US Treasury yields soar after strong data fuels bets on further rate rises ft.trib.al/JqF9G6Y

Interesting $FSLY finding from testing Meta Muse. Through a series of hundreds of tests, it appears that the $FSLY role with Meta Muse is greater than analysts have noted Over the past week, I created several test websites and ran 738 controlled tests to send traffic from Muse agents to my websites I set up analytics on my website, and I was able to track things like IP address, user agent, operating system, etc From the IP addresses, across the 738 tests, Muse sent traffic from: -Cloudflare: 37.0% -Fastly: 29.3% -Meta: 19.4% -AWS: 14.4% Cloudflare is not a surprise – as there has been a lot of analyst commentary about Muse using Cloudflare. Fastly, on the other hand, was a surprising result I ran many different situations and tasks to try to understand why, or when, Muse sends traffic through Fastly – or others. And when I looked deeper, Muse used Fastly for 100% of the tests when browser navigation was required (e.g., “go to my favorite restaurant’s website and find me the menu”). And when Muse was requested to navigate on a website (e.g., make me a doctor appointment), Fastly was used in 100% of tests Then in other situations, for example when an API is involved or a curl request required, Cloudflare was used in 100% of tests Lastly, when requesting Muse to search the web, Muse split traffic through Fastly, Cloudflare, Meta directly, and AWS

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This experience will only spread, in my view. The historical best practice to get smart on a name was read the filings, read the transcripts, and read a big stack of sell-side research, then model out the company. This hadn't really changed much in decades (outside of innovations in alternative data & expert network transcripts). Having a comprehensive offering of sell-side research was important at the institutional level. We have reached threshold on numerous fronts where a public market investor can achieve similar or deeper comprehension on a name with AI, and doesn't necessarily need that same comprehensive sell-side offering. That random sell-side report that went deep on a certain aspect of the business or industry can now be created with an AI agent sitting on the right data pipeline. One example I've shown in the past on DKNG...in the past, if I'm trying to get smarter & sharper on a deep dive on state level taxation, the right sell-side note dropping at the right time is supremely helpful. Now, I can run that analysis when I need it at the push of a button. The cohort of investors who build off of sell-side models will, very soon, be at push-button AI capabilities (and more may move modeling off Excel into JSON). The moat of the sell-side is melting. And I believe the sell-side has, collectively, overplayed their hand in being adversarial to the agentic path. My view is they will eventually fold, but not before many clients learn to build around the commercial friction and, maybe, eventually come to the same conclusion as Just Another Pod Guy. Like most things the top decile sell-side analysts will be fine, decades of investor trust and relationships will continue to monetize. But what happens to the 16th best analyst on a name? It think it's obvious the industry just needs fewer voices on a name, so how do you pivot? Corporate access has enduring value (if you don't believe it, be a fly on the wall when there is one seat at a key meeting and 5 pods wanting that same seat...). But I think it's deeper than that...how does the sell-side drive differentiated client insight? Not just regurgitate publicly available information (never much value, and now zero value). Cleveland Research to me is the working mental model of deep embedding of their analysts into the operational flow of industries driving a regular & valuable flow of investible insights.