Early bird discounts for the San Francisco World’s Fair, the biggest AIE gathering of the year, end today - prices will go up by ~$500 tonight so do please lock in ASAP! From near-universal AI tool adoption inside Shopify to internal systems for ML experimentation, auto-research, customer simulation, and ultra-low-latency search, Mikhail Parakhin joins us for a deep dive into what it actually looks like when a 20-year-old, $200B software company goes all-in on AI. We cover why Shopify has become much more vocal about its internal stack, what changed after the December model-quality inflection, and why the real bottleneck in AI coding is no longer generation, but review, CI/CD, and deployment stability. We also go inside Tangle, Tangent, SimGym, which are three major AI initiatives that Shopify is doing to make experimentation reproducible, optimization automatic, customer behavior simulatable, and search and catalog intelligence faster and cheaper at scale. Along the way, Mikhail explains UCP, Liquid AI, and why token budgets are directionally right but often measured badly, why AI-written code can still increase bugs in production, what makes Shopify’s customer simulation defensible, and what he learned from the Sydney era at Bing. We discuss: * Mikhail’s path from running a major Microsoft business unit spanning Windows, Edge, Bing, and ads to becoming CTO of Shopify * Why Shopify is talking more publicly about AI now, and why staying at the frontier has become necessary for the company * Shopify’s internal AI adoption curve, the December inflection, and why CLI-style tools are rising faster than traditional IDE-based tools * Why Jensen Huang is directionally right on token budgets, but raw token count is still the wrong way to evaluate engineering output * Why the real unlock is not more agents in parallel, but better critique loops, stronger models, and spending more on review than generation * Why AI coding can still lead to more bugs in production even if models write cleaner code on average than humans * Why Shopify built its own PR review flow, and why Mikhail thinks most off-the-shelf review tools miss the point * How PR volume, test failures, and deployment rollback are becoming the real bottlenecks in the agent era * Why Git, pull requests, and CI/CD may need a new metaphor once code is written at machine speed * What Tangle is, and how Shopify uses it to make ML and data workflows reproducible, collaborative, and production-ready from the start * Why Tangle is different from Airflow, and why content-addressed caching creates network effects across teams * What Tangent is, and how Shopify is using auto-research loops to optimize search, themes, prompt compression, storage, and more * Why Tangent is becoming a democratizing tool for PMs and domain experts, not just ML engineers * Why AutoML finally feels real in the LLM era, and where auto-research still falls short today * Why Tangle, Tangent, and SimGym become much more powerful when combined into one system * What SimGym is, why simulated customers only work if you have real historical behavior, and why Shopify’s data gives it a moat * How SimGym evolved from comparing A/B variants to telling merchants what to change on a single live storefront to raise conversions * Why customer simulation is so expensive, from multimodal models to browser farms to serving and distillation costs * How Shopify models merchant and buyer trajectories, runs counterfactuals, and thinks about interventions like discounts, campaigns, and notifications * Why category-level behavior is so different across commerce, and why ideas like Chinese Restaurant Processes are showing up again in practice * Shopify’s new UCP and catalog work, including runtime product search, bulk lookups, and identity linking * Why Shopify is using Liquid AI, and why Mikhail sees it as the first genuinely competitive non-transformer architecture he has used in practice * Where Liquid already works inside Shopify today, from low-latency query understanding to large-scale catalog and Sidekick Pulse workloads * Whether Liquid could become frontier-scale with enough compute, and why Shopify remains pragmatic and merit-based about model choice * Who Shopify is hiring right now across ML, data science, and distributed databases * The Sydney story at Bing, why its personality was not an accident, and what Mikhail learned from deliberately shaping AI character early on Mikhail Parakhin * LinkedIn: https://www.linkedin.com/in/mikhail-parakhin/ * X: https://x.com/MParakhin Timestamps 00:00:00 Introduction: Mikhail Parakhin, Microsoft, and Shopify 00:01:16 Why Shopify Is Talking More About AI 00:02:29 Internal AI Adoption at Shopify and the December Inflection 00:06:54 Token Budgets, Jensen Huang, and Why Usage Metrics Can Mislead 00:10:55 Why Shopify Built Its Own AI PR Review System 00:12:38 AI Coding, More Bugs, and the Real Deployment Bottleneck 00:14:11 Why Git, PRs, and