Shopify’s Mikhail Parakhin: Spend on AI to do what was once impossible

Mikhail Parakhin compares a company’s actions to the sequence of letters in text. Just as a language model predicts what comes next in text, he says, a company can be represented as a sequence of actions and given a digital twin.

Boris Cherny asks which difficult problems Parakhin has put to Claude and where it has surprised him. The Shopify CTO says he found a solution to a mathematical problem involving a Wasserstein loss function that he had pursued for seven years without success. He says neither he nor earlier language models had solved it.

Parakhin distinguishes this from automating work people could already do. He says the model enabled something he previously could not do, even with more time or help. For the hardest problems he sees today, he cannot solve them without the model and the model cannot solve them without him; the back-and-forth produces a combination better than either alone.

Cherny describes that as raising the ceiling as well as the floor. Parakhin agrees and says raising the ceiling matters more: ordinary work can sometimes be improved by diligence or more people, but extending what is possible has no easy substitute.

Cherny asks how to measure the business return when engineers can do something they could not before, rather than simply quantifying time saved. Parakhin says Shopify measures the easier-to-observe productivity gains carefully, including not only the number and complexity of code changes but also the complexity of projects.

Using its system for tracking work, Shopify can see whether teams complete more projects faster after accounting for complexity, Parakhin says. That allows the company to estimate productivity gains as a percentage.

Asked how a CTO should discuss the issue with a CEO, Parakhin recommends trying the largest model and comparing the results. Without that comparison, he says, a team does not know what it is missing. Shopify gives everyone unlimited tokens and runs the heaviest models it can find, he says, while investing in computing for inference, training and optimization.

Parakhin says people making decisions need to use the model and product themselves. Only then can they understand the capability, keep pushing its limits and see whether the returns justify it.

A prediction system is one project Parakhin says he is proud of. Its premise is that a company’s activities can be represented as a sequence of actions, just as text is a sequence of letters. Examples range from opening a credit line and shipping a product to accepting credit cards or taking a loan, alongside many smaller actions by employees.

From that sequence, he says, Shopify can create a digital twin and try interventions on it instead of on the real business. The company might explore what happens if a merchant advertises, ships a day faster or receives a loan, and look for a sequence of interactions that improves its chances of growth.

Parakhin says Shopify then acts in production: for example, offering a loan or sending a merchant advice about faster shipping or advertising. In his view, experimenting without putting real people at risk can help more merchants succeed, with a direct effect on Shopify’s financial results.

Cherny asks how long the system would have taken to build in the past. Parakhin says he does not think it was previously feasible: a company would have needed the right concentration of talent, the foresight to assemble it and the data collection to match. Raising the ceiling, to him, means doing something that previously would not even have been considered.

Asked what Claude changed his mind about, Parakhin says he once thought language models could not help him manage a team. He reversed that view and built a system that analyzes ongoing activity and proactively warns him when a project may miss its deadline or people appear unhappy, so he can address issues earlier.

Parakhin says the technical side of leadership has become more important. Models are good at picking up signals and trying to please, so they can give a person what was implied by the question rather than what is actually needed. Because a model can build the requested thing so quickly, a poor initial direction may have worse consequences than before, when discussion might have intervened.

Managing engineers who supervise models is different from managing engineers who write code directly, he says. A leader has to help determine how to distribute work among AI systems, track what they are doing and avoid conflicts. Parakhin says the emphasis has shifted toward team efficiency and technical decisions rather than checking every task day to day.

For other technical leaders, Parakhin advises against limiting tokens too tightly, while retaining circuit breakers for runaway processes that consume resources or cause harm. He says the difference between large and small models may seem modest at first, but testing is necessary to learn what a stronger model can do.

His “barbell” strategy is to budget for the largest models in coding, development, testing and research. For production, he recommends choosing a suitable model, often fine-tuning it, and using research to balance cost, throughput and quality. Savings from production can then fund stronger models for development. He says Claude Fable 5 was a particularly large improvement in his experience.

Parakhin says he has seen teams make the opposite trade: restricting development tokens while running unnecessarily large models in production. He believes his approach will be more competitive.

Parakhin ends with a personal example: he had hoped to work in retirement on deciphering Linear A, a Bronze Age writing system used on Crete. He jokes that Cherny posted that someone had used Claude to do it before he got there.