Sherwin Wu: belief

Transcript context

…This episode is brought to you by Datadog, now home to Eppo, the leading experimentation and feature flagging platform. Product managers at the world's best companies use Datadog, the same platform their engineers rely on every day, to connect product insights to product issues like bugs, UX friction and business impact. It starts with product analytics, where PMs can watch replays, review funnels, dive into retention, and explore their growth metrics. Where other tools stop, Datadog goes even further. It helps you actually diagnose the impact of funnel drop-offs and bugs and UX friction. Once you know where to focus, experiments prove what works. I saw this firsthand when I was at Airbnb where our experimentation platform was critical for analyzing what worked and where things went wrong. And the same team that built experimentation at Airbnb built Eppo. Datadog then lets you go beyond the numbers with session replay. Watch exactly how users interact with heat maps and scroll maps to truly understand their behavior. And all of this is powered by feature flags that are tied to real-time data so that you can roll out safely, target precisely and learn continuously. Datadog is more than engineering metrics. It's where great product teams learn faster, fix smarter, and ship with confidence. Request a demo at datadoghq.com/lenny. That's datadoghq.com/lenny. Okay. I'm going to shift to talking about the API and the platform that you all build. So you work with a lot of companies implementing your API, your platform building on your tools. You told me that you find that a lot of companies actually have negative ROI on their AI deployments, which I think is what a lot of people read about and feel and think, and it's interesting you're actually seeing that. What's going on there? What are they doing wrong? What's happening in the world of AI and deployments and ROI? Yeah. So to be clear, I don't explicitly see quantitative numbers around this. It's actually really hard to measure these things, but especially from observing some companies trying to do AI, I would not be surprised if a lot of AI deployments are actually negative ROI. I mean, part of this too is I think there's also general sentiment from folks around the country, like basically outside of tech that AI is being forced onto them. And I think part of this is probably a symptom of some negative ROI AI deployments. A couple of things I've observed around this. So one thing is, and I come back to this again and again, I think we in Silicon Valley just forget that we live in a bubble. Twitter is a bubble, sorry X is a bubble, Silicon Valley is a bubble, software engineering's a bubble. Most people in the world, most people in the US are not software engineers, are not very AI-pilled, are not following every single model release. And so are just highly out of the loop on how to use this technology. And so we always talk about all these best practices for Codex, all these Codex-pilled people within OpenAI. I'm sure everyone on X who posts are like crazy power users of these AI tools. They lean into Skills, they lean into AGENTS.md. MCPs.…

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