CEOInterviews.AI
Start App
Try: AI strategy tariffs earnings guidance layoffs interest rates growth

Jad Naous

Founder & CEO, Grepr

Search every verified Jad Naous interview, podcast appearance, and on-the-record quote — each transcript cross-checked by AI and human review to confirm speaker identity. Jad Naous, founder and CEO of Grepr, appeared in two recorded conversations in the last two months. In an October 2026 podcast with a16z General Partner Martin Casado, Naous discussed the capabilities and limits of AI in software development and operations. He described how coding agents have transformed work within his 10-person team, noting that projects that previously took six months now take roughly 10 days. Naous argued that while AI can write production code, operating real-world systems remains a harder challenge due to unpredictable workloads and failures. He also stated that AI systems can run experiments to answer operational questions but that production systems do not always behave as expected, which is why incidents occur. In a September 2026 conversation with Evan Robinson, CTO of Jitsu, Naous discussed Grepr's observability and reliability services. Robinson, an early customer, described how his company evaluates build-versus-buy decisions, saying observability is not a core differentiator for a logistics firm and that attention cost, not just monetary cost, matters when scaling. Naous also shared his perspective on AI's evolution from autocomplete to more capable coding tools, and highlighted the importance of maximizing signal-to-noise in observability without losing the ability to catch unforeseen issues.

Recent appearances

  • Why AI Can Write Code But Can’t Keep It Running (Yet)

    October 16, 2026 · 📺 a16z

    AI can write production code in minutes. But can it debug and operate that code when something goes wrong? Grepr CEO Jad Naous argues that running real-world systems is a fundamentally harder challenge, with unpredictable workloads, failures, and countless possible states. Jad joins a16z General Partner Martin Casado to discuss how coding agents have transformed engineering inside his 10-person team, including projects that once took six months and now take roughly 10 days. They also explore whether AI can troubleshoot production systems by detecting changes, forming hypotheses, and running…

    Watch on YouTube ↗

  • Jitsu's CTO on Scaling AI-Powered Delivery Infrastructure Nationwide

    September 10, 2026 · 📺 Grepr

    A recorded conversation between Jad Naous (Grepr) and Evan Robinson (Jitsu). Jitsu delivers millions of packages a year for leading brands around the world. Behind every delivery is a real-time tech stack doing route optimization, driver dispatch, geofencing, image-based proof of delivery, and customer communication. Every one of those systems generates data. A lot of it. Evan Robinson, Jitsu's CTO, has spent his career building platforms at this kind of scale. He co-founded Shipwire (acquired by Ingram Micro), ran a platform engineering team at Yahoo, and now oversees the AI-powered infrast…

    Watch on YouTube ↗

  • #42 – Killing Observability Noise: How Grepr Reduces Data by Up to 90% and Rebuilds the Pipeline ...

    April 16, 2026 · 📺 Dash0

    Jad Naous, founder and CEO of Grepr, joins Dash0’s Mirko Novakovic to tackle one of observability’s biggest problems: too much data and not enough signal. Drawing on his experience at AppDynamics and Apache Druid, Jad explains why current architectures are fundamentally broken, and how Grepr compresses, aggregates and extracts signal directly in the stream, reducing data volume by up to 90% before it ever hits tools like Datadog. They dive into real-time pattern detection across logs and traces, why rule-based pipelines fall short, and how high-signal data is the foundation for any future AI…

    Watch on YouTube ↗

  • Grepr Demo: Cut Your Datadog Log Volume 96% Without Losing Data

    January 25, 2025 · 📺 Grepr

    Grepr CEO and founder Jad Naous gives a full walkthrough of Grepr in action, cutting Datadog log volume by about 96% without throwing any data away. Starting from five Datadog agents pushing 33,000 debug logs, Jad sets up Grepr end to end and shows the reduction happen live: Sign up and create a pipeline (source, raw data store, sink) Connect Datadog as both the source and the destination with an API key Store all raw data in your own S3 bucket or in Grepr's Point your agent at Grepr's ingest URL over HTTP Watch throughput drop from 60 to 70 messages per second to about two, a 96% reduction…

    Watch on YouTube ↗

The summary and quote tags on this profile are produced with AI assistance from verified, first-person interview transcripts, then checked by our team to confirm the speaker's identity and the accuracy of every quote. See how we verify →