ShopifyのAIフェーズ移行：2026年の使用爆発、無制限のOpus-4.6トークン予算、Tangle、Tangent、SimGym — Shopify CTOミハイル・パラヒンとの対談

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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 that, uh, CLI-based tools and tools that don’t require you to look at the code becoming more popular, and you could see, yeah, various versions of, uh, Cloud Code and Codex and Pi and internal development tools taking off.

Uh, exactly, yeah, uh, and blue is our River, just internal agent for coding, where tools, uh, that require IDEs such as, uh, GitHub, Copilot or Cursor, they’re not exactly shrinking, but they’re not growing as fast. Like, uh, red, red line is, is the IDE kind of tools. So you could see that they’re, they’re not experiencing as, as fast of a growth.

[00:04:37] swyx: As I understand it, basically, every employee has their choice, right? Of choose whatever tool you use, and then you’re just kind of doing a, a daily sur-survey or something.

[00:04:47] Mikhail Parakhin: Exactly. And, uh, we- Yeah ... the, the push is to get your job done, you can use any tool, and we effectively fund unlimited tokens for everybody.

Uh, we, we do, we do try to control the models that, uh, people use, but from the bottom, not from top. Like we basically say, “Hey, please don’t use anything less than Opus four point six.”

[00:05:09] swyx: Oh .

[00:05:10] Mikhail Parakhin: Some people, some people end up using GPT five point four extra high. Some people use Opus four point six. Um, uh, you know, uh, there are some, uh, there are plus and minuses in going for full one million context window versus not.

But, uh, we try to discourage people from using anything less than that.

[00:05:28] swyx: Yeah, yeah. Got it, got it. Uh, I mean, uh, that’s, you know... The, the next chart here, it really kind of shows the expansion and the sort of December twenty twenty-five inflection, right? That, uh, people are using a lot of tokens. I think it’s also really interesting that no one was kind of abusing it in twenty twenty-five.

Like it was- Had comparatively, uh, to this year, there was almost no growth. I mean, it’s still like, you know, probably, probably gave fifty percent.

[00:05:56] Mikhail Parakhin: Yeah. This is just a different scale. It’s still exponential- Yeah, yeah ...growth at just a different- ...rate of expansion. Uh, there was inflection point, and Sean, I would claim the, the super interesting part here is that you could see that the distribution becoming more and more skewed.

Yes. The top percentiles grow faster. So that means- Yeah ...the people in the top ten percentile, they, their consumption grows faster than seventy-five and so forth. So, uh, the distribution skews more and more towards the highest users, which is... I don’t know what it tells me. It’s like it feels not ideal, to be honest.

Or maybe it’s okay. We’ll see.

[00:06:36] swyx: Why does it feel not ideal? Is, is it because of, um, quantity over quality, or what’s the concern?

[00:06:42] Mikhail Parakhin: Because take it to the limit. That means, you know, if, if this rate of separation continued- Ah, yes ...a year, there will be one person consuming all the tokens. So it’s just, it’s kinda strange.

[00:06:54] swyx: Yeah, I mean, um, uh, I, I think internal like teaching and all that, uh, will, will help sort of distribute things more widely. But in, in the early days, of course, the people who are sort of more AI-pilled will obviously find more ways to use it than the people who are less AI-pilled. Maybe let’s, let’s call it that.

I’ll just, I’ll just kinda quickly, uh, pause from the, the... You know, we will go back to the rest of the slides, but I just wanna, um, review, you know, there are a lot of CTOs of, of large companies like yourself where they’re all considering some kind of token budget, right? Like I think it’s something, something that Jensen Huang has been talking about, where like if your 200K engineer is not using 100K of tokens every year, like they’re, they’re underutilizing coding agents.

Of course, Jensen Huang would say that, but like it seems a very quantity over quality approach and like some, some people are basically saying like, well, is this comparable to judging engineer quality by lines of code, right? Which we also know is like kind of flawed, but better than nothing. So I, I don’t know if you have like a sort of management take here on, on how to view this kind of, uh, metrics.

[00:08:02] Mikhail Parakhin: Well, I mean, you’re, you’re baiting me. I, I like... This is my favorite topic. Uh, if you let me, I’ll probably talk for two hours on just this. I have a lot of things to say. Like I do think Jensen gotten a lot of bad press saying, “Oh, of course you’re, you know, this, uh, the- ...the cake seller says you don’t need enough cakes.”

You know? Like, of course. Uh, but, uh, I actually, uh, think that’s undeserved. I think he, he’s actually right. Uh, I do think- He,

[00:08:33] swyx: he’s directionally correct.

