Suggestions
Barr Moses
Co-Founder & CEO at Monte Carlo
Barr Moses, Co-Founder and CEO of Monte Carlo
Barr Moses is the Co-Founder and CEO of Monte Carlo, a data observability platform that helps organizations deliver reliable data.123 She co-founded the company in 2019 with Lior Gavish to address the challenges around data reliability that she had experienced throughout her career.3
Education and Background
- Graduated from Stanford University with a BS in Mathematics and Computational Sciences1
- Formerly VP of Customer Operations at Gainsight, where she helped teams use data as a competitive advantage2
- Served in the Israeli Air Force as a commander of an intelligence data analyst unit4
- Previously worked as a management consultant at Bain & Company and in the Statistics Department at Stanford4
Monte Carlo's Mission
Monte Carlo's mission is to accelerate the adoption of data by reducing "data downtime" - periods of time when data is wrong or inaccurate.5 The company provides a data observability platform that combines machine learning-based monitoring of data, code, and infrastructure to ensure data reliability across the data stack.5
Founding and Growth
Barr Moses decided to start Monte Carlo in 2018 after working on three different startup ideas in parallel.3 As she met with potential customers, it became clear that there was significant demand from technical leaders grappling with the consequences of data downtime.3
Today, Monte Carlo has successfully created a new category and established itself as the end-to-end data observability platform for companies like Cisco, Fox, and American Airlines.3 The company raised a seed round and acquired its first customers despite a lackluster initial slide deck.3
Highlights
Agents to drive reliable data + AI applications are here.
When we launched Monte Carlo in 2019, we wanted to help every team own the quality and trust of their data—and this is an enormous leap towards that reality. https://t.co/kDajkHsDf1
Have you seen Stanford University's new AI Index Report?
There's a ton to digest, but this takeaway stands out to me the most:
“The responsible AI ecosystem evolves—unevenly.”
In the report, the editors highlight that AI-related incidents are on the rise, but standardized evaluations for validating response quality (and safety) leave MUCH to be desired.
From a corporate perspective, there’s an observable gap between understanding the RISKS of errant model responses— and actually taking meaningful ACTION (at the data, system, code, and model response levels) to mitigate it.
The primary thrust of the AI movement seems to be this:
Build, build, and build some more…then process the consequences later.
I think we need to take a step back as data leaders and ask if that’s really an acceptable approach.
Should it be?
A couple of bright spots highlighted in the report included a few new benchmarks which all offer promising steps toward assessing the factuality and safety of model responses:
- HELM Safety
- AIR-Bench
- and FACTS
Governments are also taking notice. “In 2024, global cooperation on AI governance intensified, with organizations including the OECD, EU, U.N., and African Union releasing frameworks focused on transparency, trustworthiness, and other core responsible AI principles.”
When it comes to AI, it’s not just our customers who suffer when things go awry. Our stakeholders, our teammates, and our reputations are all on the hook for generative missteps.
In short, rewards may still be high—but the risks have never been higher.
What do you think?
Let me know in the comments!

