Production engineering as a wedge

My guests today are Animesh Koratana and Jamin Ball. 

Animesh is the founder and CEO of our portfolio company PlayerZero, which is building AI production engineers that operate complex enterprise software autonomously - resolving production incidents, catching defects before release, and building durable models of how systems actually behave.

Jamin is a partner at Altimeter Capital and the writer behind Clouded Judgement, a Substack where he analyzes emerging trends in enterprise software. 

Jamin recently sparked a debate with an essay titled “Long Live Systems of Record.” 

His core argument is that while agents are changing how software is used and where value accrues, they still depend on ground truth. Systems of record won't disappear so much as get pushed down the stack as new agent-native interfaces emerge on top.

My partner Jaya and I felt compelled to respond, with Animesh contributing insights based on what he's seeing on the ground as he builds PlayerZero. 

From our perspective, the missing layer is what happens inside the workflow itself: the judgment, exceptions, and reasoning that agents and humans apply as work gets done. We call these decision traces, and we believe the context graph they form over time will become the most valuable asset for companies building and deploying AI systems.

It's a genuine debate - and one that's only going to matter more as agents move from demos to production.

Looking forward to keeping the conversation going!

Chapters

• 00:00 Why Jamin’s essay sparked debate 
• 00:35 Jamin’s argument: agents need ground truth 
• 02:00 Animesh on why context graphs matter 
• 07:58 What today's systems of record are missing 
• 08:28 How PlayerZero thinks about context graphs 
• 10:00 How context graphs could change org structures 
• 11:10 How do you capture decisions without making people log everything? 
• 14:35 Which systems of record are most at risk 
• 17:04 Two workflows ripe for disruption: GTM and software development 
• 22:31 Animesh on where context graphs can add most value 
• 28:50 Why context graphs create moats for startups 
• 30:00 Will context graphs be industry-specific or universal? 
• 34:00 Bear case: do context graphs fail like semantic layers? 
• 43:27 2026 predictions: big AI IPOs, world models, enterprise agent adoption 
• 45:00 Hot takes: point solutions die; AI job-loss discourse hits a fever pitch 
• 47:30 Jevons paradox: why agents create more work, not less