModels #AIArchitecture #TechLeadership

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The "TAM" for AI Agents isn't software. And there is a $10 Trillion opportunity.

In this episode, Replit CEO Amjad Masad reveals why 99% of today's enterprise AI agents are just "Slop"—unreliable, generic toys that fail in production. We dive deep into the engineering reality of building autonomous agents that actually work, moving beyond simple chatbots to systems that can navigate the messy reality of enterprise infrastructure.

Amjad breaks down Replit’s "Computer Use" hack that makes agents 10x cheaper than generic models, explains why "Vibe Coding" is the future of the C-Suite, and issues a warning to technical leaders: If you want to ship fast in the AI era, you need to kill your product roadmap.

In this episode, we cover:

The "Slop" Problem: Why most LLM outputs are generic and how to inject "taste" back into software.

The Computer Use "Hack": How Replit built a programmatic verifier loop that outperforms vision-based models.

Vibe Coding: Why non-technical domain experts (HR, Sales, Marketing) will build the next generation of enterprise software.

The $10T Market: Why the Junior Developer role is disappearing and being replaced by the "Manager of Agents."

🚀 CHAPTERS

0:00 - Intro: Why most AI Agents are "Toys"

03:02 - The only 2 AI use cases making money right now

06:00 - The "Crappy Product" Strategy (Shipping fast)

10:00 - What is "AI Slop"? (And how to fix it)

14:30 - The "Deleted Database" Incident: Solving Reliability

18:00 - The "Squishy" Divide: Why Marketing Agents fail

21:45 - Vibe Coding in the Enterprise

26:00 - Model Wars: Claude Opus vs. Gemini vs. OpenAI

28:10 - The "Computer Use" Hack (10x Cheaper, 3x Faster)

36:00 - Why Product Roadmaps are Dead

43:00 - Replit is the #1 Software Vendor (Ramp Data)

49:00 - The Unit Economics of Agents (Token Costs vs. Value)

53:00 - Open Source vs. Closed: The "Cathedral of Bazaars"

59:00 - The $10 Trillion Opportunity: Replacing Labor

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#AgenticAI #Replit #VibeCoding #EnterpriseAI #LLM #SoftwareEngineering #FutureOfWork #AmjadMasad #ArtificialIntelligence #DevOps

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Inside the 'Agent Economy': How 30,000 AI Assistants Took Over JPMorgan

While most enterprises were scrambling after ChatGPT launched, JPMorgan Chase was already two years ahead. 🚀

In this episode of Beyond the Pilot, we sit down with Derek Waldron, Chief Analytics Officer at JPMorgan Chase, to reveal how the world’s largest bank built an internal AI platform that is now used by 1 in 2 employees daily.

Derek shares the contrarian insight that drove their strategy: AI models are commodities; the real moat is connectivity.

Learn how they scaled from zero to 250,000+ users, why they empowered employees to build 30,000+ of their own "Personal Agents," and how they are solving the data privacy challenge at an enterprise scale.

🔥 IN THIS EPISODE:

The "Super Intelligence" Thought Experiment: Why raw intelligence is useless without enterprise connectivity.

The Agent Economy: How JPM enabled non-technical staff to build 30,000 custom AI assistants.

The Adoption Playbook: How to break through the "30% wall" and get the majority of your workforce using AI.

Build vs. Buy: Why JPM built their own "LLM Suite" instead of waiting for vendors.

⏳ CHAPTERS:

00:00 - Introduction: The JPMorgan AI Story

01:45 - The 3 Core Principles Behind JPM’s Strategy

03:25 - The "Super Intelligence" Thought Experiment

05:00 - Data Privacy: Why JPM Doesn't Train Public Models

06:00 - Viral Adoption: From 0 to 250k Users

09:20 - Evolution of LLM Suite: From RAG to Ecosystem

14:00 - The "Moat" is Connectivity, Not the Model

23:00 - The Agent Economy: 30,000 Employee-Built Assistants

31:00 - Governance & Guardrails for AI Agents

33:00 - Crossing the Chasm: Getting to 60% Adoption

40:00 - The "Product" Mindset: Solving Business Problems First

42:30 - The Future: End-to-End Process Transformation

46:25 - The "Unsolved" Problem Derek Wants to Fix

🙏 SPECIAL THANKS TO OUR SPONSOR:

This episode is presented by Outshift by Cisco.

