
16 segments available
In this episode of AI + a16z, Distributional cofounder and CEO Scott Clark, and a16z partner Matt Bornstein, explore why building trust in AI systems matters more than just optimizing performance metrics. From understanding the hidden complexities of generative AI behavior to addressing the challenges of reliability and consistency, they discuss how to confidently deploy AI in production. Why is trust becoming a critical factor in enterprise AI adoption? How do traditional performance metrics fail to capture crucial behavioral nuances in generative AI systems? Scott and Matt dive into these questions, examining non-deterministic outcomes, shifting model behaviors, and the growing importance of robust testing frameworks. Among other topics, they cover: - The limitations of conventional AI evaluation methods and the need for behavioral testing. - How centralized AI platforms help enterprises manage complexity and ensure responsible AI use. - The rise of "shadow AI" and its implications for security and compliance. - Practical strategies for scaling AI confidently from prototypes to real-world applications. 00:01:00 - What is machine learning? 00:02:20 - The journey from tuning parameters to testing reliability 00:08:05 - Building production AI systems then and now 00:12:37 - Establishing trust in AI systems 00:17:18 - Centralization, platforms, and enterprise IT management 00:26:47 - Scaling AI usage in production 00:30:53 - How and why to test enterprise AI systems 00:38:10 - Cost management, tech debt, and prompt hygiene 00:41:44 - AI labs and enterprise users: Who influences who?
Exploring the critical importance of trust in AI performance over mere optimization.
"After helping people optimize models for about a decade and a half, I came to a really interesting realization that I was solving the wrong problem. And foundationally, it was that the thing that's ho..."
This segment discusses the realization that trust, rather than performance optimization, is the key to unlocking value from AI systems.
"more interactive and like the types of applications, the types of ways that you can use these systems becoming so much larger because I mean gen is in the the generative aspect of them is in the name,..."
This segment discusses the challenges of ensuring reliability and consistency in AI systems, emphasizing the need for robust testing methodologies.
"robustness do I now have because I've overfit this system?" And we're seeing people do the exact same thing again today with LLMs where they're focusing on these highlevel metrics, these end outputs, ..."
The speaker reflects on their journey of persistence and growth through past failures in building AI systems.
"of those past learnings all those past mistakes made a ton of mistakes as just a naive PhD student trying to build a company I won't force you to go through the mistakes that you made maybe maybe that..."
This segment discusses the collaborative nature of generative AI and its implications for product owners regarding performance and behavior management.
"original machine learning systems. I think one of the main distinctions is how atomic some of these units maybe are. And so with a lot of traditional ML and AI, it was all about can I make a specific ..."
This segment explores the inherent chaos and unpredictability of AI systems, highlighting their non-deterministic and constantly shifting nature.
"to change dramatically without me knowing or it could be that I explicitly don't want it to exhibit a specific bias or a specific type of response. And I think the main thing that's shifted though is ..."
This segment discusses the critical importance of establishing user trust in AI systems and the need for consistent verification processes to adapt to behavioral changes.
"create massive behavioral changes by the time it actually starts to affect an end user. And so it's really important and what I think a lot of firms are running into right now is if you're only lookin..."
Exploring the various attributes of AI behavior, including performance metrics and underlying characteristics.
"And and this is what you're doing at uh distributional, right? Definitely. So distributional is an enterprise platform to allow teams to test these applications in production to make sure that they ar..."
This segment discusses the importance of adapting performance metrics in AI to account for bias and unintended behaviors.
"possible. But by detecting that you have bias, maybe you want to factor that into your performance metric over time. And this is about adapting what that desired behavior over time as you learn more a..."
Exploring the benefits of centralized Gen AI platforms in managing risks and improving developer support.
"really leverage all of its resources you want to have more centralized tooling. And so much in the same way that we saw the the rise of ML and AI platforms over the last decade, we're now starting to ..."
This segment explores strategies for technology executives to centralize and streamline AI access, enhancing developer usability and platform integration.
"code base is now public or whatever it may be. So if I'm a technology executive at at a big company right now experiencing this phenomenon of little AI projects popping up everywhere, some of them doi..."
Exploring the challenges of aligning AI models with specific business goals while providing developers with efficient access to advanced tools.
"incentives are misaligned. So, OpenAI obviously wants to create the best general purpose foundational models, but an individual business may want a model that solves a very specific problem a very spe..."
This segment discusses the risks associated with overloading AI systems with excessive data and the unintended consequences that may arise from it.
"be, or turn it on to real enterprise value. And so I think the shift in mindset needs to be not just does it work the way that I want to to look at it, but does it work more holistically? Do you have ..."
This segment explores how monitoring distributional behavior shifts can provide insights into AI system performance and root causes of changes.
"little bit from maybe traditional approaches to LLM eval because instead of trying to come up with a small number of strong estimators for performance where we want to be able to conclusively say A is..."
This segment explores the challenges and trade-offs faced by enterprises when developing complex AI applications, emphasizing the need for testing and understanding both operational risks and performance impacts.
"harder problems to be completely honest. We see some firms attacking the lowhanging fruit internal chat bots to like ask questions about HR because they're afraid to take that leap to develop the the ..."
Exploring the dynamic interplay between AI labs and enterprise needs, ensuring reliable and adaptable AI solutions.
"So, it's an interesting thing you say like the the system and even the prompt sent to a language model kind of reflects the the the organization it's coming from and kind of like the behavior they're ..."