Your new favorite podcast for all things security and industry trends. Brought to you by Sumo Logic, each episode features no B.S. real talk with experts who’ve been in the trenches, helping you navigate complex tech decisions, avoid costly mistakes, and stay one step ahead.
Log in, look around, level up: Building a trial people want to explore
We turned the mic around for this episode, with Zoe Hawkins interviewing Adam White and Jake Lee about Sumo Logic’s redesigned free trial. The new 14-day sandbox comes preloaded with realistic data feeds, Cloud SIEM, and our AI agents, so no one has to build collectors or open tickets before finding out whether the platform fits. Adam and Jake walk through the guided “choose your own adventure” paths, the broad, open-ended questions that bring out the best in Mobot, and how the trial will keep pace with new releases. Security and observability practitioners who want to size up a logging platform in an afternoon, without kicking off a data engineering project, will get the most from this one.
0:00:00 – Introduction: Zoe takes the guest seat
0:00:56 – What the new 14-day sandbox trial includes
0:03:16 – Preloaded data feeds, Cloud SIM, and AI agents
0:04:50 – Why we chose to do trials differently from other vendors
0:06:58 – Moving from the sandbox to a proof of value with your own data
0:07:54 – Guided paths and choose-your-own-adventure exploration
0:13:22 – What people ask Mobot: broad questions and gap analysis
0:17:08 – Keeping the trial current as new agents and use cases launch
0:20:51 – How to sign up and what happens after 14 days
0:22:12 – Wrap-up
24 Sept 2026
Deterministic by design: Why AI doesn't get to freestyle your pipeline
We sat down with Ben Cody, SVP Product Management at Sumo Logic, to work out what data pipeline management means now that moving data from point A to point B is the easy part. Ben draws a sharp line between the two jobs AI can and cannot do here, arguing that AI should generate the configurations and flag the gaps while the pipeline itself stays deterministic and runs the same way every time. We also get into toxic data, why telemetry volumes are outrunning budgets, and the accidental coining of "vibelines." Security and SRE teams watching agent traffic inflate their ingest bill will find plenty here on shaping what they keep without giving up coverage.
15 Sept 2026
Tokenpocalypse: Who pays when the AI bill comes due
On this episode of Masters of Data, we dig into what we're calling the tokenpocalypse, the real cost of running AI at the scale most of us have quietly slid into. We trace the whiplash of the past two years, from "everyone needs the AI hammer" to backlash against obvious AI slop, then get into the harder question of what happens when a mission-critical process runs out of tokens, from chaining Claude to Databricks for a custom dashboard to the eye-watering budget Uber reportedly burned through by April. We also weigh in on cheaper Chinese frontier models, the pull toward localized and specialized AI, and whether the current build-out of AI infrastructure can realistically pay for itself. If you're the one watching the AI line item on your budget, or building workflows that quietly depend on a model always being available, this conversation is a useful gut check.
0:00:00 – Intro and cohost banter
0:00:35 – Naming the "tokenpocalypse"
0:02:03 – From "use the AI hammer" to backlash against AI slop
0:04:56 – Chaining Claude and Databricks to build a dashboard
0:06:02 – Running out of tokens on mission-critical work, and Uber's budget blowout
0:08:30 – Why dumping your entire data lake into AI wastes tokens
0:11:13 – Cheaper Chinese models and the pricing fallout
0:13:08 – From ChatGPT to Claude, and the case for localized AI
0:15:21 – Whether the AI infrastructure buildout is built for the wrong future
0:19:52 – Wrap-up
Who has been a guest on Masters of Data
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Patrick Kobly
David
AJ Bean
Jeremy Powell
Adam White
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