CI/CD May Need to Change for Agents 00:18:24 Tangle: Shopify’s Reproducible ML and Data Workflow Engine 00:21:19 Why Tangle Is Different from Airflow 00:26:14 Tangent: Auto Research for Optimization and Experimentation 00:30:07 How Tangent Democratizes Experimentation Beyond ML Engineers 00:33:06 The Limits of Auto Research 00:36:36 Why Tangle, Tangent, and SimGym Compound Together 00:37:20 SimGym: Simulating Customers with Shopify’s Historical Data 00:42:47 The Infra Behind SimGym 00:46:00 Why SimGym Gets Better with Real Customer History 00:47:30 Counterfactuals, HSTU, and Modeling Merchant Trajectories 00:51:55 CRPs, Clustering, and Category-Level Customer Behavior 00:53:30 UCP, Shopify Catalog, and Identity Linking 00:55:07 Liquid AI: Why Shopify Uses Non-Transformer Models 00:59:13 Real Shopify Use Cases for Liquid 01:03:00 Can Liquid Scale into a Frontier Model? 01:09:49 Hiring at Shopify: ML, Data Science, and Databases 01:10:43 Sydney at Bing: Personality Shaping and AI Character 01:13:32 Closing Thoughts Transcript [00:00:00] swyx: Okay. We’re here in the studio, a remote studio, with Mikhail Parakhin, CTO of Shopify. Welcome. [00:00:08] Mikhail Parakhin: Thank you. Welcome. [00:00:10] swyx: I don’t even know if I should introduce you as CTO of Shopify. I feel like you have many identities. Uh, you led sort of the, the Bing ML team, I guess, uh, uh, or ads team. I, I don’t know, I don’t know, uh, you know, it’s, uh, people va-variously refer you as like CEO or, or, uh, I don’t know what that, that, that said previous role at Microsoft was. [00:00:29] Mikhail Parakhin: Uh, that was... Yeah, my previous role w- at Microsoft was the-- I actually was the CEO of one of Microsoft’s business units, which included, as I, you know, as we discussed, all the things that people like to laugh about, uh, including Windows and Edge and Bing and ads and everything. [00:00:47] swyx: Yeah, yeah. What a, what a, what a wild time. You’ve obviously, uh, done a lot since you landed at Shopify. Uh, one of the reasons I reached out was because you started promoting more sort of internal tooling, uh, primarily Tangle, but also a lot of people have seen and adopted Tobi’s QMD, uh, and obviously, I think, uh, Shopify has always been sort of leading in terms of, uh, engineering. I think more-- it’s just more recent that you guys have been more vocal about your sort of AI adoption. Is that, is that true? [00:01:16] Mikhail Parakhin: Well, I think AI tools in general are fairly recent development, uh, and we’ve-- Shopify, you know, at this stage of its development, we’re developing AI in-in-house and other, uh, building tools that use AI and, you know, interfacing with the wider AI community, uh, you know, are on the sort of the, uh, runaway trajectory. So it just did by sort of natural byproduct. We, we talk about it more also. We just, uh, just even yesterday, Andrej Karpathy was famous in tweeting about, oh, are there some, uh, ways, uh, that, that you can organize your agents to store the data and then, uh, look up the data so that you don’t have to research or, or lose context every- Yes time. And a little bit tongue in cheek, I tweeted that, “Hey, we’ve, we’ve done it much earlier, and we even have different approaches, Tobi and I.” Tobi, of course, is a big fan of QMD, and I’m more of a SQL, SQLite fan. But, uh, yeah, very similar things that we’ve already done here. The point is, yeah, we’re very dynamic, you know, explosively growing company, and we have to be at the forefront of AI adoption, obviously. [00:02:29] swyx: Yeah. Yeah. Um, you, your team kindly prepared some slides actually that we were gonna bring up on to, uh, the screen. I think I can, I can screen share, and then we can kind of go through some of the shocking stats that maybe, maybe put some numbers to what exactly is going on. So here we have, uh- An internal AI tool adoption chart. What are we looking at here? What ? [00:02:54] Mikhail Parakhin: Yeah, this is very interesting statistics. Uh, this is number of daily active workers, you know, think of, uh, DAO, basically the active users of- [00:03:05] swyx: Yeah ... [00:03:05] Mikhail Parakhin: AI tool as a percentage of all the people in the company, right? And then- Yeah ... different AI tools. And, uh, you could see two things here is that one is the green is total. Uh, green is just total. So you could see that it approaches really % by now. It’s hard not to do your job now without interacting deeply, at least with one tool. You could see another interesting thing is just as many people commented in December was the phase transition when suddenly models gotten good enough that, that everything took off and started growing. Uh, it, it was many people noticed that the thing is that small improvements accumulated into this big change in Sep- December roughly timeframe. [00:03:52] swyx: Yeah. [00:03:52] Mikhail Parakhin: The other thing I would claim you could see is tha