Learn more about their work on the Internet of Agents and the open-source Linux Foundation project:

🔗 https://www.agentcy.org

🎙️ GUEST: 

Derek Waldron | Chief Analytics Officer, JPMorgan Chase

HOSTS:

Matt Marshall | VentureBeat

Sam Witteveen | VentureBeat

#EnterpriseAI #JPMorgan #GenerativeAI #AgenticAI #FinTech #ArtificialIntelligence #Innovation #BeyondThePilot

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We built AI agents by accident... and it worked. 🤯

In this episode of VentureBeat’s Beyond the Pilot, we go inside the engineering brain of Booking.com with Pranav Pathak (Director of Product Machine Learning). Pranav reveals how they "stumbled" into agentic architectures before the term even existed, how a simple text box revealed a massive missed revenue opportunity (the "Hot Tub" story), and exactly how they stack LLMs, RAG, and Orchestrators to handle millions of travelers without breaking the bank.

If you are building Enterprise AI, this is the blueprint for moving from "cool demo" to production scale.

🚀 In this episode, we cover:

The "Hot Tub" Revelation: How free-text AI search exposed features customers desperately wanted but couldn't find.

Real ROI Metrics: How LLMs drove a 2x increase in topic detection accuracy and freed up 1.5x of agent bandwidth.

The Booking.com AI Stack: A full breakdown of their Orchestrator → Moderation → Agent → RAG architecture.

Latency vs. Intelligence: Why they don't use GPT-5 for everything and how they decide between small models and big brains.

The Memory Problem: How to build AI that remembers user preferences without being "creepy”.

00:00 Introduction to Agentic Architectures

00:30 Meet Pranav Pathak from Booking.com

01:24 Evolution of Travel Recommendations

03:41 Impact of Gen AI on Customer Service

07:29 Building an Effective AI Stack

10:32 Agentic Systems and Best Practices

13:45 Choosing Between Building and Buying AI Solutions

18:51 Evaluating AI Models for Business Use

24:10 Challenges in Human Evaluation

25:06 Recommendation System and Data Utilization

27:26 Innovations in Travel Search

29:04 Journey and Challenges in ML Integration

32:08 Managing Memory and User Data

38:07 Future of Travel Assistance

41:33 Advice for New AI Integrations

43:57 Final Thoughts and Farewell

🔗 LINKS & RESOURCES:

OutShift by Cisco (Sponsor): outshift.cisco.com

VentureBeat: www.venturebeat.com

#ArtificialIntelligence #GenAI #Bookingcom #MachineLearning #AgenticAI #LLM #TechPodcast #EnterpriseAI

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In our inaugural episode, we sit down with Ryan Nystrom, leader of the AI team at Notion, to pull back the curtain on Notion 3.0. Ryan reveals the journey of integrating powerful AI agents into the productivity platform and draws fascinating parallels between the current AI era and the mobile revolution he witnessed at Instagram. He shares exclusive insights into the development challenges, the critical role of tools, context, and curation, and how custom agents are poised to reshape work. Plus, Ryan offers essential advice for any company diving into the AI space.

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Lift-and-shift isn’t enough. MongoDB’s Vinod Bagal breaks down how to modernize your data for AI — and why waiting could cost you your competitive edge.

Host: Sean Michael Kerner

Guest: Vinod Bagal

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AI is accelerating the cyber arms race — and former FBI agents Paul Bingham and Mike Morris say most enterprises aren’t ready. In this VB in Conversation, they break down the real-world threats targeting critical infrastructure, how AI is changing the attack surface, and why smart, layered defense starts with training the next-gen cyber workforce.

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Key Insights and Takeaways from VentureBeat Transform Event: AI and Enterprise Innovation

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