Mikhail Parakhin
CTO, Shopify · 1 appearance on the record.
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executiveengineeroperator@MParakhin ↗LinkedIn ↗shopify.com ↗
Mikhail Parakhin oversees engineering, distributed systems, search latency, and internal AI stack adoption across Shopify. He previously served as CEO of Advertising and Web Services at Microsoft, where he led initiatives including Bing Chat and Copilot, and was earlier the Chief Technology Officer of Yandex.
28statements → 10claims → 1claims resolved → 3.89/5average certainty → 2.29/5average debate potential → ≈4.5/5argument clarity, estimated →
1 supported 0 partly supported 0 contradicted 9 not checkable as stated how the 10 claims stand · each chip opens the sources
1 prediction · 9 assertions · 3 opinions · 8 insights · 6 disclosures · 1 what if · every statement was checked. The prediction and assertions are the 10 claims: statements the public record can support or contradict. 1 is resolved, and 9 name no date, number or outcome precise enough to check. Everything else (opinions, insights, what ifs, disclosures) can never be settled by the record, so it carries no assessment.
The record, in short
What the tape says about how Mikhail argues and how the claims held up. Everything they said, and everything said about them, is in the tabs below.
Their most notable supported claim
Parakhin: Bing Sydney first launched in India using Megatron, not OpenAI
“The funny thing, I mean, the most interesting anecdote is that Sydney was first shipped in India for and it was not noticed for a long time. And first implementation of Sydney didn't even have open AI model under it. It was during Megatron. Microsoft and the N…”
How they sound: not measured why? →
We measure speaking style by listening to the audio itself, and a fair number needs at least 2,000 words from one person on tape we have measured. There is too little of Mikhail Parakhin on measured tape to publish a rate. This says nothing about how they speak.
Everything Mikhail Parakhin said on Latent Space that made the record, most notable first. Filter by type, assessment or year in the ledger →
Parakhin: Top AI models write code with fewer bugs than average humans
“I would claim by now, good model writes code on average with fewer bugs than average human.”
Parakhin: Liquid AI could beat frontier models with equal compute
“I think if they if they had similar level of compute, they would be very competitive and maybe even beat the largest models, at least from what I've seen.”
Parakhin: CLI AI tools outpace IDEs like Cursor at Shopify
“The other thing I would claim you could see is that 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 Cloud Code and Codex and Pi and internal development tools taking off e…”
Parakhin: Effective PR review requires largest pro-level models, not fast tools
“At PR review time, you want to run the largest models. That means codex or cloud code is not going to cut it. You need to have pro-level models if you really want to stem the tide of bugs from going into production”
Parakhin: Slower AI PR reviews actually save total deployment time
“It actually, in terms of the overall time to deploy, it's total time savings if you spend more time on a longer model, like, thinking for an hour, because then you don't have to spend all that time During testing and rolling, you know, rolling back the deploym…”
Parakhin: Liquid is the only genuinely competitive non-transformer architecture Shopify found
“That's why we at Shopify, when we tried multiple, and we constantly try multiple models, multiple companies, we found that for small, particularly with low latency applications, when you have low latency and or if you need longer context lengths, Liquid was th…”
Parakhin: Liquid-transformer hybrids are probably the best neural network architecture available
“I think especially in their hybrid form, when combined with Transformer, like in Mamba fashion, they probably the best architecture I'm aware of, like, period.”
Parakhin: Bing Sydney's personality was deliberately engineered, not purely emergent
“What almost everybody doesn't fully realize is that it wasn't by accident that Sydney was Sydney. I mean, we spent a lot of effort on personality shaping. We, I mean, it was a bit of my Yandex legacy where previously we did this Alice digital assistant which w…”
Parakhin: Daily AI tool adoption at Shopify approaches 100 percent
“So you could see that it approaches really a hundred percent by now. It's hard not to do your job now without interacting deeply, at least with one tool.”
Parakhin: Shopify funds unlimited tokens and discourages models below Opus
“And we effectively fund unlimited tokens for everybody. We do try to control the models that people use, but from the bottom, not from top. Like we basically say, hey, please don't use anything less than Opus.”
Parakhin: Jensen Huang is directionally correct on developer token budgets
“I do think Jensen gotten a lot of bad press saying, oh, of course you're, you know, this the cake seller says we don't need enough cakes, you know, like, of course. But I actually think that's undeserved. I think he, he's actually right. I do think. He's direc…”
Parakhin: No existing third-party AI PR review tool meets his standards
“I haven't found a good PR review tool that, that does what I think should be done”
Parakhin: Human-centric CI/CD systems must be redesigned for AI agents
“Clearly the old thing were designed for humans will need to be morphed into something new.”
Parakhin: Airflow is poorly suited for rapid ML experimentation compared to Tangle
“Airflow is great, but Airflow is more about you have something and you want to repeatedly run it in production on schedule. It's less about you as a team developing things and being able to share and you grabbing the standard pipeline and saying, hey, I want t…”
Parakhin: Auto-research excels at obvious optimizations but fails on out-of-distribution tasks
“Autoresearch is very good at doing kind of obvious things that you don't have bandwidth to do, or you didn't notice, or maybe you're not aware of like some standard practices. It is not good at doing something completely out of distribution, something that, yo…”
Parakhin: Simulating customers without historical data produces ungrounded agent responses
“If you don't have the historical data, All you can do is prompt agents in the vacuum, and they will do exactly what you prompt them to do.”
Parakhin: Increasing product image size usually tanks ecommerce sales
“Usually people's intuition here, by the way, is that I increase my images. I'll have more because they look nicer. You know, designers all look sparse and big images. Like, usually your sales tank, right?”
Parakhin: AI assistants engage users best when polite but slightly edgy
“What we learned in those experiments is you want to be polite, but you want to be a little bit on edge, and that draws people in.”
Parakhin: Shopify built agent memory systems before Karpathy proposed them
“Just even yesterday, Andrej Karpathy was famous in tweeting about, oh, there's some ways that you can organize your agents to store the data and then look up the data so that you don't have to research or lose context every time. And a little bit tongue-in-che…”
Parakhin: Content-addressed caching yields big compute savings by deduplicating cross-team work
“The main savings are coming from the fact that you ran it, you got your job done, and you moved on. Then somebody else in some department you don't know existed, runs the same task, but on the newer version. Like right now, you can't, in, in most of the organi…”
Parakhin: Shopify increased search throughput 5x to 4,200 QPS via auto-research
“Our search recently we moved from, it's hard to win, quote, from 800 QPS to 4200 QPS with the same quality just by pure optimizations and not a research loop that kept Running and changing code in our index surf on the same number of machines, just increasing …”
Parakhin: Shopify aimed for 0.7 correlation between SimGym and real add-to-carts
“Internal goal was to hit 0.7 correlation with add to cart events, for example, like that, that if we run real A, B test experiment, that it should go and replicate same sort of success that, that humans had or lack thereof.”
Parakhin: Shopify uses large HSTU architecture to model merchant trajectories
“Internally we have this system. We talked about it briefly once at NeurIPS. We have a huge HSTU-based system that models the whole companies.”
Parakhin: Counterfactual behavioral modeling was impossible before LAMPS and HSTUs
“Being able to model something complex as human beings or companies and model counterfactuals on it where you can have interventions in the future and optimize when to make intervention, what kind what kind of intervention to make. It's such an unlock that prev…”
Appearances (1)
| Episode | Date | Speaking time |
|---|---|---|
| AI-Native Engineering: 100% adoption, 5x search throughput, unlimited tokens — Mikhail Par | Apr 22, 2026 | 47m |