[00:08:35] Mikhail Parakhin: Yeah. Yeah. He’s directionally correct for sure. Uh-

[00:08:37] swyx: Who knows what the right number is? Yeah.

[00:08:39] Mikhail Parakhin: The thing that I do Uh, want to say, and this is something that we learned through trial and error and very important is like two things.

One is that it’s not about just consuming tokens. Uh, you can consume tokens and, and in fact, the anti-pattern is running multiple agents, too many agents in parallel that don’t communicate with each other. That’s almost useless, uh, compared to just fewer agents and burns tokens very efficiently. Uh, setting up the right critique loop, especially with the high quality models, where one agent does something, the other one, ideally with a different model, critiques it, uh, suggests ways to improve it, the agent redoes it with this critique and, and so it takes much longer.

So people don’t like it because latency goes up. You know, they, they have to wait until this debate is happening. But, uh, the quality of the code is much higher. And another thing, just since you mentioned like, look, uh, uh, yeah, the overall budget is just like, uh, lines of codes. Lines of codes are exploding for everybody right now, or partially because AI is really mover balls, but partially just because AI can write a lot more code, you know, doesn’t get tired.

And so you have to have to have a very strong narrow waist during PR review. Otherwise, just the number of bugs will go through the roof. It’s, uh, it’s this unexpected consequence of the just volume trumping everything. I would claim by now good model writes code on average with fewer bugs than, than the average human.

But since they write so much more of it, like more of it will make it into production. So you have to- You still

[00:10:26] swyx: have

[00:10:26] Mikhail Parakhin: more bugs. Yeah. Have to have a very rigorous PR reviews, also automated of course. But, uh, yeah, that to spend a lot budget there. Like this, this for me, for me, actually, the important metric is the ratio of budget spent during code generation versus, uh, spent, uh, expensive tokens like GPT, uh, five point four Pro or, uh, uh, Deep Think from Gemini, you know, checking on PR reviews.

[00:10:55] swyx: Yeah, totally. Uh, I noticed in your chart you didn’t have any review tools. Do you just use like, like let’s say a Claude code to review tools? Or do you have another set of review tools like the Greptiles, the Code Rabbits, uh, Devin Reviews has a review tool. I don’t know if you’ve had those specialist review tools.

[00:11:13] Mikhail Parakhin: You are a little bit jumping on my store tool right now because the graphs I was only showing public tools. Uh, uh, the-- I haven’t found a good PR review tool that, that does what I think should be done. And, uh, partially my, my thinking is because it’s so... It just goes against both what people feel like emotionally they prefer and, uh, some of the, uh, you know, frankly Even business models that, that the companies run.

At peer review tool, uh, time, you want to run the largest models. That means, I don’t know, Codex or, or, uh, Cloud Code is not gonna cut it. You need to have pro-level models if you really want to, uh, stand the tide of bots from going into production. And you need us to spend a lot of time, the models taking turns, but you don’t want, like, a big swarm of, uh, of, uh, agents.

So in fact, you end up in a different dual-dualistic world where you generate not that many tokens. You, in fact, generate few tokens, but it takes f-a long time because these are expensive models taking turns rather than many, many agents trying to do many things in parallel. So that’s, that’s why I feel like I haven’t found good tools, so we are using our own for peer review for now.

[00:12:33] swyx: Yeah. Yeah. I mean, uh, I think a lot of companies are building their own, uh, especially to their needs, right?

[00:12:38] Mikhail Parakhin: Mm-hmm.

[00:12:38] swyx: Um, I, uh, you also have a chart here going back to the slides on, uh, PR merge growth, where we’re now at thirty percent, uh, month on month rather than ten percent. Uh, and also the, the estimated complexity is going up.

You know, this is productivity, right? ‘Cause y- presumably there’s more stuff going into the code base and more, more features getting worked on. I’m curious about the backlog, right? Like the, the, the-- I actually don’t mind a pro-level model taking an hour or two hours to review my PR, because I’ve dealt with humans who take a week to review my PR, right?

And I keep pinging them on Slack, “Hey, hey, review my PR.” So, you know, I think there’s some trade-off here where, like, it still doesn’t make sense.

[00:13:18] Mikhail Parakhin: Exactly. That, that’s exactly m-my point. Uh, that on one hand, you can tolerate longer latencies at, uh, PR. On the other hand, like right now, the real problem is not in spending time waiting for PR.

It’s real problem is since there’s so much more code than- Yeah ... uh, probability of at least some tests failing going up, and then you, like, keep de-failing, then you have to find the offending PR, evict it, retest it without that PR, and so d